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Ultrasonic Backscatter Communication: A Feasibility Study

Ultrasonic Backscatter Communication: A Feasibility Study

引用

IEEE International Symposium on Applications of Ferroelectrics (ISAF)

作者： Asra Ashraf Johan E. Carlson Jaap van de Beek Johan Borg Dept. of Computer Science Electrical and Space Engineering Luleå University of Technology Luleå Sweden

ISBN: (数字)9798350371901

ISBN: (纸本)9798350371918

This paper presents a feasibility study of an ultrasonic backscatter communication system designed for low-power, short-range data transmission, suitable for applications in IoT, biomedical implants, and underwater sensor networks. Our proposed system utilizes a very low power passive network to encode information by modulating the reflection properties of the backscatter medium. We demonstrate through experimental analysis the effective data transmission and detection. The key contribution of this study includes the development of a practical experimental setup using ultrasonic transducers, backscatter modulators, alongside signal processing techniques for optimal data extraction. Results indicate the successful data transmission and detection for a limited number of bits in a very high SNR regime, showcasing the performance of the transmission protocol.

关键词： Ultrasonic transducers Passive networks Wireless sensor networks Protocols Modulation Implants Acoustics Data communication Backscatter Signal to noise ratio

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An IoT enabled Malware Identification Mechanism over Digital Documents using Predictive Learning Scheme 2

An IoT enabled Malware Identification Mechanism over Digital...

引用

2nd IEEE International Conference on Advances in Computing, Communication and Applied Informatics, ACCAI 2023

作者： Radha, K. Sivagamidevi, G. Juliet, N. Niranjana, S. Nimmalaharathi Dhanalakshmi, G. Dr.Mahalingam College of Engineering and Technology Department of Computer Science and Technology Pollachi India Dhanalakshmi College of Engineering Dept of Ai & Ds Chennai India S.A. Engineering College Department of Master of Computer Applications Chennai India Jeppiaar Institute of Technology Department of ECE Chennai India SreeVidyanikethan Engineering College School of Engineering Department of Eie Andhra Pradesh Tirupati India Panimalar Engineering College Department of Information Technology Chennai India

ISBN: (纸本)9798350315905

The ability to identify malicious software is crucial to ensuring the safety of computer networks. Nevertheless, signature-based technologies now in use are inadequate for identifying zero-day assaults and polymorphic infections. That's why we need detection methods that use machine learning. The proliferation and sophistication of malware threats have elevated the problem of automated malware identification to the forefront of network security discussions. Manually analyzing all malware in a programme using conventional malware detection techniques is laborious and resource-intensive. The proliferation of the internet has led to a meteoric rise in the number of people using IoT devices. Malware assaults are growing more common as the storage capacity of IoT devices grows;as a result, detecting malware in IoT devices has become a pressing concern. For identifying malware in IoT devices, we present an ensemble categorization approach based on deep learning. For the identification of malware, we employ ANN and LSTM outputs. Our suggested technique achieves an average accuracy of 99.49% on standard datasets, which is higher than the accuracy of state-of-the-art methods. © 2023 IEEE.

关键词： Internet of things

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Optimization of Cost-based Dynamic Economic Dispatch Problem using Mathematical Programming

Optimization of Cost-based Dynamic Economic Dispatch Problem...

引用

International Conference on Innovations in Science, engineering and technology (ICISET)

作者： Mou Mahmood Nahid-Ur-Rahman Chowdhury Dept. of Computer Science and Engineering Southeast University Dhaka Bangladesh Dept. of Electrical and Electronic Engineering Ahsanullah University of Science and Technology Dhaka Bangladesh

ISBN: (数字)9798350355499

ISBN: (纸本)9798350355505

This paper introduces a robust and effective methodology for addressing the dynamic economic dispatch (DED) optimization problem, which is pivotal in power system operations and planning. DED is basically an enhanced version of static economic dispatch (SED) problem that involves scheduling a set of generation units over a typical operational period, often spanning 24 hours, to cover the predicted load demand with the on/off status of each generation unit predetermined. The literature distinguishes between two main types of DED: cost-based and price-based. This paper focuses on the cost-based approach, utilizing quadratic constrained programming (QCP) to model the problem effectively. QCP is particularly suited for this task as it optimally manages the ramp rate constraints and capacities of the available generation units maintaining the minimum operating cost. To demonstrate the effectiveness of the proposed methodology, simulations were conducted using two distinct scenarios involving 5-unit and 10-unit power dispatch strategies. The results, obtained through the general algebraic modeling system (GAMS), clearly presents the superiority of GAMS in achieving optimal solutions when compared to other existing metaheuristic techniques.

关键词： Technological innovation Renewable energy sources Costs Wind energy Software packages Power system dynamics Metaheuristics Propagation losses Mathematical models Reliability

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Secure Data Transmission Based on Artificial Intelligence Using IoT in Security Systems 5

Secure Data Transmission Based on Artificial Intelligence Us...

