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检索条件"主题词=detection algorithms"

共 7359 条记录，以下是51-60 订阅

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Specification and Evaluation of Geofence Boundary Violation detection algorithms

Specification and Evaluation of Geofence Boundary Violation ...

引用

International Conference on Unmanned Aircraft Systems (ICUAS)

作者： Stevens, Mia N. Rastgoftar, Hossein Atkins, Ella M. Univ Michigan Ann Arbor MI 48109 USA

ISBN: (纸本)9781509044948

This paper studies two methods of geofence boundary violation detection. The first method is Ray Casting, which iterates over each geofence boundary edge to determine if a given position of interest is inside the geofence. The second method, called Triangle Weight Characterization (TWC), subdivides the geofence domain into a finite number of triangles, then iterates over each triangle to determine if the given position of interest is inside the geofence. We apply the TWC and Ray Casting methods to case studies that include both keep-in and keep-out geofence boundaries.

关键词： Casting detection algorithms Aircraft Image edge detection Aerospace engineering Algorithm design and analysis Color

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Performance Effects of Dynamic Graph Data Structures in Community detection algorithms

Performance Effects of Dynamic Graph Data Structures in Comm...

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IEEE High Performance Extreme Computing Conference (HPEC)

作者： Thankachan, Rohit Varkey Swenson, Brian P. Fairbanks, James P. Georgia Inst Technol Sch Comp Sci Atlanta GA 30332 USA Georgia Tech Res Inst Atlanta GA 30332 USA

ISBN: (纸本)9781538659892

Community detection algorithms create a dynamic graph as an internal data structure for tracking agglomerative merges. This community (block) graph is modified heavily through operations derived from moving vertices between candidate communities. We study the problem of choosing the optimal graph representation for this data structure and analyze the performance implications theoretically and empirically. These costs are analyzed in the context of Peixoto's Markov Chain Monte Carlo algorithm for stochastic block model inference, but apply to agglomerative hierarchical community detection algorithms more broadly. This cost model allows for evaluating data structures for implementing this algorithm and we identify inherent properties of the algorithm that exclude certain optimizations.

关键词： Data structures detection algorithms Image edge detection Heuristic algorithms Sparse matrices Proposals Partitioning algorithms

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Evaluation of Duplicate detection algorithms: From Quality Measures to Test Data Generation 37

Evaluation of Duplicate Detection Algorithms: From Quality M...

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37th IEEE International Conference on Data Engineering (IEEE ICDE)

作者： Panse, Fabian Naumann, Felix Univ Hamburg Hamburg Germany Univ Potsdam Hasso Plattner Inst Potsdam Germany

ISBN: (纸本)9781728191843

Duplicate detection identifies multiple records in a dataset that represent the same real-world object. Many such approaches exist, both in research and in industry. To investigate essential properties of duplicate detection algorithms, such as their result quality or runtime behavior, they must be executed on suitable test data. The quality evaluation requires that these test data are labeled, constituting a ground truth. Correctly labeled, sizable, and real or at least realistic test datasets, however, are not easy to obtain, creating an obstacle for the advancement of research. In this tutorial, we present common methods to evaluate duplicate detection algorithms and to generate labeled test data. We close with a discussion of open problems.

关键词： Industries Runtime Conferences Tutorials Data engineering Object recognition detection algorithms

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Sampling Schemes and detection algorithms for Wideband Spectrum Sensing

Sampling Schemes and Detection Algorithms for Wideband Spect...

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IEEE International Symposium on Dynamic Spectrum Access Networks (DYSPAN)

作者： Sun, Zhanwei Laneman, J. Nicholas IEEE United States

ISBN: (纸本)9781479926619

This paper establishes a unified model for sub-Nyquist sampling and discusses different aliasing patterns inherent in sub-Nyquist sampling. Several sub-Nyquist sampling schemes are considered within the unified model. This paper also develops multi-channel detection for wideband spectrum sensing following a Bayesian rule based upon the new performance metrics we defined in earlier work. The paper shows that generalized co-prime sampling exhibits similar sensing performance to that of random sampling for the same performance metrics and detection algorithms, and is much easier to implement. Moreover, integer undersampling, which corresponds to the simplest sub-Nyquist sampling scheme and leads to periodic aliasing, appears to be advantageous over the more sophisticated sub-Nyquist sampling schemes in the regime that better protects the primary system.

