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Design of IoT Based Agricultural Farm Protector Drone Using Raspberry Pi

Design of IoT Based Agricultural Farm Protector Drone Using ...

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international conference on Contemporary computing and Informatics (IC3I)

作者： P. Kanimozhi Muthukumaran Kannan Christo Ananth T. Ananth Kumar S. Parkavi Computer Science and Engineering IFET College of Engineering Villupuram India Department of EIE Jaya Engineering College Chennai Tamilnadu Faculty of International Education Programs Samarkand State University Uzbekistan

In many cases, the safety of persons and commodities is now assured using surveillance drones, which are crucial technical systems. the drone is critical operational asset during surveillance operations because of its rapid deployment and accurate observation. It is crucial to safeguard farmland and agricultural areas from animals, birds, and other species. Numerous measures are attempted to deter human intrusion but they fall short in defending against the farmer's principal foes-animals that trample their crops, both domestic and wild animals must be kept out of the area. A farm protection drone has been employed to prevent such disasters. With the help of a drone technology with raspberry pi, used as animal protection to monitor the farms. To do this, this project makes use of (GSM) Global System for Mobile Communication and (GPS) Global Positioning System Module. A “SMS” the area that animals observe and detected by image processing will be sent to the farmer and forest officers.

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Envisioning 6G Molecular Communication for IoBNT Diagnostic systems

Envisioning 6G Molecular Communication for IoBNT Diagnostic ...

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2021 international conference on Green Energy, computing and Sustainable Technology, GECOST 2021

作者： Juwono, Filbert H. Reine, Regina Wong, W.K. Sim, Zee Ang Gopal, Lenin Curtin University Malaysia Department of Electrical and Computer Engineering Miri Sarawak98009 Malaysia Twigx Research LondonWC2H 9JQ United Kingdom Heriot-Watt University School of Engineering and Physical Sciences Putrajaya62200 Malaysia

ISBN: (纸本)9781665438650

With the large volume of data transfer, present 5G communication technology is unable to provide system reliability. the era of the 6th generation of communication technology (6G) has been proposed to overcome the current limitation. In the 6G era, it is expected that every thing will be connected, including the nano-things. In this paper, we envision a future diagnostic system that uses molecular communication and machine learning techniques to diagnose disease biomarkers online. this concept is one of the applications of the Internet of BioNano-things (IoBNT). the concept of molecular communication is revisited in this paper. Furthermore, the proposed diagnostic system is described in detail, including an explanation of the biomarker detection mechanism, an outmessaging interface, and algorithms for biomarker detection. Furthermore, challenges and research opportunities are also presented. © 2021 IEEE.

关键词： Data transfer

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proceedings of 6th Annual international Workshop on Innovating the Network for Data Intensive Science, INDIS 2019 - Held in conjunction with SC 2019: the international conference for High Performance computing, Networking, Storage and Analysis

Proceedings of 6th Annual International Workshop on Innovati...

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6th Annual international Workshop on Innovating the Network for Data Intensive Science, INDIS 2019

ISBN: (纸本)9781728166667

the proceedings contain 8 papers. the topics discussed include: estimation of RTT and loss rate of wide-area connections using mpi measurements;training classiï¬üers to identify TCP signatures in scientiï¬üc workï¬‚ows;G2: a network optimization framework for high-precision analysis of bottleneck and flow performance;hop recording and forwarding state logging: two implementations for path tracking in P4;SCinet DTN-as-a-service framework;co-scheduling of advance and immediate bandwidth reservations for inter-data center transfer;and sample transfer optimization with adaptive deep neural network.

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Self-Adaptive A* Algorithm with Enhanced Dynamic Window Approach for Optimal Trajectory Planning in Narrow Environments 14

Self-Adaptive A* Algorithm with Enhanced Dynamic Window Appr...

