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Autonomous Low Power Energy Management Bridge for Industry IoT Application 21

Autonomous Low Power Energy Management Bridge for Industry I...

21st International Conference on Micro and Nanotechnology for Power Generation and Energy Conversion Applications, PowerMEMS 2022

作者： Yamamoto, Koki Utsumi, Shion Fujita, Takayuki Ebara Corporation Technologies R&D and Intellectual Property Division Japan University of Hyogo Department of Electrical Engineering and Computer Science Japan University of Hyogo Advanced Medical Engineering Research Institute Japan

ISBN: (纸本)9781665493062

This work reported the autonomous battery-less power management system for the IoT (Internet of Things) application that is based on thermoelectrical energy harvesting and power management IC (PMIC). In the proposed design for targeting IoT, the bridge part that transmits the signal from the local area network e.g., Wi-Fi or BLE (Bluetooth® low energy) to the long-range protocol is essential to optimization. It is focused on the collaboration with the LPWA (Low Power Wide Area) network and BLE protocol to realize them, the programmed system controls the power management system. Lastly, the design indicated the possibility to utilize the autonomous system in IoT applications as a monitoring system in the industry with TEG (Thermoelectric Generator) harvesting. Especially, the temperature difference of about 8.6 degrees Celsius could drive the IoT utility with charging time every 3 hours 40 minutes when hot-starting from capacitance energy that should be semi-permanently utilized are evaluated. The work described that it could be effectively utilized to expand the IoT applications as close to easily and at a low cost. © 2022 IEEE.

关键词： Energy harvesting

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Hybrid Xception-ResNext BiLSTM Model with Attention and Visual Synchronization for Robust Deepfake Detection

Hybrid Xception-ResNext BiLSTM Model with Attention and Visu...

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IEEE International Conference on System, Computation, Automation and Networking (ICSCAN)

作者： M. Suresh J.Vijaya Raghavan Abhisek Roy Swet Snigdha Das Department of Computing Technologies SRM Institute of Science and Technology Chennai India Department of Electrical and Electronics Engineering Manakula Vinayagar Institute of Technology Pondicherry India Computer Science Engineering SRM Institute of Science and Technology Chennai India

ISBN: (数字)9798331510022

ISBN: (纸本)9798331510039

Deepfake technology has become a significant problem since it allows for the creationof compelling manipulated videos. This research presents a novel hybrid deepfake detection system that combines the Xception and ResNext networks with a Bidirectional Long Short-Term Memory (BiLSTM) model and Convolutional Block Attention Module (CBAM). Apart from focusing on the temporal and spatial anomalies of deepfakes, our model also includes visual synchronization analysis and physiological signal detection. Extensive experiments are conducted across several datasets, exhibiting cutting-edge performance across various manipulation scenarios. The proposed method achieves good generalization, as they are in real-world circumstances.

关键词： Deepfakes Visualization Automation Convolution Computational modeling Bidirectional long short term memory Focusing Physiology Synchronization Signal detection

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Object Detection Using Deep Learning for Blind People with Voice Feedback

Object Detection Using Deep Learning for Blind People with V...

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IEEE International Conference on System, Computation, Automation and Networking (ICSCAN)

作者： Pongiannan R K Franklin J Richard Pravin A Marissa Maria Sarvottam Kumar Mishra Department of Computing Technologies SRM Institute of Science and Technology Kattankulathur India Department of Electrical and Electronics Engineering SRM Institute of Science and Technology Kattankulathur India Computer Science Engineering SRM Institute of Science and Technology Kattankulathur India

ISBN: (数字)9798331510022

ISBN: (纸本)9798331510039

BlindSpace is an innovative application aimed at improving the lives of visually challenged individuals. Leveraging cutting-edge image captioning and object detection technologies, the app allows users to capture images using their smartphone's camera, providing detailed scene descriptions and audibly identifying objects within those images. This not only enhances users' understanding of their environment but also fosters independence and confidence. With voice commands and text-to-speech technology, the application offers a user-friendly experience and can be customized to individual preferences. Operating efficiently offline, BlindSpace stands as a significant leap in accessibility technology, bridging the gap between the visual and non-visual worlds for visually challenged individuals and promoting inclusivity and autonomy

关键词： Deep learning Visualization Technological innovation Navigation Object detection Cameras Real-time systems Text to speech Object recognition Diseases

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Dijkstra's Algorithm with Bellman-Ford for Shortest Path Discovery in Big Data 2

Dijkstra's Algorithm with Bellman-Ford for Shortest Path Dis...

