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The Latest News, Articles, and Events in Signal Processing
Nowadays, mobile devices, such as smartphones, have been widely used all over the world. In addition, the performance of image recognition has drastically increased with deep learning technologies.
Technical Co-Sponsors:
- IEEE Communications Society
- IEEE Computational Intelligence Society
- IEEE Computer Society
- IEEE Engineering in Medicine and Biology Society
- IEEE Information Theory Society
Scope
This is a great opportunity for recent postdocs that are AI enthusiasts. Knowledge in the area of material sciences is not mandatory (see the link with the job offer). So, if you are working on AI methods, but have not been working with material sciences yet, but would like to acquaint yourself with the topic, feel encouraged to apply.
There is a postdoctoral scholar opportunity at the University of Southern
California (USC) in a multidisciplinary research area involving
sample/learning complexity in machine learning; optimization theory;
information theory; and graph signal processing. More specifically, we are
looking for a candidate with a strong theoretical foundation in some of these
areas while being motivated to apply the theory to practical datasets using
The Signal Processing research group at the Universität Hamburg (http://uhh.de/inf-sp) is hiring a postdoctoral researcher for 33 months for the project "Crossmodal Processing of Audio-Visual Signals".
PhD-student positions (\approx 3 years) and Postdoc positions (1-->5 years) available for research in Wireless Communications, Information Theory, Edge and Distributed Computing, Caching. EURECOM is located in the French Riviera's silicon valley and is an English speaking Graduate school.
The list of tables of contents (TOCs) are now available for March 2019. The TOCs are formatted to the style of its publication and offers not only links to the full issue, but also to the individual articles in IEEEXplore®.
Are you an early-career researcher who enjoys finding innovative solutions to unmet clinical needs and translating deep learning in medical image analysis to the clinic? Do you have a background in medical image computing and experience with working collaboratively with clinicians and clinical image databases? Do you have a passion for developing statistical deep Bayesian methods for medical image analysis?
The Department of Medical Physics and Acoustics at the University of Oldenburg, Germany, is seeking to fill the position of a
Professor (W2) Speech Technology and Hearing Devices
commencing as soon as possible within the cluster of excellence “Hearing4all”.
Autonomous vehicles (AVs) are appearing everywhere: in the air, on the road, and even underground and underwater. Signal processing is helping to guide these vehicles more accurately, which ensures their safety and also protects the people who encounter them.
Fractional interpolation is used to provide sub-pixel level references for motion compensation in the interprediction of video coding, which attempts to remove temporal redundancy in video sequences. Traditional handcrafted fractional interpolation filters face the challenge of modeling discontinuous regions in videos, while existing deep learning-based methods are either designed for a single quantization parameter (QP), only generating half-pixel samples, or need to train a model for each sub-pixel position.
Recent studies have shown the effectiveness of using depth information in salient object detection. However, the most commonly seen images so far are still RGB images that do not contain the depth data.
Deep convolutional neural networks (CNNs) have revolutionized the computer vision research and have seen unprecedented adoption for multiple tasks, such as classification, detection, and caption generation. However, they offer little transparency into their inner workings and are often treated as black boxes that deliver excellent performance.
Defocus blur detection is an important and challenging task in computer vision and digital imaging fields. Previous work on defocus blur detection has put a lot of effort into designing local sharpness metric maps.
Compressive sensing (CS) theory opens promising avenues toward building rapid and energy-efficient sensing systems in a wide range of applications that require inherently high temporal and/or spatial resolution while exhibiting a sparse signal structure.
Industrial control systems (ICSs) manage and monitor critical civil or military infrastructure, such as water treatment facilities, power plants, electricity grids, transportation systems, oil and gas refineries, and health care. Because they are so important, ICSs are becoming attractive targets for malicious attacks that could lead to catastrophic failures with substantive impacts.
Optical fiber communication systems provide the infrastructure for high-capacity data transfer. The field has shown dramatic increases in transmission capacity, and yet the demand for capacity also grows significantly. We are in a situation in which systems must continually grow in capacity to keep pace with the demand, thereby necessitating continual innovation and technical advances.
More than one million fetal deaths occur in the United States every year. Monitoring the long-term heart rate variability provides a great amount of information about the fetal health condition which requires continuous monitoring of the fetal heart rate.
Malaysia Chapter Receives the 2018 Chapter of the Year Award! This was one of the highlighted top articles as read in the Inside the Signal Processing Newsletter, December 2018 edition. The news was really a big surprise for us as we did not expect it at all.
Image, Video, and Multidimensional Signal Processing Technical Committee (IVMSP TC) promotes and guides the advancement of the field of image, video, and multidimensional signal processing.
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