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22nd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)

作者： Danielsson, Jakob Jagemar, Marcus Behnam, Moris Sjodin, Mikael Malardalen Univ Vasteras Sweden Ericsson AB Stockholm Sweden

ISBN: (纸本)9781509065059

We discuss how to obtain information of execution characteristics, such as parallelizability and memory utilization, with the final aim to improve the performance and predictability of feature and corner detection algorithms for use in e.g. robotics and autonomous machines. Our aim is to obtain a better understanding of how computer vision algorithms use hardware resources and how to improve the time predictability and execution time of such algorithms when executing on multi-core CPUs. We evaluate a fork-join model applicable to feature detection algorithms and present a method for measuring how well the algorithm performance correlates with hardware resource usage. We have applied our method to the Featured from Accelerated Segment Test (FAST) algorithm. Our characterization of FAST reveals that it is an algorithm with excellent parallelism opportunities, resulting in an almost linear speed-up per core. Our measurements also reveal that the performance of FAST correlates very little with the number number of misses in the Ll data cache, Ll instruction cache, data translation lookaside buffer and L2 cache. Thus, the FAST algorithm will not have a negative effect on the execution time when the input data fits in the L2 cache.

关键词： Feature extraction Prediction algorithms Hardware detection algorithms Velocity measurement Monitoring

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Improving the accuracy of existing camera based fall detection algorithms through late fusion 39

Improving the accuracy of existing camera based fall detecti...

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39th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC)

作者： Baldewijns, Greet Debard, Glen Mertes, Gert Croonenborghs, Tom Vanrumste, Bart Katholieke Univ Leuven E Media Lab Campus Grp T Leuven Belgium Katholieke Univ Leuven ESAT STADIUS Leuven Belgium IMEC Med Informat Technol Dept Leuven Belgium Thomas More Kempen MOBILAB Geel Belgium Katholieke Univ Leuven ESAT PSI Leuven Belgium Katholieke Univ Leuven Dept Comp Sci DTAI Leuven Belgium Harvard Med Sch Brigham & Womens Hosp Broad Inst Massachusetts Inst Technol & Harvard Program Translat Neuropsychiat Genom Cambridge MA USA

ISBN: (纸本)9781509028092

Fall incidents remain an important health hazard for older adults. Fall detection systems can reduce the consequences of a fall incident by insuring that timely aid is given. Currently fall detection algorithms however suffer a reduction in accuracy when introduced in real-life situations. In this paper a late fusion technique is proposed that will improve the accuracy of existing fall detection systems. It combines the confidence levels of different single camera fall detection systems. Four different aggregation methods are compared to each other based on the Area Under the Curve (AUC) of precision-recall curves. Calculating the median of the confidence levels of five cameras an increase of 218% in the AUC of the precision-recall-curves is achieved compared to the AUC of the single camera fall detector. These results show that significant improvements can be made to the accuracy of single camera fall detectors in a relatively easy way.

关键词： Cameras detection algorithms Detectors Support vector machines Noise measurement Three-dimensional displays Hospitals

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A CFAR optimization for low frequency UWB SAR change detection algorithms 37

A CFAR optimization for low frequency UWB SAR change detecti...

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37th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2017

作者： Fabrin, Ana C. F. Molin, Ricardo D. Alves, Dimas I. Machado, Renato Bayer, Fabio M. Pettersson, Mats I. Federal University of Santa Maria - UFSM Santa Maria - RS97105-900 Brazil Federal University of PAMPA - UNIPAMPA Alegrete - RS97546-550 Brazil Blekinge Institute of Technology - BTH KarlskronaSE-371 79 Sweden

ISBN: (纸本)9781509049516

This paper presents a study on the constant false alarm rate (CFAR) filter design for change detection algorithms (CDA). More specifically, we are interested in CFAR filters used in CDA for low frequency ultra-wideband (UWB) synthetic aperture radar (SAR) systems. The filter design performance was evaluated in terms of false alarm rate (FAR) and probability of detection (PD). For evaluation purposes, we considered a set of SAR images obtained with the CARABAS-II system. The results are compared with the ones presented in [1], where the same CDA was considered, except for the CFAR filter. The results show that relevant FAR performance improvements can be obtained by just modifying the CFAR filter parameters taking into account the image resolution and target characteristics. © 2017 IEEE.

关键词： Synthetic aperture radar Change detection algorithms detection algorithms Algorithm design and analysis Image resolution

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Low-complexity detection algorithms Based on Matrix Partition for Massive MIMO 9

Low-complexity Detection Algorithms Based on Matrix Partitio...

