December 2022

Brain data are inherently large scale, multidimensional, and noisy. Indeed, advances in imaging and sensor technology allow recordings of ever-increasing spatio-temporal resolution. Multidimensional, as time series data are recorded at multiple locations (electrodes, voxels), from multiple subjects, under various conditions.

In the cognitive neurosciences and machine learning, we have formal ways of understanding and characterising perception and decision-making; however, the approaches appear very different: current formulations of perceptual synthesis call on theories like predictive coding and Bayesian brain hypothesis. 

While message-passing neural networks (MPNNs) are the most popular architectures for graph learning, their expressive power is inherently limited. In order to gain increased expressive power while retaining efficiency, several recent works apply MPNNs to subgraphs of the original graph. 

The IEEE Signal Processing Society Boston Chapter has been selected as the recipient of the 2022 Chapter of the Year Award!

IEEE Fellow is the highest grade of membership of the IEEE. It honors members with an outstanding record of technical achievements, contributing importantly to the advancement or application of engineering, science and technology, and bringing significant value to society.

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.

Focus stacking is an effective approach to extending the depth of field of a camera, yet is challenging with regard to 1) controlling focal planes in forming a stack and 2) fusing the focal stack into composites that are free from defocusing, i.e., all-in-focus.