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The Latest News, Articles, and Events in Signal Processing
Shiv Dutt Joshi received the B.E. degree (Hons.) in electrical and electronics engineering from the Birla Institute of Technology, Pilani, India, in 1981, and the M.Tech. degree in communications and radar engineering and the Ph.D. degree in signal processing from IIT Delhi, New Delhi, India, in 1983 and 1988, respectively.
The First TReNDS Neuroimaging Competition is LIVE! The competition features the largest normative dataset of its kind and will push the limits of current predictive technologies to help determine the translational value of the provided features for future clinical use. Find more about this unique opportunity here.
The IEEE Signal Processing Society invites nominations for the positions of the Chair, Women in Signal Processing Committee and Chair, Young Professionals Committee. The term for the two positions is three years (1 January 2021-31 December 2023). Nominations must be received no later than 10 July 2020.
We seek a highly motivated postdoctoral researcher for a cutting-edge research project sponsored by Total Exploration & Production Research & Technology, USA. The research is held at Braude College of Engineering, Israel, in close collaboration with Total and includes visiting periods at Total (Houston, USA). The postdoctoral researcher will develop novel deep learning algorithms for solving complex seismic inversion problems. Topics of interest include:
Manuscript Due: May 29, 2021
Publication Date: 1st Quarter 2022
CFP Document
September 30-October 3, 2012
Orlando, Florida
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May 22-27, 2011
Location: Prague, Czech Republic
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Manuscript Due: February 26, 2021
Publication Date: November 2021
CFP Document
A full time one-year position as a postdoctoral Fellow is available at the AViReS Lab ( https://avires.dimi.uniud.it ) of the Department of Mathematics, Computer Science and Physics (DMIF), University of Udine.
A question that naturally arises in active sensing systems, such as ToF systems, is how much volume can be sensed with a given power budget, and how this can be extended by means of some more intricate sensing scheme. The main objective of this project is the development of a very-wide-area ToF 3D sensing system which has to be outstandingly efficient regarding the power consumption.
July 6-17, 2020
NOTE: Location Changed to--Virtual Conference
December 7-10, 2020
Location: NOTE: Location changed to--Virtual Conference
August 11-13, 2021
Note: Location changed to--Virtual Conference
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.
This work presents a generalization of classical factor analysis (FA). Each of M channels carries measurements that share factors with all other channels, but also contains factors that are unique to the channel. Furthermore, each channel carries an additive noise whose covariance is diagonal, as is usual in factor analysis, but is otherwise unknown.
Space-time adaptive processing (STAP) algorithms with coprime arrays can provide good clutter suppression potential with low cost in airborne radar systems as compared with their uniform linear arrays counterparts. However, the performance of these algorithms is limited by the training samples support in practical applications.
In this article, we explore the state-space formulation of a network process to recover from partial observations the network topology that drives its dynamics. To do so, we employ subspace techniques borrowed from system identification literature and extend them to the network topology identification problem.
We consider a specific graph learning task: reconstructing a symmetric matrix that represents an underlying graph using linear measurements. We present a sparsity characterization for distributions of random graphs (that are allowed to contain high-degree nodes), based on which we study fundamental trade-offs between the number of measurements, the complexity of the graph class, and the probability of error.
Observability is a fundamental concept in system inference and estimation. This article is focused on structural observability analysis of Cartesian product networks. Cartesian product networks emerge in variety of applications including in parallel and distributed systems.
We consider the problem of learning a graph from a given set of smooth graph signals. Our graph learning approach is formulated as a constrained quadratic program in the edge weights. We provide an implicit characterization of the optimal solution and propose a tailored ADMM algorithm to solve this problem efficiently.
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