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Multi-Task Learning Via Sa-Fpn And Ej-Head
As a concise framework, Mask R-CNN achieves promising performance in object detection and instance segmentation. However, there is room for improvement in two aspects. One is that performing multi-task prediction needs more credible feature extraction and
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Zero-Crossing Precoding With Maximum Distance To The Decision Threshold For Channels With 1-Bit Quantization And Oversampling
Low-resolution devices are promising for systems that demand low energy consumption and low complexity as required in IoT systems. In this study, we propose a novel waveform for bandlimited channels with 1-bit quantization and oversampling at the receiver
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End-To-End Architectures For Asr-Free Spoken Language Understanding
Spoken Language Understanding (SLU) is the problem of extracting the meaning from speech utterances. It is typically addressed as a two-step problem, where an Automatic Speech Recognition (ASR) model is employed to convert speech into text, followed by a
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Upscaling Vector Approximate Message Passing
In this paper we consider the problem of recovering a signal x of size N from noisy and compressed measurements y = A x + w of size M, where the measurement matrix A is right-orthogonally invariant (ROI). Vector Approximate Message Passing (VAMP) demonstr
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Selection-Channel-Aware Reverse Jpeg Compatibility For Highly Reliable Steganalysis Of Jpeg Images
This paper deeply studies the principle of the recent reverse JPEG compatibility attack [1]. This analysis allows us to cast the problem of hidden data detection in DCT coefficients within hypothesis testing theory. The optimal LR test, thought efficient,
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A Novel Moving Sparse Array Geometry With Increased Degrees Of Freedom
In this paper, we propose a novel moving sparse array geometry named dilated arrays (DAs) by extending the dilation of nested arrays to other linear array structures. The theoretical analysis of dilation to other arrays is not straightforward since the re
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Robust Transmission Over Channels With Channel Uncertainty: An Algorithmic Perspective
The availability and quality of channel state information heavily influences the performance of wireless communication systems. For perfect channel knowledge, optimal signal processing and coding schemes are well studied and often closed-form solutions ar
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Effective Approximate Maximum Likelihood Estimation Of Angles Of Arrival For Non-Coherent Sub-Arrays
We consider the problem of estimating the angles of arrival (AOAs) of multiple sources from a single snapshot obtained by a set of non-coherent sub-arrays, i.e., while the antenna elements in each sub-array are coherent, each sub-array observes a differen
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Mental Fatigue Prediction From Multi-Channel Ecog Signal
Early detection of mental fatigue and changes in vigilance could be used to initiate neurostimulation to treat patients suffering from brain injury and mental disorders. In this study, we analyzed electrocorticography (ECoG) signals chronically recorded f
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Knowledge Distillation And Random Erasing Data Augmentation For Text-Dependent Speaker Verification
This paper explores the Knowledge Distillation (KD) approach and a data augmentation technique to improve the generalization ability and robustness of text-dependent speaker verification (SV) systems. The KD method consists of two neural networks, known a
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Unsupervised Change Detection For Multimodal Remote Sensing Images Via Coupled Dictionary Learning And Sparse Coding
Archetypal scenarios for change detection generally consider two images acquired through sensors of the same modality. The resolution dissimilarity is often bypassed though a simple preprocessing, applied independently on each image to bring them to the s
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Acoustic Scene Classification Using Deep Residual Networks With Late Fusion Of Separated High And Low Frequency Paths
We investigate the problem of acoustic-scene classification, using a deep residual network applied to log-mel spectrograms complemented by log-mel deltas and delta-deltas.~We design the network to take into account that the temporal and frequency axes in
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Detection Of Speech Events And Speaker Characteristics Through Photo-Plethysmographic Signal Neural Processing
The use of photoplethysmogram signal (PPG) for heart and sleep monitoring is commonly found nowadays in smartphones and wrist wearables. Besides common usages, it has been proposed and reported that person information can be extracted from PPG for other u
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Improved End-To-End Spoken Utterance Classification With A Self-Attention Acoustic Classifier
While human language provides a natural interface for human-machine communication, there are several challenges concerning extracting the intents of a speaker when interacting with a virtual agent, especially when the speaker is in a noisy acoustic enviro
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High Dynamic Range Imaging Using Deep Image Priors
Traditionally, dynamic range enhancement for images has involved a combination of contrast improvement (via gamma correction or histogram equalization) and a denoising operation to reduce the effects of photon noise. More recently, modulo-imaging methods
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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
