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IoT based Coal Mines Safety Monitoring and Alerting System 10

IoT Based Coal Mines Safety Monitoring and Alerting System

引用

10th international conference on Communication and signal Processing, ICCSP 2024

作者： Kaur, Tanmeet Ranjan, Abhinav Kumar Kumar, Sumit Kumar, Kamlesh Kathirvel, Mathivanan Greater Noida Institute of Engineering and Technology Uttar Pradesh Greater Noida India Adhiparaasakathi Engineering College TamilNadu Melmaruvathur India

ISBN: (纸本)9798350353068

Many coal excavators are worried about their security in the working environment. Inside subsurface mines, unfortunate ventilation opens laborers to poisonous gases, intensity, and residue, which can prompt disorder, injury, and demise. With the guide of an ARM regulator, this work proposes an idea for a Web of Things (IoT), remote sensor organization (WSN) that can follow temperature, moisture, and gas in an underground mine. This gadget utilizes a low-cost, less-power Arduino UNO, gas sensors, DHT-11 sensor, Hub MCU, fire sensors to recognize fire and send a caution, and LDR to distinguish light contingent upon light levels. Customary coal mining tunnel noticing structures are consistently wired association systems that expect a critical part in ensuring coal mine security. With the constant augmentation of mining zones and significance in coal mines, various laneways have become outwardly weakened domains with different mystery gambles. Besides, laying links, which is exorbitant and tedious, is awkward. To resolve the issues, a coal mineshaft security checking framework in light of a remote sensor organization and the Web of Things, which can build the observing level is made. Numerous miniature sensor hubs with little measurements and lower prices make up an IoT and remote sensor organization. © 2024 IEEE.

关键词： internet of things

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Design of RNS-KSA based 2D FIR Filter

Design of RNS-KSA Based 2D FIR Filter

引用

2024 IEEE international conference on Information technology, Electronics and Intelligent Communication systems, ICITEICS 2024

作者： Reddy, Raghavendra Rajesh, B. Nandini, A. Kumar, Prem Chakradhar, K.S. Tirupati India Tirupati India

ISBN: (纸本)9798350382693

Two-dimensional finite impulse response filters have the benefits of phase linearity and assured stability, making them appealing in applications. 2D Finite Impulse Response (FIR) filters serve a significant role in different applications, such as image processing, medical field, pattern recognition, robot vision, and seismic or geophysical signal processing. This paper presents a novel RNS-based 2D Finite Impulse Response (FIR) filter using Residue Number System (RNS) and floating-point arithmetic operations, in which Residue Number System (RNS) provides substantially lower ranges of numbers in arithmetic computations than starting numbers and Floating-point arithmetic provides more accuracy and dynamic range than fixed-point arithmetic. The Floating Point Adder in the filter is designed using a Kogge-Stone parallel prefix adder (KSA) to improve addition speed. The proposed novel 2D FIR filter can process 32, 48, and 64-bit data by just changing the corresponding RNS moduli selection which makes it a better choice over a fixed-point-based 2D FIR filter. This filter is designed in Verilog HDL for kernel sizes 3X3, 5X5 and 7X7 and synthesized using Altera's Quartus software for FPGA Board Cyclone III (EP3C16F484C6) to evaluate the design parameters of the filter. © 2024 IEEE.

关键词： FIR filters

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Electrical Impedance Tomography image Reconstruction based on Graph Laplacian and Sparse Learning 7

Electrical Impedance Tomography Image Reconstruction Based o...

引用

7th international conference on Power and Energy Applications, ICPEA 2024

作者： Shi, Shuaikai Liatsis, Panos Khalifa University Department of Computer Science Abu Dhabi United Arab Emirates Khalifa University Center for Secure Cyber-Physical Systems Abu Dhabi United Arab Emirates

ISBN: (纸本)9798350356113

Electrical Impedance Tomography (EIT) is a non-invasive diagnostic technique capable of inferring the internal conductivity distribution of a target from the measured boundary voltage signal. However, estimating high-resolution conductivity images from undersampled voltage signals is a nontrivial task due to the highly ill-posed nature of the problem. Although supervised learning methods can greatly improve the accuracy of reconstructed conductivity images, such models rely on a large number of training samples, which are difficult to obtain in practice. To address this problem, sparse regression frameworks have been introduced in EIT within the unsupervised learning setting, based on the assumption of a smaller number of inclusions. In this work, we proposed an iterative learning approach, which simultaneously incorporates spatially sparse and graph priors. Experiments conducted on both synthetic and real-world data demonstrate the superiority of the proposed method. © 2024 IEEE.

