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Innovations in High Speed Communication and signal processing (IHCSP), International Conference on

作者： G. Ravivarman Abhiraj Malhotra B Reddy Rajalakshmi G Satish Kumar R V. A. Mishra Department of Electrical and Electronics Engineering Karpagam Academy of Higher Education Coimbatore India Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Chitkara Centre for Research and Development Chitkara University Himachal Pradesh India Department of IT Prince Shri Venkateshwara Padmavathy Engineering College Chennai Adjunct Faculty School of Mangement PG Management JAIN (Deemed to be University) Bangalore Karnataka India Computer Engg Vishwakarma Institute of Technology Pune India

ISBN: (数字)9798350368949

ISBN: (纸本)9798350368956

A network of interconnected computing devices, sensors, and other electronic devices that can conversation and process information is known as the IoT. Devices on the network that have unique IP addresses may communicate with one another and with the outside world over the internet. The idea of the IoT has grown in significance because intelligent environments, including smart homes and cities, have been developed. These environments include a wide range of application disciplines. Aiming to make every day human activities more creative and competitive, such smart settings address concerns with living conditions, industrial demands, and energy usage. With the proliferation of IoT services and apps across many networks, the primary objective is now obvious. This study presented SMO-HPSO to improve identification accuracy while decreasing false alarm levels, and to lever the enormous quantity of intrusion statistics classification problems. In addition, the Random Forest Classifier (RFC) is used to find the best values for attack categorization using the UNSW NB-15 and NSL-KDD datasets. Current approaches such as RFC, DT, and SVM were contrasted with the suggested SMO-HPSO method. When tested against other algorithms, SMO-HPSO outperformed them on the NSL-KDD and UNSW NB-15 datasets. Positive outcomes, including enhanced efficiency in attack detection and categorization, have been shown by the suggested models.

关键词： Support vector machines Technological innovation Accuracy Urban areas signal processing algorithms Smart homes signal processing NSL-KDD Internet of Things Random forests

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SMD Coincident Antenna Array for 2D Field Acquisition and Reconstruction

SMD Coincident Antenna Array for 2D Field Acquisition and Re...

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International Conference on Electromagnetics in Advanced Applications (ICEAA)

作者： Francesca Ortolani Ironbridge Electronics Rome Italy

ISBN: (数字)9798350360974

ISBN: (纸本)9798350360981

Field acquisition and reconstruction by means of (near-)coincident arrays is quite consolidated in acoustics. Techniques such as Mid-Side, XY and Ambisonics, supported by algorithms for signal processing, paved the way to 2-dimensional and 3-dimensional sound recording and field synthesis, by placing microphones very close to each other in a single spot. On the other hand, the use of antenna coincident arrays for electromagnetic field acquisition is quite rare. This article proposes a preliminary study on the feasibility of coincident antenna arrays by means of surface mounted miniature chip antennas and the application of a 2-dimensional field acquisition technique such as Mid-Side to electromagnetism.

关键词： Phased arrays Three-dimensional displays Array signal processing Surface mount technology signal processing algorithms Radar Radar antennas Recording Electromagnetics Antenna arrays

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Fault Diagnosis of Precision Screw Pump Based on Multi-Scale Convolutional Neural Networks

Fault Diagnosis of Precision Screw Pump Based on Multi-Scale...

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Digital Society and Intelligent Systems (DSInS), International Conference on

作者： Ergang Luo Hongyi Gan Xiao Shang Chunbai Liu Linsen Song Zhenhui Li Li Tian Yiwen Zhang Xinming Zhang Zhi Gao Changchun University of Science and Technology Changchun Jilin China FAW Tooling Die Manufacturing Corporation Changchun Jilin China AFAW Foundry Corporation Changchun Jilin China Changchun Zhongsheng Technology Co Ltd Changchun Jilin China University-Enterprise Joint Innovation Laboratory of Intelligent Manufacturing Assembly and Inspection Technology and Equipment for Automotive Parts and Components Changchun University of Science and Technology Changchun Jilin China Foshan University Foshan Guangdong China Changchun University of Technology Changchun Jilin China

ISBN: (数字)9798331528829

ISBN: (纸本)9798331528836

In the stator coating process of new energy automobile motor manufacturing, precision screw pumps have become the key equipment due to high-efficiency transportation and good adaptability, but they are prone to failures in long-term operation, which affects production efficiency and motor performance. Traditional fault diagnosis relies on manual labour, which is inefficient and inaccurate. In this paper, the three-axis vibration signals of the screw pump are collected, and the signal processing methods such as $\mathbf{3}\boldsymbol{\sigma}$ , LSTM and robust normalization are combined with BP neural network, CNN and multi-scale convolutional neural network MSCNN, respectively, to construct a fault diagnosis model of precision screw pump based on MSCNN, with a classification accuracy of up to 92%, which realizes the timely and accurate diagnosis of the faults of the precision screw pump and improves the stability and overall performance of the stator coating process.

