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检索条件"主题词=detection algorithms"

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An Intelligent Signal detection Method Based on DNN for MBM System

An Intelligent Signal Detection Method Based on DNN for MBM ...

引用

IEEE Conference on Vehicular Technology (VTC)

作者： Chengxia Chen Ming Lei Chan Wang Minjian Zhao College of Information Science and Electronic Engineering Zhejiang University Hangzhou China

ISBN: (数字)9781728194844

ISBN: (纸本)9781728194851

Media-based modulation (MBM) is a novel method that embedding part or all of the information in the variations of the transmission media. The traditional detection algorithms for MBM system have to estimate the channel state informations (CSIs) to recover the transmitted symbols and they are affected by the number of receiving antennas (RAs). In this paper we propose a deep neural networks (DNN) detector with a novel loss function (LF) which does not require estimation of CSIs and is independent of the number of RA. Numerical results validate that the proposed DNN detector has better bit-error-ratio (BER) performance than that of the traditional detection algorithms and the performance advantage of the proposed LF is also confirmed.

关键词： Vehicular and wireless technologies Simulation Transmitting antennas Detectors Transforms detection algorithms Signal detection

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Automatic detection of Shoulder Bending Defects in Tire X-ray Images

Automatic Detection of Shoulder Bending Defects in Tire X-ra...

引用

International Conference on Computer Engineering and Applications (ICCEA)

作者： Chuanwen Lin Gang Chen Yuxiang Zhang Fei Sun School of Artificial Intelligence and Big Data Hefei University Hefei China People’s Liberation Army Guangzhou China Department of Computer Science and Technology University of Science and Technology of China Hefei China

ISBN: (数字)9781728159041

ISBN: (纸本)9781728159058

The quality of tire determines the stability of vehicles. Among the various defects of tires, shoulder bending is a common and serious one. A novel method for shoulder bending detection in tire X-ray images is introduced in this paper. Firstly, a series of pre-processing operations including image inpainting, image blurring, adaptive binarization and image segmentation will be done on the images. Then a seed based cords searching algorithm is proposed to find the cords in pixel level. Finally, a feature extraction algorithm is designed to extract the feature vector of each cords which will be fed into the final judgement. In contrast with deep learning methods, the proposed solution does not require many samples to train the network and it is more adaptable when processing images produced from the factory assembly lines. Experimental results show that when processing different types of tire X-ray images, our method is not only more robust but also with even better precision and recall.

关键词： Tires Feature extraction X-ray imaging Microsoft Windows Classification algorithms Gray-scale detection algorithms

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Automated System for Features Extraction From PCG Signal

Automated System for Features Extraction From PCG Signal

引用

International Conference on Computing Methodologies and Communication (ICCMC)

作者： Fariha Zannat Mohammad Monirujjaman Khan Saif Al Sohad North South University Bashundhara Dhaka

This paper is about an automated system for extracting valuable features from PCG signal which can be used to detect heart diseases. A Graphical user interface is being build where anyone can select a specific PCG recordings and then the system will provide S1S2 distance in milliseconds and A2P2 distance in milliseconds. At first Shannon energy envelope is used to find the S1 (first heart sound) and S2 (second heart sound) areas more easily. Peak detection algorithm is used to detect desired peaks. Using threshold algorithm unwanted peaks were removed. Then using these peak distances cardiac cycle was located. Using cardiac cycle and estimate second heart sound distance range S1S2 distance and A2P2 distance are calculated. A2P2 distance is the time difference between two components (Aortic valve closure & Pulmonic valve closure) of second heart sound(S2). S1S2 distance and A2P2 distance are two very important features of heart sound. Many heart diseases are associated with these two features. These two features can be used to detect these heart diseases. So the project will help to build an automated heart diseases detection system in future.

关键词： Heart Filtering Signal processing algorithms Feature extraction Valves detection algorithms Phonocardiography

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A multi-objective parallel detection algorithm for images of power transmission line corridors

A multi-objective parallel detection algorithm for images of...

