Junan Zhu

Works (7)

Updated: July 5th, 2023 15:41

2018 article

Performance Limits With Additive Error Metrics in Noisy Multimeasurement Vector Problems

Zhu, J., & Baron, D. (2018, August 23). IEEE Transactions on Signal Processing, Vol. 66, pp. 5338–5348.

By: J. Zhu* & D. Baron n 

author keywords: Active user detection; error metric; message passing; multi-measurement vector problem
topics (OpenAlex): Sparse and Compressive Sensing Techniques; Distributed Sensor Networks and Detection Algorithms; Blind Source Separation Techniques
TL;DR: This work proposes a novel setup for active user detection in multiuser communication and demonstrates the promise of the proposed setup, and proposes a message passing algorithmic framework that achieves the optimal performance of the algorithm. (via Semantic Scholar)
Sources: Web Of Science, NC State University Libraries
Added: October 16, 2018

2016 article

Multi-processor approximate message passing using lossy compression

Han, P., Zhu, J., Niu, R., & Baron, D. (2016, March 1). International Conference on Acoustics Speech and Signal Processing, pp. 6240–6244.

By: P. Han*, J. Zhu n, R. Niu * & D. Baron n 

topics (OpenAlex): Sparse and Compressive Sensing Techniques; Advanced Data Compression Techniques; Analog and Mixed-Signal Circuit Design
TL;DR: A communication-efficient multi-processor compressed sensing framework based on the approximate message passing algorithm is proposed, which performs lossy compression on the data being communicated between processors, resulting in a reduction in communication costs with a minor degradation in recovery quality. (via Semantic Scholar)
Sources: NC State University Libraries, NC State University Libraries
Added: August 6, 2018

2016 article

Performance Limits for Noisy Multimeasurement Vector Problems

Zhu, J., Baron, D., & Krzakala, F. (2016, December 30). IEEE Transactions on Signal Processing, Vol. 65, pp. 2444–2454.

By: J. Zhu n, D. Baron n  & F. Krzakala *

author keywords: Approximate message passing; multimeasurement vector problem; replica analysis
topics (OpenAlex): Sparse and Compressive Sensing Techniques; Blind Source Separation Techniques; Distributed Sensor Networks and Detection Algorithms
TL;DR: Numerical results illustrate that more signal vectors in the jointly sparse signal ensemble lead to a better phase transition, and the MMSE's of complex CS problems with both real and complex measurement matrices are also analyzed. (via Semantic Scholar)
Sources: Web Of Science, NC State University Libraries
Added: August 6, 2018

2016 article

Performance trade-offs in multi-processor approximate message passing

Zhu, J., Beirami, A., & Baron, D. (2016, July 1). 2016 Ieee International Symposium on Information Theory, pp. 680–684.

By: J. Zhu n, A. Beirami* & D. Baron n 

topics (OpenAlex): Sparse and Compressive Sensing Techniques; Distributed Sensor Networks and Detection Algorithms; Markov Chains and Monte Carlo Methods
TL;DR: It is proved that the achievable region of this MOP is convex, and conjecture how the combined cost of computation and communication scales with the desired mean squared error. (via Semantic Scholar)
Sources: NC State University Libraries, NC State University Libraries
Added: August 6, 2018

2015 article

Recovery From Linear Measurements With Complexity-Matching Universal Signal Estimation

Zhu, J., Baron, D., & Duarte, M. F. (2015, January 16). IEEE Transactions on Signal Processing, Vol. 63, pp. 1512–1527.

By: J. Zhu n, D. Baron n  & M. Duarte *

author keywords: Compressed sensing; MAP estimation; Markov chain Monte Carlo; universal algorithms
topics (OpenAlex): Sparse and Compressive Sensing Techniques; Blind Source Separation Techniques; Direction-of-Arrival Estimation Techniques
TL;DR: This paper considers universal CS recovery, where the statistics of a stationary ergodic signal source are estimated simultaneously with the signal itself, and focuses on a maximum a posteriori (MAP) estimation framework that leverages universal priors to match the complexity of the source. (via Semantic Scholar)
Sources: Web Of Science, NC State University Libraries
Added: August 6, 2018

2014 article

Complexity-adaptive universal signal estimation for compressed sensing

Zhu, J., Baron, D., & Duarte, M. F. (2014, June 1). 2014 IEEE Workshop on Statistical Signal Processing (SSP), pp. 388–391.

By: J. Zhu n, D. Baron n  & M. Duarte *

topics (OpenAlex): Sparse and Compressive Sensing Techniques; Blind Source Separation Techniques; Direction-of-Arrival Estimation Techniques
TL;DR: This paper significantly improves on previous work, especially for continuous-valued signals, by offering a four-stage algorithm called Complexity-Adaptive Universal Signal Estimation (CAUSE), where the alphabet size of the estimate adaptively matches the coding complexity of the signal. (via Semantic Scholar)
Sources: NC State University Libraries, NC State University Libraries
Added: August 6, 2018

2013 article

Performance regions in compressed sensing from noisy measurements

Zhu, N. J., & Baron, D. (2013, March 1). 2013 47th Annual Conference on Information Sciences and Systems (CISS).

By: N. Zhu n & D. Baron n 

topics (OpenAlex): Sparse and Compressive Sensing Techniques; Blind Source Separation Techniques; Distributed Sensor Networks and Detection Algorithms
TL;DR: It is shown that in several regions, which have different measurement rates and noise levels, the reconstruction error behaves differently, and it may be possible to develop reconstruction algorithms with lower error in that region. (via Semantic Scholar)
Sources: NC State University Libraries, NC State University Libraries
Added: August 6, 2018

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