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Date: 20 September 2023
Time: 11:00 AM ET (New York Time)
Speaker(s): Dr. Xudong Zhang, Dr. Fangzhou Wang, Dr. Hongbin Li
Date: 13 February 2023
Time: 2:30 PM ET (New York Time)
Full webinar details
Date: 18 April 2023
Time: 3:00 PM CET (Local time)
Presenter(s): Dr. Matthews Jacob
Date: 14 September 2023
Time: 7:30 AM ET (New York Time)
Speaker(s): Dr. Cheng Lu, Dr. Ya-Feng Liu, Dr. Jing Zhou
The Signal Processing, Learning, and Computing (SPLC) Group at the Baltic Institute of Advanced Technology (BPTI) seeks a postdoctoral researcher in theoretical and applied signal processing.
The successful candidate will develop new computational methods for airborne object recognition in radar signals.
One year ago, I was writing the IEEE Signal Processing Magazine 2022 May editorial when the Russian army brutally attacked Ukraine. One year after, war is always present… I can’t understand how a single man and his entourage can unleash such a killing spree and be responsible for so many deaths, especially innocent victims like children.
Humans can listen to a target speaker even in challenging acoustic conditions that have noise, reverberation, and interfering speakers. This phenomenon is known as the cocktail party effect . For decades, researchers have focused on approaching the listening ability of humans. One critical issue is handling interfering speakers because the target and nontarget speech signals share similar characteristics, complicating their discrimination.
Analyzing the magnitude response of a finite-length sequence is a ubiquitous task in signal processing. However, the discrete Fourier transform (DFT) provides only discrete sampling points of the response characteristic. This work introduces bounds on the magnitude response, which can be efficiently computed without additional zero padding. The proposed bounds can be used for more informative visualization and inform whether additional frequency resolution or zero padding is required.
Linear regression models have a wide range of applications in statistics, signal processing, and machine learning. In this Lecture Notes column we will examine the performance of the least-squares (LS) estimator with a focus on the case when there are more parameters than training samples, which is often overlooked in textbooks on estimation.
Apollo 11 was the first manned space mission to successfully bring astronauts to the Moon and return them safely. As part of NASA’s goal in assessing team and mission success, all voice communications within mission control, astronauts, and support staff were captured using a multichannel analog system, which until recently had never been made available. More than 400 personnel served as mission specialists/support who communicated across 30 audio loops, resulting in 9,000+ h of data. It is essential to identify each speaker’s role during Apollo and analyze group communication to achieve a common goal.
The underrepresentation of women in science, technology, engineering, and mathematics (STEM) fields is an issue that has been studied extensively [1] . Yet women still face many challenges, even though the demand for many STEM occupations has exploded. Many factors contribute to the low number of women in the STEM field. From an early age, girls are exposed to many cultural cues that dissuade them from participating in STEM fields. This gender bias is enforced by implicit or explicit messages from multiple sources.
Date: 25-27 June 2024
Location: Singapore
This paper deals with joint adaptive radar detection and target bearing estimation in the presence of mutual coupling among the array elements. First of all, a suitable model of the signal received by the multichannel radar is developed via a linearization procedure of the Uniform Linear Array (ULA) manifold around the nominal array looking direction together with the use of symmetric Toeplitz structured matrices to represent the mutual coupling effects. Hence, the Generalized Likelihood Ratio Test (GLRT) detector is evaluated under the assumption of homogeneous radar environment.
In the second part of the series papers, we set out to study the algorithmic efficiency of sparsity-constrained sensing. Stemmed from co-prime sampling/array, we propose a generalized framework, termed Diophantine sensing, which utilizes generic Diophantine equation theory and higher-order sparse ruler to strengthen the sampling time (delay), the degree of freedom (DoF), and the sampling sparsity, simultaneously. It is well known that co-prime sensing can reconstruct the autocorrelation of a sequence with significantly more lags based on Bézout theorem.