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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Kale, Kiran Raja Praveen, K.N. Savita Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India Faculty of Engineering and Technology Department of Computer Science Engineering Karnataka Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350357769

Fifth-generation wireless networks are ready to handle the increasing volume of mobile traffic and provide critical technological solutions to suit various service needs. Utilizing the millimeter-wave (mm-wave) spectrum is a crucial strategy for achieving large data speeds. Additionally, analogue radio-over-fiber (ARoF) has a wide range of benefits, such as affordability, little power usage, and exceptional spectrum efficiency. As a result, mm-wave ARoF becomes a strong candidate for providing becomes the standard waveform for 5G, it is crucial that mm-wave ARoF systems support it. However, in mm-wave OFDM ARoF systems, an existence of phase interference represents a substantial performance-limiting constraint. Through a painstakingly constructed test setup, we perform a thorough examination of phase noise in this study. We are able to systematically modify the system's overall last phase noise level thanks to this configuration. In addition, we provide a fresh and unique approach for OFDM transmitter phase noise compensation. Our testing confirms the algorithm's effectiveness under these circumstances and highlights the potential advantages of mm-wave OFDM ARoF for 5G to meet by evaluating how well it works over a range of period noise values and subcarrier spacings. © 2023 IEEE.

关键词： Analogue Radio-over-Fiber Beyond 5G Distortion Frequency-Selective Channels Investigation Millimeter-Wave MM-Wave OFDM Phases Spectral Efficiency and Signal-to-Noise Ratio Subcarrier Spacings

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Powering Development Wirelessly: Improvements in Far-Field Instrument Power

Powering Development Wirelessly: Improvements in Far-Field I...

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Kumar Sinha, Deepak Singh, Swati Sivaperumal Faculty of Engineering and Technology Department of Computer Science Engineering Karnataka Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India

ISBN: (纸本)9798350357769

In this study, far-field mobile electrical power options for inexpensively mobile sensors are thoroughly investigated. Emphasis is placed on situations in which sensor placements are uncertain and battery replacement is not practicable. Low-power nondirectional mobile battery backup becomes a possibility for sensors with occasional data transmission demands, estimated radiofrequency (RF) power consumption ranging from 20 to 200 W/cm2, and a necessity for small sensor dimensions. The sensor system uses an antenna to collect coming GHz electromagnetic waves, then transforms this energy into electricity for a storage device, which could be a thin-film battery, using a rectifier circuit. This method makes it possible for the full platform-which includes gauges, an inexpensively Bluetooth transmitter, including a low-power CPU for management-to function continuously. To maximize performance, particularly at reduced incident energy levels, RF power receipt and power control circuits must be designed in concert. Both hybrid and modular power management circuitry options are described and evaluated in this research. © 2023 IEEE.

关键词： Bluetooth wireless Energy harvesting and Efficiency Optimization Far-field power Low-power sensors Mobile sensors Power management Rectenna design System integration Wireless energy transfer

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Efficient Segmentation for wsn Connectivity Technologies

Efficient Segmentation for wsn Connectivity Technologies

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Vivek, V. Savita Dore, Aruna Faculty of Engineering and Technology Department of Computer Science Engineering Karnataka Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India

ISBN: (纸本)9798350357769

In wireless sensors networks, the chargeable capacity of sensor nodes serves as the primary energy source. In WSNs, grouping has emerged as a crucial strategy to save energy use and lengthen network lifespan. Transport energy is inversely correlated to the amount of distance across transmitter and destination nodes, which supports clustering. In this paper, we provide a novel fuzzy logic model for the cluster head (CH) choice, a key step in clustering in WSNs. The recommended model includes five characteristics to assess each node's suitability for CH status. The closeness to the starting place, volume, topographical suitability, and remaining power are some of these criteria. This fuzzy logic methodology is used to propose the Fuzzy Reasoning-based Energy-Efficient Grouping for WSN, with emphasis on establishing the shortest possible distance across CHs (FL-EEC/D). As a further metric of clustered methods' energy use, we also evaluate how efficiently they distribute energy among the sensors inside the WSN using the Gini index. We compare our proposed FL-EEC/D approach with the Low Energy Adaptation Clustered Architecture (LEACH), a fuzzy logic-based clustering approach, and various techniques. For various network sizes and topologies, simulation findings show considerable improvements in energy efficiency, network longevity, and balanced energy consumption across sensor nodes. Our results show that initial node depletion and half node depletion have significantly improved on average. © 2023 IEEE.

