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You searched for +publisher:"Oregon State University" +contributor:("Raich, Raviv"). Showing records 1 – 22 of 22 total matches.

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Oregon State University

1. Thangavelu, Madan Kumar. On error bounds for linear feature extraction.

Degree: MS, Computer Science, 2010, Oregon State University

 Linear transformation for dimension reduction is a well established problem in the field of machine learning. Due to the numerous observability of parameters and data,… (more)

Subjects/Keywords: Dimension reduction; Dimension reduction (Statistics)

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APA (6th Edition):

Thangavelu, M. K. (2010). On error bounds for linear feature extraction. (Masters Thesis). Oregon State University. Retrieved from http://hdl.handle.net/1957/13886

Chicago Manual of Style (16th Edition):

Thangavelu, Madan Kumar. “On error bounds for linear feature extraction.” 2010. Masters Thesis, Oregon State University. Accessed October 18, 2019. http://hdl.handle.net/1957/13886.

MLA Handbook (7th Edition):

Thangavelu, Madan Kumar. “On error bounds for linear feature extraction.” 2010. Web. 18 Oct 2019.

Vancouver:

Thangavelu MK. On error bounds for linear feature extraction. [Internet] [Masters thesis]. Oregon State University; 2010. [cited 2019 Oct 18]. Available from: http://hdl.handle.net/1957/13886.

Council of Science Editors:

Thangavelu MK. On error bounds for linear feature extraction. [Masters Thesis]. Oregon State University; 2010. Available from: http://hdl.handle.net/1957/13886


Oregon State University

2. Singh, Satpreet Harcharan. Visualization and Analysis of Sensor Data for Detecting Microclimate Cold Air Pools.

Degree: MS, 2017, Oregon State University

 Cold air pools are spatiotemporal phenomena that occur when cold air from higher elevations roll down the slope to accumulate in lower elevations. Behaviors like… (more)

Subjects/Keywords: Cold Air Pools

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APA (6th Edition):

Singh, S. H. (2017). Visualization and Analysis of Sensor Data for Detecting Microclimate Cold Air Pools. (Masters Thesis). Oregon State University. Retrieved from http://hdl.handle.net/1957/61773

Chicago Manual of Style (16th Edition):

Singh, Satpreet Harcharan. “Visualization and Analysis of Sensor Data for Detecting Microclimate Cold Air Pools.” 2017. Masters Thesis, Oregon State University. Accessed October 18, 2019. http://hdl.handle.net/1957/61773.

MLA Handbook (7th Edition):

Singh, Satpreet Harcharan. “Visualization and Analysis of Sensor Data for Detecting Microclimate Cold Air Pools.” 2017. Web. 18 Oct 2019.

Vancouver:

Singh SH. Visualization and Analysis of Sensor Data for Detecting Microclimate Cold Air Pools. [Internet] [Masters thesis]. Oregon State University; 2017. [cited 2019 Oct 18]. Available from: http://hdl.handle.net/1957/61773.

Council of Science Editors:

Singh SH. Visualization and Analysis of Sensor Data for Detecting Microclimate Cold Air Pools. [Masters Thesis]. Oregon State University; 2017. Available from: http://hdl.handle.net/1957/61773


Oregon State University

3. You, Zeyu. A statistical inference framework for finding recurring patterns in large data with applications to energy management.

Degree: MS, Electrical and Computer Engineering, 2014, Oregon State University

 We consider the problem of finding unknown patterns that are recurring across multiple sets. For example, finding multiple objects that are present in multiple images… (more)

Subjects/Keywords: recurring pattern recognition; Pattern recognition systems  – Mathematical models

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APA (6th Edition):

You, Z. (2014). A statistical inference framework for finding recurring patterns in large data with applications to energy management. (Masters Thesis). Oregon State University. Retrieved from http://hdl.handle.net/1957/50023

Chicago Manual of Style (16th Edition):

You, Zeyu. “A statistical inference framework for finding recurring patterns in large data with applications to energy management.” 2014. Masters Thesis, Oregon State University. Accessed October 18, 2019. http://hdl.handle.net/1957/50023.

MLA Handbook (7th Edition):

You, Zeyu. “A statistical inference framework for finding recurring patterns in large data with applications to energy management.” 2014. Web. 18 Oct 2019.

