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An error-efficient Gaussian filter for image processing by using the expanded operand decomposition logarithm multiplication

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Journal of Ambient Intelligence and Humanized Computing 2024年第1期15卷 1045-1052页

作者： Durgesh Nandan Kanungo, Jitendra Mahajan, Anurag Department of Electronics and Communication Engineering Jaypee University of Engineering and Technology Madhya Pradesh Guna India Department of Electronics and Telecommunication Engineering Symbiosis Institute of Technology Maharashtra Pune India

Digital signal processing and image processing applications require the noise free and efficient arithmetic operations. To overcome the noise problem, an efficient expanded operand decomposition (ExOD) based logarithm multiplication is proposed and applied for convolution of Gaussian smoothing filter to minimize the noise. The ExOD approach improves the average error percentage (AEP), mean square error (MSE) and the error rate in comparison of reported operand decomposition (OD) and improved operand decomposition (IOD). The peak signal-to-noise ratio (PSNR), MSE and the AEP of the ExOD logarithm multiplication method are improved as compared to the reported logarithm multiplication to the convolution operation. The ExOD method for logarithmic multiplication gives AEP of 0.276, 0.294 and 0.309%, whereas Mitchell’s algorithm gives 2.702, 2.661 and 2.830%, OD-Mitchell’s multiplication gives 1.441, 1.449 and 2.170% and IOD-Mitchell’s multiplication gives 1.627, 1.678 and 2.064% for 4-, 8-, and 16-bit operations respectively. ExOD-Mitchell’s multiplication gives 1.867% MSE, 45.453 db PSNR and 0.293% AEP, whereas Mitchell’s algorithm gives 18.688% MSE, 35.449 db PSNR and 2.731% AEP, OD-Mitchell’s multiplication gives 9.877% MSE, 38.218 db PSNR and 1.686% AEP and IOD-Mitchell’s multiplication gives 10.866% MSE, 37.804 db PSNR and 1.789% AEP. The proposed ExOD based Mitchell algorithm multiplication has useful applications in image processing with efficient accuracy. © Springer-Verlag GmbH Germany, part of Springer Nature 2018.

关键词： Convolution

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Reflection compensation and binary thresholding using the Homomorphic Filtering approach for composite manufacturing

Reflection compensation and binary thresholding using the Ho...

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2024 IEEE International Conference on Recent Advances in Science and engineering technology, ICRASET 2024

作者： Ravi Kumar, M.G. Sarvagya, Mrinal Thimmaiah, Sunil Manjesh, N. Channigavi, Annapooorna Vinay, N.A. Nagarjuna College of Engineering & Technology Dept of Electronics & Communication Bengaluru India Reva University School of Electronics & Communication Engineering Bengaluru India Sjc Institute of Technology Dept of Electronics & Communication Chickballapur India Dayanda Sagar College of Engineering Dept of Electronics & Communication Bengaluru India

ISBN: (纸本)9798350388602

The global aerospace manufacturing industry is witnessing a major paradigm shift in design and material choice from metals to composites such as Carbon Fiber Reinforcement Plastics (CFRP) in civil and military aircraft structure design. In prepreg-based composite fabrication, multiple sheets of prepreg material are manually laid in the desired orientation on a mold. The layout process is subjected to 100 percent ply-by-ply manual visual inspection for detecting inclusion which can arise when the protective films are missed to get removed or due to the tackiness of the prepregs layers, other materials used in the process get included between layers. Manual inspection during layup is skill-based. The black and reflective nature of the surface makes it difficult to detect the inclusions efficiently. Thus, there exists a need for an automated or semi-automated system that can aid the inspector in detecting and identifying inclusions during fabrication. This paper inculcates the preliminary work carried out towards developing a methodology to identify the inclusions using image processing algorithms. One of the challenges to using the images is the reflection from the layers. A homomorphic filtering-based approach was developed to reduce the effects of reflection and binary thresholding to detect the inclusion regions. The developed methodology was validated through experimental results. It is found that Homomorphic filtering-based reflection compensation and binary thresholding can distinguish the inclusion regions, which can be used by the inspector in the clean room environment. © 2024 IEEE.

