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Attaining Peak Performance through an Interference-Aware Joint Channel Assignment Scheme for Wireless Mesh Networks 15

Attaining Peak Performance through an Interference-Aware Joi...

引用

15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Mulla, Tahseen Shikalgar, Anisa Dept. of Computer Engineering Vasantdada Patil Pratishthan's College of Engineering & Visual Arts Sion Mumbai India Dept. of Computer Science & Engineering Walchand College of Engineering Sangli India

ISBN: (纸本)9798331300579

Wireless Mesh Networks (WMNs) have witnessed a remarkable surge in popularity across a diverse spectrum of applications, encompassing military networks and public wireless infrastructures. These networks, emerging as the cornerstone of numerous wireless communication systems, offer expansive wireless connectivity to a substantial user base. Despite these advancements, the inherent challenge of interference among nodes poses a significant hurdle, resulting in diminished performance for WMNs. The prevalence of interference within WMNs is a critical concern, adversely affecting their operational efficiency. The need to address this challenge is underscored by the imperative to optimize WMN performance. A pivotal step in this direction involves the development of channel assignment algorithms that are not only efficient but also effective in mitigating interference issues. Efforts to enhance WMN performance through advanced channel assignment strategies are paramount. By devising algorithms that intelligently allocate channels and manage interference, we can significantly elevate the overall efficiency of WMNs. This imperative undertaking is fueled by the overarching goal of ensuring seamless connectivity and robust performance in the face of escalating demands from various application domains. In summary, the increasing prominence of WMNs in diverse sectors necessitates a proactive approach in overcoming performance limitations. Through the implementation of sophisticated channel assignment algorithms, we can fortify WMNs, ensuring their continued role as a reliable backbone for wireless communication systems. © Grenze Scientific Society, 2024.

关键词： Wireless mesh networks (WMN)

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Secure Automated Inventory Management system using Abstracted System Design Microservice Architecture 14

Secure Automated Inventory Management system using Abstracte...

引用

14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Darshan, M. Raswanth, S.R. Chaitanya, V.S.S.K. Kalavalapalli, Venkata Sridhar Sai Kumar, Priyanka Srivastava, Gautam Amrita School of Computing Dept. of Computer Science and Engineering Coimbatore India University of Texas Dept. of Computer Science San Antonio United States Brandon University Dept. of Mathematics and Computer Science Brandon Canada

ISBN: (纸本)9798350335095

Managing inventory has forever been a difficult problem for e-commerce sellers. Even today manual labor is used for the major part of the job. Without much automation in this industry growth has been stagnant because of the same *** proposed system works on a microservice-based architecture that enables automated inventory management with the help of a social-media messaging bot in an abstract manner. The bot comprises an integrated hybrid recommender system at the customer's end for better recommendations based on image and textual data. The entire system is deployed on the cloud following a serverless architecture for resilient service. The proposed solution plays a key role in taking research forward in the direction of micro-service-based system design involving real-world data. The results of the experiment prove to be beneficial for customers and retailers since they are now connected using the micro-service. © 2023 IEEE.

关键词： Systems analysis

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Convolutional Neural Networks (CNN) and Facial Features based Smart Driver Alertness Detection 7

Convolutional Neural Networks (CNN) and Facial Features base...

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7th International Conference on Trends in Electronics and Informatics, ICOEI 2023

作者： Kumar, K.V Pavan Chidanand, M. Shabu, S.L.Jany Grace, L.K.Joshila Poornima, D. Sathyabama Institute of Science and Technology Dept. of Computer Science and Engineering Chennai600119 India

ISBN: (纸本)9798350397284

The development of machine learning assists drivers in the form of unmanned automobiles etc. Nearly 22% of accidents are caused by the driver's drowsiness and lack of alertness. This study intends to monitor the alertness of driver by analyzing their facial expressions and eye variations. The developed smart processing system substantially reduces highway accidents. The proposed model considers various facial characteristics like PERCLOS, Eye Aspect Ratio (EAR), blink values, and yawning. The driver is continually observed by the camera in the proposed method. HC classifiers are used to identify the driver's face. CNN uses the extracted eye images to determine whether the eyes are closed. Based on the classification results, EAR is measured. If the mouth is detected to be open, the lip distance (yawn) is measured. Finally, a warning will be generated if the driver is in a drowsy state. © 2023 IEEE.

关键词： Aspect ratio

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Machine Learning-driven Antenna Selection Strategies for Fading MIMO Systems 2

Machine Learning-driven Antenna Selection Strategies for Fad...