Vancouver:

You Z. A statistical inference framework for finding recurring patterns in large data with applications to energy management. [Internet] [Masters thesis]. Oregon State University; 2014. [cited 2019 Oct 18]. Available from: http://hdl.handle.net/1957/50023.

Council of Science Editors:

You Z. A statistical inference framework for finding recurring patterns in large data with applications to energy management. [Masters Thesis]. Oregon State University; 2014. Available from: http://hdl.handle.net/1957/50023


Oregon State University

4. Delgado, Diana (Diana Carolina). Iterative reconstruction methods of CT images using a statistical framework.

Degree: MS, Electrical and Computer Engineering, 2010, Oregon State University

 Medical imaging technologies play a vital role in early diagnosis of disease by providing internal images of the human body to medical professionals. Computed Tomography… (more)

Subjects/Keywords: Computed Tomography (CT); Tomography

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APA (6th Edition):

Delgado, D. (. C. (2010). Iterative reconstruction methods of CT images using a statistical framework. (Masters Thesis). Oregon State University. Retrieved from http://hdl.handle.net/1957/16368

Chicago Manual of Style (16th Edition):

Delgado, Diana (Diana Carolina). “Iterative reconstruction methods of CT images using a statistical framework.” 2010. Masters Thesis, Oregon State University. Accessed October 18, 2019. http://hdl.handle.net/1957/16368.

MLA Handbook (7th Edition):

Delgado, Diana (Diana Carolina). “Iterative reconstruction methods of CT images using a statistical framework.” 2010. Web. 18 Oct 2019.

Vancouver:

Delgado D(C. Iterative reconstruction methods of CT images using a statistical framework. [Internet] [Masters thesis]. Oregon State University; 2010. [cited 2019 Oct 18]. Available from: http://hdl.handle.net/1957/16368.

Council of Science Editors:

Delgado D(C. Iterative reconstruction methods of CT images using a statistical framework. [Masters Thesis]. Oregon State University; 2010. Available from: http://hdl.handle.net/1957/16368


Oregon State University

5. El Amrani, Samia. Computationally efficient block diagonalization for downlink multiuser MIMO-OFDM systems.

Degree: MS, Electrical and Computer Engineering, 2010, Oregon State University

 One of the key challenges in downlink multiuser multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems is the mitigation of the multi-access interference when… (more)

Subjects/Keywords: downlink; MIMO systems  – Mathematical models

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APA (6th Edition):

El Amrani, S. (2010). Computationally efficient block diagonalization for downlink multiuser MIMO-OFDM systems. (Masters Thesis). Oregon State University. Retrieved from http://hdl.handle.net/1957/16857

Chicago Manual of Style (16th Edition):

El Amrani, Samia. “Computationally efficient block diagonalization for downlink multiuser MIMO-OFDM systems.” 2010. Masters Thesis, Oregon State University. Accessed October 18, 2019. http://hdl.handle.net/1957/16857.

MLA Handbook (7th Edition):

El Amrani, Samia. “Computationally efficient block diagonalization for downlink multiuser MIMO-OFDM systems.” 2010. Web. 18 Oct 2019.

Vancouver:

El Amrani S. Computationally efficient block diagonalization for downlink multiuser MIMO-OFDM systems. [Internet] [Masters thesis]. Oregon State University; 2010. [cited 2019 Oct 18]. Available from: http://hdl.handle.net/1957/16857.

Council of Science Editors:

El Amrani S. Computationally efficient block diagonalization for downlink multiuser MIMO-OFDM systems. [Masters Thesis]. Oregon State University; 2010. Available from: http://hdl.handle.net/1957/16857


Oregon State University

6. Maiya, Megha. iMAC : improved medium access control for multi-channel multi-hop wireless networks.

Degree: MS, Computer Science, 2010, Oregon State University

 Trends in wireless networks are increasingly pointing towards a future with multi-hop networks deployed in multi-channel environments. In this thesis, we present the design for… (more)

Subjects/Keywords: MAC; Computer network protocols

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APA (6th Edition):

Maiya, M. (2010). iMAC : improved medium access control for multi-channel multi-hop wireless networks. (Masters Thesis). Oregon State University. Retrieved from http://hdl.handle.net/1957/18086

Chicago Manual of Style (16th Edition):

Maiya, Megha. “iMAC : improved medium access control for multi-channel multi-hop wireless networks.” 2010. Masters Thesis, Oregon State University. Accessed October 18, 2019. http://hdl.handle.net/1957/18086.

