ALGORITHM OF SKELETON-BASED STATIC HAND GESTURE RECOGNITION

2020 ◽  
pp. 13-22
Author(s):  
D. A. Kalina ◽  
R. V. Golovanov ◽  
D. V. Vorotnev
Keyword(s):  
Gesture Recognition ◽  
Graphics Processing Unit ◽  
Recognition System ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Processing Unit ◽  
The Novel ◽  
Central Processing ◽  
Graphics Processing ◽  
Artificial Network

We present the monocamera approach of static hand gestures recognition based on skeletonization. The problem of creating skeleton of the human’s hand, as well as body, became solvable a few years ago after inventing so called convolutional pose machines – the novel architecture of artificial neural network. Our solution uses such kind of pretrained convolutional artificial network for extracting hand joints keypoints with further skeleton reconstruction. In this work we also propose special skeleton descriptor with proving its stability and distinguishability in terms of classification. We considered a few widespread machine learning algorithms to build and verify different classifiers. The quality of the classifier’s recognition is estimated using the wellknown Accuracy metric, which identified that classical SVM (Support Vector Machines) with radial basis kernel gives the best results. The testing of the whole system was conducted using public databases containing about 3000 of test images for more than 10 types of gestures. The results of a comparative analysis of the proposed system with existing approaches are demonstrated. It is shown that our gesture recognition system provides better quality in comparison with existing solutions. The performance of the proposed system was estimated for two configurations of standard personal computer: with CPU (Central Processing Unit) only and with GPU (Graphics Processing Unit) in addition where the latest one provides realtime processing with up to 60 frames per second. Thus we demonstrate that the proposed approach can find an application in the practice.

scholarly journals Numerical simulation of flattened heat pipe with double heat sources for CPU and GPU cooling application in laptop computers

2020 ◽  
Author(s):  
Wisoot Sanhan ◽  
Kambiz Vafai ◽  
Niti Kammuang-Lue ◽  
Pradit Terdtoon ◽  
Phrut Sakulchangsatjatai
Keyword(s):  
Experimental Data ◽  
Heat Pipe ◽  
Graphics Processing Unit ◽  
Processing Unit ◽  
Heat Sources ◽  
Final Thickness ◽  
Laptop Computers ◽  
Central Processing ◽  
Graphics Processing ◽  
Good Agreement

Abstract An investigation of the effect of the thermal performance of the flattened heat pipe on its double heat sources acting as central processing unit and graphics processing unit in laptop computers is presented in this work. A finite element method is used for predicting the flattening effect of the heat pipe. The cylindrical heat pipe with a diameter of 6 mm and the total length of 200 mm is flattened into three final thicknesses of 2, 3, and 4 mm. The heat pipe is placed under a horizontal configuration and heated with heater 1 and heater 2, 40 W in combination. The numerical model shows good agreement compared with the experimental data with the standard deviation of 1.85%. The results also show that flattening the cylindrical heat pipe to 66.7 and 41.7% of its original diameter could reduce its normalized thermal resistance by 5.2%. The optimized final thickness or the best design final thickness for the heat pipe is found to be 2.5 mm.

scholarly journals Graphics processing unit implementation of the F-statistic for continuous gravitational wave searches

2021 ◽  
Author(s):  
Liam Dunn ◽  
Patrick Clearwater ◽  
Andrew Melatos ◽  
Karl Wette
Keyword(s):  
Gravitational Wave ◽  
Graphics Processing Units ◽  
Graphics Processing Unit ◽  
Computational Cost ◽  
Processing Unit ◽  
Central Processing ◽  
Long Baseline ◽  
Using Data ◽  
Graphics Processing ◽  
Gpu Implementation

Abstract The F-statistic is a detection statistic used widely in searches for continuous gravitational waves with terrestrial, long-baseline interferometers. A new implementation of the F-statistic is presented which accelerates the existing "resampling" algorithm using graphics processing units (GPUs). The new implementation runs between 10 and 100 times faster than the existing implementation on central processing units without sacrificing numerical accuracy. The utility of the GPU implementation is demonstrated on a pilot narrowband search for four newly discovered millisecond pulsars in the globular cluster Omega Centauri using data from the second Laser Interferometer Gravitational-Wave Observatory observing run. The computational cost is 17:2 GPU-hours using the new implementation, compared to 1092 core-hours with the existing implementation.

scholarly journals Accelerating Computer-Based Recognition of Fynbos Leaves Using a Graphics Processing Unit

2017 ◽  
Vol 29 (3) ◽  
Author(s):  
Simon Lucas Winberg ◽  
Moeko Ramone ◽  
Khagendra Naidoo
Keyword(s):  
High Resolution ◽  
Graphics Processing Unit ◽  
Rapid Prototype ◽  
Machine Learning Algorithms ◽  
Processing Unit ◽  
Time Saving ◽  
Heritage Site ◽  
Wild Fires ◽  
Alien Vegetation ◽  
Graphics Processing