引用

IEEE Information Technology and Mechatronics Engineering Conference (ITOEC)

作者： Wenjie Zheng Chengqi Li Xiangyu Cui Panfeng Shang State Grid Shandong Electric Power Research Institute Jinan China

ISBN: (数字)9781728143231

ISBN: (纸本)9781728143248

The application of deep learning-based image recognition technology in remote monitoring of power transmission lines has improved the protection level of power transmission lines. However, since the current hidden danger detection is based on a single general algorithm, the accuracy of the detection results needs to be improved. In this paper, we proposed a multi-objective parallel detection algorithm for images of power transmission line corridors based on basic feature sharing. Firstly, we tested the detection effects of different detection algorithms on various types of hidden danger images, based on which benchmark algorithms are selected and optimized for various types of hidden dangers. After that, we designed a multi-objective parallel detection framework to implement the parallel detection of the above three detection algorithms. The experimental results show that the multiobjective parallel detection algorithm proposed in this paper can improve the detection accuracy, as well the detection speed.

关键词： Feature extraction detection algorithms Object detection Power transmission lines Training Fires Machinery

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Defect detection Based on Fusion of Phase Congruency and Shear Wavelet

Defect Detection Based on Fusion of Phase Congruency and She...

引用

International Symposium on Autonomous Systems (ISAS)

作者： Rihong Li Yongfei Chen Weipeng Yang Feng Lin Hongxia Gao Guoheng Liang School of Automation Science and Engineering South China University of Technology Guangzhou China Research and Development Center Zhaoqing Honghua Electronic Technology Co. Ltd Zhaoqing China

ISBN: (数字)9781665418829

ISBN: (纸本)9781665447997

A new defect detection algorithm for patch components based on fusing phase congruency and shear wavelet is proposed. Compared to the gradient-based defect detection, the phase congruency (PC) is insensitive to variations in illumination, contrast, and noise. In this paper, we proposed an improved phase congruency method, which improves the sensitivity to defects and removes the noise interference at the price of the blurring to defect's contours. To solve this problem, we also proposed a fusing strategy to combine phase congruency and shear wavelet, which can not only fit the defect's contour precisely but also remove noise better. To evaluate the performance of the proposed method, we design a variety of experiments on patch components. Finally, the results show our method has excellent performance for the detection of patch components.

关键词： Sensitivity Autonomous systems Design methodology Lighting Interference detection algorithms

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Community detection for Mobile Money Fraud detection

Community Detection for Mobile Money Fraud Detection

引用

International Conference on Social Networks Analysis, Management and Security (SNAMS)

作者： Safa El Ayeb Baptiste Hemery Fabrice Jeanne Estelle Cherrier Normandie Univ UNICAEN ENSICAEN CNRS GREYC Caen France Orange Caen France

ISBN: (数字)9780738111803

ISBN: (纸本)9781665419734

This paper presents an overview of a first-year PhD thesis. The main idea can be expressed as a community detection issue in a real network containing data from a telecommunications operator. More precisely, mobile money transactions will be studied, with a view to detecting fraudulent ones. For this purpose, we propose to address the problem by studying different community detection approaches. Indeed, in a network, communities are constituted between users sharing common habits, therefore fraudulent transactions may appear as unusual behaviors. The originality of this work is to process real data, gathered by a telecom operator. Complex network organization is expected, making this thesis quite promising.

关键词： Social networking (online) Organizations Complex networks Tools Telecommunications Security detection algorithms

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Learning Deep Embedding for Community detection

Learning Deep Embedding for Community Detection

引用

Big Data & Artificial Intelligence & Software Engineering (ICBASE), International Conference on

作者： Yu Pan Jiupeng Yang Shuaihui Wang Junhua Zou Guyu Hu Zhisong Pan Army Engineering University Nanjing China National University of Defense Technology Changsha China

Community detection is a classic and challenging network analysis task. Inspired by the similarity between autoencoder and modularity maximization model in terms of a low-dimensional approximation of the modularity matrix. In this paper, a novel community detection framework via deep autoencoder was proposed. Firstly, the modularity is fed into the autoencoder, which can obtain a non-linear deep representation for the network. In addition, node attribute similarity was utilized to construct pairwise constraints on nodes, and then a graph regularization is introduced into the framework. The extensive experimental evaluations on real-world networks and synthetic networks demonstrate that the proposed framework achieves superior performance compared with the state-of-the-art community detection algorithms.