MLA Handbook (7th Edition):

Maiya, Megha. “iMAC : improved medium access control for multi-channel multi-hop wireless networks.” 2010. Web. 18 Oct 2019.

Vancouver:

Maiya M. iMAC : improved medium access control for multi-channel multi-hop wireless networks. [Internet] [Masters thesis]. Oregon State University; 2010. [cited 2019 Oct 18]. Available from: http://hdl.handle.net/1957/18086.

Council of Science Editors:

Maiya M. iMAC : improved medium access control for multi-channel multi-hop wireless networks. [Masters Thesis]. Oregon State University; 2010. Available from: http://hdl.handle.net/1957/18086


Oregon State University

7. Lakshminarayanan, Balaji. Probabilistic models for classification of bioacoustic data.

Degree: MS, Electrical and Computer Engineering, 2010, Oregon State University

 Probabilistic models have been successfully applied for a wide variety of problems, such as but not limited to information retrieval, computer vision, bio-informatics and speech… (more)

Subjects/Keywords: probabilistic models

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APA (6th Edition):

Lakshminarayanan, B. (2010). Probabilistic models for classification of bioacoustic data. (Masters Thesis). Oregon State University. Retrieved from http://hdl.handle.net/1957/19655

Chicago Manual of Style (16th Edition):

Lakshminarayanan, Balaji. “Probabilistic models for classification of bioacoustic data.” 2010. Masters Thesis, Oregon State University. Accessed October 18, 2019. http://hdl.handle.net/1957/19655.

MLA Handbook (7th Edition):

Lakshminarayanan, Balaji. “Probabilistic models for classification of bioacoustic data.” 2010. Web. 18 Oct 2019.

Vancouver:

Lakshminarayanan B. Probabilistic models for classification of bioacoustic data. [Internet] [Masters thesis]. Oregon State University; 2010. [cited 2019 Oct 18]. Available from: http://hdl.handle.net/1957/19655.

Council of Science Editors:

Lakshminarayanan B. Probabilistic models for classification of bioacoustic data. [Masters Thesis]. Oregon State University; 2010. Available from: http://hdl.handle.net/1957/19655


Oregon State University

8. Narasimhan, Revathy. Simultaneous Segmentation and Classification of Bird Song Using CNN.

Degree: MS, Electrical and Computer Engineering, 2016, Oregon State University

 In bioacoustics, automatic animal voice detection and recognition from audio recordings is an emerging topic for animal preservation. Our research focuses on bird bioacoustics, where… (more)

Subjects/Keywords: Birdsongs  – Data processing

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APA (6th Edition):

Narasimhan, R. (2016). Simultaneous Segmentation and Classification of Bird Song Using CNN. (Masters Thesis). Oregon State University. Retrieved from http://hdl.handle.net/1957/60040

Chicago Manual of Style (16th Edition):

Narasimhan, Revathy. “Simultaneous Segmentation and Classification of Bird Song Using CNN.” 2016. Masters Thesis, Oregon State University. Accessed October 18, 2019. http://hdl.handle.net/1957/60040.

MLA Handbook (7th Edition):

Narasimhan, Revathy. “Simultaneous Segmentation and Classification of Bird Song Using CNN.” 2016. Web. 18 Oct 2019.

Vancouver:

Narasimhan R. Simultaneous Segmentation and Classification of Bird Song Using CNN. [Internet] [Masters thesis]. Oregon State University; 2016. [cited 2019 Oct 18]. Available from: http://hdl.handle.net/1957/60040.

