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Section: Publications & Resources

Publication: IEEE Signal Processing Magazine

Named after the ancient Greek goddess of wisdom, Athens is not just another modern metropolis. With numerous ancient monuments standing sentinel along her streets and squares, Athens never tires of revealing her glorious past to her visitors.

Section: Publications & Resources

Publication: IEEE Signal Processing Magazine

The Multimedia Signal Processing Technical Committee (MMSP TC) of the IEEE Signal Processing Society (SPS) promotes the advancement of multimedia signal processing technology. 

Section: Publications & Resources

Publication: IEEE Signal Processing Magazine

In today's era of the Internet of Things (IoT), the amalgamation of information and communication technologies with actuating devices has reached all corners of the modern world. In the context of critical infrastructures, such as the power grid, this cyberphysical transformation has permeated all system levels as evident in devices ranging from crucial operational components (e.g., generators) and advanced sensors...

Section: Publications & Resources

Publication: IEEE Signal Processing Magazine

The widespread adoption of new 3D sensor and authoring technologies has made it possible to capture 3D scenes and models in real time with decent visual quality. As an example, Microsoft's Kinect and Apple's PrimeSense technology are now being used in a wide variety of interactive 3D mobile applications, including gaming and augmented reality applications.

Section: Publications & Resources

Publication: IEEE Signal Processing Magazine

Signal processing has played a foundational role in the consumer electronics revolution of the past several decades. Mobile devices, smart-home technologies, digital cameras, and countless other cutting-edge products have benefited from signal processing-enabled innovations.

Section: Publications & Resources

Publication: IEEE Transactions on Signal Processing

In this paper, we study the problem of recovering a group sparse vector from a small number of linear measurements. In the past, the common approach has been to use various “group sparsity-inducing” norms such as the Group LASSO norm for this purpose. By using the theory of convex relaxations, we show that it is also possible to use 1 -norm minimization for group sparse recovery.

Section: Publications & Resources

Publication: IEEE Transactions on Signal Processing

This paper introduces a node-asynchronous communication protocol in which an agent in a network wakes up randomly and independently, collects states of its neighbors, updates its own state, and then broadcasts back to its neighbors. This protocol differs from consensus algorithms and it allows distributed computation of an arbitrary eigenvector of the network, in which communication between agents is allowed to be directed.

Section: Publications & Resources

Publication: IEEE Transactions on Signal Processing

Spike estimation from calcium (Ca 2+ ) fluorescence signals is a fundamental and challenging problem in neuroscience. Several models and algorithms have been proposed for this task over the past decade. Nevertheless, it is still hard to achieve accurate spike positions from the Ca 2+ fluorescence signals. While existing methods rely on data-driven methods and the physiology of neurons for modeling the spiking process, this paper exploits the nature of the fluorescence responses to spikes using signal processing.

Section: Publications & Resources

Publication: IEEE Transactions on Signal Processing

It is well known that the convergence of Gaussian belief propagation (BP) is not guaranteed in loopy graphs. The classical convergence conditions, including diagonal dominance, walk-summability, and convex decomposition, are derived under pairwise factorizations of the joint Gaussian distribution. However, many applications run Gaussian BP under high-order factorizations, making the classical results not applicable.