25 accepted papers
The time allocation problem in multi-function cognitive radar systems focuses on the trade-off between scanning for newly emerging targets and tracking the previously detected targets. We formulate this as a multi-objective optimization problem and employ deep reinforcement learning to find Pareto-o…
Phased array radar (PAR) is shown to provide an enhanced performance for target tracking due to its beam agility and ability for time resource allocation. Existing algorithms for PAR resource allocation often consider standard and simplified dynamic models for target motion, which are not suitable f…
1-bit compressed sensing (1bCS) is a quantized signal acquisition technique to compress high-dimensional sparse signals. The goal is to design sensing matrices A ∈ ℝ<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m×n</sup> with the fewest possible rows…
This paper investigates the integration of Internet of Things (IoT) networks with modern massive multiple-input multiple-output (MIMO) wireless systems to enable various new use cases. Given the dynamic nature of parameters monitored by IoT nodes, efficient techniques for tracking these time-varying…
This work considers an IoT network comprising of several IoT sensor nodes (SNs), a passive intelligent reflecting surface (IRS), and a fusion center (FC). Each IoT SN observes multiple physical phenomena, and transmits its observations to the FC for post processing. This necessitates the need for ef…
In critical environments that require a high accuracy of decisions, utilizing human cognitive strengths and expertise in addition to machine observations is advantageous to improve decision quality and enhance situational awareness. While the current literature on human decision making is primarily…
Unlike decision making systems made up of physical sensors where the system parameters are known a priori and can be controlled at will, human behavior in decision making is complex and uncertain. The objective of this work is to study how humans make decisions based on internal and external sources…
In this paper, the optimal jamming strategy by an adversary in distributed detection networks is investigated. By utilizing the game-theoretical framework to characterize the interaction between the fusion center (FC) and the jammer as a repeated game, we examine how the behavior of the FC changes w…
In this paper, we address the problem of one-bit compressed sensing using the data-driven deep learning approach. Our approach uses an untrained neural network to reconstruct sparse vectors from their one-bit measurements. We define a new cost function using the untrained network, which maximizes th…
In this paper, we present a new detector for the detection of sparse stochastic signals using 1-bit data in two-level two- degree tree-structured sensor networks (2L-2D TSNs). Related prior work mostly concentrates on parallel sensor networks (PSNs). However, PSNs may sometime become impractical in…
We consider the distributed stochastic optimization problem of minimizing a nonconvex function f in an adversarial setting. All the w worker nodes in the network are expected to send their stochastic gradient vectors to the fusion center (or server). However, some (at most α-fraction) of the nodes m…
In this paper, we address the classical problem of one-bit compressed sensing. We present a deep learning based reconstruction algorithm that relies on a generative model. The generator which is a neural network, learns a mapping from a low dimensional space to a higher dimensional set comprising of…
We consider the problem of sparse signal reconstruction from noisy 1-bit compressed measurements using a statistically dependent signal, as an aid. We assume that this signal does not share joint sparse representation with the sparse signal and call it a heterogeneous side-information. We assume tha…
In this paper, we consider the problem of sparse signal detection with compressed measurements in a Bayesian framework. Multiple nodes in the network are assumed to observe sparse signals. Observations at each node are compressed via random projections and sent to a centralized fusion center. Motiva…
This paper studies clustering using a Kolmogorov-Smirnov based K-means algorithm. All data sequences are assumed to be generated by unknown continuous distributions. The pairwise KS distances of the distributions are assumed to be lower bounded by a certain positive constant. The convergence analysi…
The emerging paradigm of Human-Machine Inference Networks (HuMaINs) combines complementary cognitive strengths of humans and machines in an intelligent manner to tackle various inference tasks and achieves higher performance than either humans or machines by themselves. While inference performance o…
Random distortion testing (RDT) addresses the problem of testing whether or not a random signal, Ξ, deviates by more than a specified tolerance, τ, from a fixed value, ξ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> [1]. The test is nonparamet…
We explore the design of an effective crowdsourcing system for an M -ary classification task. Crowd workers complete simple binary microtasks whose results are aggregated to give the final decision. We consider the scenario where the workers have a reject option so that they are allowed to skip micr…
Present day distributed inference systems consist of sensors with different modalities working as a system to perform specific tasks. With multiple sensors sensing heterogeneous data over multiple time instants, diversity is an inherent aspect of such systems. In this work, we take the first step to…
In this work, we investigate the accelerated proximal gradient method for nonconvex programming (APGnc). The method compares between a usual proximal gradient step and a linear extrapolation step, and accepts the one that has a lower function value to achieve a monotonic decrease. In specific, under…
Performing likelihood ratio based detection with high dimensional multimodal data is a challenging problem since the computation of the joint probability density functions (pdfs) in the presence of intermodal dependence is difficult. While some computationally expensive approaches have been proposed…
In this work, we use crowds for acoustic classification of animal species in supervised and unsupervised manners. We demonstrate the effectiveness of the proposed triplet based crowdsourcing systems via actual experiments. Moreover, we propose a generalized 1-bit RPCA algorithm to further improve cl…
In this paper, we propose a new approach for robust compressive sensing (CS) using memristor crossbars that are constructed by recently invented memristor devices. The exciting features of a memristor crossbar, such as high density, low power and great scalability, make it a promising candidate to p…
In this paper, we study the problem of generating uniform random point samples on a domain of d dimensional space based on a minimum distance criterion between point samples (Poisson-disk sampling or PDS). First, we formally define PDS via the pair correlation function (PCF) to quantitatively evalua…
We study the problem of adaptive sensor management for target tracking, where at every instant we search for the best sensors to be activated at the next time step. In our problem formulation, the measurements may be corrupted by correlated noises, and the impact of correlated measurements on sensor…