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Complexity Reduced IIR Filter Design for FPGA 2

Complexity Reduced IIR Filter Design for FPGA

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2nd IEEE International Conference on system Analysis and Intelligent Computing, SAIC 2020

作者： Sergiyenko, Anatoliy Serhienko, Anastasia Igor Sikorsky Kyiv Polytechnic Institute Dept. of Computer Engineering Kyiv Ukraine Igor Sikorsky Kyiv Polytechnic Institute Dept. of System Programming and Specialized Computer Systems Kyiv Ukraine

ISBN: (纸本)9781728190822

In this paper, a method of high order IIR filter design is proposed in which the coefficients in the canonical binary number system representation are searched by the simulated annealing algorithm. The high-quality IIR filters are designed using the all-pass filter stages, methods of masking filters and multiplied delays. It was proven, that if the coefficients of the multiplierless filter have no more than three summands in their representation, then its pipelined implementation in FPGA has the highest clock frequency. The use of the VHDL language in all the steps of the filter design helps to speed-up and improve the filter optimization. © 2020 IEEE.

关键词： IIR filters

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Monte-Carlo Tree Search with Tree Shape Control

Monte-Carlo Tree Search with Tree Shape Control

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IEEE Ukraine Conference on Electrical and computer Engineering

作者： Oleksandr I. Marchenko Oleksii O. Marchenko Department of System Programming and Specialized Computer Systems National Technical University of Ukraine "Igor Sikorsky Kyiv polytechnic institute"

ISBN: (纸本)9781509030071

The paper, basing on analysis of the Monte-Carlo Tree Search (MCTS) method and specific features of its behavior for various cases of usage, proposes a new variant of the method, which was called as Monte-Carlo Tree Search with Tree Shape Control (MCTS-TSC) and which uses original Depth-Width Criteria (DWCs) for both tree shape estimation and control during search and for estimation and selection of potentially better options for search continuation. Proposed Tree Shape Control (TSC) technique can be used with some other tuning, pruning, and learning techniques. Besides, it can provide better scheduling of MCTS parallelization.

关键词： Monte-Carlo Tree Search (MCTS) Game tree MCTS improvement techniques Exploitation-exploration dilemma Tree shape control MCTS parallelization

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Image noise reduction by denoising autoencoder

Image noise reduction by denoising autoencoder

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IEEE International Conference on Dependable systems, Services and Technologies (DESSERT)

作者： Lev Yasenko Yaroslav Klyatchenko Oksana Tarasenko-Klyatchenko Department of System Programming and Specialized Computer Systems National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute" Kyiv Ukraine

ISBN: (数字)9781728199573

ISBN: (纸本)9781728199580

Neural networks are used in many tasks today. One of them is the images processing. Autoencoder is very popular neural networks for such problems. Denoising autoencoder is an important autoencoder because some tasks we need a preprocessed image to get less noisy result. This research describes ways to analyze noisy images produced by a physically-based render engine and how to reduce that noise. The results showed that the algorithms are logarithmic.

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Using Natural Version Redundancy of FPGA Projects in Area of Critical Applications

Using Natural Version Redundancy of FPGA Projects in Area of...

引用

IEEE International Conference on Dependable systems, Services and Technologies (DESSERT)

作者： Oleksandr Drozd Vitaliy Romankevich Mykola Kuznietsov Myroslav Drozd Oleksandr Martynyuk Department of Computer Intelligent Systems and Networks Odessa National Polytechnic University Odessa Ukraine System programming and specialized computer systems National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute" Kyiv Ukraine Department of Information Systems Odessa National Polytechnic University Odessa Ukraine

ISBN: (数字)9781728199573

ISBN: (纸本)9781728199580

The paper focuses on the role of version redundancy, which increases with the expansion of critical applications and the tightening of their functional safety requirements. International standards governing these requirements have already fixed the importance of version redundancy to counter common cause failures in safety-related systems. The problem of hidden faults inherent in these systems is equally acute, undermining confidence in the fault tolerance of circuit solutions important for functional safety. We suggest that this problem be seen as a growth problem where the system rises to the level of diversification in modes of operation by dividing them into normal and emergency and checkability circuits, but components continue to be stamped at a lower level of replication. Then the solution to the problem is to raise the components to the level of the system by developing their version redundancy. We demonstrate this development for FPGA components with LUT-oriented architecture and offer a method to improve the FPGA-ready project by natural version redundancy of its program code. The method generates a set of program code versions and selects versions that improve the checkability of circuits with respect to constant faults and the trustworthiness of results, respectively, in normal and emergency mode to reduce a set of hidden faults. The method is illustrated by an iterative array multiplier implemented in an FPGA project. Versions are generated, evaluated, and selected using the program developed for this.

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Speaker's Emotions Recognition Module Based on the GoogleLeNet Neural Network

Speaker's Emotions Recognition Module Based on the GoogleLeN...

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2022 International Conference on Smart Information systems and Technologies, SIST 2022

作者： Tereikovskyi, Ihor Tereikovska, Liudmyla Kryvoruchko, Olena Tereikovskyi, Oleh Tyshchenko, Dmytro Franchuk, Tamara National Technical University Of Ukraine Igor Sikorsky Kyiv Polytechnic Institute Dept. Of System Programming And Specialized Computer Systems Kyiv Ukraine Kyiv National University Of Construction And Architecture Dept. Of Information Technology For Design And Applied Mathematics Kyiv Ukraine Kyiv National University Of Trade And Economics Dept. Of Software Engineering And Cyber Security Kyiv Ukraine National Technical University Of Ukraine Igor Sikorsky Kyiv Polytechnic Institute Dept. Of Department Of Computer Engineering Kyiv Ukraine

ISBN: (数字)9781665467902

ISBN: (纸本)9781665467902

The article is devoted to the development of means for recognition of the emotions of the speaker, based on the neural network analysis of fixed fragments of the voice signal. The possibility of improving recognition tools through the use of a convolutional neural network of the GoogLeNet type is shown. The expediency of training the network on the examples of the TESS database is determined. A procedure for representing a voice signal in the form of a square grayscale image has been developed. Based on the use of the specified database, the modification of the GoogLeNet network was carried out. The architecture and software implementation of the recognition module have been developed. With the help of computer experiments, it has been established that after 150 epochs of training on a fairly limited training sample, the modified GoogLeNet makes it possible to achieve an accuracy of recognition of the speaker's emotions of approximately 0.97 on test examples. The obtained accuracy is comparable to the accuracy of the best modern systems of this purpose and confirms the promise of using neural network models like GoogLeNet for emotion recognition based on voice signal analysis. The need for further research aimed at reducing the resource intensity of the neural network model is shown. © 2022 IEEE.

关键词： Emotion Recognition

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