Приведение плотных матриц с элементами из GF(2) к ступенчатому виду на платформе NVIDIA CUDA
An approach is described to implementation of the Method of Four Russians for reducing the dense matrices over GF(2) to row echelon form using the NVIDIA CUDA platform. Estimates of the algorithm running time and recommendations on choosing the algorithm parameters are given. It is shown that the developed implementation is most effective in comparison with the existing solutions for matrices of a size 2^17 x 2^17.
Low-cost gaze tracking systems are in great demand due to their wide range of application. Commonly, extra devices are needed (for instance, head mounted cameras); however, in this investigation gaze tracking is performed in real-time based on the video stream from an infrared video camera. A comparative analysis of the existing analogues was executed and the main features of gaze tracking systems were highlighted and prioritized. These features are price, tracking accuracy, angle error, flexibility, and usability.
A methodology was developed which allows to calculate a gaze direction vector according to the relative position of eye center and corneal reflection from an infrared diode. The centers of an eye and reflection are estimated using the vector field of image gradients and additional weighting. CUDA technology is used to accelerate the developed algorithms.
The main advantage of the developed algorithm is the ability to detect and continuously track pupils’ centers, regardless of the head position, which significantly extends the scope of the gaze tracking system under consideration.
We present optimization guidelines and implementations of cryptographic hash functions GOST R 34.11-94 and GOST R 34.11-2012. Results for x86_64 CPUs and NVIDIA CUDA-capable GPUs are provided for our and several other well-known implementations. It is shown that the new standard may be twice as fast as the old one on modern CPUs, but it may be slower on embedded devices and GPUs. The results given for our implementation are the fastest among all the tested implementations on both platforms.
In this work, we describe the problem of automated pollen recognition using images from lighting microscope. Automated pollen recognition related to such important tasks as honey quality control, air quality control for helping to asthma and allergy patients, paleopalynology, forensic palynology. We describe the problem solution based on machine learning and CUDA. Extracted features and preprocessing steps are described. Results are compared on dataset of 5 specie. The best model is convolutional neural network with 89% of accuracy. Its performance was particularly up twice using CUDA.
This proceedings publication is a compilation of selected contributions from the “Third International Conference on the Dynamics of Information Systems” which took place at the University of Florida, Gainesville, February 16–18, 2011. The purpose of this conference was to bring together scientists and engineers from industry, government, and academia in order to exchange new discoveries and results in a broad range of topics relevant to the theory and practice of dynamics of information systems. Dynamics of Information Systems: Mathematical Foundation presents state-of-the art research and is intended for graduate students and researchers interested in some of the most recent discoveries in information theory and dynamical systems. Scientists in other disciplines may also benefit from the applications of new developments to their own area of study.