关键词： Cluster Head Election Clustering Algorithms Energy Efficiency Fuzzy Inference Systems Fuzzy Logic Model Gini Index Measurement Network Longevity Routing Protocols Separation Distance Wireless Sensor Networks

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AI-Based Secure Software-Defined Controller to Assist Alzheimer’s Patients in Their Daily Routines 11th

AI-Based Secure Software-Defined Controller to Assist Alzhei...

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11th International Conference on Frontiers of Intelligent Computing: Theory and Applications, FICTA 2023

作者： Nithya, S. Palanisamy, Satheesh Kumar Obaid, Ahmed J. Apinaya Prethi, K.N. Alkhafaji, Mohammed Ayad Department of Computer Science and Engineering Kumaraguru College of Technology Coimbatore India Department of Electronics and Communication Engineering Coimbatore Institute of Technology Coimbatore India Faculty of Computer Science and Mathematics University of Kufa Kufa Iraq College of Engineering Technology National University of Science and Technology Dhi Qar Iraq

ISBN: (纸本)9789819967056

According to reports, 60–70% of the estimated 50 million dementia patients globally have Alzheimer's disease. Forgetting earlier encounters or occurrences is one of the illness’ early warning signals. People with Alzheimer's frequently repeat words and inquiries, forget discussions, appointments, or activities, and then fail to recall them later, as well as routinely misplace their items and become lost in places they are acquainted with. There is no proven medication for Alzheimer's disease that can stop the brain's disease process in its tracks. The goal of this study is to develop a personal assistant that can remind a person with Alzheimer's or Parkinson's disease of important chores. The suggested remedy is a convolutional neural network-based voice-based smart controller. It makes it easier for the user to remember where they put the thing. The suggested solution also contains various extra characteristics that will be beneficial to both the Alzheimer's patient and regular people. The controller also often sends an alert at predetermined intervals so that individuals can pick up their phones, remind themselves of tasks, and see their own to-do list. The controller sends a notification before the meeting if the user has a meeting booked. This smart controller will serve as an assistant to the individual with Alzheimer’s and might also be helpful to ordinary human beings. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Speech recognition

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The Role of Modern Machine Intelligence in Decoding Activity Patterns from Visual Data of Various Organisms

The Role of Modern Machine Intelligence in Decoding Activity...

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2023 International Conference on Recent Advances in Science and engineering technology, ICRASET 2023

作者： Gowrishankar, J. Kumar, Ritesh Srivastava, Dharmesh Kumar Faculty of Engineering and Technology Department of Computer Science Engineering Karnataka Bangalore India Maharishi University of Information Technology Maharishi School of Engineering & Technology Uttar Pradesh India Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India

ISBN: (纸本)9798350306927

The integration of modern machine intelligence presents a transformative paradigm in the study of organisms' behaviors through visual data analysis. Leveraging deep learning algorithms like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), this approach automates the extraction of intricate patterns from images and videos, enabling comprehensive insights into diverse organisms. Object detection algorithms further facilitate precise tracking. By surpassing traditional methods, it enhances accuracy and scalability. Ethical considerations guide responsible research. This fusion of technology and ecology fosters a deeper understanding of natural ecosystems, paving the way for sustainable coexistence with the environment and the preservation of biodiversity. © 2023 IEEE.

关键词： Data handling

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Nodal Transmission in 5G Wireless Networking

Nodal Transmission in 5G Wireless Networking

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Agrawal, Trapty Kakati, Pallabi Das, Sunanda Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India Faculty of Engineering and Technology Department of Computer Science Engineering Karnataka Bangalore India

ISBN: (纸本)9798350357769

The increase in data transmission network speed and reliability is inextricably linked to the fast growth of information technology. This calls for the ongoing improvement and upgrading of current standards. Next-generation communication networks are being developed and deployed with diligence by several organizations. This article provides a thorough examination of the software options available that are designed for analyzing and evaluating data network behaviors. It specifically emphasizes tools with substantial built-in functionality and flexibility that are ideally suited for mobile communication systems. It becomes clear that the NS3 network simulator is the best option. This article provides an overview as well as a unique NS3 solution that makes it possible to estimate the dependability of data transmission from a network node's first movement to its removal from a base station. © 2023 IEEE.

