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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Sribhavisha, Kr.B Manasa, Dampetla Prakash, Deviprabha R Narayanan, Ss Adithya Mohanty, Aparna Sumathi, G. Vellore Institute of Technology School of Electronics and Communication Engineering Vellore India

ISBN: (纸本)9798350372120

Beverage refers to a liquid food or drink which is normally consumed for its refreshing, appetizing and stimulating effects. Quality control is essential for any product being realized. It holds a main role in beverage industry to hold the product uniformity and dependability in order to ensure customer satisfaction, brand reputation and regulatory compliance. Beverage safety can be ensured through proper monitoring of the entire production chain. Ensuring the nutritional value ensures the quality of the product. Real time monitoring of beverages provide real-time visibility into the production process allowing to monitor critical parameters such as temperature, pH, color and dissolved oxygen. By detecting deviations in real-time, it can help in taking immediate corrective actions to prevent quality issues. The advent of IoT has revolutionized the food and beverage industry. Implementing IoT solutions in this sector can significantly enhance efficiency, quality control. This work presents an IoT based liquid quality analyzer system using a microcontroller (ESP32) in which multiple sensors are incorporated to measure pH, temperature, gas sensor and turbidity. The main objective is to provide a real time, accurate measurements for ensuring the quality check of commonly used beverages like Pepsi, 7 up and normal water. The analyzer system processes the sensor data using algorithms and displays the real time measured values using LCD display as well in graphical form. © 2024 IEEE.

关键词： Microcontrollers

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Low-light image enhancement using retinex based an extended ResNet model

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Multimedia Tools and Applications 2024年 1-16页

作者： Anila, V.S. Nagarajan, G. Perarasi, T. Department of Electronics and Communication Engineering Puducherry Technological University Puducherry India Department of Electronics and Communication Engineering Bannari Amman Institute of Technology Tamil Nadu India

Images are the primary inputs to the computer vision applications. The quality of the captured images is depending on the characteristics of the environment. A poor visual background can result in dark images. The darker environment has less illumination of light. So, the captured image is of low quality, thus the images are termed as low light images. If this kind of image is the input to computer vision applications such as classification, clustering, and recognition, then the efficiency becomes very low. To improve the image quality this paper introduces deep learning-based architecture. This paper modifies the existing ResNet50 architecture by adding extra layers to make it compatible with the present objective. To improve the novelty of the model, it has been trained from the base layer with the low-light dataset. The proposed model is trained with different optimizers. Machine learning models are trained using a variety of optimizers, each with distinct properties that influence the model's convergence, learning speed and generalization ability to new data. The performance of the model is evaluated by using the performance parameters PSNR, Entropy, SSIM, MSE and Global Illumination. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Image quality

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A P4-Based Approach to Traffic Isolation and Bandwidth Management for 5G Network Slicing

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Tsinghua Science and technology 2025年第1期30卷 171-185页

作者： Wenji He Haipeng Yao Huan Chang Yunjie Liu School of Information and Communication Engineering Beijing University of Posts and TelecommunicationsBeijing 100876China School of Information and Electronics Beijing Institute of TechnologyBeijing 100081China

With various service types including massive machine-type communication(mMTC)and ultra-reliable low-latency communication(URLLC),fifth generation(5G)networks require advanced resources management *** a method to segment network resources logically,network slicing(NS)addresses the challenges of heterogeneity and scalability prevalent in these *** software-defined networking(SDN)technologies,lack the flexibility needed for precise control over network resources and fine-grained packet *** has led to significant developments in programmable switches,with programming protocol-independent packet processors(P4)emerging as a transformative programming language.P4 endows network devices with flexibility and programmability,overcoming traditional SDN limitations and enabling more dynamic,precise network slicing *** our work,we leverage the capabilities of P4 to forge a groundbreaking closed-loop architecture that synergizes the programmable data plane with an intelligent control *** set up a token bucket-based bandwidth management and traffic isolation mechanism in the data plane,and use the generative diffusion model to generate the key configuration of the strategy in the control *** comprehensive experimentation,we validate the effectiveness of our architecture,underscoring its potential as a significant advancement in 5G network traffic management.

