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Fusion-Based Digital Twin Framework with Convolutional Neural Networks (CNN) for Real-Time Threat Detection and Image Enhancement in Low-Light IoT Environments

Fusion-Based Digital Twin Framework with Convolutional Neura...

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Integrated Intelligence and Communication Systems (ICIICS), International Conference on

作者： K. Ashok Kumar Pareshwar Prasad Barmola S. P. Velmurugan Manjunath A C Umang Soni Ramya Maranan Department of Physics B V Raju Institute of Technology Narsapur Medak India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun India Department of Electronics and Communication Engineering Kalasalingam Academy of Research and Education Krishankoil India Department of Mechanical Engineering Brindavan college of Engineering Bangalore India Faculty of Engineering and Technology Parul institute of Engineering and Technology Vadodara India Department of Research and Innovation Saveetha School of Engineering SIMATS Chennai India

ISBN: (数字)9798331504960

ISBN: (纸本)9798331504977

Image acquisition in low-light environments often suffers from poor contrast, high noise, and loss of fine details, making effective enhancement challenging. Conventional enhancement methods frequently fail to balance detail preservation and noise suppression. This paper introduces a novel fusion-based technique for enhancing images captured in low-light settings. The proposed method integrates multi-scale decomposition, adaptive histogram equalization, and wavelet transform to improve image quality. Multi-scale decomposition captures critical structural information, adaptive histogram equalization adjusts intensity levels for enhanced contrast, and wavelet transform reduces noise while refining image details. Evaluation on standard low-light image datasets demonstrates that this method outperforms current state-of-the-art techniques. The enhanced images show a peak signal-to-noise ratio (PSNR) of 32.45 dB and a structural similarity index (SSIM) of 0.87, reflecting an 18% improvement in PSNR and a 12% gain in SSIM over traditional methods. Additionally, the technique reduces noise variance by 20% and enhances visual clarity, making it suitable for applications such as surveillance, medical imaging, and autonomous driving. In conclusion, this fusion-based approach provides an effective solution for low-light image enhancement, maintaining critical details and achieving improved visual quality through balanced contrast enhancement and noise reduction.

关键词： Wavelet transforms Histograms Visualization PSNR Noise Noise reduction Adaptive equalizers Real-time systems Convolutional neural networks Image enhancement

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Sensing metric-based hybrid underlay/overlay transmission framework for CR-IoT networks for smart city applications

Sensing metric-based hybrid underlay/overlay transmission fr...

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7th IET Smart Cities Symposium (SCS 2023)

作者： Indu Bala Maad M. Mijwil Mostafa Abotaleb El-Sayed M. El-kenawy Abdelhameed Ibrahim School of Electrical and Electronics Engineering Lovely Professional University Punjab India Computer Techniques Engineering Department Baghdad College of Economic Sciences University Baghdad Iraq Department of System Programming South Ural State University Chelyabinsk Russia Department of Communications and Electronics Delta Higher Institute of Engineering and Technology Egypt Computer Engineering and Control Systems Department Faculty of Engineering Mansoura University Mansoura Egypt

With the increasing number of IoT devices, there is a growing need for bandwidth to support their communication. Unfortunately, there is a shortage of available bandwidth due to preallocated bands for various services. To address this issue, Cognitive Internet of Things (CR-IoT) enables devices to optimize their efficiency and enhance their communication capabilities by intelligently accessing available bandwidth. This is achieved through the use of soft sensing metrics, where devices continuously monitor the RF environment and transmit data opportunistically in overlay mode if a free channel is detected, or in underlay mode if not. In this paper, a soft sensing metric based hybrid transmission framework is proposed for CR-IoT devices to meet the data rate requirement for the smart city applications. The efficacy of this approach is demonstrated through simulation results.

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AI-Driven Test and Measurement Automation in electronics Manufacturing

AI-Driven Test and Measurement Automation in Electronics Man...

