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检索条件"主题词=Detection algorithms"

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Automatic Tuning of MST Segmentation of Mammograms for Registration and Mass detection algorithms

Automatic Tuning of MST Segmentation of Mammograms for Regis...

引用

Proceedings of the Digital Image Computing: Technqiues and Applications (DICTA)

作者： Mariusz Bajger Fei Ma Murk J. Bottema School of Computer Science Engineering and Mathematics Flinders University Adelaide SA Australia

A technique utilizing an entropy measure is developed for automatically tuning the segmentation of screening mammograms by minimum spanning trees (MST). The lack of such technique has been a major obstacle in previous work to segment mammograms for registration and applying mass detection algorithms. The proposed method is tested on two sets of mammograms: a set of 55 mammograms chosen from a publicly available Mini-MIAS database, and a set of 37 mammograms selected from a local database. The method performance is evaluated in conjunction with three different preprocessing filters: gaussian, anisotropic and neutrosophic. Results show that the automatic tuning has the potential to produce state-of-the art segmentation of mass-like objects in mammograms. The neutrosophic filtering provided the best performance.

关键词： detection algorithms Image segmentation Cancer Databases Coronary arteriosclerosis Humans Image registration Breast tissue Muscles Diseases

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Design of adaptive detection algorithms for surveillance radar

Design of adaptive detection algorithms for surveillance rad...

引用

IEEE International Conference on Radar

作者： W.L. Melvin J.R. Guerci M.J. Callahan M.C. Wicks Georgia Tech Research Institute Georgia Institute of Technology Atlanta GA USA Dejense Advanced Research Projects Agency Arlington VA USA US Air Force Research Laboratory Rome NY USA

Multidimensional adaptive signal processing algorithms are vital components of advanced radar systems, improving detection and throughput performance by exploiting signal diversity. Our focus in this paper centers on some implementation issues facing adaptive radar algorithms. To this end, we mainly discuss challenges associated with clutter heterogeneity and nonstationarity. Furthermore, we overview some algorithm-based solutions to improve detection performance.

关键词： Algorithm design and analysis detection algorithms Surveillance Signal processing algorithms Radar detection Multidimensional signal processing Adaptive signal processing Radar signal processing Adaptive signal detection Throughput

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Performance Analysis of Learning-based Intent Drift detection algorithms in Next Generation Networks

Performance Analysis of Learning-based Intent Drift Detectio...

引用

IEEE Latin-American Conference on Communications, LATINCOM

作者： Chukwuemeka Muonagor Mounir Bensalem Admela Jukan Technical University of Braunschweig Germany

ISBN: (数字)9798331521110

ISBN: (纸本)9798331521127

Intent-Based Networking (IBN) is a known concept for enabling the autonomous configuration and self-adaptation of networks. One of the major issues in IBN is maintaining the applied intent due to the effects of drifts over time, which is the gradual degradation in the fulfillment of the intents, before they fail. Despite its critical role to intent assurance and maintenance, intent drift detection was largely overlooked in the literature. To fill this gap, we propose a learning-based intent drift detection algorithm for predictive maintenance of intents and analyze its performance by applying various unsupervised known learning techniques available as open-source (Affinity Propagation, DBSCAN, Gaussian Mixture Models, Hierarchical clustering, K-Means clustering, OPTICS, One-Class SVM). We apply these techniques for intent-drift detection and analyze them comparatively on their efficiency in detecting drifts. The results show that DBSCAN is the most efficient model for detecting the intent drifts. The worst performance is measured by the Affinity Propagation model, reflected in its poorest accuracy and latency values. To the best of our knowledge, this is the first work to address the problem of intent drift detection, and analyze its efficiency for intent maintenance.

关键词： Support vector machines Affinity propagation Machine learning Optics Maintenance Performance analysis detection algorithms Next generation networking Gaussian mixture model Predictive maintenance

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Impact of Evolutionary Community detection algorithms for Edge Selection Strategies

Impact of Evolutionary Community Detection Algorithms for Ed...

