2022 New Trends in Signal Processing (NTSP)最新文献

Implementation of True Current Amplifiers via Commercial Integrated Circuits 用商用集成电路实现真电流放大器

2022 New Trends in Signal Processing (NTSP) Pub Date : 2022-10-12 DOI: 10.23919/NTSP54843.2022.9920476

V. Biolková, D. Biolek, Z. Kolka

引用次数: 1

Intrusion Detection by Artificial Neural Networks 基于人工神经网络的入侵检测

2022 New Trends in Signal Processing (NTSP) Pub Date : 2022-10-12 DOI: 10.23919/NTSP54843.2022.9920388

M. Turčaník, J. Baráth

引用次数: 1

Analysis of Communication Protocols of UAV Control Sets 无人机控制集通信协议分析

2022 New Trends in Signal Processing (NTSP) Pub Date : 2022-10-12 DOI: 10.23919/NTSP54843.2022.9920433

Pavel Kozak, V. Platenka, Marketa Vrsecka

{"title":"Analysis of Communication Protocols of UAV Control Sets","authors":"Pavel Kozak, V. Platenka, Marketa Vrsecka","doi":"10.23919/NTSP54843.2022.9920433","DOIUrl":"https://doi.org/10.23919/NTSP54843.2022.9920433","url":null,"abstract":"This article explores control signal protocols for controlling UAV (Unmanned Aerial Vehicles). It is aimed at commercially available UAVs that are intended for high-quality private or affordable professional use, but at the same time operate in the 2.4 GHz license-free band. Representatives of control sets with different types of communication protocols were selected for the measurement so that they could be compared. The initial measurement revealed the behavior of the control signals (modulation type, security, FH (Frequency Hopping), existence of a return channel...), thereby creating three different groups of control devices for measurement. Only some control units can actively react to interference. The communication protocols of the UAV control sets are designed to be able to ensure a reliable connection in an environment where unintentional collisions can occur relatively often. The analysis of the control protocol took place on the basis of the interference of a very small part of the frequency spectrum (one channel intended for FH) and the subsequent analysis of the behavior of this interference. The knowledge gained about the ability to avoid collisions in the spectrum can be used to create effective intentional jamming in the future.","PeriodicalId":103310,"journal":{"name":"2022 New Trends in Signal Processing (NTSP)","volume":"146 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132697133","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Software Tool for Pronunciation Training of Specific English Terminology 特定英语术语发音训练软件工具

2022 New Trends in Signal Processing (NTSP) Pub Date : 2022-10-12 DOI: 10.23919/NTSP54843.2022.9920469

Jan Malucha

引用次数: 0

Evaluation of the Cost-Effective Indoor Wireless Positioning System Using RSSI Method 基于RSSI方法的室内无线定位系统性价比评估

2022 New Trends in Signal Processing (NTSP) Pub Date : 2022-10-12 DOI: 10.23919/NTSP54843.2022.9920440

Stanislav Drozd, Juraj Tomlain, Martin Marko, O. Teren, J. Tomlain

引用次数: 2

New Concept of Analogue Adaptive Filter Based on Fully Analogue Artificial Neural Network 基于全模拟人工神经网络的模拟自适应滤波器新概念

2022 New Trends in Signal Processing (NTSP) Pub Date : 2022-10-12 DOI: 10.23919/NTSP54843.2022.9920436

F. Paulů, J. Hospodka

引用次数: 1

Astronomical Objects Classification by Convolutional Neural Network Algorithms Layers 基于卷积神经网络算法层的天体分类