The Cape Floristic Kingdom (CFK) is the most diverse floristic kingdom in the world and has been declared an international heritage site. However, it is under threat from wild fires and invasive species. Much of the work of managing this natural resource, such as removing alien vegetation or fighting wild fires, is done by volunteers and casual workers. Many fynbos species, for which the Table Mountain National Park is known, are difficult to identify, particularly by non-expert volunteers. Accurate and fast identification of plant species would be beneficial in these contexts. The Fynbos Leaf Optical Recognition Application (FLORA) was thus developed to assist in the recognition of plants of the CFK. The first version of FLORA was developed as a rapid prototype in MATLAB; it utilized sequential algorithms to identify plant leaves, and much of this code was interpreted M files. The initial implementation suffered from slow performance, though, and could not run as a lightweight standalone executable, making it cumbersome. FLORA was thus re-developed as a standalone C++ version that was subsequently enhanced further by accelerating critical routines, by running them on a graphics processing unit (GPU). This paper presents the design and testing of both the C++ version and the GPU-accelerated version of FLORA. Comparative testing was done on all three versions of FLORA, viz., the original MATLAB prototype, the C++ non-accelerated version, and the C++ GPU-accelerated version to show the performance and accuracy of the different versions. The accuracy of the predictions remained consistent across versions. The C++ version was noticeable faster than the original prototype, achieving an average speed-up of 8.7 for high-resolution 3456x2304 pixel images. The GPU-accelerated version was even faster, saving 51.85 ms on average for high-resolution images. Such a time saving would be perceptible for batch processing, such as rebuilding feature descriptors for all the leaves in the leaf database. Further work on this project involves testing the system with a wider variety of leaves and trying different machine learning algorithms for the leaf prediction routines.

scholarly journals Analysis of Fast Fourier Transformations algorithm for CUDA Architecture

2012 ◽  
Vol 53 ◽  
Author(s):  
Beatričė Andziulienė ◽  
Evaldas Žulkas ◽  
Audrius Kuprinavičius
Keyword(s):  
Graphics Processing Unit ◽  
General Purpose ◽  
Fast Fourier Transformation ◽  
Processing Unit ◽  
Data Allocation ◽  
Analysis Method ◽  
Central Processing ◽  
Execution Speed ◽  
Cuda Architecture ◽  
Graphics Processing

In this work Fast Fourier transformation algorithm for general purpose graphics processing unit processing (GPGPU) is discussed. Algorithm structure and individual stages performance were analysed. With performance analysis method algorithm distribution and data allocation possibilities were determined, depending on algorithm stages execution speed and algorithm structure. Ratio between CPU and GPU execution during Fast Fourier transform signal processing was determined using computer-generated data with frequency. When adopting CPU code for CUDA execution, it not becomes more complex, even if stream procesor parallelization and data transfering algorith stages are considered. But central processing unit serial execution).

scholarly journals Paralelização do Algoritmo Floyd-Warshall usando GPU

2013 ◽  
Author(s):  
Roussian R. A. Gaioso ◽  
Walid A. R. Jradi ◽  
Lauro C. M. de Paula ◽  
Wanderley De S. Alencar ◽  
Wellington S. Martins ◽  
...  
Keyword(s):  
Graphics Processing Unit ◽  
Central Processing Unit ◽  
Processing Unit ◽  
Central Processing ◽  
Graphics Processing

Este artigo apresenta uma implementação paralela baseada em Graphics Processing Unit (GPU) para o problema da identificação dos caminhos mínimos entre todos os pares de vértices em um grafo. A implementação é baseada no algoritmo Floyd-Warshall e tira o máximo proveito da arquitetura multithreaded das GPUs atuais. Nossa solução reduz a comunicação entre a Central Processing Unit (CPU) e a GPU, melhora a utilização dos Streaming Multiprocessors (SMs) e faz um uso intensivo de acesso aglutinado em memória para otimizar o acesso de dados do grafo. A vantagem da implementação proposta é demonstrada por vários grafos gerados aleatoriamente utilizando a ferramenta GTgraph. Grafos contendo milhares de vértices foram gerados e utilizados nos experimentos. Os resultados mostraram um excelente desempenho em diversos grafos, alcançando ganhos de até 149x, quando comparado com uma implementação sequencial, e superando implementações tradicionais por um fator de quase quatro vezes. Nossos resultados confirmam que implementações baseadas em GPU podem ser viáveis mesmo para algoritmos de grafos cujo acessos à memória e distribuição de trabalho são irregulares e causam dependência de dados.

scholarly journals Modular Microservice based GPU Utilization Manager with Gunicorn

2020 ◽  
pp. 230-237
Keyword(s):  
Performance Monitoring ◽  
Graphics Processing Unit ◽  
Processing Unit ◽  
New Era ◽  
Central Processing ◽  
Massively Parallel Computing ◽  
Improved Performance ◽  
Graphics Processing ◽  
Mathematical Operations ◽  
Speed Of Analysis

