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Extended Object Tracking Using Hierarchical Truncation Measurement Model With Automotive Radar
Motivated by real-world automotive radar measurements that are distributed around object (e.g., vehicles) edges with a certain volume, a novel hierarchical truncated Gaussian measurement model is proposed to resemble the underlying spatial distribution of
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Federating Solar, Storage And Communications In The Electric Grid And Internet Of Things
A futuristic infrastructure model is envisioned with distributed modules that can produce solar energy, have a storage system and provide services of lighting, electric-vehicle charging and communications. A stochastic model is formulated for the solar po
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An Online Kernel Scalar Quantization Scheme For Signal Classification
Distributed relay networking is one way of enabling connectivity between users that lack the necessary infrastructure to communicate with each other. An important advantage of such networks is the restoration of wireless communication coverage in the case
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Robust Parameter Estimation Of Contaminated Damped Exponentials
Parameter estimation of damped exponential signals has wide applications including fault detection and system parameter identification, etc. However, existing methods for estimating parameters of damped exponentials are either sensitive to noise or restri
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Speech Synthesis Using Eeg
In this paper we demonstrate speech synthesis using different electroencephalography (EEG) feature sets recently introduced in [1]. We make use of a recurrent neural network (RNN) regression model to predict acoustic features directly from EEG features. W
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Achieving Fully-Digital Performance By Hybrid Analog/Digital Beamforming In Wide-Band Massive-Mimo Systems
In this paper, we study the realization of any given fully-digital precoder (FDP) by hybrid analog/digital precoding (HADP) in wide-band mmWave systems. We first formulate the massive-MIMO OFDM-based HADP system design and then, introduce the notion of pe
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Chirping Up The Right Tree: Incorporating Biological Taxonomies Into Deep Bioacoustic Classifiers
Class imbalance in the training data hinders the generalization ability of machine listening systems. In the context of bioacoustics, this issue may be circumvented by aggregating species labels into super-groups of higher taxonomic rank: genus, family, o
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Unsupervised Domain Adaptation For Semantic Segmentation With Symmetric Adaptation Consistency
Unsupervised domain adaptation, which leverages label information from other domains to solve tasks on a domain without any labels, can alleviate the problem of the scarcity of labels and expensive labeling costs faced by supervised semantic segmentation.
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Unsupervised Feature Enhancement For Speaker Verification
The task of making speaker verification systems robust to adverse scenarios remains a challenging and an active area of research. We developed an unsupervised feature enhancement approach in log-filter bank space with the end goal of improving speaker ver
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Adaptive Matched Filter Using Non-Target Free Training Data
The problem of detecting a subspace signal in colored Gaussian noise with unknown covariance matrix is investigated when the training data may contain samples with target signal. The target signal is assumed that it lies in a subspace spanned by columns o
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Towards Real-Time, Multi-View Video Stereopsis
We present a real-time, multi-view video stereopsis (RTMVS) algorithm. This algorithm processes five synchronized video streams from cameras of a stationary camera array using a commodity laptop computer equipped with an Nvidia GPU. It provides 3D visuali
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Structured Citation Trend Prediction Using Graph Neural Networks
Academic citation graphs represent citation relationships between publications across the full range of academic fields. Top cited papers typically reveal future trends in their corresponding domains which is of importance to both researchers and practiti
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Robust Rank Constrained Sparse Learning: A Graph-Based Method For Clustering
Graph-based clustering is an advanced clustering techniuqe, which partitions the data according to an affinity graph. However, the graph quality affects the clustering results to a large extent, and it is difficult to construct a graph with high quality,
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Parsing Map Guided Multi-Scale Attention Network For Face Hallucination
Face hallucination that aims to transform a low-resolution (LR) face image to a high-resolution (HR) one is an active domain-specific image super-resolution problem. The performance of existing methods is usually not satisfactory, especially when the upsc
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On Distributed Stochastic Gradient Descent For Nonconvex Functions In The Presence Of Byzantines
We consider the distributed stochastic optimization problem of minimizing a nonconvex function $f$ in an adversarial setting. All the $w$ worker nodes in the network are expected to send their stochastic gradient vectors to the fusion center (or server).
