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Synthetic Aperture Radar (SAR) data enables direct observation of land surface at repetitive intervals and therefore allows temporal detection and monitoring of land changes. However, the problem of radar automatic change detection is made more dif􀅿cult, mainly with the presence of speckle noise. This paper presents a new method for SAR image change detection using the Dezert-Smarandache Theory (DSmT). First, a Gamma distribution function is used to characterize globally the radar texture data and allows mass assignment throw Kullback-Leibler distance. Then, local pixel measurements are introduced to re􀅿ne the mass attribution and take into account the context information. Finally, DSmT is carried out by comparing the modelling results between temporal images. The originality of the proposed method is on the one hand, the use of DSmT which achieve a plausible and paradoxical reasoning comparing to classical Dempster-Shafer Theory (DST). On the other hand, the given approach characterizes the radar texture data with a Gamma distribution which allows a better representation of the speckle. The radar texture is being usually modeled by a Gaussian model in previous DST and DSmT fusion works.

Synthetic Aperture Radar (SAR) data enables direct observation of land surface at repetitive intervals and therefore allows temporal detection and monitoring of land changes. However, the problem of radar automatic change detection is made more dif􀅿cult, mainly with the presence of speckle noise. This paper presents a new method for SAR image change detection using the Dezert-Smarandache Theory (DSmT). First, a Gamma distribution function is used to characterize globally the radar texture data and allows mass assignment throw Kullback-Leibler distance. Then, local pixel measurements are introduced to re􀅿ne the mass attribution and take into account the context information. Finally, DSmT is carried out by comparing the modelling results between temporal images. The originality of the proposed method is on the one hand, the use of DSmT which achieve a plausible and paradoxical reasoning comparing to classical Dempster-Shafer Theory (DST). On the other hand, the given approach characterizes the radar texture data with a Gamma distribution which allows a better representation of the speckle. The radar texture is being usually modeled by a Gaussian model in previous DST and DSmT fusion works.

Contributors: Jeremy M. Wolfe. Data collection began: 4/4/18 This experiment investigates how changing the input from a simulated AI can affect the decisions made by human observers in a two-alternative forced choice task (like the decision to recall a woman for further examination in mammography). Using a signal detection framework, we developed an analogue task that can be tested with non-experts in the lab.

A study about the relationships between individual differences in two psychophysical components of the Signal Detection Theory "perceptual sensitivity" and "response bias" as a function of adults' food neophobia or the fear of novel food.

Recent news cast doubts on London Interbank Offered Rate (LIBOR) integrity. Given its economic importance and the delay with which authorities realize about this situation, we aim to find an objective method in order to detect departures in the LIBOR rate that from the expected behavior. We analyze several interest rates time series and we detect an anomalous behavior in LIBOR, specially during the period of the financial crisis of 2008. Our findings could be consistent with data manipulation.

Recent news cast doubts on London Interbank Offered Rate (LIBOR) integrity. Given its economic importance and the delay with which authorities realize about this situation, we aim to find an objective method in order to detect departures in the LIBOR rate that from the expected behavior. We analyze several interest rates time series and we detect an anomalous behavior in LIBOR, specially during the period of the financial crisis of 2008. Our findings could be consistent with data manipulation.

This paper discusses the first experiment in a series designed to systematically understand the different characteristics of an automated system that lead to trust in automation. We also discuss a simple process model, which helps us understand the results. Our experimental paradigm suggests that participants are agnostic to the automation s behavior; instead, they merely focus on alarm rate. A process model suggests this is the result of a simple reward structure and a non-explicit cost of trusting the automation.

This paper discusses the first experiment in a series designed to systematically understand the different characteristics of an automated system that lead to trust in automation. We also discuss a simple process model, which helps us understand the results. Our experimental paradigm suggests that participants are agnostic to the automation s behavior; instead, they merely focus on alarm rate. A process model suggests this is the result of a simple reward structure and a non-explicit cost of trusting the automation.

Basic quantum detection and estimation theory, applications to optics, photon counting, and filtering theory are studied. Recent work on the restoration of degraded optical images received at photoelectrically emissive surfaces is also reported, the data used by the method are the numbers of electrons ejected from various parts of the surface.

