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How Would Emergency Communication based On LoRaWAN Perform? Empirical Findings of signal Propagation in Rural Areas 20

How Would Emergency Communication Based On LoRaWAN Perform? ...

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20th Global Information systems for Crisis Response and Management conference, ISCRAM 2023

作者： Kuntke, Franz Bektas, Merve Buhleier, Laura Pohl, Ella Schiller, Rebekka Reuter, Christian Technical University of Darmstadt PEASEC Germany

ISBN: (纸本)9798218217495

Low Power Wide Area Network (LPWAN) technologies are typically promoted for internet-of-Things (IoT) applications, but are also of interest for emergency communications systems when regular fixed and mobile networks break down. Although LoRaWAN is a frequently used representative here, there are sometimes large differences between the proposed range and the results of some practical evaluations. Since previous work has focused on urban environments or has conducted simulations, this work aims to gather concrete knowledge on the transmission characteristics in rural environments. Extensive field studies with varying geographic conditions and comparative tests in urban environments were performed using two different hardware implementations. Overall, it was found that the collected values in rural areas are significantly lower than the theoretical values. Nevertheless, the results certify that LoRaWAN technology has a high range that cannot be achieved with other common technologies for emergency communications. © 2023 Information systems for Crisis Response and Management, ISCRAM. All rights reserved.

关键词： Wide area networks

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An Improved Blind Deconvolution for Restoration of Blurred images Using Ringing Removal Processing 4th

An Improved Blind Deconvolution for Restoration of Blurred I...

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4th international conference on Computer and Communication Technologies, IC3T 2022

作者： Dimlo, U. M. Fernandes Narasimharao, Jonnadula Laxmaiah, Bagam Srinath, E. Rani, D. Sandhya Sandhyarani Kumar, Voruganti Naresh Department of CSE Sreyas Institute of Engineering and Technology Telangana Hyderabad India Department of CSE CMR Technical Campus Telangana Hyderabad India Department of CSE Keshav Memorial Institute of Technology UGC Autonomous Telangana Hyderabad India CMR Technical Campus Telangana Hyderabad India

ISBN: (纸本)9789811985621

One of the most difficult challenges in image processing is restoring a defocused image by reducing blur and noise. Blurring characterizes image deterioration, and recovery is accomplished by point spread function estimation and ideal image estimation processing was repeated. Ringing, or wavelike artifacts that arise along strong edges, is a difficult challenge in latent image restoration. Therefore, this paper will introduce an improved blind deconvolution for restoring blurry images using ringing removal process. This paper provides an improved deconvolution technique that uses blur kernel prediction based on dark channels before achieving clear image recovery. To hold picture information and beautify the rims of the ringing impact created at some point of the authentic clean picture recuperation process, an easy bilateral clear out is used. The ringing removal method L0 regularization is used with the restoration process, which can estimate a sharper image. By removing the difference map from the final deconvolution result, it is possible to get a clearer picture without ringing. Finally, the results are presented in terms of performance parameters such as signal-to-noise ratio (SNR), mean squared error (MSE), and peak signal-to-noise ratio (PSNR).The results show that the performance parameters of the improved blind deconvolution model are superior compared to existing image blur removal algorithms. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： image reconstruction

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Key-Independent image Encryption using DNA Encoding and Chaotic Dynamics

Key-Independent Image Encryption using DNA Encoding and Chao...

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international conference on Communications and signal Processing

作者： V Maharaja Vannia Moorthi Megha R Krishna Gopika Anil Kumar V Thanikaiselvan S Subashanthini Rengarajan Amirtharajan School of Electronics Engineering Vellore Institute of Technology Vellore India School Of Computer Science Engineering And Information Systems Vellore Institute of Technology Vellore India School of Electrical & Electronics Engineering SASTRA Deemed University Thanjavur

ISBN: (数字)9798350353068

ISBN: (纸本)9798350353075

Privacy violations are common in our technology-driven world, where almost everyone interacts with internet-connected electronic devices. To address this, cryptography has emerged, concealing data during transmission to deter hackers. Cryptography involves basic mathematical operations, exchanging data based on a shared key known only to the sender and receiver. However, this method suffers from key dependency at the receiver’s end, requiring constant key knowledge. To overcome this issue, a novel image encryption method has been introduced in this paper that is independent of the key on the receiver side. This method leverages techniques such as DNA Encoding, Chaotic Dynamics, and Key Hiding Mechanisms to increase the security of the sent image. The proposed method has been tested experimentally, and the results demonstrate its effectiveness with entropy values exceeding 7.995 and correlation coefficient values below 1% for the vertical, horizontal, and diagonal directions. Overall, the proposed image encryption scheme offers a promising solution for protecting sensitive image data in various applications, combining advanced encryption techniques with key management enhancements to ensure confidentiality and integrity.

关键词： Correlation coefficient Privacy image coding Chaotic communication Computer hacking DNA Receivers

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Hypertension Classification for Fundus image based on Improving Clahe Morphology in Wavelet Transform and ResUNet 11th

Hypertension Classification for Fundus Image Based on Improv...

