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Automated ECG signal processing can assist in diagnosing several heart diseases. Many R peak detection methods have been studied because the accuracy of R peak detection significantly affects the quality of subsequent ECG feature extraction. Two important steps in R peak detection algorithm that draw attention over researchers are the preprocessing and thresholding stages. Among several methods, wavelet...
For decades iris recognition has been widely studied by the scientific community due to its almost unique and stable patterns. Iris recognition biometric systems apply mathematical pattern-recognition techniques to an iris' image of an individual's eye to extract its feature vector. Comparing the dissimilarities from two feature vectors with an acceptance threshold, the system decides if the two vectors...
The methods currently used for transformer tap estimation are not robust against measurement errors, while the well-documented Least Absolute Value (LAV) State Estimator (SE) is not robust against transformer tap errors. This paper addresses these shortcomings by introducing the so-called Sparse Extended Least Absolute Value (SELAV) SE. By strategically modifying the formulation of LAV SE, the “sparse”...
Lempel-Ziv complexity is the basis for widely used compression algorithms. It has also been proposed as the basis for a distance metric to measure evolutionary distance. In this work we present an extension of the latter idea to develop a low complexity robust species-specific genomic signature. This signature can be used to identify biological organisms using only a small fragment of their genome...
A robust kernel-based machine learning localization scheme using time of arrival (TOA) or time difference of arrival (TDOA) in none-line-of-sight (NLOS) environments is proposed. The scheme can provide accurate position estimation while the reference nodes are coarsely and randomly distributed in the area of interests. Moreover, the scheme is insensitive with respect to random TOA synchronization...
Mutual subspace method (MSM), which is one of image-based approaches, showed strong discrimination capability in gait recognition. In general, 2D image matrices are transformed into 1D image vectors to be used as input into MSM, and then principal component analysis (PCA) is applied to 1D vectors to generate a subspace. However, due to the high dimensionalities of 1D vectors, the evaluation accuracy...
We propose a novel scoring concept for visual place recognition based on nearest neighbor descriptor voting and demonstrate how the algorithm naturally emerges from the problem formulation. Based on the observation that the number of votes for matching places can be evaluated using a binomial distribution model, loop closures can be detected with high precision. By casting the problem into a probabilistic...
We propose a vision-based method that localizes a ground vehicle using publicly available satellite imagery as the only prior knowledge of the environment. Our approach takes as input a sequence of ground-level images acquired by the vehicle as it navigates, and outputs an estimate of the vehicle's pose relative to a georeferenced satellite image. We overcome the significant viewpoint and appearance...
To deal with the time-varying and non-linear problems in near infrared (NIR) spectroscopy modeling, the recursive modeling algorithm has been introduced within a justin-time framework by a moving window. Recursive strategy is quite effective by adding of new samples and discarding oldest samples. For fermentation process, while moving window is adopted to facing the changing of target property, the...
Preliminary-summation-based PCA (PS-PCA), was recently proposed to handle the non-Gaussian features of industrial processes. However, when PS-PCA is applied to the monitoring of data with outliers, the “summation infection” phenomenon occurs, which makes PS-PCA ineffective. To eliminate the influence of outliers, this paper proposes a novel robust PS-PCA (RPS-PCA) which distinguishes outliers from...
Face recognition in real scenarios is mainly affected by illumination variation and occlusion, and therefore in order to develop a robust face recognition system these issues should be handled simultaneously. To this aim, the steps involved in the presented framework are (i) computationally simple and efficient preprocessing chain that eliminates major effects of illumination variation and noise while...
The primary objective of this paper is to explore the applicability of sparse representation based classification (SRC), particularly at the fingerprint recognition problem. This paper proposes sparse proximity based fingerprint matching methodology. The sparse representation based classification problem can be solved as representing the test sample in terms of training set with some sparse residual...
In this paper, we address the problem of automated pose classification and segmentation of the left ventricle (LV) in 2D echocardiographic images. For this purpose, we compare two complementary approaches. The first one is based on engineering ad-hoc features according to the traditional machine learning paradigm. Namely, we extract phase features to build an unsupervised LV pose estimator, as well...
In the calculation of rank minimization, the non-negative sparse low-rank representation classification (NSLRRC) regularizes nuclear norm's each singular value equally, but this limits its flexibility and ability to solve many practical problems, where the singular values with clear physical meanings ought to be treated differently. In this paper, a weighted non-negative sparse low-rank representation...
One of the major reasons for the performance degradation of a speaker verification (SV) system in real-world conditions is its inability to spot speech regions due to the presence of noise. This work focuses on the role of voice activity detection (VAD) methods in alleviating such shortcomings. The experiments are conducted on the core-core task of the speakers in the wild (SITW) challenge. Two VAD...
Recognition of Face in a group of people is a bit of difficult in now days, in this paper, a new method called Fuzzy Logic Local Ternary Pattern has been introduced. This FLTP method is a commanding technique to identify the faces clearly and even their emotions too. Here, several videos are taken in to the database and compares with query image. Using FLTP, recognizes the person is present in those...
Watermarking is an important technical way to realize copyright protection of intellectual property. The traditional video watermarking can cause distortion to host video in a certain extent, and has a weak robustness against strong geometric attacks. Firstly, a Nonnegative Matrix Factorization with Sparseness Constraints on Parts (NMFSCP) method is proposed in this paper. Secondly, the NMFSCP is...
Feature refers to some relevant information which is present on images or faces. Feature extraction used to extract those features from the face. Among that bulk of keypoints, only robust features are detected by using feature descriptors. This paper analyzes 2 robust feature detector and descriptors are: Scale-Invariant Feature Transform (SIFT) and Speeded Up Robust Features (SURF). These two robust...
This paper proposes a novel facial image representation Block-based Local Contrast Patterns (BLCP) for illumination-robust face recognition. This method is based on an effective texture descriptor local contrast patterns (LCP). We use the directed and undirected difference masks to calculate three types of local intensity contrasts: directed, undirected, and maximum difference responses. These response...
When emotion recognition systems are used in new domains, the classification performance usually drops due to mismatches between training and testing conditions. Annotations of new data in the new domain is expensive and time demanding. Therefore, it is important to design strategies that efficiently use limited amount of new data to improve the robustness of the classification system. The use of...
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