In this paper, we determine the connective K-cohomology with reality of elementary abelian $2$-groups as a module over $\mathbb{Z}[v_1,a]$, where $v_1$ is the equivariant Bott class and $a$ the Euler class of the sign representation. This gives in particular a new approach to the computation of the connective real K-theory of such groups. The originality here is to make all computations in the $\mathbb{Z}/2$-equivariant stable category, considering only $\mathbb{Z}/2$-equivariant cohomology theories, and to use relative homological algebra over certain subalgebras of the equivariant Steenrod algebra to perform explicit computations.

Radar systems must detect targets in the presence of clutter and jamming (colored noise) signals. By exploiting signal diversity, space-time adaptive processing (STAP) improves radar detection performance in colored noise-limited environments. This set of lecture notes describes fundamental aspects of space-time detection theory in Gaussian noise. We first describe basic detection theory; key aspects of the discussion include the formation of a sufficient statistic, threshold setting, matched filtering, and the notion of a whitening filter. We then examine the role space-time signal diversity plays in enhancing target detection performance in colored noise environments. Next, we define commonly used performance metrics, including signal-to- interference-plus-noise ratio (SINR) loss factors. The latter part of the paper describes practical aspects of space-time detection: we consider various detector structures, estimation of unknown parameters, and the impact of clutter heterogeneity on detection performance.

This work is a theoretical study of microwave photonic links which use optical frequency modulation (FM) and filter-slope discrimination for demodulation. The high modulation efficiency of optical FM devices is attractive for achieving low noise-figure links, but linear demodulation of the signals is also desired. In order to design discriminators which produce low distortion, this paper presents general full-signal and small-signal models of the effects of arbitrary optical filtering on an FM link, including the interaction between FM and residual IM. The small signal model is used to derive figures of merit for the linearity, noise and dynamic range of the FM links. The results of the models invalidate a common assumption: that linear FM to IM discrimination is possible. Instead, the discrimination must be from FM to amplitude modulation of the electric field for the link response to be linear. The linearity of links using two different sets of FIR discriminator filters, designed using the minimax relative error and maximally linear criteria, are compared in order to evaluate the better design method. The evolution of link linearity with filter order is studied. The analytical models are compared to numerical simulations of the filters and the models are found to be consistent. Finally, a Monte Carlo simulation is used to analyze the sensitivity to errors in fabrication for high-order filters realized in planar lightwave circuits (PLC) with a lattice filter architecture.

The most common hazard in forest is forest fire. Forest fires are as ancient as the forests themselves which destroy the forests, and can be a great threat to people who live in forests as well as wildlife. They pose a peril not only to the forest wealth but also to the entire regime utterly distressing the bio diversity, the ecology and the environment of a region. The present methods of detection of forest fire using satellite are widely considered to be scarce to foreknow the fires in the forest. Moreover, the satellite based methods of forest fire detection predict the forest fire only after the fire blowout uncontrollable and this method is considered to be futile to forecast the forest fire. Hence, a smart system is introduced which comprises of multiple classifiers to classify the forest fire attributes and fusion methods using Dezert-Smarandache theory, are considered to combine the data and to forecast the fire more accurately and effectively . The experimental results demonstrate the combined approach, which yields better accuracy in envisaging the forest fire.

The most common hazard in forest is forest fire. Forest fires are as ancient as the forests themselves which destroy the forests, and can be a great threat to people who live in forests as well as wildlife. They pose a peril not only to the forest wealth but also to the entire regime utterly distressing the bio diversity, the ecology and the environment of a region. The present methods of detection of forest fire using satellite are widely considered to be scarce to foreknow the fires in the forest. Moreover, the satellite based methods of forest fire detection predict the forest fire only after the fire blowout uncontrollable and this method is considered to be futile to forecast the forest fire. Hence, a smart system is introduced which comprises of multiple classifiers to classify the forest fire attributes and fusion methods using Dezert-Smarandache theory, are considered to combine the data and to forecast the fire more accurately and effectively . The experimental results demonstrate the combined approach, which yields better accuracy in envisaging the forest fire.

