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The main body of the literature states that Artificial Neural Networks must be regarded as a "black box" without further interpretation due to the inherent difficulties for analyze the weights and bias terms. Some authors claim that ANN trained as a regression device tend to organize itself by specializing some neurons to learn the main relationships embedded in the training set, while other...
The Teager-Kaiser energy operator (TKO) belongs to a class of autocorrelators and their linear combination that can track the instantaneous energy of a nonstationary sinusoidal signal source. TKO-based monocomponent AM-FM demodulation algorithms work under the basic assumption that the operator outputs are always positive. In the absence of noise, this is assured for pure sinusoidal inputs and the...
This paper investigates the optimization of the generalized likelihood ratio test (GLRT) eigenvalue-based spectrum sensing detector in terms of decision thresholds and sensing time. In order to guarantee the interests of primary and secondary users simultaneously, the sensing performance is assessed using the total error rate, i.e., the summation of probabilities of false alarm and missed detection...
Probability density function (pdf) estimation of sea clutter in synthetic aperture radar (SAR) imagery has a fundamental role in constructing a constant false alarm rate (CFAR) based ship detector. This paper proposes a semi-parametric sea clutter modeling method for SAR amplitude imagery. The pdf of sea clutter is estimated point by point for each amplitude value, by selecting an optimal component...
Traditional hyperspectral anomaly detection methods either model the global background or the local neighborhood, that bring some apparent drawbacks, such as the unreasonable assumption of uni-modular background in global detectors, or the high false alarms by sliding windows in local detectors. In this paper, a source component-based anomaly detection approach is proposed. It first extracts the source...
In this paper, we define and analyse some properties of a class of order filters. These filters can be regarded as adaptive L-Filters which can be tuned by setting only one parameter instead of N, where N is the filter size. Deterministic and statistical properties are discussed. Experimental results obtained on both synthetic and real images show that the noise reduction effect of the new filter...
Microcalcification (MC) detection in mammograms can be hampered by a number of factors ranging from imaging noise to inhomogeneity in breast tissue. Consequently, owning to the variability among subjects in their mammograms, the detection accuracy often varies from case to case even for a well-developed MC detector. To account for this variability, we propose to use a Bayes' risk approach to define...
This paper deals with stochastic resonance and its application in sine detection. The nonlinear physical phenomenon of stochastic resonance generally occurs in bistable systems excited by a random noise plus a sine. Such systems force cooperation between the input noise and the input sine: Provided a fine tuning between the noise amplitude and the dynamics, the system reacts periodically. The interesting...
We present a high speed implementation of maximum-likelihood estimator using FPGA as positioning method for monolithic scintillation crystal based PET detector. Instead of using channel distribution parameters to recalculate the fitting curves, we suggest using real possibility distribution function for channels of all possible positions, which largely reduces the requirement of computation resources...
In numerous RF communication applications there is a need to analyze detector performance in noise, interference and fading environments. In detection usually multiple pulses or signal samples are integrated to improve performance. A Gram-Charlier expansion is often used to approximate the probability density function (PDF) with known moments. In this study, Linear-law detector performance is simulated...
In 1950 Abraham Wald proved that every admissible statistical decision rule is either a Bayesian procedure or the limit of a sequence of such procedures. He thus provided a decision-theoretic justification for the use of Bayesian inference, even for non-Bayesian problems. It is often assumed that his result also justified the use of Bayesian priors to solve such problems. However, the principles one...
A cognitive radio has to perform spectrum sensing to detect the vacant channel. The detection faces some challenges due to the required performance and limited knowledge on the primary signals and the channel. In this paper we proposed a blind spectrum sensing method for cognitive radio network. The proposed method based on the difference on distribution of the condition between when the transmission...
Radio Spectrum sensing has been a topic of strong research in the last years due to its importance to Cognitive Radio (CR) systems. However, in Cognitive Radio Networks (CRNs) with multiple Primary Users (PUs), the Secondary Users (SUs) can often detect PUs that are located outside the sensing range, due to the level of the aggregated interference caused by that PUs. This effect, known as Spatial...
Introduced to the hyperspectral imaging community in the WHISPERS 2010 Reykjavik meeting, the “clairvoyant fusion” methodology for creating detection algorithms has since been applied to a variety of binary decision problems. These are summarized here. For any detection problem amenable to a fusion approach, an infinity of “flavors” can be devised. This paper describes what problems have been formulated...
We study different linear precoding strategies and their asymptotic detection performance for decentralized detection of unknown deterministic signals. Two precoding schemes are proposed: a random precoding scheme which generates its precoding vectors following a certain distribution, and a signassisted precoding scheme which incorporates the sign information of the signal components into the precoding...
We introduce and discuss photon-processing detectors and we compare them with photon-counting detectors. By estimating a relatively small number of attributes for each collected photon, photon-processing detectors may help understand and solve a fundamental theoretical problem of any imaging system based on photon-counting detectors, namely null functions. We argue that photon-processing detectors...
In this paper, performance of the energy detector is analyzed in blind cooperative relay networks operating over independent and identically distributed (IID) Rayleigh fading channels. First, utilizing the closed-form expression of the probability density function (PDF) of the dual-hop relay link along with an alternative series form representation of the generalized marcum-Q function, exact average...
In this paper, we analyze the performance of an energy detector operating over Gamma shadowed Rice fading channels, namely Rice fading channels with the fluctuating line-of-sight components following the Gamma distribution. This composite multi-path/shadowing model has been shown to provide remarkably accurate fading characterization while leading to closed-form expressions for important channel statistics...
In this paper, spectrum sensing in cognitive radio systems is studied for non-Gaussian channels in the presence of prior distribution uncertainty. In most practical cases, some amount of prior information about signals of primary users is available to secondary users but that information is never perfect. In order to design optimal spectrum sensing algorithms in such cases, we propose to employ the...
In this work, we studied the effect of multi-user and multipath, on the performance of a serial acquisition search system, using adaptive threshold with constant false alarm rate, in Rayleigh fading channels. Since the received signal levels in mobile communications are unknown and location varying, the acquisition schemes for pseudo-noise (PN) sequences with fixed thresholds may cause too many false...
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