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In this paper, a Sequential Fuzzy Indexing Tables classifier is proposed for problems that require fast online operation. Its base idea comes from fuzzy hypermatrices (which are specialized versions of fuzzy look-up tables) that realize nearest-neighbor classification in order to recognize patterns similar to known ones. It is done by mapping the problem space into the memory in form of multidimensional...
Channel-Optimized Vector Quantization (COVQ) is an alternative to Vector Quantization (VQ) in the scenario of transmission over noisy channels. The codebook design is an optimization problem in which a set of vectors must be optimized to represent the signals to be quantized. This paper presents a new approach to COVQ codebook design, which is a challenging optimization problem. The proposed technique...
In order to solve the problem of traditional methods having low accuracy in recognizing rolling bearings' faults and to reduce time in building a training model, this paper puts forward a method of recognizing faults based on wavelet packet transformation and PCA-PSO-MSVM. First of all, we extracted the energy values after all kinds of faulty signals have been transformed through wavelet packet, and...
When traditional sample selection methods are used to compress large data sets, the computational complexity turns out to be very high and it is really time consuming. To avoid these shortcomings, we propose a new method to select samples based on non-stable cut points. With the basic characteristic of convex function that its extreme values occur at the endpoints of intervals, the method measures...
A improved swarm optimization method based on particle swarm optimization (PSO) and simplified swarm optimization (SSO) is proposed to adjust the weight in artificial neural network. This method is a modification of traditional PSO and SSO, and combines them to a new optimization method (PSOSSO for short). The proposed method overcomes some of the drawbacks of SSO and improves its ability to train...
Melt index is considered one of the most important variables in determining chemical product quality and thus reliable prediction of melt index (MI) is essential in practical propylene polymerization processes. In this paper, a fuzzy support vector regression (FSVR) based model for propylene polymerization process is developed to predict the MI of polypropylene from other easily measured process variables...
In this study, we present a new phoneme-based deep neural network (DNN) framework for single microphone speech enhancement. While most speech enhancement algorithms overlook the phoneme structure of the speech signal, our proposed framework comprises a set of phoneme-specific DNNs (pDNNs), one for each phoneme, together with an additional phoneme-classification DNN (cDNN). The cDNN is responsible...
Many artificial speech bandwidth extension (ABE) approaches perform source-filter decomposition of the input narrowband speech, with subsequent computation of upper frequency band (UB) spectral envelope posteriors. In this paper we perform a direct comparison of HMM- and deep neural network (DNN)-based modeling of likelihoods or posteriors for ABE UB envelope estimation. DNN-based approaches turn...
The computational complexity of kernel methods grows at least quadratically with respect to the training size and hence low rank kernel approximation techniques are commonly used. One of the most popular approximations is constructed by sub-sampling the training data. In this paper, we present a sampling algorithm called Enhanced Distance Subset Approximation (EDSA) based on a novel kernel function...
The aim of this study is the quantification of the effectiveness of a computer-assisted system to improve upper extremity function (Armeo Spring system) in hemiplegic children with Cerebral Palsy using clinical-functional scales and upper limb kinematics. Eight children with hemiplegia were evaluated by clinical examination (Quality of Upper Extremities Skills Test and Melbourne Assessment) and 3D...
Wearable devices allow the seamless and inexpensive gathering of biomedical signals such as electrocardiograms (ECG), photoplethysmograms (PPG), and respiration traces (RESP). They are battery operated and resource constrained, and as such need dedicated algorithms to optimally manage energy and memory. In this work, we design SAM, a Subject-Adaptive (lossy) coMpression technique for physiological...
The analysis of quantitative and quality indicators of ratings of technical and electrotechnical universities of the world through international, national and private criteria is considered and made. A prospective student undertaking a similar analysis will have the opportunity to select a profession taking into account many of the same factors.
With the rapid growth of the number of short text, how to effectively realize the automatic classification of short text is needed to be solved in the information domain. According to the characteristics of short text, this paper proposes Bagging_NB & Bagging_BSJ, which are two classification algorithms based on the improvement of current integrated classifiers. Traditional classifier NB, SVM,...
Position-patch based face hallucination approaches have been proposed to replace the probabilistic graph-based or manifold learning-based models recently. In this paper, we propose a novel position-based face hallucination method based on locality-constrained matrix regression (LcMR). LcMR uses nuclear norm to characterize the reconstruction error straightforward, thus preserving the essential structural...
In this paper, we propose a principled framework for pornographic image recognition. Specifically, we present our definition of pornographic images, which characterizes the pornographic contents in images as the exposure of private body parts. As the private body parts often lie in local image regions, we model each image as a bag of local image patches (instances), and assume that for each pornographic...
In the literature, various techniques for supervised/ semi-supervised classification of satellite imageries require manual selection of samples for each class. In this paper, we propose a spectral-slope based classification technique, which automates the process of initial labeling of a set of sample points. These are subsequently used in a supervised classifier as training samples and it performs...
This paper presents a novel pose-indexed based multi-view (PIMV) face alignment framework. Most of the current cascaded regression face alignment methods generally start with a mean shape. However, when the initial shape is far from the ground truth, the performance significantly deteriorates. Our approach aims to obtain a preferable initial shape from a pose-indexed shape searching space. This space...
In this paper, we consider the problem of example based single image super-resolution. Our main contribution is introducing a new framework that makes no assumption about the structural similarity between the high-resolution (HR) and low-resolution (LR) manifolds. Instead, we use a subspace affinity measure to exploit the similarity between each HR and LR subspace. First, we train both LR and HR manifolds...
In this work, we propose a multi-view temporal video segmentation approach that employs a Gaussian scoring process for determining the best segmentation positions. By exploiting the semantic action information that the dense trajectories video description offers, this method can detect intra-shot actions as well, unlike shot boundary detection approaches. We compare the temporal segmentation results...
We present a fully trainable solution for binarization of degraded document images using extremely randomized trees. Unlike previous attempts that often use simple features, our method encodes all heuristics about whether or not a pixel is foreground text into a high-dimensional feature vector and learns a more complicated decision function. We introduce two novel features, the Logarithm Intensity...
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