The most common hazard in forest is forest fire. Forest fires are as ancient as the forests themselves which destroy the forests, and can be a great threat to people who live in forests as well as wildlife. They pose a peril not only to the forest wealth but also to the entire regime utterly distressing the bio diversity, the ecology and the environment of a region. The present methods of detection of forest fire using satellite are widely considered to be scarce to foreknow the fires in the forest. Moreover, the satellite based methods of forest fire detection predict the forest fire only after the fire blowout uncontrollable and this method is considered to be futile to forecast the forest fire. Hence, a smart system is introduced which comprises of multiple classifiers to classify the forest fire attributes and fusion methods using Dezert-Smarandache theory, are considered to combine the data and to forecast the fire more accurately and effectively . The experimental results demonstrate the combined approach, which yields better accuracy in envisaging the forest fire.

This article is from PLoS ONE , volume 9 . Abstract The detection of evolving communities in dynamic complex networks is a challenging problem that recently received attention from the research community. Dynamics clearly add another complexity dimension to the difficult task of community detection. Methods should be able to detect changes in the network structure and produce a set of community structures corresponding to different timestamps and reflecting the evolution in time of network data. We propose a novel approach based on game theory elements and extremal optimization to address dynamic communities detection. Thus, the problem is formulated as a mathematical game in which nodes take the role of players that seek to choose a community that maximizes their profit viewed as a fitness function. Numerical results obtained for both synthetic and real-world networks illustrate the competitive performance of this game theoretical approach.

Reliable and consistent definition of analytical and intuitive processes have been difficult to establish. Often, the two processes have been defined by verbal properties such as fast or slow, conscious or nonconscious and require working memory or do not require working memory. An alternative, operational definition is provided by the Precise/Not Precise-model (PNP-model) which define the processes by modeling answer distributions (Sundh, Collsiöö, Millroth, & Juslin, 2021). It suggests that analytic responses result in a leptokurtic answer distribution as they rely on precise, deterministic processes. This means that if a normative strategy, such as a mental algorithm, is applied flawlessly it consistently leads to a normative answer. However, if one applies the wrong strategy or if a mistake occurs in the execution of the deterministic, analytical process, it can result in potentially large errors. On the other hand, intuitive processes rely on mental models of the state and function of the world which are learned from past experience. These mental models provide a range of more or less plausible answers which result in non-precise, gaussian response distribution. Many dual process accounts of intuition and analysis have addressed the interplay between analytical and intuitive processes (Kahneman, 2011; Evans & Stanovich, 2013; Frederick, 2005). The role of analytical processes has been described as a supervisory system to the more error-prone intuitive processes. That is, the analytical processes “intervene” when the “lower-order” intuitive processes fail to provide a normative solution (Evans & Stanovich, 2013). However, when viewing intuition as based on mental models that are shaped by passed experience, the role of intuitive processes could also be to evaluate the plausibility of the answer provided by the deterministic, analytic process. That is, the role of the intuitive process is to make sure that the solution reached by an analytic algorithm that is plausible given one’s past experience of the states of the world. If the result of an algorithm is implausible, another algorithm should be applied. When defining analytical processes as the application of content-independent logical or mathematical rules, the context should be irrelevant to reaching a correct solution when relying only on analytical processes. The intuitive process, on the other hand, uses contextual cues to suggest a range of plausible answers. This provides information that can be used either to evaluate answers suggested by a deterministic algorithm (i.e. does the answer fall within the range of plausible answers?) or to provide an (most often) approximately correct but non-precise answer. Consider for example the bat and ball-problem from the Cognitive Reflection Test (Frederick, 2005): A bat and a ball cost $1.1. A bat cost $1 more than a ball. How much does a ball cost? People mainly provide two answers to this problem: $.1 and $0.05. $.1 is the most common answer although it is wrong. The correct answer is $0.05. When applying the PNP-model, the two leptokurtic answer distributions tells us that the answers are mainly a result of analytical, deterministic thinking. Most people rely on an incorrect, but simpler, algorithm which we call the additive algorithm: 1 + x = 1.1. The normative solution is more advanced and require more numerical skills: (1 + x) + x = 1.1. In the original article, Frederick (2005) argued that the participants make the mistake because the additive solution is an intuitive answer where the answer “springs quickly to mind” and the analytical/reflective processes fail to intervene. However, from our perspective, the analytical processes fail as it relies on a faulty analytic algorithm (the additive algorithm) and the intuitive processes fail to “intervene”. We hypothesize that the intuitive supervision fails because the answer provided by the additive algorithm falls within the range of what is intuitively plausible. If one should alter the numbers in the bat and ball-problem so that the additive algorithm leads to an implausible answer, the intuitive supervisory process should detect this and initiate search for an algorithm that leads to a more plausible answer. There are two ways that the additive algorithm can be implausible. Consider the following example: A loaf of bread and a 2-liter coca cola cost $4.5 in total. The loaf of bread cost $0.5 more than two-liter coca cola. How much does 2-liter coca cola cost? If the additive algorithm is applied the coca cola would cost $4 (0.5 + x = 4.50). This is an implausible answer because (1) we may know from prior, real-world experience that a 2-liter coca cola usually does not cost that much (a contextual cue) and (2) the loaf of bread is supposed to cost more than the coca-cola, still the coca cola would cost $4 of the total of $4.5 for both products. This makes the additive algorithm mathematically implausible (mathematical cue). The plausibility of the additive algorithm as a solution to the bat and ball problem can therefore be varied by altering the difference of cost between the two products which provides numerical cues of its plausibility. Less relative difference in cost between products makes the additive algorithm mathematically less plausible. Furthermore, the absolute difference of the solutions provided by the normative algorithm and the additive algorithm can also be manipulated, although the additive algorithm always results in the double cost compared to the normative algorithm. In problems where the absolute difference of additive and normative algorithm is greater, the additive algorithm also seems less plausible. As the intuitive processes relies on contextual cues to judge the plausibility of a solution, it should be easier to detect the faulty additive algorithm when there are contextual cues available. The intuitive supervisory process can be regarded as a signal detection process. Its function is to detect signals that are implausible solutions, i.e. solutions that are not consistent with the mental models about states and functions of the world. In this study, we aim to apply the signal detection theory (SDT) to investigate the properties of the intuitive supervision system. We do this by showing multiple content-modified versions of the bat and ball-problem to the participants combined with a suggested solution (either a normative solution or the solution reached by the additive algorithm). The participants will have to judge if the solution provided is false or true. The participants will also provide a certainty rating for each answer that will be used to further investigate the participants’ sensitivity to detect incorrect solutions. SDT allows for a measurement of the participants ability to discriminate faulty and correct solutions that is not affected by response bias.