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14th international conference on Indoor Positioning and Indoor Navigation - Work-in Progress Papers, IPIN-WiP 2024

作者： Yan, Jiaquan Zhou, Zineng Luo, Haiyong Zhao, Fang Xiong, You Wang, Juan Ma, XuePeng Beijing University of Posts and Telecommunications No 10 Xitucheng Road Haidian District Beijing China Institute of Computing Technology Chinese Academy of Sciences No.6 Kexueyuan South Road Haidian District Beijing China University of Chinese Academy of Sciences Beijing China Shouguang Cheng Zhi Feng Xing Technology Co. Ltd. China

Path planning in narrow environments presents substantial challenges for autonomous driving and robotic navigation. this paper introduces an innovative adaptive search method tailored for the hybrid A* algorithm, designed to fully leverage the capabilities of differential drive models. this method enhances the algorithm's ability to generate trajectories that are more in tune with the dynamics of differential drive chassis, particularly in narrow passages. Here, the algorithm adaptively transitions between hybrid A* and traditional A* strategies, utilizing variable search step lengths to effectively balance precision and computational efficiency. Furthermore, we introduce a novel reward function for the hybrid A* algorithm, which incorporates considerations for safety and the costs associated with in-place rotations. this reward function takes into account the number of nearby obstacles, a safety cost, a rotation cost, and a zero-speed cost, all aimed at minimizing unnecessary rotations and optimizing the overall path planning process. To implement our approach in real-world scenarios, we present an Enhanced Dynamic Window Approach (EDWA) that employs multi-scale path sampling to more effectively navigate complex environments with sharp turns. Simulation results demonstrate the effectiveness and superiority of our proposed algorithms in managing narrow path navigation. the improved hybrid A* and DWA algorithms notably enhance safety, efficiency, and trajectory smoothness, showing significant advancements over traditional methods. © 2024 Copyright for this paper by its authors.

关键词： Trajectories

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7th international Symposium on Intelligent Informatics, ISI 2022

7th International Symposium on Intelligent Informatics, ISI ...

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7th international Symposium on Intelligent Informatics, ISI 2022

the proceedings contain 37 papers. the special focus in this conference is on Intelligent Informatics. the topics include: Segmentation Approach for60;Nucleus Cytoplasm of60;Ewing Sarcoma;Deep Neuroevolution Squ...

ISBN: (纸本)9789811980930

the proceedings contain 37 papers. the special focus in this conference is on Intelligent Informatics. the topics include: Segmentation Approach for Nucleus Cytoplasm of Ewing Sarcoma;Deep Neuroevolution Squeezes More Out of Small Neural Networks and Small Training Sets: Sample Application to MRI Brain Sequence Classification;abstractive Text Summarization of Hindi Corpus Using Transformer Encoder-Decoder Model;automatic Text Classification for Web-Based Malayalam Documents;question and Answer Generation from Text Using Transformers;A Comparative Study of Spam SMS Detection Techniques for English Content Using Supervised Machine Learning algorithms;Evaluation of Tweet Sentiments Using NLP;cascaded Feature Vector Assisted Blood Vessel Segmentation from Retinal Images;Unsupervised Deep Clustering and Reinforcement Learning Can Accurately Segment MRI Brain Tumors with Very Small Training Sets;towards Frugal Artificial Intelligence: Exploring Neural Network Pruning and Binarization;EEG-Based Emotion Recognition Using an Ensemble Learning Algorithm;imaging and Vision Development Platform with Algorithm Library for Intelligent Vision systems;pulse Decomposition Analysis Based Non-invasive Diabetes Detection System;noise Classification and Removal in Compressively Sensed Surveillance Videos Using Statistical Measures;DNA and Improved Sine Map Based Video Encryption;the Analysis of Srgb Color Space Based Density for Brain Tumor Segmentation;improved Kapur Entropy-Based Lung Nodule Segmentation in X-ray Images;comparative Analysis of Various Standards for Medical Image Compression;Repetitive Filtering-Based Intra Prediction Scheme for HEVC;identification and Counting of Blood Cells Using Machine Learning and Image Processing;practical Implications of Dequantization on Machine Learning algorithms: A Survey;EDGE-Based ML in W-Band Target Micro-Doppler Feature Extraction;emotion Detection Using Speech Analysis;FED6G: Chameleon Learning for Network Slice Management in Beyond 5G

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Earthquake Predictions Using Machine Learning and Acoustic Emissions

Earthquake Predictions Using Machine Learning and Acoustic E...

