You searched for subject:(Compressed sensing).
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University of Toronto
1.
Makhzani, Alireza.
Compressed Sensing for Jointly Sparse Signals.
Degree: 2012, University of Toronto
URL: http://hdl.handle.net/1807/33438 
► Compressed sensing is an emerging field, which proposes that a small collection of linear projections of a sparse signal contains enough information for perfect reconstruction…
(more)
▼ Compressed sensing is an emerging field, which proposes that a small collection of linear projections of a sparse signal contains enough information for perfect reconstruction of the signal. In this thesis, we study the general problem of modeling and reconstructing spatially or temporally correlated sparse signals in a distributed scenario. The correlation among signals provides an additional information, which could be captured by joint sparsity models. After modeling the correlation, we propose two different reconstruction algorithms that are able to successfully exploit this additional information. The first algorithm is a very fast greedy algorithm, which is suitable for large scale problems and can exploit spatial correlation. The second algorithm is based on a thresholding algorithm and can exploit both the temporal and spatial correlation. We also generalize the standard joint sparsity model and propose a new model for capturing the correlation in the sensor networks.
MAST
Advisors/Committee Members: Valaee, Shahrokh, Electrical and Computer Engineering.
Subjects/Keywords: Compressed Sensing; 0544
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APA (6th Edition):
Makhzani, A. (2012). Compressed Sensing for Jointly Sparse Signals. (Masters Thesis). University of Toronto. Retrieved from http://hdl.handle.net/1807/33438
Chicago Manual of Style (16th Edition):
Makhzani, Alireza. “Compressed Sensing for Jointly Sparse Signals.” 2012. Masters Thesis, University of Toronto. Accessed September 19, 2019.
http://hdl.handle.net/1807/33438.
MLA Handbook (7th Edition):
Makhzani, Alireza. “Compressed Sensing for Jointly Sparse Signals.” 2012. Web. 19 Sep 2019.
Vancouver:
Makhzani A. Compressed Sensing for Jointly Sparse Signals. [Internet] [Masters thesis]. University of Toronto; 2012. [cited 2019 Sep 19].
Available from: http://hdl.handle.net/1807/33438.
Council of Science Editors:
Makhzani A. Compressed Sensing for Jointly Sparse Signals. [Masters Thesis]. University of Toronto; 2012. Available from: http://hdl.handle.net/1807/33438
2.
Ryan Paderna.
Channel Estimation and Detection for Advanced Digital Terrestrial Television System : 次世代地上デジタルテレビシステムのための伝搬路推定と復調法; ジセダイ チジョウ デジタル テレビ システム ノ タメノ デンパンロ スイテイ ト フクチョウホウ.
Degree: 博士(工学), 2018, Nara Institute of Science and Technology / 奈良先端科学技術大学院大学
URL: http://hdl.handle.net/10061/12512
Subjects/Keywords: Compressed sensing
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APA (6th Edition):
Paderna, R. (2018). Channel Estimation and Detection for Advanced Digital Terrestrial Television System : 次世代地上デジタルテレビシステムのための伝搬路推定と復調法; ジセダイ チジョウ デジタル テレビ システム ノ タメノ デンパンロ スイテイ ト フクチョウホウ. (Thesis). Nara Institute of Science and Technology / 奈良先端科学技術大学院大学. Retrieved from http://hdl.handle.net/10061/12512
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Paderna, Ryan. “Channel Estimation and Detection for Advanced Digital Terrestrial Television System : 次世代地上デジタルテレビシステムのための伝搬路推定と復調法; ジセダイ チジョウ デジタル テレビ システム ノ タメノ デンパンロ スイテイ ト フクチョウホウ.” 2018. Thesis, Nara Institute of Science and Technology / 奈良先端科学技術大学院大学. Accessed September 19, 2019.
http://hdl.handle.net/10061/12512.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Paderna, Ryan. “Channel Estimation and Detection for Advanced Digital Terrestrial Television System : 次世代地上デジタルテレビシステムのための伝搬路推定と復調法; ジセダイ チジョウ デジタル テレビ システム ノ タメノ デンパンロ スイテイ ト フクチョウホウ.” 2018. Web. 19 Sep 2019.
Vancouver:
Paderna R. Channel Estimation and Detection for Advanced Digital Terrestrial Television System : 次世代地上デジタルテレビシステムのための伝搬路推定と復調法; ジセダイ チジョウ デジタル テレビ システム ノ タメノ デンパンロ スイテイ ト フクチョウホウ. [Internet] [Thesis]. Nara Institute of Science and Technology / 奈良先端科学技術大学院大学; 2018. [cited 2019 Sep 19].
Available from: http://hdl.handle.net/10061/12512.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Paderna R. Channel Estimation and Detection for Advanced Digital Terrestrial Television System : 次世代地上デジタルテレビシステムのための伝搬路推定と復調法; ジセダイ チジョウ デジタル テレビ システム ノ タメノ デンパンロ スイテイ ト フクチョウホウ. [Thesis]. Nara Institute of Science and Technology / 奈良先端科学技術大学院大学; 2018. Available from: http://hdl.handle.net/10061/12512
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Queens University
3.
Abou Saleh, Ahmad.
Source-Channel Mappings with Applications to Compressed Sensing
.
Degree: Electrical and Computer Engineering, 2011, Queens University
URL: http://hdl.handle.net/1974/6614
► Tandem source-channel coding is proven to be optimal by Shannon given unlimited delay and complexity in the coders. Under low delay and low complexity constraints,…
(more)
▼ Tandem source-channel coding is proven to be optimal by Shannon given unlimited
delay and complexity in the coders. Under low delay and low complexity constraints,
joint source-channel coding may achieve better performance. Although digital joint
source-channel coding has shown a noticeable gain in terms of reconstructed signal
quality, coding delay, and complexity, it suffers from the leveling-off effect. However, analog systems do not suffer from the leveling-off effect. In this thesis, we investigate the advantage of analog systems based on the Shannon-Kotel’nikov approach and
hybrid digital-analog coding systems, which combine digital and analog schemes to achieve a graceful degradation/improvement over a wide range of channel conditions.
First, we propose a low delay and low complexity hybrid digital-analog coding that is able to achieve high (integer) expansion ratios ( >3). This is achieved by combining
the spiral mapping with multiple stage quantizers. The system is simulated for a 1 : 3 bandwidth expansion and the behavior for a 1 : M (with M an integer >3) system is studied in the low noise level regime.
Next, we propose an analog joint source-channel coding system that is able to achieve
a low (fractional) expansion ratio between 1 and 2. More precisely, this is an N : M
bandwidth expansion system based on combining uncoded transmission and a 1 : 2 bandwidth expansion system (with N < M < 2N).Finally, a 1 : 2 analog bandwidth expansion system using the (Shannon-Kotel’nikov) Archimedes’ spiral mapping is used in the compressed sensing context, which is inherently analog, to increase the system’s immunity against channel noise. The proposed system is compared to a conventional compressed sensing system that assumes noiseless transmission and a compressed sensing based system that account for noise during signal reconstruction.
Subjects/Keywords: source-channel mappings;
compressed sensing
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APA (6th Edition):
Abou Saleh, A. (2011). Source-Channel Mappings with Applications to Compressed Sensing
. (Thesis). Queens University. Retrieved from http://hdl.handle.net/1974/6614
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Abou Saleh, Ahmad. “Source-Channel Mappings with Applications to Compressed Sensing
.” 2011. Thesis, Queens University. Accessed September 19, 2019.
http://hdl.handle.net/1974/6614.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Abou Saleh, Ahmad. “Source-Channel Mappings with Applications to Compressed Sensing
.” 2011. Web. 19 Sep 2019.
Vancouver:
Abou Saleh A. Source-Channel Mappings with Applications to Compressed Sensing
. [Internet] [Thesis]. Queens University; 2011. [cited 2019 Sep 19].
Available from: http://hdl.handle.net/1974/6614.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Abou Saleh A. Source-Channel Mappings with Applications to Compressed Sensing
. [Thesis]. Queens University; 2011. Available from: http://hdl.handle.net/1974/6614
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
4.
Manoel, Antonio André Monteiro.
Statistical physics for compressed sensing and information hiding.
Degree: PhD, Física, 2015, University of São Paulo
URL: http://www.teses.usp.br/teses/disponiveis/43/43134/tde-08102015-140952/
;
► This thesis is divided into two parts. In the first part, we show how problems of statistical inference and combinatorial optimization may be approached within…
(more)
▼ This thesis is divided into two parts. In the first part, we show how problems of statistical inference and combinatorial optimization may be approached within a unified framework that employs tools from fields as diverse as machine learning, statistical physics and information theory, allowing us to i) design algorithms to solve the problems, ii) analyze the performance of these algorithms both empirically and analytically, and iii) to compare the results obtained with the optimal achievable ones. In the second part, we use this framework to study two specific problems, one of inference (compressed sensing) and the other of optimization (information hiding). In both cases, we review current approaches, identify their flaws, and propose new schemes to address these flaws, building on the use of message-passing algorithms, variational inference techniques, and spin glass models from statistical physics.
Esta tese está dividida em duas partes. Na primeira delas, mostramos como problemas de inferência estatística e de otimização combinatória podem ser abordados sob um framework unificado que usa ferramentas de áreas tão diversas quanto o aprendizado de máquina, a física estatística e a teoria de informação, permitindo que i) projetemos algoritmos para resolver os problemas, ii) analisemos a performance destes algoritmos tanto empiricamente como analiticamente, e iii) comparemos os resultados obtidos com os limites teóricos. Na segunda parte, este framework é usado no estudo de dois problemas específicos, um de inferência (compressed sensing) e outro de otimização (ocultação de dados). Em ambos os casos, revisamos abordagens recentes, identificamos suas falhas, e propomos novos esquemas que visam corrigir estas falhas, baseando-nos sobretudo em algoritmos de troca de mensagens, técnicas de inferência variacional, e modelos de vidro de spin da física estatística.
Advisors/Committee Members: Vicente, Renato.
Subjects/Keywords: Bayesian inference; compressed sensing; compressed sensing; esteganografia; inferência Bayesiana; steganography
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APA (6th Edition):
Manoel, A. A. M. (2015). Statistical physics for compressed sensing and information hiding. (Doctoral Dissertation). University of São Paulo. Retrieved from http://www.teses.usp.br/teses/disponiveis/43/43134/tde-08102015-140952/ ;
Chicago Manual of Style (16th Edition):
Manoel, Antonio André Monteiro. “Statistical physics for compressed sensing and information hiding.” 2015. Doctoral Dissertation, University of São Paulo. Accessed September 19, 2019.
http://www.teses.usp.br/teses/disponiveis/43/43134/tde-08102015-140952/ ;.
MLA Handbook (7th Edition):
Manoel, Antonio André Monteiro. “Statistical physics for compressed sensing and information hiding.” 2015. Web. 19 Sep 2019.
Vancouver:
Manoel AAM. Statistical physics for compressed sensing and information hiding. [Internet] [Doctoral dissertation]. University of São Paulo; 2015. [cited 2019 Sep 19].
Available from: http://www.teses.usp.br/teses/disponiveis/43/43134/tde-08102015-140952/ ;.
Council of Science Editors:
Manoel AAM. Statistical physics for compressed sensing and information hiding. [Doctoral Dissertation]. University of São Paulo; 2015. Available from: http://www.teses.usp.br/teses/disponiveis/43/43134/tde-08102015-140952/ ;
University of Wollongong
5.
Tang, Van Ha.
Compressed sensing for enhanced through-the-wall radar imaging.
Degree: PhD, 2015, University of Wollongong
URL: ;
https://ro.uow.edu.au/theses/4643
► Through-the-wall radar imaging (TWRI) is an emerging technology that aims to capture scenes behind walls and other visually opaque materials. The abilities to sense…
(more)
▼ Through-the-wall radar imaging (TWRI) is an emerging technology that aims to capture scenes behind walls and other visually opaque materials. The abilities to sense through walls are highly desirable for both military and civil applications, such as search and rescue missions, surveillance, and reconnaissance. TWRI systems, however, face with several challenges including prolonged data acquisition, large objects, strong wall clutter, and shadowing effects, which limit the radar imaging performances and hinder target detection and localization.