关键词： Inspection

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PixNet for early diagnosis of COVID-19 using CT images

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Multimedia Tools and Applications 2025年第10期84卷 8179-8203页

作者： Pravin, Sheena Christabel Rohith, G. V, Kiruthika Saranya, J. Latha, B. Vigneshwar, K.R. Krishna, S. Vamsi Nambirajan, Hemkumar Sumitra, Y. School of Electronics Engineering Vellore Institute of Technology Chennai India Rajalakshmi Engineering College Chennai India

Globally, COVID-19 has impacted severely the healthcare systems and the patients as well. Reverse Transcription-Polymerase Chain Reaction (RT-PCR) tests can be effectively supplemented with computed tomography images. Recent research on CT-based screening found that COVID-19 infection is linked to abnormalities in chest Computed Tomography (CT). However, it is difficult to distinguish these from the general abnormalities that are caused in the lungs. Although COVID-19 RT-PCR testing is exceedingly precise, its sensitivity varies based on the sampling technique and the period as well. Some studies have even shown that RT-PCR testing displays very low COVID-19 sensitivity. This motivated the authors to propose a new deep-learning model called PixNet that can detect positive and negative cases accurately. We compared the effectiveness of the proposed model against several state-of-the-art models trained on CT images. On analysis, it is found that the proposed model displays 96% classification accuracy in diagnosing COVID-19 infection. The proposed algorithm automatically detects the infection owing to COVID-19 from CT scan images, which may be an effective screening tool for Clinicians. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： COVID-19

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Effective Chirp Modulation communication System Design for LEO Satellite IoT

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IEEE Internet of Things Journal 2025年第11期12卷 16962-16976页

作者： Zhao, Wenqing Zhang, Haozhe Zou, Jun Liu, Guangzu Shu, Feng Nanjing University of Science and Technology School of Electronic and Optical Engineering Nanjing China Hainan University School of Information and Communication Engineering Haikou China

The high-dynamic Doppler frequency offset presents great challenges to communications in the Low-Earth Orbit (LEO) satellite Internet of Things (IoT) scenarios, including the high complexity receiver synchronization algorithm, frequency tracking capability and high receiver sensitivity. In this paper, we propose an effective communication system architecture based on chirp modulation for LEO satellite IoT with reference to LoRa system. Our approach introduces an improved chirp modulation based on Low-Rate Optimization (LRO) with specified frequency interval and a CRC-assisted decoding scheme. Simulation results show that the proposed optimal LRO factor can improve the spectral efficiency. Furthermore, we propose a low complexity synchronization algorithm and LRO-based frequency tracking strategy. The simulation results confirm the feasibility of the proposed communication system for LEO satellite IoT, which demonstrates robust performance under high-dynamic Doppler frequency offsets. © 2014 IEEE.

关键词： Chirp modulation

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The Extended Hybrid Carrier-Based Multiple Access technology for High Mobility Scenarios

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China communications 2024年第1期21卷 53-68页

作者： Ge Song Xiaojie Fang Xuejun Sha School of Electronics and Information Engineering Harbin Institute of TechnologyHarbin 150001China

The hybrid carrier(HC)system rooted in the carrier fusion concept is gradually garnering *** this paper,we study the extended hybrid carrier(EHC)multiple access scheme to ensure reliable wireless *** employing the EHC modulation,a power layered multiplexing framework is realized,which exhibits enhanced interference suppression capability owing to the more uniform energy distribution *** implementation method and advantage mechanism are explicated respectively for the uplink and downlink,and the performance analysis under varying channel conditions is *** addition,considering the connectivity demand,we explore the non-orthogonal multiple access(NOMA)method of the EHC system and develop the EHC sparse code multiple access *** proposed scheme melds the energy spread superiority of EHC with the access capacity of NOMA,facilitating superior support for massive connectivity in high mobility *** results have verified the feasibility and advantages of the proposed *** with existing HC multiple access schemes,the proposed scheme exhibits robust bit error rate performance and can better guarantee multiple access performance in complex scenarios of nextgeneration communications.