:Graphics processing unit (GPU) is a computer programmable chip that could perform rapid mathematical operations that can be accelerated with massive parallelism. In the early days, central processing unit (CPU) was responsible for all computations irrespective of whether it is feasible for parallel computation. However, in recent years GPUs are increasingly used for massively parallel computing applications, such as training Deep Neural Networks. GPU’s performance monitoring plays a key role in this new era since GPUs serve an inevitable role in increasing the speed of analysis of the developed system. GPU administration comes in picture to efficiently utilize the GPU when we deal with multiple workloads to run on the same hardware. In this study, various GPUparameters are monitored and help to keep them in safe levels and also to keep the improved performance of the system. This study,

scholarly journals GPU Accelerated PIC and SIC for OFDM-NOMA

Electronics ◽  
2019 ◽  
Vol 8 (3) ◽  
pp. 257 ◽  
Author(s):  
Talgat Manglayev ◽  
Refik Kizilirmak ◽  
Nor Hamid
Keyword(s):  
Interference Cancellation ◽  
Orthogonal Frequency Division Multiplexing ◽  
Multiple Access ◽  
Graphics Processing Unit ◽  
Processing Unit ◽  
Parallel Interference Cancellation ◽  
Central Processing ◽  
Fifth Generation ◽  
Access Scheme ◽  
Graphics Processing

Non-orthogonal multiple access (NOMA) is a candidate multiple access scheme for the fifth-generation (5G) cellular networks. In NOMA systems, all users operate at the same frequency and time, which poses a challenge in the decoding process at the receiver side. In this work, the two most popular receiver structures, successive interference cancellation (SIC) and parallel interference cancellation (PIC) receivers, for NOMA reverse channel are implemented on a graphics processing unit (GPU) and compared. Orthogonal frequency division multiplexing (OFDM) is considered. The high computational complexity of interference cancellation receivers undermines the potential deployment of NOMA systems. GPU acceleration, however, challenges this weakness, and our numerical results show speedups of about from 75–220-times as compared to a multi-thread implementation on a central processing unit (CPU). SIC and PIC multi-thread execution time on different platforms reveals the potential of GPU in wireless communications. Furthermore, the successful decoding rates of the SIC and PIC are evaluated and compared in terms of bit error rate.

Parallel Ellipsoid Collision Detection With Applications in Contact Dynamics

2010 ◽  
Author(s):  
Arman Pazouki ◽  
Hammad Mazhar ◽  
Dan Negrut
Keyword(s):  
Collision Detection ◽  
Graphics Processing Unit ◽  
Contact Dynamics ◽  
Contact Detection ◽  
Processing Unit ◽  
Depth Of Penetration ◽  
Central Processing ◽  
Dynamics Of Multibody Systems ◽  
Graphics Processing ◽  
Contact Parameters

This work concentrates on the contact detection of ellipsoids, an enhancement to collision detection which can be used to study the dynamics of multibody systems with frictional contact. A first method for contact detection is posed as an unconstrained optimization problem. This method, while computationally demanding, can find the contact parameters as well as determine the state of the contact of two ellipsoids. Next, a method is presented that is approximately two orders of magnitudes more efficient in finding the contact state of two ellipsoids. However, it cannot find the contact parameters such as contact normal, depth of penetration, etc. Finally, a parallel algorithm for the ellipsoid contact detection problem is presented. The algorithm is implemented on a ubiquitous Graphics Processing Unit (GPU) card and shown to achieve a speedup of up to 70× over a Central Processing Unit (CPU) based method. The proposed methodology is expected to have an impact in granular flow dynamics applications.

State Estimation Based on Ensemble DA–DSVM in Power System

2019 ◽  
Vol 29 (05) ◽  
pp. 653-669
Author(s):  
Shunjiang Wang ◽  
Baoming Pu ◽  
Ming Li ◽  
Weichun Ge ◽  
Qianwei Liu ◽  
...  
Keyword(s):  
Power Systems ◽  
Power System ◽  
State Estimation ◽  
Graphics Processing Unit ◽  
Estimation Algorithm ◽  
The State ◽  
Support Vector ◽  
Processing Unit ◽  
Central Processing ◽  
Calculation Results

This paper investigates the state estimation problem of power systems. A novel, fast and accurate state estimation algorithm is presented to solve this problem based on the one-dimensional denoising autoencoder and deep support vector machine (1D DA–DSVM). Besides, for further reducing the computation burden, a partitioning method is presented to divide the power system into several sub-networks and the proposed algorithm can be applied to each sub-network. A hybrid computing architecture of Central Processing Unit (CPU) and Graphics Processing Unit (GPU) is employed in the overall state estimation, in which the GPU is used to estimate each sub-network and the CPU is used to integrate all the calculation results and output the state estimate. Simulation results show that the proposed method can effectively improve the accuracy and computational efficiency of the state estimation of power systems.

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