We propose a method to generate a warning system for the early detection of time clusters applied to public health surveillance data. This new method relies on the evaluation of a return period associated to any new count of a particular infection reported to a surveillance system. The method is applied to Salmonella surveillance in France and compared to the model developed by Farrington et al.

Distinguishing a dark matter interaction from an astrophysical neutrino-induced interaction will be major challenge for future direct dark matter searches. In this paper, we consider this issue within non-relativistic Effective Field Theory (EFT), which provides a well-motivated theoretical framework for determining nuclear responses to dark matter scattering events. We analyze the nuclear energy recoil spectra from the different dark matter-nucleon EFT operators, and compare to the nuclear recoil energy spectra that is predicted to be induced by astrophysical neutrino sources. We determine that for 11 of the 14 possible operators, the dark matter-induced recoil spectra can be cleanly distinguished from the corresponding neutrino-induced recoil spectra with moderate size detector technologies that are now being pursued, e.g., these operators would require 0.5 tonne years to be distinguished from the neutrino background for low mass dark matter. Our results imply that in most models detectors with good energy resolution will be able to distinguish a dark matter signal from a neutrino signal, without the need for much larger detectors that must rely on additional information from timing or direction.

Identifying the true theory of dark matter depends crucially on accurately characterizing interactions of dark matter (DM) with other species. In the context of DM direct detection, we present a study of the prospects for correctly identifying the low-energy effective DM-nucleus scattering operators connected to UV-complete models of DM-quark interactions. We take a census of plausible UV-complete interaction models with different low-energy leading-order DM-nuclear responses. For each model (corresponding to different spin-, momentum-, and velocity-dependent responses), we create a large number of realizations of recoil-energy spectra, and use Bayesian methods to investigate the probability that experiments will be able to select the correct scattering model within a broad set of competing scattering hypotheses. We conclude that agnostic analysis of a strong signal (such as Generation-2 would see if cross sections are just below the current limits) seen on xenon and germanium experiments is likely to correctly identify momentum dependence of the dominant response, ruling out models with either "heavy" or "light" mediators, and enabling downselection of allowed models. However, a unique determination of the correct UV completion will critically depend on the availability of measurements from a wider variety of nuclear targets, including iodine or fluorine. We investigate how model-selection prospects depend on the energy window available for the analysis. In addition, we discuss accuracy of the DM particle mass determination under a wide variety of scattering models, and investigate impact of the specific types of particle-physics uncertainties on prospects for model selection.

