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Application of the Continuous Fourier Transformation in signal Theory 3

Application of the Continuous Fourier Transformation in Sign...

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3rd ieee international conference on System Analysis and intelligent Computing, SAIC 2022

作者： Kharkevych, Yurii Mukhin, Oleg Hrabova, Uliana Makarchuk, Andriy Kal'chuk, Inna Kharkevych, Galyna Lesya Ukrainka Volyn National University Faculty of Information Technology and Mathematics Lutsk Ukraine National Technical University of Ukraine 'Igor Sikorsky Kyiv Polytechnic Institute' Kyiv Ukraine Lesya Ukrainka Volyn National University Faculty of International Relations Lutsk Ukraine

ISBN: (数字)9798350396744

ISBN: (纸本)9798350396744

There are a number of methods in systems analysis that are used to solve a wide range of applied problems, including digital signal processing. The method of discrete Fourier transformation is one of the widely used methods. The method of discrete Fourier transformation allows us to analyze, convert and synthesize signals. It is not possible using analog processing. The discrete Fourier transformation is particularly used to work with digital signals processing. However, sometimes it is necessary to work with continuous signals, so it is impossible to use the Fourier discrete transformation. In such cases, we use the so-called continuous Fourier transformation, which is a continuous analogue of a discrete transformation and has a similar content. Due to the specifics of the continuous Fourier transformation as a method for solving classical problems of systems analysis, there is a need to study the number of its properties, including its convergence. The relation between the smoothness of the summing Abel-Poisson function, which models the studied signal, and the convergence rate of its continuous Fourier transformation has been established. Since the summing Abel-Poisson functions are harmonic, as well as the signals that occur in real conditions, the results obtained in this paper take into account not only the smoothness of the function, but also its periodicity. Having taken into account this aspect, we could indicate the method of signal recovery, which gives better approximate properties than those previously studied in foreign and domestic scientific works. © 2022 ieee.

关键词： systems analysis

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MMV based EMG signal Reconstruction for Remote Rehabilitation

MMV based EMG Signal Reconstruction for Remote Rehabilitatio...

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ieee international conference on robotics and Biomimetics

作者： Ruihui Zhao Haoxiang Zhang Yehao Ma Robotics Institute Ningbo University of Technology Ningbo China

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

To overcome the limitations of low compression rates and extended reconstruction times in the Single Measurement Vector (SMV) model for electromyography (EMG) signal processing, this paper presents a compression and reconstruction algorithm based on the Multiple Measurement Vector (MMV) model. The EMG signals undergo preprocessing, and the optimal combination of the Discrete Sine Transform (DST) sparse basis and the BPBDMtx measurement matrix is selected. signal reconstruction is then performed using the Orthogonal Matching Pursuit (MMV-OMP) algorithm. Experimental results show that the proposed MMV-based compression sensing algorithm significantly outperforms the traditional SMV model in terms of reconstruction quality, compression rate, and reconstruction speed. This method offers a novel pathway for efficient EMG signal reconstruction and has broad potential for applications in sensor technology and remote rehabilitation.

关键词： Telemedicine Matching pursuit algorithms signal processing algorithms Transforms Reconstruction algorithms Robot sensing systems Electromyography Vectors signal reconstruction Sparse matrices

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Deep Learning Networks for Human Knee Abnormality Detection Based on Surface EMG signals 47

Deep Learning Networks for Human Knee Abnormality Detection ...

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47th international conference on Telecommunications and signal processing, TSP 2024

作者： Mekruksavanich, Sakorn Phaphan, Wikanda Jitpattanakul, Anuchit School of Information and Communication Technology University of Phayao Department of Computer Engineering Phayao Thailand Research Group in Statistical Learning and Inference King Mongkut's University of Technology North Bangkok Faculty of Applied Science Department of Applied Statistics Bangkok Thailand Intelligent and Nonlinear Dynamic Innovations Research Center King Mongkut's University of Technology North Bangkok Department of Mathematics Faculty of Applied Science Bangkok Thailand

