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You searched for +publisher:"Syracuse University" +contributor:("Pramod k. Varshney"). Showing records 1 – 13 of 13 total matches.

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Syracuse University

1. Iyengar, Satish Giridhar. Decision-Making with Heterogeneous Sensors - A Copula Based Approach.

Degree: PhD, Electrical Engineering and Computer Science, 2011, Syracuse University

  Statistical decision making has wide ranging applications, from communications and signal processing to econometrics and finance. In contrast to the classical one source-one receiver… (more)

Subjects/Keywords: Biometrics; Detection Theory; Hypothesis Testing; Multimodal; Multisensor Fusion; Neural Synchrony; Electrical and Computer Engineering

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APA (6th Edition):

Iyengar, S. G. (2011). Decision-Making with Heterogeneous Sensors - A Copula Based Approach. (Doctoral Dissertation). Syracuse University. Retrieved from https://surface.syr.edu/eecs_etd/310

Chicago Manual of Style (16th Edition):

Iyengar, Satish Giridhar. “Decision-Making with Heterogeneous Sensors - A Copula Based Approach.” 2011. Doctoral Dissertation, Syracuse University. Accessed December 15, 2019. https://surface.syr.edu/eecs_etd/310.

MLA Handbook (7th Edition):

Iyengar, Satish Giridhar. “Decision-Making with Heterogeneous Sensors - A Copula Based Approach.” 2011. Web. 15 Dec 2019.

Vancouver:

Iyengar SG. Decision-Making with Heterogeneous Sensors - A Copula Based Approach. [Internet] [Doctoral dissertation]. Syracuse University; 2011. [cited 2019 Dec 15]. Available from: https://surface.syr.edu/eecs_etd/310.

Council of Science Editors:

Iyengar SG. Decision-Making with Heterogeneous Sensors - A Copula Based Approach. [Doctoral Dissertation]. Syracuse University; 2011. Available from: https://surface.syr.edu/eecs_etd/310

2. He, Hao. Heterogeneous Sensor Signal Processing for Inference with Nonlinear Dependence.

Degree: PhD, Electrical Engineering and Computer Science, 2015, Syracuse University

  Inferring events of interest by fusing data from multiple heterogeneous sources has been an interesting and important topic in recent years. Several issues related… (more)

Subjects/Keywords: Copula Theory; Dependent observations; Detection; Estimation; Sensor Fusion; Engineering

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APA (6th Edition):

He, H. (2015). Heterogeneous Sensor Signal Processing for Inference with Nonlinear Dependence. (Doctoral Dissertation). Syracuse University. Retrieved from https://surface.syr.edu/etd/390

Chicago Manual of Style (16th Edition):

He, Hao. “Heterogeneous Sensor Signal Processing for Inference with Nonlinear Dependence.” 2015. Doctoral Dissertation, Syracuse University. Accessed December 15, 2019. https://surface.syr.edu/etd/390.

MLA Handbook (7th Edition):

He, Hao. “Heterogeneous Sensor Signal Processing for Inference with Nonlinear Dependence.” 2015. Web. 15 Dec 2019.

Vancouver:

He H. Heterogeneous Sensor Signal Processing for Inference with Nonlinear Dependence. [Internet] [Doctoral dissertation]. Syracuse University; 2015. [cited 2019 Dec 15]. Available from: https://surface.syr.edu/etd/390.

Council of Science Editors:

He H. Heterogeneous Sensor Signal Processing for Inference with Nonlinear Dependence. [Doctoral Dissertation]. Syracuse University; 2015. Available from: https://surface.syr.edu/etd/390


Syracuse University

3. Cao, Nianxia. SENSOR MANAGEMENT FOR LOCALIZATION AND TRACKING IN WIRELESS SENSOR NETWORKS.

Degree: PhD, Electrical Engineering and Computer Science, 2016, Syracuse University

  Wireless sensor networks (WSNs) are very useful in many application areas including battlefield surveillance, environment monitoring and target tracking, industrial processes and health monitoring… (more)

Subjects/Keywords: Localization; Sensor management; Tracking; Wireless sensor networks; Engineering

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APA (6th Edition):

Cao, N. (2016). SENSOR MANAGEMENT FOR LOCALIZATION AND TRACKING IN WIRELESS SENSOR NETWORKS. (Doctoral Dissertation). Syracuse University. Retrieved from https://surface.syr.edu/etd/563

Chicago Manual of Style (16th Edition):

Cao, Nianxia. “SENSOR MANAGEMENT FOR LOCALIZATION AND TRACKING IN WIRELESS SENSOR NETWORKS.” 2016. Doctoral Dissertation, Syracuse University. Accessed December 15, 2019. https://surface.syr.edu/etd/563.