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9th International Conference on Wireless Communication and Signal Processing (WCSP)

作者： Wu, Haijian Lin, Jun Zhang, Chuan Wang, Zhongfeng Nanjing Univ Sch Elect Sci & Engn Nanjing Jiangsu Peoples R China Southeast Univ Natl Mobile Commun Res Lab Nanjing Jiangsu Peoples R China

ISBN: (纸本)9781538620625

Massive Multiple-Input Multiple-Output (MIMO) is one of the key technologies in the fifth generation (5G) wireless communication for much higher throughput. However, current detection algorithms for massive MIMO suffer from large computational complexity. The Neumann series based approximated matrix inverse is a good tradeoff between detection performance and computational complexity. In this paper, compared to the traditional Neumann series based method, a matrix partition (MP) method is proposed to significantly reduce the number of required multiplications and additions while maintain comparable or even better detection performance. The presented MP method innovates the construction of pre-conditioner matrix and employs the Neumann series method in an intelligent way. Simulation results from a 128 x 16 massive MIMO system with 16-QAM modulation demonstrate that the proposed MP method can reduce the number of multiplications and additions by 68% and 70%, respectively.

关键词： MIMO Computational complexity Uplink Symmetric matrices Signal detection detection algorithms

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QRS Complex detection and Measurements algorithms for Multichannel ECGs in Cardiac Resynchronization Therapy Patients

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IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE 2018年 6卷 1900211-1900211页

作者： Curtin, Antonia E. Burns, Kevin, V Bank, Alan J. Netoff, Theoden, I Univ Minnesota Biomed Engn Dept Minneapolis MN 55455 USA United Heart & Vasc Clin Res Dept St Paul MN 55102 USA

We developed an automated approach for QRS complex detection and QRS duration (QRSd) measurement that can effectively analyze multichannel electrocardiograms (MECGs) acquired during abnormal conduction and pacing in heart failure and cardiac resynchronization therapy (CRT) patients to enable the use of MECGs to characterize cardiac activation in such patients. The algorithms use MECGs acquired with a custom 53-electrode investigational body surface mapping system and were validated using previously collected data from 58 CRT patients. An expert cohort analyzed the same data to determine algorithm accuracy and error. The algorithms: 1) detect QRS complexes;2) identify complexes of the most prevalent morphology and morphologic outliers;and 3) determine the array-specific (i.e., anterior and posterior) and global QRS complex onsets, offsets, and durations for the detected complexes. The QRS complex detection algorithm had a positive predictivity and sensitivity of >= 96% for complex detection and classification. The absolute QRSd error was 17 +/- 14 ms, or 12%, for array-specific QRSd and 12 +/- 10 ms, or 8%, for global QRSd. The absolute global QRSd error (12 ms) was less than the interobserver variation in that measurement (15 +/- 10 ms). The sensitivity, positive predictivity, and error of the algorithms were similar to the values reported for current state-of-the-art algorithms designed for and limited to simpler data sets and conduction patterns and within the variation found in clinical 12-lead ECG QRSd measurement techniques. These new algorithms permit accurate, real-time analysis of QRS complex features in MECGs in patients with conduction disorders and/or pacing.

关键词： Biomedical signal processing classification algorithms detection algorithms electrocardiology

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Performance Analysis of Several Pitch detection algorithms on Simulated and Real Noisy Speech Data

Performance Analysis of Several Pitch Detection Algorithms o...

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European Signal Processing Conference

作者： Denis Jouvet Yves Laprie Speech Group Inria Villers-les-Nancy France CNRS Villers-les-Nancy France Universite de Lorraine Villers-les-Nancy France

This paper analyses the performance of a large bunch of pitch detection algorithms on clean and noisy speech data. Two sets of noisy speech data are considered. One corresponds to simulated noisy data, and is obtained by adding several types of noise signals at various levels on the clean speech data of the Pitch-Tracking Database from Graz University of Technology (PTDB-TUG). The second one, SPEECON, was recorded in several different acoustic environments. The paper discusses the performance of pitch detection algorithms on the simulated noisy data, and on the real noisy data of the SPEECON corpus. Also, an analysis of the performance of the best pitch detection algorithm with respect to estimated signal-to-noise ratio (SNR) shows that very similar performance is observed on the real noisy data recorded in public places, and on the clean data with addition of babble noise.

关键词： Speech Noise measurement detection algorithms Signal to noise ratio Signal processing algorithms Frequency-domain analysis Databases

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Pre-Coding using Different detection algorithms in MIMO System

Pre-Coding using Different Detection Algorithms in MIMO Syst...

