The Principal Component Analysis (PCA) is considered to be a quintessential data preprocessing tool in many machine learning applications. But the high dimensionality and massive scale of data in several of these applications means the traditional centralized PCA solutions are fast becoming irrelevant for them. 

The Signal Processing Society (SPS) has 12 Technical Committee that support a broad selection of signal processing-related activities defined by the scope of the Society.

Do you know someone who has made notable contributions to signal processing or service to the IEEE Signal Processing Society? Now is your chance to recognize them by nominating them for an SPS award!

Elisa Konofagou is the Robert and Margaret Hariri Professor of Biomedical Engineering and Professor Radiology as well as Director of the Ultrasound and Elasticity Imaging Laboratory at Columbia. Her main interests are in the development of novel elasticity imaging techniques and therapeutic ultrasound methods.

Prof. Christian Jutten received Ph.D. (1981) and Doctor es Sciences (1987) degrees from Grenoble Institute of Technology, France. He was Associate Professor (1982-1989), Professor (1989-2019), and in September 2019, became Professor Emeritus at University Grenoble Alpes. 

With the goal of turning data into information, and information into insight, the Sarvajanik College of Engineering & Technology (SCET), in association with IEEE SPS SCET Student Branch Chapter, Gujarat Section and IEEE SCET Student Branch organized a National Level Project Competition on Data Science Challenge 1.0 (DSC 1.0).

The 8th IEEE World Forum on the Internet of Things (WFIoT2022) seeks submissions and proposals for original technical papers and presentations that address the Internet of Things (IoT), its theoretical and technological building blocks, the applications that drive the growth and evolution of IoT.

The Signal Processing Society (SPS) has 12 Technical Committee that support a broad selection of signal processing-related activities defined by the scope of the Society.

In this talk, we investigate the model-driven deep learning for multiple input-multiple output (MIMO) detection. In particular, the MIMO detector is specially designed by unfolding an iterative algorithm and adding some trainable parameters. 

A brief introduction to state estimation in multi-object system that arises from applications where the number of objects and their states are unknown and vary randomly with time. State space model (SSM) is a fundamental concept in system theory that permeated through many fields of study.