检索结果-内蒙古大学图书馆

8th International Conference on Identification, Information and Knowledge in the Internet of Things, IIKI 2019

作者： Yu, Jie Liu, Yulan Bai, Yang Liu, Feiliang Science and Technology on Electromagnetic Scattering Laboratory Beijing1100854 China Chaohu University Hefei230000 China

With the rapid development of Unmanned Aerial Vehicle (UAV), stealth warship and stealth tank, the mission of radar has become more difficult. The modern radar system must detect and track target through strong clutter from many aspect, such as ground and sea. Radar detection for low slow small （LSS）targets has gradually become a new focus. LSS target has a small Radar Cross Section (RCS) because of its small size, making the SNR of the echo signal very low. At the same time, the Doppler frequency of slow target is very low, making it difficult for conventional signal processing methods to separate the Doppler information from the clutter spectrum. Thus, it does make sense to study how to detect the LSS target in strong clutter efficiently. In this paper, the distribution characteristics of radar clutter and coherent accumulation algorithm for LSS target detection in complex electromagnetic environment are studied. On the base of that, a double-threshold target detection method based on coherent accumulation is proposed. To check out the efficiency of the detection method, an experiment has been conducted, and the data collected was used to test the detection algorithm, and the result has been shown in this paper. © 2020 The Authors. Published by Elsevier B.V.

关键词： Antennas

来源：评论

学校读者我要写书评

暂无评论

基于改进MEANSHIFT的可见光低小慢目标跟踪算法

引用

信号处理 2022年第4期38卷 824-834页

作者：胡亮杨德贵王行廖祥红中南大学航空航天学院湖南长沙410083 中南大学自动化学院湖南长沙410083

由于低小慢目标强机动性、易畸变等特点导致对其跟踪定位误差大、精确度低,本文针对这一难题提出了一种基于多特征融合与区域生长的Meanshift低小慢目标跟踪算法。首先根据人工标定的目标初始位置截取ROI(region of interest)区域,提取... 详细信息

由于低小慢目标强机动性、易畸变等特点导致对其跟踪定位误差大、精确度低,本文针对这一难题提出了一种基于多特征融合与区域生长的Meanshift低小慢目标跟踪算法。首先根据人工标定的目标初始位置截取ROI(region of interest)区域,提取ROI区域的灰度直方图以及HOG(梯度方向直方图)特征,融合两特征建立目标的二维描述模板,然后结合目标模板与候选模板之间的Bhattacharyya相似系数以及ROI区域与候选区域之间的Hu矩的欧氏距离构建新的算法收敛判据,最后利用区域生长方法分析目标面积变化建立模板更新机制。通过在公开数据集LaSOT以及自行采集的4个图像序列上与同类算法的实验表现,表明本文算法对低小慢目标强机动与畸变不敏感,跟踪效果稳定,在一定的约束条件下,算法的跟踪精度可到达95%以上,具有较强的应用价值。

关键词：低小慢目标跟踪特征融合 Meanshift 区域生长 Hu不变矩

来源：评论

学校读者我要写书评

暂无评论

Local Patch Network with Global Attention for Infrared small Target Detection

arXiv

引用

arXiv 2021年

作者： Chen, Fang Gao, Chenqiang Liu, Fangcen Zhao, Yue Zhou, Yuxi Meng, Deyu Zuo, Wangmeng School of Communication and Information Engineering Chongqing University of Posts and Telecommunications Chongqing400065 China Chongqing Key Laboratory of Signal and Information Processing Chongqing University of Posts and Telecommunications Chongqing400065 China Macau Institute of Systems Engineering Macau University of Science and Technology Taipa999078 China School of Mathematics and Statistics Xi’an Jiaotong University Shanxi Xi’an710049 China School of Computer Science and Technology Harbin Institute of Techonlogy Heilongjiang Harbin47822 China