关键词： Task analysis detection algorithms Software engineering

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Fuzzy Sugeno λ-measures and theirs applications to community detection problems.*

Fuzzy Sugeno λ-measures and theirs applications to communit...

引用

IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： Inmaculada Gutiérrez Daniel Gómez Javier Castro Rosa Espínola Faculty of Statistics Complutense University Madrid Spain Instituto de Evaluación Sanitaria. Complutense University Faculty of Statistics Complutense University Madrid Spain

ISBN: (数字)9781728169323

ISBN: (纸本)9781728169330

In this paper we propose a new framework for community detection problems. The starting point is a n-vector which defines some evidence about the elements of a finite set. This vector is used to build an interaction measure between the n elements of the set to which it refers. This interaction measure is represented by a Sugeno λ-measure to which we make it being also a fuzzy measure. Then, we obtain the weighted graph associated with this new capacity measure. To carry on with it, we make use of the Shapley value. We also introduce the notion of extended vector fuzzy graph, which relates a graph with the capacity measure introduced in this work. Finally, we use a community detection method, based on Louvain algorithm, to search a cluster structure in the weighted graph. This partition is based on the relations among the individuals obtained from the initial vector. Let us note that in the case that there exist some connections among the elements, apart from their affinity, we can combine this extra information with that given by the vector, in order to obtain groups with highly-knit elements among which there are strong relations.

关键词： Atmospheric measurements Particle measurements Additives Weight measurement Extraterrestrial measurements Tools detection algorithms

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detection of Chipless RFID Tag Using a Single Antenna RFID Reader System

Detection of Chipless RFID Tag Using a Single Antenna RFID R...

引用

IEEE Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA)

作者： Meriam A. Bibile Grishma Khadka Nemai C. Karmakar Electrical and Computer System Engineering Monash University Melbourne Victoria Australia

ISBN: (数字)9781728194370

ISBN: (纸本)9781728194387

The detection of different types of chipless Radio Frequency Identification (RFID) tags using a single antenna reader is presented in this paper. The performance is evaluated through experiment and measured using Chipless RFID reader system with single antenna and compared with results from vector network analyser. It explains the operation of the reader and how the pic-microcontroller is programmed with the detection algorithm. Two types of tags have been tested, a printed tag which is flexible and will be in high demand for commercialisation and a copper tag which has more durability. The post processing of the measured results to obtain the tag ID is performed using MATLAB. Adaptive Wavelet based detection algorithm is used for the decoding of the tag ID. After the tag ID has been decoded it is sent to a display screen on the DSP unit and/ or via RS-232 to the host computer application. There is no need of human interaction for the reader to interrogate the tag as the user is able to save the tag data received by the reader in a specified database/server.

关键词： Antenna measurements Semiconductor device measurement Copper detection algorithms RFID tags Intelligent systems Matlab

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A Proximity Based Community detection in Temporal Graphs

A Proximity Based Community Detection in Temporal Graphs

引用

IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT)

作者： H L Advaith S Adarsh G Akshay P Sreeram G P Sajeev Dept of Computer Science and Engineering Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (数字)9781728168289

ISBN: (纸本)9781728168296

With very fast global instant messaging services, communication networks of today are changing rapidly. To accurately represent such dynamic networks, a special type of a graph, known as temporal graph is indispensable. Majority of the community detection algorithms are designed for static graphs. So, an efficient conversion of temporal graphs to static graphs, while retaining important temporal information, enables the use of any standard community detection algorithm easily. This paper proposes a novel community detection method by constructing static graphs from temporal networks. The proposed method is validated through experiments. Results show that our technique leads to considerable runtime reduction upon dealing with large graphs comprise multiple community structures.

关键词： Social network services detection algorithms Image edge detection Data mining Instant messaging Numerical models Heuristic algorithms

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