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Enhancing Bikers' Safety and Coordination: A LoRa-Enabled Wearable System for Smart Cycling

Enhancing Bikers' Safety and Coordination: A LoRa-Enabled We...

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Soft Computing for Security Applications (ICSCSA), International Conference on

作者： Krishna Prasad D S Sandeep Kumar S Deeksha G D Manasa Rao Megha S Shashwathi N Dept. of Computer & Communication Engineering NMAM Institute of Technology (NMAMIT) NITTE (Deemed to be University) Nitte Karnataka India

ISBN: (数字)9798331515720

ISBN: (纸本)9798331515737

In recent years, the popularity of biking, especially mountain biking, has surged, drawing enthusiasts seeking adventure and adrenaline amidst rugged landscapes. However, with this surge in popularity comes an increased awareness of the safety challenges inherent in the sport, particularly in remote or challenging terrains where traditional communication methods often fail to provide adequate support. Recognizing these challenges, the paper presents a LoRa-enabled wearable system tailored to enhance safety and coordination for mountain bikers, focusing on challenging terrain scenarios. By incorporating GPS functionality, the system delivers crucial information such as status updates and emergency notifications, thereby markedly elevating safety standards for riders. Furthermore, the project enables real-time communication in network-deficient areas, effectively surmounting communication hurdles posed by mountainous landscapes. Leveraging LoRa technology extends the communication range, guaranteeing uninterrupted connectivity and empowering bikers to traverse rugged terrains with heightened assurance and security.

关键词： LoRa Real-time systems Safety Security Wearable devices Usability Surges Standards Global Positioning System Sports

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A Deep Learning Approach to Maximizing Electrostatic Sieve Efficiency in Regolith Beneficiation

A Deep Learning Approach to Maximizing Electrostatic Sieve E...

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IEEE International Conference on Big Data

作者： Kalpit M. Vadnerkar Emmanuela Amen Eze Rinoj Gautam Daoru Han Xin Liang Tong Shu Dept. of Electrical and Computer Engr. Clemson University Clemson SC USA Dept. of Mechanical and Aerospace Engr. Missouri University of Science and Technology Rolla MO USA Dept. of Computer Science and Engr. University of North Texas Denton TX USA Department of Computer Science University of Kentucky Lexington KY USA Dept. of Computer Science and Engineering University of North Texas Denton TX USA

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

This study investigates the optimization of an electrostatic sieve designed for lunar regolith beneficiation. Two parameters of the electrostatic sieve, 1) the voltage amplitude and 2) angle of inclination, were chosen as variables in the optimization process. Numerical simulations revealed that increasing voltage amplitude significantly enhances sieve performance over the sieve angle. However, optimal separation required careful voltage adjustment for specific sieve angles. A comprehensive dataset incorporating additional parameters was then created to train Machine Learning (ML) and Deep Learning (DL) models for further optimization. The ML/DL models were trained on a small subset of the original dataset to predict the yield. We showcase the benefits of leveraging DL techniques to improve the electrostatic sieve for regolith beneficiation via tailored evaluations. Our model, trained on lower-yield examples, accurately (92%) identifies parameter combinations that increase yields above 30%. It leads to a near-optimal yield with 10× reduction on runtime when compared with exhaustive simulations. This not only reduces the reliance on resource-intensive numerical simulations but also offers a rapid, validated approach to optimizing equipment for lunar mining operations.

