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IEEE Transactions on Green communications and Networking 2024年

作者： Turk, Sare Ece Ahrazoglu, Evla Safahan Erdogan, Eylem Altunbas, Ibrahim Istanbul Technical University Department of Electronics and Communication Engineering Istanbul34469 Turkey Politecnico di Milano Department of Electronic Information and Bioengineering Milan20133 Italy Izmir Institute of Technology Department of Electrical and Electronics Engineering Izmir35433 Turkey

Satellites and high altitude platform station (HAPS) systems are expected to become the key elements of non-terrestrial networks with the recent advances on sixth-generation (6G) wireless networks. In this article, motivated by the mounting interest in HAPS systems both from academia and industry, we propose a multi-HAPS aided optical satellite communication architecture using hybrid radio frequency (RF)/free space optical (FSO) communication to provide enhanced throughput, and reliability. The proposed architecture can be used in railways, transcontinental highways or maritime communications to provide enhanced coverage, throughput and reliability. To quantify the overall performance of the proposed scenario, outage probability, ergodic capacity, throughput, energy efficiency are obtained and validated. Additionally, the impact of outdated channel state information and channel estimation errors are considered, severely affecting the system performance by causing signal-to-noise ratio loss and outage floors. Furthermore, we find optimum HAPS distance and obtain the number of HAPS systems that is required for reliable communications. The results show that serial placement of HAPS systems at optimum distances can enhance the system performance and energy efficiency. © 2017 IEEE.

关键词： Channel estimation

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Enhancing Pervasive Developmental Disorder Interventions for Children through Deep Learning Algorithms in Virtual Reality

Enhancing Pervasive Developmental Disorder Interventions for...

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Automation, Computing and Renewable Systems (ICACRS), International Conference on

作者： A. Reethika S. Priyadharsini G. Vijaykumar A. Rasheedha G. Devi P.Kanaga Priya Department of Electronics and Communication Engineering Sri Ramakrishna Engineering College Coimbatore India Department of Electronics and Communication Engineering KPR Institute of Engineering and Technology Coimbatore India Department of Biomedical Engineering Sri Shanmugha College of Engineering & Technology Salem India Department of Biomedical Engineering Sri Ramakrishna Engineering College Coimbatore India Department of computer science and engineering SNS college of technology Coimbatore India Department of computer science and engineering KPR Institute of Engineering and Technology Coimbatore India

Pervasive Developmental Disorders (PDDs) represent a complex set of neurodevelopmental conditions in which a children has a challenges in social communication, repetitive behaviors, and restricted interests. Early intervention is serious in improving the lives of children with PDDs. Autism spectrum can be identified at the age of 2 years based on the behavior symptoms. This abstract introduces a novel approach to PDD intervention that leverages deep learning algorithms within a Virtual Reality (VR) environment. This research explores the Deep learning algorithms are utilized to create adaptive and personalized experiences, which adapt in real-time to the child’s unique needs, preferences, and progress. These algorithms harness vast datasets of behavioral and physiological information, allowing for precise and individualized interventions. The VR environment serves as a safe and controlled space for children with PDDs to practice social interactions, communication skills, and sensory integration. It provides immersive scenarios and simulations designed to mimic real-life situations, facilitating gradual exposure and desensitization to sensory challenges.

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Data-Driven Drive Safety with Cloud-based Adaptive Cruise Control using K-Nearest Neighbors Algorithm

Data-Driven Drive Safety with Cloud-based Adaptive Cruise Co...

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E-mobility, Power Control and Smart Systems (ICEMPS), International Conference on

作者： Subramani Roy Choudri T. J. Nagalakshmi Peddi Sai Krishna K. Balasubadra S K Mouleeswaran S.P. Maniraj Department of Computer Science and Engineering Vardhaman College of Engineering Hyderabad Telangana India Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Computer Science and Engineering SRM Institute of Science and Technology Kattankulathur Chenagalpattu Chennai Tamil Nadu India Department of Information Technology R.M.D. Engineering College Kavaraipettai Chennai Tamil Nadu India Department of Computer Science and Engineering Dayananda Sagar University Bangalore Karnataka India Department of Computer Science and Communication SRM Institute of Science and Technology Chennai Tamil Nadu India

