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Detection with multiple distributions is considered. Rather than formulating the problem with multiple hypotheses, we formulate the problem in a binary hypothesis testing framework by a multiple model approach. Three classes of the Multi-Model Detection (MMD) problems are considered: simplex, compound, and mixture. Three concepts of optimality are given for these three problems, including Uniformly...
The electric strength level of the electronics is determined by serial impact of the EOS with increasing amplitude. EOS impact repeats until functional or parametric failure is registered. For the test it is important to determine the electric strength level with acceptable accuracy. This requires the decrease of the amplitude increment coefficient. However, decreasing the value of the coefficient...
This paper studies the problem of classifying some Gaussian samples into one of two parametric probabilistic models, also called sources, when the parameter and the a priori probability of each source are unknown. Each source is governed by an univariate normal distribution whose mean is unknown. A training sequence is available for each source in order to compensate the lack of prior information...
Atrial fibrillation (AF) is one of the most common cardiac arrhythmia and effects nearly 1–2 of every 100 persons of the population. This paper evaluates the effectiveness of Machine Learning (ML) approach to detect AF episodes. Features, determined exclusively on the basis of beat intervals, are classified with linear classifier. Performances of the proposed approach are evaluated by means of the...
Systems based on PUFs derive secrets from physical variation and it is difficult to measure the security level of the obtained PUF response bits in practice. We evaluate raw PUF data to assess the quality of the physical source to detect undesired imperfections in the circuit to provide feedback for the PUF designer and improve the achieved security level. Complementing previous work on correlations...
This paper addresses the problem of detecting the presence of a complex-valued, possibly improper, but unknown signal, common among two or more sensors (channels) in the presence of spatially independent, unknown, possibly improper and colored, noise. Past work on this problem is limited to signals observed in proper noise. A source of improper noise is IQ imbalance during down-conversion of bandpass...
A reconfigurable over-the-air chamber represents a reverberation chamber whose walls are lined with antennas that are terminated in reconfigurable impedances, allowing synthesis of a wide range of channel conditions for over-the-air testing of mobile wireless devices. While these chambers have potential for practical device testing, finding the right impedances to achieve the desired channel characteristics...
This paper proposes a practical implementation for the generation of real-time K-Distributed correlated sea-clutter in firmware. The method uses a dual cumulative distribution function (CDF) based look-up method to transpose a complex uniformly distributed random variable (RV) to the required RV. The clutter is correlated by means of a filter process before translation, and it is shown that this technique...
As of today, diagnosis of ADHD is highly dependent on subjective observations, which has motivated researchers to investigate quantitative methods for the discrimination of ADHD and Non-ADHD subjects using EEG data. The goal of the effort reported here is to classify subjects with high accuracy, as well as to do so based on a select few channels. By making use of AR model features, several classifiers...
A statistical approach is proposed to detect parametric fault in linear and weakly non-linear analog circuits by mapping faults to a statistical metric, Bhattacharyya distance which is measured from the Probability Density Function (PDF) of the outputs. The non-Gaussian Auto-regressive (AR) model is used to estimate the PDF. To validate the proposed statistical approach, we have simulated two benchmark...
In this paper we develop statistical detection theory for graph signals. In particular, given two graphs, namely, a background graph that represents an usual activity and an alternative graph that represents some unusual activity, we are interested in answering the following question: To which of the two graphs does the observed graph signal fit the best? To begin with, we assume both the graphs are...
Deep convolutional neural networks (DCNN's) have shown great value in approaching highly challenging problems in image classification. Based on the successes of DCNNs in scene classification and object detection and localization it is natural to consider whether they would be effective for much simpler computer vision tasks. Our work involves the application of a DCNN to the relatively simple task...
The least squares density difference change detection test (LSDD-CDT) has proven to be an effective method in detecting concept drift by inspecting features derived from the discrepancy between two probability density functions (pdfs). The first pdf is associated with the concept drift free case, the second to the possible post change one. Interestingly, the method permits to control the ratio of...
A general MOPSO algorithm was applied to ZDT1-4. Bias in the archive solutions was observed in the initialisation of the archive solutions. The bias continued until simulation end because a general MOPSO algorithm does not contain any explicit way to correct bias in its archive. Pareto dominance testing was discovered to be a main contributor to the bias. Bias was also introduced by the target's problem...
The problem of detecting a change in distribution of a sequence of independent and identically distributed (IID) random variables is addressed. Unlike previous approaches to sequential change detection, which assume a known initial probability density function (PDF) for the sequence, in this paper we address the case where the initial distribution of the sequence is unknown. An optimal stopping approach...
We consider a distributed composite hypothesis testing problem in which sensor nodes share a collision channel to send their decisions and the fusion center (FC) has a limited time to collect these decisions. When the FC does not have enough time to collect all local decisions successfully, we propose a transmission protocol called sensor censoring random access as the multiple access scheme used...
We give a general unified method that can be used for L1 closeness testing of a wide range of university structured distribution families. More specifically, we design a sample optimal and computationally efficient algorithm for testing the equivalence of two unknown (potentially arbitrary) university distributions under the Ak-distance metric: Given sample access to distributions with density functions...
In industry, embedded systems continue to become even more complex. The development is done increasingly in a virtual environment to accelerate the prototyping and the implementation. Independent from this fact, testing is still the essential activity for verification and validation and obtaining confidence in the quality and reliability of the product. The main driver behind innovation is software,...
Taxicab demand discovering is one of the most fundamental issues of taxicab services. Most of the regions in one city suffer the demand and supply disequilibrium problem. It causes the difficulty in scheduling taxicabs for taxicab companies. It will be solved by modeling the regional demand of taxicabs by using trajectory data. In this paper, we propose a method to model regional taxicab demand. Firstly,...
Generating large amounts of semantically-rich data for testing big data workflows is paramount for scalable performance benchmarking and quality assurance in modern machine-learning and analytics workloads. The most obvious use case for such a generative algorithm is in conjunction with a big data application blueprint, which can be used by developers (to test their emerging big data solutions) as...
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