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The Latest News, Articles, and Events in Signal Processing
ICASSP 2022 is pioneering a new event, the SPS Entrepreneurship Forum. The aim of this 1-day event is to promote entrepreneurship amongst the signal processing community, by sharing entrepreneurship journeys, discussing challenges and opportunities in translating signal processing research into commercial applications...
Date: October 26-November 11, 2022
Location: Yokohama, Japan
CFP Announcement
The 8th IEEE World Forum on the Internet of Things (WFIoT2022) seeks submissions and proposals for original technical papers and presentations that address the Internet of Things (IoT), its theoretical and technological building blocks, the applications that drive the growth and evolution of IoT.
Submission Deadline: April 10, 2022
Call for Proposals Document
Dr. Sanjit K. Mitra, Distinguished Professor Emeritus of Electrical and Computer Engineering at University of California, Santa Barbara, has been elected recently to the Engineering Academy of Japan (EAJ) as a foreign associate. EAJ is composed of leading experts from academia, industry, and government institutions who possess a wide range of knowledge and have made outstanding contributions in engineering and technological sciences, and closely related fields...
The IEEE Transactions on Multimedia invites nominations for the position of Editor-in-Chief for a 3-year term starting 1 January 2023. Nominations should be submitted to the Steering Committee Chair, Wenwu Zhu, IEEE Transactions on Multimedia, via email to wwzhu@tsinghua.edu.cn.
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 Signal Processing Society (SPS) has 12 Technical Committee that support a broad selection of signal processing-related activities defined by the scope of the Society.
The IEEE Signal Processing Society (SPS) is excited to announce a new initiative to support SPS refers to the pre-college educational years collectively, and commonly includes elementary, middle, and high schools.
Do you have a colleague whose signal processing knowledge and expertise might be of greater interest to the broader Signal Processing Society community? SPS is now soliciting nominations for speakers to serve for two Society programs, Distinguished Lecturer Program (DL) and Distinguished Industry Speaker Program (DIS).
With the goal of turning data into information, and information into insight, the Sarvajanik College of Engineering & Technology (SCET), in association with IEEE SPS SCET Student Branch Chapter, Gujarat Section and IEEE SCET Student Branch organized a National Level Project Competition on Data Science Challenge 1.0 (DSC 1.0).
Manuscript Due: 10 December 2022
Publication Date: September 2023
CFP Document
A brief introduction to state estimation in multi-object system that arises from applications where the number of objects and their states are unknown and vary randomly with time. State space model (SSM) is a fundamental concept in system theory that permeated through many fields of study.
We are seeking a highly motivated and skilled PhD student to work with us on the development of a digital phenotyping strategy for children with autism. The PhD student will use machine learning approaches to provide automated measures of body movement and social scenes for children with autism, with the goal to support automated autism diagnosis and/or fine-grained characterization of autistic symptoms.
The large antenna arrays with hybrid analog and digital (HAD) architectures can provide a large aperture with low cost and hardware complexity, resulting in enhanced direction-of-arrival (DOA) estimation and reduced power consumption. This paper investigates the trade-off between DOA estimation and power consumption in large antenna arrays with HAD architectures.
We present a general nonlinear Bayesian filter for high-dimensional state estimation using the theory of reproducing kernel Hilbert space (RKHS). By applying the kernel method and the representer theorem to perform linear quadratic estimation in a functional space, we derive a Bayesian recursive state estimator for a general nonlinear dynamical system in the original input space. Unlike existing nonlinear extensions of the Kalman filter where the system dynamics are assumed known, the state-space representation for the Functional Bayesian Filter (FBF) is completely learned online from measurement data in the form of an infinite impulse response (IIR) filter or recurrent network in the RKHS, with universal approximation property.
In this paper, we propose CE-BASS, a particle mixture Kalman filter which is robust to both innovative and additive outliers, and able to fully capture multi-modality in the distribution of the hidden state. Furthermore, the particle sampling approach re-samples past states, which enables CE-BASS to handle innovative outliers which are not immediately visible in the observations, such as trend changes.
Time-of-arrival (TOA) based localization plays a central role in current and future localization systems. Such systems, exploiting the fine delay resolution properties of wideband and ultra-wideband (UWB) signals, are particularly attractive for ranging under harsh propagation conditions in which significant multipath may be present. While multipath has been traditionally considered detrimental in the design of TOA estimators, it can be exploited to benefit ranging.
Target detection is studied for a cloud multiple-input multiple-output (MIMO) radar using quantized measurements. According to the local sensor quantization strategies and fusion strategies, this paper discusses three methods: quantize local test statistics which are linearly fused (QTLF), quantize local test statistics which are optimally fused (QTOF), and quantize local received signals which are optimally fused (QROF).
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