Council of Science Editors:

Narasimhan R. Simultaneous Segmentation and Classification of Bird Song Using CNN. [Masters Thesis]. Oregon State University; 2016. Available from: http://hdl.handle.net/1957/60040


Oregon State University

9. Pei, Yuanli. Learning with Partial Supervision for Clustering and Classification.

Degree: PhD, 2017, Oregon State University

 In the field of machine learning, clustering and classification are two fundamental tasks. Traditionally, clustering is an unsupervised method, where no supervision about the data… (more)

Subjects/Keywords: Partial Supervision

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APA (6th Edition):

Pei, Y. (2017). Learning with Partial Supervision for Clustering and Classification. (Doctoral Dissertation). Oregon State University. Retrieved from http://hdl.handle.net/1957/61476

Chicago Manual of Style (16th Edition):

Pei, Yuanli. “Learning with Partial Supervision for Clustering and Classification.” 2017. Doctoral Dissertation, Oregon State University. Accessed October 18, 2019. http://hdl.handle.net/1957/61476.

MLA Handbook (7th Edition):

Pei, Yuanli. “Learning with Partial Supervision for Clustering and Classification.” 2017. Web. 18 Oct 2019.

Vancouver:

Pei Y. Learning with Partial Supervision for Clustering and Classification. [Internet] [Doctoral dissertation]. Oregon State University; 2017. [cited 2019 Oct 18]. Available from: http://hdl.handle.net/1957/61476.

Council of Science Editors:

Pei Y. Learning with Partial Supervision for Clustering and Classification. [Doctoral Dissertation]. Oregon State University; 2017. Available from: http://hdl.handle.net/1957/61476

10. Qin, Lu. An ensemble method for macrosoma prediction.

Degree: MS, Computer Science, 2014, Oregon State University

 Macrosomia is a medical term describing a new baby born with an excessive birth weight (greater than 4000g). Fetal macrosomia may lead to both pregnancy… (more)

Subjects/Keywords: macrosomia; Birth weight  – Forecasting

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APA (6th Edition):

Qin, L. (2014). An ensemble method for macrosoma prediction. (Masters Thesis). Oregon State University. Retrieved from http://hdl.handle.net/1957/46799

Chicago Manual of Style (16th Edition):

Qin, Lu. “An ensemble method for macrosoma prediction.” 2014. Masters Thesis, Oregon State University. Accessed October 18, 2019. http://hdl.handle.net/1957/46799.

MLA Handbook (7th Edition):

Qin, Lu. “An ensemble method for macrosoma prediction.” 2014. Web. 18 Oct 2019.

Vancouver:

Qin L. An ensemble method for macrosoma prediction. [Internet] [Masters thesis]. Oregon State University; 2014. [cited 2019 Oct 18]. Available from: http://hdl.handle.net/1957/46799.

Council of Science Editors:

Qin L. An ensemble method for macrosoma prediction. [Masters Thesis]. Oregon State University; 2014. Available from: http://hdl.handle.net/1957/46799

11. Goska, Benjamin J. Simulator for optimizing performance and power of embedded multicore processors.

Degree: MS, Electrical and Computer Engineering, 2012, Oregon State University

 This work presents improvements to a multi-core performance/power simulator. The improvements which include updated power models, voltage scaling aware models, and an application specific benchmark,… (more)

Subjects/Keywords: Low power digital; Multiprocessors  – Energy consumption  – Computer simulation

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APA (6th Edition):

Goska, B. J. (2012). Simulator for optimizing performance and power of embedded multicore processors. (Masters Thesis). Oregon State University. Retrieved from http://hdl.handle.net/1957/29069

Chicago Manual of Style (16th Edition):

Goska, Benjamin J. “Simulator for optimizing performance and power of embedded multicore processors.” 2012. Masters Thesis, Oregon State University. Accessed October 18, 2019. http://hdl.handle.net/1957/29069.

MLA Handbook (7th Edition):

Goska, Benjamin J. “Simulator for optimizing performance and power of embedded multicore processors.” 2012. Web. 18 Oct 2019.

Vancouver:

Goska BJ. Simulator for optimizing performance and power of embedded multicore processors. [Internet] [Masters thesis]. Oregon State University; 2012. [cited 2019 Oct 18]. Available from: http://hdl.handle.net/1957/29069.