关键词： network slicing P4 traffic isolation bandwidth management diffusion model

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An Optimization Approach to DWDM Network Reconfiguration through Reinforcement Learning

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SN Computer Science 2024年第8期5卷 1069页

作者： Prakash, Deepthi Managuli, Manjunath Department of Electronics and Communication Engineering Akash Institute of Engineering and Technology Karnataka Bengaluru India Department of Electronics and Communication Engineering KLS Gogte Institute of Technology Karnataka Belagavi India

Optical networks must support dynamic reconfiguration to fulfill the requirements of the upcoming generationof data-hungry applications. Reconfiguration can lead to increased network utilization, higher bit rates, increased capacity, and lower QoS violations. With the objective of maximization of utilization under multiple constraints of latency, jitter and transmission quality, this paper proposes a reinforcement-learning-based reconfiguration for dense wavelength division multiplexing (DWDM) Networks. The reconfiguration is addressed both at the local switch and global network level for higher performance gains. A mixed strategy with strategy selection based on reinforcement learning is proposed in this study, as opposed to earlier works that used a single reconfiguration strategy such as wavelength selection, transceiver parameter tuning, route path planning, bandwidth adjustment, etc. A simulation of the proposed solution is performed by integrating MatLab with the OptiSystem Simulator. As compared to existing work, the proposed solution increased network utilization by 4.10% and reduced QoS violations by 2.2 times. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： DWDM networks Dynamic reconfiguration Jitter Latency QoS violation Reinforcement learning

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Prediction of circularity of the holes in electrical discharge machining of inconel alloy using machine learning technique

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Interactions 2024年第1期245卷 1-10页

作者： Sujatha, M. Kavitha, B.C. Vimalarani, G. David, D. Beulah Muthukumar, K. Ram, S. Department of Electrical and Electronics Engineering Easwari Engineering College Chennai India Department of Electronics and Communication Engineering Hindustan Institute of Technology and Science Chennai India Department of Data Analytics Institute of Information Technology Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Thandalam Tamilnadu Chennai India Department of Electrical and Electronics Engineering Sri Krishna College of Engineering and Technology Coimbatore India

Accuracy and finishing of the component is playing an essential role in metal forming process. The quality of the product is depending on accuracy of the shape and size. The measurement of holes is a challenging task in electric discharge machining (EDM) of materials. Sometimes, the diameter of the holes are different and its seems to be conical shape. The circularity of the hole is not maintained due to over current, low voltage and improper electrode materials. In this article, the circularity of the holes and its dimensions are analyzed by coordinate measuring machine (CMM). The experimental results are compared with predicted results with the aid of machine learning technique through regularized random forest (RRF) model. Inconel 718 alloy is used as a work material for this experimental investigation. The material characterization and its performance against the EDM process are studied. Prediction of circularity of the holes by machining of inconel alloy is perfectly done by machine learning technique. It’s used to enhance the accuracy of the hole and performance of the process. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Metal forming

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A Review on Auto Delivery BOT 15

A Review on Auto Delivery BOT

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Vijetha, T.S. Akshay Chaitrashree, M.G. Pavan, K.H. Puthran, Pratham M. Department of Electronics and Communication Engineering India Electronics and Communication Engineering Alva's Institute of Engineering and Technology Mijar India

ISBN: (纸本)9798331300579

Autonomous delivery bots have completely transformed the logistics and transportation sectors. In the proposed project we ensure the secured delivery of the items from the warehouse to the customers location. wherein we use the modern technologies such as the gps tracking and the on site decision systems which will help the system throughout its journey from the warehouse to the delivery point. This whole process would we automated only the destination location need to be fed to the system. These automated delivery bots have a large area of application either it be medical field or the military fields or the grocery delivery etc, these bots can pave their way to the destination and complete the delivery process without human interference. © Grenze Scientific Society, 2024.

关键词： Bot (Internet)

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A robust transmission with enhancement of 5G PHY using FBMC and AMC for machine-to-machine communication node

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Internet of Things and Cyber-Physical Systems 2023年第1期3卷 323-332页

作者： Sharma, Vipin Arya, Rajeev Kumar Kumar, Sandeep Electronics & Communication Engineering Department National Institute of Technology Patna800005 India Electronics & Communication Engineering Department National Institute of Technology Karnataka Surathkal 575014 India

Advancement of 5G new radios has enabled more robust communication for the Machine-to-Machine (M2M) communication node, using filter bank multicarrier (FBMC). This paper focuses on robust transmission over random fluctuations of the channel and also enhances the battery life for the massive machine type communication (mMTC) node. Filter bank multicarrier and Adaptive Modulation Coding (AMC) have been utilized together to enhance the performance of the 5G (NR) PHY layer. A frame-to-frame implementation is used to diminish the impact of fading using AMC, while efficient utilization of spectrum is achieved using FBMC. The selection of the AMC profile is obtained through the analysis of uplink packets using the Distance Statistics (DS). The FBMC is incorporated with 5G PHY in place of OFDM to achieve the optimum utilization of spectrum and also obtain a significant reduction in peak to average power ratio (PAPR) for robust transmission, which saves 10% of the battery requirement. On the basis of channel state information, distance statistics were employed to optimize the AMC. The optimum selection of AMC with FBMC will reduce the bit error rate (BER) against multipath fading and ensure the better utilization of available spectrum to attain the optimum utilization of the power amplifier. © 2023 The Authors