This study aims at analyzing whether being exposed to conspiracy theory content does have an effect on the extent to which individuals believe in conspiracy theories and whether this relationship can be explained through effectance motivation, anthropomorphism, and illusionary pattern detection.

This study aims at analyzing whether being exposed to conspiracy theory content does have an effect on the extent to which individuals believe in conspiracy theories and whether this relationship can be explained through effectance motivation, anthropomorphism, and illusionary pattern detection.

Helicopter Health Usage Monitoring Systems (HUMS) have potential for providing data to support increasing the service life of a dynamic mechanical component in the transmission of a helicopter. Data collected can demonstrate the HUMS condition indicator responds to a specific component fault with appropriate alert limits and minimal false alarms. Defining thresholds for specific faults requires a tradeoff between the sensitivity of the condition indicator (CI) limit to indicate damage and the number of false alarms. A method using Receiver Operating Characteristic (ROC) curves to assess CI performance was demonstrated using CI data collected from accelerometers installed on several UH60 Black Hawk and AH64 Apache helicopters and an AH64 helicopter component test stand. Results of the analysis indicate ROC curves can be used to reliably assess the performance of commercial HUMS condition indicators to detect damaged gears and bearings in a helicopter transmission.

Human labeled datasets, along with their corresponding evaluation algorithms, play an important role in boundary detection. We here present a psychophysical experiment that addresses the reliability of such benchmarks. To find better remedies to evaluate the performance of any boundary detection algorithm, we propose a computational framework to remove inappropriate human labels and estimate the intrinsic properties of boundaries.

Design of distributed order statistic constant false alarm rate (OS-CFAR) detection systems with data fusion was investigated. Its performance for different fusion rules and for a variety of nonhomogeneous backgrounds such as clutter edges and interfering targets was analyzed. Issues related to sampling and quantization in distributed detection systems were addressed. Sampling schemes for signal detection based on Ali-Silvey distance measures were derive. Performance enhancement over uniform sampling was shown. A number of collaborative research projects with Rome Laboratory engineers were carried out. The most notable one was the development of a prototype of an expert system CFAR (ES-CFAR) processor. This processor intelligently selects the CFAR algorithm based upon the observed characteristics of the environment. Substantial performance improvement over a conventional CFAR processor was demonstrated. Distributed detection, Data fusion, Detection theory.

Proliferation of WMD, especially biological warfare (BW) weapons, continues apace in today's world, both by nation states and terrorist groups. BW weapons may provide these players a capability which is as potentially devastating as a nuclear weapon at a much lower cost. They will continue to threaten regional stability and may be the primary strategic, as well as terrorist, threat against the United States in the future. An effective and credible biological defense program will minimize the impact of biological weapons in the conduct of U.S. military operations. This goal will be achieved only when our forces have real time detection and identification of BW attack. This paper details the progress made in BW agent detection in the six years since the Gulf War. With two new systems, we have the technology to provide the combatant commander with a credible bio-detection array. What we have failed to do in the near term is to supply the requisite force structure in the Active Component to make the technology work for the commander.

A review of detection threshold concepts is followed by an investigation into published theory for the detection of signals in noise. A set of empirical formulae relating minimum detectable signal to some basic sonar system parameters is presented. The formulae are compared and a recommendation made as to which is the most useful for the calculation of an omnidirectional narrowband lofargram minimum detectable signal for power detection of sinusoidal signals in Gaussian noise.

For more details, see the University of Florida George A. Smathers library catalog: http://uf.catalog.fcla.edu/permalink.jsp?20UF030478119

In this study, we try to develop a method for detecting cheating in the laboratory based on signal detection theory. Signal detection theory is a famous and well-studied theory (e.g., Tanner & Swets, 1954) that is used to test an ability to discriminate signal and noise. We will create two different environments to discuss whether individuals cheat by comparing their sensitivity indices (See more details in “Data collection procedures”).

