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检索条件"主题词=detection algorithms"

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Onset detection of Tabla Strokes using LP Analysis

Onset detection of Tabla Strokes using LP Analysis

引用

International Conference on Signal Processing and Communications, SPCOM

作者： R Gowriprasad. K.Sri Rama Murty Indian Institute of Technology Hyderabad India

ISBN: (数字)9781728188959

ISBN: (纸本)9781728188966

Onset detection is an important first step in music analysis. We propose a pre-processing scheme for improved onset detection of complex strokes of Indian percussion instruments with resonance characteristics. In this work, we explore the onset detection of Tabla (Indian percussion instrument) strokes. The resonance characteristics of tabla strokes poses challenges to onset detection. In such cases, the energy-based and spectral flux-based onset detectors are often inaccurate on the raw signal. We propose an onset detection algorithm addressing these challenges using Linear Prediction (LP) analysis and Hilbert envelope (HE) in tandem. Tabla signal is modeled using LP, and its residual highlights the onset time instances very well. Unipolar nature of HE on top of LP residual further enhances the onset instances. Onset detection is performed using energy based, spectral flux based and the state of the art Machine Learning based onset detectors on the Hilbert envelope of LP residual (HELP). Experiments were performed on tabla solo played at various tempi and the results show that the HELP based approach provides 12% relative improvement in F-measures compared to the performance on raw tabla signal.

关键词： Instruments Detectors Music detection algorithms Task analysis Time-domain analysis Frequency-domain analysis

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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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Sparse Activity, Timing detection and Channel Estimation for Grant-Free Uplink Communications

Sparse Activity, Timing Detection and Channel Estimation for...

引用

IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

作者： Chu-Tung Liu Hsuan-Jung Su Yasuhiro Takano Graduate Institute of Communication Engineering National Taiwan University Taipei Taiwan Kobe University Kobe Japan

ISBN: (数字)9781728144900

ISBN: (纸本)9781728144917

This paper considers a grant-free uplink scenario in which the user activity (UA), the timing offsets (TOs), and the channel state information (CSI) of user equipments (UEs) are unknown to the serving base station (BS). In this scenario, the number of potential UEs is large while only a few UEs are active. Due to the sparse nature of the UA, a detection algorithm using approximate message passing (AMP), a compressed sensing (CS) algorithm, is proposed to recover the UA, TOs, and CSI. The proposed algorithm maps the asynchronous system to a virtual system and applies AMP to it. After AMP converges, to minimize the detection error probability, a Bayes test is derived and utilized. Besides, state evolution (SE) analysis is conducted on the virtual system to predict the performance of the proposed algorithm. Simulation results show that when the number of pilot symbols is large enough, the proposed algorithm can achieve the same detection error probability and channel estimation error as those of an ideal system in which the UEs are synchronized. Analytical results show that SE is able to predict the minimum number of pilot symbols needed to achieve a required detection error probability, and predict the performance when the number of pilot symbols is large.

关键词： Channel estimation detection algorithms Timing Uplink Message passing Approximation algorithms Prediction algorithms

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A YOLOv3-based Learning Strategy for Real-time UAV-based Forest Fire detection

A YOLOv3-based Learning Strategy for Real-time UAV-based For...

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Chinese Control and Decision Conference, CCDC

作者： Zhentian Jiao Youmin Zhang Lingxia Mu Jing Xin Shangbin Jiao Han Liu Ding Liu Department of Automation and Information Engineering Xian University of Technology Xian China Department of Mechanical Industrial & Aerospace Engineering Concordia University Montreal Canada

ISBN: (数字)9781728158556

ISBN: (纸本)9781728158563

Forest resources safety is of paramount importance for natural and public security. Forest fire detection methods have been attracted much attention recently, but the performance in terms of comprehensiveness, rapidity, and accuracy is still not satisfactory. A deep learning fire detection algorithm is proposed in this paper, aiming at improving the detection accuracy and efficiency by using the unmanned aerial vehicle (UAV). A large-scale YOLOv3 network is firstly developed which can ensure the detection accuracy. The algorithm is then applied to UAV forest fire detection (UAV-FFD) platform, where the fire images can be captured by the UAV and transmitted to the ground-station in real time. The testing results indicate that the recognition rate of the detection algorithm is about 91%, and the frame rate can reach up to 30 FPS (Frames Per Second). It shows strong potential in real-time application for precision forest fire detection.

关键词： Forestry Feature extraction Real-time systems Unmanned aerial vehicles detection algorithms Cloud computing Edge computing

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Performance Improvement of Energy detection in Cognitive Radio Under Noise Uncertainty

Performance Improvement of Energy Detection in Cognitive Rad...

