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检索条件"主题词=Detection algorithms"

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Blind detection algorithm based on adaptive chaotic particle swarm optimization

Blind detection algorithm based on adaptive chaotic particle...

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Congress on Image and Signal Processing, CISP

作者： Chuang Yu Zhang-Hui Yu Shu-Juan Yu School of Information Science & Engineering Shenyang Ligong University Shenyang China Institute of Electronic Science and Engineering Nanjing University of Posts and Telecommunications Nanjing China

For the blindness in initialization of particle swarm optimization algorithm and the phenomenon of premature occurred in the course of evolution, a new algorithm is proposed in this paper: blind detection algorithm based on adaptive chaotic particle swarm optimization. The performance simulation shows that, using of randomness and ergodicity of chaos, the improved algorithm compared with the method in the literature has more advantages like good global convergence, fast convergence speed and low bit error rate. This new algorithm can solve blind detection problem successfully.

关键词： Particle swarm optimization Convergence Optimization Signal processing algorithms Chaos Signal to noise ratio detection algorithms

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Anomaly detection using impedance measurements on the boundary

Anomaly detection using impedance measurements on the bounda...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Y.G. Cho O. Kwon J.K. Seo E.J. Woo J.R. Yoon Graduate School of East-West Medical Sciences Kyung Hee University Kyongju South Korea Department of Mathematics Yonsei University Seoul South Korea School of Electronics and Information Kyung Hee University Kyongju South Korea School of Mathematics Korea Institute of Advanced Study Seoul South Korea

We describe a new anomaly detection algorithm based on an electrical impedance tomography (EIT) technique. When only the boundary current and voltage measurements are available, it is not practically feasible to reconstruct accurate high-resolution cross-sectional resistivity images of a subject. In this paper, we focus our attention on the detection of the location and size of anomalies with resistivity values different from the background tissues. We show the performance of the algorithm from experimental results using a 32-channel EIT system and saline phantoms. The algorithm is applicable to the detection of cancerous tissues in the breast.

关键词： Impedance measurement Conductivity Tomography Biomedical imaging detection algorithms Image reconstruction Mathematics Imaging phantoms Cancer detection Breast

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Optimal detection in MIMO Systems Using Spatial Sigma-Delta ADCs

Optimal Detection in MIMO Systems Using Spatial Sigma-Delta ...

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Asilomar Conference on Signals, Systems & Computers

作者： Toan-Van Nguyen Sajjad Nassirpour Italo Atzeni Antti Tölli A. Lee Swindlehurst Duy H. N. Nguyen Department of Electrical and Computer Engineering San Diego State University San Diego CA USA Centre for Wireless Communications University of Oulu Finland Department of Electrical Engineering and Computer Science University of California Irvine CA USA

ISBN: (数字)9798350354058

ISBN: (纸本)9798350354065

The spatial Sigma-Delta architecture can be used to reduce the quantization noise and thus improve the effective resolution of few-bit analog-to-digital converters (ADCs) for certain spatial frequencies of interest. This paper proposes a novel data detection scheme based on the variational Bayes (VB) inference framework for multiple-input multiple-output (MIMO) systems that utilize first-order spatial Sigma-Delta ADCs. We derive a closed-form expression to approximate the posterior distributions of the transmitted data symbols, which are then used for their estimation. Simulation results show that the proposed detection scheme achieves a detection performance comparable to unquantized systems and has a lower symbol error rate (SER) than the conventional quantized VB and linear minimum mean-squared error (LMMSE) methods. The effects of the azimuth range, and the antenna spacing and wavelength on the SER performance of all detection algorithms are also extensively analyzed.

关键词： Sigma-delta modulation Quantization (signal) Simulation Noise Symbols Massive MIMO Bayes methods Iterative methods detection algorithms Spatial resolution

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KERNEL SPARSE REPRESENTATION FOR HYPERSPECTRAL TARGET detection

KERNEL SPARSE REPRESENTATION FOR HYPERSPECTRAL TARGET DETECT...

