IEEE ICASSP 2020 Virtual Conference May 2020

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  • Deep Soft Interference Cancellation For Mimo Detection

    00:15:41
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    Symbol detection in multiuser multiple-input multiple-output (MIMO) setups is a challenging task. A family of algorithms capable of reliably recovering multiple symbols is based on interference cancellation. However, these methods assume that the channel
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  • Drift Detection And Correction Post-Tracking

    00:13:19
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    Accurate object tracking is a challenging problem due to numerous factors, that may cause the tracker to drift away from the target object. Typically, the output of a tracker is a bounding box (BB); such BB may not well discriminate the object from its ba
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  • Doa Tracking Via Signal-Subspace Projector Update

    00:16:49
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    We develop a novel direction-of-arrival (DOA) tracking method in which we directly operate the signal-subspace projector instead of tracking the subspace eigenbasis. In each time frame, we employ a multidimensional subspace fitting approach to track the s
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  • Neural Attentive Multiview Machines

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    An important problem in multiview representation learning is finding the optimal combination of views with respect to the specific task at hand. To this end, we introduce NAM: a Neural Attentive Multiview machine that learns multiview item representations
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Sound event detection (SED) and acoustic scene classification (ASC) are major tasks in environmental sound analysis. Considering that sound events and scenes are closely related to each other, some works have addressed joint analyses of sound events and a
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  • Indoor Heading Direction Estimation Using Rf Signals

    00:13:15
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    Heading direction information is crucial to many ubiquitous computing applications. The main stream has been resort- ing to inertial sensors, such as accelerometer, gyroscope and magnetometer, which suffer from severe accumulative errors or large degradat
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  • A Wifi-Based Passive Fall Detection System

    00:12:47
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    Fall detection systems based on WiFi signals are gaining popularity recently. However, most of the existing works relying on training are environment-dependent. In this paper, we propose DeFall, a novel WiFi-based environment-independent fall detection sy
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  • A Real-Time Deep Network For Crowd Counting

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    Automatic analysis of highly crowded people has attracted extensive attention from computer vision research. Previous approaches for crowd counting have already achieved promising performance across various benchmarks. However, to deal with the real situa
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