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检索条件"任意字段=Conference on Signal and Data Processing of Small Targets"

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Compressed data aggregation for energy efficient wireless sensor networks

Compressed data aggregation for energy efficient wireless se...

引用

Annual IEEE Communications Society conference on Sensor and Adhoc Communications and Networks

作者： Xiang, Liu Luo, Jun Vasilakos, Athanasios School of Computer Engineering Nanyang Technological University Singapore Singapore Department of Electrical and Computer Engineering National Technical University of Athens Greece

ISBN: (纸本)9781457700934

As a burgeoning technique for signal processing, compressed sensing (CS) is being increasingly applied to wireless communications. However, little work is done to apply CS to multihop networking scenarios. In this paper, we investigate the application of CS to data collection in wireless sensor networks, and we aim at minimizing the network energy consumption through joint routing and compressed aggregation. We first characterize the optimal solution to this optimization problem, then we prove its NP-completeness. We further propose a mixed-integer programming formulation along with a greedy heuristic, from which both the optimal (for small scale problems) and the near-optimal (for large scale problems) aggregation trees are obtained. Our results validate the efficacy of the greedy heuristics, as well as the great improvement in energy efficiency through our joint routing and aggregation scheme. © 2011 IEEE.

关键词： Wireless sensor networks

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Multiple target detection for an antenna array using outlier rejection methods

Multiple target detection for an antenna array using outlier...

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International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： D.P. Michal D.R. Fuhrmann Electronic Systems and Signals Research Laboratory Department of Electrical Engineering Washington University Saint Louis MO USA

When there are multiple targets in a Gaussian noise background, single target detection methods can cause missed detections. The missed detections result because targets are present in the data used to estimate the covariance. Range bins containing targets differ from target-free range bins and are considered to be outliers. Different outlier rejection methods for multivariate data are developed. These methods include single outlier detection via hypothesis testing and multiple outlier detection using model order methods. Range bins detected as outliers are removed from the covariance estimate and target detection is performed. Simulation results for the various outlier rejection methods are presented.< >

关键词： Object detection Antenna arrays Testing Radar detection Matched filters Gaussian noise signal to noise ratio Covariance matrix Statistical analysis Laboratories

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Urban Road Autonomous Driving Vehicle Target Detection Algorithm Based on Improved YOLOv8

Urban Road Autonomous Driving Vehicle Target Detection Algor...

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6th International conference on Intelligent Computing and signal processing (ICSP)

作者： Yunfeng Gu Yujia Zheng Tian Ding Xiaohua Song Xiaoyan Zhang School of Electronic Information Engineering Xi'an Technological University Xi'an Shaanxi China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

Traffic target detection is one of the key technologies in the field of automatic driving, and timely and accurate detection of traffic targets is of great significance to improve driving safety and prevent traffic accidents. To address these problems of high equipment requirements, susceptibility to the background interference, and low detection accuracy of small targets in the traditional target detection model, a target detection algorithm to improve YOLOv8 is proposed. The method of introducing the SENetV2 module in the C2f module of the backbone network is used to improve the model's ability to recognize the important features of the detected target. A cross-scale feature fusion structure in the neck network is utilized to enhance the model's adaptability to scale changes and its ability to detect small-scale objects; the detection head of YOLOv8 is replaced with Dynamic Head to improve the model's performance in recognizing small targets in complex backgrounds. The training validation on the BDD100k dataset shows that the proposed improved algorithm improves the average precision (mAP) by 1.7% compared with the original algorithm., and the number of parameters and computation amount decrease by 26% and 15%, respectively, so that the model has a greater improvement in the ability of detecting small targets under complex backgrounds.

关键词： Adaptation models Accuracy Target recognition Computational modeling Heuristic algorithms signal processing algorithms Object detection Feature extraction Neck Vehicle dynamics

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A Improved Histogram Probabilistic Multiple Hypothesis Tracking Algorithm for Maneuvering Weak Target

A Improved Histogram Probabilistic Multiple Hypothesis Track...