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2nd International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2024

作者： Karthikeyan, M. Sekar, Satheeshkumar Basha, H. Anwer Epsiba, P. Ganesh, E.N. Meenakshi, B. Srm Institute of Science and Technology School of Computing Faculty of Engineering and Technology Department of Computing Technologies Kattankulathur Tamil Nadu Chennai India Mphasis Corporation ChandlerAZ United States Saveetha College of Liberal Arts & Sciences Simats University Department of Computer Science Tamil Nadu Chennai India Sri Indu College of Engineering and Technology Department of Electronics and Communication Engineering Telangana Hyderabad India Department of Electronics and Communication Engineering Avadi Tamil Nadu Chennai India Sri Sairam Engineering College Department of Electrical and Electronics Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350368413

Many applications, from logistics to social networks, rely on quickly navigating the shortest pathways inside large-scale graphs. For shortest route finding in huge data settings, this study aims to compare Dijkstra's method with the Bellman-Ford algorithm. Applying these methods to large datasets will allow us to compare their speed, accuracy, and scalability. Graph density and negative weight cycles are two of the intrinsic properties of big data settings that will be used to determine which method is most suited to each. Optimal algorithm selection for given data situations is guided by insights into each algorithm's operational strengths and shortcomings, which are revealed via rigorous testing and performance metrics analysis. This comparison helps real-world applications of each method and provides valuable information for future advancements in large data processing shortest route calculation algorithms. In a study comparing Dijkstra's Algorithm and Bellman-Ford using the RandomGraph1 dataset, the results showed that for nodes 100-500, the former's ms value ranged from 5.2 to 52.3 and the latter's ms value ranged from 6.8 to 68.9. Similarly, for nodes 100-500, the MB value range for Dijkstra's Algorithm was 10.2-46.7 and for Bellman-Ford, it was 14.8-71.9. Data from a different SparseGraph1 dataset reveals that, for values between 100 and 500, Dijkstra's Algorithm has a percentage range of 98.5 to 99.8, while for Bellman-Ford, it varies from 97.2 to 99.3. © 2024 IEEE.

关键词： Graph algorithms

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Automating Theory of Mind Assessment with a LLaMA-3-Powered Chatbot: Enhancing Faux Pas Detection in Autism 14

Automating Theory of Mind Assessment with a LLaMA-3-Powered ...

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14th International Conference on computer and Knowledge engineering, ICCKE 2024

作者： Fallah, Avisa Nazari, Mohammad Ali Keramati, Ali Mirfazeli, Fatemeh Sadat School of Mathematics Statistics and Computer Science University of Tehran Tehran Iran National Brain Centre Iran University of Medical Sciences Tehran Iran Department of Neuroscience Advanced Technologies in Medicine Iran University of Medical Sciences Image Brain Institute Tehran Iran School of Electrical and Computer Engineering University of Tehran Tehran Iran Mental Health Research Center Department of Psychiatry School of Medicine Psychosocial Health Research Institute Advanced Technologies in Medicine National Brain Centre Iran University of Medical Sciences Tehran Iran

ISBN: (纸本)9798331511272

The Theory of Mind (ToM) is critical for social interactions, enabling individuals to understand and predict others’ mental states. Deficits in ToM, particularly in detecting faux pas, are common among individuals with Autism Spectrum Disorder (ASD). Traditional methods for assessing ToM, such as the Faux Pas Recognition Test (FPRT), require significant human involvement, which can limit accessibility and scalability. This study aims to explore how large language model (LLM)powered chatbots, specifically utilizing the LLaMA-3 model, can enhance the assessment of ToM by automating and improving the administration of the FPRT. The methodology involved creating a dual-phase process where the chatbot first assesses user responses and then provides rehabilitative guidance to correct misunderstandings. Additionally, the system employs a scoring mechanism combining automated evaluation with human oversight to ensure accuracy and consistency, with system prompts crafted based on chain-of-thought prompting to enhance the depth and reliability of the assessments. Our findings demonstrate the effectiveness of LLM-powered chatbots in delivering dynamic and supportive ToM assessments, suggesting its potential for broader application for both artificial intelligence and psychological practices. ©2024 IEEE.

关键词： Chain-of-thought Faux Pas Recognition Test Interactive Chatbot language Models Theory of Mind

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Quality of Service Factor based Unfailing Route Formation in Wireless Sensor Network

Quality of Service Factor based Unfailing Route Formation in...

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2023 IEEE International Conference on Intelligent and Innovative technologies in Computing, electrical and Electronics, ICIITCEE 2023

作者： Yuvarani, S. Gayathri, A. Velmurugan, K.Jayasakthi Meenakshi, V. Sadhana, S. Srinivasan, C. Department of Computer Applications Tamil Nadu Perambalur India Tamil Nadu Chennai India Jeppiaar Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India School of Electrical and Electronics Engineering Sathyabama Institute of Science and Technology Tamil Nadu Chennai India Tamil Nadu Namakkal India Vee Eee Technologies Solution Pvt Ltd Research & Development Division Tamil Nadu Chennai India

ISBN: (纸本)9781665462631

Nowadays, several analytical methods exist to make unfailing route formation. However, it is still challenging work because the wireless medium is unreliable. Sensor nodes are utilized for widespread distribution. Thus, security issues greatly affect wireless sensor networks (WSN). The Quality of Service Factor-based Unfailing Route (QFUR) Formation in WSN is introduced to solve these issues. In this approach, Quality of Service (QoS) factors like sensor node delay, packet drop, and residual energy is verified to determine whether the sensor node is normal. The value of the QoS factor is less than the threshold for that node to be a normal sensor node in the WSN. This approach is serious to notice and separate the compromised nodes from evading misinformation through the falsified data inserted by the opponent over compromised nodes. The Reinforcement Learning (RL) algorithm computes the reward value based on node energy utilization, hop count, dropped rate, and delay. The simulation outcomes demonstrate a lesser delay and lesser packet loss in the network. Furthermore, it improves the opponent node's detection and minimizes the false negative ratio in the WSN. © 2023 IEEE.