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International Conference on Science technology engineering and Management (ICONSTEM)

作者： Anurag Vijay Agrawal Kommisetti Murthy Raju Aparna P Gade Sravya A Chandrashekhar J. Ramya Department of Electronics and Communication Engineering J. P. Institute of Engineering and Technology Meerut Uttar Pradesh India Department of Electronics and Communication Engineering Shri Vishnu Engineering College For Women Bhimavaram Andha Pradesh India Department of Computer Science Engineering RMK College of Engineering and Technology Puduvoyal Tamil Nadu India Department of Electronics and Communication Engineering Shri Vishnu Engineering College for Women Bhimavaram Andhra Pradesh India Department of Mechanical Engineering Faculty of Science and Technology ICFAI Foundation for Higher Education Hyderabad Telangana India Department of Artificial Intelligence and Data Science OMR St. Joseph's College of Engineering Chennai Tamil Nadu India

ISBN: (数字)9798350365092

ISBN: (纸本)9798350365108

Innovative strategies to increase output while decreasing expenses and enhancing product quality are essential in the dynamic electronics manufacturing industry. A robust AI-based system for automating test and measurement tasks in electronics manufacture is detailed in this research. Manual testing and measurement is a part of traditional production procedures; it's labor-intensive, time-consuming, and prone to mistakes. Smarter, more adaptable, and more efficient processes are achieved through the automation of tests and measurements facilitated by AI. The proposed approach makes use of machine learning techniques to sift through mountains of industrial data. In order to identify patterns, outliers, and problems, the AI system examines both past and present data. Reducing the likelihood of defective items reaching the final stage of production, this proactive technique helps identify and rectify issues fast. Product quality, cost, scrap rate, and production yield are all enhanced by AI-driven test and measurement automation in electronics manufacturing. In keeping with Industry 4.0, this ground-breaking method improves the manufacturing processes of electronic devices. Industry intelligence, sustainability, and efficiency are all enhanced by it.

关键词： Manufacturing processes Production Metrology Product design Mathematics Quality assessment Task analysis

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A Wideband WiFi/NB-IoT On-Glass Antenna for Smart Building Applications

A Wideband WiFi/NB-IoT On-Glass Antenna for Smart Building A...

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European Conference on Antennas and Propagation, EuCAP

作者： Muhammad Uzair Juin Acharjee Akkarat Boonpoonga Dirk Heberling Suramate Chalermwisutkul High Freqeuncy Systems Laboratory King Mongkut's University of Technology North Bangkok Bangkok Thailand Department of Electronics and Communication Engineering St. Thomas' College of Engineering and Technology Kolkata India Department of Electrical and Computer Engineering Faculty of Engineering King Mongkut's University of Technology North Bangkok Bangkok Thailand Institute of High Frequency Technology RWTH Aachen University Aachen Germany Fraunhofer Institute for High Frequency Physics and Radar Techniques FHR Wachberg Germany

ISBN: (数字)9788831299107

ISBN: (纸本)9798350366327

In this work, an on-glass antenna is proposed for IoT and smart building applications. The dielectric properties of five common glass types used in architecture were investigated. Clear glass, 3 mm thick, was selected as the substrate. The antenna is a rectangular monopole with a coplanar waveguide feed and an asymmetric ground. It was designed and simulated using Ansys HFSS, then fabricated using clear glass and copper tape. Measurements of the reflection coefficient and radiation pattern showed good agreement with simulations. The antenna exhibits a bidirectional radiation pattern, with 2.8 dBi gain and 94.82% radiation efficiency at 2.1 GHz and 3.9 dBi gain and 95.72% radiation efficiency at 2.4 GHz, respectively. A −10 dB bandwidth of 650 MHz (29.3%) centered at 2.215 GHz was achieved. The proposed antenna shows promise for integration with architectural glass elements in smart building applications, such as smart glass doors and windows.