This paper investigates the detection of information hidden by the Least Significant Bit (LSB) matching scheme. In a theoretical context of known image media parameters, two important results are presented. First, the use of hypothesis testing theory allows us to design the Most Powerful (MP) test. Second, a study of the MP test gives us the opportunity to analytically calculate its statistical performance in order to warrant a given probability of false-alarm. In practice when detecting LSB matching, the unknown image parameters have to be estimated. Based on the local estimator used in the Weighted Stego-image (WS) detector, a practical test is presented. A numerical comparison with state-of-the-art detectors shows the good performance of the proposed tests and highlights the relevance of the proposed methodology.

This paper investigates the detection of information hidden by the Least Significant Bit (LSB) matching scheme. In a theoretical context of known image media parameters, two important results are presented. First, the use of hypothesis testing theory allows us to design the Most Powerful (MP) test. Second, a study of the MP test gives us the opportunity to analytically calculate its statistical performance in order to warrant a given probability of false-alarm. In practice when detecting LSB matching, the unknown image parameters have to be estimated. Based on the local estimator used in the Weighted Stego-image (WS) detector, a practical test is presented. A numerical comparison with state-of-the-art detectors shows the good performance of the proposed tests and highlights the relevance of the proposed methodology.

In this work, we derive the nuclear form factor for the spin-independent collision between the WIMPs and nucleus in terms of the relativistic mean field (RMF) theory. Comparison with the traditional form factors which are commonly used in literature is given and it is found that our results are slightly above that of the 2PF model by 4% to 8%, but deviate from the Helm form factor by 15% to 25% for the whole recoil energy spectrum of 0$\sim $ 100 keV. Moreover, taking Xe and Ge as examples, we show the dependence of the form factor on the recoil energy.

We propose an Euclidean geometric representation for the classical detection theory. The proposed representation is so generic that can be employed to almost all communication problems. The hypotheses and observations are mapped into R^N in such a way that a posteriori probability of an hypothesis given an observation decreases exponentially with the square of the Euclidean distance between the vectors corresponding to the hypothesis and the observation.

In quantum detection theory, the optimum detection operators must commute; admitting simultaneous approximate measurement of noncommuting observables cannot yield a lower Bayes cost. In addition, the lower bounds on mean square errors of parameter estimates, predicted by the quantum mechanical Cramer-Rao inequality, cannot be reduced by such means.

In quantum detection theory, the optimum detection operators must commute; admitting simultaneous approximate measurement of noncommuting observables cannot yield a lower Bayes cost. In addition, the lower bounds on mean square errors of parameter estimates, predicted by the quantum mechanical Cramer-Rao inequality, cannot be reduced by such means.

This cross-sectional study aims to improve our understanding of the overconfidence in errors bias by addressing some methodological limitations of previous studies. Our study has introduced a continuous confidence scale to allow a more detailed analysis of confidence calibration using type 2 Signal Detection Theory. Findings from this study may help address whether the pattern referred to as overconfidence in errors bias might be better explained by participants being unable to tell when they are confident or not, also known as a resolution issue.

This cross-sectional study aims to improve our understanding of the overconfidence in errors bias by addressing some methodological limitations of previous studies. Our study has introduced a continuous confidence scale to allow a more detailed analysis of confidence calibration using type 2 Signal Detection Theory. Findings from this study may help address whether the pattern referred to as overconfidence in errors bias might be better explained by participants being unable to tell when they are confident or not, also known as a resolution issue.

This cross-sectional study aims to improve our understanding of the overconfidence in errors bias by addressing some methodological limitations of previous studies. Our study has introduced a continuous confidence scale to allow a more detailed analysis of confidence calibration using type 2 Signal Detection Theory. Findings from this study may help address whether the pattern referred to as overconfidence in errors bias might be better explained by participants being unable to tell when they are confident or not, also known as a resolution issue.

We propose a novel statistical method for detection of objects in noisy images. The method uses results from percolation and random graph theories. We present an algorithm that allows to detect objects of unknown shapes in the presence of nonparametric noise of unknown level. The noise density is assumed to be unknown and can be very irregular. Our procedure substantially differs from wavelets-based algorithms. The algorithm has linear complexity and exponential accuracy and is appropriate for real-time systems. We prove results on consistency and algorithmic complexity of our procedure.