2022 New Trends in Signal Processing (NTSP) Pub Date : 2022-10-12 DOI: 10.23919/NTSP54843.2022.9920384

D. Kyselica, Linda Jurkasová, R. Ďurikovič, J. Silha

{"title":"Astronomical Objects Classification by Convolutional Neural Network Algorithms Layers","authors":"D. Kyselica, Linda Jurkasová, R. Ďurikovič, J. Silha","doi":"10.23919/NTSP54843.2022.9920384","DOIUrl":"https://doi.org/10.23919/NTSP54843.2022.9920384","url":null,"abstract":"Our work focuses on application of modern experimental Machine Learning (ML) algorithms toward the space objects classification. Two types of data are analyzed, frame objects present on the astronomical Flexible Image Transport System (FITS) frames and space objects’ light curves, which could be considered as a footprint for given object. In our work we will present ML algorithm used for recognition of frame objects present in the FITS frames by using their specific shape. Presented algorithm is a Convolutional Neural Network (CNN) of 9 layers. The input to the network is a small 50x50 image which must contain only one object for the network to correctly classify it. This could later be used as subnet in region-based CNN after finding regions of interest in full FITS image. Additionally, we present results of applying CNN neural network based on ResNet architecture to classify light curves to categories based on their shape. For deep learning we used primarily public catalogue light curves of selected populations of upper stages e.g., Falcon 9, Atlas Centaur 5, Delta 4 which usually contain simpler features in their photometric series. The modeling software Blender was also used to generate synthetic light curves for training purposes. Algorithm can identify correctly more than 84% of tested objects. In near future we plan to extend the algorithm to identify more complex objects such as box-wing and single box-wing satellites.","PeriodicalId":103310,"journal":{"name":"2022 New Trends in Signal Processing (NTSP)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127928373","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Use of 3D Printing for Sierpinski Fractal Antenna Manufacturing 利用3D打印技术制造Sierpinski分形天线

2022 New Trends in Signal Processing (NTSP) Pub Date : 2022-10-12 DOI: 10.23919/NTSP54843.2022.9920393

M. Richterová, J. Olivová, M. Popela, V. Blažek

引用次数: 0

Contrast Quality Measure: Full-Reference Image Quality Assessment Metric for Infrared Images 对比质量度量:红外图像的全参考图像质量评估度量

2022 New Trends in Signal Processing (NTSP) Pub Date : 2022-10-12 DOI: 10.23919/NTSP54843.2022.9920403

Nenad Stojanović, Boban P. Bondzulic, B. Pavlović, V. Ristić

{"title":"Contrast Quality Measure: Full-Reference Image Quality Assessment Metric for Infrared Images","authors":"Nenad Stojanović, Boban P. Bondzulic, B. Pavlović, V. Ristić","doi":"10.23919/NTSP54843.2022.9920403","DOIUrl":"https://doi.org/10.23919/NTSP54843.2022.9920403","url":null,"abstract":"The paper proposes an objective image quality assessment measure with full referencing. The measure is based on a comparison of the contrast of the original image and the image with the degradation. Discrete cosine transform coefficients are used for contrast estimation. By applying the measure, a scalar value is obtained that reflects the quality of the test (degraded) image. The proposed measure is tested on an infrared image dataset developed by the Military Academy in Belgrade, Serbia. The performance of the measure was compared with other well-known objective full-reference image quality assessment metrics, which were developed for the images in visible domain. It was shown that measure performance can be improved with the adequate selections of the block dimensions and the number of discrete cosine transform coefficients during the calculation of image quality value. The proposed measure obtained a correlation with the subjective scores near 82%, which puts the measure into the top three of all tested image quality assessment measures. The proposed measure showed the best performance on the images distorted by Gaussian blurring, where the level of agreement with the subjective scores is over 97%, according to which the measure stands out as the best compared to other tested measures.","PeriodicalId":103310,"journal":{"name":"2022 New Trends in Signal Processing (NTSP)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127029693","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

CFAR Algorithm for Improving Detections on Radar Raw Data Matrices 改进雷达原始数据矩阵检测的CFAR算法

2022 New Trends in Signal Processing (NTSP) Pub Date : 2022-10-12 DOI: 10.23919/NTSP54843.2022.9920396

J. Perdoch, S. Gazovová, M. Pacek, Z. Matousek, J. Ochodnicky

引用次数: 1