Search results
Enter terms
630 results found.
Section: Publications & Resources
This article presents an adaptive multi-sensing (MS) framework for a network of densely deployed solar energy harvesting wireless nodes. Each node is mounted with heterogeneous sensors to sense multiple cross-correlated slowly-varying parameters/signals.
Section: Publications & Resources
This article presents limited feedback-based precoder quantization schemes for Interference Alignment (IA) with bounded channel state information (CSI) uncertainty. Initially, this work generalizes the min-max mean squared error (MSE) framework, followed by the development of robust precoder and decoder designs based on worst case MSE minimization.
Section: Publications & Resources
Structure inference is an important task for network data processing and analysis in data science. In recent years, quite a few approaches have been developed to learn the graph structure underlying a set of observations captured in a data space. Although real-world data is often acquired in settings where relationships are influenced by a priori known rules, such domain knowledge is still not well exploited in structure inference problems.
Section: Publications & Resources
Publication: IEEE Transactions on Signal Processing
Distributed data clustering in sensor networks is receiving increasing attention with the development of network technology. A variety of algorithms for distributed data clustering have been proposed recently. However, most of these algorithms have trouble with either non-Gaussian shaped data clustering or model order selection problem.
Section: Publications & Resources
Publication: IEEE Transactions on Signal Processing
This paper proposes a novel algorithm to determine the optimal orientation of sensing axes of redundant inertial sensors such as accelerometers and gyroscopes (gyros) for increasing the sensing accuracy. In this paper, we have proposed a novel iterative algorithm to find the optimal sensor configuration.
Section: Publications & Resources
Publication: IEEE Transactions on Signal Processing
In this paper, we study the problem of compressed sensing using binary measurement matrices and ℓ1 -norm minimization (basis pursuit) as the recovery algorithm. We derive new upper and lower bounds on the number of measurements to achieve robust sparse recovery with binary matrices.
Section: Community & Involvement
Signal Processing Society Technical Working Groups
Section: Community & Involvement
h2.field__item {display:none;}Primary Technical Committee:Sensor Array and Multichannel Technical Committee
Section: Community & Involvement
Chair: Ahmet M. ElbirIstinye UniversityIstanbul, TurkeyRegion: Reg 8Gender: MTWG Term: FirstTerm Ends: 31 December 2026
Section: Community & Involvement
Meeting Calendar | Meeting Minutes