关键词： Deep learning Runtime Computational modeling Moon Neural networks Voltage Numerical simulation Numerical models Electrostatics Optimization

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Hybrid CNN LSTM Approach for Sentiment Analysis of Bengali Language Comment on Facebook

Hybrid CNN LSTM Approach for Sentiment Analysis of Bengali L...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： SM Nuruzzaman Nobel S M Masfequier Rahman Swapno Ramachandra A C Hasibul Hossain Shajeeb Md Babul Islam Rezaul Haque Dept of Computer Science and Engineering Bangladesh University of Business and Technology Dhaka Bangladesh Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru Dept. of Computer Modeling Electronic and System Engineering UNICAL Rende Italy Dept of CSE East West University Dhaka Bangladesh

This study addresses the challenge of sentiment analysis in Bengali Facebook comments, where users express their opinions on various topics. Existing sentiment analysis techniques have difficulty achieving high accuracy in this situation, which has inspired the creation of a fresh methodology. To improve sentiment analysis of Bengali Facebook comments, we suggest a model that combines a hybrid convolutional neural network (CNN) and a long short-term memory network (LSTM). Our research adds to the expanding area of sentiment analysis, especially for languages with few resources, such as Bengali. The success of the Hybrid CNN LSTM model opens the door for more precise and context-aware sentiment analysis on social media platforms, allowing for a better understanding of user sentiments and boosting user experiences. Our study focused on dividing comments into five groups according to their emotional content: troll, not bully, sexual, religious, and threat. We carefully generated the handmade dataset and ensured the quality and relevance of a dataset of 50,000 Bengali comments before using it to train and test our algorithm. The Hybrid CNN LSTM model outperforms conventional approaches and achieves an outstanding accuracy rate of 92.09%, demonstrating amazing efficiency in sentiment recognition. This dramatic increase in precision indicates that machine learning techniques are successful in sentiment analysis of Bengali Facebook comments.

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Design Objectives for Synthesis of Graphene PN Junction Circuits Based on Two-Level Representation

Design Objectives for Synthesis of Graphene PN Junction Circ...

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Euromicro Symposium on Digital System Design

作者： Subrata Das Arighna Deb Petr Fiser Debesh Kumar Das Dept of Computer Science & Engineering Supreme Knowledge Foundation Group of Institutions Hooghly India School of Electronics Engineering Kalinga Institute of Industrial Technology Bhubaneswar India Faculty of Information Technology Czech Technical University Prague Czech Republic Dept. of Computer Science & Engineering Jadavpur University Kolkata India

ISBN: (数字)9798350380385

ISBN: (纸本)9798350380392

The development of electrostatically doped graphene PN-junctions shows promise for creating efficient low-power, high-speed circuits. In recent years, there has been a considerable interest in the synthesis of graphene PN junction logic circuits. However, existing synthesis methods lack assessment based on technology-specific cost metrics (e.g., the number of graphene PN junction gates, constant inputs), leading to insufficiently addressed design objectives. In this paper, we introduce synthesis approaches for graphene PN-junction circuits based on Sum-of-Products (SoP) and Exclusive Sum-of-Products (ESoP) function representations. Experimental results indicate that ESoP-based synthesis significantly reduces the number of graphene PN junction gates, constant inputs, and switching activity compared to SoP-based approaches. Overall, ESoP-based synthesis is deemed more suitable than SoP-based methods for designing graphene PN-junction logic circuits.

关键词： Measurement Costs Digital systems Logic circuits Graphene Switches Logic gates Circuit synthesis Logic Junctions

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Prediction and detection of nutrition deficiency using machine learning

Prediction and detection of nutrition deficiency using machi...

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Device Intelligence, Computing and Communication Technologies, (DICCT), International Conference on

作者： Amit Kumar Mishra Neha Tripathi Ashish Gupta Deepak Upadhyay Neeraj Kumar Pandey Dept. of Computer Science & Engineering Graphic Era Hill University Dehradun India Dept. of Computer Science & Engineering Graphic Era (Deemed to be University) Dehradun India Dept. of Computer Science & Engineering Tula’s Institute Dehradun India School of Computing DIT University Dehradun India

The plant producers have a hard time identifying nutritional inadequacies in their crops. The capacity to recognize these comprehensive nutritional deficiencies could help regulate crops properly. Using image processing, Convolutional Neural Network (CNN), the researchers were able to categorize and identify complete nutritional deficiencies in various cultivars. The prototypes would provide prescribed plant fertilizers once nutrient insufficiency was recognized. Iron (Fe), magnesium (Mg), potassium (K), nitrogen (N), calcium (Ca) and complete nutrition were examined. For classifying the image processing techniques were used to turn the images into grayscale & binary data. Using identification and prediction, CNN predicts complete nutritional deficiencies in the plant. CNN high accuracy of detection and diagnosis of nutrient deficits in different cultivars, according to the results compared with Artificial Neural Network (ANN) and DenseNet-121. The design has been tested and the results demonstrate a better way to classify and diagnose complete nutritional deficits in different cultivars.