引用

11th international conference on Future Data and Security Engineering, FDSE 2024

作者： Vo Thi Hong, Tuyet Binh, Nguyen Thanh Ho Chi Minh City University of Foreign Ho Chi Minh City Viet Nam

ISBN: (纸本)9789819604364

The wavelet transform gives a multilevel for the input stage of feature extraction. The disadvantage of the wavelet domain is the threshold to become the key for the inversion period. The lack of information in medical images is a vital issue for feature maps. Therefore, the input shape of the digital signal needs to improve intelligibility with the background. Machine learning or deep learning cannot easily solve this problem with the basic backbone for feature extraction. In this research, the proposed method for hypertension classification based on the morphology concept in wavelet transform presents another way to parallelize feature maps. The proposed method includes: the pre-processing for parameters by CLAHE morphology, the decomposition for multilevel of parameter input, ResUNet for extraction the feature maps, the inversion to synthesis the feature maps for hypertension classification in wavelet domain. The final results were compared with the recent methods by Accuracy, Sensitivity and Specificity in STARE dataset. These values of comparison are 95.75%, 93.88% and 94.16%, respectively. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deep learning

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internet of Things-based 3-Lead ECG signal Acquisition System

Internet of Things-Based 3-Lead ECG Signal Acquisition Syste...

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international conference on Advances in IoT, Security with AI, ICAISA 2023

作者： Sinha, Pranamya Arora, Anuja Kumar, Sunil Bhardwaj, Daya Kumar, Ravi Shaheed Rajguru College of Applied Sciences for Women University of Delhi Delhi India

ISBN: (纸本)9789819950874

IoT, also known as the internet of Things, has been a promising technology that involves numerous devices worldwide to stay connected to collect, share and analyze data, thereby building invocations that can help humanity. The IoT-enabled system consists of a smart device connected to processors, sensors, and communication hardware to receive data from the environment, process it into digital signals, and transfer it to devices for analysis. internet of Things-based 3-Lead ECG signal Acquisition System is an efficient trio of components. It is a patient-centric monitoring system, which allows them to actively collect real-time rhythmic contraction and relaxation of cardiac muscles as signals and store the data in the database. It can then be analyzed using personal devices like laptops and smartphones. Providing a window for actionable insights in cases of emergencies. In this paper, we have constructed an IoT-based ECG system by taking into account the basis of ECG mechanism. It obtains impulses via electrodes from specific locations on the body as input for the AD8232 sensor operating at a supply range of 2–3.5 V. The sensor filters biopotential signals, amplifies them, and transfers them to the microprocessor (Arduino board Uno). The board obtains the signals from the sensor and, as per the instructions given via the code through the compatible software, it processes the signals, and sends them as an output for visualization by the system connected to the board. The system produces an ECG pattern in the Arduino Ide Serial plotter and provides heart rate and heart rate variability. The values can be obtained from a serial monitor and are analyzed using different applications. IoT-based ECG can be incorporated into ambulances for diagnosing various cardiovascular conditions of the patients remotely by the doctor. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Electrocardiograms

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Infrared fiber bundle image super-resolved based on computational imaging 3

Infrared fiber bundle image super-resolved based on computat...

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3rd international conference on Defence technology, ICDT 2022

作者： Wang, Bowen Laboratory Nanjing University of Science and Technology Jiangsu Province Nanjing210094 China Jiangsu Key Laboratory of Spectral Imaging and Intelligent Sense Nanjing University of Science and Technology Jiangsu Province Nanjing210094 China

As the representative of flexibility in optical imaging media, in recent years, fiber bundles have emerged as a promising architecture in the development of compact visual systems. Dedicated to tackling the problems of universal honeycomb artifacts and low signal-to-noise ratio (SNR) imaging in fiber bundles, the iterative super-resolution reconstruction network based on a physical model is proposed. Under the constraint of solving the two subproblems of data fidelity and prior regular term alternately, the network can efficiently "regenerate"the lost spatial resolution with deep learning. By building and calibrating a dual-path imaging system, the real-world dataset where paired low resolution (LR) - high resolution (HR) images on the same scene can be obtained simultaneously. Numerical results on both USAF target and complex target objects demonstrate that the algorithm can restore high contrast images without pixilated noise. On the basis of super-resolution reconstruction, compound eye image composition based on fiber bundle is also realized in this paper for the actual imaging requirements. The proposed work is the first to apply a physical model-based network structure to fiber bundle imaging in the long-wave infrared band, effectively promoting the engineering application of thermal radiation detection. © Published under licence by IOP Publishing Ltd.

关键词： Computational Imaging

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Smart Agriculture System for Plant Disease Detection and Irrigation Management Using Machine Learning and IoT 5

Smart Agriculture System for Plant Disease Detection and Irr...

