The IEEE Signal Processing Society is excited to announce we are partnering with the IEEE Humanitarian Activities Committee (HAC) to conduct a special Call for Proposals to support projects in SPS chapters that utilize Signal Processing technologies to address local community challenges.

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.

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 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. 

Privacy issues and communication costs are both major concerns in distributed optimization in networks. There is often a tradeoff between them because encryption methods used for privacy-preservation often introduce significant communication overhead.

In past decades, conventional communication primarily focused on how to accurately and effectively transmit symbols, which is categorized as the first level of communications by Shannon and Weaver. With the developments of cellular communication systems, the achieved transmission rate is gradually approaching to the Shannon limit. 

The IEEE Signal Processing Society invites nominations for the SPS Challenges and Data Collections Chair position. The term for the position is two years (1 January 2023-31 December 2024).  Nominations must be received no later than 15 June 2022.

Neuro-imaging is safe and non-invasive, which is widely used in the early diagnosis and prediction of Alzheimer's disease, Parkinson's disease, Autism and other brain diseases. With the rapid development of intelligent computing technology, it is possible to use neuro-imaging data for intelligent diagnosis of brain diseases.

Practically anyone can now generate a realistic-looking deepfake video. Many methods to detect deepfakes were recently proposed by the research community. However, it is still unclear how realistic deepfake videos are for an average person and whether the algorithms are significantly better than humans at detecting them.