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IEEE International Geoscience and Remote Sensing Symposium

作者： Yi Chen Nasser M. Nasrabadi Trac D. Tran Department of Electrical and Computer Engineering The Johns Hopkins University US Army Research Laboratory

ISBN: (纸本)9781467311601

In this paper, we present a nonlinear kernel-based target detection algorithm for hyperspectral images. The proposed approach relies on the sparse representation of an unknown sample with respect to both background and target training samples in a high-dimensional feature space induced by a kernel function. The sparse representation vector can be recovered via a kernelized greedy algorithm, where the kernel trick is used to avoid explicit evaluations of the data in the feature space. The spatial smoothness in hyperspectral images is also taken into account through a kernelized joint sparsity model. The detection decision is then made by comparing the reconstruction accuracy in terms of the background and target sub-dictionaries. The detection algorithm in a high-dimensional feature space implicitly exploits the higher-order structure (correlations) within the data which cannot be captured by a linear model. Therefore, projecting the pixels into a kernel feature space and kernelizing the linear sparse representation model improves the separability between the background and target classes, leading to a more accurate detection performance.

关键词： Eigenvector Seeds sparse representation Object detection detection algorithms hyperspectral imagery greedy algorithm Kernel BACKGROUND pixel

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Computational load reduction for anomaly detection in hyperspectral images: An experimental comparative analysis

Computational load reduction for anomaly detection in hypers...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： N. Acito G. Corsini M. Diani Dipartimento di Ingegneria dellInformazione Università di Pisa Pisa Italy

In this manuscript we investigate the efficient implementation of anomaly detection strategies in hyperspectral images. We especially focus on methods to reduce the computational complexity for a fast implementation of the detection algorithms. In particular, we consider two strategies based on data fusion methods applied to the outputs of the optical heads of the hyperspectral sensor. Furthermore, we consider, two computationally efficient implementations of anomaly detection where the well known RX algorithm is applied to hyperspectral data after dimensionality reduction. The detection performances of the anomaly detection strategies are compared using real data acquired by the MIVIS sensor. An estimate of the reduction of the computational load achieved with the different techniques is also provided.

关键词： Hyperspectral imaging Image analysis Hyperspectral sensors detection algorithms Optical sensors Head Military computing Computational complexity Signal processing algorithms Surveillance

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detection of focal epileptiform activity in the EEG: an SVD and dipole model approach

Detection of focal epileptiform activity in the EEG: an SVD ...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： B. Vanrumste R.D. Jones P.J. Bones Department of Electrical and Computer Engineering University of Canterbury Christchurch New Zealand Department of Medical Physics & Bioengineering Christchurch Hospital Christchurch New Zealand Department of Medicine Christchurch School of Medicine and Health Science Christchurch New Zealand

An algorithm has been developed for detection of epileptiform activity in the EEG. The EEG is divided into overlapping epochs, which undergo two steps. The first is singular value decomposition (SVD) which identifies the number of uncorrelated active sources in an epoch. In the second step, EEG dipole source analysis, using a single dipole model, is applied to the EEG. This yields dipole parameters and a relative residual energy (RRE). The detection algorithm triggers an EEG epoch when SVD indicates a dominant source and the RRE is low. The algorithm is applied to simulated EEG generated by two sources which are synchronously and asynchronously active. For the synchronous case the critical measure is the RRE whereas for the asynchronous case both the SVD and RRE are critical. The algorithm has also been applied to real EEG containing two spikes and an eye-blink artifact. The SVD indicated a dominant active source and the RRE was low for all three events. These preliminary results demonstrate the potential of the method for detection of spikes and seizures with a focal origin.

关键词： Epilepsy Electroencephalography Brain modeling Singular value decomposition detection algorithms Bones Physics Biomedical engineering Hospitals Synchronous generators

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Small Targets Under Drone Perspective Based on Improved YOLOv5s detection Algorithm

Small Targets Under Drone Perspective Based on Improved YOLO...

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Signal Processing and Intelligent Computing (SPIC), International Conference on

作者： Xinyue Feng Jun Liu Zhichao Zhang School of Automation and Electrical Engineering Shenyang Ligong University Shenyang China

ISBN: (数字)9798350368888

ISBN: (纸本)9798350368895

Research aimed at the detection of ground targets using UAVs for shooting, the phenomenon of missed detections and false detections still exists, and the algorithm cannot simultaneously consider detection accuracy and parameter size. Therefore, this paper uses the improved YOLOv5s algorithm to further study the above problems. Firstly, a Simple Patches of Convolution-Mixer (CSPCM) is added to both the Head and Backbone network structures of the original model, which uses deep convolution to help better fuse the information of the two dimensions and achieve higher accuracy; In addition, a small target detection layer is set up in the neck to expand the perception field of the model, so that the algorithm model can be handy when dealing with small target detection tasks. Secondly, the lightweight convolutional network module GhostNetV2 is used as the backbone of the algorithm, reducing feature redundancy in channels. Finally, YOLOv5-CG was tested on VisDrone2019. The new model reduced the number of parameters by 1468493 and the model size by 2.4M, mAP@0.5 Improved by 4.1%, mAP@0.5 0.95 increased by 1.9%; The experimental results indicate that Yolov5-CG can achieve parameter simplification while ensuring improved accuracy, and can be better used for small object detection.