We propose a novel statistical method for detection of objects in noisy images. The method uses results from percolation and random graph theories. We present an algorithm that allows to detect objects of unknown shapes in the presence of nonparametric noise of unknown level. The noise density is assumed to be unknown and can be very irregular. Our procedure substantially differs from wavelets-based algorithms. The algorithm has linear complexity and exponential accuracy and is appropriate for real-time systems. We prove results on consistency and algorithmic complexity of our procedure.

This paper presents a review of state-of-art in the Magnetic Flux Leakage (MFL) sensor technology, which plays an important role in Nondestructive Testing (NDT) to detect crack and corrosion in ferromagnetic material. The demand of more reliable MFL tools and signal acquisition increase as it has a direct impact on structure integrity and can lead to be major catastrophic upon questionable signal analysis. This is because the size, cost, efficiency, and reliability of the extensive MFL system for NDT applications primarily depend on signal acquisition as a qualitative measure in producing a trustworthy analysis. Therefore, the selection of appropriate tools and methodology plays a major role in determining the comprehensive performance of the system. This paper also reviews an Artificial Neural Network (ANN) and Finite Element Method (FEM) in developing an optimum permeability standard on the test piece.

This paper presents a review of state-of-art in the Magnetic Flux Leakage (MFL) sensor technology, which plays an important role in Nondestructive Testing (NDT) to detect crack and corrosion in ferromagnetic material. The demand of more reliable MFL tools and signal acquisition increase as it has a direct impact on structure integrity and can lead to be major catastrophic upon questionable signal analysis. This is because the size, cost, efficiency, and reliability of the extensive MFL system for NDT applications primarily depend on signal acquisition as a qualitative measure in producing a trustworthy analysis. Therefore, the selection of appropriate tools and methodology plays a major role in determining the comprehensive performance of the system. This paper also reviews an Artificial Neural Network (ANN) and Finite Element Method (FEM) in developing an optimum permeability standard on the test piece.

This paper presents a review of state-of-art in the Magnetic Flux Leakage (MFL) sensor technology, which plays an important role in Nondestructive Testing (NDT) to detect crack and corrosion in ferromagnetic material. The demand of more reliable MFL tools and signal acquisition increase as it has a direct impact on structure integrity and can lead to be major catastrophic upon questionable signal analysis. This is because the size, cost, efficiency, and reliability of the extensive MFL system for NDT applications primarily depend on signal acquisition as a qualitative measure in producing a trustworthy analysis. Therefore, the selection of appropriate tools and methodology plays a major role in determining the comprehensive performance of the system. This paper also reviews an Artificial Neural Network (ANN) and Finite Element Method (FEM) in developing an optimum permeability standard on the test piece.

This paper presents a review of state-of-art in the Magnetic Flux Leakage (MFL) sensor technology, which plays an important role in Nondestructive Testing (NDT) to detect crack and corrosion in ferromagnetic material. The demand of more reliable MFL tools and signal acquisition increase as it has a direct impact on structure integrity and can lead to be major catastrophic upon questionable signal analysis. This is because the size, cost, efficiency, and reliability of the extensive MFL system for NDT applications primarily depend on signal acquisition as a qualitative measure in producing a trustworthy analysis. Therefore, the selection of appropriate tools and methodology plays a major role in determining the comprehensive performance of the system. This paper also reviews an Artificial Neural Network (ANN) and Finite Element Method (FEM) in developing an optimum permeability standard on the test piece.

This paper presents a review of state-of-art in the Magnetic Flux Leakage (MFL) sensor technology, which plays an important role in Nondestructive Testing (NDT) to detect crack and corrosion in ferromagnetic material. The demand of more reliable MFL tools and signal acquisition increase as it has a direct impact on structure integrity and can lead to be major catastrophic upon questionable signal analysis. This is because the size, cost, efficiency, and reliability of the extensive MFL system for NDT applications primarily depend on signal acquisition as a qualitative measure in producing a trustworthy analysis. Therefore, the selection of appropriate tools and methodology plays a major role in determining the comprehensive performance of the system. This paper also reviews an Artificial Neural Network (ANN) and Finite Element Method (FEM) in developing an optimum permeability standard on the test piece.