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International Conference on Innovations in Information Technology

作者： Morteza Alijani Anas Osman University of Trento Trento Italy

ISBN: (数字)9781728181844

ISBN: (纸本)9781728181851

Spectrum scarcity has been one of the most conspicuous constraints in networking, amidst the growing demand for 5G and Internet of things (IoT) services. Consequently, a cognitive radio networking system (CRN) presents as a reliable solution to counter the current spectrum limitations. Many spectrum sensing techniques have been integrated with CRN to address the spectrum shortage; through the detection of the frequency band and investigating the status of the primary user (PU). Nonetheless, energy detection approaches are perceived as one of the most effective solutions for spectrum sensing, as they can operate in a non-causal manner. Energy detection works with a single or double threshold. Each threshold plays a fundamental role taking into account the stochastic properties of the detected signal and consequently the calculation of the optimal energy level. Despite various research efforts towards improving the energy detection scheme, the proposed algorithms still struggle in coping with noise uncertainty and signal-to-noise ratio (SNR) wall. The main contribution of this paper is the implementation and comparison between a single (conventional) and double energy detection algorithms, in terms of the characteristics, performance, and simulation results. Finally, we analyze the noise effect on the energy detection approach and present opportunistic potential solutions such as Machine learning (ML) and Graph signal processing (GSP) techniques.

关键词： Sensors Uncertainty Signal to noise ratio Cognitive radio Technological innovation Information technology detection algorithms

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A trust attack detection algorithm based on improved K-means clustering

A trust attack detection algorithm based on improved K-means...

引用

IEEE Joint International Information Technology and Artificial Intelligence Conference (ITAIC)

作者： Junjie Jia Yi Li College of Computer Science and Engineering Northwest Normal University Lanzhou China

ISBN: (数字)9781728152448

ISBN: (纸本)9781728152455

In the current unsupervised attack detection, the classification attribute of proxy attack has the problem that the resolution of attack users and ordinary users is not high, which affects the recommendation accuracy. This paper proposes a trust attack detection algorithm based on K-means clustering and user rating interval weighted RDMA (SRDMA). In this algorithm, the user reputation IGR classification feature attribute is introduced, and the two classification features with the highest discrimination are extracted by using the distinction coefficient, and then the traditional K-means clustering algorithm is improved through the user reputation IGR. The improved K-means clustering algorithm can cluster most attackers together. Finally, in view of the fact that there are misjudged real users in the attack user set after clustering, this paper uses user rating interval weighted RDMA algorithm (SRDMA) for secondary classification, so as to gather as few real users as possible, and reduce the impact of trust attack on the recommendation system. The experimental results of real MovieLens show that the proposed attack detection algorithm based on improved K-means clustering and SRDMA can effectively detect the random proxy attack profile model, and the detection effect is obviously better than the comparison algorithm.

关键词： Clustering algorithms Filtering algorithms Feature extraction Classification algorithms Reliability detection algorithms Information technology

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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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DNN Based Iterative detection for High Order QAM OFDM Systems With Insufficient Cyclic Prefix

DNN Based Iterative Detection for High Order QAM OFDM System...

引用

IEEE International Conference on Communications in China (ICCC)

作者： Huan Cai Chunming Zhao Wei Shi National Mobile Communications Research Laboratory Southeast University Nanjing China Purple Mountain Laboratories Nanjing China

ISBN: (数字)9781728173276

ISBN: (纸本)9781728173283

In this paper, we consider high order QAM orthogonal frequency-division multiplexing (OFDM) systems with insufficient cyclic prefix (CP) which will lead to intersymbol interference (ISI) and intercarrier interference (ICI) in the receiver. To cope with the error floor induced by ICI and ISI in one-tap equalization and iterative serial interference cancellation (ISIC), we develop a maximum likelihood (ML) based iterative grouping detection algorithm (ML-IG), which utilizes the correlation among the received signals on adjacent subcarriers to improve the detection accuracy. Since ML-IG for OFDM systems with high order QAM modulation is of significant complexity, detection network (DetNet) based iterative grouping detection algorithm (DetNet-IG) is designed to imitate ML-IG. Simulations show that both ML-IG and DetNet-IG can provide better BER performance than ISIC, and DetNet-IG exhibits distinguished robustness against channel model incongruity.

关键词： Quadrature amplitude modulation OFDM Iterative algorithms Robustness Channel models detection algorithms Computational complexity

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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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Hyperspectral Target detection by Fractional Fourier Transform

Hyperspectral Target Detection by Fractional Fourier Transfo...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Xiaobin Zhao Wei Li Tao Shan Lu Li Ran Tao School of Information and Electronics Beijing Institute of Technology China School of Automation Beijing Information Science and Technology University Beijing China

ISBN: (数字)9781728163741

ISBN: (纸本)9781728163758

Target detection in hyperspectral images (HSI) is an important technique and many target detection algorithms have been developed in recent years. The most widely detection algorithms by the original spectral characteristics may lack the ability of target signal enhancement and background suppression. This paper presents an efficient algorithm for detecting hyperspectral targets based on fractional Fourier transform (FrFT). Firstly, fractional Fourier transform primary search is used as preprocessing to obtain the better intermediate domain features with complementary characteristics between the original reflection spectrum and the Fourier transform domain. Secondly, fractional Fourier transform secondary search and constrained energy minimization (FrFT-CEM) was adopted to find an optimal fractional order to distinguish the target from the background. The proposed method has been proved to be superior in two real hyperspectral data sets.

关键词： Hyperspectral imaging Object detection Fourier transforms Feature extraction Detectors Anomaly detection detection algorithms

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