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2nd IEEE International Conference on Advancements and Key Challenges in Green Energy and Computing, AKGEC 2024

作者： Upadhyay, Deepak Sharma, Kunj Bihari Gupta, Mridul Upadhyay, Abhay Venu, Nookala Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era deemed to be University Dept. of Computer Science and Engineering Dehradun India Madhav Institute of Technology & Science Deemed University Centre for Internet of Things Gwalior India

ISBN: (纸本)9798350390179

In this study, we investigate better performing antenna selection strategies in wireless communication systems based on machine learning algorithms. Reinforcement learning as well as neural networks among others are analysed for different metrics such a bit error rate (BER), signal-to-noise ratio (SNR) and data throughput. These results show that these methods are working well against the dynamic changing of channel conditions to provide reliable and efficient. The computational complexity analysis suggests affordable overhead, allowing the feasibility of real-time implementation. Although this method can simulate the scenario in a real environment, it is not scalable due to simulation fidelity and antenna array. On the one hand, future research will refine and expand upon these ML models with real-time adaptation capabilities - On the other hand significant field trials are needed to put this work into practice for specific applications making sure that we develop robust adaptive wireless communication systems in practice. © 2024 IEEE.

关键词： Adversarial machine learning

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SafeSync: A Comprehensive Safety and Monitoring System for Two-Wheeler Users Using Machine Learning, ESP32, GPS, and GSM 6

SafeSync: A Comprehensive Safety and Monitoring System for T...

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6th IEEE International Conference on Recent Advances in Intelligent Computational Systems, RAICS 2024

作者： Sunil, K.S. Adithyan, P. Krishnan, Anjana Hashim Sharaf, C.P. Paul, Jerin John, Salvi Hanna Government Engineering College Dept. of Computer Science & Engg. Kerala Idukki India

ISBN: (纸本)9798350381689

Our work, 'SafeSync' proposes a complete safety and monitoring system aimed to address the considerable hazards encountered by two-wheeler users, such as accidents and rash driving scenarios. Unlike other systems, SafeSync combines powerful technology such as ESP32, GPS, and GSM with an innovative application of machine learning methods. This system specializes in detecting and alerting to accidents, tracking vehicles in real time, and identifying risky driving behaviour. SafeSync goes beyond traditional safety measures by not only identifying ongoing accidents but also forecasting potential events. This predictive feature enables the system to proactively inform users or authorities in advance, assisting in the prevention of accidents. The Flutter-built dedicated mobile app serves as a user-friendly interface, offering real-time updates on the vehicle's status and location. The program also allows users to customise monitoring settings and add parental restrictions. By actively recognizing and warning users to dangerous practises, SafeSync strives to improve rider safety and promote responsible driving behaviour. Finally, the initiative helps overall road safety by providing a solid solution for two-wheeler users to monitor and enhance their road safety. © 2024 IEEE.

关键词： Accident Alert ESP32 GPRS GPS GSM MPU6050 Rash Driving

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Towards Robust mmWave-based Human Activity Recognition using Large Simulated dataset for Model Pretraining

Towards Robust mmWave-based Human Activity Recognition using...

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2024 IEEE International Conference on Big data, Bigdata 2024

作者： Joshi, Vinay Xu, Shengkai Cao, Qiming Zhu, Yi Wang, Pu Xue, Hongfei Unc Charlotte Dept. of Computer Science Charlotte United States Purdue University Electrical and Computer Engineering West Lafayette United States Wayne State University Dept. of Computer Science Detroit United States

ISBN: (纸本)9798350362480

Human activity recognition (HAR) is crucial for real-world applications such as healthcare, surveillance, and smart homes. Among sensing technologies, millimeter wave (mmWave) sensors stand out due to their contactless nature, high sensitivity, and ability to operate in low-light environments while preserving privacy. However, the scarcity of mmWave sensing data limits the generalizability of mmWave-based HAR systems. To address this, we propose mmAP, a data augmentation and pretraining framework that synthesizes a large mmWave dataset using human mesh data, followed by pretraining a robust and general mmWave heatmap encoder using a multi-modal masked autoencoder framework using the synthesized data. We enhance the model's robustness with heatmap-specific data perturbations and perform task-specific fine-tuning on a small real-world dataset. The experiment results over the baseline demonstrate the effectiveness of the proposed mmAP framework. © 2024 IEEE.

关键词： Large datasets

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EEG-Based Alzheimer's Disease Detection Using Deep Learning Techniques 12

EEG-Based Alzheimer's Disease Detection Using Deep Learning ...

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12th IEEE Region 10 Humanitarian Technology Conference, R10-HTC 2024

作者： Ancy, S.A. Panachakel, Jerrin Thomas College of Engineering Dept. of Computer Science and Engineering Trivandrum India College of Engineering Dept. of Electronics and Comm. Engg. Trivandrum India

ISBN: (纸本)9798350340204

Alzheimer's disease (AD) is a neurological condition characterized by the degeneration of neuron cells, leading to cognitive impairment. This project investigates the effectiveness of EEG-based features for detecting Alzheimer's Disease using deep learning techniques. The dataset comprises EEG recordings from healthy controls, Frontotemporal Dementia (FTD) patients, and patients diagnosed with AD. data preprocessing involved segmenting the EEG signals into fixed-length epochs and filtering the baseline EEG into alpha, beta, and theta frequency bands. Features such as Mean Phase Coherence (MPC), Differential Entropy (DE), and Mean Squared Coherence (MSC) were computed pairwise for each epoch and frequency band. A feature matrix was constructed for each subject by stacking these features, which were then converted into images and used as input data for a ResNet50-based pre-trained model. The model was tested in leave-one-out cross-validation setting and achieved results comparable to the state-of-the-art in distinguishing between EEG recordings from healthy and AD-affected subjects. © 2024 IEEE.