Infrared small target detection plays an important role in the infrared search and tracking applications. In recent years, deep learning techniques were introduced to this task and achieved noteworthy effects. Following general object segmentation methods, existing deep learning methods usually processed the image from the global view. However, the imaging locality of small targets and extreme class-imbalance between the target and background pixels were not well-considered by these deep learning methods, which causes the low-efficiency on training and high-dependence on numerous data. A local patch network (LPNet) with global attention is proposed in this paper to detect small targets by jointly considering the global and local properties of infrared small target images. From the global view, a supervised attention module trained by the small target spread map is proposed to suppress most background pixels irrelevant with small target features. From the local view, local patches are split from global features and share the same convolution weights with each other in a patch net. By leveraging both the global and local properties, the data-driven framework proposed in this paper has fused multi-scale features for small target detection. Extensive synthetic and real data experiments show that the proposed method achieves the state-of-the-art performance compared with existing both conventional and deep learning methods. © 2021, CC BY.

关键词： Infrared imaging

来源：评论

学校读者我要写书评

暂无评论

Image Electronic Evidence Screening Based on Improved SSD

Image Electronic Evidence Screening Based on Improved SSD

引用

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

作者： Yafei Liu Liehui Jiang Tieming Liu Youwei Zhang Zhongyuan Network Security Research institute Zhengzhou University Zhengzhou China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China Zhengzhou Xinda Institute of Advanced Technology Zhengzhou China

ISBN: (纸本)9781665447300

With the development of information technology, electronic evidence plays an increasingly important role in the judicial trial. In Judicial Forensics, it is difficult to extract effective electronic evidence accurately and quickly from a large number of electronic data. Traditional means generally take artificial identification to collect, which takes a long time and is not efficient. Using SSD target detection and recognition algorithm instead of traditional means can effectively reduce the time of screening evidence, but the basic SSD neural network is prone to miss detection for small targets. To solve the above problems, an improved SSD-based image electronic evidence screening method is proposed. This method optimizes the SSD neural network adaptively, introduces the attention mechanism module in the shallow convolution layer of the network to improve the representation ability of the feature map, and fuses the image features obtained from different convolution layers with multi-scale features to increase the shallow feature information. The experimental data are used to test the improved algorithm and analyze the experimental results. It is found that compared with the original SSD neural network algorithm, the detection mean average precision of the improved algorithm is increased by 4.7%, reaching 84.3%, which shows the feasibility and effectiveness of the improved algorithm.

关键词： Image recognition Convolution Target recognition Fuses Neural networks Stacking signal processing algorithms

来源：评论

学校读者我要写书评

暂无评论

MALTA-Cz: a radiation hard full-size monolithic CMOS sensor with small electrodes on high-resistivity Czochralski substrate

引用

JOURNAL OF INSTRUMENTATION 2023年第9期18卷 P09018页

作者： Pernegger, H. Allport, P. Berlea, D. V. Birman, A. Bortoletto, D. Buttar, C. Charbon, E. Dachs, F. Dao, V. Denizli, H. Dobrijevic, D. Dyndal, M. Fenigstein, A. de Acedo, L. Flores Sanz Freeman, P. Gabrielli, A. Gazi, M. Gonella, L. Gonzalez, V. Gustavino, G. Haim, A. Kugathasan, T. Leblanc, M. Munker, M. Oyulmaz, K. Y. Piro, F. Riedler, P. Sandaker, H. Schioppa, E. J. Sharma, A. Snoeys, W. Sanchez, C. Solans Suligoj, T. Toledano, E. van Rijnbach, M. Nunez, M. Vazquez Weick, J. Worm, S. Zoubir, A. M. CERN EP Dept Espl Particules 1 CH-1211 Meyrin Switzerland Univ Birmingham Sch Phys & Astron Birmingham B15 2TT England DESY Platanenallee 6 D-15738 Zeuthen Germany Univ Oxford Dept Phys Clarendon Lab Pk Rd Oxford OX1 3PU England Univ Glasgow Phys & Astron Dept Kelvin Bldg Glasgow G12 8QQ Scotland Ecole Polytech Fed Lausanne EPFL Adv Quantum Architecture AQUA Lab Rue Maladiere 71C CH-2002 Neuchatel Switzerland Bolu Abant Izzet Baysal Univ Dept Phys Bolu Turkiye Univ Zagreb Fac Elect Engn & Comp Unska Ul 3 Zagreb 10000 Croatia Univ Valencia Dept Elect Engn Ave UnivSN46100 Burjassot Valencia Spain Univ Oslo Dept Phys Postboks 1048 N-0316 Oslo Norway Etesian Semicond Ha Oren St 5 IL-3009500 Ramat Yishai Israel Tower Semicond Shaul Amor Ave 20 Migdal Haemeq Israel Tech Univ Darmstadt Fachbereich Elektrotech & Informat Tech Otto Berndt Str 2 D-64287 Darmstadt Germany AGH Univ Sci & Technol Krakow Poland Univ Geneva Geneva Switzerland Univ Salento Lecce Italy