Subjects/Keywords: Through-the-wall radar imaging; compressed sensing; Bayesian compressed sensing
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APA (6th Edition):
Tang, V. H. (2015). Compressed sensing for enhanced through-the-wall radar imaging. (Doctoral Dissertation). University of Wollongong. Retrieved from ; https://ro.uow.edu.au/theses/4643
Chicago Manual of Style (16th Edition):
Tang, Van Ha. “Compressed sensing for enhanced through-the-wall radar imaging.” 2015. Doctoral Dissertation, University of Wollongong. Accessed September 19, 2019.
; https://ro.uow.edu.au/theses/4643.
MLA Handbook (7th Edition):
Tang, Van Ha. “Compressed sensing for enhanced through-the-wall radar imaging.” 2015. Web. 19 Sep 2019.
Vancouver:
Tang VH. Compressed sensing for enhanced through-the-wall radar imaging. [Internet] [Doctoral dissertation]. University of Wollongong; 2015. [cited 2019 Sep 19].
Available from: ; https://ro.uow.edu.au/theses/4643.
Council of Science Editors:
Tang VH. Compressed sensing for enhanced through-the-wall radar imaging. [Doctoral Dissertation]. University of Wollongong; 2015. Available from: ; https://ro.uow.edu.au/theses/4643
University of Alberta
6.
Fang, Hao.
Parallel Sampling and Reconstruction with Permutation in
Multidimensional Compressed Sensing.
Degree: MS, Department of Electrical and Computer
Engineering, 2013, University of Alberta
URL: https://era.library.ualberta.ca/files/s4655h990
► The advent of compressed sensing provides a new way to sample and compress signals. In this thesis, a parallel compressed sensing architecture is proposed, which…
(more)
▼ The advent of compressed sensing provides a new way to
sample and compress signals. In this thesis, a parallel compressed
sensing architecture is proposed, which samples a two-dimensional
reshaped multidimensional signal column by column using the same
sensing matrix. Compared to architectures that sample a
vector-reshaped multidimensional signal, the sampling device in the
parallel compressed sensing architecture stores a smaller-sized
sensing matrix and has lower computational complexity. Besides, the
reconstruction of the multidimensional signal can be conducted in
parallel, which reduces the computational complexity and time for
reconstruction at the decoder side. In addition, when parallel
sampling is not required but analog compressed sensing is desired,
an alternative architecture proposed in this thesis, named parallel
compressed sensing reconstruction architecture, can be used. In
both proposed architectures, permutation is introduced and shown to
enable the reduction of the required number of measurements for a
given desired reconstruction error performance.
Subjects/Keywords: multidimensional signal processing; permutation; compressed sensing
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APA (6th Edition):
Fang, H. (2013). Parallel Sampling and Reconstruction with Permutation in
Multidimensional Compressed Sensing. (Masters Thesis). University of Alberta. Retrieved from https://era.library.ualberta.ca/files/s4655h990
Chicago Manual of Style (16th Edition):
Fang, Hao. “Parallel Sampling and Reconstruction with Permutation in
Multidimensional Compressed Sensing.” 2013. Masters Thesis, University of Alberta. Accessed September 19, 2019.
https://era.library.ualberta.ca/files/s4655h990.
MLA Handbook (7th Edition):
Fang, Hao. “Parallel Sampling and Reconstruction with Permutation in
Multidimensional Compressed Sensing.” 2013. Web. 19 Sep 2019.
Vancouver:
Fang H. Parallel Sampling and Reconstruction with Permutation in
Multidimensional Compressed Sensing. [Internet] [Masters thesis]. University of Alberta; 2013. [cited 2019 Sep 19].
Available from: https://era.library.ualberta.ca/files/s4655h990.
Council of Science Editors:
Fang H. Parallel Sampling and Reconstruction with Permutation in
Multidimensional Compressed Sensing. [Masters Thesis]. University of Alberta; 2013. Available from: https://era.library.ualberta.ca/files/s4655h990
University of Alberta
7.
Lu, Ran.
The Strong Restricted Isometry Property of Sub-Gaussian
Matrices and the Erasure Robustness Property of Gaussian Random
Frames.
Degree: MS, Department of Mathematical and Statistical
Sciences, 2016, University of Alberta
URL: https://era.library.ualberta.ca/files/cj9602064j
► In this thesis we will study the robustness property of sub-gaussian random matrices. We first show that the nearly isometry property will still hold with…
(more)
▼ In this thesis we will study the robustness property
of sub-gaussian random matrices. We first show that the nearly
isometry property will still hold with high probability if we erase
a certain portion of rows from a sub-gaussian matrix, and we will
estimate the erasure ratio with a given small distortion rate in
the norm. With this, we establish the strong restricted isometry
property (SRIP) and the robust version of Johnson-Lindenstrauss
(JL) Lemma for sub-gaussian matrices, which are essential in
compressed sensing with corruptions. Then we fix the erasure ratio
and deduce the lower and upper bounds of the norm after a erased
sub-gaussian matrix acting on a vector, and in this case we can
also obtain the corresponding SRIP and the robust version of JL
Lemma. Finally, we study the robustness property of Gaussian random
finite frames, we will improve existing results.
Subjects/Keywords: restricted isometry property; random matrix; compressed sensing
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APA (6th Edition):
Lu, R. (2016). The Strong Restricted Isometry Property of Sub-Gaussian
Matrices and the Erasure Robustness Property of Gaussian Random
Frames. (Masters Thesis). University of Alberta. Retrieved from https://era.library.ualberta.ca/files/cj9602064j
Chicago Manual of Style (16th Edition):
Lu, Ran. “The Strong Restricted Isometry Property of Sub-Gaussian
Matrices and the Erasure Robustness Property of Gaussian Random
Frames.” 2016. Masters Thesis, University of Alberta. Accessed September 19, 2019.
https://era.library.ualberta.ca/files/cj9602064j.
MLA Handbook (7th Edition):
Lu, Ran. “The Strong Restricted Isometry Property of Sub-Gaussian
Matrices and the Erasure Robustness Property of Gaussian Random
Frames.” 2016. Web. 19 Sep 2019.
Vancouver:
Lu R. The Strong Restricted Isometry Property of Sub-Gaussian
Matrices and the Erasure Robustness Property of Gaussian Random
Frames. [Internet] [Masters thesis]. University of Alberta; 2016. [cited 2019 Sep 19].
Available from: https://era.library.ualberta.ca/files/cj9602064j.
Council of Science Editors:
Lu R. The Strong Restricted Isometry Property of Sub-Gaussian
Matrices and the Erasure Robustness Property of Gaussian Random
Frames. [Masters Thesis]. University of Alberta; 2016. Available from: https://era.library.ualberta.ca/files/cj9602064j
Universiteit Utrecht
8.
Burgh, H.K. van der.
Reducing geometric distortions of Diffusion-Weighted Imaging using Compressed Sensing.
Degree: 2014, Universiteit Utrecht
URL: http://dspace.library.uu.nl:8080/handle/1874/294002
► Diffusion-weighted imaging (DWI) is a type of contrast imaging used in Magnetic Resonance Imaging (MRI) that visualizes the amount of diffusion of water molecules in…
(more)
▼ Diffusion-weighted imaging (DWI) is a type of contrast imaging used in Magnetic Resonance Imaging (MRI) that visualizes the amount of diffusion of water molecules in tissue. Tumors are well visible on DWI images. DWI is often acquired with Echo Planar Imaging (EPI) techniques. Unfortunately, these techniques lead to geometric distortions in the diffusion-weighted images. This causes problems in locating the position of the tumor exactly, which is required for radiotherapy.
In this thesis, an approach called
Compressed Sensing (CS) was investigated as a technique to reduce the geometric distortions. In theory, the distortions are reduced by obtaining less MR data during scan acquisition (undersampling). By enforcing sparsity of the data in a transform domain, a well reconstructed image can be obtained as the solution of an appropriate minimization problem. The reconstruction algorithm used to solve this problem was cFISTA, a modification of FISTA developed by Beck and Teboulle.
Five undersampling strategies were retrospectively used on a DWI patient data set and the best strategy among these five was identified. The reconstruction quality of the whole image and the quality of the tumor reconstruction were assessed using the so-called Structure Similarity Image Measure. A strategy called centerincreased gave the best balance between the average percentage of the MR data required for high quality reconstruction and the variation between the test images, for both the tumor reconstruction and reconstruction of the whole image. High quality reconstructions were obtained for this strategy, when on average only 20% of the MR data was included.
Advisors/Committee Members: Sleijpen, G.L.G..
Subjects/Keywords: compressed sensing; DW-MRI; reconstruction; minimization problem
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APA (6th Edition):
Burgh, H. K. v. d. (2014). Reducing geometric distortions of Diffusion-Weighted Imaging using Compressed Sensing. (Masters Thesis). Universiteit Utrecht. Retrieved from http://dspace.library.uu.nl:8080/handle/1874/294002
Chicago Manual of Style (16th Edition):
Burgh, H K van der. “Reducing geometric distortions of Diffusion-Weighted Imaging using Compressed Sensing.” 2014. Masters Thesis, Universiteit Utrecht. Accessed September 19, 2019.
http://dspace.library.uu.nl:8080/handle/1874/294002.
MLA Handbook (7th Edition):
Burgh, H K van der. “Reducing geometric distortions of Diffusion-Weighted Imaging using Compressed Sensing.” 2014. Web. 19 Sep 2019.
Vancouver:
Burgh HKvd. Reducing geometric distortions of Diffusion-Weighted Imaging using Compressed Sensing. [Internet] [Masters thesis]. Universiteit Utrecht; 2014. [cited 2019 Sep 19].
Available from: http://dspace.library.uu.nl:8080/handle/1874/294002.
Council of Science Editors:
Burgh HKvd. Reducing geometric distortions of Diffusion-Weighted Imaging using Compressed Sensing. [Masters Thesis]. Universiteit Utrecht; 2014. Available from: http://dspace.library.uu.nl:8080/handle/1874/294002
University of Southern California
9.
Tehrani, Arash Saber.
Neighbor discovery in device-to-device communication.
Degree: PhD, Electrical Engineering, 2015, University of Southern California
URL: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/625930/rec/4350
► The neighbor discovery in wireless networks is the problem of devices identifying other devices which they can communicate to effectively, i.e., those whose signal can…
(more)
▼ The neighbor discovery in wireless networks is the
problem of devices identifying other devices which they can
communicate to effectively, i.e., those whose signal can be
received with a power great enough. The neighbor discovery is
crucial and is the first task which must be performed in the an
ad-hoc or device-to-device network, is the prerequisite for channel
estimation, scheduling, and communication. ❧ This dissertation
presents two solutions for the neighbor discovery problem:
compressed sensing neighbor discovery and ZigZag scheme. The former
relies on the connection between the neighbor discovery and
compressed sensing as number of neighbors a device has is small
compares to the total number of nodes and thus sparse vector
approximation methods can be applied. Exploiting our recent work on
optimal deterministic
sensing matrices, we show that the discovery
time can be reduced to K log(N/K). The latter is based on serial
interference cancellation that can provide further efficiency
improvement, i.e., discovery period on the scale of K. ❧ We show a
theoretical connection between the performance of these schemes and
channel coding through which we derive performance bounds for the
methods in the ideal setting. To analyze the performance of the
methods, we compare them against each other and against random
access scheme. ❧ We further extend the ZigZag discovery to support
devices equipped with directional steering beam antennas. Note that
such extension is not straight forward as devices need to scan
their surrounding and two devices must agree on becoming the
neighbor as both should aim their beams at one another in future
communications. ❧ Furthermore, as the neighbor discovery methods
perform poorly in the presence of interference from non-neighbors,
we suggest a cellular based scheduling to overcome methods. We
further analyze the performance of these methods in a realistic
environment with presence of shadowing and fading, and introduce
theoretic and practical measures to protect the performance of
these methods against interference caused by
non-neighbors.
Advisors/Committee Members: Caire, Giuseppe (Committee Chair), Molisch, Andreas F. (Committee Member), Goldstein, Larry (Committee Member).
Subjects/Keywords: neighbor discovery; peer discovery; compressed sensing; D2D
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APA (6th Edition):
Tehrani, A. S. (2015). Neighbor discovery in device-to-device communication. (Doctoral Dissertation). University of Southern California. Retrieved from http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/625930/rec/4350
Chicago Manual of Style (16th Edition):
Tehrani, Arash Saber. “Neighbor discovery in device-to-device communication.” 2015. Doctoral Dissertation, University of Southern California. Accessed September 19, 2019.
http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/625930/rec/4350.
