Large telecommunications networks are designed to achieve high reliability with hardware and software redundancy that is managed through complex fault-tolerant mechanisms for error detection and recovery. Because of t...
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Large telecommunications networks are designed to achieve high reliability with hardware and software redundancy that is managed through complex fault-tolerant mechanisms for error detection and recovery. Because of the fault tolerant mechanisms, when errors do occur they do not always cause failures and, hence, it can be difficult to detect anomalous behavior of the system and to determine its root cause. In this paper, using sequential system performance data, we present the application of multivariate change detection algorithms and visual analytics methods for detecting and diagnosing anomalous behavior with low latency in telecommunications systems. Such methods, coupled with domain knowledge, are efficient and effective for detecting and diagnosing anomalies as compared to log analysis. We demonstrate our methods with real data from a large system.
There are a variety of sensor systems deployed at border crossings and ports of entry throughout the world that scan for illicit nuclear material. These systems employ detection algorithms that interpret the output of...
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There are a variety of sensor systems deployed at border crossings and ports of entry throughout the world that scan for illicit nuclear material. These systems employ detection algorithms that interpret the output of the scans and determine whether additional investigation is warranted. In this work, we demonstrate an approach for comparing the performance of such detection algorithms. We optimize each algorithm by minimizing risk, which considers the probability distribution of threat sources and the consequence of detection errors. Our method is flexible and is easily adapted to many different assumptions regarding the probability of a conveyance containing illicit material and the relative consequences of false positive and false negative errors. This approach can help developers and decision makers identify optimal settings for these algorithms. We illustrate the method by comparing the risk from two families of detection algorithms and discuss the generalizability of the method.
The implementation of wideband MIMO systems poses a major challenge to hardware designers due to the huge processing power required for MIMO detection. To achieve this goal with a complete VLSI solution, channel codin...
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The implementation of wideband MIMO systems poses a major challenge to hardware designers due to the huge processing power required for MIMO detection. To achieve this goal with a complete VLSI solution, channel coding and MIMO detection are preferably separated so that each of them can be fitted into a single chip. In this paper, a comparative study is presented regarding various uncoded adaptive and non-adaptive MIMO detection algorithms. Intended to serve as a reference for system designers, this comparison is performed from several different perspectives including theoretical formulation, simulated BER/PER performance, and hardware complexity. All the simulations are conducted within MIMO-OFDM framework and with a packet structure similar to that of the IEEE 802.11a/g standard. As the comparison results show, the RLS algorithm appears to be an affordable solution for a wideband MIMO system targeted at gigabit wireless transmission. As a direct result of this work, an ASIC for a 25 MHz wideband 8 /spl times/ 8 MIMO-OFDM system using RLS has been designed and fabricated.
Background subtraction is a popular algorithm for video object segmentation. It identifies foreground objects by comparing the input images with a pure background image. In camera-motion compensated sequences, small e...
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Background subtraction is a popular algorithm for video object segmentation. It identifies foreground objects by comparing the input images with a pure background image. In camera-motion compensated sequences, small errors in the motion estimation can lead to large image differences along sharp edges. Consequently, the errors in the image registration finally lead to segmentation errors. This paper proposes a computationally efficient approach to detect image areas having a high risk of showing misregistration errors. Furthermore, we describe how existing change detection algorithms can be modified to avoid segmentation errors in these areas. Experiments show that our algorithm can improve the segmentation quality. The algorithm is memory efficient and suitable for real-time processing.
Community detection is one of the most widely-used graph analytics. As recent community detection algorithms have been targeting large-scale networks, an emerging problem is how best to evaluate the output of these al...
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ISBN:
(数字)9781728174457
ISBN:
(纸本)9781728174570
Community detection is one of the most widely-used graph analytics. As recent community detection algorithms have been targeting large-scale networks, an emerging problem is how best to evaluate the output of these algorithms. Common measures such as modularity have several well-known issues, so comparisons against a notion of a “ground truth” community structure, such as in the Lancicinetti-Fortunato-Radicchi (LFR) benchmark, is preferred. This current work targets the parallel generation of graphs matching the specifications of the LFR benchmark. We are able to generate such graphs at the billion-edge scale in seconds, giving orders-of-magnitude speedup relative to prior work.
Advanced Traffic Management Systems (ATMS) provide the means for local transportation officials to monitor traffic conditions, adjust traffic operations, and respond to accidents. By providing early traffic incident d...
