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The Latest News, Articles, and Events in Signal Processing
September 21-23, 2020
NOTE: Location changed to--Virtual Conference
The list of tables of contents (TOCs) are now available for July 2019. The TOCs are formatted to the style of its publication and offers not only links to the full issue on the front cover, but also links to the individual articles to take you directly to IEEEXplore®; simply mouse-over the article title and click on it.
The IEEE Signal Processing Society (SPS) is dedicated to providing webinars on technical and technological innovations (and their applications) in signal processing from top researchers and thought leaders in academia and industry on the SPS Resource Center.
Effective immediately, all SPS Student Branch Chapters are now eligible to request up to $500 annually to help support invited speakers, career development workshops, social functions in connection to Student Branch Chapter meetings, and other related activities.
The IEEE Signal Processing Society is proud to launch a new gold fully open access (OA) journal, the IEEE Open Journal of Signal Processing (OJ-SP), spanning the full scope of the Society’s fields of interest. OJ-SP, which will be fully compliant with funder mandates, including Plan S, will begin accepting submissions in fall 2019 and publish its first articles in early 2020.
Signal processing works around us constantly, doing unseen work to keep us connected in infinite ways, including wi-fi internet. In our new video, Signal Processing for Wi-Fi, learn how signal processing methods are used to overcome challenges, letting you stay connected across multiple devices simultaneously and without interruption.
Amazon AI has open positions in Palo Alto, California, for applied scientists (both interns and full time)
in all areas of human language technology, with special focus on Deep Learning (DL) models for
speech recognition, machine translation and text-to-speech.
Recent years have witnessed the explosive growth of mobile data traffic, which has led to ever-increasing demand for high system throughput, massive user access, heterogeneous data traffic, high bandwidth efficiency, and ultra-low latency.
Information is the key element in modern factory automation and process control systems, and one of the most difficult tasks is to provide, distribute, and properly process it. Information transfer and processing in this scenario depend to a large extent on appropriate communication systems, usually referred to as “industrial networks.”
Radar was developed during World War II for defense and security applications, and it was initially used for detecting aircrafts and missiles, replacing short range and narrow field-of-view acoustic devices.
IEEE Signal Processing Society (SPS) President, Ali H. Sayed, is seeking nominations for the positions of Chair and Vice Chair of the IEEE SPS Fellow Evaluation Committee. The term of appointment for each position is one year, renewable (1 January 2020-31 December 2020).
The saliency detection technologies are very useful to analyze and extract important information from given multimedia data, and have already been extensively used in many multimedia applications. Past studies have revealed that utilizing the global cues is effective in saliency detection. Nevertheless, most of prior works mainly considered the single-scale segmentation when the global cues are employed. In this paper, we attempt to incorporate the multi-scale global cues for saliency detection problem.
With the development of video coding technology, high-efficiency video coding (HEVC) has become a promising alternative, compared with the previous coding standards, for example, H.264. In general, H.264 to HEVC transcoding can be accomplished by fully H.264 decoding and fully HEVC encoding, which suffers from considerable time consumption on the brute-force search of the HEVC coding tree unit (CTU) partition for rate-distortion optimization (RDO).
Predicting articulatory movements from audio or text has diverse applications, such as speech visualization. Various approaches have been proposed to solve the acoustic-articulatory mapping problem. However, their precision is not high enough with only acoustic features available. Recently, deep neural network (DNN) has brought tremendous success in various fields, like speech recognition and image processing.
The aim of this paper is to present a new method for skin tumor segmentation in the 3D ultrasound images. We consider a variational formulation, the energy of which combines a diffuse interface phase field model (regularization term) and a log-likelihood computed using nonparametric estimates (data attachment term).
The procedure for extracting a cryptographic key from noisy sources, such as biometrics and physically uncloneable functions (PUFs), is known as fuzzy extractor (FE). Although FE constructions deal with discrete sources, most noisy sources are continuous. In the continuous case, it is required to transform the source to a discrete one.
Spectrum auction is an effective approach to improve the spectrum utilization, by leasing an idle spectrum from primary users to secondary users. Recently, a few differentially private spectrum auction mechanisms have been proposed, but, as far as we know, none of them addressed the differential privacy in the setting of double spectrum auctions.
In this paper, the achievable secrecy rate of a relay-assisted massive multiple-input multiple-output (MIMO) downlink is investigated in the presence of a multi-antenna active/passive eavesdropper. The excess degrees-of-freedom offered by a massive MIMO base-station (BS) are exploited for sending artificial noise (AN) via random and null-space precoders.
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