引用

5th international conference on Sustainable Technologies for Industry 5.0, STI 2023

作者： Islam, Saiful Hasan, Md. Zahidul Kabir, Syed Ahsanul Green University of Bangladesh Dept. of Cse Bangladesh

ISBN: (纸本)9798350394290

Food security and crop productivity are seriously threatened by plant diseases, which calls for early identification and treatment. Researchers have been working for farmers using smart agriculture systems based on the internet of Things (IoT) for years. They demonstrated a number of disease classification models, highlighting the applications of radial basis function and convolutional neural networks (CNNs), This study presents a comprehensive Smart Agriculture System for Plant Disease Detection aimed at minimizing damage and curbing disease spread. Leveraging advanced technologies encompassing machine learning, computer vision, and IoT sensors, the system monitors crop health in real time. Its design facilitates automated disease detection, outbreak prediction, and treatment recommendation. The system's foundation on image processing for disease detection and IoT integration for precise irrigation management encompasses image acquisition, segmentation, feature extraction, machine learning, decision-making algorithms, and data analysis. Furthermore, the results validate the efficacy of the system in disease detection and irrigation control, while acknowledging the need for further refinement. After successful integration of Raspberry Pi, NodeMCU, sensors, and motor pump, the study provides essential data tables that clarify optimal environmental conditions for different crops and the comparative precision of disease detection methods. The mobile application of the prototype facilitates monitoring and diagnosis. As the agricultural landscape evolves toward Industry 5.0, this study contributes to a sustainable, technology-driven future where smart agriculture harmonizes human expertise and technological innovation. © 2023 IEEE.

关键词： internet of things

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Priority-based Resource Allocation in Vehicular Communication Using Q-Learning

Priority-Based Resource Allocation in Vehicular Communicatio...

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Electronics, Communication and signal Processing (ICECSP), international conference on

作者： Divyanshu Pandey K. L. V. Sai Prakash Sakuru Dept. of Electronics & Communication National Institute of Technology Warangal Hanamkonda TS India

ISBN: (数字)9798350364590

ISBN: (纸本)9798350375381

Vehicular communication has the potential to revolutionize road safety, traffic efficiency, infotainment services, and autonomous driving. However, as urban traffic complexity increases, intelligent transportation solutions are essential. Priority-based resource allocation is crucial for optimizing network performance and information transmission efficiency in the internet of Vehicles (IoV). In this context, Q- learning optimizes resource allocation in vehicular communication systems, prioritizing real-time traffic conditions. The Q-learning model effectively allocates resources based on priority, continuously improving strategy over time. This study shows the significant potential of the Q-Learning model for optimizing priority-based resource allocation in vehicular communication, enhancing road safety and efficiency.

关键词： Adaptation models Q-learning Transportation Information processing signal processing Road safety Robustness Real-time systems Resource management internet of Vehicles

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CNN Model Compression by Merit-based Distillation 19

CNN Model Compression by Merit-Based Distillation

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19th IEEE international Colloquium on signal Processing and Its Applications, CSPA 2023

作者： Morikawa, Takumi Kameyama, Keisuke University of Tsukuba Degree Programs in Systems and Information Engineering Graduate School of Science and Technology Tsukuba Japan University of Tsukuba Faculty of Engineering Information and Systems Tsukuba Japan

ISBN: (纸本)9781665476928

Deep learning has advanced dramatically in recent years, and especially large convolutional neural networks (CNNs) have shown outstanding performance in a wide variety of tasks. However, such large-scale CNNs may not be able to be utilized in practice because they require a lot of computational resources. Therefore, there is a need to realize a CNN in a small model that has high performance similar to the large CNN. One method to solve these problems is Distillation, which uses a model of large scale and high performance as the teacher and a small model as the student model, and performs model compression by transferring the knowledge of the teacher model to the student model. However, in distillation, even when the teacher model incorrectly guesses the training input, the student model learns to mimic the teacher. This can prevent the student model from learning better. In this paper, we propose Meritbased Distillation, in which the accuracy of the teacher model's output determines the degree to which the student model mimics the teacher model's output, thereby preventing the student to learn undesired mappings. In the experiment, the effect of the Merit-based Distillation is evaluated. Consequently, the proposed method outperforms the improvements in the generalization by the student models trained by other known methods. © 2023 IEEE.

关键词： image classification

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Thirteenth international conference on signal Processing systems, ICSPS 2021

Thirteenth International Conference on Signal Processing Sys...

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13th international conference on signal Processing systems, ICSPS 2021

ISBN: (纸本)9781510653177

The proceedings contain 49 papers. The topics discussed include: classification model of optical character recognition failures in unrecovered slider serial numbers in hard disk drive manufacturing and image capture processes;three-stage deep learning system for recognizing contaminated serial numbers in hard disk drive: a comparison study with two-stage deep learning model;influenza virus detection technology based on organic electrochemical transistor;robust adaptive beamforming based on sampling covariance matrix reconstruction and steering vector estimation;design and test of signal processing hardware for two-dimensional phased array digital multi-beam system;low-light image enhancement based on variational Retinex model;mixture correntropy unscented Kalman filter for power system dynamic state estimation;and a method of multi-target bearings-only association and location with two platforms.

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