Compact convolutional neural network cascadefor face detection

This paper presents a new solution to the frontal face detection problem based on a compact convolutional neural networks cascade. Test results on an FDDB dataset show that it is able to compete with state-of-the-art algorithms. This proposed detector is implemented using three technologies: SSE/AVX/AVX2 instruction sets for Intel CPUs, Nvidia CUDA, and OpenCL. The detection speed of our approach exceeds considerably all the existing CPUbased and GPU-based algorithms. Thanks to its high computational efficiency, our detector can process 4K Ultra HD video stream in real time (up to 27 fps) on mobile platforms while searching objects with a dimension of 60×60 pixels or higher. At the same time, its processing speed is almost independent of the background and the number of objects in a scene. This is achieved by asynchronous computation of stages in the cascade

Text classification using convolutional neural network committee training

The method of classification of textual information based on the apparatus of convolutional neural networks is considered. The word-by-word text conversion into dense vectors is considered. Testing was conducted on the text data of the sample “The 20 Newsgroups”, this sample contains texts distributed in 20 classes. The accuracy, the best of the convolutional neural network used in this work, on the test sample was ~ 74%. The accuracy of voting of neural networks using the Bagging algorithm was ~ 81.5%. Based on the review of similar solutions, a comparison was made with the following text classification algorithms: using the support vector machine (SVM, 82.84%), naive bayes classifier (81%), k nearest neighbor algorithm (75.93%), a bag of words

Electroencephalogram Analysis Based on Gramian Angular FieldTransformation

This paper addresses the problem of motion imagery classification from electroencephalogram signals which related with manydifficulties such on human state, measurement accuracy, etc. Artificial neural networks are a good tool to solve such kind of problems.Electroencephalogram is time series signals therefore, a Gramian Angular Fields conversion has been applied to convert it into images.GAF conversion was used for classification EEG with Convolutional Neural Network (CNN). GAF images are represented as a Gramianmatrix where each element is the trigonometric sum between different time intervals. Grayscale images were applied for recognition toreduce numbers of neural network parameters and increase calculation speed. Images from each measuring channel were connectedinto one multi-channel image. This article reveals the possible usage GAF conversion of EEG signals to motion imagery recognition,which is beneficial in the applied fields, such as implement it in brain-computer interfac

Structural model constructing for optical handwritten character recognition

The article is devoted to the development of the algorithms for optical handwritten character recognition based on the structural models constructing. The main advantage of these algorithms is the low requirement regarding the number of reference images. The one-pass approach to a thinning of the binary character representation has been proposed. This approach is based on the joint use of Zhang-Suen and Wu-Tsai algorithms. The effectiveness of the proposed approach is confirmed by the results of the experiments. The article includes the detailed description of the structural model constructing algorithm's steps. The proposed algorithm has been implemented in character processing application and has been approved on MNIST handwriting characters database. Algorithms that could be used in case of limited reference images number were used for the comparison

Genetic Structure of Mongolic-Speaking Kalmyks

This is the publisher's version, also available electronically from http://digitalcommons.wayne.edu/humbiol/vol73/iss6/4.Genetic polymorphisms of blood groups ABO and RH D, serum proteins HP, TF, and GC, and red cell enzymes ACP1, PGM1, ESD, GLO1, and SOD-A have been reported for three tribes (Torguts, Derbets, and Buzavs) of the Volga’s Kalmyk-Oyrats. The Kalmyks exhibit genetic markers that are characteristic of Central Asian populations, namely, high allelic frequencies for ABO*B, TF*C2, GC*1F, ESD*2, and GLO1*2, and the rare incidence of individuals with the RH-negative phenotype. Genetic distance measures reveal that close genetic affinities exist between the Derbets and Buzavs, but both populations differ significantly from the Torguts. Collectively as an ethnic group, the Kalmyks genetically resemble the contemporary Buryats of the Baikal region of southeastern Siberia and the Mongols of Mongolia. The transplantation of the Kalmyk-Oyrats from their homeland near Lake Baikal to their current residence (4500 km) near the Caspian Sea and their subsequent isolation for more than 300 years have not appreciably altered the gene frequencies from the parental populations for frequencies of standard genetic markers

Using artificial neural networks to solve text classification problems

The article is devoted to neural network text classification algorithms. This paper presents the main components of the text classification system, as well as the same problems associated with the use of the architecture of convolutional neural networks. The algorithm for obtaining vector representations of the dictionary is described

Advanced quasistatic approximation

The quasistatic approximation (QSA) is an efficient method of simulating laser- and beam-driven plasma wakefield acceleration, but it becomes imprecise if some plasma particles make long longitudinal excursions in a strongly nonlinear wave, or if waves with non-zero group velocity are present in the plasma, or the plasma density gradients are sharp, or the beam shape changes rapidly. We present an extension to QSA that is free from its limitations and retains its main advantages of speed and reduced dimensionality. The new approach takes into account the exchange of information between adjacent plasma layers. We introduce the physical model, describe its numerical implementation, and compare the simulation results with available analytical solutions and other codes