Depleted Monolithic Active Pixel Sensor (DMAPS) sensors developed in the Tower Semiconductor 180 nm CMOS imaging process have been designed in the context of the ATLAS ITk upgrade Phase-II at the HL-LHC and for future collider experiments. The "MALTA-Czochralski (MALTA-Cz)" full size DMAPS sensor has been developed with the goal to demonstrate a radiation hard, thin CMOS sensor with high granularity, high hit-rate capability, fast response time and superior radiation tolerance. The design targets radiation hardness of > 10(15) (1 MeV) n(eq)/cm(2) and 100 Mrad TID. The sensor shall operate as tracking sensor with a spatial resolution of approximate to 10 mu m and be able to cope with hit rates in excess of 100 MHz/cm(2) at the LHC bunch crossing frequency of 40 MHz. The 512 x 512 pixel sensor uses small collection electrodes (3.5 mu m) to minimize capacitance. The small pixel size (36.4 x 36.4 mu m(2)) provides high spatial resolution. Its asynchronous readout architecture is designed for high hit-rates and fast time response in triggered and trigger-less detector applications. The readout architecture is designed to stream all hit data to the multi-channel output which allows an off-sensor trigger formation and the use of hit-time information for event *** sensor manufacturing has been optimised through process adaptation and special implant designs to allow the manufacturing of small electrode DMAPS on thick high-resistivity p-type Czochralski substrate. The special processing ensures excellent charge collection and charge particle detection efficiency even after a high level of radiation. Furthermore the special implant design and use of a Czochralski substrate improves the sensor's time resolution. This paper presents a summary of sensor design optimisation through process and implant choices and TCAD simulation to model the signal response. Beam and laboratory test results on unirradiated and irradiated sensors have shown excellent detection efficiency a

关键词： Detector modelling and simulations II (electric fields, charge transport, multiplicationand induction, pulse formation, electron emission, etc) Particle tracking detectors (Solid-statedetectors) Radiation-hard detectors Solid state detectors

来源：评论

学校读者我要写书评

暂无评论

Towards an OFDM radar waveform for detection of far located targets with relatively low radar cross sections

引用

DIGITAL signal processing 2021年 114卷 103051-103051页

作者： Omar, Samir-Mohamad Kteish, Zeinab Fadel, Hasan Lebanese Univ Dept Phys & Elect Fac Sci 1 Beirut Lebanon Int Univ Beirut BIU Sch Engn CCE Dept Beirut Lebanon

The joint use of the OFDM waveform for radar as well as for communications has gained high attention recently. One problem that arises from using this waveform is the high spur level that appears on the range-velocity profile where it may eclipse an existing target of relatively small radar cross-section (RCS). This spur is due to nulling some subcarriers such as the DC-subcarrier. Another problem that results from using this waveform is the small maximum range which is far less than the unambiguous range. In our paper, we propose two diverse solutions to overcome the aforementioned problems. The first is based on using the Autoregressive (AR) model to address the problem of the high spur level. The second relies on repeating the same QAM modulated symbol through some subcarriers of the same OFDM symbol to address the problem of the relatively small maximum range. Our analysis and simulations show that applying our proposed methodology contributes to efficiently reducing the spur level from one side, and extending the maximum range to the limit of the unambiguous range from the other side. Furthermore, a trade-off is offered between the data rate of the communication system and the maximum range of the radar. (C) 2021 Elsevier Inc. All rights reserved.

