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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...
Hardware implementation of machine learning methods is often considered challenging, however, it brings major benefits, such as speed of operation and energy efficiency. Having in mind these benefits, we evaluate the performance and complexity of four probability density estimators (PDE) that we consider interesting for hardware implementation of the probabilistic neural network (PNN). We report results...
Most Wi-Fi based localization algorithms are cooperative as user device is required to associate with an AP. However, user may not associate with AP in scenarios such as supermarkets which calls for non-cooperative localization. In this paper, the probe request (PR) frame sent by device is analyzed and the weighted kernel density estimation assisted Bayes (w-KAB) algorithm is utilized for localization...
The problem of probability density function reconstruction from statistical moments has been studied since 19th century. However, there has not been an attempt toapply the moments directly to classification problems. In thismanuscript, we proposed a classification method of orthogonalmoments for classification of two-dimensional data. We comparedthe proposed moment-based classifier with a well-known...
We review recent theoretical results in maximum entropy (MaxEnt) PDF projection that provide a theoretical framework for fusing the information from multiple features for the purpose of general statistical inference. Given a high-dimensional input data vector x, and several dimension-reducing feature transformations zi = Ti(x), we consider the problem of estimating the probability density function...
Survival information potential (SIP) is defined by the survival distribution function instead of the probability density function (PDF) of a random variable. SIP can be used as a risk function equipped with learning error compensation ability while this SIP based risk function does not involve the estimation of PDF. This is desirable for a robust learning application in view of the error compensation...
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...
This paper proposes a new land cover mapping algorithm that combines the strengths of random forest (RF) with a Markov random field (MRF) model. The idea is to transform the observed data into the decision domain of weak classifiers inside an RF. Due to how RF are trained, these decisions can be considered to be independent from each others, and therefore the joint probability density function in...
This paper explains the way of unification of flame and smoke detection algorithms by merging the common steps into a single processing flow. Scenario, discussed in the current manuscript, considers using fixed surveillance cameras that allows using background subtraction to detect changes in a scene. Due to imperfection of background subtraction, foreground pixels, belonging to the same real object,...
This report proposes a detection method based on using kurtosis and maximum amplitude information, both extracted from the spectral domain. At the end we face a classification problem which is solved by introducing a linear decision boundary. This new method is proved to result into overall good and stable detection results.
We experience changes in stationarity/time variance in many practical applications. Since changes modify the operational framework the application is working with, its accuracy performance is in turn affected. When changes can occur, we need to detect them as soon as possible, in general by inspecting features extracted from data, and afterwards intervene to mitigate their effects. In this paper,...
In this paper, the generalized two-dimensional Fisher's linear discriminant (G-2DFLD) method is extended by incorporating Gaussian probability distribution information into the definition of the between-class and within-class scatter matrices to develop a novel Gaussian probabilistic generalized two-dimensional linear discriminant analysis (GPG-2DLDA). A Gaussian probability density function (pdf)...
This work investigates the design scheme of orthogonal frequency division multiplexing (OFDM) training sequences with low peak to average power ratio (PAPR) under spectral constraints. Sequences with low PAPR can be used for synchronization and channel estimation in communication systems. In practical systems, there are always spectral constraints such as the reserved tones and DC-offset subcarriers...
Detecting abnormal usages in intelligent systems, especially in smart home systems, is an important task. By exploring log data, useful information and patterns can be discovered which may help users/organizations to better understand the usage of their appliances and to distinguish unnecessary usages as well as abnormal problems which can cause waste, damages, or even fire. This work proposes several...
Despite the rapidly growing interest, progress in the study of relations between physiological abnormalities and mental disorders is hampered by complexity of the human brain and high costs of data collection. The complexity can be captured by deep learning approaches, but they still may require significant amounts of data. In this paper, we seek to mitigate the latter challenge by developing a generator...
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...
Interstitial cells of Cajal (ICC) play a central role in coordinating normal gastrointestinal (GI) motility. Depletion of ICC numbers and network integrity contributes to major functional GI motility disorders. However, the mechanisms relating ICC structure to GI function and dysfunction remains unclear, partly because there is a lack of large-scale ICC network imaging data across a spectrum of depletion...
In this paper an artificial intelligence based framework for fault detection and diagnosis to support supervision of the cardboard production is presented. Cutting accuracy significantly affects the quality of the product and because there are many different causes of errors, their identification requires a sound knowledge and experience of the service staff. The authors observed that the sources...
A non-parametric probability density function (pdf) estimation technique is presented. The estimation consists in approximating the unknown pdf by a network of Gaussian Radial Basis Functions (GRBFs). Complexity analysis is introduced in order to select the optimal number of GRBFs. Results obtained on real data show the potentiality of this technique.
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...
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