Council of Science Editors:

Goska BJ. Simulator for optimizing performance and power of embedded multicore processors. [Masters Thesis]. Oregon State University; 2012. Available from: http://hdl.handle.net/1957/29069

12. Jin, Gaole. On surrogate supervision multi-view learning.

Degree: MS, Electrical and Computer Engineering, 2012, Oregon State University

 Data can be represented in multiple views. Traditional multi-view learning methods (i.e., co-training, multi-task learning) focus on improving learning performance using information from the auxiliary… (more)

Subjects/Keywords: multi-view learning; Supervised learning (Machine learning)

…Raich of Oregon State University. 14 where (α)+ is α for α > 0 and 0 otherwise… …Oregon State University. 19 where (t)+ = max{0, t}. In SVM, a classifier… …ICASSP) with Dr. Raviv Raich of the Oregon State University and Dr. David Miller of the… 

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APA (6th Edition):

Jin, G. (2012). On surrogate supervision multi-view learning. (Masters Thesis). Oregon State University. Retrieved from http://hdl.handle.net/1957/37997

Chicago Manual of Style (16th Edition):

Jin, Gaole. “On surrogate supervision multi-view learning.” 2012. Masters Thesis, Oregon State University. Accessed October 18, 2019. http://hdl.handle.net/1957/37997.

MLA Handbook (7th Edition):

Jin, Gaole. “On surrogate supervision multi-view learning.” 2012. Web. 18 Oct 2019.

Vancouver:

Jin G. On surrogate supervision multi-view learning. [Internet] [Masters thesis]. Oregon State University; 2012. [cited 2019 Oct 18]. Available from: http://hdl.handle.net/1957/37997.

Council of Science Editors:

Jin G. On surrogate supervision multi-view learning. [Masters Thesis]. Oregon State University; 2012. Available from: http://hdl.handle.net/1957/37997

13. Behmardi, Behrouz. A probabilistic framework and algorithms for modeling and analyzing multi-instance data.

Degree: PhD, Electrical and Computer Engineering, 2012, Oregon State University

 Multi-instance data, in which each object (e.g., a document) is a collection of instances (e.g., word), are widespread in machine learning, signal processing, computer vision,… (more)

Subjects/Keywords: Multi-instance learning; Entropy (Information theory)

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APA (6th Edition):

Behmardi, B. (2012). A probabilistic framework and algorithms for modeling and analyzing multi-instance data. (Doctoral Dissertation). Oregon State University. Retrieved from http://hdl.handle.net/1957/35782

Chicago Manual of Style (16th Edition):

Behmardi, Behrouz. “A probabilistic framework and algorithms for modeling and analyzing multi-instance data.” 2012. Doctoral Dissertation, Oregon State University. Accessed October 18, 2019. http://hdl.handle.net/1957/35782.

MLA Handbook (7th Edition):

Behmardi, Behrouz. “A probabilistic framework and algorithms for modeling and analyzing multi-instance data.” 2012. Web. 18 Oct 2019.

Vancouver:

Behmardi B. A probabilistic framework and algorithms for modeling and analyzing multi-instance data. [Internet] [Doctoral dissertation]. Oregon State University; 2012. [cited 2019 Oct 18]. Available from: http://hdl.handle.net/1957/35782.

Council of Science Editors:

Behmardi B. A probabilistic framework and algorithms for modeling and analyzing multi-instance data. [Doctoral Dissertation]. Oregon State University; 2012. Available from: http://hdl.handle.net/1957/35782

14. Briggs, Forrest. Multi-instance multi-label learning : algorithms and applications to bird bioacoustics.

Degree: PhD, Computer Science, 2013, Oregon State University

 We consider the problem of supervised classification of bird species from audio recordings in a real-world acoustic monitoring scenario (i.e. audio data is collected in… (more)

Subjects/Keywords: multi-instance; Machine learning

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APA (6th Edition):

Briggs, F. (2013). Multi-instance multi-label learning : algorithms and applications to bird bioacoustics. (Doctoral Dissertation). Oregon State University. Retrieved from http://hdl.handle.net/1957/44820

Chicago Manual of Style (16th Edition):

Briggs, Forrest. “Multi-instance multi-label learning : algorithms and applications to bird bioacoustics.” 2013. Doctoral Dissertation, Oregon State University. Accessed October 18, 2019. http://hdl.handle.net/1957/44820.

MLA Handbook (7th Edition):

Briggs, Forrest. “Multi-instance multi-label learning : algorithms and applications to bird bioacoustics.” 2013. Web. 18 Oct 2019.