One of the great attractions of minimal super-unified supersymmetric models is the prediction of a massive, stable, weakly interacting particle (the lightest supersymmetric partner, LSP) which can have the right relic abundance to be a cold dark matter candidate. In this paper we investigate the identity, mass, and properties of the LSP after requiring gauge coupling unification, proper electroweak symmetry breaking, and numerous phenomenological constraints. We then discuss the prospects for detecting the LSP from (1) LSP annihilations into positrons, anti-protons, and gamma rays in the galactic halo, (2) large underground arrays to detect upward going muons arising from LSP capture and annihilation in the sun and earth, (3) elastic collisions on matter in a table top apparatus, and (4) production of LSPs or decays into LSPs at high energy colliders. Our conclusions are that space annihilation experiments and large underground detectors are of limited help in initially detecting the LSP although perhaps they could provide confirmation of a signal seen in other experiments, while table top detectors have considerable discovery potential. Colliders, however, might be the best dark matter detectors of all.

The book deals with the basic aspects of the statistical theory of detecting radar signals and measuring their parameters in the presence of Gaussian interferences, gives the theory and the principles of designing devices with optimum detection and measuring as well as a great number of examples permitting the reader to familiarize himself more quickly with the main problems of the theory and its applications.

Terrorist actions are often preceded by preparatory activities that are at least slightly anomalous compared to the activities of the general population (such as unusual purchasing patterns). Analysis of patterns of such anomalies will detect potential preparations for terrorist activity. However, each such anomaly is what is known technically as a fuzzy signal : each anomaly taken individually might indicate terrorist preparations, but usually does not. This paper describes the hypothetical computer system FUSEDOT (FUzzy Signal Expert system for the Detection Of Terrorism preparations). As the name suggests, FUSEDOT applies artificial intelligence expert system technology to the fuzzy signals presented by certain anomalous data, such as interpersonal relationships, financial relationships, travel patterns, purchasing patterns, patterns of Internet usage, and personal background. The components of the system data acquisition, data processing, report generation, and feedback and further system development are described in outline. System limitations are described. FUSEDOT is compared with other systems for the analysis of massive amounts of data to detect terrorism, specifically the Novel Intelligence from Massive Data program, and the Terrorist Information Awareness program. In comparison with these programs, FUSEDOT will implement some superior technology, will be easier to develop, and poses fewer privacy concerns. NATO member nations would do well to consider the development of FUSEDOT, which can be a valuable asset in the defence against terrorism.

This article is from Frontiers in Psychology , volume 5 . Abstract The paradigm of Signal Detection Theory (SDT) was used to analyze the ability of professional pianists (N = 16) and string players (N = 15) to discriminate small F0 differences between consecutive musical tones, presented in pairs, with identical and with different (bright and dull) timbres. The sensitivity (d′) and response bias (c) were heavily dependent on the timbral arrangement of the pairs of tones (the “comparable tones”), which can be interpreted as the influence of timbre-induced pitch shift on F0 discrimination. The participants were somewhat biased to “miss” signals when comparable tones had identical timbres and to make “false alarms” when the tones had different timbres. The d′ was lowest when the tones with a lower F0 in those stimulus-pairs containing tones with different timbres had a brighter timber, and highest when both tones had bright timbre. On average, the string players had a somewhat higher d′ and their perception was slightly less influenced by timbre-induced pitch shift when compared to the pianists. Nevertheless, the dependence of d′ and c on the timbral arrangement of the tones was registered in the case of all the participants at all the investigated pitch regions around D#3, D4, and C#5. Furthermore, the presence of a silence of 3.5 s—a silence interval—between the tones to be compared had an impact on both d′- and c-values as well as on the degree of vulnerability to timbre-induced pitch shift.

The search for Tactical Ballistic Missile (TBM) launchers is modeled mathematically using the Random Search Model. Special provisions are made in the model to account for the fact that the launcher is not always exposed to detection by the searcher. The probability that the launcher is exposed to detection is assumed to be both deterministic and stochastic. The tactical implications of the results of the model are discussed and several methods are provided to improve search performance based on an analysis of the model parameters