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Enhanced Non-Local Cascading Network With Attention Mechanism For Hyperspectral Image Denoising
Because of the complexity of imaging environment, hyperspectral remote sensing images (HSIs) often suffer from different kinds of noise. Despite the success in natural image denoising, most of the existing CNN-based HSIs denoising methods still suffer fro
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Slogd: Speaker Location Guided Deflation Approach To Speech Separation
Speech separation is the process of separating multiple speakers from an audio recording. In this work we propose to separate the sources using a Speaker LOcalization Guided Deflation (SLOGD) approach wherein we estimate the sources iteratively. In each i
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Semi-Supervised Sentence Classification Based On User Polarity In The Social Scenarios
The data sparsity is the main challenge in sentence classification in social scenarios, the recent methods incorporate user information by encoding user node in the user-relation network to alleviate this issue. However, the connection between users is no
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Interpretability-Guided Convolutional Neural Networks For Seismic Fault Segmentation
Delineating the seismic fault, which is an important type of geologic structures in seismic images, is a key step for seismic interpretation. Comparing with conventional methods that design a number of hand-crafted features based on the observed character
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Minimal Adversarial Perturbations In Mobile Health Applications: The Epileptic Brain Activity Case Study
Today, the security of wearable and mobile-health technologies represents one of the main challenges in the Internet of Things (IoT) era. Adversarial manipulation of sensitive health-related information, e.g., if such information is used for prescribing m
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A Fast And Accurate Frequent Directions Algorithm For Low Rank Approximation Via Block Krylov Iteration
It is known that frequent directions (FD) is a popular deterministic matrix sketching method for low rank approximation. However, FD and its randomized variants usually meet high computational cost or computational instability in dealing with large-scale
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Privacy-Preserving Pattern Recognition Using Encrypted Sparse Representations In L0 Norm Minimization
In this paper, we propose a privacy-preserving pattern recognition method that uses encrypted sparse representations in L0 norm minimization. We prove, theoretically, that the proposal has exactly the same dictionary and sparse coefficient estimation perf
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Label Propagation Adaptive Resonance Theory For Semi-Supervised Continuous Learning
Semi-supervised learning and continuous learning are fundamental paradigms for human-level intelligence. To deal with real-world problems where labels are rarely given and the opportunity to access the same data is limited, it is necessary to apply these
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Efficient Super-Resolution Two-Dimensional Harmonic Retrieval Via Enhanced Low-Rank Structured Covariance Reconstruction
This paper develops an enhanced low-rank structured covariance reconstruction (LRSCR) method based on the decoupled atomic norm minimization (D-ANM), for super-resolution two-dimensional (2D) harmonic retrieval with multiple measurement vectors. This LRSC
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Deep Flow Collaborative Network For Online Visual Tracking
The deep learning-based visual tracking algorithms such as MDNet achieve high performance leveraging to the feature extraction ability of a deep neural network. However, the tracking efficiency of these trackers is not very high due to the slow feature ex
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On The Impact Of Language Familiarity In Talker Change Detection
The ability to detect talker changes when listening to conversational speech is fundamental to the perception and understanding of multi-talker speech. In this paper, we propose a novel experimental paradigm to provide insights on the impact of language f
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Addressing Accent Mismatch In Mandarin-English Code-Switching Speech Recognition
Automatic speech recognition systems suffer from accuracy degradation when code-switching (multiple languages are spoken in a single utterance) is encountered. This is especially common for non-native speakers where there is a mismatch between speech and
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Expression-Guided Eeg Representation Learning For Emotion Recognition
Learning a joint and coordinated representation between different modalities can improve multimodal emotion recognition. In this paper, we propose a deep representation learning approach for emotion recognition from electroencephalogram (EEG) signals guid
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Fusionndvi: A Novel Fusion Method For Ndvi In Remote Sensing
Normalized difference vegetation index (NDVI) is widely utilized to examine vegetation coverage and estimate crop yield. To obtain a high-resolution (HR) NDVI, fusion techniques, which first generates a HR multispectral (MS) image by fusing a low-resoluti
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Dynamic Resource Optimization And Altitude Selection In Uav-Based Multi-Access Edge Computing
The aim of this work is to develop a dynamic optimization strategy to allocate communication and computation resources in a Multi-access Edge Computing (MEC) scenario, where Unmanned AerialVehicles (UAVs) act as flying base station platforms endowed with
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Cp-Gan: Context Pyramid Generative Adversarial Network For Speech Enhancement
