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The Latest News, Articles, and Events in Signal Processing
Graph-based transforms have been shown to be powerful tools in terms of image energy compaction. However, when the size of the support increases to best capture signal dependencies, the computation of the basis functions becomes rapidly untractable. This problem is in particular compelling for high dimensional imaging data such as light fields. The use of local transforms with limited supports is a way to cope with this computational difficulty.
This paper proposes a multi-layer neural network structure for few-shot image recognition of novel categories. The proposed multi-layer neural network architecture encodes transferable knowledge extracted from a large annotated dataset of base categories. This architecture is then applied to novel categories containing only a few samples.
Anonymous authentication (AA) schemes are used by an application provider to grant services to its n users for pre-defined k times after they have authenticated themselves anonymously. These privacy-preserving cryptographic schemes are essentially based on the secret key that is embedded in a trusted platform module (TPM).
This article proposes an algorithm which allows Alice to simulate the game played between her and Eve. Under the condition that the set of detectors that Alice assumes Eve to have is sufficiently rich (e.g. CNNs), and that she has an algorithm enabling to avoid detection by a single classifier (e.g adversarial embedding, gibbs sampler, dynamic STCs), the proposed algorithm converges to an efficient steganographic algorithm.
The use of microwave tomography (MWT) in an industrial drying process is demonstrated in this feasibility study with synthetic measurement data. The studied imaging modality is applied to estimate the moisture content distribution in a polymer foam during the microwave drying process. Such moisture information is crucial in developing control strategies for controlling the microwave power for selective heating.
Perfusion computed tomography (PCT) is critical in detecting cerebral ischemic lesions. PCT examination with lowdose scans can effectively reduce radiation exposure to patients at the cost of degraded images with severe noise, and artifacts. Tensor total variation (TTV) models are powerful tools that can encode the regional continuous structures underlying a PCT object.
We consider the problem of localizing the source using range, and range-difference measurements. Both the problems are non-convex, and non-smooth, and are challenging to solve. In this article, we develop an iterative algorithm - Source Localization Via an Iterative technique (SOLVIT) to localize the source using all the distinct range-difference measurements, i.e., without choosing a reference sensor.
Personal Sound Zones (PSZ) systems aim to render independent sound signals to multiple listeners within a room by using arrays of loudspeakers. One of the algorithms used to provide PSZ is Weighted Pressure Matching (wPM), which computes the filters required to render a desired response in the listening zones while reducing the acoustic energy arriving to the quiet zones.
The study of label noise in sound event recognition has recently gained attention with the advent of larger and noisier datasets. This work addresses the problem of missing labels, one of the big weaknesses of large audio datasets, and one of the most conspicuous issues for AudioSet. We propose a simple and modelagnostic method based on a teacher-student framework with loss masking to first identify the most critical missing label candidates, and then ignore their contribution during the learning process.
We propose a novel modified Mel-discrete cosine transform (MMD) filter bank structure, which restricts the overlap of each filter response to its immediate neighbor. In contrast to the well-known triangular filters employed in the extraction of the Mel-frequency cepstral coefficients (MFCC), the proposed filter structure has a smoother response and offers discrete cosine transformation and Mel-scale filtering in a single operation.
This work examines a distributed learning problem where the agents of a network form their beliefs about certain hypotheses of interest. Each agent collects streaming (private) data and updates continually its belief by means of a diffusion strategy, which blends the agent’s data with the beliefs of its neighbors. We focus on weakly-connected graphs, where the network is partitioned into sending and receiving sub-networks, and we allow for heterogeneous models across the agents.
Recently, deep neural network (DNN) based methods for low-dose CT have been investigated to achieve excellent performance in both image quality and computational speed. However, almost all methods using DNNs for low-dose CT require clean ground truth data with full radiation dose to train the DNNs. In this work, we attempt to train DNNs for low-dose CT reconstructions with reduced tube current by investigating unsupervised training of DNNs for denoising sensor measurements or sinograms without full-dose ground truth images.
Regularization by denoising (RED) is an image reconstruction framework that uses an image denoiser as a prior. Recent work has shown the state-of-the-art performance of RED with learned denoisers corresponding to pre-trained convolutional neural nets (CNNs). In this work, we propose to broaden the current denoiser-centric view of RED by considering priors corresponding to networks trained for more general artifact-removal.
One challenging aspect in face anti-spoofing (or presentation attack detection, PAD) refers to the difficulty of collecting enough and representative attack samples for an application-specific environment. In view of this, we tackle the problem of training a robust PAD model with limited data in an application-specific domain.
With the rapid progress in recent years, techniques that generate and manipulate multimedia content can now provide a very advanced level of realism. The boundary between real and synthetic media has become very thin. On the one hand, this opens the door to a series of exciting applications in different fields such as creative arts, advertising, film production, and video games. On the other hand, it poses enormous security threats. Software packages freely available on the web allow any individual, without special skills, to create very realistic fake images and videos.
Lecture Date: December 7, 2020 (Virtual Lecture)
Chapter: Beijing
Chapter Chair: Qiuqi Ruan
Topic: Binary optimizations in signal processing
Date: October 28, 2020
Time: 11:00 AM EDT (New York Time)
Title: Joint Optimization of Radio and Computational
Resources in Mobile Edge Computing
Registration | Full webinar details
In recent years, we have seen the emergence of new compute-intensive and delay-critical mobile applications, such as virtual/augmented reality, online gaming, ultra-high-definition video streaming and autonomous driving. Multi-access edge computing (MEC) has become a key technology in 5G networks to shift computational tasks from resource-limited mobile devices to nearby servers placed at the edge of the network.
White Paper Due: November 1, 2020
Publication Date: November 2021
CFP Document
White Paper Due: February 1, 2021
Publication Date: March 2022
CFP Document
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