关键词： extended hybrid carrier system high mo-Received:Jun.09,2023 Revised:Nov.20,2023 Editor:Yuanwei Liu bility communications multiple access

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Machine Learning Based Dent Damage Detection Using Image Processing Methods 2

Machine Learning Based Dent Damage Detection Using Image Pro...

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2nd International Conference on Emerging Trends in Information technology and engineering, ic-ETITE 2024

作者： Elaiyarani, K. Nigileeswari, B. Rani, G. Tharagai Rajeswari, J. Vignesh, S. Electronics and Communication Engineering Agni College of Technology Chennai India Electronics and Communication Engineering Dhanalakshmi Srinivasan College of Engineering and Technology Chennai India

ISBN: (纸本)9798350328202

Finding cracks and dents in pressed panel goods is a crucial step in assessing their quality. This study presents a novel edge line evaluation method for non-invasive crack detection based on obtaining the captured object's outline. The objective of this proposed work is to use convolutional neural network (CNN) algorithm to develop an automated system that can accurately recognize dents on cars or other metal surfaces. The suggested system uses YOLO V3, R-CNN algorithms, and image processing approaches to find dents or fissures in metal or concrete surfaces. A competitive edge should prioritize improved accuracy, quicker dent detection, wider applicability, and cost-effectiveness. © 2024 IEEE.

关键词： Crack detection

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Classification of Ayurvedic Medicinal Plant Leaf using KNN Classifier and FPGA 10

Classification of Ayurvedic Medicinal Plant Leaf using KNN C...

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10th International Conference on Advanced Computing and communication Systems, ICACCS 2024

作者： Jaffino, G. Jose, J. Prabin School of Electronics Engineering Vellore Institute of Technology Vellore India Aditya Engineering College Department of Electronics and Communication Engineering Surampalem India

ISBN: (纸本)9798350384369

The precise recognition of the appropriate botanical specimens utilized in the formulation of medicinal remedies assumes paramount significance within the domain of the Ayurvedic pharmaceutical industry. The primary criteria essential for the discernment of a medicinal plant include the configuration, hue, and consistency of its leaves. The chromatic and textural attributes exhibited by both sides of the leaf encompass determinative parameters crucial for species identification. Conducting real-time visual analysis of Ayurvedic medicinal plants is a demanding and formidable undertaking. This work introduces a methodology for the real-time classification of Ayurvedic medicinal plant leaves through the utilization of the FPGA (Field Programable gate array) system. The approach aims to facilitate efficient and accurate classification, enhancing the identification process of these plants based on their leaves. In the first stage, an average filter was employed to facilitate the image-smoothing process, thereby eliminating the inherent noise in the leaves of Ayurvedic medicinal plants. The pre-processed image is subsequently segmented by employing the Canny edge detection technique. Statistical characteristics including boundary points, connected components, object area, major axis length, filled area of the object, perimeter, eccentricity, equivalent diameter, and mean intensity are quantified and extracted from the images. The K-nearest neighbor algorithm is employed for the purpose of categorizing distinct Ayurvedic medicinal plant leaves, relying on the features that have been extracted. The proposed method system has been created and implemented on the Zynq-7000 platform utilizing the Xilinx System Generator approach. © 2024 IEEE.

关键词： Edge detection

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Clustered OLSR routing and FPGA parameter analysis for mobile ad hoc communication

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International Journal of electronics Letters 2025年第2期13卷 227-237页

作者： Kumar, Arvind Kumar, Adesh Agrawal, Anurag Vijay Department of Electrical and Electronics Engineering School of Advanced Engineering UPES Dehradun India Department of Electronics and Communication Engineering Bhagwant Institute of Technology (U.P.) 251315 Muzaffarnagar India