Vancouver:

Briggs F. Multi-instance multi-label learning : algorithms and applications to bird bioacoustics. [Internet] [Doctoral dissertation]. Oregon State University; 2013. [cited 2019 Oct 18]. Available from: http://hdl.handle.net/1957/44820.

Council of Science Editors:

Briggs F. Multi-instance multi-label learning : algorithms and applications to bird bioacoustics. [Doctoral Dissertation]. Oregon State University; 2013. Available from: http://hdl.handle.net/1957/44820

15. Qiao, Tianzhu. Position estimation in indoor localization system.

Degree: PhD, Electrical and Computer Engineering, 2014, Oregon State University

 Indoor positioning systems can be used for many applications such as indoor navigation, emergence response, asset monitoring, and shopper assistance. Due to the weak received… (more)

Subjects/Keywords: location estimation; Indoor positioning systems (Wireless localization)

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APA (6th Edition):

Qiao, T. (2014). Position estimation in indoor localization system. (Doctoral Dissertation). Oregon State University. Retrieved from http://hdl.handle.net/1957/53199

Chicago Manual of Style (16th Edition):

Qiao, Tianzhu. “Position estimation in indoor localization system.” 2014. Doctoral Dissertation, Oregon State University. Accessed October 18, 2019. http://hdl.handle.net/1957/53199.

MLA Handbook (7th Edition):

Qiao, Tianzhu. “Position estimation in indoor localization system.” 2014. Web. 18 Oct 2019.

Vancouver:

Qiao T. Position estimation in indoor localization system. [Internet] [Doctoral dissertation]. Oregon State University; 2014. [cited 2019 Oct 18]. Available from: http://hdl.handle.net/1957/53199.

Council of Science Editors:

Qiao T. Position estimation in indoor localization system. [Doctoral Dissertation]. Oregon State University; 2014. Available from: http://hdl.handle.net/1957/53199

16. Mbuthia, Juliana. Parameter estimation of Gaussian hierarchical model using Gibbs sampling.

Degree: MS, Electrical And Computer Engineering, 2014, Oregon State University

 Gibbs sampling method is an important tool used in parameter estimation for many probabilistic models. Specifically, for many scenarios, it is difficult to generate high-dimensional… (more)

Subjects/Keywords: Parameter estimation

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APA (6th Edition):

Mbuthia, J. (2014). Parameter estimation of Gaussian hierarchical model using Gibbs sampling. (Masters Thesis). Oregon State University. Retrieved from http://hdl.handle.net/1957/51386

Chicago Manual of Style (16th Edition):

Mbuthia, Juliana. “Parameter estimation of Gaussian hierarchical model using Gibbs sampling.” 2014. Masters Thesis, Oregon State University. Accessed October 18, 2019. http://hdl.handle.net/1957/51386.

MLA Handbook (7th Edition):

Mbuthia, Juliana. “Parameter estimation of Gaussian hierarchical model using Gibbs sampling.” 2014. Web. 18 Oct 2019.

Vancouver:

Mbuthia J. Parameter estimation of Gaussian hierarchical model using Gibbs sampling. [Internet] [Masters thesis]. Oregon State University; 2014. [cited 2019 Oct 18]. Available from: http://hdl.handle.net/1957/51386.

Council of Science Editors:

Mbuthia J. Parameter estimation of Gaussian hierarchical model using Gibbs sampling. [Masters Thesis]. Oregon State University; 2014. Available from: http://hdl.handle.net/1957/51386

17. Bryant, Douglas W. (Douglas Wesley). Algorithms for massive biological datasets.

Degree: PhD, Computer Science, 2011, Oregon State University

 Within the past several years the technology of high-throughput sequencing has transformed the study of biology by offering unprecedented access to life's fundamental building block,… (more)

Subjects/Keywords: High-throughput sequencing; Nucleotide sequence  – Computer programs

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APA (6th Edition):

Bryant, D. W. (. W. (2011). Algorithms for massive biological datasets. (Doctoral Dissertation). Oregon State University. Retrieved from http://hdl.handle.net/1957/25504

Chicago Manual of Style (16th Edition):

Bryant, Douglas W (Douglas Wesley). “Algorithms for massive biological datasets.” 2011. Doctoral Dissertation, Oregon State University. Accessed October 18, 2019. http://hdl.handle.net/1957/25504.