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Intelligent and Fuzzy systems - Intelligence and Sustainable Future proceedings of the INFUS 2023 conference

作者： Sioutas, Ioannis Drakaki, Maria Linardos, Vasileios Tzionas, Panagiotis Department of Science and Technology University Center of International Programmes of Studies International Hellenic University 14th Km Thessaloniki Nea Moudania Thessaloniki570 01 Greece Department of Industrial Engineering and Management International Hellenic University P.O. Box 141 Thessaloniki57400 Greece

ISBN: (纸本)9783031397769

the impacts from earthquakes can be devastating, therefore accurate earthquake forecasting is of ultimate importance towards effective disaster risk reduction and improved disaster preparedness. Earthquake prediction is the holy grail of earthquake science yet advances in this field have not shown results that will contribute significantly to a reduction of associated disaster losses. In this paper, Machine Learning (ML) and Deep Learning (DL) methods have been employed to forecast the time before a defect fails by using the acoustic signals of a laboratory fault. A Kaggle dataset was used containing experimental laboratory earthquake data. Various algorithms were employed including the CatBoost, Random Forest, Extreme Gradient Boosting, AdaBoost and Convolutional Neural Networks (CNNs). the forecasts consider the instantaneous physical characteristics of the acoustic signal. the best accuracy was achieved by applying rolling forecast using the AdaBoost Regressor algorithm. Promising results have shown the potential of the proposed method. Future research could explore the applicability of the method with continuous signals from seismic waves to provide further insights into earthquake prediction. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Earthquakes

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CoViT-Net: A Pre-trained Hybrid Vision Transformer for COVID-19 Detection in CT-Scans 4th

CoViT-Net: A Pre-trained Hybrid Vision Transformer for COVID...

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4th international conference on Frontiers in computing and systems, COMSYS 2023

作者： Das, Ankit Banik, Debapriya Roy, Kaushiki Chan, Gordon K. Bhattacharjee, Debotosh Department of Computer Science and Engineering Amity University Kolkata700135 India Department of Computer Science and Engineering ICFAI University Tripura799210 India Institute of Engineering and Management Kolkata700091 India University of Engineering and Management Kolkata700160 India Department of Oncology and the Department of Experimental Oncology Cross Cancer Institute University of Alberta EdmontonABT6G 1Z2 Canada Department of Computer Science and Engineering Jadavpur University Kolkata700032 India

ISBN: (纸本)9789819726103

the coronavirus disease, COVID-19, that grasped the earth since 2019, has taken a serious toll on humanity. COVID-19 cases were spiking exponentially in densely populated regions, and the health infrastructure was challenged daily. As such, Artificial Intelligence (AI) had to step in to improvise medical imaging applications. One such use case was the classification of Computerized Tomography (CT) Scan images into COVID-infected and un-infected categories. thus it would effectively classify a patient being either COVID Positive or Negative. Computer Vision (CV) algorithms manifest the potential to solve such image classification problems. In retrospect, deep learning-based architectures have been very productive. However, Vision Transformers (ViT) have taken over CV since its inception. In this study, we propose CoViT-Net: A pre-trained hybrid Vision Transformer architecture for detecting COVID-19 in CT scan images. Our proposed CoViT-Net has a unique architecture incorporating a ViT-S/16 feature extractor, pre-trained on the ImageNet21k dataset, and a modified MLP block with dense layers on top. CoViT-Net has thereby achieved a state-of-the-art accuracy score of 0.9638. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： COVID-19

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MPICC: Multiple-Precision Inter-Combined MAC Unit with Stochastic Rounding for Ultra-Low-Precision Training 25

MPICC: Multiple-Precision Inter-Combined MAC Unit with Stoch...

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30th Asia and South Pacific Design Automation conference, ASP-DAC 2025

作者： Huang, Leran Liu, Yongpan Lin, Xinyuan Wei, Chenhan Sun, Wenyu Wang, Zengwei Cao, Boran Zhang, Chi Fu, Xiaoxia Zhao, Wentao Zhang, Sheng Tsinghua Shenzhen International Graduate School Key Laboratory of Advanced Sensor and Integrated System Shenzhen China Tsinghua University Department of Electronic Engineering Beijing China