关键词： 5G mobile communication systems

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Smart-Grid Monitoring using IoT with Modified Lagranges Key Based Data Transmission

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Intelligent Automation & Soft Computing 2023年第3期35卷 2875-2892页

作者： C.K.Morarji N.Sathish Kumar Department of Electronics and Communication Engineering Rohini College of Engineering and TechnologyKanyakumariTamilnadu629401India Department of Electronics and Communication Engineering Sri Ramakrishna Engineering CollegeCoimbatoreTamilnadu641022India

One of the recent advancements in the electrical power systems is the smart-grid *** the effective functioning of the smart grid,the process like monitoring and controlling have to be given *** this paper,the Wireless Sensor Network(WSN)is utilized for tracking the power in smart grid *** smart grid is used to produce the electricity and it is connected with the sensor to transmit or receive the *** data is transmitted quickly by using the Probabilistic Neural Network(PNN),which aids in identifying the shortest path of the *** transmitting the data from the smart grid to the(Internet of Things)IoT web page,it is secured by introducing the secret keys between the neighbouring nodes through the process of *** this method,the combination of Lagrange’s theorem and the Location Based Key(LBK)management is used for better security *** approach deli-vers optimal performance in terms of security,throughput,packet loss and delay,which are comparatively better than the existing methods.

关键词： WSN smart grid Lagrange LBK PNN PV system IoT SCADA

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Duplicate image detection using deep learning modified SVM and k-NN classification method for multimedia application

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Soft Computing 2024年第13-14期28卷 7659-7670页

作者： Singh, Mahesh K. Kumar, Sanjeev Ranjan, Rajeev Nandan, Durgesh Department of Electronics and Communication Engineering Aditya Engineering College Surampalem India Department of Electronics and Communication Engineering Chandigarh University Punjab Gharuan India School of Computer Science and Artificial Intelligence SR University Telangana Warangal India

This paper discussed an efficient technique to detect near-duplicate images with higher accuracy. The proposed method improved the existing accuracy of near-duplicate image detection and classification using Haar wavelet feature extraction and classification algorithm. Due to advancements in digital image acquisition and image editing software, near-duplicate image counterfeiting has become predictable. This expanding problem needs the creation of sophisticated detection algorithms capable of effectively preventing the spread of near-duplicate image deception. Support vector machine (SVM) and K-near neighbor (KNN) classifiers are used in the classification stage of a modified deep learning model with a convolutional neural network (CNN). The classification model finds the better result on the Imperial College London dataset, with 99.12% using CNN with KNN classifier and 98.63% for CNN with SVM classifier. The classification model also gives better results on the Nanyang Technological University dataset, with 98.36% using CNN with the KNN classifier and 97.85% for CNN with the SVM classifier. The model is tested using publicly available images to find the results. According to the comparison results, this model performed significantly better result than the previous model. As a result, digital image forensics has become more critical, particularly in identifying image duplication. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Support vector machines

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OSNR Monitoring for QPSK and QAM in Fiber-Optic Networks Using Machine Learning 5th

OSNR Monitoring for QPSK and QAM in Fiber-Optic Networks ...

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5th International Conference on Advances in Distributed Computing and Machine Learning, ICADCML 2024

作者： Sahu, Shakrajit Clement, J. Christopher School of Electronics Engineering Department of Communication Engineering Vellore Institute of Technology Vellore632014 India

ISBN: (纸本)9789819735228

This work discusses performance analysis of OSNR monitoring QPSK and QAM in optical fiber communication systems which are simple and cost-effective. The key future of advanced fiber-optic networks, live tracking for optical signal-to-noise ratio will become essential for optical system accomplishment. The optical fiber link generates chromatic dispersion and polarization-mode dispersion travel with the photonics signal, i.e., establish optical signal-to-noise ratio observe error. A low-bandwidth photodetector is important for the optical signal-to-noise ratio monitoring scheme, i.e., lower the cost and decrease the effects of chromatic dispersion and polarization-mode dispersion. We need to indicate an optical neural network for an optical signal-to-noise ratio monitoring scheme. Chromatic dispersion is the best estimation parameter to transmit light of different wavelengths at different speeds. The most potent modulation format for calculating bit error rate and optical signal-to-noise ratio is quadrature phase-shift keying, and quadrature amplitude modulation (4-QAM, 16-QAM, 64-QAM, and 256-QAM). The simulation results express that the preferred optical signal-to-noise ratio monitoring schemes achieve the best classification accuracy and low computational complexity. The simulation results find that the optical signal signal-to-noise ratio ranges from 0 to 20 dB for QPSK and QAM signals. The suggested method executes a high optical signal signal-to-noise ratio evaluation precision with a higher approximation error below 0.5 dB. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Machine learning

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