MLA Handbook (7th Edition):

Cao, Nianxia. “SENSOR MANAGEMENT FOR LOCALIZATION AND TRACKING IN WIRELESS SENSOR NETWORKS.” 2016. Web. 15 Dec 2019.

Vancouver:

Cao N. SENSOR MANAGEMENT FOR LOCALIZATION AND TRACKING IN WIRELESS SENSOR NETWORKS. [Internet] [Doctoral dissertation]. Syracuse University; 2016. [cited 2019 Dec 15]. Available from: https://surface.syr.edu/etd/563.

Council of Science Editors:

Cao N. SENSOR MANAGEMENT FOR LOCALIZATION AND TRACKING IN WIRELESS SENSOR NETWORKS. [Doctoral Dissertation]. Syracuse University; 2016. Available from: https://surface.syr.edu/etd/563


Syracuse University

4. Nadendla, Venkata Sriram Siddhardh. On the Design and Analysis of Secure Inference Networks.

Degree: PhD, Electrical Engineering and Computer Science, 2016, Syracuse University

  Parallel-topology inference networks consist of spatially-distributed sensing agents that collect and transmit observations to a central node called the fusion center (FC), so that… (more)

Subjects/Keywords: Byzantine Attack; Eavesdropping; Inference Networks; Jamming; Security; Engineering

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APA (6th Edition):

Nadendla, V. S. S. (2016). On the Design and Analysis of Secure Inference Networks. (Doctoral Dissertation). Syracuse University. Retrieved from https://surface.syr.edu/etd/590

Chicago Manual of Style (16th Edition):

Nadendla, Venkata Sriram Siddhardh. “On the Design and Analysis of Secure Inference Networks.” 2016. Doctoral Dissertation, Syracuse University. Accessed December 15, 2019. https://surface.syr.edu/etd/590.

MLA Handbook (7th Edition):

Nadendla, Venkata Sriram Siddhardh. “On the Design and Analysis of Secure Inference Networks.” 2016. Web. 15 Dec 2019.

Vancouver:

Nadendla VSS. On the Design and Analysis of Secure Inference Networks. [Internet] [Doctoral dissertation]. Syracuse University; 2016. [cited 2019 Dec 15]. Available from: https://surface.syr.edu/etd/590.

Council of Science Editors:

Nadendla VSS. On the Design and Analysis of Secure Inference Networks. [Doctoral Dissertation]. Syracuse University; 2016. Available from: https://surface.syr.edu/etd/590

5. El Bardan, Raghed. Resource Allocation for Interference Management in Wireless Networks.

Degree: PhD, Electrical Engineering and Computer Science, 2016, Syracuse University

  Interference in wireless networks is a major problem that impacts system performance quite substantially. Combined with the fact that the spectrum is limited and… (more)

Subjects/Keywords: Coding theory; Cognitive Radio Networks; Game theory; Interference; Matching theory; Resource allocation; Engineering

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APA (6th Edition):

El Bardan, R. (2016). Resource Allocation for Interference Management in Wireless Networks. (Doctoral Dissertation). Syracuse University. Retrieved from https://surface.syr.edu/etd/663

Chicago Manual of Style (16th Edition):

El Bardan, Raghed. “Resource Allocation for Interference Management in Wireless Networks.” 2016. Doctoral Dissertation, Syracuse University. Accessed December 15, 2019. https://surface.syr.edu/etd/663.

MLA Handbook (7th Edition):

El Bardan, Raghed. “Resource Allocation for Interference Management in Wireless Networks.” 2016. Web. 15 Dec 2019.

Vancouver:

El Bardan R. Resource Allocation for Interference Management in Wireless Networks. [Internet] [Doctoral dissertation]. Syracuse University; 2016. [cited 2019 Dec 15]. Available from: https://surface.syr.edu/etd/663.