ISBN: (数字)9798350394399

ISBN: (纸本)9798350394405

An innovative new technique cloud-based adaptive cruise control (ACC) uses data analysis and cloud computing to make driving safer and more efficient. Current ACC systems in the cloud depend on static models or rules, to handle the ever-changing nature of traffic. It provide a data-driven method for ACC in the cloud that makes use of the k-nearest neighbours (KNN) algorithm to adapt the vehicle's speed and distance based on learning from both past and present driving data. For this ACC system in the cloud and develop a protocol for communication between vehicles and the cloud, as well as a distance and speed controller based on KNN. It tests the method against both traditional ACC and human drivers in a simulated environment to view how well it performs. In a number of traffic situations, the method outperforms both the human driver and traditional ACC in terms of safety, comfort, and fuel economy. The system's cloud-based architecture allows it to access and understand vast volumes of real-time data, providing drivers with an exhaustive and current data of the environment ahead. The system simulates real-world studies to test the effectiveness of the proposed method, demonstrating how it may improve road safety and traffic flow. When data-driven decision-making is combined with ACC it demonstrates how cloud-based technology may be used to improve road safety and advance the field of intelligent transportation.

关键词： Training Cloud computing Computational modeling Biological system modeling Decision making Road safety Real-time systems

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A Comparative Approach on Enhancing Lifetime of Wireless Sensor Networks 5

A Comparative Approach on Enhancing Lifetime of Wireless Sen...

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5th International Conference on Contemporary Computing and Informatics, IC3I 2022

作者： Sultana, Shakira Bordoloi, Dibyhash Singh, Charanjeet Srivastava, Arun Pratap N., Thiyagarajan N., Chinthamu Loyola Academy Department of Electronics and Communication Technology Telangana Alwal India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun India Deenbandhu Chhotu Ram University of Science and Technology Electronics and Communication Department Murthal India Lloyd Institute of Engineering and Technology Department of CSE Uttar Pradesh Greater Noida India Sri Eshwar College of Engineering Department of Electronics and Communication Engineering Tamilnadu Coimbatore India CTO Candidate DallasTX United States

ISBN: (纸本)9798350398267

Wireless Sensor Networks are crucial elements that are employed to assess the environment, in addition, to utilizing the sensor information for additional computing including climate forecast, human medical-related forecasts, transportation management, and so on. Because of the significant operational expense of the system, the sensor network of the system is anticipated to run independently in the area for an extended length of time in these circumstances. As a consequence of this, the exhaustion of single sensor nodes may have an impact on the network's operations. As a result, in an attempt to extend the network lifespan, an energy-efficient alternative must be proposed. Wireless sensor network technologies have emerged in past decades as an evolving innovation for monitoring & recording ambient physical variables utilizing many sensors. For the organizing of gathered data, centralized sites are required. Multi-hop communication is used to transfer data to the sink nodes communications are needed. To eliminate undesired transmissions, the most well-known WSN approach known as information aggregating is utilized. It must gather information from various sensors & transmit it to the ground station. The procedure of gathering information is repeated many times to maximize information effectiveness. This technology contributes to lower transmitting information power costs. © 2022 IEEE.

关键词： Sensor nodes

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Usage of ML and IoT in Healthcare Diagnose During Pandemic 3

Usage of ML and IoT in Healthcare Diagnose During Pandemic

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3rd International Conference on Intelligent engineering and Management, ICIEM 2022

作者： Shashi Srikanth, V. Biswas, Prarthita Chinnammal, V. Bhosale, Supriya Ashok Degadwala, Sheshnag Chaudhary Charan Singh University Uttar Pradesh Meerut India School of CS and IT Jain University Karnataka Bangalore India Adamas University Barasat Department School of Education Kolkata India Rajalakshmi Institute of Engineering Electronics and Communication Engineering Tamilnadu Chennai India Sanjivani College of Engineering Maharashtra Kopargaon India Sigma Institute of Engineering Department Department of Computer Engineering Gujarat India

ISBN: (数字)9781665467568

ISBN: (纸本)9781665467568

The pandemic crisis has obliterated human existence as we know it, as well as regional, social, and commercial action, as well as compelled human civilization in living inside the defined perimeter. Uses of IoT with ML in health care applications is described in this article. The created ML with IoT dependent observation prototype assists for tracing COVID-19 positive detected persons using prior information and isolates them from non-infected individuals. By anticipating as well as analyzing information with AI, proposed ML-IoT system employs parallel computing to track pandemic sickness and also to avoid pandemic disease. The use of machine learning-dependent IoT for COVID in health conditions diagnose likely to be demonstrated the effectiveness for detection and prevention of CORONAVIRUS transmission. It still effects in better way on lowering preventive expenditures also leds to better treatment for infected individuals. In terms of monitoring and tracking, the recommended technique is 95% accurate. The findings will aid for stopping the pandemic's spread and providing assistance to the healthcare sector. © 2022 IEEE.