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International Conference on Current Trends in Computer, Electrical, Electronics and Communication

作者： Purnima K. Sharma Dinesh Sharma Akansha Gupta SVEC Tadepalligudem India GE University Dehradun India

ISBN: (纸本)9781538632444

Pre-coding is a general understanding of beamforming to achieve the transmission diversity of the signal. Basically transmitter sends the information which is already coded according to the known knowledge about the channel conditions to the receiver. The pre-coding technique leads to achieve a good performance by eliminating the degradation effects even in the correlation environment upon the channel in the communication system. In this paper, the different detection algorithms: Zero forcing (ZF), Minimum mean square error (MMSE) and Block diagonalization (BD) are used in pre-coding. Many solutions have been proposed for the Co-Channel Interference problem. The main aim of this paper is to select the most suitable Coding techniques to adopt. The analysis is carried by assuming the channel as Rayleigh and modulation scheme is QPSK with 4×4 MIMO system.

关键词： Pre-coding detection algorithms Channel state information (CSI) Zero forcing (ZF) Minimum mean square error (MMSE) Block diagonalization (BD).

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A review of community detection algorithms in signed social networks

A review of community detection algorithms in signed social ...

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International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS)

作者： Ruby Inderjeet Kaur Ajay Kumar Garg Engineering College Ghaziabad Uttar Pradesh IN

Now a days social network analysis has gained much attention. A community in social networks indicates that nodes within the group are densely connected and connections between groups are sparse. These social networks can be modeled as signed social networks that contain both positive and negative links or relations. In the social networks community mining or detection is the task of grouping the nodes together on the basis of their linked pattern. In the literature, various community detection algorithms have been proposed. This paper represents an overview of community detection algorithms in the signed social networks along with their classification. Then after we have a comparative study of the community detection algorithms.

关键词： Clustering algorithms Social network services Partitioning algorithms detection algorithms Image edge detection Heuristic algorithms Classification algorithms

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On the Robustness of Machine Learning Based Malware detection algorithms

On the Robustness of Machine Learning Based Malware Detectio...

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International Joint Conference on Neural Networks

作者： Weiwei Hu Ying Tan Key Laboratory of Machine Perception (MOE) and Department of Machine Intelligence School of Electronics Engineering and Computer Science Peking University

ISBN: (纸本)9781509061839

With the rapid popularity of the Internet, a large amount of new malware is produced every day, while the traditional signature based malware detection algorithm is unable to detect such unseen malware. In recent years, many machine learning based algorithms have been proposed to detect new malware, and several of these algorithms are able to achieve quite good detection performance when supplied with plenty of training data. However, most of these algorithms just focus on how to improve the classification performance, while the robustness is not taken into consideration. This paper performs a detailed analysis on the robustness of four well-known machine learning based malware detection approaches, i.e. the DLL and API feature, the string feature, PE-Miner and the byte level N-Gram feature. We proposed two pretense approaches under which malware is able to pretend to be benign and bypass the detection algorithms. Experimental results show that the performances of these detection algorithms decline greatly under the pretense approaches. The lack of robustness makes these algorithms unable to be used in real world applications. In future works of machine learning based malware detection, researchers have to take the problem of robustness seriously.

关键词： detection algorithms Spyware Robustness Artificial Intelligence Machine Learning algorithms Dynamic link libraries

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Network intrusion detection using machine learning anomaly detection algorithms

Network intrusion detection using machine learning anomaly d...

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IEEE Signal Processing and Communications Applications (SIU)

作者： M. Elif KarsligЕl A. Gökhan Yavuz M. Amaç Güvensan Khadija Hanifi Hasan Bank Bilgisayar Mühendisliği Bölümü Yıldız Teknik Üniversitesi İstanbul Türkiye Yildiz Teknik Universitesi Istanbul TR Bilgisayar Mühendisliği Bölümü Yıldız Teknik Universitesi İstanbul Türkiye

Attacks on the network are exceptional cases that are not observed in normal traffic behavior. In this work, in order to detect network attacks, using k-means algorithm a new semi-supervised anomaly detection system has been designed and implemented. During the training phase, normal samples were separated into clusters by applying k-means algorithm. Then, in order to be able to distinguish between normal and abnormal samples - according to their distances from the clusters' centers and using a validation dataset-a threshold value was calculated. New samples that are far from the clusters' centers more than the threshold value is detected as anomalies. We used NSL-KDD - a labelled dataset of network connection traces-for testing our method's effectiveness. The experiments result on the NSL-KDD data set, shows that we achieved an accuracy of 80.119%.

关键词： Dogs Intrusion detection Nanoelectromechanical systems Clustering algorithms Algorithm design and analysis Conferences detection algorithms

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