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This paper presents that support vector machine (SVM) is used to classify three gait patterns: level walking, stair ascent and stair descent based on ground reaction force (GRF). The recognition process consists of three stages: i) a three layers wavelet packet analysis is used for feature extraction, with which squared and standard deviation of decomposition coefficients compose features; ii) with...
Most Online Social Networks (OSNs) allow registered members to leave comments on particular entities. An entity can either be a person, a location, or a product. These comments have already become an important reference for many people in the daily life. However, a popular entity usually receives an extensive number of comments and it has become infeasible for users to read through all of them. In...
Spontaneous reporting systems of adverse drug events have been widely established in many countries to collect as could as possible all adverse drug events to facilitate the detection of suspected ADR signals via some statistical or data mining methods. Unfortunately, due to privacy concern or other reasons, the reporters sometimes may omit consciously some attributes, causing many missing values...
Approximate computing explores opportunities that emerge when applications can tolerate error or inexactness. These applications, which range from multimedia processing to machine learning, operate on inherently noisy and imprecise data. We can trade-off some loss in output value integrity for improved processor performance and energy-efficiency. As memory accesses consume substantial latency and...
Nearest neighbour search is a core process in many data mining algorithms. Finding reliable closest matches of a query in a high dimensional space is still a challenging task. This is because the effectiveness of many dissimilarity measures, that are based on a geometric model, such as lp-norm, decreases as the number of dimensions increases. In this paper, we examine how the data distribution can...
Signatures are the single most widely used method of identifying an individual but they carry with them an alarmingly significant number of vulnerabilities, implying the need for an effective and robust method of precisely identifying an individual's signature. The signature of an individual is visually acquired by using a pen-based tracking system [1], [2]. This paper considers the possibility of...
In this paper, we propose a novel method for fingerprint indexing based on local patterns of ridge flow centered on minutiae. These local descriptors are projected on a learned dictionary of ridge flow patches, with a sparsity-inducing algorithm. We show that this sparse decomposition allows to replace the ridge flow patches by a compressed signature with a reduced loss of accuracy. We experimented...
Data reduction as a critical step in the process of data pre-processing presents a central point of interest across a wide variety of fields. Data pre-processing has a significant impact on the performance of any machine learning algorithm. In this context, we focus our research paper on investigating the data pre-processing phase of a recent evolutionary algorithm named the Dendritic Cell Algorithm...
This paper describes the methodology for implementation of artificial neural networks with adaptable parameters (weights, connections, number of neurons) on fixed-point embedded systems. Components of neuron unit and interconnecting matrix are discussed. Particular example of implementation on PIC18F46K80 is given. Results are discussed in appropriate part.
The Canny algorithm has been extensively adopted to perform edge detection in images. The Derivative of Gaussian (DoG) proposed by Canny has been shown to be the optimal edge detector to compute the image gradient due to its robustness to noise. However, the DoG has some important drawbacks in relation to images with thin edges of a few pixels width and junctions. The excessive blurring provided by...
This research analyzed the use of daubechies wavelet as a feature extraction and confusion matrix as the principal parameter of accuracy percentage level in neural network. Detection process began with image pre-processing, lung area segmentation, feature extraction, and training phase. Classifications of the system output consisted of normal lung, pleural effusion, and pulmonary tuberculosis. Seventy...
Cross validation method has been widely used for estimation of perfomance of classifiers and statistical method. However, compared with other resampling methods, cross-validation has not yet theoretically investigated since the sampling scheme is based not only on stochasticity but also on set-based processing. This paper proposes a new framework for evaluation of cross-validation methods based on...
Rule extraction is an important issue in data mining and knowledge discovery. The effective computation of rule extraction has a direct bearing on the efficiency of knowledge acquisition. In data mining and machine learning tasks, some of the irrelevant attributes not only influence the performance of rule extraction algorithms but also decrease classification accuracy. To acquire brief decision rules,...
Feature subset selection, as an important processing step to knowledge discovery and machine learning, is effective method in reducing irrelevant and or redundant features, compressing repeated data, and improving classification accuracy. Rough set theory is an important tool to select feature subset from high-dimensional data. In this work, feature subset selection based on fuzzy rough set is introduced,...
A texture descriptor based on a set of indices of degrees of local approximating polynomials is proposed in this paper. An image is split into non-overlapping patches, reshaped into one-dimensional source vectors and convolved with the polynomial approximation kernels of various degrees p. As a result, a set of approximations is obtained. For each element of the source vector, these approximations...
For typical indoor positioning systems employing a training/positioning model based on Wi-Fi fingerprints, significant training costs extremely restrict this kind of indoor localization system to be widely deployed and implemented with real location based applications. In this paper, we present a crowd-based approach to solve this problem, which automatically collects and constructs fingerprints database...
In this paper we present a higher order moment-matching algorithm for computing the distribution parameters of nonlinear transformation random variables. The new algorithm has two distinct aspects compared to the standard Unscented Kalman Filter (UKF). First, the sigma points are computed in two steps using the covariance matrix and higher-order moments. Second, the associated weights are positive...
In this paper, we consider the localization problem on wireless sensor networks in indoor environments, in the case when low cost ranging techniques are used. Each sensor node seeks to estimate its local map (i.e., its own position and that of the sensor nodes in its neighborhood) by collecting noisy measurements of the received signal strength indicator (RSSI) from packets sent by its neighbors....
Industrial applications often require processing data with large dynamic ranges at low sample rates. As algorithms become more complex, handling the data range of variables required for fixed-point implementations becomes time consuming, and can also lead to inefficient designs. Floating-point solutions leverage these limitations trading automatic data range handling for a usually higher implementation...
The role of Numerical Integration in the evaluation of definite improper integrals is being increasingly appreciated as there are no simple analytical results available. In this paper the authors explore four such quadrature formulae and their performance in evaluating Logarithmic integrals, a class of definite improper integrals and one of the important integrals in Number Theory. The performance...
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