关键词： Antenna measurements Smart buildings Indium tin oxide Glass Reflection coefficient Internet of Things Substrates Wideband Gain Antenna radiation patterns

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Efficient and Non-Invasive Rice Moisture Quantification Using Coupled Lines Microwave Sensor Design

Efficient and Non-Invasive Rice Moisture Quantification Usin...

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International Conference on Information technology (INCIT)

作者： Pratya Phanomupatum Ravee Phromloungsri Decha Wongpatsa Tiwat Pongthavornkamol Phatcharanat Saeng-On Somchat Sonasang Electronics Faculty of Industrial Technology Nakhon Phanom University Nakhon Phanom Thailand Computer and Communication Engineering Faculty of Technology Udon Thani Rajabhat University Udon Thani Thailand Faculty of Agriculture and Technology Nakhon Phanom University Nakhon Phanom Thailand Location and Automatic Identification System Research Team Location and Automatic NECTEC National Science and Technology Development Agency Pathum Thani Thailand Electrical Elements and Wireless Devices Unit (EEWDU) Faculty of Industrial Technology Nakhon Phanom University Nakhon Phanom Thailand

ISBN: (数字)9798350366303

ISBN: (纸本)9798350366310

This research presents the design, simulation, and experimental validation of a novel microwave sensor for noninvasive moisture measurement in rice. The sensor employs coupled microstrip lines fabricated on an FR4 substrate, designed to operate at a center frequency of 900 MHz with a quarter-wavelength configuration. The Advanced Design System software was utilized for the simulation, where key parameters such as S 21 and phase response were analyzed across a frequency range of 0.1 to 3 GHz. The sensor was fabricated and tested using a Vector Network Analyzer, where the moisture content in rice samples was varied incrementally, and the sensor’s performance was evaluated based on the changes in transmission characteristics. The results demonstrated the sensor’s potential for accurate moisture detection in rice, showcasing its feasibility as a noninvasive measurement technique in agricultural applications. This study contributes to the development of efficient, real-time moisture measurement technologies in the food processing industry.

关键词： Microwave measurement Sensitivity Moisture measurement Moisture Network analyzers Microwave sensors Vectors Real-time systems System software Substrates

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Entanglement properties of optomagnonic crystal from nonlinear perspective

arXiv

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arXiv 2024年

作者： Wanic, M. Jasiukiewicz, C. Toklikishvili, Z. Jandieri, V. Trybus, M. Jartych, E. Mishra, S.K. Chotorlishvili, L. Department of Physics and Medical Engineering Rzeszów University of Technology Rzeszów35-959 Poland Faculty of Exact and Natural Sciences Tbilisi State University Chavchavadze av.3 Tbilisi0128 Georgia Faculty of Engineering University of Duisburg-Essen CENIDE - Center for Nanointegration Duisburg-Essen DuisburgD-47048 Germany Lublin University of Technology Department of Electronics and Information Technology Faculty of Electrical Engineering and Computer Science Lublin20-618 Poland Varanasi221005 India

Optomagnonics is a new field of research in condensed matter physics and quantum optics focused on strong magnon-photon interactions. Particular interest concerns realistic, experimentally feasible materials and prototype cheap elements for futuristic nanodevices implemented in the processing or storing of quantum information. Hence, magnon-photon entanglement is an essential ingredient since it allows manipulation and control of the quantum states of bipartite systems. Quantifying the entanglement between two continuous bosonic modes, such as magnons and photons, is not trivial. The state-of-the-art for today is the logarithmic negativity, calculated through the quantum Langevin equations subjected to thermal noise. However, due to its complexity, this method requires further approximation. Namely, after the linearization procedure, quantum operators are replaced by their semiclassical expectation values calculated near the steady state. The cost of this approximation is the loss of information about the phases of propagated quantum states. In the present work, we propose a new procedure that avoids the linearization of dynamics. Prior analyzing the quantum entanglement, we explore the nonlinear semiclassical dynamics in detail and precisely define the phase space. The typical nonlinear dynamical system holds bifurcation points and fixed points of different characters in its phase space: Saddle points, Stable and unstable spiral, and Stable node points. Here, we show that methods of the qualitative theory of nonlinear differential equations are also relevant for optomagnonic problems. Our main finding is that entanglement is not defined in the Saddle Point region. On the other hand, the maximum of the entanglement corresponds to the region near the border between the Stable node and Stable spiral regions. Our approach is quite general. However, in numerical calculations, we considered a particular system: optomagnonic crystal based on the yttrium iron garnet (YIG)