Council of Science Editors:

El Bardan R. Resource Allocation for Interference Management in Wireless Networks. [Doctoral Dissertation]. Syracuse University; 2016. Available from: https://surface.syr.edu/etd/663


Syracuse University

6. Kailkhura, Bhavya. Distributed Inference and Learning with Byzantine Data.

Degree: PhD, Electrical Engineering and Computer Science, 2016, Syracuse University

  We are living in an increasingly networked world with sensing networks of varying shapes and sizes: the network often comprises of several tiny devices… (more)

Subjects/Keywords: Byzantines; Corrupted Data; Distributed Inference; Distributed Learning; Engineering

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APA (6th Edition):

Kailkhura, B. (2016). Distributed Inference and Learning with Byzantine Data. (Doctoral Dissertation). Syracuse University. Retrieved from https://surface.syr.edu/etd/629

Chicago Manual of Style (16th Edition):

Kailkhura, Bhavya. “Distributed Inference and Learning with Byzantine Data.” 2016. Doctoral Dissertation, Syracuse University. Accessed December 15, 2019. https://surface.syr.edu/etd/629.

MLA Handbook (7th Edition):

Kailkhura, Bhavya. “Distributed Inference and Learning with Byzantine Data.” 2016. Web. 15 Dec 2019.

Vancouver:

Kailkhura B. Distributed Inference and Learning with Byzantine Data. [Internet] [Doctoral dissertation]. Syracuse University; 2016. [cited 2019 Dec 15]. Available from: https://surface.syr.edu/etd/629.

Council of Science Editors:

Kailkhura B. Distributed Inference and Learning with Byzantine Data. [Doctoral Dissertation]. Syracuse University; 2016. Available from: https://surface.syr.edu/etd/629


Syracuse University

7. Li, Qunwei. On Classification in Human-driven and Data-driven Systems.

Degree: PhD, Electrical Engineering and Computer Science, 2018, Syracuse University

  Classification systems are ubiquitous, and the design of effective classification algorithms has been an even more active area of research since the emergence of… (more)

Subjects/Keywords: Engineering

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APA (6th Edition):

Li, Q. (2018). On Classification in Human-driven and Data-driven Systems. (Doctoral Dissertation). Syracuse University. Retrieved from https://surface.syr.edu/etd/991

Chicago Manual of Style (16th Edition):

Li, Qunwei. “On Classification in Human-driven and Data-driven Systems.” 2018. Doctoral Dissertation, Syracuse University. Accessed December 15, 2019. https://surface.syr.edu/etd/991.

MLA Handbook (7th Edition):

Li, Qunwei. “On Classification in Human-driven and Data-driven Systems.” 2018. Web. 15 Dec 2019.

Vancouver:

Li Q. On Classification in Human-driven and Data-driven Systems. [Internet] [Doctoral dissertation]. Syracuse University; 2018. [cited 2019 Dec 15]. Available from: https://surface.syr.edu/etd/991.

Council of Science Editors:

Li Q. On Classification in Human-driven and Data-driven Systems. [Doctoral Dissertation]. Syracuse University; 2018. Available from: https://surface.syr.edu/etd/991


Syracuse University

8. ZHENG, YUJIAO. Distributed Estimation and Performance Limits in Resource-constrained Wireless Sensor Networks.

Degree: PhD, Electrical Engineering and Computer Science, 2014, Syracuse University

  Distributed inference arising in sensor networks has been an interesting and promising discipline in recent years. The goal of this dissertation is to investigate… (more)

Subjects/Keywords: Bayesian estimation; Kalman filtering; Particle filtering; Sensor networks; Target tracking; Engineering

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APA (6th Edition):

ZHENG, Y. (2014). Distributed Estimation and Performance Limits in Resource-constrained Wireless Sensor Networks. (Doctoral Dissertation). Syracuse University. Retrieved from https://surface.syr.edu/etd/71

Chicago Manual of Style (16th Edition):

ZHENG, YUJIAO. “Distributed Estimation and Performance Limits in Resource-constrained Wireless Sensor Networks.” 2014. Doctoral Dissertation, Syracuse University. Accessed December 15, 2019. https://surface.syr.edu/etd/71.

MLA Handbook (7th Edition):

ZHENG, YUJIAO. “Distributed Estimation and Performance Limits in Resource-constrained Wireless Sensor Networks.” 2014. Web. 15 Dec 2019.

Vancouver:

ZHENG Y. Distributed Estimation and Performance Limits in Resource-constrained Wireless Sensor Networks. [Internet] [Doctoral dissertation]. Syracuse University; 2014. [cited 2019 Dec 15]. Available from: https://surface.syr.edu/etd/71.