MLA Handbook (7th Edition):
Tehrani, Arash Saber. “Neighbor discovery in device-to-device communication.” 2015. Web. 19 Sep 2019.
Vancouver:
Tehrani AS. Neighbor discovery in device-to-device communication. [Internet] [Doctoral dissertation]. University of Southern California; 2015. [cited 2019 Sep 19].
Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/625930/rec/4350.
Council of Science Editors:
Tehrani AS. Neighbor discovery in device-to-device communication. [Doctoral Dissertation]. University of Southern California; 2015. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/625930/rec/4350
University of Victoria
10.
Li, Wanbo.
Wireless ECG system with bluetooth low energy and compressed sensing.
Degree: Department of Electrical and Computer Engineering, 2016, University of Victoria
URL: http://hdl.handle.net/1828/7398
► Electrocardiogram (ECG) is a noninvasive technology widely used in health care systems for diagnosis of heart diseases, and a wearable ECG sensor with long-term monitoring…
(more)
▼ Electrocardiogram (ECG) is a noninvasive technology widely used in health care systems for diagnosis of heart diseases, and a wearable ECG sensor with long-term monitoring is necessary for real-time heart disease detection. However, the conventional ECG is restricted considering the physical size and power consumption of the system. In this thesis, we propose a Wireless ECG System with Bluetooth Low Energy (BLE) and
Compressed Sensing (CS).
The proposed Wireless ECG System includes an ECG sensor board based on a BLE chip, an Android application and a web service with a database. The ECG signal is first collected by the ECG Sensor Board and then transmitted to the Android application through BLE protocol. At last, the ECG signal is uploaded to the cloud database from the Android app. We also introduce
Compressed Sensing into our system with a novel sparse
sensing matrix, data compression and a modified Compressive Sampling Matching Pursuit (CoSaMP) reconstruction algorithm. Experiment results show that the amount of data transmitted is reduced by about 57% compared to not using
Compressed Sensing, and reconstruction time is 64% less than using Orthogonal Matching Pursuit (OMP) or Iterative Re-weighted Least Squares (IRLS) algorithm.
Advisors/Committee Members: Dong, Xiaodai (supervisor).
Subjects/Keywords: Bluetooth Low Energy; Compressed Sensing; ECG compression
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APA (6th Edition):
Li, W. (2016). Wireless ECG system with bluetooth low energy and compressed sensing. (Masters Thesis). University of Victoria. Retrieved from http://hdl.handle.net/1828/7398
Chicago Manual of Style (16th Edition):
Li, Wanbo. “Wireless ECG system with bluetooth low energy and compressed sensing.” 2016. Masters Thesis, University of Victoria. Accessed September 19, 2019.
http://hdl.handle.net/1828/7398.
MLA Handbook (7th Edition):
Li, Wanbo. “Wireless ECG system with bluetooth low energy and compressed sensing.” 2016. Web. 19 Sep 2019.
Vancouver:
Li W. Wireless ECG system with bluetooth low energy and compressed sensing. [Internet] [Masters thesis]. University of Victoria; 2016. [cited 2019 Sep 19].
Available from: http://hdl.handle.net/1828/7398.
Council of Science Editors:
Li W. Wireless ECG system with bluetooth low energy and compressed sensing. [Masters Thesis]. University of Victoria; 2016. Available from: http://hdl.handle.net/1828/7398
11.
Carpentier, Alexandra.
De l'échantillonage optimal en grande et petite dimension : On optimal sampling in high and low dimension.
Degree: Docteur es, Informatique, 2012, Université Lille I – Sciences et Technologies
URL: http://www.theses.fr/2012LIL10041
► Pendant ma thèse, j’ai eu la chance d’apprendre et de travailler sous la supervision de mon directeur de thèse Rémi, et ce dans deux domaines…
(more)
▼ Pendant ma thèse, j’ai eu la chance d’apprendre et de travailler sous la supervision de mon directeur de thèse Rémi, et ce dans deux domaines qui me sont particulièrement chers. Je veux parler de la Théorie des Bandits et du Compressed Sensing. Je les voie comme intimement liés non par les méthodes mais par leur objectif commun: l’échantillonnage optimal de l’espace. Tous deux sont centrés sur les manières d’échantillonner l’espace efficacement : la Théorie des Bandits en petite dimension et le Compressed Sensing en grande dimension. Dans cette dissertation, je présente la plupart des travaux que mes co-auteurs et moi-même avons écrit durant les trois années qu’a duré ma thèse.
During my PhD, I had the chance to learn and work under the great supervision of my advisor Rémi (Munos) in two fields that are of particular interest to me. These domains are Bandit Theory and Compressed Sensing. While studying these domains I came to the conclusion that they are connected if one looks at them trough the prism of optimal sampling. Both these fields are concerned with strategies on how to sample the space in an efficient way: Bandit Theory in low dimension, and Compressed Sensing in high dimension. In this Dissertation, I present most of the work my co-authors and I produced during the three years that my PhD lasted.
Advisors/Committee Members: Munos, Rémi (thesis director).
Subjects/Keywords: Théorie des bandits stochastiques; Compressed sensing; 006.31
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APA (6th Edition):
Carpentier, A. (2012). De l'échantillonage optimal en grande et petite dimension : On optimal sampling in high and low dimension. (Doctoral Dissertation). Université Lille I – Sciences et Technologies. Retrieved from http://www.theses.fr/2012LIL10041
Chicago Manual of Style (16th Edition):
Carpentier, Alexandra. “De l'échantillonage optimal en grande et petite dimension : On optimal sampling in high and low dimension.” 2012. Doctoral Dissertation, Université Lille I – Sciences et Technologies. Accessed September 19, 2019.
http://www.theses.fr/2012LIL10041.
MLA Handbook (7th Edition):
Carpentier, Alexandra. “De l'échantillonage optimal en grande et petite dimension : On optimal sampling in high and low dimension.” 2012. Web. 19 Sep 2019.
Vancouver:
Carpentier A. De l'échantillonage optimal en grande et petite dimension : On optimal sampling in high and low dimension. [Internet] [Doctoral dissertation]. Université Lille I – Sciences et Technologies; 2012. [cited 2019 Sep 19].
Available from: http://www.theses.fr/2012LIL10041.
Council of Science Editors:
Carpentier A. De l'échantillonage optimal en grande et petite dimension : On optimal sampling in high and low dimension. [Doctoral Dissertation]. Université Lille I – Sciences et Technologies; 2012. Available from: http://www.theses.fr/2012LIL10041
12.
Cui, Xuelin.
Joint CT-MRI Image Reconstruction.
Degree: PhD, Electrical Engineering, 2018, Virginia Tech
URL: http://hdl.handle.net/10919/86177
► Modern clinical diagnoses and treatments have been increasingly reliant on medical imaging techniques. In return, medical images are required to provide more accurate and detailed…
(more)
▼ Modern clinical diagnoses and treatments have been increasingly reliant on medical imaging
techniques. In return, medical images are required to provide more accurate and detailed
information than ever. Aside from the evolution of hardware and software, multimodal
imaging techniques offer a promising solution to produce higher quality images by fusing
medical images from different modalities. This strategy utilizes more structural and/or
functional image information, thereby allowing clinical results to be more comprehensive
and better interpreted.
Since their inception, multimodal imaging techniques have received a great deal of attention
for achieving enhanced imaging performance. In this work, a novel joint reconstruction
framework using sparse computed tomography (CT) and magnetic resonance imaging (MRI)
data is developed and evaluated. The method proposed in this study is part of the planned
joint CT-MRI system which assembles CT and MRI subsystems into a single entity. The
CT and MRI images are synchronously acquired and registered from the hybrid CT-MRI
platform. However, since their image data are highly undersampled, analytical methods, such
as filtered backprojection, are unable to generate images of sufficient quality. To overcome
this drawback, we resort to
compressed sensing techniques, which employ sparse priors that
result from an application of L1-norm minimization.
To utilize multimodal information, a projection distance is introduced and is tuned to tailor
the texture and pattern of final images. Specifically CT and MRI images are alternately
reconstructed using the updated multimodal results that are calculated at the latest step
of the iterative optimization algorithm. This method exploits the structural similarities
shared by the CT and MRI images to achieve better reconstruction quality. The improved
performance of the proposed approach is demonstrated using a pair of undersampled CT-MRI
body images and a pair of undersampled CT-MRI head images. These images are
tested using joint reconstruction, analytical reconstruction, and independent reconstruction
without using multimodal imaging information. Results show that the proposed method
improves about 5dB in signal-to-noise ratio (SNR) and nearly 10% in structural similarity
measurements compared to independent reconstruction methods. It offers a similar quality
as fully sampled analytical reconstruction, yet requires as few as 25 projections for CT and
a 30% sampling rate for MRI. It is concluded that structural similarities and correlations
residing in images from different modalities are useful to mutually promote the quality of
image reconstruction.
Advisors/Committee Members: Mili, Lamine M. (committeechair), Beex, Aloysius A. (committee member), Abbott, Amos L. (committee member), Yu, Hengyong (committee member), Cao, Guohua (committee member).
Subjects/Keywords: Medical Imaging; Image Reconstruction; Compressed Sensing
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APA (6th Edition):
Cui, X. (2018). Joint CT-MRI Image Reconstruction. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/86177
Chicago Manual of Style (16th Edition):
Cui, Xuelin. “Joint CT-MRI Image Reconstruction.” 2018. Doctoral Dissertation, Virginia Tech. Accessed September 19, 2019.
http://hdl.handle.net/10919/86177.
MLA Handbook (7th Edition):
Cui, Xuelin. “Joint CT-MRI Image Reconstruction.” 2018. Web. 19 Sep 2019.
Vancouver:
Cui X. Joint CT-MRI Image Reconstruction. [Internet] [Doctoral dissertation]. Virginia Tech; 2018. [cited 2019 Sep 19].
Available from: http://hdl.handle.net/10919/86177.
Council of Science Editors:
Cui X. Joint CT-MRI Image Reconstruction. [Doctoral Dissertation]. Virginia Tech; 2018. Available from: http://hdl.handle.net/10919/86177
Delft University of Technology
13.
Gishkori, S.S.
Compressive Sampling for PPM and FSK Modulated Signals:.
Degree: 2009, Delft University of Technology
URL: http://resolver.tudelft.nl/uuid:badbddb8-9f61-48a5-ac50-fc3e0bd719e9
► Efficiency of the Analog to Digital Converters (ADCs) has always been an issue of concern, especially, when it comes to sampling wide band signals which…
(more)
▼ Efficiency of the Analog to Digital Converters (ADCs) has always been an issue of concern, especially, when it comes to sampling wide band signals which require extremely high sampling rates. As the systems with wide band signals are gaining the front position in digital communications, the need to find ways to reduce the sampling rates of ADCs but still maintaining exact reconstruction, is becoming ever more resurgent. In this thesis we present the utilization of a newly discovered technique to reduce the sampling rates much below the Nyquist rates. We explore the combination of Compressive Sampling (CS) with Pulse Position Modulation (PPM) and Frequency Shift Keying (FSK) modulation schemes. CS has been suggested for 'sparse signals'. Sparsity helps represent the signal in much less dimensions. We evaluate the suitability of this technique for PPM and FSK modulated signals in multipath fading environments so as to reduce the complexity at the receiver side. We detect the signals without having to first estimate the channel. We present scenarios where we can achieve the most from the combination of CS and PPM/FSK. We extend this frame work to Ultra Wide Band (UWB) applications. We also give theoretical expressions for the error probabilities of our signal models. The real challenge, with regard to using CS, is to reconstruct the signal from its reduced dimensions. In this respect, we have opted for the Orthogonal Matching Pursuit (OMP) algorithm for most of the cases, nonetheless, we elaborate on other available reconstruction methods as well.
Advisors/Committee Members: Leus, G.J.T., Van der Veen, A.J..
