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In this paper, we propose an adaptive framework for the variable step size of the fractional least mean square (FLMS) algorithm. The proposed algorithm named the robust variable step size-FLMS (RVSS-FLMS), dynamically updates the step size of the FLMS to achieve high convergence rate with low steady state error. For the evaluation purpose, the problem of system identification is considered. The experiments...
Rolling element bearings play an important role in ensuring the availability of industrial machines. Unexpected bearing failures in such machines during field operation can lead to machine breakdown, which may have some pretty severe implications. However, the insufficiency of labeled samples is major problem for handling fault diagnosis problem. To address such concern, we propose a semi-supervised...
In this work, we develop a robust controller based on adaptive sliding mode strategy to control the position of electro-pneumatic system. The Sliding Mode Control (SMC) is renowned for its precision, robustness against uncertainties and disturbance. However, it suffers from chattering phenomenon. To overcome it, we substitute the discontinuous term of SMC by a proportional derivative term and to improve...
Fault estimation is considered as one of the most essential problem in diagnosis domain. In this paper, a simultaneous state and fault estimation is treated for state time delay system. The fault estimation technique is based on H∞ optimization method to addressed the subject of robustness in the presence of disturbance and uncertainties. The fault estimation is made by using the Lyapunov Krasovskii...
The traditional random sample consensus (RANSAC) algorithm is capable of estimating a model with fewer data points and almost unaffected by noise. There are several drawbacks of such algorithm including detection errors, unstable threshold and massive calculation. By analyzing the spatial relations of graphics pixels, a hypothetical circle is firstly formed with three hypothetical points which are...
Despite of having no explicit shape model, multi-atlas approaches to image segmentation have proved to be a top-performer for several diverse datasets and imaging modalities. In this paper, we show how one can directly incorporate shape regularization into the multi-atlas framework. Unlike traditional methods, our proposed approach does not rely on label fusion on the voxel level. Instead, each registered...
Representation-based classifiers (RCs) including sparse RC (SRC) have attracted intensive interest in pattern recognition in recent years. In our previous work, we have proposed a general framework called atomic representation-based classifier (ARC) including many popular RCs as special cases. Despite the empirical success, ARC and conventional RCs utilize the mean square error (MSE) criterion and...
We propose a novel background subtraction method for robust region extraction of moving objects in the dynamic background. In our method, a set of recently observed frame (reference) images is used as a background model. To withstand the constant fluctuations of background appearance, a current frame (input) image is compared with every reference image, and pixels in the input image deviated from...
The presented work proposes a simple feature extraction technique which is designed for robust detection of event related potentials (ERP). This technique was tested to detect the N400 which is an ERP generally associated with recall. The chief advantages of the proposed technique are that it is robust to different ocular artifacts and yet sensitive to event related potentials. Further each signal...
Spectral analysis of neighborhood graphs is one of the most widely used techniques for exploratory data analysis, with applications ranging from machine learning to social sciences. In such applications, it is typical to first encode relationships between the data samples using an appropriate similarity function. Popular neighborhood construction techniques such as k-nearest neighbor (k-NN) graphs...
Building Radiation Hybrid (RH) maps is a challenging process. Traditional RH mapping techniques are very time consuming, and do not work well on noisy datasets. In this presented research, we propose a new approach that uses resampling technique with consensus clustering technique to filter out unreliable markers, and build robust RH maps in a short time. The main aims of using the proposed approach...
Outlier detection algorithms are often computationally intensive because of their need to score each point in the data. Even simple distance-based algorithms have quadratic complexity. High-dimensional outlier detection algorithms such as subspace methods are often even more computationally intensive because of their need to explore different subspaces of the data. In this paper, we propose an exceedingly...
Skypatterns are an elegant answer to the pattern explosion issue, when a set of measures can be provided. Skypatterns for all possible measure combinations can be explored thanks to recent work on the skypattern cube. However, this leads to too many skypatterns, where it is difficult to quickly identify which ones are more important. First, we introduce a new notion of pattern steadiness which measures...
Fuzzy density-based clustering has been a challenge. Research has been focused on fuzzyfying the DBSCAN algorithm. Different methods have been proposed that use a fuzzy definition of core points within the DBSCAN algorithm. Our approach adapts the membership degree calculation known from fuzzy c-means by replacing the need for a distinguished centroid point by a more general cluster skeleton. These...
Identifying influential spreaders in online social networks (OSNs) has long been an important but difficult problem to be addressed. Distinguished from previous works that mainly focused on the stationary features of users' influence, we systematically study the variations of users' spreading capability given the fact that influential spreaders are more likely to be the targets of various cyber attacks...
A series of online multi-task learning (OMTL) algorithms have been proposed to avoid the expensive training cost and poor adaptability of traditional batch multi-task learning (MTL) algorithms in recent years. However, these OMTL algorithms usually assume that all tasks are closely related, which may not hold in practical scenarios. More importantly, their theoretical reliability is weakened due to...
In fingerprint recognition system, minutiae-based matching algorithms are most intensively researched. However, in most minutia-based methods, the similarity score is given based on the main score of matched minutiae. And the boosted information is not effectively used in the final similarity score computation. Based on the observation, we extract several features as the supplementary scores. And...
This paper proposes an inaudible and robust audio-information-hiding scheme based on the singular-spectrum analysis (SSA) and a psychoacoustic model. SSA is used to decompose the host signals into several additive oscillatory components. The hidden information is embedded into the host signals by modifying amplitudes of some oscillatory components. To satisfy the inaudibility, we propose a novel method...
Robust vessel segmentation of fundus images is of great interest for better diagnosis of many diseases like diabetic retinopathy, retinopathy of prematurity, vein occlusions and so on. In this paper, we propose a novel example-based vessel segmentation method, based on learning the mapping relationship between fundus images and their corresponding ground truths. Firstly, the training images and their...
We investigate resilient consensus in a network of integer-valued agents in the presence of malicious agents. The goal of the healthy, normal agents is to form consensus in their state values, which may be disturbed by the non-normal, malicious agents. In this paper, we study update rules of the agents having asynchrony in the update times and also interagent communications with non-uniform, time...
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