Council of Science Editors:

ZHENG Y. Distributed Estimation and Performance Limits in Resource-constrained Wireless Sensor Networks. [Doctoral Dissertation]. Syracuse University; 2014. Available from: https://surface.syr.edu/etd/71


Syracuse University

9. Liu, Sijia. Resource Management for Distributed Estimation via Sparsity-Promoting Regularization.

Degree: PhD, Electrical Engineering and Computer Science, 2016, Syracuse University

  Recent advances in wireless communications and electronics have enabled the development of low-cost, low-power, multifunctional sensor nodes that are small in size and communicate… (more)

Subjects/Keywords: convex optimization; distributed estimation; resource management; sparsity; wireless sensor networks; Engineering

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APA (6th Edition):

Liu, S. (2016). Resource Management for Distributed Estimation via Sparsity-Promoting Regularization. (Doctoral Dissertation). Syracuse University. Retrieved from https://surface.syr.edu/etd/441

Chicago Manual of Style (16th Edition):

Liu, Sijia. “Resource Management for Distributed Estimation via Sparsity-Promoting Regularization.” 2016. Doctoral Dissertation, Syracuse University. Accessed December 15, 2019. https://surface.syr.edu/etd/441.

MLA Handbook (7th Edition):

Liu, Sijia. “Resource Management for Distributed Estimation via Sparsity-Promoting Regularization.” 2016. Web. 15 Dec 2019.

Vancouver:

Liu S. Resource Management for Distributed Estimation via Sparsity-Promoting Regularization. [Internet] [Doctoral dissertation]. Syracuse University; 2016. [cited 2019 Dec 15]. Available from: https://surface.syr.edu/etd/441.

Council of Science Editors:

Liu S. Resource Management for Distributed Estimation via Sparsity-Promoting Regularization. [Doctoral Dissertation]. Syracuse University; 2016. Available from: https://surface.syr.edu/etd/441

10. Subramanian, Arun. Hypothesis Testing Using Spatially Dependent Heavy-Tailed Multisensor Data.

Degree: PhD, Electrical Engineering and Computer Science, 2014, Syracuse University

  The detection of spatially dependent heavy-tailed signals is considered in this dissertation. While the central limit theorem, and its implication of asymptotic normality of… (more)

Subjects/Keywords: Detection; Heavy-tailed signals; Hypothesis testing; Inference; Information fusion; Statistical dependence; Engineering

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APA (6th Edition):

Subramanian, A. (2014). Hypothesis Testing Using Spatially Dependent Heavy-Tailed Multisensor Data. (Doctoral Dissertation). Syracuse University. Retrieved from https://surface.syr.edu/etd/192

Chicago Manual of Style (16th Edition):

Subramanian, Arun. “Hypothesis Testing Using Spatially Dependent Heavy-Tailed Multisensor Data.” 2014. Doctoral Dissertation, Syracuse University. Accessed December 15, 2019. https://surface.syr.edu/etd/192.

MLA Handbook (7th Edition):

Subramanian, Arun. “Hypothesis Testing Using Spatially Dependent Heavy-Tailed Multisensor Data.” 2014. Web. 15 Dec 2019.

Vancouver:

Subramanian A. Hypothesis Testing Using Spatially Dependent Heavy-Tailed Multisensor Data. [Internet] [Doctoral dissertation]. Syracuse University; 2014. [cited 2019 Dec 15]. Available from: https://surface.syr.edu/etd/192.

Council of Science Editors:

Subramanian A. Hypothesis Testing Using Spatially Dependent Heavy-Tailed Multisensor Data. [Doctoral Dissertation]. Syracuse University; 2014. Available from: https://surface.syr.edu/etd/192

11. Peng, Renbin. Noise-Enhanced and Human Visual System-Driven Image Processing: Algorithms and Performance Limits.

Degree: PhD, Electrical Engineering and Computer Science, 2011, Syracuse University

  This dissertation investigates the problem of image processing based on stochastic resonance (SR) noise and human visual system (HVS) properties, where several novel frameworks… (more)

Subjects/Keywords: Computer Vision; Human Visual System; Image Processing; Medical Imaging; Performance Limits; Stochastic Resonance Noise; Electrical and Computer Engineering

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APA (6th Edition):

Peng, R. (2011). Noise-Enhanced and Human Visual System-Driven Image Processing: Algorithms and Performance Limits. (Doctoral Dissertation). Syracuse University. Retrieved from https://surface.syr.edu/eecs_etd/311

Chicago Manual of Style (16th Edition):

Peng, Renbin. “Noise-Enhanced and Human Visual System-Driven Image Processing: Algorithms and Performance Limits.” 2011. Doctoral Dissertation, Syracuse University. Accessed December 15, 2019. https://surface.syr.edu/eecs_etd/311.