Subjects/Keywords: Compressive Sampling; PPM; FSK; UWB; Compressed Sensing
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APA (6th Edition):
Gishkori, S. S. (2009). Compressive Sampling for PPM and FSK Modulated Signals:. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:badbddb8-9f61-48a5-ac50-fc3e0bd719e9
Chicago Manual of Style (16th Edition):
Gishkori, S S. “Compressive Sampling for PPM and FSK Modulated Signals:.” 2009. Masters Thesis, Delft University of Technology. Accessed September 19, 2019.
http://resolver.tudelft.nl/uuid:badbddb8-9f61-48a5-ac50-fc3e0bd719e9.
MLA Handbook (7th Edition):
Gishkori, S S. “Compressive Sampling for PPM and FSK Modulated Signals:.” 2009. Web. 19 Sep 2019.
Vancouver:
Gishkori SS. Compressive Sampling for PPM and FSK Modulated Signals:. [Internet] [Masters thesis]. Delft University of Technology; 2009. [cited 2019 Sep 19].
Available from: http://resolver.tudelft.nl/uuid:badbddb8-9f61-48a5-ac50-fc3e0bd719e9.
Council of Science Editors:
Gishkori SS. Compressive Sampling for PPM and FSK Modulated Signals:. [Masters Thesis]. Delft University of Technology; 2009. Available from: http://resolver.tudelft.nl/uuid:badbddb8-9f61-48a5-ac50-fc3e0bd719e9
Rochester Institute of Technology
14.
Dominguez, Miguel.
Structure-Constrained Basis Pursuit for Compressively Sensing Speech.
Degree: MS, Electrical Engineering, 2016, Rochester Institute of Technology
URL: https://scholarworks.rit.edu/theses/9002
► Compressed Sensing (CS) exploits the sparsity of many signals to enable sampling below the Nyquist rate. If the original signal is sufficiently sparse, the…
(more)
▼ Compressed Sensing (CS) exploits the sparsity of many signals to enable sampling below the Nyquist rate. If the original signal is sufficiently sparse, the Basis Pursuit (BP) algorithm will perfectly reconstruct the original signal. Unfortunately many signals that intuitively appear sparse do not meet the threshold for "sufficient sparsity". These signals require so many CS samples for accurate reconstruction that the advantages of CS disappear. This is because Basis Pursuit/Basis Pursuit Denoising only models sparsity. We developed a "Structure-Constrained Basis Pursuit" that models the structure of somewhat sparse signals as upper and lower bound constraints on the Basis Pursuit Denoising solution. We applied it to speech, which seems sparse but does not compress well with CS, and gained improved quality over Basis Pursuit Denoising. When a single parameter (i.e. the phone) is encoded, Normalized Mean Squared Error (NMSE) decreases by between 16.2% and 1.00% when sampling with CS between 1/10 and 1/2 the Nyquist rate, respectively. When bounds are coded as a sum of Gaussians, NMSE decreases between 28.5% and 21.6% in the same range. SCBP can be applied to any somewhat sparse signal with a predictable structure to enable improved reconstruction quality with the same number of samples.
Advisors/Committee Members: Behnaz Ghoraani, Andres Kwasinski, Sohail Dianat.
Subjects/Keywords: Basis pursuit; Compressed sensing; Speech coding
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APA (6th Edition):
Dominguez, M. (2016). Structure-Constrained Basis Pursuit for Compressively Sensing Speech. (Masters Thesis). Rochester Institute of Technology. Retrieved from https://scholarworks.rit.edu/theses/9002
Chicago Manual of Style (16th Edition):
Dominguez, Miguel. “Structure-Constrained Basis Pursuit for Compressively Sensing Speech.” 2016. Masters Thesis, Rochester Institute of Technology. Accessed September 19, 2019.
https://scholarworks.rit.edu/theses/9002.
MLA Handbook (7th Edition):
Dominguez, Miguel. “Structure-Constrained Basis Pursuit for Compressively Sensing Speech.” 2016. Web. 19 Sep 2019.
Vancouver:
Dominguez M. Structure-Constrained Basis Pursuit for Compressively Sensing Speech. [Internet] [Masters thesis]. Rochester Institute of Technology; 2016. [cited 2019 Sep 19].
Available from: https://scholarworks.rit.edu/theses/9002.
Council of Science Editors:
Dominguez M. Structure-Constrained Basis Pursuit for Compressively Sensing Speech. [Masters Thesis]. Rochester Institute of Technology; 2016. Available from: https://scholarworks.rit.edu/theses/9002
University of Rochester
15.
Zerom, Petros.
Image reconstruction and discrimination at low light
levels.
Degree: PhD, 2013, University of Rochester
URL: http://hdl.handle.net/1802/27903
► Quantum imaging is a recent and promising branch of quantum optics that exploits the quantum nature of light. Improving the limitations imposed by classical sources…
(more)
▼ Quantum imaging is a recent and promising branch of
quantum optics that exploits
the quantum nature of light.
Improving the limitations imposed by classical sources
of light in
optical imaging techniques or overcoming the classical boundaries
of image
formation is one of the key motivations in quantum
imaging. In this thesis, I describe
certain aspects of both
quantum and thermal ghost imaging and I also study image
discrimination with high fidelity at low light levels.
First of
all, I present a theoretical and experimental study of
entangled-photon
compressive ghost imaging. In quantum ghost
imaging using entangled photon pairs,
the brightness of readily
available sources is rather weak. The usual technique of
image
acquisition in this imaging modality is to raster scan a
single-pixel single-photon
sensitive detector in one arm of a
ghost imaging setup. In most imaging modalities,
the number of
measurements required to fully resolve an object is dependent on
the measurement’s Nyquist limit. In the first part of the thesis, I
propose a ghost
imaging (GI) configuration that uses bucket
detectors (as opposed to a raster scanning
detector) in both arms
of the GI setup. High resolution image reconstruction using
only
27% of the measurement’s Nyquist limit using compressed sensing
algorithms
are presented.
The second part of my thesis deals with
thermal ghost imaging. Unlike in quantum
GI, bright and spatially
correlated classical sources of radiation are used in thermal
GI.
Usually high-contrast speckle patterns are used as sources of the
correlated beams
of radiation. I study the effect of the field
statistics of the illuminating source on the
quality of ghost
images. I show theoretically and experimentally that a thermal GI
setup can produce high quality images even when low-contrast
(intensity-averaged)
speckle patterns are used as an illuminating
source, as long as the collected signal is
mainly caused by the
random fluctuation of the incident speckle field, as opposed to
other noise sources.
In addition, I describe transverse image
discrimination and recognition using holographic
matched filtering
techniques using heralded single photons from a spontaneous
parametric downconversion source. Heralded single photons are used
for encoding and
discriminating images from our predefined
orthogonal basis set. Our basis set constitutes
two locally
spatially orthogonal objects. We show that if the object is a
member
of a predefined set, we can discriminate the objects in the
set with high confidence
levels.
Subjects/Keywords: Compressed sensing; Ghost imaging; Speckle imaging
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Manager
APA (6th Edition):
Zerom, P. (2013). Image reconstruction and discrimination at low light
levels. (Doctoral Dissertation). University of Rochester. Retrieved from http://hdl.handle.net/1802/27903
Chicago Manual of Style (16th Edition):
Zerom, Petros. “Image reconstruction and discrimination at low light
levels.” 2013. Doctoral Dissertation, University of Rochester. Accessed September 19, 2019.
http://hdl.handle.net/1802/27903.
MLA Handbook (7th Edition):
Zerom, Petros. “Image reconstruction and discrimination at low light
levels.” 2013. Web. 19 Sep 2019.
Vancouver:
Zerom P. Image reconstruction and discrimination at low light
levels. [Internet] [Doctoral dissertation]. University of Rochester; 2013. [cited 2019 Sep 19].
Available from: http://hdl.handle.net/1802/27903.
Council of Science Editors:
Zerom P. Image reconstruction and discrimination at low light
levels. [Doctoral Dissertation]. University of Rochester; 2013. Available from: http://hdl.handle.net/1802/27903
University of Rochester
16.
Hu, Yue.
Novel compressed sensing algorithms with applications to
magnetic resonance imaging.
Degree: PhD, 2014, University of Rochester
URL: http://hdl.handle.net/1802/28286
► Magnetic Resonance Imaging (MRI) is a widely used non-invasive clinical imaging modality. Unlike other medical imaging tools, such as X-rays or computed tomography (CT), the…
(more)
▼ Magnetic Resonance Imaging (MRI) is a widely used
non-invasive clinical imaging modality. Unlike other medical
imaging tools, such as X-rays or computed tomography (CT), the
advantage of MRI is that it uses non-ionizing radiation. In
addition, MRI can provide images with multiple contrast by using
different pulse sequences and protocols. However, acquisition
speed, which remains the main challenge for MRI, limits its
clinical application. Clinicians have to compromise between spatial
resolution, SNR, and scan time, which leads to sub-optimal
performance. </br>
The acquisition speed of MRI
can be improved by collecting fewer data samples. However,
according to the Nyquist sampling theory, undersampling in k-space
will lead to aliasing artifacts in the recovered image. The recent
mathematical
theory of compressed sensing has been developed to
exploit the property of sparsity for signals/images. It states that
if an image is sparse, it can be accurately reconstructed using a
subset of the k-space data under certain conditions.
</br>
Generally, the reconstruction is
formulated as an optimization problem. The sparsity of the image is
enforced by using a sparsifying transform. Total variation (TV) is
one of the commonly used methods, which enforces the sparsity of
the image gradients and provides good image quality. However, TV
introduces patchy or painting-like artifacts in the reconstructed
images. We introduce novel regularization penalties involving
higher degree image derivatives to overcome the practical problems
associated with the classical TV scheme. Motivated by novel
reinterpretations of the classical TV regularizer, we derive two
families of functionals, which we term as isotropic and anisotropic
higher degree total variation (HDTV) penalties, respectively. The
numerical comparisons of the proposed scheme with classical TV
penalty, current second order methods, and wavelet algorithms
demonstrate the performance improvement. Specically, the proposed
algorithms minimize the staircase and ringing artifacts that are
common with TV schemes and wavelet algorithms, while better
preserving the singularities. </br>
Higher
dimensional MRI is also challenging due to the above mentioned
trade-offs. We propose a three-dimensional (3D) version of HDTV
(3D-HDTV) to recover 3D datasets. One of the challenges associated
with the HDTV framework
is the high computational complexity of
the algorithm. We introduce a novel computationally efficient
algorithm for HDTV regularized image recovery problems. We find
that this new algorithm improves the convergence rate by a factor
of ten compared to the previously used method. We demonstrate the
utility of 3D-HDTV regularization in the context of compressed
sensing, denoising, and deblurring of 3D MR dataset and
fluorescence microscope images. We show that
3D-HDTV outperforms
3D-TV schemes in terms of the signal to noise ratio (SNR) of the
reconstructed images and its ability to preserve ridge-like details
in the 3D datasets.<br</br>
To address speed
limitations…
Subjects/Keywords: Compressed sensing; Image processing; Magnetic resonance imaging
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APA ·
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Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Hu, Y. (2014). Novel compressed sensing algorithms with applications to
magnetic resonance imaging. (Doctoral Dissertation). University of Rochester. Retrieved from http://hdl.handle.net/1802/28286
Chicago Manual of Style (16th Edition):
Hu, Yue. “Novel compressed sensing algorithms with applications to
magnetic resonance imaging.” 2014. Doctoral Dissertation, University of Rochester. Accessed September 19, 2019.
http://hdl.handle.net/1802/28286.
MLA Handbook (7th Edition):
Hu, Yue. “Novel compressed sensing algorithms with applications to
magnetic resonance imaging.” 2014. Web. 19 Sep 2019.
Vancouver:
Hu Y. Novel compressed sensing algorithms with applications to
magnetic resonance imaging. [Internet] [Doctoral dissertation]. University of Rochester; 2014. [cited 2019 Sep 19].
Available from: http://hdl.handle.net/1802/28286.
Council of Science Editors:
Hu Y. Novel compressed sensing algorithms with applications to
magnetic resonance imaging. [Doctoral Dissertation]. University of Rochester; 2014. Available from: http://hdl.handle.net/1802/28286
University of Rochester
17.
Yang, Zhili (1985 - ); Jacob, Mathews.
Efficient reconstruction algorithms for fast
MRI.