MLA Handbook (7th Edition):

Bryant, Douglas W (Douglas Wesley). “Algorithms for massive biological datasets.” 2011. Web. 18 Oct 2019.

Vancouver:

Bryant DW(W. Algorithms for massive biological datasets. [Internet] [Doctoral dissertation]. Oregon State University; 2011. [cited 2019 Oct 18]. Available from: http://hdl.handle.net/1957/25504.

Council of Science Editors:

Bryant DW(W. Algorithms for massive biological datasets. [Doctoral Dissertation]. Oregon State University; 2011. Available from: http://hdl.handle.net/1957/25504

18. Guo, Jia. Compensation method of the excess loop delay in continuous-time delta-sigma ADCs based on model matching approach.

Degree: MS, Electrical and Computer Engineering, 2014, Oregon State University

 Continues-Time (CT) Delta-Sigma (ΔΣ) Analog-to-Digital Converters (ADCs) have one important constrain, namely the excess loop delay. Most previous excess lop delay compensation methods need to… (more)

Subjects/Keywords: Continuous-time delta sigma ADCs; Analog-to-digital converters

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APA (6th Edition):

Guo, J. (2014). Compensation method of the excess loop delay in continuous-time delta-sigma ADCs based on model matching approach. (Masters Thesis). Oregon State University. Retrieved from http://hdl.handle.net/1957/46910

Chicago Manual of Style (16th Edition):

Guo, Jia. “Compensation method of the excess loop delay in continuous-time delta-sigma ADCs based on model matching approach.” 2014. Masters Thesis, Oregon State University. Accessed October 18, 2019. http://hdl.handle.net/1957/46910.

MLA Handbook (7th Edition):

Guo, Jia. “Compensation method of the excess loop delay in continuous-time delta-sigma ADCs based on model matching approach.” 2014. Web. 18 Oct 2019.

Vancouver:

Guo J. Compensation method of the excess loop delay in continuous-time delta-sigma ADCs based on model matching approach. [Internet] [Masters thesis]. Oregon State University; 2014. [cited 2019 Oct 18]. Available from: http://hdl.handle.net/1957/46910.

Council of Science Editors:

Guo J. Compensation method of the excess loop delay in continuous-time delta-sigma ADCs based on model matching approach. [Masters Thesis]. Oregon State University; 2014. Available from: http://hdl.handle.net/1957/46910

19. Duong, Thai (Thai Phu). Data Collection in Sensor Networks via the Novel Fast Markov Decision Process Framework.

Degree: MS, Computer Science, 2015, Oregon State University

 We investigate the data collection problem in sensor networks. The network consists of a number of stationary sensors deployed at different sites for sensing and… (more)

Subjects/Keywords: Sensor Network; Sensor networks

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APA (6th Edition):

Duong, T. (. P. (2015). Data Collection in Sensor Networks via the Novel Fast Markov Decision Process Framework. (Masters Thesis). Oregon State University. Retrieved from http://hdl.handle.net/1957/56020

Chicago Manual of Style (16th Edition):

Duong, Thai (Thai Phu). “Data Collection in Sensor Networks via the Novel Fast Markov Decision Process Framework.” 2015. Masters Thesis, Oregon State University. Accessed October 18, 2019. http://hdl.handle.net/1957/56020.

MLA Handbook (7th Edition):

Duong, Thai (Thai Phu). “Data Collection in Sensor Networks via the Novel Fast Markov Decision Process Framework.” 2015. Web. 18 Oct 2019.

Vancouver:

Duong T(P. Data Collection in Sensor Networks via the Novel Fast Markov Decision Process Framework. [Internet] [Masters thesis]. Oregon State University; 2015. [cited 2019 Oct 18]. Available from: http://hdl.handle.net/1957/56020.