关键词： Quality of service

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Error exponent of activated non-signaling assisted classical-quantum channel coding

arXiv

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arXiv 2024年

作者： Oufkir, Aadil Tomamichel, Marco Berta, Mario Institute for Quantum Information RWTH Aachen University Aachen Germany Centre for Quantum Technologies Department of Electrical and Computer Engineering National University of Singapore Singapore

We provide a tight asymptotic characterization of the error exponent for classical-quantum channel coding assisted by activated non-signaling correlations. Namely, we find that the optimal exponent - also called reliability function - is equal to the well-known sphere packing bound, which can be written as a single-letter formula optimized over Petz-Rényi divergences. Remarkably, there is no critical rate and as such our characterization remains tight for arbitrarily low rates below the capacity. On the achievability side, we further extend our results to fully quantum channels. Our proofs rely on semi-definite program duality and a dual representation of the Petz-Rényi divergences via Young inequalities. © 2024, CC BY.

关键词： Quantum channel

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Matrix Spectral Factorization for Alpert Multiwavelet Filter Bank

Matrix Spectral Factorization for Alpert Multiwavelet Filter...

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National Conference with International Participation (TELECOM)

作者： Vasil Kolev Todor Cooklev Bulgarian Academy of Sciences Institute of Information and Communication Technologies Sofia Bulgaria Department of Electrical and Computer Engineering Purdue University Fort Wayne Indiana USA

ISBN: (数字)9798350355000

ISBN: (纸本)9798350355017

Multiwavelets can be orthogonal, symmetric, smooth, and with short support, all at the same time. However, the design of multifilters with these properties remains a challenging problem. Obtaining multifilters by using matrix spectral factorization (MSF) is still quite unknown in the signal processing community. There are a number of numerical approaches to MSF, but they usually cannot handle the case when the determinant of the matrix product filter has zeros on the unit circle (i.e. singularity), which is precisely of greatest interest in the construction of multiwavelets. We describe a novel design of Alpert's multiwavelet filter using Bauer's method for MSF of a product multifilter. We apply the proposed multifilter for image denoising of gray images and compare with the orthogonal GHM and CL multifilters.

关键词： Symmetric matrices Filter banks Signal processing algorithms Filtering algorithms Signal processing Encoding Telecommunications Image denoising

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Neural Networks for Effective Member Density Monitoring in IoT-Enabled Smart Fitness Centers

Neural Networks for Effective Member Density Monitoring in I...

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2024 Asian Conference on Intelligent technologies, ACOIT 2024

作者： Ranganathan, Chitra Sabapathy Riazulhameed, Arshadh Ariff Mohamed Abuthahir Nandagopal, V. Mohankumar, N. Saju Raj, T. Muthulekshmi, M. Mphasis Corporation Tamil Nadu Chennai India Blackstraw Technologies Pvt Ltd Tamil Nadu Chennai India Department of Electrical and Electronics Engineering School of Engineering Mohan Babu University Andhra Pradesh Tirupathi India Nagpur Campus Maharashtra Pune India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamil Nadu Chennai India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Chennai India

ISBN: (纸本)9798350374933

Improving user experience and operational management in the quickly growing field of IoT-enabled smart fitness centers relies heavily on accurate member density monitoring. This paper provides a new method that uses neural networks to effectively monitor member density in these kinds of settings. Incorporating Internet of Things (IoT) sensors into the fitness center's layout to gather occupancy data in real-time is essential to our process. Feed this data into a neural network architecture that was built to estimate densities. It demonstrates the accuracy, scalability, and durability of our system in various operating circumstances by evaluating its performance via real-world experiments and comprehensive simulations. It communicates about how our method may be used in smart fitness centers that are connected to the IoT to improve management efficiency, resource allocation, and user happiness. Our efforts help push the current state of the art in the use of artificial intelligence methods for improving the efficiency of advanced gyms. © 2024 IEEE.

关键词： Electronic health record

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On the composable security of weak coin flipping

arXiv

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arXiv 2024年

作者： Wu, Jiawei Hu, Yanglin Bansal, Akshay Tomamichel, Marco Centre for Quantum Technologies National University of Singapore Singapore Department of Computer Science Virginia Polytechnic Institute State University United States Department of Electrical and Computer Engineering National University of Singapore Singapore

Weak coin flipping is a cryptographic primitive in which two mutually distrustful parties generate a shared random bit to agree on a winner via remote communication. While a stand-alone secure weak coin flipping protocol can be constructed from noiseless communication channels, its composability has not been explored. In this work, we demonstrate that no weak coin flipping protocol can be abstracted into a simple black box resource with composable security. Despite this, we also establish the overall stand-alone security of weak coin flipping protocols under sequential composition. © 2024, CC BY-NC-ND.

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