关键词： Fault diagnosis Vibrations Accuracy Neural networks signal processing algorithms Fasteners Stators Motors Convolutional neural networks Long short term memory

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Masked Face Inpainting Using Denoising Diffusion Probabilistic Models

Masked Face Inpainting Using Denoising Diffusion Probabilist...

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signal processing and Intelligent Computing (SPIC), International Conference on

作者： Bin Zhao Fang Li Fengxiang Li Shenyang Ligong University Shenyang China Liaoning Guidaojiaotong Polytechnic Institute Shenyang China

ISBN: (数字)9798350368888

ISBN: (纸本)9798350368895

Image inpainting in computer vision is a challenging task, as most existing approaches are trained for certain mask distributions, which limiting their generalization capability when confronted with unknown mask types. However, rather than producing semantically significant texture, training with pixel-level and perceptual losses frequently results in the extrapolation of basic texture in absent areas. In this paper, we propose an image inpainting approach based on Denoising Diffusion Probabilistic Model (DDPM). We utilize a pretrained unconditional DDPM as a generative prior. We alter the generative process of the model, enabling accelerated resampling for unknown regions based on known information from the given image. Since our approach does not involve any adjustments or modifications to the DDPM network itself, it can handle any form of mask and generate high-quality and diverse outcomes. The CelebA-HQ dataset experimental results show that our approach produces state-of-the-art performance.

关键词： Training Extrapolation Computer vision Limiting Image synthesis Noise reduction signal processing algorithms Diffusion models Probabilistic logic Faces

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Maximizing the Partial Decode-and-Forward Rate in the Gaussian MIMO Relay Channel

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IEEE TRANSACTIONS ON signal processing 2021年 69卷 1548-1562页

作者： Hellings, Christoph Gest, Patrick Wiegart, Thomas Utschick, Wolfgang Tech Univ Munich Methoden Signalverarbeitung Munich Germany Swiss Fed Inst Technol Dept Phys Zurich Switzerland TUM Inst Commun Engn Munich Germany

It is known that circularly symmetric Gaussian signals are the optimal input signals for the partial decode-and-forward (PDF) coding scheme in the Gaussian multiple-input multiple-output (MIMO) relay channel, but there is currently no method to find the optimal covariance matrices nor to compute the optimal achievable PDF rate since the optimization is a non-convex problem in its original formulation. In this paper, we show that it is possible to find a convex reformulation of the problem by means of an approximation and a primal decomposition. We derive an explicit solution for the inner problem as well as an explicit gradient for the outer problem, so that the efficient cutting-plane method can be applied for solving the outer problem. Based on these ingredients, we propose a convergent algorithm whose output is an approximation of the globally optimal solution along with a certificate of accuracy in terms of a maximum distance to the true global optimum. In numerical simulations, this distance converged to values close to zero in all considered instances of the problem, showing that the proposed method manages to find the global optimum in all these instances.

关键词： Relays MIMO communication Covariance matrices signal processing algorithms Approximation algorithms Transmitting antennas Technological innovation Gaussian relay channel multiple-input multiple-output (MIMO) partial decode-and-forward convex optimization sensitivity analysis cutting plane method generalized eigenvalue decomposition

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SPA 2024 Tutorial

SPA 2024 Tutorial

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signal processing algorithms, Architectures, Arrangements and Applications (SPA)

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Dog Breed Detection using YOLOv7 and WOA-CNN

Dog Breed Detection using YOLOv7 and WOA-CNN

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International Conference on Communications and signal processing

作者： Aniket Singh Rushil Kothari V. Arulalan Department of Computing Technologies SRM Institute of Science and Technology Kattankulathur Chengalpattu District Tamil Nadu India

ISBN: (数字)9798350353068

ISBN: (纸本)9798350353075

The need to accurately identify dog breeds is important due to their popularity and role as companions. With an increase in the variety of dog breeds worldwide, there is a pressing demand for better classification methods. Advances in artificial intelligence have resulted in the creation of a new model that combines YOLOv7 with the Whale Optimization Algorithm (WOA) in a Convolutional Neural Network (CNN) setup, designed specifically for detecting dog breeds. This model uses the extensive Stanford Dogs dataset, which includes 120 unique breeds, and has been thoroughly tested. It has shown to be more accurate and precise than current methods. The effectiveness of this model in identifying dog breeds marks a significant improvement in the field and suggests its usefulness in various areas such as veterinary services, dog behavior studies, and improved pet management systems. Additionally, this method showcases the benefits of merging advanced machine learning techniques with nature-inspired algorithms to address complex identification challenges, making a significant contribution to the development of AI-based solutions in animal identification and related areas.