关键词： Covariance matrix

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An Analysis of Encapsulating Security Payload in Wireless Network Security

An Analysis of Encapsulating Security Payload in Wireless Ne...

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Emerging Research in Computational Science (ICERCS), International Conference on

作者： Devika A. Verma Vaishali Singh Beemkumar. N Ritu Shree Deepak Minhas S. Yuvaraj Department of Computer Engineering Vishwakarma Institute of Information Technology Pune India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Mechanical Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Bangalore Karnataka India Department of Computer Science & Application Vivekananda Global University Jaipur India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Department of Electronics and Communication Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai

The Encapsulating Safety Payload (ESP) is a necessary part of the IPsec suite of protocols, imparting a critical layer of security within a Wi-Fi community. This paper aims to evaluate ESP and its software in wireless community protection comprehensively. ESP capabilities as a secured payload encapsulating data via imparting integrity, authentication, and confidentiality. It commonly accomplishes this by using a mixture of cryptography and tunneling to shield the contents of the packets from alteration, eavesdropping, and unauthorized get entry. Moreover, this paper outlines the distinctive ESP modes and cipher suites, presenting how they may be used to defend data being despatched over a Wi-Fi community. ESP additionally affords numerous safety enhancements, such as protection from replay assaults, packet length control, and critical negotiation. Furthermore, this paper explores the challenges associated with implementing and deploying ESP in wireless networks. Subsequently, the paper will pay attention to the numerous overall performance issues for ESP utilization by analyzing the effect of latency, throughput, and power intake on ESP-enabled Wi-Fi networks.

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Chatbots and Customer Service: Enhancing Marketing Management with AI Technologies

Chatbots and Customer Service: Enhancing Marketing Managemen...

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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Rajchandar, K. Hari Krishna, Somanchi Khalil Wani, Showkat Pathak, Supriya Jain, Amit Kumar Naveen Kumar, C.M. Sr University School of Cs & Ai Telangana Warangal India Vignana Bharathi Institute of Technology Department of Business Management Telangana State 501301 India Saudi Electronic University College of Administrative and Financial Sciences Department of Business Administration Riyadh Saudi Arabia Oriental University Faculty of Management and Commerce Madhya Pradesh Indore India Poornima University Department of Electrical & Electronics Engineering Rajasthan JaipurIS-2027-2031 India Malnad College of Engineering Department of Computer Science & Business Systems Karnataka Hassan573202 India

ISBN: (纸本)9798331508685

By increasing engagement, expediting client contacts, and boosting satisfaction through AI-driven solutions, chatbot integration in customer service has revolutionised marketing management. With an emphasis on how model assessment techniques - such as accuracy, precision, recall, and F1 score - can be applied to enhance chatbot efficacy in customer service scenarios, this paper investigates the role of machine learning models in optimising chatbot performance. In order to appropriately meet a variety of consumer demands, this research offers perspectives into the selection, training, and evaluation of chatbot models by analysing a range of AI technologies, such as natural language processing (NLP) and predictive analytics. The influence of chatbots on customer loyalty and retention is highlighted in this article, which also explores the benefits and limitations of utilising AI in real-time consumer interactions. According to research, well-considered ML models can produce more precise and sympathetic answers, which enhances brand perception and opens up new channels for CRM in marketing plans. © 2025 IEEE.

关键词： Sales

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Speech Measuring Following Semiconductor Feature for Acoustic Using Biodegradable Material

Speech Measuring Following Semiconductor Feature for Acousti...

