Foreground detection is an important preliminary step of many video analysis systems. Many algorithms have been proposed in the last years, but there is not yet a consensus on which approach is the most effective, not...
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Foreground detection is an important preliminary step of many video analysis systems. Many algorithms have been proposed in the last years, but there is not yet a consensus on which approach is the most effective, not even limiting the problem to a single category of videos. This paper aims at constituting a first step towards a reliable assessment of the most commonly used approaches. In particular, four notable algorithms that perform foreground detection have been evaluated using quantitative measures to assess their relative merits and demerits. The evaluation has been carried out using a large, publicly available dataset composed by videos representing different realistic applicative scenarios. The obtained performance is presented and discussed, highlighting the conditions under which algorithm can represent the most effective solution.
The performance of mode division multiplexing (MDM) systems is limited by mode-dependent loss (MDL), which seriously deteriorates the unitarity of the transmission matrix, making the traditional detection algorithms i...
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The performance of mode division multiplexing (MDM) systems is limited by mode-dependent loss (MDL), which seriously deteriorates the unitarity of the transmission matrix, making the traditional detection algorithms ineffective. In order to mitigate the effect of MDL in MDM transmission and improve the efficiency of digital signal processing at the receiver, a novel coordinate descent (CD)-based box-constrained detection algorithm using adaptive moment estimate (CDbox-Adam) is proposed. In the process of each coordinate update for the CDbox detection algorithm, the adaptive moment estimation (Adam) method adaptively updates the step size for different parameters from the first moment estimate and the second moment estimate of the gradients. To simplify the process of each coordinate updating of the CDbox-Adam algorithm, the CDbox-Adamax detection algorithm is also proposed. The CDbox-Adamax detection algorithm generalizes the 2-norm update rule of weights to the infinite norm. At the same time, an early stop criterion is proposed to efficiently avoid unnecessary iterations. Simulation results show that the early stop criterion reduces the number of iterations of the proposed detection algorithms. For a 12-mode MDM system impaired by MDL, the average number of iterations of the proposed detection algorithms is less than the CDbox detection algorithm. Thus, the proposed detection algorithms present lower computational complexity than the CDbox detection algorithm. Furthermore, compared with the conventional minimum mean square error (MMSE) detection algorithm, the proposed detection algorithms, CDbox-Adam and CDbox-Adamax, gain signal to noise ratio (SNR) improvement of 2.3 dB and 1.6 dB at BER = 10(-5) when MDL = 5 dB with QPSK and 16QAM, respectively. In the presence of MDL = 5 dB and 10 dB, when compared with the proposed detection algorithms with perfect CSI, the proposed detection algorithms with LS channel estimation requires 5.1 dB and 2.5 dB more of SNR to get
The purpose of this paper is to analyze the behavior of several jump detection algorithms when applied to the same real data (geophysical signals) and to compare these algorithms from different points of view: complex...
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The purpose of this paper is to analyze the behavior of several jump detection algorithms when applied to the same real data (geophysical signals) and to compare these algorithms from different points of view: complexity, efficiency, robustness, and ability to characterize the detected jumps. Three types of algorithms are investigated: "filtered derivatives" detectors, cumulative sum (cusum) tests, and Willsky's generalized likelihood ratio (GLR) algorithm. A modified version of this last test is elaborated, and a new detector, mixing GLR and cusum tests, is presented.
Incident management is a major problem in traffic control. Traffic incidents are the cause for more than half of all traffic delays. The research undertaken by the University of Minnesota has reached a point where goo...
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Incident management is a major problem in traffic control. Traffic incidents are the cause for more than half of all traffic delays. The research undertaken by the University of Minnesota has reached a point where good knowledge on different incident detection techniques has been achieved. The computer program presented in this paper is designed to assist researchers in testing incident detection algorithms. The primary gains by using this program are the reduction of time needed for algorithm testing and the flexibility on designing the test site. With this program the user can assign individual threshold sets in every section, and use multiple algorithms simultaneously. The algorithms included in the version presented in this paper are DELOS(3,3), CALIFORNIA, Alg. #7, and Alg. #8. Some of the features that make this program unique are its ability to combine measurements on the field to create “pseudo” detectors, its capability to automatically judge if a detection is valid, and its ability to combine incident detection algorithms to improve detection performance.