关键词： OFDM radar Periodogram Autoregressive Radar cross-section Maximum range

来源：评论

学校读者我要写书评

暂无评论

Maritime small targets Detection Method for Passive Bistatic Radar Using Non-Cooperative Radar as Illuminator of Opportunity

Maritime Small Targets Detection Method for Passive Bistatic...

引用

2019 IEEE International Conference on signal, Information and data processing, ICSIDP 2019

作者： Jie, Song Guoqing, Wang Caisheng, Zhang Research Institute of Information Naval Aviation University YanTai China

ISBN: (纸本)9781728123455

This paper proposes the maritime small targets detection method of passive bistatic radar (PBR) using a noncooperative pulse radar as an illuminator. Compared with other opportunity illuminators, the transmitting signal of a radar usually has a better ambiguity function, which leads to a higher range resolution, but there is a problem that radar transmitting signal parameters are unknown, and radar signal processing, such as pulse compression, non-coherent integration, and CFAR processing cannot be conducted accurately. The proposed method estimates radar signal parameters from the direct-path signal, and uses estimated parameters to radar signal processing of the scattered echo. The experiments use the shore-based maritime surveillance radar as a transmitter and ships in the harbor as the targets, which show that the proposed method can be applied to maritime small targets detection successfully. © 2019 IEEE.

关键词： Radar signal processing

来源：评论

学校读者我要写书评

暂无评论

MALTA-Cz: A radiation hard full-size monolithic CMOS sensor with small electrodes on high-resistivity Czochralski substrate

arXiv

引用

arXiv 2023年

作者： Pernegger, Heinz Allport, P. Berlea, D.V. Birman, A. Bortoletto, D. Buttar, C. Charbon, E. Dachs, F. Dao, V. Denizli, H. Dobrijevic, D. Dyndal, M. Fenigstein, A. de Acedo, L. Flores Sanz Freeman, P. Gabrielli, A. Gazi, M. Gonella, L. Gonzalez, V. Gustavino, G. Haim, A. Kugathasan, T. LeBlanc, M. Munker, M. Oyulmaz, K.Y. Piro, F. Riedler, P. Sandaker, H. Schioppa, E.J. Sharma, A. Snoeys, W. Sanchez, C. Solans Suligoj, T. Toledano, E. van Rijnbach, M. Nunez, M. Vazquez Weick, J. Worm, S. Zoubir, A.M. CERN Geneva Switzerland University of Birmingham Birmingham United Kingdom DESY Zeuthen Germany University of Oxford Oxford United Kingdom University of Glasgow Glasgow United Kingdom EPFL Lausanne Switzerland Bolu Abant Izzet Bazsal Merkez Bolu Turkey University Zagreb Zagreb Croatia Universitat de València València Spain University of Oslo Oslo Norway Etesian Semiconductor Ramat Yishai Israel Tower Semiconductor Migdal HaEmek Israel Technische Universität Darmstadt Darmstadt Germany

Depleted Monolithic Active Pixel Sensor (DMAPS) sensors developed in the Tower Semiconductor 180 nm CMOS imaging process have been designed in the context of the ATLAS ITk upgrade Phase-II at the HL-LHC and for future collider experiments. The "MALTA-Czochralski (MALTA-Cz)" full size DMAPS sensor has been developed with the goal to demonstrate a radiation hard, thin CMOS sensor with high granularity, high hit-rate capability, fast response time and superior radiation tolerance. The design targets radiation hardness of > 1015 (1 MeV) neq/cm2 and 100 Mrad TID. The sensor shall operate as tracking sensor with a spatial resolution of ≈10 μm and be able to cope with hit rates in excess of 100 MHz/cm2 at the LHC bunch crossing frequency of 40MHz. The 512×512 pixel sensor uses small collection electrodes (3.5 μm) to minimize capacitance. The small pixel size (36.4 × 36.4 μm2) provides high spatial resolution. Its asynchronous readout architecture is designed for high hit-rates and fast time response in triggered and trigger-less detector applications. The readout architecture is designed to stream all hit data to the multi-channel output which allows an off-sensor trigger formation and the use of hit-time information for event tagging. The sensor manufacturing has been optimised through process adaptation and special implant designs to allow the manufacturing of small electrode DMAPS on thick high-resistivity p-type Czochralski substrate. The special processing ensures excellent charge collection and charge particle detection efficiency even after a high level of radiation. Furthermore the special implant design and use of a Czochralski substrate improves the sensor’s time resolution. This paper presents a summary of sensor design optimisation through process and implant choices and TCAD simulation to model the signal response. Beam and laboratory test results on unirradiated and irradiated sensors have shown excellent detection efficiency after a dose of 2 × 1015 1 MeV ne

