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 geof...
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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.
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 b...
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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.
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 de...
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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.
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....
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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.
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 ...
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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.
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 ventu...
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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.
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 alg...
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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.
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 howeve...
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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.
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...
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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.
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...
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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.
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