Impulse noise is known to be highly damaging to the performance of a multicarrier system. To be able to deal with impulsive disturbances, the receiver has to recognize which symbol was contaminated by the impulse nois...
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ISBN:
(纸本)9539676940
Impulse noise is known to be highly damaging to the performance of a multicarrier system. To be able to deal with impulsive disturbances, the receiver has to recognize which symbol was contaminated by the impulse noise. We analyze performance of a selection of impulse noise detection algorithms. Impulse noise can be detected in the time domain, in the frequency domain, or by combining the time and frequency domain analysis. The choice of the impulse noise detection algorithm depends on the characteristics of an impulse.
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.
Two major tasks of partial discharges (PD) measurements may be distinguished, (i) providing general evidence and the type of PD (detection) and (ii) the location of the PD. Dependent on the type of device under test t...
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ISBN:
(纸本)9781424416219
Two major tasks of partial discharges (PD) measurements may be distinguished, (i) providing general evidence and the type of PD (detection) and (ii) the location of the PD. Dependent on the type of device under test the two issues have changing priority. For the on-line/on-site PD location in power transformers unconventional PD measuring methods like acoustic ultra-sonic measurements or electromagnetic measurements up to the UHF (ultra high frequencies) range are performed, while the Time Domain Reflectometry (TDR) of electric PD signals is a standard technique for the location of PD in power cables.
This paper develops several parallel algorithms for target detection in hyperspectral imagery, considered to be a crucial goal in many remote sensing applications. In order to illustrate parallel performance of the pr...
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This paper develops several parallel algorithms for target detection in hyperspectral imagery, considered to be a crucial goal in many remote sensing applications. In order to illustrate parallel performance of the proposed parallel algorithms, we consider a massively parallel Beowulf cluster at NASA's Goddard Space Flight Center. Experimental results, collected by the AVIRIS sensor over the World Trade Center, just five days after the terrorist attacks, indicate that commodity cluster computers can be used as a viable tool to increase computational performance of hyperspectral target detection applications.
Spectrum sensing is a critical function for enabling dynamic spectrum access (DSA) in wireless networks that utilize cognitive radio (CR). In DSA networks, unlicensed secondary users can gain access to a licensed spec...
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Spectrum sensing is a critical function for enabling dynamic spectrum access (DSA) in wireless networks that utilize cognitive radio (CR). In DSA networks, unlicensed secondary users can gain access to a licensed spectrum band as long as they do not interfere with primary users. Spectrum sensing is subject to errors in the form of false alarms and missed detections. False alarms cause spectrum under-use by secondary users, and missed detections cause interference to primary users. Although existing research has demonstrated the utility of a Markov chain for modeling the spectrum access pattern of primary users over time, little effort has been directed toward spectrum sensing based upon such models. In this paper, we develop soft-input sequence detection algorithms of Markov sources in noise for spectrum sensing in DSA networks. We assign different Bayesian cost factors for missed detections and false alarms, and we show that a suitably modified Forward-Backward sequence detection algorithm is optimal in minimizing the detection risk. Along the way, we observe new fundamental limitations that we call "Risk Floor" and "Limiting ROC" for energy detection and coherent detection due to the PU's spectrum access pattern.
We propose novel detection algorithms for linear modulations transmitted over nonlinear satellite channels, also impaired by additive white Gaussian noise. These algorithms are derived by using a Volterra-series expan...
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We propose novel detection algorithms for linear modulations transmitted over nonlinear satellite channels, also impaired by additive white Gaussian noise. These algorithms are derived by using a Volterra-series expansion of the useful signal and by applying the sum-product algorithm to a suitably-designed factor graph. Being soft-input soft-output (SISO) in nature, the proposed detectors can be adopted in turbo processing without additional modifications. Typical linear modulations employed in satellite transmissions are considered in the numerical results. When compared with the optimal detection algorithm for these channels, whose complexity is exponential in the channel memory, the proposed schemes result very appealing in terms of tradeoff between performance and computational complexity. Particularly, the proposed schemes can approach the optimal performance with a complexity only linear in the channel memory.
An intrusion detection system (IDS), that monitors passively specific computing resources, and reports anomalous or intrusive activities, is becoming an important component in the security system of information infras...
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ISBN:
(纸本)0769514057
An intrusion detection system (IDS), that monitors passively specific computing resources, and reports anomalous or intrusive activities, is becoming an important component in the security system of information infrastructure. algorithms for detecting intrusions are under rapid development, but far from being mature. One interesting and difficult issue is how to study and test a new intrusion detection algorithm against a variety of (perhaps simulated) intrusive activities under realistic background traffic. A flexible and general-purpose platform for testing intrusion detection algorithms is clearly desirable. This paper presents such a software platform, called IntruDetector. With this platform, detection algorithms can be tested directly in a real environment with a wide range of intrusive activities. The data of normal system activities are directly collected from the live environment, and are mixed with intrusive activities that are simulated by hybrid simulation. The main properties of this approach are: (1) the background traffic is realistic; (2) it allows flexible simulation of various types of intrusions; and (3) normal system operation will not be disrupted by virtually simulated destructive intrusions during testing.
A cellular model for simulating 2D grayscale images of smoke clouds is offered. The model is intended for adjustment of forest fire detection algorithms based on image analysis. Simulation of realistic smoke cloud ima...
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A cellular model for simulating 2D grayscale images of smoke clouds is offered. The model is intended for adjustment of forest fire detection algorithms based on image analysis. Simulation of realistic smoke cloud image well-known to be the fire character, in long image sequence allows estimating detection probability.
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