Council of Science Editors:

Duong T(P. Data Collection in Sensor Networks via the Novel Fast Markov Decision Process Framework. [Masters Thesis]. Oregon State University; 2015. Available from: http://hdl.handle.net/1957/56020

20. Venkatram, Hariprasath. Energy and area efficient techniques for data converters.

Degree: PhD, Electrical Engineering and Computer Science, 2013, Oregon State University

 Data converters are ubiquitous building blocks of a signal chain. The rapid increase in communication and connectivity devices presents new avenues for pushing the state(more)

Subjects/Keywords: ADC; Analog-to-digital converters  – Design and construction

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APA (6th Edition):

Venkatram, H. (2013). Energy and area efficient techniques for data converters. (Doctoral Dissertation). Oregon State University. Retrieved from http://hdl.handle.net/1957/42265

Chicago Manual of Style (16th Edition):

Venkatram, Hariprasath. “Energy and area efficient techniques for data converters.” 2013. Doctoral Dissertation, Oregon State University. Accessed October 18, 2019. http://hdl.handle.net/1957/42265.

MLA Handbook (7th Edition):

Venkatram, Hariprasath. “Energy and area efficient techniques for data converters.” 2013. Web. 18 Oct 2019.

Vancouver:

Venkatram H. Energy and area efficient techniques for data converters. [Internet] [Doctoral dissertation]. Oregon State University; 2013. [cited 2019 Oct 18]. Available from: http://hdl.handle.net/1957/42265.

Council of Science Editors:

Venkatram H. Energy and area efficient techniques for data converters. [Doctoral Dissertation]. Oregon State University; 2013. Available from: http://hdl.handle.net/1957/42265

21. Tjahja, Teresa V. Supervised Hierarchical Segmentation for Bird Bioacoustics.

Degree: MS, Computer Science, 2015, Oregon State University

 Bioacoustics analysis can be used to conduct environmental monitoring by detecting the presence of birds species. This analysis usually involves identifying the species from their… (more)

Subjects/Keywords: supervised segmentation; Bioacoustics

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APA (6th Edition):

Tjahja, T. V. (2015). Supervised Hierarchical Segmentation for Bird Bioacoustics. (Masters Thesis). Oregon State University. Retrieved from http://hdl.handle.net/1957/56051

Chicago Manual of Style (16th Edition):

Tjahja, Teresa V. “Supervised Hierarchical Segmentation for Bird Bioacoustics.” 2015. Masters Thesis, Oregon State University. Accessed October 18, 2019. http://hdl.handle.net/1957/56051.

MLA Handbook (7th Edition):

Tjahja, Teresa V. “Supervised Hierarchical Segmentation for Bird Bioacoustics.” 2015. Web. 18 Oct 2019.

Vancouver:

Tjahja TV. Supervised Hierarchical Segmentation for Bird Bioacoustics. [Internet] [Masters thesis]. Oregon State University; 2015. [cited 2019 Oct 18]. Available from: http://hdl.handle.net/1957/56051.

Council of Science Editors:

Tjahja TV. Supervised Hierarchical Segmentation for Bird Bioacoustics. [Masters Thesis]. Oregon State University; 2015. Available from: http://hdl.handle.net/1957/56051

22. Cao, Jinzhou. Linearity enhancement techniques for data converters.

Degree: PhD, Electrical and Computer Engineering, 2015, Oregon State University

 Data converters are essential interface circuits between the analog world that people live in and the digital processors that people live with. Linearity, which often… (more)

Subjects/Keywords: data converter; Analog-to-digital converters  – Calibration

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APA (6th Edition):

Cao, J. (2015). Linearity enhancement techniques for data converters. (Doctoral Dissertation). Oregon State University. Retrieved from http://hdl.handle.net/1957/55404

Chicago Manual of Style (16th Edition):

Cao, Jinzhou. “Linearity enhancement techniques for data converters.” 2015. Doctoral Dissertation, Oregon State University. Accessed October 18, 2019. http://hdl.handle.net/1957/55404.

MLA Handbook (7th Edition):

Cao, Jinzhou. “Linearity enhancement techniques for data converters.” 2015. Web. 18 Oct 2019.

Vancouver:

Cao J. Linearity enhancement techniques for data converters. [Internet] [Doctoral dissertation]. Oregon State University; 2015. [cited 2019 Oct 18]. Available from: http://hdl.handle.net/1957/55404.

Council of Science Editors:

Cao J. Linearity enhancement techniques for data converters. [Doctoral Dissertation]. Oregon State University; 2015. Available from: http://hdl.handle.net/1957/55404

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