No abstract

In the distributed remote (CEO) source coding problem, many separate encoders observe independently noisy copies of an underlying source. The rate loss is the difference between the rate required in this distributed setting and the rate that would be required in a setting where the encoders can fully cooperate. In this sense, the rate loss characterizes the price of distributed processing. We survey and extend the known results on the rate loss in various settings, with a particular emphasis on the case where the noise in the observations is Gaussian, but the underlying source is general.

Semantic signal processing and communications are poised to play a central part in developing the next generation of sensor devices and networks. A crucial component of a semantic system is the extraction of semantic signals from the raw input signals, which has become increasingly tractable with the recent advances in machine learning (ML) and artificial intelligence (AI) techniques. The accurate extraction of semantic signals using the aforementioned ML and AI methods, and the detection of semantic innovation for scheduling transmission and/or storage events are critical tasks for reliable semantic signal processing and communications. In this work, we propose a reliable semantic information extraction framework based on our previous work on semantic signal representations in a hierarchical graph-based structure. The proposed framework includes a time integration method to increase fidelity of ML outputs in a class-aware manner, a graph-edit-distance based metric to detect innovation events at the graph-level and filter out sporadic errors, and a Hidden Markov Model (HMM) to produce smooth and reliable graph signals. The proposed methods within the framework are demonstrated individually and collectively through simulations and case studies based on real-world computer vision examples.

Multi-antenna radars exhibit positively correlated detection performance with the number of elements utilized. The feasibility of refining antenna arrays to reduce cost of operation with only marginal loss of performance has attracted significant attention as utilizing a large number of elements may be prohibitively costly in terms of computation and power. Under cognitive radar paradigm, the goal is to choose an optimal or near optimal subset of elements from an antenna array of pre-specified geometry while meeting certain performance and cost criteria. In this work, we present optimization based selection methods for certain array geometries to select the best K element sub-array in terms of Cramér-Rao lower bound (CRB) on direction-ofarrival (DoA) estimations. Our results indicate that it is possible to reduce K up to a certain point without significant reduction in DoA estimation performance. The maximum possible reduction in K depends on the operating signal-to-noise ratio (SNR) and how much performance loss is tolerated. Thus, once the operating SNR is known, it is possible to utilize fewer array elements with slight decrease in performance.