Degree: PhD, 2014, University of Rochester
URL: http://hdl.handle.net/1802/28560
► In this dissertation, we focus on the problems of fast and accurate reconstruction of undersampled dynamic MRI data sets. Our approaches make use of the…
(more)
▼ In this dissertation, we focus on the problems of
fast and accurate reconstruction
of undersampled dynamic MRI data
sets. Our approaches make use of the natural
sparsity of images
and belong to the family of compressed sensing algorithms.
The
compressed sensing theory relies on random sampling. In reality,
people prefer
non-Cartesian MRI sampling trajectories such as
radial or spiral ones in order
to mimic random cases. First, we
propose a novel NUFFT scheme to reconstruct
Non-Cartesian MRI
dataset with high accuracy and low memory demands. Second,
we
develop a unified framework for nonlocal mean regularization
algorithms
to finely recover the MRI images from undersampled
datasets. Finally, we show
that our algorithms significantly
improved the Cardiac MRI reconstruction in both
signal to noise
ratio and visual effects.
Subjects/Keywords: Compressed sensing; Dynamic MRI; Nonlocal; NUFFT
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APA ·
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Export
to Zotero / EndNote / Reference
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APA (6th Edition):
Yang, Zhili (1985 - ); Jacob, M. (2014). Efficient reconstruction algorithms for fast
MRI. (Doctoral Dissertation). University of Rochester. Retrieved from http://hdl.handle.net/1802/28560
Chicago Manual of Style (16th Edition):
Yang, Zhili (1985 - ); Jacob, Mathews. “Efficient reconstruction algorithms for fast
MRI.” 2014. Doctoral Dissertation, University of Rochester. Accessed September 19, 2019.
http://hdl.handle.net/1802/28560.
MLA Handbook (7th Edition):
Yang, Zhili (1985 - ); Jacob, Mathews. “Efficient reconstruction algorithms for fast
MRI.” 2014. Web. 19 Sep 2019.
Vancouver:
Yang, Zhili (1985 - ); Jacob M. Efficient reconstruction algorithms for fast
MRI. [Internet] [Doctoral dissertation]. University of Rochester; 2014. [cited 2019 Sep 19].
Available from: http://hdl.handle.net/1802/28560.
Council of Science Editors:
Yang, Zhili (1985 - ); Jacob M. Efficient reconstruction algorithms for fast
MRI. [Doctoral Dissertation]. University of Rochester; 2014. Available from: http://hdl.handle.net/1802/28560
Harvard University
18.
Markovich, Thomas.
Towards Realistic Correlated Electronic Dynamics: New Developments in the Modeling of Harmonic Bath Models and Many Body Dispersion Interactions.
Degree: PhD, 2017, Harvard University
URL: http://nrs.harvard.edu/urn-3:HUL.InstRepos:41142045
► In this work we develop and characterize tools to compute realistic environmental models and accurate molecular structures, with the ultimate goal of enabling accurate correlated…
(more)
▼ In this work we develop and characterize tools to compute realistic environmental models and accurate molecular structures, with the ultimate goal of enabling accurate correlated electronic dynamics.
The procedure to compute environmental models starts with {\it ab inito} molecular dynamics, from which an autocorrelation function is computed. In this work we introduce super-resolution as a technique for recovering high resolution bath models from one quarter of the data that the Fourier transform requires. We further characterize the method and the closely related compressed sensing, to better understand transferability to other problems in chemistry. While work remains to improve upon our factor of four undersampling, we are optimistic that super-resolution will provide a path forward to computing accurate environmental models.
To accurately model the structure and energetics of many materials, particularly those in condensed phase, it is frequently necessary to include dispersion in density functional theory (DFT) calculations. One of the most accurate techniques for including dispersion in DFT is the many-body dispersion (MBD) model, which models the dispersion energy as the correlation energy of an auxiliary quantum harmonic oscillator system. In this work we present gradients of MBD model with respect to the ions, unit cell parameters, and the charge density, which yield the forces, cell stresses, and dispersion potential respectively. To make the MBD model applicable to a wide range of systems, we develop an efficient implementation of the MBD gradients and energies within the Quantum ESPRESSO, FHI-AIMS, Octopus, and QChem software packages. We present results for gradient and unit cell optimizations for a wide range of systems, and find good agreement with reference values. In addition, we characterize the MBD's dependence on both the application of MBD self consistently and the inclusion of the Hirhsfeld volume gradients and find both are important for accurate forces. We present a framework for combining the MBD model with new exchange correlation functionals and provide this fitting data for twenty-four DFT exchange-correlation functionals. While signficant work still remains in further benchmarking the MBD model, we are encouraged by current results.
Chemical Physics
Advisors/Committee Members: Aspuru-Guzik, Alan (advisor), Ni, Kang-Kuen (committee member), Kaxiras, Efthimios (committee member).
Subjects/Keywords: Many Body Dispersion; Compressed Sensing; Dispersion
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APA (6th Edition):
Markovich, T. (2017). Towards Realistic Correlated Electronic Dynamics: New Developments in the Modeling of Harmonic Bath Models and Many Body Dispersion Interactions. (Doctoral Dissertation). Harvard University. Retrieved from http://nrs.harvard.edu/urn-3:HUL.InstRepos:41142045
Chicago Manual of Style (16th Edition):
Markovich, Thomas. “Towards Realistic Correlated Electronic Dynamics: New Developments in the Modeling of Harmonic Bath Models and Many Body Dispersion Interactions.” 2017. Doctoral Dissertation, Harvard University. Accessed September 19, 2019.
http://nrs.harvard.edu/urn-3:HUL.InstRepos:41142045.
MLA Handbook (7th Edition):
Markovich, Thomas. “Towards Realistic Correlated Electronic Dynamics: New Developments in the Modeling of Harmonic Bath Models and Many Body Dispersion Interactions.” 2017. Web. 19 Sep 2019.
Vancouver:
Markovich T. Towards Realistic Correlated Electronic Dynamics: New Developments in the Modeling of Harmonic Bath Models and Many Body Dispersion Interactions. [Internet] [Doctoral dissertation]. Harvard University; 2017. [cited 2019 Sep 19].
Available from: http://nrs.harvard.edu/urn-3:HUL.InstRepos:41142045.
Council of Science Editors:
Markovich T. Towards Realistic Correlated Electronic Dynamics: New Developments in the Modeling of Harmonic Bath Models and Many Body Dispersion Interactions. [Doctoral Dissertation]. Harvard University; 2017. Available from: http://nrs.harvard.edu/urn-3:HUL.InstRepos:41142045
Universiteit Utrecht
19.
Zwaan, I.N.
Compressed Sensing accelerated radial acquisitions for dynamic Magnetic Resonance Imaging.
Degree: 2013, Universiteit Utrecht
URL: http://dspace.library.uu.nl:8080/handle/1874/262831
► We present a flexible method dubbed Accelerated Radial Compressed Sensing (ARCS) which uses Compressed Sensing to reconstruct 2D and 3D radial data. Our tests on…
(more)
▼ We present a flexible method dubbed Accelerated Radial
Compressed Sensing (ARCS) which uses
Compressed Sensing to reconstruct 2D and 3D radial data. Our tests on 2D radial data show that ARCS is competitive in quality with traditional CS reconstruction methods (which reconstruct Cartesian data) and is five to twenty times as fast at the same time. Therefore, we believe that ARCS is a novel approach that warrants additional research.
Advisors/Committee Members: Sleijpen, G.L.G., Seevinck, P.R..
Subjects/Keywords: Compressed Sensing; CS; Magnetic Resonance Imaging; MRI; Accelerated Radial Compressed Sensing; ARCS
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APA (6th Edition):
Zwaan, I. N. (2013). Compressed Sensing accelerated radial acquisitions for dynamic Magnetic Resonance Imaging. (Masters Thesis). Universiteit Utrecht. Retrieved from http://dspace.library.uu.nl:8080/handle/1874/262831
Chicago Manual of Style (16th Edition):
Zwaan, I N. “Compressed Sensing accelerated radial acquisitions for dynamic Magnetic Resonance Imaging.” 2013. Masters Thesis, Universiteit Utrecht. Accessed September 19, 2019.
http://dspace.library.uu.nl:8080/handle/1874/262831.
MLA Handbook (7th Edition):
Zwaan, I N. “Compressed Sensing accelerated radial acquisitions for dynamic Magnetic Resonance Imaging.” 2013. Web. 19 Sep 2019.
Vancouver:
Zwaan IN. Compressed Sensing accelerated radial acquisitions for dynamic Magnetic Resonance Imaging. [Internet] [Masters thesis]. Universiteit Utrecht; 2013. [cited 2019 Sep 19].
Available from: http://dspace.library.uu.nl:8080/handle/1874/262831.
Council of Science Editors:
Zwaan IN. Compressed Sensing accelerated radial acquisitions for dynamic Magnetic Resonance Imaging. [Masters Thesis]. Universiteit Utrecht; 2013. Available from: http://dspace.library.uu.nl:8080/handle/1874/262831
EPFL
20.
Haghighatshoar, Saeid.
Compressed Sensing of Memoryless Sources: A Deterministic Hadamard Construction.
Degree: 2014, EPFL
URL: http://infoscience.epfl.ch/record/203719
► Compressed sensing is a new trend in signal processing for efficient sampling and signal acquisition. The idea is that most real-world signals have a sparse…
(more)
▼ Compressed sensing is a new trend in signal processing for efficient sampling and signal acquisition. The idea is that most real-world signals have a sparse representation in an appropriate basis and this can be exploited to capture the sparse signal by taking only a few linear projections. The recovery is possible by running appropriate low-complexity algorithms that exploit the sparsity (prior information) to reconstruct the signal from the linear projections (posterior information). The main benefit is that the required number of measurements is much smaller than the dimension of the signal. This results in a huge gain in sensor cost (in measurement devices) or a dramatic saving in data acquisition time. However, some difficulties naturally arise in applying the
compressed sensing to real-world applications such as robustness issues in taking the linear projections and computational complexity of the recovery algorithm. In this thesis, we design structured matrices for
compressed sensing. In particular, we claim that some of the practical difficulties can be reasonably solved by imposing some structure on the measurement matrices. The thesis evolves around the Hadamard matrices which are {+1,-1}-valued matrices with many applications in signal processing, coding, optics and mathematics. As the title of the thesis implies, there are two main ingredients to this thesis. First, we use a memoryless assumption for the source, i.e., we assume that the nonzero components of the sparse signal are independently generated by a given probability distribution and their position is completely random. This allows us to use tools from probability, information theory and coding theory to rigorously assess the achievable performance. Second, using the mathematical properties of the Hadamard matrices, we design measurement matrices by selecting specific rows of a Hadamard matrix according to a deterministic criterion. We call the resulting matrices ``partial Hadamard matrices''. We design partial Hadamard matrices for three signal models: memoryless discrete signals and sparse signals with linear or sub-linear sparsity. A signal has linear sparsity if the number k of its nonzero components is proportional to n, the dimension of signal, whereas it has a sub-linear sparsity if k scales like O(n^α) for some α ∈ (0,1). We develop tools to rigorously analyze the performance of the proposed constructions by borrowing ideas from information theory and coding theory. We also extend our construction to distributed (multi-terminal) signals. Distributed
compressed sensing is a ubiquitous problem in distributed data acquisition systems such as ad-hoc sensor networks. From both a theoretical and an engineering point of view, it is important to know how many measurement per dimension are necessary from different terminals in order to have a reliable estimate of the distributed data. We theoretically analyze this problem for a very simple setup where the components of the distributed signal are generated by a joint…
Advisors/Committee Members: Rimoldi, Bixio.
Subjects/Keywords: Compressed sensing; Hadamard matrices; Deterministic matrix construction; Sparse Fast Hadamard Transform (SFHT); Distributed compressed sensing
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APA ·
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APA (6th Edition):
Haghighatshoar, S. (2014). Compressed Sensing of Memoryless Sources: A Deterministic Hadamard Construction. (Thesis). EPFL. Retrieved from http://infoscience.epfl.ch/record/203719
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Haghighatshoar, Saeid. “Compressed Sensing of Memoryless Sources: A Deterministic Hadamard Construction.” 2014. Thesis, EPFL. Accessed September 19, 2019.
http://infoscience.epfl.ch/record/203719.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Haghighatshoar, Saeid. “Compressed Sensing of Memoryless Sources: A Deterministic Hadamard Construction.” 2014. Web. 19 Sep 2019.