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 suffe...
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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.
In this paper we derive a quality measure for analysing the performance of algorithms which seek to find boundaries and boundary models between regions of differing mean grey-level value. The measure can be used to co...
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In this paper we derive a quality measure for analysing the performance of algorithms which seek to find boundaries and boundary models between regions of differing mean grey-level value. The measure can be used to compare different approaches and, more importantly, it can also be used as a first step to building self-optimising vision systems that can automatically optimise important parameters at each level of the system.
Identifying the true type of a computer file can be a difficult problem. Previous methods of file type recognition include fixed file extensions, fixed "magic numbers" stored with the files, and proprietary ...
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Identifying the true type of a computer file can be a difficult problem. Previous methods of file type recognition include fixed file extensions, fixed "magic numbers" stored with the files, and proprietary descriptive file wrappers. All of these methods have significant limitations. This paper proposes algorithms for automatically generating "fingerprints" of file types based on a set of known input files, then using the fingerprints to recognize the true type of unknown files based on their content, rather than metadata associated with them. Recognition is performed by three different algorithms based on: byte frequency analysis, byte frequency cross-correlation analysis, and file header/trailer analysis. Tests were run to measure the accuracy of these algorithms. The accuracy varied from 23% to 96% depending upon which algorithm was used. These algorithms could be used by virus scanning packages, firewalls, intrusion detection systems, forensic analyses of computer hard drives, Web browsers, or any other program that needs to identify the types of files for proper operation. File type detection is also important to the operating systems for correct identification and handling of files regardless of file extension.
Spectrum sensing is a critical requirement for proposed Dynamic Spectrum Access networks both to avoid causing interference to primary users and to maximize the throughput of the secondary network. Traditional sensing...
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Spectrum sensing is a critical requirement for proposed Dynamic Spectrum Access networks both to avoid causing interference to primary users and to maximize the throughput of the secondary network. Traditional sensing algorithms are performance limited by the length of time they can observe the primary user's transmission. We implement for demonstrative purposes a number of sequence detection spectrum sensing algorithms that take into account the transitions in the primary user's channel access, resulting in better sensing performance. The demonstration consists of primary and secondary data links that are both streaming video in the same frequency band, forcing the secondary link to opportunistically access the spectrum. Various sensing algorithms are selectable and configurable at the secondary transmitter, including multiple sequence detection algorithms and energy detection. The superior performance of the sequence detection algorithms is evident at the receivers through a number of metrics, including video quality, plots of the historical data rate, and estimates of the detection and false alarm probabilities.
Layered space-time code technique is a good scheme to improve the wireless transmission rate and reliability. The paper discusses four detection algorithms for the Vertical-Bell Laboratories Layered Space-Time (V-BLAS...
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Layered space-time code technique is a good scheme to improve the wireless transmission rate and reliability. The paper discusses four detection algorithms for the Vertical-Bell Laboratories Layered Space-Time (V-BLAST) system. Then, it analyzes the process of algorithms in detail and simulates them. The bit error rate (BER) for the four detection algorithms in different channel situations are compared.
The capability of identifying physical structures in an unknown environment is important for autonomous mobile robot navigation and scene understanding. A methodology for detecting corridor and door structures in an i...
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The capability of identifying physical structures in an unknown environment is important for autonomous mobile robot navigation and scene understanding. A methodology for detecting corridor and door structures in an indoor environment is proposed, and the performances of the corridor detection algorithm and door detection algorithm applied in different environments are evaluated. In the proposed algorithms, we utilize a feedback mechanism based hypothesis generation and verification (HGV) method to detect corridor and door structures using low level line features in video images. The proposed method consists of low, intermediate, and high level processing stages which correspond to the extraction of low-level features, the formation of hypotheses, and the verification of hypotheses using a feedback mechanism, respectively. The system has been tested on a large number of real corridor images captured by a moving robot in a corridor. The experimental results validated the effectiveness and robustness of the proposed methods with respect to different viewpoints, different robot moving speed, under different illumination conditions and reflection variations.
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