MLA Handbook (7th Edition):

Peng, Renbin. “Noise-Enhanced and Human Visual System-Driven Image Processing: Algorithms and Performance Limits.” 2011. Web. 15 Dec 2019.

Vancouver:

Peng R. Noise-Enhanced and Human Visual System-Driven Image Processing: Algorithms and Performance Limits. [Internet] [Doctoral dissertation]. Syracuse University; 2011. [cited 2019 Dec 15]. Available from: https://surface.syr.edu/eecs_etd/311.

Council of Science Editors:

Peng R. Noise-Enhanced and Human Visual System-Driven Image Processing: Algorithms and Performance Limits. [Doctoral Dissertation]. Syracuse University; 2011. Available from: https://surface.syr.edu/eecs_etd/311

12. Kar, Swarnendu. Collaborative Estimation in Distributed Sensor Networks.

Degree: PhD, Electrical Engineering and Computer Science, 2013, Syracuse University

  Networks of smart ultra-portable devices are already indispensable in our lives, augmenting our senses and connecting our lives through real time processing and communication… (more)

Subjects/Keywords: Baseline Drift; Estimaton Theory; Spatial Collaboration; Spatial Whitening; Library and Information Science

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APA (6th Edition):

Kar, S. (2013). Collaborative Estimation in Distributed Sensor Networks. (Doctoral Dissertation). Syracuse University. Retrieved from https://surface.syr.edu/eecs_etd/333

Chicago Manual of Style (16th Edition):

Kar, Swarnendu. “Collaborative Estimation in Distributed Sensor Networks.” 2013. Doctoral Dissertation, Syracuse University. Accessed December 15, 2019. https://surface.syr.edu/eecs_etd/333.

MLA Handbook (7th Edition):

Kar, Swarnendu. “Collaborative Estimation in Distributed Sensor Networks.” 2013. Web. 15 Dec 2019.

Vancouver:

Kar S. Collaborative Estimation in Distributed Sensor Networks. [Internet] [Doctoral dissertation]. Syracuse University; 2013. [cited 2019 Dec 15]. Available from: https://surface.syr.edu/eecs_etd/333.

Council of Science Editors:

Kar S. Collaborative Estimation in Distributed Sensor Networks. [Doctoral Dissertation]. Syracuse University; 2013. Available from: https://surface.syr.edu/eecs_etd/333

13. Vempaty, Aditya. Reliable Inference from Unreliable Agents.

Degree: PhD, Electrical Engineering and Computer Science, 2015, Syracuse University

  Distributed inference using multiple sensors has been an active area of research since the emergence of wireless sensor networks (WSNs). Several researchers have addressed… (more)

Subjects/Keywords: Detection and Estimation; Distributed Inferece; Human-Machine Systems; Reliable Systems; Engineering

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APA (6th Edition):

Vempaty, A. (2015). Reliable Inference from Unreliable Agents. (Doctoral Dissertation). Syracuse University. Retrieved from https://surface.syr.edu/etd/332

Chicago Manual of Style (16th Edition):

Vempaty, Aditya. “Reliable Inference from Unreliable Agents.” 2015. Doctoral Dissertation, Syracuse University. Accessed December 15, 2019. https://surface.syr.edu/etd/332.

MLA Handbook (7th Edition):

Vempaty, Aditya. “Reliable Inference from Unreliable Agents.” 2015. Web. 15 Dec 2019.

Vancouver:

Vempaty A. Reliable Inference from Unreliable Agents. [Internet] [Doctoral dissertation]. Syracuse University; 2015. [cited 2019 Dec 15]. Available from: https://surface.syr.edu/etd/332.

Council of Science Editors:

Vempaty A. Reliable Inference from Unreliable Agents. [Doctoral Dissertation]. Syracuse University; 2015. Available from: https://surface.syr.edu/etd/332

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