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Intelligent Data Communication Technologies and Internet of Things (IDCIoT), International Conference on

作者： Shrikant Upadhyay Rakesh Kumar Binod Kumar Barkha Kumari Ankita Lakra Sampurna Mandal Department of Electronics and Communication Engineeing MLR Insitute of Technology Hyderabad India Wipro Engineering Edge Hyderabad India Facultyt of CSE & IT Jharkhand Rai University Ranchi India Department of Computer Science and Engineering (AI & AIML) GH Raisoni College of Engineering and Management Pune Faculty of Computing and IT Usha Martin Univerity Ranchi India Department of Computer Science and Engineering-Artifical Intelligence Brainware University Kolkata India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

This research paper primarily focuses to identify the various feature removal presence used for addresser recognition and validation. The current template i.e. classical and typical are considered to examine its role in different acoustic surroundings and also try to measure feature removal in acoustic condition considering factors like jitters, noise, echo etc. which truly a factor that affect the speech performance and degrades the overall performance of any system. Effort has been put to inspect the attainment of various feature removal approach with the help of classifier considering different structures. The behavior of different feature extraction removal simulated using Hindi samples and it will be analysed in terms of efficiency and error rate. The result may be useful for suitable for existing as well as any proposed model by changing its classifiers. Speech of different ages as well groups are used to analysed and implementation using 16-bit TMS 320 C5X semiconductor devices with the feature extraction and algorithm may improve the overall efficiency of the proposed system. Bio-degradable material can also help to reduce the acoustic level at different of speaker identifications. The feature of semiconductor helps to understand and also add the improvements in proper selection of feature extraction that consume less time in evaluating the algorithm and provide better efficiency in different acoustic conditions. Biodegradable material has not used so far in various studies and biodegradable paly a very important role in acoustic condition as we are dealing with acoustic condition and it is very important to take care of acoustic to come up with better efficiency.

关键词： Semiconductor device modeling Biodegradable materials Semiconductor device measurement Error analysis Biological system modeling Noise Feature extraction Acoustics Mel frequency cepstral coefficient Standards

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Architecture-based Evaluation of VGG16 and ResNet Models for an Online Deep Learning Environment for Medical Applications

Architecture-based Evaluation of VGG16 and ResNet Models for...

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Innovations in Science and technology for Sustainable Development (ICISTSD), International Conference on

作者： Hari Priyanka Chilakalapudi Ramya Venkatesan Yedukondalu Kamatham Department of Computer Science and Engineering Faculty of Engineering and Technology Annamalai University Chidambaram India Department of Computer Science and Engineering Matrusri Engineering College Hyderabad India Department of Electronics and Communication Engineering CVR College of Engineering Hyderabad India

ISBN: (纸本)9781665499378

Image classification using deep learning models has evolved impressively well in the past decade. Datasets containing millions of images grouped into thousands of classes have been used to train and test these models. Medical image classification however still faces the challenge of scarcity in datasets. Gathering data from various locations and placing it in a commonly accessed dataset is highly time-consuming. Diseases need real-time response just like any other mission-critical operation and online deep learning could be handy. There are many pre-trained models which acquired good accuracy on large datasets. But as the depth of the model increases the time taken to train the model and the number of computations also increase. In this paper, we evaluated two models with different architectures. VGG16 is a 16-layer normal stack of convolutional layers and ResNet50V2 is a stack of residual blocks with skip connections and 50 layers. We used a computer Tomography (CT) Lung image dataset to classify images into COVID, healthy and pneumonia images. We found that VGG16 is taking lesser time and computations with reduced loss when compared to the ResNet50V2 model. We finally conclude that ResNet50V2 is taking more time to train images as the model is 50 layers deep, whereas the VGG16 model is only 16 layers deep. Also, images that show mild infection were predicted as healthy images by ResNet50V2 but predicted correctly by the VGG16 model.

关键词： Deep learning COVID-19 Technological innovation Computational modeling Computed tomography Predictive models Real-time systems

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