关键词： Internet of things

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Performance Evaluation of IoT Architecture for Heterogeneous Networks

Performance Evaluation of IoT Architecture for Heterogeneous...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Monika Kashyap Zaineb Naaz Neeti Bansal Vidushi Sharma Department of Computer Science and Engineering Gautam Buddha University Greater Noida India Department of Electronics and Communication Engineering Meerut Institute of Engineering and Technology Meerut India

With the increased deployment of IoT networks in different applications, the need to develop resource-efficient IoT networks arises. The traditional networks based on a single communication and connectivity protocol (either wired or wireless) fail to serve the dynamic requirements of the IoT. Heterogeneous networks combining wired (Ethernet, Power Line communication, etc.) and wireless (ZigBee, Wi-Fi, etc.) protocols are the future networks that will serve the purpose of IoT in the best possible manner. A real-world test bed analysis is essential to analyze and enhance the efficiency of hybrid wired and wireless-based heterogeneous networks. The paper presents an experimental investigation of traditional wired and hybrid (partially wired and wireless) networks based on IoT architectures. The results depict a reduction in the PDR (packet delivery ratio) and an increase in the packet loss rate for hybrid topology compared to a completely wired architecture.

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Evaluating the Performance of Deep Learning Models in Predicting Brain Ischemic Stroke Risk using EEG Signals.

Evaluating the Performance of Deep Learning Models in Predic...

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International conference of electronics, communication and Aerospace Technology (ICECA)

作者： N. Shyamala S.Roji Marjorie Department of Computer Science and Engineering Saveetha School of engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai TamilNadu India Department of Electronics and Communication Engineering SRM Institute of Science and TechnologyKattankulathur Chennai TamilNadu India

ISBN: (数字)9798350367904

ISBN: (纸本)9798350367911

Stroke is the third most common cause of fatalities worldwide. It’s a hazardous condition that may cut off blood flow to the brain and result in death, serious illness, or disability. In order to begin treatment early and reduce the death rate, an exact stroke prediction is essential. Electrical activity inside the brain is reflected in electroencephalography (EEG) data, which provide a non-invasive method of monitoring brain performing to detect abnormalities relating to stroke risk. The intention is to evaluate the way deep learning models predict the risk brain stroke using EEG signals. For training and validate the models, a large set of electroencephalogram (EEG) data from patients with different stroke risk levels was used Using neural network architectures, which include recurrent and convolutional neural networks (RNNs), intends to identify EEG data’s temporal and spatial features that are predictive of the emergence of a *** the convolutional layer, preprocessed data is fed into the three-layer CNN-model. From the input data, the layer of convolution extracts features that are useful. after feature extraction, fully interconnected (FC) layers are used to process the features by changing their weights. RNNs are made to handle sequential data by keeping memory states, particularly LSTM (Long Short-Term Memory) networks. RNN excels at recognizing sequential data’s In order to take benefit of both architectures, a hybrid approach which utilizes CNN for extraction of features and RNN for modeling sequences can be implemented. In order to simulate temporal dependencies, layers of CNN may first extract spatial information, which are then given to RNN layers. The hybrid algorithm achieved $\mathbf{9 0. 0 2 \%}$ accuracy.

关键词： Deep learning Predictive models Stroke (medical condition) Brain modeling Feature extraction Prediction algorithms Electroencephalography Data models Robustness Convolutional neural networks

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A Highly Efficient Ternary Adder and Carbon Nanotube FET in VLSI-A Review

A Highly Efficient Ternary Adder and Carbon Nanotube FET in ...

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Sustainable Computing and Integrated communication in Changing Landscape of AI (ICSCAI), International Conference on

作者： Sandeep Kumar Arjun Singh Arun Kumar Singh Department Of Electronics and Communication Engineering G.L.BAJAJ Institute of Technology & Management Greater Noida India Department Of Computer Science & Engineering Greater Noida Institute of Technology Greater Noida India

ISBN: (数字)9798350355338

ISBN: (纸本)9798350355345

This review article shows the benefits of replacing binary logic with ternary logic and the application of carbon nanotube FET in adder circuits that reduce interconnect and improve the delay, power dissipation, and speed. Future nanotechnology faces the limitation of CMOS like DIBL, the effect of the short channels, high leakage current, and temperature-dependent threshold voltage. These are overcome by carbon nanotube FET. CMOS was replaced by carbon nanotube FET due to having similarity in structure as the channel of its form by tubes of carbon which works on ballistic transport and chirality. Currently, numerous researches are going on to develop and investigate the use of Carbon Nanotube FET in half and full adder. In this article, a comparison study for half and full adder using carbon nanotube FET is done and parameters like propagation delay, power dissipation, and transistor count are compared. This analysis shows that Carbon nanotube FET-based digital circuits are much more efficient as compared to CMOS logic. The performance parameter and results are simulated using HSPICE software.