关键词： Self-supervised learning

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An Overview of Recent Research on IoT-based Energy Management System in Smart Homes 1

引用

18th international conference on Intelligent Information Hiding and Multimedia signal Processing, IIH-MSP 2022

作者： Sung, Tien-Wen Huang, Zeming Liang, Qiaoxin Fujian Provincial Key Laboratory of Big Data Mining and Applications College of Computer Science and Mathematics Fujian University of Technology Fuzhou350118 China

ISBN: (数字)9789819906055

ISBN: (纸本)9789819906048

Increasing load demand and energy crisis issues are becoming more and more concerned. Nowadays, the demand for electricity in household life is rising sharply and the structure of electricity consumption is gradually becoming complex. With the development of the internet of Things technology, it provides a better platform for the improvement of the energy management system. The home energy management system (HEMS) based on the internet of Things comes into being, which can integrate the management of all home power loads and distributed energy, realize the optimal configuration of energy, and help users save electricity. This paper firstly describes the basic concept of IoT smart home energy management system, then describes the framework of HEMS, and finally reviews the current research status in this field from three modules, namely, data collection module, communication module, and management and optimization module, and discusses its technical challenges and future research directions. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Energy management systems

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A Dynamic Trust Chain Measurement Method based on Mixed Granularity of Call Sequence 8

A Dynamic Trust Chain Measurement Method Based on Mixed Gran...

引用

8th international conference on signal and image Processing, ICSIP 2023

作者： Zhang, Chen Xu, Chao Cheng, Wei Hua Xia, Yi Liu, Yu State Grid Jiangsu Electric Power Co. Ltd Yangzhou Power Supply Branch Yangzhou China Jiangsu Electric Power Information Technology Co. Ltd Nanjing China

ISBN: (纸本)9798350397932

Trusted computing technology aims to protect the computer or embedded system from tampering. based on the idea of trust chain establishment, it establishes a trust chain for the whole process from the embedded system power-on to the application running in the operating system. However, the trust chain uses the static measurement scheme before starting each component, so the system state information recorded by the trust chain can only reflect the state of the program in the system when it is started, but not the state at run time. In view of the above problems, by analyzing the credibility theorem of process operation defined in the non-interference model, a dynamic measurement model design idea using the synchronous comparison between the hypothetical system and the real system is proposed. based on this design idea, this paper analyzes the running model of the program through the application call sequence, and improves the model from the perspective of measurement granularity to improve the running efficiency of measurement. based on this, a dynamic integrity measurement method that can measure the program dynamically in real time is proposed. Experiment results demonstrate that the abnormal state of the process can be well detected by the proposed method at a small cost. © 2023 IEEE.

关键词： Embedded systems

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Air-Fuzz: Feasibility Analysis of Fuzzing-based Side-Channel Information Leakage Attack in Air-Gapped Networks 1

引用

25th international conference on Information Security Applications, WISA 2024

作者： Kim, Yeon-Jin Park, Na-Eun Lee, Il-Gu Sungshin Women’s University 2 Bomun-ro 34da-gil Seongbuk-gu Seoul02844 Korea Republic of

ISBN: (数字)9789819616244

ISBN: (纸本)9789819616237

As the number of cyberattacks via networks increases, air-gapped networks that are physically isolated from the internet are recommended to protect systems and industrial facilities. However, previous research has highlighted the possibility of data exfiltration attacks in air-gapped environments by exploiting computer systems or peripheral devices. Consequently, research to identify potential attack types and vectors in air-gapped environments is required. This study proposes an attack model that uses fuzzing on internet of Things connectivity devices to exfiltrate data within an air-gapped environment through side-channel signal patterns. We conducted an experiment to verify the feasibility of the attack model in a noisy channel and compared its performance with that of conventional attack models affected by interference and noise. By injecting Gaussian noise into wireless signals encoded with data, we compared the Bit Error Rate (BER) of the proposed attack model with that of conventional models. The BER of the proposed attack model was improved by approximately 59.43% compared with that of conventional attack models. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Information leakage

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Advanced YOLO-based Trash Classification and Recycling Assistant for Enhanced Waste Management and Sustainability 2

Advanced YOLO-based Trash Classification and Recycling Assis...

引用

2nd international conference on Intelligent Cyber Physical systems and internet of Things (ICoICI)

作者： Kumar, M. Guru Vimal Kumar, Madde Rao, K. Narayana Rao, P. Syamala Tirumala, Arepalli Patnala, Eswar Vel Tech Rangarajan Dr Sagunthala R&D Inst Sci & Dept Comp Sci & Engn Chennai Tamil Nadu India Guru Nanak Inst Dept Comp Sci & Engn IoT Tech Campus Hyderabad India RISE Krishna Sai Prakasam Grp Inst Dept Comp Sci & Engn Ongole AP India SRKR Engn Coll Dept IT Bhimavaram AP India SRM Univ AP Dept Elect & Commun Engn Mangalagiri Andhra Pradesh India Koneru Lakshmaiah Educ Fdn Dept Comp Sci & Engn Vaddeswaram AP India

ISBN: (纸本)9798331540661;9798331540678

The ever-growing global population has heightened resource consumption and waste generation, emphasizing the quick need for effective waste management to safeguard the environment. Unfortunately, the recycling industry grapples with persistent challenges, primarily in accurate trash classification, a critical factor for successful recycling. Manual sorting, often prone to errors due to subjective human judgment, hampers the recycling process, contributing to inefficiencies. Furthermore, the inherent risks associated with direct contact during the sorting of hazardous materials pose serious health concerns for the workers involved. In response to these challenges, we propose a revolutionary solution: the Trash Classification and Recycling Assistant utilizing YOLO variants V5-V7. This system, rooted in image classification techniques, seeks to elevate the precision of trash sorting. Notably, YOLO variant V7 emerges as the frontrunner, showcasing remarkable accuracy improvements. By utilizing the capabilities of advanced technology, this innovative approach not only streamlines waste sorting processes but also mitigates health risks linked to manual handling of toxic materials. The integration of YOLO variants V5-V7 represents a pivotal step towards ushering in a new era of efficiency and accuracy in recycling practices, thus significantly contributing to the overarching goal of environmental sustainability.

