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The Latest News, Articles, and Events in Signal Processing

IEEE Transactions on Computational Imaging

Given a spectral library, sparse unmixing aims to estimate the fractional proportions in each pixel of a hyperspectral image scene. However, the ever-growing dimensionality of spectral dictionaries strongly limits the performance of sparse unmixing algorithms. In this study, we propose a novel dictionary pruning (DP) approach to improve the performance of sparse unmixing algorithms, making them more accurate and time-efficient.

IEEE Transactions on Computational Imaging

In cell and molecular biology, the fusion of green fluorescent protein (GFP) and phase contrast (PC) images aims to generate a composite image, which can simultaneously display the functional information in the GFP image related to the molecular distribution of biological living cells and the structural information in the PC image such as nucleus and mitochondria. In this paper, we propose a detail preserving cross network (DPCN), which consists of a structural-guided functional feature extraction branch (SFFEB), a functional-guided structural feature extraction branch (FSFEB) and a detail preserving module (DPM), to address the GFP and PC image fusion issue.

IEEE/ACM Transactions on Audio, Speech, and Language Processing

Automatic speech recognition (ASR) technologies have been significantly advanced in the past few decades. However, recognition of overlapped speech remains a highly challenging task to date. To this end, multi-channel microphone array data are widely used in current ASR systems.

IEEE/ACM Transactions on Audio, Speech, and Language Processing

In music source separation, the number of sources may vary for each piece and some of the sources may belong to the same family of instruments, thus sharing timbral characteristics and making the sources more correlated. This leads to additional challenges in the source separation problem.

IEEE/ACM Transactions on Audio, Speech, and Language Processing

A key task for speech recognition systems is to reduce the mismatch between training and evaluation data that is often attributable to speaker differences. Speaker adaptation techniques play a vital role to reduce the mismatch. Model-based speaker adaptation approaches often require sufficient amounts of target speaker data to ensure robustness. 

IEEE Signal Processing Letters

Point Clouds (PCs) have recently been adopted as the preferred data structure for representing 3D visual contents. Examples of Point Cloud (PC) applications range from 3D representations of small objects up to large scenes, both still or dynamic in time. PC adoption triggered the development of new coding, transmission, and display methodologies that culminated in new international standards for PC compression. 

IEEE Signal Processing Letters

Video inpainting aims to fill missing regions with plausible content in a video sequence. Deep learning-based video inpainting methods have made promising progress over the past few years. However, these methods tend to generate degraded completion content, such as missing textural details.

IEEE Open Journal of Signal Processing

Quadrature spatial modulation (QSM) isa recently proposed multiple-input multiple-output (MIMO) wireless transmission paradigm that has garnered considerable research interest owing to its relatively high spectral efficiency. QSM essentially enhances the spatial multiplexing gain while maintaining all the inherent advantages of spatial modulation (SM).

IEEE Open Journal of Signal Processing

Identification of decompressed JPEG images, especially those compressed with high JPEG quality factors, is a challenging issue in image forensics. Furthermore, the applicability of the existing JPEG forensic detectors in forgery localization is limited by their inability to cope with spatial misalignment in the 8×8 JPEG grid.

IEEE Journal of Selected Topics in Signal Processing

Automotive imaging radars require high angular resolution which can be achieved by a large antenna aperture. In order to obey Nyquist spatial sampling rate, a large number of array elements and receive channels is required. In practice, this solution results in a prohibitively high cost and complexity. 

IEEE Journal of Selected Topics in Signal Processing

We propose a high-resolution imaging radar system to enable high-fidelity four-dimensional (4D) sensing for autonomous driving, i.e., range, Doppler, azimuth, and elevation, through a joint sparsity design in frequency spectrum and array configurations. To accommodate a high number of automotive radars operating at the same frequency band while avoiding mutual interference, random sparse step-frequency waveform (RSSFW) is proposed to synthesize a large effective bandwidth to achieve high range resolution profiles.

IEEE Journal of Selected Topics in Signal Processing

Automotive radar is used in many applications of advanced driver assistance systems and is considered as one of the key technologies for highly automated driving. An overview of state-of-the-art signal processing in automotive radar is presented along with current research directions and practical challenges.

I-DeepLearn aims to increase diversity in computer science and related field by inviting students from grades 10-12 to experience machine learning (Artificial Intelligence) and deep learning (AI processing) and their applications, specifically in the healthcare domain. In this one-week online workshop, students will learn about essential concepts in machine learning and deep learning through a dynamic mix of hands-on programming projects and interactive discussions.

Friedrich-Alexander-University Erlangen-Nuremberg

The Faculty of Engineering at Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) invites applications for a

Tenure Track Professorship for Intelligent Speech Interfaces

(W1 / Assistant Professor)

University College London

PhD Studentship - UCL Institute of Healthcare Engineering EPSRC PhD Studentships on Healthy Ageing

Project Title:  Lab-on-an-App: AI Empowered Point-of-Care Diagnostics for Ageing Population

Project Supervisor:

  • Professor Miguel Rodrigues | Dept Electronic and Electrical Engineering | University College London

Project Co-Supervisors:

Institute of Sound and Vibration Research, University of Southampton

We invite applications for the post of Lecturer or Associate Professor in the Signal Processing, Audio and Hearing research group at the Institute of Sound and Vibration Research (ISVR), University of Southampton. We are open to applicants from a broad range of disciplines from within the fields of Acoustic, Audio and Speech Signal Processing. https://jobs.soton.ac.uk/Vacancy.aspx?ref=1350921DA-R

Prof. Mihaela van der Schaar is the John Humphrey Plummer Professor of Machine Learning, Artificial Intelligence and Medicine at the University of Cambridge, a Fellow at The Alan Turing Institute in London, and a Chancellor’s Professor at UCLA.

Mónica F. Bugallo received her Ph.D. in computer science and engineering from University of A Coruña, Spain. She is a Professor of Electrical and Computer Engineering and the Vice Provost for Faculty Affairs, and Diversity, Equity and Inclusion at Stony Brook University, NY, USA. Bugallo is the current Chair of the IEEE SPS Signal Processing Theory and Methods Technical Committee, Senior Associate Editor of the IEEE Signal Processing Letters and Associate Editor of the IEEE Transactions on Signal Processing. 

Xuedong Huang is a Microsoft Technical Fellow and Azure AI Chief Technology Officer. He is responsible for Microsoft’s Azure AI engineering and research to bring the dream of making machines see, hear and understand human beings a reality.

Dr. Supavadee Aramvith received her B.S. (first class honors) degree in Computer Science from Mahidol University, Bangkok, Thailand, in 1993. She received her M.S. and Ph.D. degrees in Electrical Engineering from the University of Washington, Seattle, USA, in 1996 and 2001, respectively. 

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