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The Latest News, Articles, and Events in Signal Processing
Webinar Details: 21 March 2018 from 11:00am - 12:00pm (EDT)
Attendee Registration Link: Register for Webinar, by Dr. Xiao-Ping (Steven) Zhang
Topic: Fundamentals on Signal Processing for Finance and Economics
The IEEE Signal Processing Society is very excited to offer the next webinar in our new series featuring “technical and technological innovations and their applications in signal processing” from top researchers and thought leaders in academia and industry.
Call for Proposals Document
Submission Deadline: May 7, 2018
Signal processing is always at the heart of the technology that differentiates today’s generations from those of the past is reaffirmed once again by the special issue on Machine Learning for Cognition in Radio Communications and Radar in IEEE Journal of Selected Topics in Signal Processing, February 2018.
The November 2017 special issue of SPM on deep learning for visual understanding surveyed deep-learning solutions under reinforcement; weakly supervised and multimodal settings, investigated their robustness; and presented overviews of their applications in domain adaptation, hashing, semantic segmentation, metric learning, inverse problems in imaging, image-to-text generation, and picture-quality assessment.
Postdoctoral researcher - (computational linguistics or computer science)
Models of Intercomprehension in Speech and Language
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. Visit the SPS Job Submission Form.
Please visit the Conferences and Events page on the IEEE Signal Processing Society website for Upcoming Lectures by Distinguished Lecturers.
For our March 2018 issue, we cover recent patents granted in the area of image segmentation
In patent no 9,886,647 systems, devices, and methods are presented for segmenting an image of a video stream with a client device by accessing a set of images within a video stream, identifying an object of interest within one or more images of the set of images, and detecting a region of interest within the one or more images.
The Signal Processing Society (SPS) has 12 Technical Committees and 3 Special Interest Groups (SIGs) that support a broad selection of signal processing-related activities defined by the scope of the Society. Technical Committees and Special Interest Groups are actively involved in awards, conferences, publications, and educational activities, and provide technical advice to the Society's leadership.
The IEEE Signal Processing Society welcomes proposals from prospective organizers of 2018-2019 winter schools under the S3P (Seasonal Schools in Signal Processing) Program. S3P seasonal schools are typically either summer schools or winter schools, and are geared towards providing graduate students, early stage researchers, and practitioners with background on selected topics in signal processing.
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...
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.
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.
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.
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.
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.
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