关键词： 5G Wireless Networking Communication Networks Data Transmission Interference Analysis Network Security Nodal Transmission NS3 Network Simulator Signal Quality And Distribution Algorithms Simulation Modeling

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Beyond The Internet of Things: Imagining IoT's Structure and Potential Routes

Beyond The Internet of Things: Imagining IoT's Structure and...

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2023 International Conference on Advances in Computation, Communication and Information technology, ICAICCIT 2023

作者： Varalakshmi, K.R. Mahesh, T.R. Singh, Akhilendra Pratap Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India Faculty of Engineering and Technology Department of Computer Science Engineering Karnataka Bangalore India Maharishi University of Information Technology Maharishi School of Engineering & Technology Uttar Pradesh India

ISBN: (纸本)9798350344387

wireless sensor system technology has made it possible for ubiquitous sensing, which affects many aspects of contemporary life. Indicators of environmental health, from sensitive ecologies as well as biodiversity to urban settings, may now be measured, inferred, and understood. The Internet of Things is enabled by the extensive utilization of these devices in a network that facilitates communication and action. In this network, the actuators and sensors smoothly interact with our environment as data is exchanged across platforms to provide a unified operational view. Near field communication (NFC) is the upcoming technology that will enable the transformation of the worldwide web into a fully integrated Future Internet. This technology is being advanced through the adoption of variousenabling device methods, such as tags, audience members, encased sense and actuators nodes, and nodes for near field interaction. The demand for data-on-demand, using complex and straightforward queries, increases significantly as studies transition from web to web2 to web3. This study presents a cloud-centric vision for the worldwideimplementation of the Internet of Things. The future of IoT research is expected to be influenced by many significant technology enablers and their corresponding application domains, which are emphasized. A cloud architecture based on the interplay of public as well as private clouds and employing Aneka is shown. Study conducted elaborate on the need of unity, the global web, and distributed processing in our last section of our IoT vision for the community of technical researchers. © 2023 IEEE.

关键词： Cloud-centric Internet of Things Data storage and analytics Internet of Things (IoT) New protocols Participatory Sensing Quality of Service (QoS) Radio Frequency Identification (RFID) User-cantered cloud-based paradigm Visualization Wireless Sensor Networks

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Robust Machine Learning Technique to Classify COVID-19 Using Fusion of Texture and Vesselness of X-Ray Images

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computer Modeling in engineering & Sciences 2024年第2期138卷 1981-2004页

作者： Shaik Mahaboob Basha Victor Hugo Cde Albuquerque Samia Allaoua Chelloug Mohamed Abd Elaziz Shaik Hashmitha Mohisin Suhail Parvaze Pathan Department of Electronics and Communication Engineering N.B.K.R.Institute of Science&TechnologyVidyanagarAndhra Pradesh524413India Department of Teleinformatics Engineering Federal University of CearáFortaleza60455-970Brazil Information Technology Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh84428Saudi Arabia Department of Mathematics Faculty of ScienceZagazig UniversityZagazig44519Egypt Faculty of Computer Science and Engineering Galala UniversitySuez435611Egypt Artificial Intelligence Research Center(AIRC) College of Engineering and Information TechnologyAjman UniversityAjmanUnited Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblos13-5053Lebanon Department of Avionics Indian Institute of Space Science and TechnologyThiruvananthapuramKerala695547India Department of Veterinary Clinical Sciences University of MinnesotaSt.PaulMinnesota55108USA