Vancouver:
Haghighatshoar S. Compressed Sensing of Memoryless Sources: A Deterministic Hadamard Construction. [Internet] [Thesis]. EPFL; 2014. [cited 2019 Sep 19].
Available from: http://infoscience.epfl.ch/record/203719.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Haghighatshoar S. Compressed Sensing of Memoryless Sources: A Deterministic Hadamard Construction. [Thesis]. EPFL; 2014. Available from: http://infoscience.epfl.ch/record/203719
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
UCLA
21.
Barekat, Farzin.
Applications of Stochastic Simulation and Compressed Sensing to Large Systems.
Degree: Mathematics, 2014, UCLA
URL: http://www.escholarship.org/uc/item/57j3m7fr
► In this dissertation, three new algorithms for three distinct problems are proposed. The three distinct problems considered here have applications to stochastic modeling, compressive sensing,…
(more)
▼ In this dissertation, three new algorithms for three distinct problems are proposed. The three distinct problems considered here have applications to stochastic modeling, compressive sensing, and numerical solutions of partial differential equations. A common aspect of these problems is that to obtain accurate results require an ever increasing number of unknown variables. Since the proposed algorithms are more efficient than the state of the art methods used for these problems, the use of these new algorithms allows one to compute solutions to these problems with substantially higher accuracy. In addition, some theoretical analysis is provided relating to the investigated problems and the proposed algorithms.
Subjects/Keywords: Mathematics; Compressed Modes; Compressed Plane Waves; Compressive Sensing; Shift Orthogonal Basis Functions; Stochastic Modeling
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APA (6th Edition):
Barekat, F. (2014). Applications of Stochastic Simulation and Compressed Sensing to Large Systems. (Thesis). UCLA. Retrieved from http://www.escholarship.org/uc/item/57j3m7fr
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Barekat, Farzin. “Applications of Stochastic Simulation and Compressed Sensing to Large Systems.” 2014. Thesis, UCLA. Accessed September 19, 2019.
http://www.escholarship.org/uc/item/57j3m7fr.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Barekat, Farzin. “Applications of Stochastic Simulation and Compressed Sensing to Large Systems.” 2014. Web. 19 Sep 2019.
Vancouver:
Barekat F. Applications of Stochastic Simulation and Compressed Sensing to Large Systems. [Internet] [Thesis]. UCLA; 2014. [cited 2019 Sep 19].
Available from: http://www.escholarship.org/uc/item/57j3m7fr.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Barekat F. Applications of Stochastic Simulation and Compressed Sensing to Large Systems. [Thesis]. UCLA; 2014. Available from: http://www.escholarship.org/uc/item/57j3m7fr
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
University of Oxford
22.
Mendoza-Smith, Rodrigo.
Numerical algorithms for the mathematics of information.
Degree: PhD, 2017, University of Oxford
URL: http://ora.ox.ac.uk/objects/uuid:451a418b-eca0-454f-8b54-7b6476056969
;
https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.757711
► This thesis presents a series of algorithmic innovations in Combinatorial Compressed Sensing and Persistent Homology. The unifying strategy across these contributions is in translating structural…
(more)
▼ This thesis presents a series of algorithmic innovations in Combinatorial Compressed Sensing and Persistent Homology. The unifying strategy across these contributions is in translating structural patterns in the underlying data into specific algorithmic designs in order to achieve: better guarantees in computational complexity, the ability to operate on more complex data, highly efficient parallelisations, or any combination of these.
Subjects/Keywords: Topological Data Analysis; Computational Algebraic Topology; Compressed sensing (Telecommunication); Numerical Analysis; Persistent Homology; Parallel Algorithms; Compressed sensing; Combinatorial compressed sensing; Expander graphs
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APA ·
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Export
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APA (6th Edition):
Mendoza-Smith, R. (2017). Numerical algorithms for the mathematics of information. (Doctoral Dissertation). University of Oxford. Retrieved from http://ora.ox.ac.uk/objects/uuid:451a418b-eca0-454f-8b54-7b6476056969 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.757711
Chicago Manual of Style (16th Edition):
Mendoza-Smith, Rodrigo. “Numerical algorithms for the mathematics of information.” 2017. Doctoral Dissertation, University of Oxford. Accessed September 19, 2019.
http://ora.ox.ac.uk/objects/uuid:451a418b-eca0-454f-8b54-7b6476056969 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.757711.
MLA Handbook (7th Edition):
Mendoza-Smith, Rodrigo. “Numerical algorithms for the mathematics of information.” 2017. Web. 19 Sep 2019.
Vancouver:
Mendoza-Smith R. Numerical algorithms for the mathematics of information. [Internet] [Doctoral dissertation]. University of Oxford; 2017. [cited 2019 Sep 19].
Available from: http://ora.ox.ac.uk/objects/uuid:451a418b-eca0-454f-8b54-7b6476056969 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.757711.
Council of Science Editors:
Mendoza-Smith R. Numerical algorithms for the mathematics of information. [Doctoral Dissertation]. University of Oxford; 2017. Available from: http://ora.ox.ac.uk/objects/uuid:451a418b-eca0-454f-8b54-7b6476056969 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.757711
23.
Sampaio, Phillipe Rodrigues.
Teoria, métodos e aplicações de otimização multiobjetivo.
Degree: Mestrado, Ciência da Computação, 2011, University of São Paulo
URL: http://www.teses.usp.br/teses/disponiveis/45/45134/tde-25042011-122013/
;
► Problemas com múltiplos objetivos são muito frequentes nas áreas de Otimização, Economia, Finanças, Transportes, Engenharia e várias outras. Como os objetivos são, geralmente, conflitantes, faz-se…
(more)
▼ Problemas com múltiplos objetivos são muito frequentes nas áreas de Otimização, Economia, Finanças, Transportes, Engenharia e várias outras. Como os objetivos são, geralmente, conflitantes, faz-se necessário o uso de técnicas apropriadas para obter boas soluções. A área que trata de problemas deste tipo é chamada de Otimização Multiobjetivo. Neste trabalho, estudamos os problemas dessa área e alguns dos métodos existentes para resolvê-los. Primeiramente, alguns conceitos relacionados ao conjunto de soluções são definidos, como o de eficiência, no intuito de entender o que seria a melhor solução para este tipo de problema. Em seguida, apresentamos algumas condições de otimalidade de primeira ordem, incluindo as do tipo Fritz John para problemas de Otimização Multiobjetivo. Discutimos ainda sobre algumas condições de regularidade e total regularidade, as quais desempenham o mesmo papel das condições de qualificação em Programação Não-Linear, propiciando a estrita positividade dos multiplicadores de Lagrange associados às funções objetivo. Posteriormente, alguns dos métodos existentes para resolver problemas de Otimização Multiobjetivo são descritos e comparados entre si. Ao final, aplicamos a teoria e métodos de Otimização Multiobjetivo nas áreas de Compressed Sensing e Otimização de Portfolio. Exibimos então testes computacionais realizados com alguns dos métodos discutidos envolvendo problemas de Otimização de Portfolio e fazemos uma análise dos resultados.
Problems with multiple objectives are very frequent in areas such as Optimization, Economy, Finance, Transportation, Engineering and many others. Since the objectives are usually conflicting, there is a need for appropriate techniques to obtain good solutions. The area that deals with problems of this type is called Multiobjective Optimization. The aim of this work is to study the problems of such area and some of the methods available to solve them. Firstly, some basic concepts related to the feasible set are defined, for instance, efficiency, in order to comprehend which solution could be the best for this kind of problem. Secondly, we present some first-order optimality conditions, including the Fritz John ones for Multiobjective Optimization. We also discuss about regularity and total regularity conditions, which play the same role in Nonlinear Multiobjective Optimization as the constraint qualifications in Nonlinear Programming, providing the strict positivity of the Lagrange multipliers associated to the objective functions. Afterwards, some of the existing methods to solve Multiobjective Optimization problems are described and compared with each other. At last, the theory and methods of Multiobjective Optimization are applied into the fields of Compressed Sensing and Portfolio Optimization. We, then, show computational tests performed with some of the methods discussed involving Portfolio Optimization problems and we present an analysis of the results.
Advisors/Committee Members: Birgin, Ernesto Julian Goldberg.
Subjects/Keywords: compressed sensing; compressed sensing; multiobjective optimization; nonlinear programming; otimização de portfolio; otimização multiobjetivo; portfolio optimization; programação não-linear
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APA (6th Edition):
Sampaio, P. R. (2011). Teoria, métodos e aplicações de otimização multiobjetivo. (Masters Thesis). University of São Paulo. Retrieved from http://www.teses.usp.br/teses/disponiveis/45/45134/tde-25042011-122013/ ;
Chicago Manual of Style (16th Edition):
Sampaio, Phillipe Rodrigues. “Teoria, métodos e aplicações de otimização multiobjetivo.” 2011. Masters Thesis, University of São Paulo. Accessed September 19, 2019.
http://www.teses.usp.br/teses/disponiveis/45/45134/tde-25042011-122013/ ;.
MLA Handbook (7th Edition):
Sampaio, Phillipe Rodrigues. “Teoria, métodos e aplicações de otimização multiobjetivo.” 2011. Web. 19 Sep 2019.
Vancouver:
Sampaio PR. Teoria, métodos e aplicações de otimização multiobjetivo. [Internet] [Masters thesis]. University of São Paulo; 2011. [cited 2019 Sep 19].
Available from: http://www.teses.usp.br/teses/disponiveis/45/45134/tde-25042011-122013/ ;.
Council of Science Editors:
Sampaio PR. Teoria, métodos e aplicações de otimização multiobjetivo. [Masters Thesis]. University of São Paulo; 2011. Available from: http://www.teses.usp.br/teses/disponiveis/45/45134/tde-25042011-122013/ ;
University of New South Wales
24.
Movahed, Amin.
Iterative Receiver Techniques for Source/Channel Decoding Using Compressed Sensing.
Degree: Engineering & Information Technology, 2016, University of New South Wales
URL: http://handle.unsw.edu.au/1959.4/56996
;
https://unsworks.unsw.edu.au/fapi/datastream/unsworks:42259/SOURCE02?view=true
► This thesis addresses the signal reconstruction problem in the quantised compressed sensing (CS) framework. In particular, signal reconstructionalgorithms for the scenario where the CS measurements…
(more)
▼ This thesis addresses the signal reconstruction problem in the quantised
compressed sensing (CS) framework. In particular, signal reconstructionalgorithms for the scenario where the CS measurements are quantised to only one bit, referred to as 1-bit CS, are considered. The results in thisthesis demonstrate that dramatic performance gain for 1-bit CS system models is achievable for signal reconstruction and transmission in thepresence of noisy channels. The three main problems considered in this work are as follows.The first problem focuses on the signal reconstruction from noisy 1-bit CS where there is measurement noise inside the quantiser and in the formof additive white Gaussian noise (AWGN). We propose an iterative algorithm, inspired by a multi-user detection method in the field of wirelesscommunications. Our design shows significant signal reconstruction performance improvement compared to the-state-of-the-art reconstructionalgorithms in the case where the elements of the signal are from a finite set of values.The second problem relates to the signal reconstruction scenario where the 1-bit CS measurements are contaminated with random bit-flips. As areconstruction solution, we propose a reconstruction algorithm, referred to as noise adaptive restricted shrinkage (NARSS). It has been shownthrough the numerical experiments that the NARSS algorithm outperforms other recent algorithms in the context of 1-bit CS with random bit-flipsboth in terms of reconstruction performance and complexity.The 1-bit CS reconstruction problem in the presence of AWGN channels is the third problem considered in this thesis. Inspired by turbo codes incommunications theory, we introduce a joint source-channel encoding/decoding scheme for transmission of 1-bit CS signals over an AWGNchannel. We refer to the proposed method as turbo-CS. In this thesis, we suggest three different soft-in/soft-out (SISO) 1-bit CS decoders that areapplied in the turbo-CS decoder setup. While the first two proposed SISO 1-bit CS decoders are heuristic-based, the third SISO 1-bit CS decoderis based on a message passing method. We analyse the convergence behaviour of the turbo-CS decoder through calculating the extrinsicinformation transfer (EXIT) chart which helps us to modify and improve the performance of the turbo-CS decoder through a training-based method.
