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Meta-Learning for improving rare word recognition in end-to-end ASR
We propose a new method of generating meaningful embeddings for speech, changes to four commonly used meta learning approaches to enable them to perform keyword spotting in continuous signals and an approach of combining their o ... Read More >
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Semantic Communication Systems for Speech Transmission
Semantic communications could improve the transmission efficiency significantly by exploring the semantic information. In this paper, we make an effort to recover the transmitted speech signals in the semantic communication syst ... Read More >
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An FPGA Implementation of Convolutional Spiking Neural Networks for Radioisotope ...
This paper details the FPGA implementation methodology for Convolutional Spiking Neural Networks (CSNN) and applies this methodology to low-power radioisotope identification using high-resolution data. Power consumption of 75 mW ... Read More >
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Maximum Likelihood Constraint Inference from Stochastic Demonstrations
When an expert operates a perilous dynamic system, ideal constraint information is tacitly contained in their demonstrated trajectories and controls. The likelihood of these demonstrations can be computed, given the system dynam ... Read More >
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Estimation and Distributed Eradication of SIR Epidemics on Networks
This work examines the discrete-time networked SIR (susceptible-infected-recovered) epidemic model, where the infection and recovery parameters may be time-varying. We provide a sufficient condition for the SIR model to converge ... Read More >
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Prior Image-Constrained Reconstruction using Style-Based Generative Models
Obtaining a useful estimate of an object from highly incomplete imaging measurements remains a holy grail of imaging science. Deep learning methods have shown promise in learning object priors or constraints to improve the condi ... Read More >
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Computing the Discrete Fourier Transform of signals with spectral frequency supp ...
We consider the problem of finding the Discrete Fourier Transform (DFT) of $N-$ length signals with known frequency support of size $k$. When $N$ is a power of 2 and the frequency support is a spectral set, we provide an $O(k \l ... Read More >
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Rigid and non-rigid motion compensation in weight-bearing cone-beam CT of the kn ...
Involuntary subject motion is the main source of artifacts in weight-bearing cone-beam CT of the knee. To achieve image quality for clinical diagnosis, the motion needs to be compensated. We propose to use inertial measurement u ... Read More >
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Hyperspectral Denoising Using Unsupervised Disentangled Spatio-Spectral Deep Pri ...
Image denoising is often empowered by accurate prior information. In recent years, data-driven neural network priors have shown promising performance for RGB natural image denoising. Compared to classic handcrafted priors (e.g., ... Read More >
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Deep learning based electrical noise removal enables high spectral optoacoustic ...
Image contrast in multispectral optoacoustic tomography (MSOT) can be severely reduced by electrical noise and interference in the acquired optoacoustic signals. Signal processing techniques have proven insufficient to remove th ... Read More >