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The Latest News, Articles, and Events in Signal Processing
Ghost imaging has recently been successfully achieved in the X-ray regime. Due to the penetrating power of X-rays this immediately opens up the possibility of ghost-tomography. No research into this topic currently exists in the literature. Here, we present adaptations of conventional X-ray tomography techniques to this new ghost-imaging scheme. Several numerical implementations for tomography through X-ray ghost-imaging are considered.
Matched field processing (MFP) compares the measures to the modeled pressure fields received at an array of sensors to localize a source in an ocean waveguide. Typically, there are only a few sources when compared to the number of candidate source locations or range-depth cells. We use sparse Bayesian learning (SBL) to learn a common sparsity profile corresponding to the location of present sources.
Supervised learning-based methods for source localization, being data driven, can be adapted to different acoustic conditions via training and have been shown to be robust to adverse acoustic environments. In this paper, a convolutional neural network (CNN) based supervised learning method for estimating the direction of arrival (DOA) of multiple speakers is proposed.
This paper investigates sound-field modeling in a realistic reverberant setting. Starting from a few point-like microphone measurements, the goal is to estimate the direct source field within a whole three-dimensional (3-D) space around these microphones. Previous sparse sound field decompositions assumed only a spatial sparsity of the source distribution, but could generally not handle reverberation.
Acoustic source localization and tracking is a well-studied topic in signal processing, but most traditional methods incorporate simplifying assumptions such as a point source, free-field propagation of the sound wave, static acoustic sources, time-invariant sensor constellations, and simple noise fields.
The IEEE Journal of Selected Topics in Signal Processing solicits special issues on topics that cover the entire scope of the IEEE Signal Processing Society, as outlined in the SPS Constitution, Article II:
"The Signal Processing Society's Field of Interest shall be the theory and application of filtering, coding, transmitting, estimating, detecting, analyzing, recognizing, synthesizing, recording, and reproducing signals by digital or analog devices or techniques. The term `signal' includes audio, video, speech, image, communication, geophysical, sonar, radar, medical, musical, and other signals
Lecture Date: May 8, 2019
Chapter: Germany
Chapter Chair: Wolfgang Utschick
Topic: Speech Recognition: What’s Left?
Applicants are sought for a postdoctoral position on a project of auditory perception in complex listening environments. The project employs signal processing and computational models as well as experimental techniques (recordings of brain activity) to explore mechanisms underlying auditory scene analysis and their translation into smart audio technologies.
The Department of Electrical and Electronic Engineering, Southern University of Science and Technology (SUSTech) now invites applications for the faculty position in the Department of Electrical and Electronic Engineering. It is seeking to appoint a number of tenured or tenure track positions in all ranks.
Deputy Director-190339
Primary Location
Italy-La Spezia
NATO Body
Centre for Maritime Research and Experimentation (CMRE)
Schedule
Full-time
Euro (EUR) Yearly
Grade A.6
Description
:
1. GENERAL JOB DESCRIPTION
1.1 POST IDENTIFICATION
Primary Location: Italy – La Spezia
CTTC is seeking to hire an Early Stage Researcher (ESR) to join the WindMill project. The project offers an excellent research and training programme:
Most evolutionary algorithms and other meta-heuristic search methods typically assume that there are explicit objective functions available for fitness evaluations. In the real world, such explicit objective functions may not exist in many cases. Instead, computationally very intensive numerical simulations, such as computational fluid dynamic simulations or finite element analysis, must be conducted.
Neural networks with rectified linear unit (ReLU) activation functions (a.k.a. ReLU networks) have achieved great empirical success in various domains. Nonetheless, existing results for learning ReLU networks either pose assumptions on the underlying data distribution being, e.g., Gaussian, or require the network size and/or training size to be sufficiently large.
In the special issue in Proceedings of the IEEE in April 2019, the editors have collected and presented recent works on innovative approaches and emerged technologies for coping with dynamicity, heterogeneity, and the scale, which have been central to (or even enablers of) recent advances in communications and networking technologies.
Lecture Date: May 9, 2019
Chapter: Santa Clara Valley
Chapter Chair: Yang Lei
Topic: Solving the Cocktail Party Problem for Hearing Aids:
Solutions, Challenges and Opportunities
The task of Heterogeneous Face Recognition consists in matching face images that are sensed in different domains, such as sketches to photographs (visual spectra images), and thermal images to photographs or near-infrared images to photographs. In this paper, we suggest that the high-level features of Deep Convolutional Neural Networks trained in visual spectra images are potentially domain independent and can be used to encode faces sensed in different image domains.
In psychology, it is known that facial dynamics benefit the perception of identity. This paper proposes a novel deep network framework to capture identity information from facial dynamics and their relations. In the proposed method, facial dynamics occurred from a smile expression are analyzed and utilized for facial authentication. Detailed changes in the local regions of a face such as wrinkles and dimples are encoded in the facial dynamic feature representation.
In real-world applications, different kinds of learning and prediction errors are likely to incur different costs for the same system. Moreover, in practice, the cost label information is often available only for a few training samples. In a semi-supervised setting, label propagation is critical to infer the cost information for unlabeled training data.
Auction is an effective way to allocate goods or services to bidders who value them the most. The rapid growth of e-auctions facilitates online transactions but poses new and distinctive challenges. It is difficult to establish trust among sellers, buyers, and auctioneers without centralized auction websites or platforms (the auctioneer) which collect bids and derive the auction results. However, these third parties may be untrustworthy, and malicious sellers or buyers may refuse to deliver the goods or payment according to the protocol.
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