关键词： Calcium Crops Prototypes Machine learning Gray-scale Iron Magnesium

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A Hybrid Algorithm to Dual Sparse Sampling Measurement in Time-Resolved Electromagnetic Near-Field Scanning

A Hybrid Algorithm to Dual Sparse Sampling Measurement in Ti...

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IEEE International Symposium on Electromagnetic Compatibility (EMC)

作者： Yanming Zhang Steven Gao Lijun Jiang Dept. of Electronic Engineering Chinese University of Hong Kong Hong Kong Dept. of Electrical and Computer Engineering Missouri University of Science and Technology Rolla MO USA

ISBN: (数字)9798350360394

ISBN: (纸本)9798350360400

Time-resolved electromagnetic near-field scanning is vital for antenna measurement and addressing complex electromagnetic interference and compatibility issues. However, the swift acquisition of high-resolution spatiotemporal data remains challenging due to physical constraints, such as moving the probe position and allowing sufficient time for sampling. This paper introduces a novel hybrid approach that combines Kriging for sparse spatial measurement, compressed sensing (CS) for sparse temporal sampling, and dynamic mode decomposition (DMD) for a comprehensive analysis of dual-sparse sampling electromagnetic near-field data. CS optimizes sparse sampling in the time domain, capitalizing on the inherent sparsity within electromagnetic radiated signals, resulting in reliable representation of time-domain signals and reducing the required time samples. Latin hypercube sampling guides the probe position, facilitating sparse measurement in the space domain. DMD extracts meaningful insights from the resulting sparse spatiotemporal data, producing sparse dynamic modes and temporal evolution information. Subsequently, Kriging is employed to infer missing spatial measurements for each sparse dynamic mode. Finally, the entire spatiotemporal signals are reconstructed based on interpolated dynamic modes and temporal evolution information. Validation of the proposed method is demonstrated with an example using crossed dipole antennas as the device under test. The Kriging-CS-DMD framework effectively reconstructs electromagnetic fields with precision while concurrently reducing the measurement workload in both the time and space domains. This methodology holds promise for various applications, including space-time-modulated electronic devices.

关键词： Antenna measurements Time measurement Spatiotemporal phenomena Sensors Electromagnetic fields Data mining Electromagnetics Time-domain analysis Probes Compressed sensing

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Addressing Semantic Similarity Attacks in Online Social Networks

Addressing Semantic Similarity Attacks in Online Social Netw...

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Blockchain and Distributed Systems Security (ICBDS), IEEE International Conference on

作者： Amardeep Singh Monika Singh Computer Science & Engg. Dept. SVIET Banur India Computer Science & Engg. Apex Institute of Technology Mohali India

An efficient technique for preserving user privacy of users while publishing data is anonymization. Banks, Social Network service providers and hospitals are examples of data owners/stakeholders who anonymize their customers' data before releasing it in order to protect users' privacy. However, trustworthy information consumers still value anonymous data. Numerous anonymization models and techniques have been developed for releasing data while preserving user privacy. These models/algorithms de-identify user data, which is typically presented as graphs. Giving clear perspectives on the entire information privacy field, including recent anonymization research, tabular and SN data, is crucial. In this article, we proposed an approach for anonymization to address privacy issues in social networks specifically addressing semantic similarity attacks. Far as we are aware, our proposed technique is capable of resolving the issues of semantic similarity between sensitive attributes efficiently. The results of the proposed approach have been evaluated using the dataset of around 6000 users of a real-time social network viz. Twitter. Evaluation metrics of APL (Average Path Length) show that our technique can protect privacy and also maintain the utility of data while publishing it for public use.