Advisors/Committee Members: Reed, Mark, Engineering & Information Technology, UNSW Canberra, UNSW, Pickering, Mark, Engineering & Information Technology, UNSW Canberra, UNSW.
Subjects/Keywords: Iterative decoding; Compressed sensing; Turbo decoding; 1-bit compressed sensing; Soft-in soft-out decoding; Message passing methods
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APA (6th Edition):
Movahed, A. (2016). Iterative Receiver Techniques for Source/Channel Decoding Using Compressed Sensing. (Doctoral Dissertation). University of New South Wales. Retrieved from http://handle.unsw.edu.au/1959.4/56996 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:42259/SOURCE02?view=true
Chicago Manual of Style (16th Edition):
Movahed, Amin. “Iterative Receiver Techniques for Source/Channel Decoding Using Compressed Sensing.” 2016. Doctoral Dissertation, University of New South Wales. Accessed September 19, 2019.
http://handle.unsw.edu.au/1959.4/56996 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:42259/SOURCE02?view=true.
MLA Handbook (7th Edition):
Movahed, Amin. “Iterative Receiver Techniques for Source/Channel Decoding Using Compressed Sensing.” 2016. Web. 19 Sep 2019.
Vancouver:
Movahed A. Iterative Receiver Techniques for Source/Channel Decoding Using Compressed Sensing. [Internet] [Doctoral dissertation]. University of New South Wales; 2016. [cited 2019 Sep 19].
Available from: http://handle.unsw.edu.au/1959.4/56996 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:42259/SOURCE02?view=true.
Council of Science Editors:
Movahed A. Iterative Receiver Techniques for Source/Channel Decoding Using Compressed Sensing. [Doctoral Dissertation]. University of New South Wales; 2016. Available from: http://handle.unsw.edu.au/1959.4/56996 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:42259/SOURCE02?view=true
25.
Lazarus, Carole.
L'échantillonnage compressif en IRM : conception optimisée de trajectoires d’échantillonnage pour accélérer l’IRM : Compressed Sensing in MRI : optimization-based design of k-space filling curves for accelerated MRI.
Degree: Docteur es, Imagerie et physique médicale, 2018, Paris Saclay
URL: http://www.theses.fr/2018SACLS309
► L'imagerie par résonance magnétique (IRM) est l'une des modalités d'imagerie les plus puissantes et les plus sures pour examiner le corps humain. L'IRM de haute…
(more)
▼ L'imagerie par résonance magnétique (IRM) est l'une des modalités d'imagerie les plus puissantes et les plus sures pour examiner le corps humain. L'IRM de haute résolution devrait aider à la compréhension et le diagnostic de nombreuses pathologies impliquant des lésions submillimétriques ou des maladies telles que la maladie d'Alzheimer et la sclérose en plaque. Bien que les systèmes à haut champ magnétique soient capables de fournir un rapport signal-sur-bruit permettant d'augmenter la résolution spatiale, le long temps d'acquisition et la sensibilité au mouvement continuent d'entraver l'utilisation de l'IRM de haute résolution. Malgré le développement de méthodes de correction du mouvement et du bruit physiologique, le long temps d'acquisition reste un obstacle majeur à l'IRM de haute résolution, en particulier dans les applications cliniques.Au cours de la dernière décennie, la nouvelle théorie du
compressed sensing (CS) a proposé une solution prometteuse pour réduire le temps d'examen en IRM. Après avoir expliqué la théorie du
compressed sensing, ce projet de thèse propose une étude empirique et quantitative du facteur de sous-échantillonnage maximum réalisable grâce au CS pour l'imagerie pondérée en T ₂ *.En outre, l'application de CS en IRM repose généralement sur l'utilisation de courbes d'échantillonnage simples telles que les lignes droites, spirales ou des légères variations de ces formes élémentaires qui ne tirent pas pleinement parti des degrés de liberté offerts par le hardware et ne peuvent être facilement adaptées à une distribution d'échantillonnage arbitraire. Dans cette thèse, j'ai introduit une méthode appelée SPARKLING, qui permet de surmonter ces limitations en adoptant une approche radicalement nouvelle de la conception de l'échantillonnage de l'espace-k. L'acronyme SPARKLING signifie Spreading Projection Algorithm for Rapid K-space sampLING. C'est une méthode flexible inspirée des techniques de stippling qui génère automatiquement, grâce à un algorithme d'optimisation, des courbes d'échantillonnage non-cartésiennes optimisées et compatibles avec les contraintes hardware de l'IRM en termes d'amplitude de gradient maximale et d'accélération maximale. Ces courbes d'échantillonnage sont conçues pour répondre à des critères clés pour un échantillonnage optimal : une distribution contrôlée des échantillons et une couverture de l'espace-k localement uniforme. Avant de s'engager dans des acquisitions, nous avons vérifié que notre système de gradient était bien capable d'exécuter ces trajectoires complexes. Nous avons implémenté une méthode de mesure de phase et avons observé une très bonne adéquation entre trajectoires prescrites et mesurées.Enfin, en alliant une efficacité d'échantillonnage avec le
compressed sensing et l'imagerie parallèle, les trajectoires SPARKLING ont permis de réduire jusqu'à 20 fois le temps d'acquisition d'un examen IRM T ₂ * par rapport aux acquisitions cartésiennes de référence, sans détérioration de la qualité d'image. Ces résultats expérimentaux ont été obtenus à 7 Tesla…
Advisors/Committee Members: Ciuciu, Philippe (thesis director).
Subjects/Keywords: IRM; Imagerie par résonance magnétique; Compressed sensing; Accélération; SPARKLING; MRI; Magnétic resonance imaging; Compressed sensing; Accélération; SPARKLING; K-space-trajectories
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APA (6th Edition):
Lazarus, C. (2018). L'échantillonnage compressif en IRM : conception optimisée de trajectoires d’échantillonnage pour accélérer l’IRM : Compressed Sensing in MRI : optimization-based design of k-space filling curves for accelerated MRI. (Doctoral Dissertation). Paris Saclay. Retrieved from http://www.theses.fr/2018SACLS309
Chicago Manual of Style (16th Edition):
Lazarus, Carole. “L'échantillonnage compressif en IRM : conception optimisée de trajectoires d’échantillonnage pour accélérer l’IRM : Compressed Sensing in MRI : optimization-based design of k-space filling curves for accelerated MRI.” 2018. Doctoral Dissertation, Paris Saclay. Accessed September 19, 2019.
http://www.theses.fr/2018SACLS309.
MLA Handbook (7th Edition):
Lazarus, Carole. “L'échantillonnage compressif en IRM : conception optimisée de trajectoires d’échantillonnage pour accélérer l’IRM : Compressed Sensing in MRI : optimization-based design of k-space filling curves for accelerated MRI.” 2018. Web. 19 Sep 2019.
Vancouver:
Lazarus C. L'échantillonnage compressif en IRM : conception optimisée de trajectoires d’échantillonnage pour accélérer l’IRM : Compressed Sensing in MRI : optimization-based design of k-space filling curves for accelerated MRI. [Internet] [Doctoral dissertation]. Paris Saclay; 2018. [cited 2019 Sep 19].
Available from: http://www.theses.fr/2018SACLS309.
Council of Science Editors:
Lazarus C. L'échantillonnage compressif en IRM : conception optimisée de trajectoires d’échantillonnage pour accélérer l’IRM : Compressed Sensing in MRI : optimization-based design of k-space filling curves for accelerated MRI. [Doctoral Dissertation]. Paris Saclay; 2018. Available from: http://www.theses.fr/2018SACLS309
University of Notre Dame
26.
Zhanwei Sun.
Performance Metrics, Sampling Schemes, and Detection
Algorithms for Wideband Spectrum Sensing</h1>.
Degree: PhD, Electrical Engineering, 2013, University of Notre Dame
URL: https://curate.nd.edu/show/td96k071s1s
► This dissertation studies the problem of wideband spectrum sensing for cognitive radio by partitioning into four fundamental elements: system modeling, performance metrics, sampling schemes,…
(more)
▼ This dissertation studies the problem of
wideband spectrum
sensing for cognitive radio by partitioning into
four fundamental elements: system modeling, performance metrics,
sampling schemes, and detection algorithms. Each element can
potentially couple individual channels, and appropriate designs of
wideband spectrum
sensing should consider the four elements
jointly. We propose a p-sparse model to
characterize the primary occupancy in a band of channels as a
Bernoulli process, and suggest a pair of new performance metrics
more appropriate for wideband spectrum
sensing, specifically, the
probability of insufficient spectrum opportunities PISO and the
probability of excessive interference opportunities PEIO. We
suggest two narrower band Nyquist sampling schemes with
correspondingly much lower rates than wideband Nyquist rate, i.e.,
partial-band Nyquist sampling (PBNS) and sequential narrow band
Nyquist sampling (SNNS), and establish a unified sub-Nyquist
sampling structure, within which we study several important
sub-Nyquist sampling schemes in literature. We investigate the
aliasing patterns inherent in sub-Nyquist sampling and identify two
extremes, specifically, uniform aliasing and periodic aliasing, and
develop corresponding detection algorithms that allow tradeoffs
between primary protection and secondary opportunities relevant to
the goal of channel detection characterized Pm, the probability of
missed detection, and Pf, the probability of false alarm, as well
as the goal of wideband detection characterized by PISO and
PEIO. For performance metrics that couple
individual channels, multi-channel detection algorithms have an
advantage over channel-by-channel detection algorithms even for
Nyquist sampling that give independent observations across
channels. Most importantly, integer undersampling (IU), which
corresponds to the simplest sub-Nyquist sampling scheme, exhibits
the best observed
sensing performance in the regime of better
protection for the primary system, i.e., the regime of low PM and
high PF, or the regime of low PEIO and high PISO, for moderate and
high signal-to-noise ratio (SNR ≥ 0 dB); on the other hand, SNNS
exhibits globally best performance for low SNR (< 0 dB) for
the cases studied. These observations discourage studies on the
design of more sophisticated sub-Nyquist sampling schemes and
development of more advanced sparse reconstruction algorithms to
the problem of wideband spectrum
sensing, since their performance
is inferior to either IU or SNNS depending on the system parameters
and the detection regime considered.
Advisors/Committee Members: Yih-Fang Huang, Committee Member, J. Nicholas Laneman, Committee Chair, Thomas Fuja, Committee Member, Martin Haenggi, Committee Member.
Subjects/Keywords: sub-Nyquist sampling; wideband spectrum sensing; dynamic spectrum access; compressed sensing; cognitive radio
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APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
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to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Sun, Z. (2013). Performance Metrics, Sampling Schemes, and Detection
Algorithms for Wideband Spectrum Sensing</h1>. (Doctoral Dissertation). University of Notre Dame. Retrieved from https://curate.nd.edu/show/td96k071s1s
Chicago Manual of Style (16th Edition):
Sun, Zhanwei. “Performance Metrics, Sampling Schemes, and Detection
Algorithms for Wideband Spectrum Sensing</h1>.” 2013. Doctoral Dissertation, University of Notre Dame. Accessed September 19, 2019.
https://curate.nd.edu/show/td96k071s1s.
MLA Handbook (7th Edition):
Sun, Zhanwei. “Performance Metrics, Sampling Schemes, and Detection
Algorithms for Wideband Spectrum Sensing</h1>.” 2013. Web. 19 Sep 2019.
Vancouver:
Sun Z. Performance Metrics, Sampling Schemes, and Detection
Algorithms for Wideband Spectrum Sensing</h1>. [Internet] [Doctoral dissertation]. University of Notre Dame; 2013. [cited 2019 Sep 19].
Available from: https://curate.nd.edu/show/td96k071s1s.
Council of Science Editors:
Sun Z. Performance Metrics, Sampling Schemes, and Detection
Algorithms for Wideband Spectrum Sensing</h1>. [Doctoral Dissertation]. University of Notre Dame; 2013. Available from: https://curate.nd.edu/show/td96k071s1s
University of Toronto
27.
Goukhshtein, Maxim.
Distributed Coding of Compressively Sensed Sources.