ISBN: (纸本)9798400706356

Recent studies have proved the feasibility of ultra-low-precision (≤ 8-bit) training. However, most of the existing operational circuits support only a few higher precisions (FP16, FP32, etc.) for multiplication, and the bit width for accumulation cannot be reduced, which results in low area and power efficiencies. In this paper, we propose MPICC, a multiple-precision Multiply-Accumulate (MAC) unit designed for ultra-low-precision training. It supports inter-combined computations across 18 different precision combinations, including LOG4 (radix-4 FP4), FP4, FP6, FP8, and INT4. It also reduces the accumulation precision from FP16 to FP12 through an optimized Stochastic Rounding (SR) strategy to further save logic resources. Moreover, a low-cost emulating controller, which time-division multiplexed the low-precision MAC unit, is also designed to accomplish high-precision computations for critical DNN layers. Compared with the existing multiple-precision computing units, the area/energy efficiencies of this design are improved by 1.17×/1.19× at FP8, and 4.69×/3.64× at FP4, respectively. the SR strategy further reduces the area/power consumption by 15.6%/14.9% of the floating-point accumulator. © 2025 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.

关键词： Stochastic systems

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Intelligence Embedded Image Caption Generator using LSTM based RNN Model 6

Intelligence Embedded Image Caption Generator using LSTM bas...

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6th IEEE international conference on Communication and Electronics systems, ICCES 2021

作者： Verma, Akash Saxena, Harshit Jaiswal, Mugdha Tanwar, Poonam Fet Manav Rachna International Institute of Research and Sciences Cse Faridabad India

ISBN: (纸本)9781665435871

Humans tend to extract information from everything they see be it living or non-living. this whole phenomenon motivated us to move in this direction and explore the field of computer vision and how this can be used with recurrent neural networks to generate captions from any image. By witnessing the recent increase in natural language processing-based applications;various other researchers have also worked on this concept and produced commendable results. Describing an image is not an easy task to implement, the structure and semantics of a sentence hold an important weight age in sentence formation. this paper approaches the problem of caption generation with an LSTM (Long-Short Term Memory) based RNN model and builds architecture based on the same to generate efficient and meaningful captions by training the dataset effectively. Flicker8k dataset is used to train our model and worked well. the accuracy of the model is evaluated based on standard evaluation metrics. © 2021 IEEE.

关键词： Long short-term memory

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Object-aware Multi-criteria Decision-Making Approach using the Heuristic data-driven theory for Intelligent Transportation systems 10

Object-aware Multi-criteria Decision-Making Approach using t...

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10th IEEE international conference on Data Science and Advanced Analytics, DSAA 2023

作者： Mekala, M.S. Eyad, Elyan Srivastava, Gautam Robert Gordon University School of Computing AberdeenAB10 7AQ United Kingdom Brandon University Dept. of Math and Computer Science BrandonMB R7A 6A9 Canada

ISBN: (纸本)9798350345032

Sharing up-to-date information about the surrounding measured by On-Board Units (OBUs) and Roadside Units (RSUs) is crucial in accomplishing traffic efficiency and pedestrians safety towards Intelligent Transportation systems (ITS). Transferring measured data demands geq10Gbit/s transfer rate and geq1GHz bandwidth though the data is lost due to unusual data transfer size and impaired line of sight (LOS) propagation. Most existing models concentrated on resource optimization instead of measured data optimization. Subsequently, RSU-LiDARs have become increasingly popular in addressing object detection, mapping and resource optimization issues of Edge-based Software-Defined Vehicular Orchestration (ESDVO). In this regard, we design a two-step data-driven optimization approach called Object-aware Multi-criteria Decision-Making (OMDM) approach. First, the surroundings-measured data by RSUs and OBUs is processed by cropping object-enabled frames using YoLo and FRCNN at RSU. the cropped data likely share over the environment based on the RSU Computation-Communication method. Second, selecting the potential vehicle/device is treated as an NP-hard problem that shares information over the network for effective path trajectory and stores the cosine data at the fog server for end-user accessibility. In addition, we use a nonlinear programming multi-tenancy heuristic method to improve resource utilization rates based on device preference predictions (Like detection accuracy and bounding box tracking) which elaborately concentrate in future work. the simulation results agree with the targeted effectiveness of our approach, i.e., mAP(geq71%) with processing delay (leq3.5times 10thbits/slot), and transfer delay (leq3Sms). Our simulation results indicate that our approach is highly effective. © 2023 IEEE.

关键词： Object detection

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