关键词： detection algorithms Performance metrics primary system broadband Aliasing Scheme Random sampling Sampling

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Adaptive detection algorithms in DS-CDMA

Adaptive detection algorithms in DS-CDMA

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IEEE International Conference on Universal Personal Communications (ICUPC 98)

作者： Melvasalo, M Virtamo, J VTT FIN-02044 Espoo Finland

ISBN: (纸本)0780351061

In Direct Sequence (DS) Code Division Multiple Access (CDMA) systems, multiuser detection offers important performance gains compared to conventional detection. The optimum multiuser detection is computationally very complex and requires knowledge e.g. of the signal energies and cross-correlations. In this paper two adaptive suboptimal multiuser detection algorithms are investigated in the symbol-synchronous case and tested by simulations. The algorithms are based on the classical projection theorem of Hilbert spaces and the idea is to minimize the error vector between the matched filter output and the detected information bit. Both the algorithms have computational complexity linearly dependent on the number of users. The simulations show that it is possible to achieve the multistage detector performance based only on knowledge of the matched filter output.

关键词： Computational complexity Computational modeling detection algorithms Detectors Hilbert space Matched filters Multiaccess communication Multiuser detection Performance gain Testing

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Performance Evaluation of Community detection algorithms Based on Relationship Strength Measurement 10

Performance Evaluation of Community Detection Algorithms Bas...

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10th International Conference on Ubiquitous and Future Networks (ICUFN)

作者： Behera, Soom Lu, Haoye Nayak, Amiya Univ Ottawa Sch Elect Engn & Comp Sci Ottawa ON Canada

ISBN: (纸本)9781538646465

The study and analysis of networks have been a significant field of research as it holds its roots in varied disciplines like Biology, Chemistry, Sociology, Computer Applications and many more. Human beings have ventured into the era of a network with the rise of all kinds of networks such as the Internet and Social networks. detection of community structure in real networks is vital in terms of both theoretical and practical value. Many community detection methods are derived from specific backgrounds and their reliability is still questionable. Researchers hardly focus on the general definition of communities and the general community detection algorithms. The discrepancy between the two might pose some obstacles to the optimization of them so that it is hard to use one algorithm to perfect the other. Recently, Lu et al. [1] have proposed a general method for constructing network model from the real-world problem. Also, they have given a general definition of community structure as well as a complete procedure for detecting communities. In this paper, we apply the efficient resistance distance function [2] on Lu et al.'s algorithm and compare its performance with other community detection algorithms that allow for overlaps.

关键词： detection algorithms Resistance Measurement Social network services Greedy algorithms Biology Network topology"

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Investigating Execution-Characteristics of Feature-detection algorithms 22

Investigating Execution-Characteristics of Feature-Detection...

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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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Community Deception or: How to Stop Fearing Community detection algorithms (Extended Abstract) 34

Community Deception or: How to Stop Fearing Community Detect...

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34th IEEE International Conference on Data Engineering Workshops (ICDEW)

作者： Fionda, Valeria Pirro, Giuseppe Univ Calabria Dept Math & Comp Sci Arcavacata Di Rende Italy CNR ICAR Arcavacata Di Rende Italy

ISBN: (纸本)9781538655207

Community deception is the problem of hiding a community from community detection algorithms. This is an important task whenever a group (e.g., activists, police enforcements) want to observe and cooperate in a social network while avoiding to be detected. We formalize the community deception problem and propose an efficient algorithm, based on the concept of community safeness, which allows to achieve deception by carefully identifying and rewiring a certain number of the community members' connections. Deception can be practically achieved in social networks like Facebook or Twitter by friending (following) or unfriending (unfollowing) network members. We validated our approach and compared it with related research on a variety of (large) real networks with encouraging results.

关键词： detection algorithms Image edge detection Knowledge engineering Electronic mail Law enforcement Facebook

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Experimental Comparison of Hardware-Amenable Spike detection algorithms for iBMIs 9

Experimental Comparison of Hardware-Amenable Spike Detection...

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9th IEEE/EMBS International Conference on Neural Engineering (NER)

作者： Shaikh, Shoeb So, Rosa Libedinsky, Camilo Basu, Arindam Nanyang Technol Univ Singapore Singapore Inst Infocomm Res Singapore Singapore Natl Univ Singapore SiNAPSE Singapore Singapore

ISBN: (纸本)9781538679210

This paper presents an experiment based comparison of absolute threshold (AT) and non-linear energy operator (NEO) spike detection algorithms in intra-cortical Brain Machine Interfaces (iBMIs). Results show an average increase in decoding performance of approximate to 5% in monkey A across 28 sessions recorded over 6 days and approximate to 2% in monkey B across 35 sessions recorded over 8 days when using NEO over AT. To the best of our knowledge, this is the first ever reported comparison of spike detection algorithms in an iBMI experimental framework involving two monkeys. Based on the improvements observed in an experimental setting backed by previously reported improvements in simulation studies, we advocate switching from state of the art spike detection technique - AT to NEO.

关键词： Decoding Brain-computer interfaces Electrodes Standards Sorting detection algorithms Hardware

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