Degree: 2017, University of Toronto
URL: http://hdl.handle.net/1807/79233
► In this work we propose a new method for compressing multiple correlated sources with a very low-complexity encoder in the presence of side information. Our…
(more)
▼ In this work we propose a new method for compressing multiple correlated sources with a very low-complexity encoder in the presence of side information. Our approach uses ideas from compressed sensing and distributed source coding. At the encoder, syndromes of the quantized compressively sensed sources are generated and transmitted. The decoder uses side information to predict the compressed sources. The predictions are then used to recover the quantized measurements via a two-stage decoding process consisting of bitplane prediction and syndrome decoding. Finally, guided by the structure of the sources and the side information, the sources are reconstructed from the recovered measurements. As a motivating example, we consider the compression of multispectral images acquired on board satellites, where resources, such as computational power and memory, are scarce. Our experimental results exhibit a significant improvement in the rate-distortion trade-off when compared against approaches with similar encoder complexity.
M.A.S.
Advisors/Committee Members: Draper, Stark C, Electrical and Computer Engineering.
Subjects/Keywords: Compressed sensing; Data compression; Distributed source coding; Multispectral image compression; Remote sensing; 0544
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APA (6th Edition):
Goukhshtein, M. (2017). Distributed Coding of Compressively Sensed Sources. (Masters Thesis). University of Toronto. Retrieved from http://hdl.handle.net/1807/79233
Chicago Manual of Style (16th Edition):
Goukhshtein, Maxim. “Distributed Coding of Compressively Sensed Sources.” 2017. Masters Thesis, University of Toronto. Accessed September 19, 2019.
http://hdl.handle.net/1807/79233.
MLA Handbook (7th Edition):
Goukhshtein, Maxim. “Distributed Coding of Compressively Sensed Sources.” 2017. Web. 19 Sep 2019.
Vancouver:
Goukhshtein M. Distributed Coding of Compressively Sensed Sources. [Internet] [Masters thesis]. University of Toronto; 2017. [cited 2019 Sep 19].
Available from: http://hdl.handle.net/1807/79233.
Council of Science Editors:
Goukhshtein M. Distributed Coding of Compressively Sensed Sources. [Masters Thesis]. University of Toronto; 2017. Available from: http://hdl.handle.net/1807/79233
University of Utah
28.
Chen, Liyong.
Image reconstruction in dynamic contrast enhanced magnetic resonance imaging.
Degree: PhD, Bioengineering, 2012, University of Utah
URL: http://content.lib.utah.edu/cdm/singleitem/collection/etd3/id/690/rec/1290
► Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) is a powerful tool to detect cardiac diseases and tumors, and both spatial resolution and temporalresolution are important…
(more)
▼ Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) is a powerful tool to detect cardiac diseases and tumors, and both spatial resolution and temporalresolution are important for disease detection. Sampling less in each time frame and applying sophisticated reconstruction methods to overcome image degradations is a common strategy in the literature.In this thesis, temporal TV constrained reconstruction that was successfully applied to DCE myocardial perfusion imaging by our group was extended to three-dimensional (3D) DCE breast and 3D myocardial perfusion imaging, and the extension includesdifferent forms of constraint terms and various sampling patterns. We also explored some other popular reconstruction algorithms from a theoretical level and showed that they can be included in a unified framework.Current 3D Cartesian DCE breast tumor imaging is limited in spatiotemporal resolution as high temporal resolution is desired to track the contrast enhancementcurves, and high spatial resolution is desired to discern tumor morphology. Here temporal TV constrained reconstruction was extended and different forms of temporal TV constraints were compared on 3D Cartesian DCE breast tumor data with simulated undersampling. Kinetic parameters analysis was used to validate the methods.
Subjects/Keywords: Cardiac; Compressed sensing; Constrained reconstruction; Image reconstruction; MRI
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APA (6th Edition):
Chen, L. (2012). Image reconstruction in dynamic contrast enhanced magnetic resonance imaging. (Doctoral Dissertation). University of Utah. Retrieved from http://content.lib.utah.edu/cdm/singleitem/collection/etd3/id/690/rec/1290
Chicago Manual of Style (16th Edition):
Chen, Liyong. “Image reconstruction in dynamic contrast enhanced magnetic resonance imaging.” 2012. Doctoral Dissertation, University of Utah. Accessed September 19, 2019.
http://content.lib.utah.edu/cdm/singleitem/collection/etd3/id/690/rec/1290.
MLA Handbook (7th Edition):
Chen, Liyong. “Image reconstruction in dynamic contrast enhanced magnetic resonance imaging.” 2012. Web. 19 Sep 2019.
Vancouver:
Chen L. Image reconstruction in dynamic contrast enhanced magnetic resonance imaging. [Internet] [Doctoral dissertation]. University of Utah; 2012. [cited 2019 Sep 19].
Available from: http://content.lib.utah.edu/cdm/singleitem/collection/etd3/id/690/rec/1290.
Council of Science Editors:
Chen L. Image reconstruction in dynamic contrast enhanced magnetic resonance imaging. [Doctoral Dissertation]. University of Utah; 2012. Available from: http://content.lib.utah.edu/cdm/singleitem/collection/etd3/id/690/rec/1290
29.
Li, Kaiyu.
A Compressed Sensing Approach to Uncertainty Propagation for Approximately Additive Functions.
Degree: 2016, Texas A&M University
URL: http://hdl.handle.net/1969.1/158971
► Computational models for numerically simulating physical systems are increasingly being used to support decision-making processes in engineering. Processes such as design decisions, policy level analyses,…
(more)
▼ Computational models for numerically simulating physical systems are increasingly being used to support decision-making processes in engineering. Processes such as design decisions, policy level analyses, and experimental design settings are often guided by information gained from computational modeling capabilities. To ensure effective application of results obtained through numerical simulation of computational models, uncertainty in model inputs must be propagated to uncertainty in model outputs. For expensive computational models, the many thousands of model evaluations required for traditional Monte Carlo based techniques for uncertainty propagation can be prohibitive. This paper presents a novel methodology for constructing surrogate representations of computational models via
compressed sensing. Our approach exploits the approximate additivity inherent in many engineering computational modeling capabilities. We demonstrate our methodology on some analytical functions, with comparison to the Gaussian process regression, and a cooled gas turbine blade application. We also provide some possible methods to build uncertainty information for our approach. The results of these applications reveal substantial computational savings over traditional Monte Carlo simulation with negligible loss of accuracy.
Advisors/Committee Members: Allaire, Douglas (advisor), Bhattacharya, Raktim (committee member), Malak, Richard (committee member).
Subjects/Keywords: compressed sensing; uncertainty propagation
…3.12 ECDF of compressed sensing approach with Fourier basis function and
real function on… …1-D simple Fourier function. . . . . . . . . . . . . . . .
42
3.13 Compressed sensing… …43
3.14 ECDF of compressed sensing approach with Legendre basis result and real
function… …3.19 Compressed sensing approach with Legendre basis result and real function
on 2-D Legendre… …polynomials function. . . . . . . . . . . . . . . . . . .
48
3.20 ECDF of compressed sensing…
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APA ·
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Manager
APA (6th Edition):
Li, K. (2016). A Compressed Sensing Approach to Uncertainty Propagation for Approximately Additive Functions. (Thesis). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/158971
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Li, Kaiyu. “A Compressed Sensing Approach to Uncertainty Propagation for Approximately Additive Functions.” 2016. Thesis, Texas A&M University. Accessed September 19, 2019.
http://hdl.handle.net/1969.1/158971.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Li, Kaiyu. “A Compressed Sensing Approach to Uncertainty Propagation for Approximately Additive Functions.” 2016. Web. 19 Sep 2019.
Vancouver:
Li K. A Compressed Sensing Approach to Uncertainty Propagation for Approximately Additive Functions. [Internet] [Thesis]. Texas A&M University; 2016. [cited 2019 Sep 19].
Available from: http://hdl.handle.net/1969.1/158971.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Li K. A Compressed Sensing Approach to Uncertainty Propagation for Approximately Additive Functions. [Thesis]. Texas A&M University; 2016. Available from: http://hdl.handle.net/1969.1/158971
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
University of Alberta
30.
Shaghaghi, Mahdi.
Parameter Estimation in Low-Rank Models from Small Sample
Size and Undersampled Data: DOA and Spectrum Estimation.
Degree: PhD, Department of Electrical and Computer
Engineering, 2014, University of Alberta
URL: https://era.library.ualberta.ca/files/7s75dg15t
► In estimation theory, a set of parameters are estimated from a finite number of measurements (samples). In general, the quality of estimation degrades as the…
(more)
▼ In estimation theory, a set of parameters are
estimated from a finite number of measurements (samples). In
general, the quality of estimation degrades as the number of
samples is reduced. In this thesis, the problem of parameter
estimation in low-rank models from a small number of samples is
studied. Specifically, we consider two related problems that fit in
this system model: direction-of-arrival (DOA) and spectrum
estimation. We focus on subspace based DOA estimation methods which
present a good compromise between performance and complexity.
However, these methods are exposed to performance breakdown for a
small number of samples. The reason is identified to be the
intersubspace leakage where some portion of the true signal
subspace resides in the estimated noise subspace. A two-step
algorithm is proposed to reduce the amount of the subspace leakage.
Theoretical derivations and simulation results are given to show
the improvement achieved by the introduced method. Furthermore, the
dynamics of the DOA estimation method in the breakdown region has
been investigated, which led to identification of a problem named
root-swap where a root associated with noise is mistakenly taken
for a root associated with the signal. Then, an improved method is
introduced to remedy this issue. Spectrum estimation from
undersampled data (samples obtained at a rate lower than the
Nyquist rate) is studied next. Specifically, the performance of the
averaged correlogram for undersampled data is theoretically
analyzed for the finite length sample size as well as
asymptotically. This method partitions the spectrum into a number
of segments and estimates the average power within each segment
from samples obtained at a rate lower than the Nyquist rate.
However, the frequency resolution of the estimator is restricted to
the number of spectral segments, and the estimation made for each
segment has also limited accuracy. Therefore, it is of significant
importance to analyze the performance of this method especially in
the case that only a finite number of samples is available. We
derive the bias and variance of the averaged correlogram for
undersampled data for finite-length signals, and we show the
associated tradeoffs among the resolution, the accuracy, and the
complexity of the method. Finally, spectrum estimation from
compressive measurements is studied. The number of such
measurements is much less than the number of Nyquist samples, and
they are obtained by correlating the signal with a number of
sensing waveforms. We specifically consider signals composed of
linear combinations of sinusoids. Albeit these type of signals have
a sparse model, their representation in the Fourier basis exhibits
frequency leakage. This problem results in the poor performance of
the conventional compressive sensing recovery algorithms that rely
on the Fourier basis. We introduce an improved model-based
reconstruction algorithm which has a performance close to the
Cramer-Rao bound, which we also derive.
Subjects/Keywords: Compressed sensing; DOA estimation; Undersampled data; Sub-Nyquist sampling; Spectrum estimation
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APA ·
Chicago ·
MLA ·
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CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Shaghaghi, M. (2014). Parameter Estimation in Low-Rank Models from Small Sample
Size and Undersampled Data: DOA and Spectrum Estimation. (Doctoral Dissertation). University of Alberta. Retrieved from https://era.library.ualberta.ca/files/7s75dg15t
Chicago Manual of Style (16th Edition):
Shaghaghi, Mahdi. “Parameter Estimation in Low-Rank Models from Small Sample
Size and Undersampled Data: DOA and Spectrum Estimation.” 2014. Doctoral Dissertation, University of Alberta. Accessed September 19, 2019.
https://era.library.ualberta.ca/files/7s75dg15t.
MLA Handbook (7th Edition):
Shaghaghi, Mahdi. “Parameter Estimation in Low-Rank Models from Small Sample
Size and Undersampled Data: DOA and Spectrum Estimation.” 2014. Web. 19 Sep 2019.
Vancouver:
Shaghaghi M. Parameter Estimation in Low-Rank Models from Small Sample
Size and Undersampled Data: DOA and Spectrum Estimation. [Internet] [Doctoral dissertation]. University of Alberta; 2014. [cited 2019 Sep 19].
Available from: https://era.library.ualberta.ca/files/7s75dg15t.
Council of Science Editors:
Shaghaghi M. Parameter Estimation in Low-Rank Models from Small Sample
Size and Undersampled Data: DOA and Spectrum Estimation. [Doctoral Dissertation]. University of Alberta; 2014. Available from: https://era.library.ualberta.ca/files/7s75dg15t
◁ [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] ▶
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Open Access Theses and Dissertations