ISBN: (纸本)9798350365597

Early knee problem management relies on precise identification and classification of abnormalities. Surface electromyography (sEMG) and goniometer signals offer non-invasive screening for muscle activity and joint angle patterns, yet their complexity poses challenges in extracting critical diagnostic information. This paper proposes a novel deep-learning method using sEMG and goniometer data for knee abnormality diagnosis. The proposed ResNeXt model, employing CNNs and multi-kernel modules, is evaluated on the UCIEMG dataset. Experimental results demonstrate ResNeXt's superior accuracy, precision, recall, and F1-score compared to baseline models (CNN and LSTM). Res NeXt achieves the best performance with combined EMG and goniometer data, reaching 96.37% accuracy and 93.77% F1-score, with fewer trainable parameters, indicating computational efficiency. The findings indicate ResNeXt's effectiveness in identifying knee abnormalities using biosensor data, particularly sEM G and goniometer signals, aiding early disease detection and treatment. © 2024 ieee.

关键词： Learning systems

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A Divider-less General PLL Lock Assist and Automatic Frequency Calibration System for Millimeter-Wave Sub-Sampling Phase-Locked Loops 29

A Divider-less General PLL Lock Assist and Automatic Frequen...

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29th ieee international conference on Electronics, Circuits and systems (ieee ICECS)

作者： Kurth, Patrick Nickel, Philipp Hecht, Urs Gerfers, Friedel Tech Univ Berlin Mixed Signal Circuit Design Einsteinufer 17 D-10587 Berlin Germany

ISBN: (数字)9781665488235

ISBN: (纸本)9781665488235

In this paper, a truly divider-less, robust and highly configurable lock assist and automatic frequency calibration (AFC) for sub-sampling phase-locked loops (SSPLL) is demonstrated. The design uses a secondary, low-frequency ring-oscillator charge-pump PLL and exploits shifted-phase sub-sampling to obtain frequency information. A fractional ratio between the reference clock of the 56 GHz SSPLL and the sample clock of the AFC is leveraged to achieve sub-harmonic and arbitrary slow frequency detection. Apart from the analog front-end with a high-speed track-and-hold and a simple low-speed, low-resolution analog-to-digital converter, the entire lock detection, frequency processing and calibration is implemented as digital logic.

关键词： Sub-Sampling Phase-Locked Loop Automatic Frequency Calibration Lock Assist

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An Effective Defense Method based on Hash Authentication against Mode-switching Attack of Ormon PLC 7

An Effective Defense Method based on Hash Authentication aga...

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7th international conference on intelligent Computing and signal processing, ICSP 2022

作者： Gao, Jianlei Li, Yaobing Li, Yun Gong, Tianyu Xu, Fengjuan Zhao, Qian Jiang, Hao Jia, Jiongxuan China Industrial Control Systems Cyber Emergency Response Team Institute of Baozhang Beijing China

ISBN: (纸本)9781665478571

Industrial control system (ICS) integrating different software and hardware is not only to collect, obtain and calculate data information, but also to control the machine or production process so that it can run automatically. Ormon PLCs are widely used in critical manufactures and have adopted factory interface network service (Fins) protocol to exchange message. The recent articles indicate that Ormon PLCs used Fins Ethernet protocol were exposed to mode-switching malicious network attack. However, the traditional detection methods based on function codes and Snort detection rules could not get an ideal result. In this paper, we propose a method that can overcome these problems in dealing with mode-switching attack by an external hash authentication device embedding asymmetric encryption. This security device built into a hash authentication mechanism, which is responsible for identifying and distinguishing the interaction information message between the upper computer and PLC interaction information. In order to validate our method, some experiments have been done to show that defend method can disrupt successfully the communication link between the attacking computer and PLC device to defend this malicious cyber attacks efficiently. © 2022 ieee.

关键词： Network security

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Facial Emotion Recognition using YOLO based Deep Learning Classifier 1

Facial Emotion Recognition using YOLO based Deep Learning Cl...