关键词： Electrodes

来源：评论

学校读者我要写书评

暂无评论

IMMPDAF Approach for L-Band Radar Multiple Target Tracking 19

IMMPDAF Approach for L-Band Radar Multiple Target Tracking

引用

INFOTEH-JAHORINA 19th International Symposium (INFOTEH)

作者： Kovaoevic, Branko Ivkovic, Dej An Radosavljevic, Zvonko Univ Belgrade Sch Elect Engineer Belgrade Serbia Mil Tech Inst Belgrade Serbia

ISBN: (纸本)9781728147758

The interacting multiple model (IMM) and probabilistic data association (PDA) filter (IMMPDAF), is developed to support the air surveillance of the Track While Scan (TWS) radar systems. Take advantage of both, IMM and PDA algorithms to increase the tracking efficiency of fast maneuvers targets. The known algorithm was implemented in a radar tracking application for the purpose of modernizing the 'L' band radar, as a replacement for the conventional NN (Nearest Neighbor) algorithm which proved to be ineffective in situations with airborne targets in the air. In extensive experiments with complex, target maneuver and non-maneuver scenarios has been shown that proposed algorithm to have robust tracking characteristics with small position and velocity error.

关键词： IMMPDAF multiple target tracking radar signal processing

来源：评论

学校读者我要写书评

暂无评论

Biosensors Based Controller for small Unmanned Aerial Vehicle Navigation

Biosensors Based Controller for Small Unmanned Aerial Vehicl...

引用

IEEE Southeastcon

作者： Mylena McCoggle Shyra Wilson Andrea Rivera Rocio Alba-Flores Valentin Soloiu University of Michigan Dearborn MI USA Kennesaw State University Kennesaw GA USA Interamerican University Bayamon Puerto Rico Georgia Southern Univeisity Statesboro GA USA

This work describes a system that uses electromyography (EMG) signals obtained from muscle sensors and an Artificial Neural Network (ANN) for signal classification and pattern recognition that is used to control a small unmanned aerial vehicle using specific arm movements. The main objective of this endeavor is the development an intelligent interface that allows the user to control the flight of a drone beyond direct manual control. The biosensors used in this work were the MyoWare Muscle sensors which contain two EMG electrodes and were used to collect signals from the posterior (extensor) and anterior (flexor) forearm, and the bicep. The collection of the raw signals from each sensor were performed using an Arduino Uno. data processing algorithms were developed with the purpose of classifying the signals generated by the arm’s muscles when performing specific movements, namely: flexing, resting, arm-up, and arm motion from right to left. With these arm motions, roll control of the drone was achieved. MATLAB software was utilized to condition the signals and prepare them for the classification stage. To generate the input vector for the ANN and perform the classification, the root mean squared, and the standard deviation were processed for the signals from each electrode. The neuromuscular information was trained using an ANN with a single 10 neurons hidden layer to categorize the four targets. The result of the classification shows that an accuracy of 97.5% was obtained for a single user. Afterwards, classification results were used to generate the appropriate control signals from the computer to the drone through a Wi-Fi network connection. These procedures were successfully tested, where the drone responded successfully in real time to the commanded inputs.

关键词： Electrodes Artificial neural networks Autonomous aerial vehicles Electromyography Software Real-time systems Biosensors

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：