引用

5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Senthil Kumar, S. Maruthaveni, R. Jayanthi, N. Yadav, Ajay Kumar Kalaiarasan, T.R. Sri Ramakrishna Mission Vidyalaya College of Arts and Science Department of Information Technology Coimbatore India Dr.S N S Rajalakshmi College of Arts and Science Department of Computer Science Coimbatore India Deemed to Be University Periyar Maniammai Institute of Science and Technology Department of Commerce Thanjavur India United Institute of Management Department of Computer Application U.P. Prayagraj India Sri Krishna College of Engineering and Technology Dept of Information Technology Coimbatore India

ISBN: (纸本)9798350383522

The Internet of Things (IoT), a rapidly expanding and constant technology today, enables the efficient exchange of information and control of electronic equipment. As technology grows more and more integrated into our daily lives, IoT serves as a bridge between human intelligence and artificial intelligence. Although the Internet of Things (IoT) presents numerous benefits for efficiency and creativity, it also poses serious privacy and security concerns. The main topic of the research is the enormous challenges in protecting IoT environments caused by the sheer number and variety of connected devices. This study's main goal is to look into how machine learning methods may be used to lessen the ongoing security vulnerabilities in IoT environments. We intend to create more resilient and intelligent security systems that can adapt to the changing threat environment by utilising machine learning's capabilities. The proposed study provides a thorough examination of the various elements and traits that make up IoT systems, with a focus on the current security measures. By taking a look at these elements, we may determine which strategies and techniques stand the highest chance of improving IoT security. This makes it possible for IoT deployments to be safer and more dependable in a technological setting that is quickly changing. © 2024 IEEE.

关键词： Camera Drowsiness Eye Face detection Hardware device Opencv Python

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Advancing Image Forgery Detection: A CNN-Based Approach

Advancing Image Forgery Detection: A CNN-Based Approach

引用

International Conference on Computation System and information technology for Sustainable Solutions (CSITSS)

作者： Nalini N Sai Rakshitha Nithyashree H V Rakshitha S L Motamarri Sai Shravan K Sai Uttej Dept. of Computer Science and Engineering Nitte Meenakshi Institute of Technology Bengaluru Karnataka India

ISBN: (数字)9798331505462

ISBN: (纸本)9798331505479

In the digital age, image forgery is a significant concern due to advanced editing tools. This study addresses the need for reliable forgery detection, focusing on copy-move, splicing, and retouching techniques. Using Convolutional Neural Networks (CNNs) known for learning hierarchical image features, the research developed a robust detection system. The CNN model, trained on a diverse dataset which has over 10,000+ images and it is split into test and train data for better classification results, detected subtle inconsistencies indicative of manipulation, achieving a peak accuracy of 94% over 50 training epochs. Test cases showed the system effectively detected forged images and identified forgery types, though it had shortcomings with invalid inputs. The CNN architecture employed convolutional and max-pooling layers with specific hyperparameters to enhance feature extraction. This paper offers a valuable tool for digital forensics experts, law enforcement, and journalists to ensure the authenticity of visual content.

关键词： Training Visualization Accuracy Law enforcement Splicing Digital forensics Feature extraction Forgery Real-time systems Convolutional neural networks

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Short-Range Propagation Characteristics in an Ice-Covered Lake 14

Short-Range Propagation Characteristics in an Ice-Covered La...

引用

14th IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2024

作者： Yang, Chaoran Ling, Qing Sheng, Xueli Xiu, Xian Liu, Zixiao Jakobsson, Andreas Harbin Eng. University National Key Laboratory of Underwater Acoustic Technology China Key Laboratory of Marine Information Acquisition and Security China College of Underwater Acoustic Engineering Harbin Engineering University China Lund University Dept. of Mathematical Sciences Sweden

ISBN: (纸本)9798350366556

This paper examines the short-range propagation characteristics of sound wave propagation in an ice covered lake experiment. The studied measurements were made in February 2024 in the shallow Song-Hua lake in northern China. The experiment illustrates how the wave propagation notably varies as a function of dept. and due to changes in the water temperature, but also how the ice cover influences the sound propagation. © 2024 IEEE.

关键词： Ice

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Large-Signal Characterization of Piezoelectric Resonators for Power Conversion

Large-Signal Characterization of Piezoelectric Resonators fo...

引用

Annual IEEE Conference on Applied Power Electronics Conference and Exposition (APEC)

作者： Amanda K. Jackson Jason W. Perreault Jeffrey H. Lang David J. Perreault Dept. of Electrical Engineering and Computer Sciences Massachusetts Institute of Technology U.S.A

Magnetics are key components of conventional power converters, but they are often the bottleneck to achieving high power density due to their size, weight, and poor performance at small sizes. Piezoelectric devices, when operated in their inductive regime, can serve a purpose similar to that of magnetic components and offer favorable scaling properties as components are miniaturized. Several sources have demonstrated the viability of piezoelectric-based power converters, but selection of the optimal material and component size is limited by a lack of data on the performance of these materials at high drive levels. This work aims to fill that gap by collecting data to examine the variation in resonator quality factor across a range of drive levels for multiple resonator sizes, frequencies, and materials. By normalizing the collected data, material trends are derived that can predict resonator losses under high drive levels, offering more insight into realistic converter operation than the currently available small-signal data sheet values. Based on these trends, implications for converter efficiency and selection of material and dimensions are discussed.