关键词： YOLO Accuracy Head Fuses Redundancy Signal processing algorithms Neck detection algorithms Testing Drones

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Nonparametric spatial analysis for mine line detection

Nonparametric spatial analysis for mine line detection

引用

OCEANS

作者： J. C. Hyland C. M. Smith Naval Surface Warfare Center Panama FL USA

ISBN: (纸本)9781424449606

The development of tools for automated analysis of data collected for mine countermeasures (MCM) search, classify and map (SCM) has predominantly focused on automated target recognition for individual mines. As MCM target detection becomes more automated, detecting spatial patterns in the positioning of mines should follow. Natural restrictions on mine deployment such as depth and bottom contour as well as mine deployment methodology and protocol effectively create mine line patterns that are typical to coastal areas. We have developed a mine line detection algorithm that uses nonparametric spatial analysis to identify mine-like contacts (MLCs) that fit into mine line patterns dictated by standard mine laying protocol in coastal regions. In general, a set of MLCs reported from a mine detection sensor will consist of both actual mines and false alarms; the algorithm classifies every MLC in this set into one of three classes: fixed - the MLC?fits into a mine line pattern with fixed mine spacing; random -the MLC?fits into a mine line pattern with random mine spacing; or false alarm-?-the MLC?does not fit into a mine line pattern and is subsequently not considered a mine. The latest version of the algorithm utilizes nonparametric techniques and requires no knowledge of either the detection sensor's probability of detection (P d ) or probability of false alarm (P fa ). A nonparametric anomaly detector first identifies regions with unusually high MLC density for more detailed analysis. Suspected regions are subsequently analyzed for unusually regular patterns of MLCs. The probabilities that the observed target densities and target patterns could occur simply by chance are estimated by comparison with results from nearby areas using basic information about mine deployment. To ensure that a mine line's full extent is captured, the algorithm attempts to grow a line after it is detected. And a more detailed probability analysis distinguishes random mine lines from fixed-space

关键词： Protocols Sea measurements Pattern analysis Data analysis Data mining Target recognition Object detection detection algorithms Algorithm design and analysis Standards development

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A fast scene change detection on MPEG coding parameter domain

A fast scene change detection on MPEG coding parameter domai...

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IEEE International Conference on Image Processing

作者： M. Sugano Y. Nakajima H. Yanagihara A. Yoneyama KDD R&D Laboratories Japan

This paper proposes a very fast and accurate scene change detection algorithm on MPEG coding parameter domain. The fast operation can be obtained by spatio-temporally subsampling coding information and by exploiting only coding parameters extracted in variable length decoding (VLD) stage, while the accurate detection is accomplished by examining the statistical characteristics of various scene changes on the coding parameter domain. The computer simulation shows that the proposed algorithm can accomplish detection more than 5 times faster than that of real-time playback for MPEG-2 video sequences using a standard workstation. It is also shown that most of abrupt scene changes, dissolve transitions, and wipe transitions have been successfully detected.

关键词： Layout Change detection algorithms Decoding detection algorithms Motion detection Discrete cosine transforms Research and development Video sequences Workstations Gunshot detection systems

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detection algorithm of video image distance based on rectangular pattern

Detection algorithm of video image distance based on rectang...

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Congress on Image and Signal Processing, CISP

作者： Wei Geng Xiaobo Lu Li Yang Wujun Chen Yang Liu School of Automation Southeast University Nanjing China School of Transportation Southeast University Nanjing China

ISBN: (纸本)9781467309653

As the traditional visibility instrument is expensive, its application field is limited and it is difficult to realize high density coverage on road, realizing the detection and precaution of the foggy weather, especially for the agglomerate fog. This paper puts forward a detection algorithm of video image distance based on rectangular pattern,making it successful to overcome the defects of the traditional methods. The method uses parameters of camera calibration to establish the transformation model of video image distance, which simplifies the original method in the field of calibration. Making it possible to realize high density coverage of camera in the highway, and we use it to make precaution and take safety management of highway in foggy weather condition, especially a sudden agglomerate fog weather condition.

关键词： Cameras Roads Calibration Meteorology detection algorithms

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