关键词： Electroencephalography

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The Low-Complexity Timing Synchronization Circuits for IEEE 802.15.4a/z IR-UWB Systems 6

The Low-Complexity Timing Synchronization Circuits for IEEE ...

引用

6th IEEE Novel Intelligent and Leading Emerging sciences Conference, NILES 2024

作者： Wang, Kuan-Wei Shen, Chung-An Huang, Chin-Ya National Taiwan University of Science and Technology Electronic and Computer Engineering Dept. Taipei Taiwan

ISBN: (纸本)9798350378511

This paper presents the design and implementation of a low-complexity timing synchronization circuit for the IEEE 802.15.4a/z IR-UWB wireless communication standard. The circuit structure of a low-complexity correlator for complex ternary values is designed. Moreover, a two-stage synchronization approach is utilized for a more accurate sample alignment. Therefore, a balance between the performance of synchronization and hardware complexity is achieved. The proposed synchronization architecture is designed and implemented based on the Xilinx ZC706 FPGA platform. The experimental results show that the proposed design saves 74% of buffer and 87.4% of multiplier utilization. © 2024 IEEE.

关键词： Ultra-wideband (UWB)

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Detect Bangladeshi Mango Leaf Diseases Using Lightweight Convolutional Neural Network 3

Detect Bangladeshi Mango Leaf Diseases Using Lightweight Con...

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3rd International Conference on Electrical, computer and Communication engineering, ECCE 2023

作者： Mahbub, Nosin Ibna Naznin, Feroza Hasan, Md Imran Shifat, Syed Mahfuzur Rahman Hossain, Md. Alamgir Islam, Md Zahidul Islamic University Dept. of Information and Communication Technology Kushtia7003 Bangladesh Green University Dept. of Computer Science and Engineering Dhaka Bangladesh Islamic University Dept. of Computer Science and Engineering Kushtia7003 Bangladesh University of Global Village Dept. of Computer Science and Engineering Barishal Bangladesh

ISBN: (纸本)9798350345360

This research concentrates on the diagnosis of common mango leaf diseases in Bangladesh using image processing via deep learning. Mango production could be raised by at least 28% globally if the crop could be safeguarded from a variety of diseases. However, without the assistance of an expert, it is challenging for the farmer to detect the disease at the appropriate time. Few studies have been conducted to identify the mango leaf disease present in Bangladesh. So far, no study has been done to identify the seven distinct mango leaf diseases reported in Bangladesh. We proposed a lightweight convolutional neural network (LCNN) in this paper to accurately classify seven distinct mango leaf diseases as well as normal mango leaf. To assess the proposed LCNN model, performance is compared to several pre-trained models such as VGG16, Resnet50, Resnet101, and Xception, and it is found that LCNN achieves the highest testing accuracy (98%). © 2023 IEEE.

关键词： Convolutional neural networks

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IoT Intrusion Detection: Evaluating ML-Based IDS on Image and Network Traffic datasets 9

IoT Intrusion Detection: Evaluating ML-Based IDS on Image an...

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9th IEEE International Symposium on Smart Electronic Systems, iSES 2023

作者： Pathak, Manan Patel, Anuj Shah, Bela Cspit Charotar University of Science and Technology Dept. of Computer Science and Engineering Anand India

ISBN: (纸本)9798350383249

With the increasing pervasiveness of Internet of Things (IoT) devices, ensuring their security has become a critical concern. Advanced intrusion detection systems (IDS) leveraging machine learning (ML) and Deep learning(DL) techniques offer promising solutions for protecting IoT networks. However, the success of ML models depends heavily on preprocessing and feature extraction processes. This research presents a survey on ML-based IDS for IoT, focusing on ML models applied to both image(IEEE dataport) and UNSW-NB15 datasets. The survey evaluates several feature extraction methods, including image filters and Machine learning models for the image dataset. For the UNSW-NB15 dataset, a range of preprocessing techniques specific to network traffic analysis, such as statistical features and protocol-specific features, are considered. In addition to feature extraction, various ML algorithms are assessed on both datasets. The evaluation is based on accuracy, precision, recall, and F1 score performance metrics, providing a comprehensive analysis of the ML model's effectiveness in detecting intrusions. This research contributes to the field of IoT security by providing insights into the performance of feature extraction algorithms and ML models on both image and network traffic datasets. The findings offer guidance for developing effective IDS solutions that can accurately detect and mitigate intrusions in IoT networks, enhancing the overall security of IoT devices and systems. © 2023 IEEE.

关键词： Internet of things

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