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Statistical Principles of Time Reversal

Time reversal is a physical principle well known for its deterministic focusing effect. Recently discovered statistical effects show that the time reversal focusing spot is not a point but has a Bessel power distribution. This finding offers accurate and reliable speed estimation indoors, where multipaths are abundant, with mostly nonline-of-sight (NLOS) conditions, and enable various indoor applications, such as wireless sensing and tracking. No known techniques can thrive in such scenarios. In essence, time reversal is an effective tool that embraces multipaths as virtual sensors with hundreds of thousands of degrees of freedom for our utilization.

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Going for Sustainable Conferences

The research landscape is evolving very dynamically. This column reflects on it from a conference viewpoint and focuses on the importance of creating a more sustainable culture for the conference portfolio that the IEEE Signal Processing Society (SPS) offers. Among the different considerations, the role that virtual conferences can play is highlighted.

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Signal Processing at 75: More Dynamic and Pervasive Than Ever

The year 2023 marked the 75th anniversary of the IEEE Signal Processing Society (SPS), which was founded in 1948 as the “Professional Group on Audio” of the Institute of Radio Engineers (IRE), becoming the first IEEE Society. (The IRE, founded in 1912 with a focus on radio and then electronics, together with the American Institute of Electrical Engineers, founded in 1884 with an emphasis on power and utilities, were united in 1963 to form IEEE.)

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The Discrete Cosine Transform and Its Impact on Visual Compression: Fifty Years From Its Invention

Compression is essential for efficient storage and transmission of signals. One powerful method for compression is through the application of orthogonal transforms, which convert a group of N data samples into a group of N transform coefficients. In transform coding, the N samples are first transformed, and then the coefficients are individually quantized and entropy coded into binary bits. The transform serves two purposes: one is to compact the energy of the original N samples into coefficients with increasingly smaller variances so that removing smaller coefficients have negligible reconstruction errors, and another is to decorrelate the original samples so that the coefficients can be quantized and entropy coded individually without losing compression performance. 

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The Magical Art of Technical Presentations

At the time of publication, all of the links in this article were operational. However, since we do not host the videos, we have no control over whether or not they will continue to be active. In many cases, similar or related videos can be found by typing the performer’s name in an appropriate search engine.

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The Submerged Part of the AI-Ceberg

This article discusses the contradiction between the exploding energy demand of artificial intelligence (AI) and the information and communication (ICT) industry as a whole and the parallel strong request for energy sobriety imposed by the need to mitigate the impact of climate change and the anticipated collapse of civilization as we know it.

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