The IEEE Workshop on Signal Processing Systems (SiPS) is a premier international forum addressing current and future challenges and new research directions on the design and implementation of signal processing systems.
Rabab Ward
Chair, 2018-2019 SPS Nominations and Appointments Committee
In accordance with the Bylaws of the IEEE Signal Processing Society, the membership will elect, by direct ballot, THREE Members-at-Large to the Board of Governors for three-year terms commencing 1 January 2019 and ending 31 December 2021, as well as TWO Regional Directors-at-Large...
Within scientific research, engineering, and consumer electronics, there is a multitude of new discrete sensor-interfaced devices. Maintaining high accuracy in signal quantization while staying within the strict power-budget of these devices is a very challenging problem. Traditional paths to solving this problem include researching more energy-efficient digital topologies as well as digital scaling.
Sensor networks deployed in high-latency environments such as underwater acoustic and satellite channels find critical applications in disaster prevention and tactical surveillance. The sensors in these networks have limited energy reserves.
Researchers from Google, Verily Life Sciences, and Stanford School of Medicine have developed an algorithm to predict cardiovascular risk factors from retinal fundus photographs. Their model was trained from 48,101 patients from the UK Biobank and 236,234 patients from EyePACS using deep learning, and validated on 12,026 patients from the UK Biobank and 999 patients from EyePACS.
Semantic segmentation is the task of labeling every pixel in an image with a predefined object category. It has numerous applications in scenarios where the detailed understanding of an image is required, such as in autonomous vehicles and medical diagnosis. This problem has traditionally been solved with probabilistic models known as conditional random fields (CRFs) due to their ability to model the relationships between the pixels being predicted.
Traditionally, analytical methods have been used to solve imaging problems such as image restoration, inpainting, and superresolution (SR). In recent years, the fields of machine and deep learning have gained a lot of momentum in solving such imaging problems, often surpassing the performance provided by analytical approaches.
Visual perception is one of our most essential and fundamental abilities that enables us to make sense of what our eyes see and interpret the world that surrounds us. It allows us to function and, thus, our civilization to survive. No sensory loss is more debilitating than blindness as we are, above all, visual beings. Close your eyes for a moment after reading this sentence and try grabbing something in front of you, navigating your way in your environment, or just walking straight, reading a book, playing a game, or perhaps learning something new.
Prosthetic limbs have improved significantly over the past several years, and signal processing has played a key role in allowing these devices to operate more smoothly and precisely on command. Now, researchers are taking the next step forward by using signal processing approaches and methods to develop prosthetics that not only function reliably and efficiently but give wearers more natural control over artificial arms, hands, and legs.
Three new members-at-large will take their seats on the IEEE Signal Processing Society (SPS) Board of Governors (BoG) beginning 1 January 2018 and will serve until 31 December 2020. Nine candidates competed for the three member-at-large positions. The successful candidates represent a broad spectrum of the SPS. The successful candidates are: Shoji Makino, Athina P. Petropulu, Paris Smaragdis.
@ieeeSPS
11.3K subscribers‧180 videos