Manual investigation of chest radiography(CXR)images by physicians is crucial for effective decision-making in COVID-19 ***,the high demand during the pandemic necessitates auxiliary help through image analysis and machine learning *** study presents a multi-threshold-based segmentation technique to probe high pixel intensity regions in CXR images of various pathologies,including normal *** information is extracted using gray co-occurrence matrix(GLCM)-based features,while vessel-like features are obtained using Frangi,Sato,and Meijering *** learning models employing Decision Tree(DT)and RandomForest(RF)approaches are designed to categorize CXR images into common lung infections,lung opacity(LO),COVID-19,and viral pneumonia(VP).The results demonstrate that the fusion of texture and vesselbased features provides an effective ML model for aiding *** ML model validation using performance measures,including an accuracy of approximately 91.8%with an RF-based classifier,supports the usefulness of the feature set and classifier model in categorizing the four different ***,the study investigates the importance of the devised features in identifying the underlying pathology and incorporates histogrambased *** analysis reveals varying natural pixel distributions in CXR images belonging to the normal,COVID-19,LO,and VP groups,motivating the incorporation of additional features such as mean,standard deviation,skewness,and percentile based on the filtered ***,the study achieves a considerable improvement in categorizing COVID-19 from LO,with a true positive rate of 97%,further substantiating the effectiveness of the methodology implemented.

关键词： Chest radiography(CXR)image COVID-19 classifier machine learning random forest texture analysis

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Visualizing Wellness: The Impact of Deep Learning on Healthcare Image Analysis

Visualizing Wellness: The Impact of Deep Learning on Healthc...

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2023 International Conference on Advances in Computation, Communication and Information technology, ICAICCIT 2023

作者： Sinha, Deepak Kumar Naval, Preeti Fiza, Samreen Faculty of Engineering and Technology Department of Computer Science Engineering Karnataka Bangalore India Maharishi University of Information Technology Maharishi School of Engineering & Technology Uttar Pradesh India Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India

ISBN: (纸本)9798350344387

Deep convolution networks have quickly established itself as the main method for deciphering medical pictures due to their widespread use. This extensive study examined almost 300 recent papers, mostly from the preceding year, that summarized the basic artificial intelligence ideas related to medical picture analysis. Applications of deep learning were explored in detail in a number of disciplines, including object recognition, picture grouping, classification, and registering. This book presents a succinct summary of studies conducted in the fields of neurological disorders, retinal cells, asthma, malignant scans, breasts, cardiovascular and cardiac pelvic, and nerve imaging. Study conducted concludes our assessment with a review of the state-of-the-art at this time, a thorough investigation of outstanding problems, and suggestions for further research. All areas, including computerized pathology, musculoskeletal, pulmonary, the heart, breasts, muscles and tendons, retinal, and neurology imaging, are summarized in a summary of current studies. This thorough review ends with an assessment of the field's current state, a critical analysis of ongoing issues, and specific suggestions for further research and development. © 2023 IEEE.

关键词： Classification Convolutional Neural Networks (CNNs) Deep Learning Detection Healthcare Image Analysis Image Processing and Medical Image Analysis Localization Machine Learning Medical Imaging

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Control of Master-Slave DC Microgrid with Optical Fiber Communication Link 15

Control of Master-Slave DC Microgrid with Optical Fiber Comm...

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15th International Renewable Energy Congress, IREC 2025

作者： Alfergani, Asma Shaltami, Farhat M. Alfaitor, Khalid Ateea Khalil, Ashraf Mohamed, Faisal University of Benghazi Electrical and Electronics Engineering Department Benghazi Libya College of Electrical and Electronics Technology - Benghazi Benghazi Libya University of Benghazi Faculty of Information Technology Department of Computer Networks and Communications Benghazi Libya Dtu Engineering Technology Technical University of Denmark Kongens LyngbyDK-2750 Denmark Authority of Natural Science Research and Technology Tripoli Libya

ISBN: (纸本)9798331516772

In this paper, a model for the master-slave DC Microgrid over a fiber optic link is proposed. The delay margin is calculated according to Lyapunov-Krasovskii functional using the LMI toolbox in Matlab with the binary iteration algorithm. The optical fiber link is implemented using optisystem simulator. The optical fiber link is set to transmit the digitalized reference current from master controller to the slave controller. The link tested at 100Mbps, 1Gbps and 2.5Gbps data rate values and several optical fiber lengths and wavelength of 1550nm and 1400nm. The results show that the performance of the link is sufficient at received power more than -30dBm for all data rate values at 1550nm. While at 1400nm, the attenuation rate is very high. Therefore, the link must be 10km to achieve acceptable performance. This wavelength is used in this simulation to avoid the C band and O band, which used for telecommunication purposes. © 2025 IEEE.

关键词： MATLAB

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