关键词： Machine learning algorithms Neural networks Merging signal processing algorithms Dogs Pressing signal processing

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A DNN Based Adaptive Filter for Speech Enhancement

A DNN Based Adaptive Filter for Speech Enhancement

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Data Science and Information System (ICDSIS), IEEE International Conference on

作者： S Pradeep Kumar Kancharla Anitha Sheela Department of ECE Kakatiya Institute of Technology and Science Warangal India Department of ECE Jawaharlal Nehru Technological University Hyderabad India

ISBN: (数字)9798350381665

ISBN: (纸本)9798350381672

The challenge of voice improvement in signal processing is enhancing the quality of speech signals recorded in noisy or unreliable environments. Digital communications, speech preparation for hearing aids, and recognition of speech all work better when speech augmentation is used. Enhancement of speech results in improving the transparency, ease of understanding and sound attributes of messages. Background noise is muted through speech enhancement. Numerous speech processing applications have emerged as a result of speech’s practicality. In this paper, a DNN-based adaptive filter is used to improve the speech quality. DNN estimates filter coefficients. The proposed DNN-based algorithm works well. It accurately estimates filter coefficients from clamorous speech. DNNs can learn well. This enables the proposed algorithm to improve speech quality and intelligibility.

关键词： Training Adaptive filters signal processing algorithms Speech recognition Artificial neural networks Speech enhancement Filtering algorithms

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Recognition of Chinese Gong-Che Notation Characters Based on YOLOv8

Recognition of Chinese Gong-Che Notation Characters Based on...

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6th International Conference on Intelligent Computing and signal processing (ICSP)

作者： Yuqian Zhang Liumei Zhang Yuanjiao Hu Tengzhuo Ju School of Computer Science Xi'an Shiyou University Xi’ an China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

Although Optical Music Recognition (OMR) technology has matured for general music notation like staff notation, it is rarely used in traditional Chinese music notation, such as Gong-che notation. It is difficult to accurately identify Chinese characters in Gong-che notation with traditional methods. To address this, we propose the YOLOv8 method to enhance Gong-che notation characters recognition accuracy. We adopt lightweight and medium-weight YOLOv8 models and explore the optimal network structure to enhance the generalization ability of characters recognition. Data augmentation techniques such as image translation, rotation and noise processing are used to solve the diversity of characters recognition and enhance the robustness of the model. Experimental results demonstrate that the YOLOv8m model achieved an accuracy improvement from 74.95% to 84.19% in Gong-che notation characters recognition, surpassing the performance of the YOLOv8n model. This validates the remarkable performance of the YOLOv8 network in Gong-che notation characters recognition tasks.

关键词： Accuracy Optical character recognition Noise signal processing algorithms Optical imaging Data augmentation Rhythm Robustness Character recognition Optical signal processing

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Sound-based Bee Colony State Analysis Using Compact MFCC Patterns

Sound-based Bee Colony State Analysis Using Compact MFCC Pat...

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International Symposium on Parallel and Distributed processing with Applications, ISPA

作者： Weihai Huang Weize Yang Zhicong Luo Jun Qi Tingting Zhang Xiangzeng Kong College of Mechanical and Electrical Engineering Fujian Agriculture and Forestry University Fuzhou China Department of Computing Xi’an Jiaotong-Liverpool University Suzhou China School of Future Technology Fujian Agriculture and Forestry University Fuzhou China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

Bees play an important role in agricultural production. However, beekeeping relies on experienced beekeepers to take time and effort to maintain the bee colonies. To lower the threshold of beekeeping and improve efficiency, we proposed a sound-based bee colony state analysis model using compact Mel frequency cepstral coefficient (MFCC) patterns. Facing high-dimensional bee colony sound signals, we obtained MFCCs from the signals and constructed a set of filters to extract MFCC-based compact features called compact MFCC patterns. After extracting compact features, the feature set was given to the support vector machine classifier. We recorded the sounds of bee colonies under normal conditions and in the absence of the queen bee to verify the proposed model. While significantly compressing the dimensionality of MFCCs, the model still achieved an accuracy score of 99.30% in distinguishing the presence of the queen bee. The extracted compact MFCC patterns effectively and compactly characterize the information related to the bee colony states in the bee colony sound signals, giving the model an excellent ability to discriminate the state of the bee colony.

关键词： Support vector machines Analytical models Time-frequency analysis Filters signal processing algorithms Production Feature extraction Mel frequency cepstral coefficient Time-domain analysis Monitoring

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