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GLOBECOM 2020 - 2020 IEEE Global Communications Conference

作者： Paolo Barsocchi Stefano Chessa Luca Foschini Dimitri Belli Michele Girolami Istituto di Scienza e Tecnologie dell'Informazione ISTI-CNR National Council of Research Pisa Italy Dell'Informazione ISTI-CNR University of Pisa Italy University of Bologna Bologna Italy University of Pisa Italy

ISBN: (数字)9781728182988

ISBN: (纸本)9781728182995

The combination of the edge computing paradigm with Mobile CrowdSensing (MCS) is a promising approach. However, the selection of the proper edge nodes is a crucial aspect that greatly affects the performance of the extended architecture. This work studies the performance of an edge-based MCS architecture with ParticipAct, a real-word experimental dataset. We present a community-based edge selection strategy and we measure two key-metrics, namely latency and the number of requests satisfied. We show how they vary by adopting three evolutionary community detection algorithms, TILES, Infomap and iLCD configured by changing several configuration settings. We also study the two metrics, by varying the number of edge nodes selected so that to show its benefit.

关键词： Crowdsensing Image edge detection Computer architecture Market research Broadband communication detection algorithms Edge computing

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Comparative Research of Dynamic Target detection algorithms Based on Static Background

Comparative Research of Dynamic Target Detection Algorithms ...

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Progress in Electromagnetic Research Symposium (PIERS)

作者： Chao Li Song Ran Lan Lin Tongji University Shanghai China

ISBN: (纸本)9781665409889

Dynamic target detection is a process of capturing moving objects in videos. detection results are affected by various factors that limit the algorithms’ further development. For example, mutual occlusion between objects can disrupt the overall structure of the target, which can easily lead to missed and false detections. In addition, when the light intensity increases or decreases, the pixel values of the input image also change, causing the results inaccurate. Considering the above factors, we need to understand how external conditions affect the accuracy and real-time performance of the algorithms to help us select the appropriate method in different scenes. In this paper, we propose a method for selecting suitable algorithms for different static scenes. It evaluates the number of dynamic targets and the light variation in the detected static background, which can help us select a suitable detection algorithm. First, we use the inter-frame difference, background difference, and the optical flow method to detect a single human target with a simple motion state in a closing scene. Then we apply these three algorithms to detect multiple targets in an open scene. In this situation, the algorithms need to deal with more complex information, bring a significant challenge for real-time and accuracy. During the process, the running frame rate is adjusted by modifying parameters in the experiments, improving the algorithms’ real-time performance and accuracy. Finally, we compare the performance data of these algorithms in different scenarios and give an evaluation and analysis for each algorithm. In brief, we provide a basis and reference for selecting detection algorithms for different static scenes, which is of great significance.

关键词： Costs Heuristic algorithms Dynamics Object detection Real-time systems detection algorithms Electromagnetics

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Review and Comparison of QRS detection algorithms for Arrhythmia Diagnosis

Review and Comparison of QRS Detection Algorithms for Arrhyt...

引用

IEEE International Conference on Signal Processing, Computing and Control (ISPCC)

作者： Kirti Harsh Sohal Shruti Jain Jaypee University of Information Technology Solan H.P India

ISBN: (纸本)9781728139890

The electrocardiogram is the graphical representation of the bio potential generated by the movement of the heart and is used to diagnose different types of heart diseases. The main purpose is to compute Heart Rate and RR interval using QRS complex detection algorithm. Several algorithms for QRS complex detection have been discussed on the basis of pre-processing and decision making stages. Evaluation of these algorithms is supported on the basis of different performance parameters such as sensitivity and positive predictive rate. After reviewing different papers, authors came across Entropy based wavelet transform, which gives better performance for QRS detection. The implementation of an existing algorithm on different ECG databases is done. Further QRS based heart diseases were discussed based on Heart Rate and RR intervals.

关键词： Electrocardiogram QRS complex Arrhythmia Heart Rate QRS complex Electrocardiography Heart Rate Cardiac Arrhythmias Heart Diseases detection algorithms RR interval Vital Signs characteristic parameter algorithms

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Assessment of the single data set detection algorithms under template mismatch

Assessment of the single data set detection algorithms under...

