Radial sampling pattern is an important signal acquisition strategy in magnetic resonance imaging (MRI) owing to better immunity to motion-induced artifacts and less pronounced aliasing due to undersampling compared to the Cartesian sampling. 

Dr. Sanjit Kumar Mitra is an Distinguished Professor Emeritus of Electrical and Computer Engineering, University of California, Santa Barbara, and Stephen and Etta Varra Professor Emeritus of Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles.

The IEEE Transactions on Multimedia is now accepting nominations for the 2023 Multimedia Prize Paper Award.  Nominations are due no later than 31 March 2023.

Decentralized stochastic gradient descent (SGD) is a driving engine for decentralized federated learning (DFL). The performance of decentralized SGD is jointly influenced by inter-node communications and local updates.

Human centric visual analysis tasks are essential to computer vision since humans are the key element for cameras to analyze. In this talk, I will mainly focus on 4 visual analysis tasks on human hand, gesture, pose, and action respectively.

IEEE Signal Processing Society Past President Ahmed Tewfik, in his capacity as Chair of the Society’s Nominations and Appointments Committee, invites nominations for the IEEE Signal Processing Society Officer positions of President-Elect for the term 1 January 2024-31 December 2025, Vice President-Conferences for the term of 1 January 2024-31 December 2026 and Vice President-Publications for the term of 1 January 2024-31 December 2026.

IEEE SPS has built a streamlined mechanism for employers to add a job announcement by simply filling in a simple job opportunity submission Web form related to a particular TC field. To submit job announcements for a particular Technical Committee, the submission form can be found by visiting the page below and selecting a particular TC.

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

Audio pattern recognition is an important research topic in the machine learning area, and includes several tasks such as audio tagging, acoustic scene classification, music classification, speech emotion classification and sound event detection. In this blog, we introduce pretrained audio neural networks (PANNs) trained on the large-scale AudioSet dataset. These PANNs are transferred to other audio related tasks. We investigate the performance and computational complexity of PANNs modeled by a variety of convolutional neural networks. We propose an architecture called Wavegram-Logmel-CNN using both log-mel spectrogram and waveform as input feature.

Recent years, face recognition has made a remarkable breakthrough due to the emergence of deep learning. However, compared with frontal face recognition, many deep face recognition models still suffer serious performance degradation when handling profile faces. To address this issue, we propose a novel Frontal-Centers Guided Loss (FCGFace) to obtain highly discriminative features for face recognition. Most existing discriminative feature learning approaches project features from the same class into a separated latent subspace.