关键词： Performance evaluation Reviews Integrated circuit interconnections Very large scale integration CNTFETs Power dissipation Delays Transistors Integrated circuit modeling Adders

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Improving Cloud Data Security with Alexnet Learning Model Compare Stochastic Gradient Descent with Accuracy

Improving Cloud Data Security with Alexnet Learning Model Co...

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Artificial Intelligence For Internet of Things (AIIoT), International Conference on

作者： E. Thriveni R Mahaveerakannan K. Sudhakar Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Chennai Tamilnadu India Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Chennai Tamilnadu India

ISBN: (数字)9798350372120

ISBN: (纸本)9798350372137

The suggested method of protecting cloud data involves setting up a server to function as a third party, similar to Alexnet, and using Advanced Encryption Standard for data stored in private clouds. Stochastic Gradient Descent is used to calculate accuracy the user and the cloud in order to ensure security between the two parties. Supplies and Procedures: In this work, two groups—AlexNet and Stochastic Gradient Descent—are compared. For statistical analysis, 1300 instruction and 423 testing dataset samples from the UNSW-NB15 dataset were used. N=10, an alpha value of 0.05, a G-Power value of 0.8%, and a 95% confidence interval were employed in the ClinCalc software for each approach. Findings and Discussion: Novel Alexnet (91.081%) exhibits better accuracy than SGD (87.9396%). The significance value, as determined by looking at the Independent Sample t-Test findings, is 0.011(p<0.05). It is statistically significant as a result. Conclusion: Based on the research findings, the Novel AlexNet classifier method outperforms the SGD in terms of improving the precision of cloud data that contains sensitive information using the UNSW-NB15 dataset.

关键词： Cloud computing Accuracy Statistical analysis Data security Stochastic processes Data models Software

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GUI Based Modeling and Simulation of Maximal Calcium Ion Bifurcation on Neuron Response and its Applications

GUI Based Modeling and Simulation of Maximal Calcium Ion Bif...

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Innovative Practices in Technology and Management (ICIPTM), International Conference on

作者： Taslima Ahmed Zaiba Ishrat Satvik Vats Sandhya Bharadwaj Hitendra Singh Department of Electronics and Communication Engineering IIMT College of Engineering Greater Noida India Department of Electronics and Communication Engineering Meerut Institute of Technology Meerut India Department of Computer and Science Engineering Graphic Era Hill University Dehradun India Department of ECE IIMT College of Engineering Greater Noida India Delhi Technological University Delhi India

ISBN: (数字)9798350307757

ISBN: (纸本)9798350307764

In spite of sodium, potassium and chloride ions, calcium, magnesium and cobalt ions also plays a vital role for complex signaling in brain. Calcium ions (Ca 2+ ) regulate neuronal activity in homo- and hetero cellular control brain physiology. Without calcium ion signaling, all transcriptional networks, and ion exchangers in brain may cause sudden stop of all physiological functions. Wrong interactions between Ca 2+ and reactive other species signaling coordinate in neurodegenerative disorders, perturbed energy metabolism, and alterations of disease-related synaptic dysfunction. Therefore, in this paper, review of Ca 2+ has been modeled with original H-H equations and discusses deregulation of Ca 2+ for different neurological related diseases as Alzheimer, Parkinson, coma etc. These channels activate due to depolarization and inactivate very little and slowly during the whole action potential generation phase. Calcium ion bifurcation was first characterized by Fatt and Katz (1953). From researcher’s simulation and formulation it was concluded, there are various subtypes channel in neuronal cells .These sub-channels serve a vital role in cellular signaling in neuron cell. Based on the neural functionality, calcium ion bifurcation is depended on calcium channel types L ca , T ca and N ca type. L ca -type currents are the main calcium ion response observed in muscular cells, where they initiate function of the respective cells. T ca type calcium ion response is activated by depolarization of ion. N ca type calcium ion channel provided only neuronal *** paper represents the bifurcation level of calcium ion response on the neuron functionality and an investigation on the properties of Ca 2+ ion response has been observed. This paper also explains the extension of original H-H model for neuron at excitable state and is analyzed with GUI panel with proper simulated version.

关键词： Pathology Reviews Calcium Sodium Neurons Bifurcation Ions

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