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IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)

作者： E. Aboutanios B. Mulgrew School of Engineering and Electronics University of Edinburgh Edinburgh UK

The detection of signals with known templates embedded in zero-mean coloured Gaussian interference is relevant to many fields such as radar, sonar, seismology and biomedicine to name a few. Traditional detection algorithms, such as the GLRT and AMF, require a training data set. Recently, single data set (SDS) algorithms, namely the GMLED and MLED, have been proposed to deal with the case where training data may not be available. In this paper, we examine the performance of these algorithms under template (or steering vector) mismatch. We identify three types of mismatch, namely the spatial steering vector mismatch, temporal steering vector mismatch and mismatch in both steering vectors. In each mismath case we derive the expected signal to noise ratio loss with respect to the corresponding matched case. Simulation results are given which show that the SDS algorithms are more sensitive to mismatch mainly due to the interaction between the signal and subspaces estimation. However, this increased sensitivity to mismatch is closely related to the ability to resolve close signals. Therefore, the SDS algorithms exhibit higher resolution

关键词： detection algorithms Sensor arrays Signal processing algorithms Sonar detection Training data Testing Radar detection Interference Biosensors Signal detection

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New Approaches for Lowering Path Expansion Complexity of K-Best MIMO detection algorithms

New Approaches for Lowering Path Expansion Complexity of K-B...

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IEEE International Conference on Communications (ICC)

作者： J. Pons P. Duvaut Conexant Systems Inc. Red Bank NJ USA

The present paper proposes two original approaches to reduce computational resources necessary for expanding survivor paths while searching in a K-best manner the MIMO detection tree. The first approach involves precomputing some products that are so far recomputed for each path expansion. The second technique is a new method to compute the path metrics. In all K-best detection techniques proposed so far, the complexity necessary to expand a path grows with the path depth in the tree. The originality of our approach stems from the fact that the path expansion complexity decreases with the path depth. Since the number of expanded paths increases with their depth, our approach better balances the path expansion complexity and the number of expanded paths at a given depth, which can yield a significant complexity reduction. We then present a new K-best Hard-Output Lattice Decoding (K-HOLD) algorithm that combines both proposed techniques. A complexity analysis shows that K-HOLD can reduce by up to 70% the overall path expansion complexity as compared to the less-complex known algorithms. This advantage comes about at no cost in terms of performance degradation.

关键词： MIMO detection algorithms Decoding Sorting Peer to peer computing Lattices Degradation Detectors Communications Society USA Councils

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IntruDetector: a software platform for testing network intrusion detection algorithms

IntruDetector: a software platform for testing network intru...

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Annual Computer Security Applications Conference

作者： Tao Wan Xue Dong Yang Department of Computer Science University of Regina Regina SAS Canada

ISBN: (纸本)0769514057

An intrusion detection system (IDS), that monitors passively specific computing resources, and reports anomalous or intrusive activities, is becoming an important component in the security system of information infrastructure. algorithms for detecting intrusions are under rapid development, but far from being mature. One interesting and difficult issue is how to study and test a new intrusion detection algorithm against a variety of (perhaps simulated) intrusive activities under realistic background traffic. A flexible and general-purpose platform for testing intrusion detection algorithms is clearly desirable. This paper presents such a software platform, called IntruDetector. With this platform, detection algorithms can be tested directly in a real environment with a wide range of intrusive activities. The data of normal system activities are directly collected from the live environment, and are mixed with intrusive activities that are simulated by hybrid simulation. The main properties of this approach are: (1) the background traffic is realistic; (2) it allows flexible simulation of various types of intrusions; and (3) normal system operation will not be disrupted by virtually simulated destructive intrusions during testing.

关键词： Software testing Intrusion detection Software algorithms Algorithm design and analysis System testing Traffic control Face detection detection algorithms Data security Performance analysis

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A comparative study of anomaly detection algorithms for detection of SIP flooding in IMS

A comparative study of anomaly detection algorithms for dete...

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International Conference on Internet Multimedia Services Architecture and Applications, IMSAA

作者： M. Ali Akbar Zeeshan Tariq Muddassar Farooq Next Generation Intelligent Networks Research Center (nexGIN RC) National University of Computer and Emerging Sciences Islamabad Pakistan

The IP multimedia subsystem (IMS) framework uses session initiation protocol (SIP) for signaling and control of sessions. In this paper, we first demonstrate that SIP flooding attacks on IMS can result in denial of service to the legitimate users. Afterwards, we report our comparative study of three well-known anomaly detection algorithms, adaptive threshold, cumulative sum, and Hellinger distance for detection of flood attacks in IMS. We evaluate the accuracy of the algorithms using a comprehensive traffic dataset that consists of varying benign and malicious traffic patterns.

关键词： detection algorithms Floods Protocols Web server Computer crime Telecommunication traffic Internet telephony Computer hacking TCPIP Next generation networking

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