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1st international conference on Electronics, Communication and signal processing, ICECSP 2024

作者： Vanamoju, Sri Vaibhav Mohan Dev Vineetha, Moturi Veda Tekchandani, Hitesh Joshi, Pallavi Shukla, Praveen Kumar Khanna, Anita Electronics and Communication Koneru Lakshmaiah Education Foundation Hyderabad India Artificial Intelligence and Data Science Koneru Lakshmaiah Education Foundation Hyderabad India Amrita Vishwa Vidyapeetham Mysuru Department of Computer Science Mysuru India School of Computing and Intelligent Systems Manipal University Jaipur Jaipur India Electrical Engineering Guru Ghasidas Vishwavidyalaya Bilaspur Bilaspur India

ISBN: (纸本)9798350364590

Understanding facial expressions is key to successful communication. It's like decoding a secret language that helps us know if someone is happy, sad, angry, surprised, or something else altogether. Harnessing facial emotion recognition (FER) in machines holds the potential to revolutionize human-computer interaction, improve customer service experiences and provide invaluable support in medical diagnosis. Machines can comprehend human's needs more effectively by reading the emotions from their facial expression and therefore this results in a more natural conversation between them and the machine. Various machine and deep learning methodologies have been implemented for FER. In this research work, the authors propose the state-of-the-art YOLO (You Only Look Once) based deep learning classifier for FER. The YOLO algorithm is a single-shot approach that efficiently classifies objects in a single pass. It achieves this by using a single neural network to predict bounding boxes and target class probabilities by utilizing the entire image as input. YOLOv8 is a recent iteration of the YOLO object detection algorithm is popular for its speed and accuracy which offers improvements over prior versions, making it a suitable choice for real-time intended task of FER. With the proposed classifier, the authors achieved test accuracy of 72.56% on the FER-2013 dataset in a seven-class FER task. © 2024 ieee.

关键词： Adversarial machine learning

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Machine Learning Application for Classification of Brain Tumors Through MR Images to Detect Malignancy 4

Machine Learning Application for Classification of Brain Tum...

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4th ieee international conference on Computing, Communication, and intelligent systems, ICCCIS 2023

作者： Chanda, Kunal Tiwari, Jay Verdhan Banik, Souvik Ahmed, Washef Advanced Signal Processing Group Centre for Development of Advanced Computing Kolkata India Medical Electronics Ministry of Electronics and Information Technology New Delhi India

ISBN: (纸本)9798350306118

In the present paper classification of Brain Tumours through MRI imaging as Fine, Medium and Coarse textures is proposed. The other methods of classification of brain tumours that are frequently used are based on size, shape and spread of the tumour. The importance of classifying tumours based on texture is that there is correlation between malignancy of the tumour and the texture of the tumour. The other methods normally don't give this information. In this paper relation between texture of the tumour and it's malignancy has been explored. Four methods of feature extraction have been used, in order to obtain texture information of the tumour. They are, Gray Level Co-occurrence Matrix (GLCM), Gradient Matrix (GM), Laplacian Of Gaussian (LoG) and the Run-Length Matrix (RLM). Once the features are extracted, they are passed to Decision Tree classification algorithm. The classification algorithms classified the extracted features as having Fine, Medium or Coarse textures. The result obtained showed correlation between texture of the tumour and its malignancy. If the texture is predicted as Fine, then it is most likely a non-malignant, or benign tumour, and its diagnosis is most likely going to give a Complete response. A Medium texture would most likely give a Partial response, and is not very malignant. This is important, since knowing this information can benefit the patient and clinicians. This improves the diagnostic confidence of the clinicians and allows them to diagnose and report multiple patients that results in lower cost of the examination as well as access to diagnostics to people living in remote areas. © 2023 ieee.

关键词： Decision trees

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Optimizing SSVEP Detection in Reduced-Channel BCI-EEG Devices: Synchronization and Power Dynamics Across Brain Regions 1

Optimizing SSVEP Detection in Reduced-Channel BCI-EEG Device...