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signal, Information and data processing (ICSIDP), IEEE International conference on

作者： Wei Shangguan Ying Lu Jinping Sun National Laboratory of Radar Signal Processing Xidian University Xi'an China School of Electronic and Information Engineering Behang University Beijing China

ISBN: (数字)9781728123455

ISBN: (纸本)9781728123462

A improved histogram probabilistic multiple hypothesis tracking (H-PMHT) algorithm based on the extended set-membership filtering (ESMF) is proposed for maneuvering weak targets detection and tracking. As an effective track-before-detect (TBD) method, H-PMHT algorithm is applied to synthesizing target measurements from multi-frame radar observation data directly, so as to avoid the problem of information loss in traditional threshold detection. Since the prior knowledge of maneuvering weak targets in real situations is always unknown but bounded, the ESMF algorithm is applied to obtain the estimated state from the synthesized measurements. Simulation results show that the ESMF based H-PMHT algorithm is capable of detecting and tracking dim targets, and has a better performance in tracking accuracy compared with the Kalman filter based H-PMHT algorithm.

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Target detection and identification using canonical correlation analysis and subspace partitioning

Target detection and identification using canonical correlat...

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International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Wei Wang Tulay Adali Darren Emge Department of CSEE University of Maryland Baltimore County Baltimore MD USA US Army Edgewood Chemical and Biological Center MD USA

We present a data-driven approach for target detection and identification based on a linear mixture model. Our aim is to determine the existence of certain targets in a mixture without specific information on the targets or the background, and to identify the targets from a given library. We use the maximum canonical correlation between the target set and the observations as the detection score, and use coefficients of the canonical vector to identify the indices of the present components from the given target library. The performance of the detector is enhanced using subspace partitioning on the target library. Both simulation and experimental results are presented to demonstrate the effectiveness of the proposed method in Raman spectroscopy for detection of surface-deposited chemical agents.

关键词： Object detection Libraries Raman scattering Spectroscopy Chemicals Detectors Testing Least squares methods Biomedical signal processing Contracts

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Time-reversal imaging and classification for distant targets in a shallow water channel

Time-reversal imaging and classification for distant targets...

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International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： N. Dasgupta L. Carin Department of Electrical and Computer Engineering Duke University Durham NC USA

Time-reversal imaging (TRI) is analogous to matched-field processing, although TRI is typically very wideband and is capable of performing target classification (in addition to localization). We apply the time-reversal technique to locate man-made cylindrical targets moving in a shallow ocean channel at long range, as well as to classify them from natural false targets like a school of fish. We present imaging and classification on simulated scattering data, for both target classes. In addition to the imaging, we explore extraction of features from the time-reversal data, with these applied to subsequent target classification. Time-reversal implementation requires a fast forward model, which we implement by a normal-mode model. We present the underlying theory of TRI, feature extraction and target classification via a relevance vector machine (RVM).

关键词： Oceans Acoustic scattering Sea surface Feature extraction Surface acoustic waves Ultrasonic imaging Acoustic waves Transceivers Acoustic imaging Wideband

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ICA-Based Connectivity on Brain Networks Using fMRI

ICA-Based Connectivity on Brain Networks Using fMRI

引用

6th International IEEE EMBS conference on Neural Engineering (NER)

作者： Eddin, Anas Salah Wang, Jin Sargolzaei, Saman Gaillard, William D. Adjouadi, Malek Florida Int Univ Coll Engn & Comp 10555 W Flagler StEAS 2672 Miami FL 33174 USA

ISBN: (纸本)9781467319690

This study introduces a novel data-driven approach for constructing large-scale functional brain networks. These networks are constructed by converting raw functional magnetic resonance imaging data into graphs using independent components analysis (ICA). Empirical evaluations were performed using data collected from three sites, which are part of a pediatric epilepsy consortium. The test data contained 30 control subjects and 29 pediatric epilepsy patients all of which were performing an auditory decision descriptive task, a language task paradigm. This approach is augmented by a unique graph thresholding technique based on the graph density function. The constructed networks were then analyzed using graph theoretical measures. The proposed network construction approach is weighed in merit to the traditional correlation approach and a modified version of it. The obtained results show that the ICA-based approaches improve considerably the delineation process of the patients' population from the controls' population, whereas the traditional methods show considerable overlap between the two populations. Furthermore, an investigation on the topology of the networks constructed show that all methods lead to a small-world topology conforming to previous brain functional studies.

关键词： biomedical MRI graphs independent component analysis medical disorders medical signal processing neurophysiology paediatrics

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Robust Cooperative Localization in a dynamic environment Using Factor Graphs and Probability data Association Filter 20

Robust Cooperative Localization in a dynamic environment Usi...