The topic of speech enhancement has been largely improved recently, especially with the development of generative adversarial networks (GANs). However prior methods simply follow the GAN architectures from computer vision tasks without specific designs fo
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Towards Linking The Lakh And Imslp Datasets
This paper investigates the problem of matching a MIDI file against a large database of piano sheet music images. Previous sheet-audio and sheet-MIDI alignment approaches have primarily focused on a 1-to-1 alignment task, which is not a scalable solution
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Efficient Shallow Wavenet Vocoder Using Multiple Samples Output Based On Laplacian Distribution And Linear Prediction
This paper presents a novel way for an efficient implementation scheme of shallow WaveNet vocoder with multiple samples (segment) output based on the use of Laplacian distribution and linear prediction. In our previous work, we have proposed a shallow arc
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End-To-End Spoken Language Understanding Without Matched Language Speech Model Pretraining Data
In contrast to conventional approaches to spoken language understanding (SLU) that consist of cascading a speech recognizer with a natural language understanding component, end-to-end (E2E) approaches for SLU infer semantics directly from the speech signa
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Two-Dimensional Doa Estimation For Coprime Planar Array: A Coarray Tensor-Based Solution
Coprime arrays can cope with the underdetermined case for direction-of-arrival (DOA) estimation. However, the popular matrix-based coarray signal processing approaches suffer performance loss on the underlying characteristics among the multi-dimensional s
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Voice Activity Detection For Transient Noisy Environment Based On Diffusion Nets
We address voice activity detection in acoustic environments of transients and stationary noises, which often occur in real-life scenarios. We exploit unique spatial patterns of speech and non-speech audio frames by independently learning their underlying
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Chronological Age Estimation Under The Guidance Of Age-Related Facial Attributes
Although the researches of facial attributes' analysis have been launched for decades, the estimation of chronological age attribute remains a big challenge. Previous researchers have found that some facial attributes (e.g., gender and race attributes) ha
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Real-Time Acoustic Scene Classification For Hearing Aids
Acoustic scene classification is a popular topic mostly combining the fields of audio signal processing and machine learning. Particularly the detection and classification of acoustic scenes and events (DCASE) challenge, which is held each year, increased
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IEEE ICASSP 2020 - State of the Society, Town Hall
IEEE ICASSP 2020 - State of the Society, Town Hall, by Dr. Ahmed Tewfik, May 2020.
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Moga: Searching Beyond Mobilenetv3
The evolution of MobileNets has laid a solid foundation for neural network applications on mobile end. With the latest MobileNetV3, neural architecture search again claimed its supremacy in network design. Unfortunately, till today all mobile methods main
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A Geometric Approach For Unsupervised Similarity Learning
Metric learning groups similar examples together, while moving away dissimilar ones. This is a crucial task in image processing and computer vision. However, existing metric learning approaches require huge number of labeled examples for their success. In
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Enhanced Adversarial Strategically-Timed Attacks Against Deep Reinforcement Learning
Recent deep neural networks based techniques, especially those equipped with the ability of self-adaptation in the system level such as deep reinforcement learning (DRL), are shown to possess many advantages of optimizing robot learning systems (e.g., aut
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Genetic Algorithm Optimized Support Vector Machine In Noma-Based Satellite Networks With Imperfect Csi
With the help of a power-domain non-orthogonal multiple access (NOMA) scheme, satellite networks can simultaneously serve multiple users within limited time/spectrum resource block. However, the existence of channel estimation errors inevitably degrade th
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Statistical Signal Processing Approach For Rain Estimation Based On Measurements From Network Management Systems
In this paper we apply statistical signal processing methodologies on a real-world application of using Commercial Microwave Links (CMLs) as opportunistic sensors for rain monitoring. We formulate an appropriate parameter estimation problem, taking advant
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Low Complexity Nlms For Multiple Loudspeaker Acoustic Echo Canceller Using Relative Loudspeaker Transfer Functions
Speech signals captured by a microphone mounted to a smart soundbar or speaker are inherently contaminated by echos. Modern smart devices are usually characterized by low computational capabilities and low memory resources; in these cases, a low-complexit
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Fcem: A Novel Fast Correlation Extract Model For Real Time Steganalysis Of Voip Stream Via Multi-Head Attention
Extracting correlation features between codes-words with high computational efficiency is crucial to steganalysis of Voice over IP (VoIP) streams. In this paper, we utilized attention mechanisms, which have recently attracted enormous interests due to the