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1st international conference on Smart Mechatronics, ICSMech 2024

作者： Amri, M. Faizal Aprilia, Dina Robiyana, Iqbal Suhendra, M. Agung Budiman, Arief Yuliani, Asri Rizki Research Center of Smart Mechatronic National Research and Innovation Agency West Java Indonesia Universitas Mandiri Faculty of Science Department of Physics Subang Indonesia Faculty of Economics and Business Education Universitas Pendidikan Indonesia Management Study Program West Java Indonesia Research Center for Artificial Intelligence and Cyber Security National Research and Innovation Agency Bandung Indonesia

ISBN: (纸本)9798350368918

Steady-state visual evoked potentials (SSVEP) are widely used in EEG-based brain-computer interfaces (BCIs) due to their fast response times and minimal subject training requirements. These signals, elicited by visual stimuli at specific frequencies, enable users to interact with external systems by focusing on flickering stimuli. SSVEP responses are strongest in occipital, though contributions from other brain areas, such as the parietal, frontal and central regions, are also relevant. With the growing adoption of portable EEG devices, understanding channel performance across brain regions is essential, especially given the limitations of fewer channels and reduced signal quality. In this paper, we evaluated the synchronization and power dynamics of EEG channels using Phase Locking Value (PLV) and Event-Related Spectral Perturbation (ERSP) metrics. Using a public dataset with limited subjects and two frequency stimuli (8 Hz and 14 Hz), the study offers insights into optimal channel selection and regional contributions, enhancing future SSVEP-based BCI design and performance. From this study, it was concluded that the 3 best channels according to their respective stimulus frequencies (8 Hz and 14 Hz) and PLV+ERSP values were as follows (Oz: 0.813 0.757, C4: 0.823 0.706, C5: 0.818 0.713). © 2024 ieee.

关键词： Biomedical signal processing

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Garbage classification recognition based on improved YOLOv8 model

Garbage classification recognition based on improved YOLOv8 ...

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signal processing and intelligent Computing (SPIC), international conference on

作者： Lijuan Qin Xiaoyu Tang Feng Zhang School of Information Science and Engineering Shenyang Ligong University Shenyang China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Science

ISBN: (数字)9798350368888

ISBN: (纸本)9798350368895

The use of intelligent garbage classification methods and devices for waste sorting is set to become the future trend in waste management. With the development of high-precision image recognition algorithms, research in intelligent garbage classification has gradually matured. The core lies in utilizing image recognition technology to analyze garbage image information, thereby surpassing the limitations of manual sorting and achieving higher standards of speed and accuracy. This paper proposes a target detection scheme based on the introduction of the C2f_RepGhost module into YOLOv8, aiming to construct a model system for efficient garbage identification and classification. Thanks to its streamlined design and real-time response characteristics, this model can operate efficiently on platforms such as smart mobile devices, enabling real-time intelligent automatic classification of various types of waste.

关键词： Waste management Accuracy Image recognition Computational modeling Streaming media Real-time systems Data models Usability Standards Sorting

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Ramanujan Sums-Wavelet Transform: A New Approach to signal processing 3

Ramanujan Sums-Wavelet Transform: A New Approach to Signal P...

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3rd international conference on intelligent Computing, Instrumentation and Control Technologies, ICICICT 2022

作者： Sajikumar, S. Vinod, S. College of Engineering Trivandrum Department of Mathematics Kerala Thiruvananthapuram India

ISBN: (数字)9781665410052

ISBN: (纸本)9781665410052

Ramanujan Sum is a trigonometric summation introduced by Srinivasa Ramanujan in 1918. Recently, this summation technique is widely used in digital signal processing applications like pattern recognition, feature extraction, signal analysis, image compression, etc. Ramanujan Sums-Wavelet Transform (RSWT) is a signal analysis tool introduced by Chen et al. in 2013, in which the multiresolution representation nature of the wavelet transform and the energy conservation property of the Ramanujan Sums transform are blended to extract more useful information from the signal. Ordinary wavelet transform can be obtained as a special case of RSWT and hence it provides us with a multitude of ways to analyze signals under consideration. In this paper, we discuss RSWT in one-dimensional signal analysis, especially in time series analysis. © 2022 ieee.

关键词： Discrete wavelet transforms

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