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An improved AI-driven Data Analytics model for Modern Healthcare Environment 15

An improved AI-driven Data Analytics model for Modern Health...

引用

15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Patil, Sachin C. Takkalapally, Prashamsh Kasula, Balaram Yadav Logeshwaran, J. United States Lewis University Department of Engineering Computing and Mathematical Sciences United States University Of The Cumberlands Dept of Information Technology WilliamsburgKY United States Department of Computer Science Karnataka Bengaluru560029 India

ISBN: (纸本)9798350370249

AI-driven statistics analytics is a swiftly advancing and impactful era that is transforming the face of healthcare. By leveraging the energy of AI computing and gadget studying, healthcare organizations can speedy gain insights from their huge datasets, offering a greater comprehensive and personalized approach to hospital therapy and populace health management. This paper explores the advantages of AI-driven statistics analytics in healthcare settings, masking key benefits along with progressed analysis and treatment, better-affected person effects, and financial savings. Moreover, this paper addresses the main challenges associated with AI-pushed analytics and offers potential solutions to enhance accuracy and relevance. In the long run, statistics analytics powered by way of AI gives powerful opportunities to improve healthcare outcomes, and its use is expected to expand within the coming years. © 2024 IEEE.

关键词： Electronic health record

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Robotics Process Automation Technologies for Data Collection and Analysis

Robotics Process Automation Technologies for Data Collection...

引用

International Conference on Computing and Networking technology (ICCNT)

作者： K Usman Tm Hayath Mohammed Shafiulla Dadapeer Dept. of Computer Science and Engineering Ballari Institute of Technology and Management Ballari Karnataka India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Data collection and analysis are critical aspects of various business processes. However, these tasks can be time-consuming, prone to errors, and delays employee productivity when done manually, especially when we have to deal with huge data. Robotic Process Automation (RPA) technologies offer a promising solution in form of a BOT, by automating tedious tasks related with data handling. A survey by The Economist corporation believe that automation kick launches digital transformation, robotic process automation (RPA) has evolved from an emerging technology to a must-have digital transformation tool for enterprises to compete globally. An increasing number of companies are now using RPA to automate normal business processes in a non-intrusive and cost-effective way. On the other hand, decision-making, pattern recognition, and many other business-related tasks benefit greatly from the use of big data and analytics. This paper explores the application of RPA in data collection and analysis, examining its functionalities, benefits, and limitations. In practical, Bot produces error-free work and takes 94.44% less time for data collection compared to a manual process, which encourages all firms to adopt to RPA technologies.

关键词： Intelligent automation Surveys Productivity Digital transformation Decision making Manuals Chatbots Pattern recognition Delays Business

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A Hybrid Deep Learning Approach for Sentiment Analysis of Dementia Care

A Hybrid Deep Learning Approach for Sentiment Analysis of De...

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2023 IEEE Conference on engineering Informatics, EI 2023

作者： Lal, Mily Bhende, Manisha Goel, Akanksha Tamrakar, Poi Saoji, Saurabh Dr. D.Y. Patil School of Science & Technology Dr D. Y. Patil Vidyapeeth Pune India PCET's Nutan Maharashtra Institute of Engineering and Technology Dept. of Computer Engineering Pune India

ISBN: (纸本)9798350338522

Sentiment analysis plays a vital role in comprehending the emotions, viewpoints, and sentiments expressed in social media content, particularly in the domain of dementia care. Deep learning models have exhibited substantial potential across a range of natural language processing tasks, including sentiment analysis. In this paper, we introduce an innovative hybrid deep learning approach for analyzing sentiment in social media data related to dementia care. This method incorporates Deep Belief Networks (DBN), Long Short-Term Memory (LSTM), and Convolutional Neural Networks (CNNs) to capture both local and global dependencies present in Twitter data. To ensure a comprehensive understanding of the text, we employ FastText word embeddings, enabling a nuanced semantic interpretation of the dataset. Our experimental results unequivocally establish the superiority of this hybrid model when compared to prior methods, achieving an impressive accuracy rate of 80.34% in sentiment classification. The outcomes of this research provide valuable insights into the emotional expression within social media content associated with dementia care. Healthcare professionals and institutions can leverage these insights to gain a deeper understanding of the emotional states, attitudes, and viewpoints of the online community, ultimately facilitating the development of enhanced strategies for dementia care and elevating the overall quality of patient support and healthcare delivery. © 2023 IEEE.

关键词： Embeddings

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