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Advanced Traffic Management Systems (ATMS) provide the means for local transportation officials to monitor traffic conditions, adjust traffic operations, and respond to accidents. By providing early traffic incident detection and management, and by redistributing traffic to less congested portions of the highway network, ATMS can influence vehicle operators' route choices. COMPASS, a state-of-the-art advanced traffic management system implemented in the Metropolitan Toronto area, has adopted most of the Intelligent Vehicle-Highway Systems (IVHS) technologies. This paper describes the logic and implementation of the automatic incident detection for COMPASS. The purpose of incident detection is to identify the potential occurrence of incidents in a traffic stream by analyzing the flow characteristics of the traffic stream. The output of the incident detection function will form the basis for incident verification by the operator and implementation of traffic response plans. Two incident detection algorithms have been developed for the system, namely the All Purpose Incident detection (APID) algorithm and the Double Exponential Smoothing (DES) algorithm. The APID algorithm is based on the California incident detection algorithms which have the general structure of a binary decision tree. The algorithm has been designed to handle different traffic patterns. For example, the light/medium traffic incident detection routines are more suitable for detecting incidents at light/medium traffic conditions than the general incident detection routine. Furthermore, the false alarm rate may be reduced by introducing the compression wave test and persistence test. The DES algorithm makes use of a short-term forecasting technique for detecting irregularities of a traffic variable (e.g. volume) in a time series. A tracking signal is obtained by dividing the cumulative error of a traffic variable (e.g. volume) by the current standard deviation of the same variable. An incident will be
The experience of ubiquitous and seamless access to heterogeneous mobile communication networks is one of the core issues of today's research. One important issue is the reliable detection of spectrum holes. in th...
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The experience of ubiquitous and seamless access to heterogeneous mobile communication networks is one of the core issues of today's research. One important issue is the reliable detection of spectrum holes. in this paper, we address three important spectrum holes detection algorithms in cognitive wireless networks: MTM-SVD based spectrum holes detection algorithm, Kalman filtering Predictor and Particle filtering predictor. A detailed analysis and the specific steps are given.
This paper presents experimental results from the hardware implementation of a power conversion system (PCS), and its islanding control and detection algorithms, for a distributed generation (DG) unit's interface ...
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This paper presents experimental results from the hardware implementation of a power conversion system (PCS), and its islanding control and detection algorithms, for a distributed generation (DG) unit's interface to the utility grid. The control and detection schemes implemented are shown in simulations to be stable and effective. The algorithms allow for not only transient control between modes of operation (grid-connected to stand-alone, islanding, modes) but for the elimination of nondetection zones (NDZ) and the autonomous operation of the PCS from the grid. The simulations show that the concepts of the algorithms are feasible, while the hardware implementation, via power electronics building blocks (PEBBs), shows the results that validate them.
We consider a combined space-time (ST) block coding and direct-sequence code division multiple access (DS/CDMA) scheme for downlink transmissions over multipath fading channels, in which ST block coding is performed a...
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We consider a combined space-time (ST) block coding and direct-sequence code division multiple access (DS/CDMA) scheme for downlink transmissions over multipath fading channels, in which ST block coding is performed at the chip level, i.e. after code spreading. For this scheme, we develop low complexity and low decoding delay linear ST single-user detection algorithms. In addition, we demonstrate that by exploiting the signal structure imposed by the chip-level ST block coding, blind channel multipath estimation (with only a scalar ambiguity) is feasible, even with the assignment of a single code vector to each user.
We analyze robustness of several multichannel radar detection algorithms to mismatches in the steering vector. In particular, we address the scenario of a staged detection, where a history of measurements provides the...
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We analyze robustness of several multichannel radar detection algorithms to mismatches in the steering vector. In particular, we address the scenario of a staged detection, where a history of measurements provides the region of interest and a-priori polarimetric target signatures for a target. The four second stage detection approaches - optimally weighed span, Kellypsilas and Robeypsilas detectors, and Gerlachpsilas secondary data free detector - are considered using these polarimetric features. The evaluation based on recordings of real natural scenes and both real and artificially inserted extended objects concerns robustness of these algorithms to mismatches in the steering vector. It has been observed - for different ground clutter environments into which extended target is placed - that Kellypsilas detector provides the best detection but may suffer severely from the mismatch, Gerlachpsilas detector while allowing higher probability of false alarms, produces the most stable performance and Robeypsilas matched filter gives a suitable trade-off.
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