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A Comparative Analysis of Deep Learning Approaches in Intrusion Detection System 6

A Comparative Analysis of Deep Learning Approaches in Intrus...

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6th International Conference on Recent Trends on Electronics, Information, Communication and technology, RTEICT 2021

作者： Das, Abhijit Balakrishnan, S.G. Sai Vidya Institute of Technology Affiliated to VTU Dept. of Information Science and Engineering Bengaluru India Mahendra Engineering College Dept. of Computer Science Engineering Salem India

ISBN: (纸本)9781665435598

With computers' growth, network-based technology, including advanced communication features, Internet of Things (IoT), automation, and upcoming fifth generation (5G) mobile technology, network security has become challenging to secure applications, systems, and networks. Rapid increase of network devices has created many new attacks and therefore presented significant difficulties for network security to identify threats correctly. Intrusion detection systems (IDSs) guarantee the network's confidentiality, integrity, and availability by monitoring network traffic and blocking any potential intrusions. Despite significant research efforts, IDS still confronts many difficulties in increasing detection efficiency and decreasing false alarm rates. Intrusion detection systems are using machine learning and deep learning-based IDS to identify intrusions throughout the network as quickly as possible. This paper identifies the idea of IDS and the following details the taxonomy developed based on the prominent Machine Learning (ML) and Deep Learning (DL) methods used in NIDS system design. An in-dept. analysis of NIDS-based papers is discussed to outline the benefits and weaknesses of the various options. New technology, including ML and DL, and current advances in these NIDS technologies are described with the methodology, assessment metrics, and dataset selections. Through highlighting the existing research difficulties with the longterm scope of the NIDS study to improve machine learning and deep learning-based NIDS. Many novel techniques are using to deal with Intrusion detection systems. However, most are not quick enough to adapt to cyber security defense systems' dynamic and complex nature as the threats surface is growing exponentially with different devices' interfaces. This paper proposes a review of various Intrusion detection system (IDS) capabilities and assets using deep learning techniques. It also suggests a novel idea to automatically adapt the network intru

关键词： Network security

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Dynamic Asset Allocation Using Trading Agent

Dynamic Asset Allocation Using Trading Agent

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IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)

作者： Shingchern D. You Chien-Hung Liu Ching-Cheng Yen Dept. of Computer Science and Information Engineering National Taipei Universiyt of Technology Taipei Taiwan

We study the use of PPO (Proximal Policy Optimization) algorithm for trading ETFs belonging to different asset classes. The studied asset classes include common stocks, bonds, REIT (Real Estate Investment Trust), gold, and future contracts on agricultural commodities. When properly training the PPO agent with the proposed ratio allocation strategy, the agent outperforms the static, periodical rebalancing approach.

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Transforming Brownfield Factories: Unleashing the Potential with Co-engineering and Virtual Commissioning

Transforming Brownfield Factories: Unleashing the Potential ...

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Annual Conference of Industrial Electronics Society

作者： Oskar Wintercorn Khalid Atta Peter Jeppsson Cristina Paniagua Jan Van Deventer Dept. of Computer Science Electrical and Space Engineering Luleå University of Technology Luleå Sweden

Manufacturing lines are subject to continual change as components inevitably wear down over time, necessitating replacement. Often, the exact original parts may no longer be readily available, or an upgrade to integrate new technology becomes desirable. The crux of these scenarios lies in swiftly and seamlessly incorporating these changes, minimizing disruption to the existing processes. To address this, we put forward a concept utilizing a digital twin, complemented by an open-source industrial IoT framework. This combination facilitates both software and hardware loop integration, paving the way for efficient co-engineering development with existing legacy systems. Through this approach, we enable swift technological integration, keeping the manufacturing process agile and up-to-date.

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