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20th International conference on Information Fusion (Fusion)

作者： Gulati, Dhiraj Zhang, Feihu Malovetz, Daniel Clarke, Daniel Knoll, Alois Fortiss GmbH Munich Germany Northwestern Polytech Univ Sch Marine Sci & Technol Xian 710072 Shaanxi Peoples R China Cogsense Technol Ltd Stanmore Greater London England Tech Univ Munich Garching Germany

ISBN: (纸本)9780996452700

Autonomous vehicles operating in dynamic environments rely on precise localization. In this paper we present a novel approach for cooperative localization of vehicular systems and an infrastructure RADAR which is resilient against outliers generated from the RADAR. The problem of cooperative localization is represented as a factor graph, where interrelated topologies ( including that of outliers) are added as constraint factor between vehicle states. Corresponding probabilities for multiple topologies between states of the two vehicles are calculated using the Probability data Association Filter and assigned to the respective edges in the graph. Simulation results indicate that this technique has significant benefits in the context of improving the resilience against outliers while optimizing joint state estimates. The methodology presented in this paper has the potential to provide a robust and flexible framework for cooperative localization in the presence of clutter, obscuration and targets entering and leaving the field of view.

关键词： Estimation Field programmable gate arrays OFDM Modulation Filtration Clocks signal processing algorithms

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Vibration data Acquisition and Fault Diagnosis System of Hydro Turbine Based on CompactRIO

Vibration Data Acquisition and Fault Diagnosis System of Hyd...

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5th International conference on Mechanical, Automotive and Materials Engineering (CMAME)

作者： Gong Weiming Zhao Ming Ge Xinfeng Ding Yongsheng Du Jinqi Zhang Yizhong Jiangdu Water Conservancy Project Adm Off Jiangsu Yangzhou 225200 Jiangsu Peoples R China Yunnan Power Grid Co Ltd Res Inst Kunming 650217 Yunnan Peoples R China Hohai Univ Coll Energy & Elect Engn Nanjing 211100 Jiangsu Peoples R China Yunnan Elect Test & Res Inst Grp CO LTD Kunming 650217 Yunnan Peoples R China Hunan Elect Power Res Inst State Grid Changsha 410007 Hunan Peoples R China

ISBN: (纸本)9781538604328

This paper uses NI CompactRIO hardware platform, the CRIO has a small, convenient and suitable for harsh environments. Based on Labview software platform, developed vibration condition monitor data acquisition and processing system, using the FPGA module and real-time processing module, realized high accuracy real-time and high sampling rate, and through wavelet analysis, processing the collected the signal. Due to the time-frequency characteristics of the wavelet analysis, the analysis of singularity of the fault signal has more advantages than the traditional spectrum analysis. Through the wavelet analysis, vibration CompactRIO system can accurate extraction of fault feature of vibration unit..

关键词： CompactRIO data Acquisition Hydro Turbine Condition monitoring Fault diagnosis

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Research on Domain Event Extraction Methods Based on Pre-Trained Model

Research on Domain Event Extraction Methods Based on Pre-Tra...

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2022 International conference on signal processing, Computer Networks, and Communications, SPCNC 2022

作者： Meng, Fanshen Zhang, Yonggang Liu, Xuhong Liu, Xiulei Yu, Junyang Institute of Data Science and Information Analysis Beijing Information Science & Technology University Beijing100029 China Center for Information Research Academy of Military Sciences Beijing100142 China Beijing Key Laboratory of Network Culture and Digital Communication Beijing Information Science & Technology University Beijing100029 China Beijing Advanced Innovation Center of Materials Genome Engineering Beijing Information Science & Technology University Beijing100029 China School of Software Henan University Henan 475001 China

ISBN: (纸本)9781510664616

One of the important research directions in information extraction is event extraction(EE). It aims at recognizing event types and event arguments from natural language texts, which is an important technical basis for artificial intelligence application that serves for the information work in business, science and technology, military and other fields. Currently, data annotation samples of the relevant field based on encyclopedia and news data are relatively rare and lack relevant datasets. Therefore, there are only a few public research on the event extraction in the relevant intelligence field. By integrating universal information extraction, the event extraction method which use the pre-trained model for the relevant intelligence field can handle the problem of rare data annotation samples for the event extraction in the relevant field. By expanding training samples automatically, the context information of encyclopedia and news data is learnt effectively to extract relevant events from encyclopedia and news data. Compared with other event extraction methods, using precision, recall and F1 value as assessment indicators, in event recognition tasks, the value of F1 enhances 1.26%, and in argument recognition tasks, the value of F1 enhances 1.58%. The method can significantly boost the extraction performance in small samples shown in the experimental results. © 2023 SPIE.

关键词： Information retrieval

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