The research letter emphasises the hardware chip design of homogeneous clustered optimised link state routing (HMC-OLSR) protocol for Mobile Ad-hoc Networks (MANET). The homogeneously distributed clustering allows for parallel processing and distributes the burden evenly among the clusters to reduce the network complexity and computational time. The chip design is evaluated based on field programmable gate array (FPGA) performance metrics for the proposed HMC-OLSR protocol, which significantly reduced hardware utilisation by 27.45%, 48.8% of slices, 28.71%, 55.82% of flip-flops, 8.35%, 13.60% of look-up tables (LUTs), and 36.4% of input/output blocks (IoBs) compared to existing OLSR and destination-Sequenced distance vector (DSDV) protocols, respectively. The recommended protocol also outperformed OLSR and DSDV routing protocols in FPGA timing constraints such as minimum time before the clock, maximum time after the clock, and minimum propagation period. The novelty of the work is in the incorporation of an FPGA-integrated scalable network design that surpasses maximum throughput, packet delivery ratio (PDR), minimum power consumption, end-to-end delay, and control overhead. © 2025 Informa UK Limited, trading as Taylor & Francis Group.

关键词： FPGA MANET node and routing OLSR

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Deep learning countermeasures for detecting replay speech attacks: a review

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International Journal of Speech technology 2025年第1期28卷 39-51页

作者： Veesa, Suresh Singh, Madhusudan Department of Electronics and Communication Engineering National Institute of Technology Nagaland Chumukedima Nagaland Dimapur797103 India

Automatic speaker verification (ASV) systems are widely accepted for biometric authentication in real-time applications. However, such ASV systems need robust protection against well-known replay attacks, especially for highly sensitive applications. This study provides a detailed overview of existing deep learning based solutions developed for replay attack detection task. Convolution neural network architectures are most widely explored for countermeasure development. In this study, existing deep learning frameworks are categorized into four groups, namely, spectrogram based deep neural networks (DNN), handcraft features based DNN, source features based DNN and end-to-end DNN frameworks. They have demonstrated notable performances in replay speech detection context. However, their generalisation remains questionable due to the potential challenges posed by unknown types of replay attacks in the future. The study highlights that excitation source features explorations in DNN framework are limited. The study also discussed few existing Gaussian mixture model (GMM) based excitation source explorations, indicating better results may be obtained over existing solutions, using alternative source features based GMM/DNN methods in future. Hence, excitation source information (either implicit or explicit form) may be further explored with GMM/DNN methods. The study concludes with a discussion on potential of excitation source information in replay detection context, following with possible future directions using source information. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Convolutional neural networks

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A SECURE INTERNET OF THINGS MODEL USING BLOCKCHAIN WITH INTEGRATED POWER OPTIMIZATION

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Telecommunications and Radio engineering (English translation of Elektrosvyaz and Radiotekhnika) 2024年第9期83卷 55-67页

作者： Tirumala Venkateswarlu, V. Riyazuddien, Shaik Zelani, Shaik Khader Khan, Pathan Mahamood Prasad, Satamraju Krishna Department of Electronics and Communication Engineering RISE Krishna Sai Prakasam Group of Institutions Ongole Andhra Pradesh Valluru India Department of Electronics and Communication Engineering Vasireddy Venkatadri Institute of Technology Andhra Pradesh India Department of Electronics and Communication Engineering Malineni Lakshmiah Women’s Engineering College Andhra Pradesh Pulladigunta India Vasireddy Venkatadri Institute of Technology Andhra Pradesh India

Wireless sensor networks (WSNs) base Internet of Things (IoT) have emerged as a pivotal technology for monitoring and collecting data in diverse applications, including environmental monitoring, industrial automation, and healthcare. However, the limited energy resources of sensor nodes present a significant challenge in ensuring the long-term sustainability and effectiveness of these networks. Energy optimization in WSNs has become a critical research focus to extend network lifetime, enhance reliability, and reduce operational costs. Security is another important issue that needs developer attention. This paper presents an implementation of blockchain security architecture for data storage and access control. A node synchronization-based energy optimization algorithm is developed and integrated into this system to enhance the lifetime of the resource-constrained IoT networks. Results after the implementation and evaluation demonstrate that the proposed model outperforms the existing models in terms of network lifetime prolonging, latency, and throughput. Use of blockchain makes the system secured for storage and authenticated accessing. Overall, the system is reliable, flexible, and robust for applications running on low-power WSNs. © 2024 by Begell House, Inc.

关键词： Internet of things

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