关键词： Trash Classification and Recycling YOLO variants trash sorting waste sorting processes toxic materials

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Fuzzy-based Deep Reinforcement Learning for Frost Forecasting in IoT Edge-enabled Agriculture 8

Fuzzy-based Deep Reinforcement Learning for Frost Forecastin...

引用

8th international Iranian conference on signal Processing and Intelligent systems, ICSPIS 2022

作者： Herabad, Mohammadsadeq Garshasbi Afshar, Niloufar Pakzad Tehran Institute of Technology Iran Iran Telecom Research Center Iran

ISBN: (纸本)9781665476232

The critical impacts of climate change on agriculture can reduce agricultural productivity and surge prices. With the current issues in the agricultural sector, agricultural engineers have studied the advent of cyber-physical systems to handle these problems, especially in frost forecasting for crop management. This paper discusses the concepts of frost forecasting in smart agriculture and designs a three-layer infrastructure for smart farming equipped with the internet of Things (IoT) technology. In addition, a novel Fuzzy-based Deep Reinforcement Learning (FDRL) is proposed to predict frosts in IoT-enabled agriculture. We use a dataset that includes five years of climate conditions of Khalkhal city in northwest of Iran in order to evaluate the performance of the proposed approach. The obtained results from implementations are assessed based on different metrics, such as Mean Absolute Error (MAE) and F-score, and compared with other Machine Learning (ML) algorithms. The results show the outperformance of FDRL compared to different ML-based algorithms. © 2022 IEEE.

关键词： internet of things

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IoT based Smart systems using Artificial Intelligence and Machine Learning: Accessible and Intelligent Solutions 6

IoT Based Smart Systems using Artificial Intelligence and Ma...

引用

6th international conference on Information systems and Computer Networks, ISCON 2023

作者： Ahmed, Bilal Shuja, Mirza Mishra, Hari Mohan Qtaishat, Ahmed Kumar, Mukesh Lovely Professional University School of Computer Application Punjab Phagwara India Sohar University Department of Information Technology Sohar Oman

ISBN: (纸本)9798350346961

The internet of Things (IoT) has grown in importance in both the technological and social spheres as consumers want smarter homes, more efficient businesses, and more efficient healthcare systems. The social setting is very important for this. Communication networks provide uninterrupted internet connectivity for both fixed and mobile devices like PCs and smartphones. Customers may now keep up their level of engagement while interacting with one or more apps at any time and from any location, thanks to developments in information and communication technologies. Many kinds of smart devices might potentially coordinate their responses to events, sending out alerts or keeping records of the system's condition in line with a predetermined policy. Constant progress toward intelligent solutions is improving many facets of modern life, including manufacturing, transportation, electronic healthcare, electronic education, and many more. Machine-to-machine (M2M) communication in the IoT is autonomous and dynamic. Diverse approaches are being developed to address the challenges posed by disparate data sources, fluctuating signal quality, and overwhelming quantities of data. From the perspectives of intelligent system technologies, security, vulnerabilities, and the potential role that intelligent solutions based on Machine Learning (ML) and Artificial Intelligence (AI) may play, this article examines the IoT. Concerns about privacy and safety have arisen as a major obstacle to the widespread adoption of the internet of Things (IoT). We propose that the Standard Security Framework (SSF) be made mandatory for all distributed networks to ensure that hardware and software modules may communicate with one another regardless of their respective platforms. In addition, the effectiveness and security of new innovations in using fog and edge computing in conjunction with cloud applications have been studied.. © 2023 IEEE.

关键词： internet of things

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Comparative Evaluation of Fixed Windowing Strategies on CT Brain images Using Multiple Deep Learning Models

Comparative Evaluation of Fixed Windowing Strategies on CT B...

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international IEEE conference on signal-image Technologies and internet-based System

作者： Supatta Viriyavisuthisakul Natsuda Kaothanthong Parinya Sanguansat Toshihiko Yamasaki Dittapong Songsaeng School of Management Technology Sirinhorn International Institute of Technology Thammasat University Pathumthani Thailand Faculty of Engineering and Technology Panyapiwat Institute of Management Nonthaburi Thailand Department of Electrical Engineering The University of Tokyo Tokyo Japan Department of Radiology Siriraj Hospital Faculty of Medicine Mahidol University Bangkok Thailand

Window setting in CT brain images is the crucial pre-processing step to examine the abnormalities for diagnosing disease. Recently, many methods have been proposed to determine the suitable window automatically instead of fixing the window. However, fixed windowing methods may still be used in clinical practice due to their simplicity and ease of use. Here, we propose to evaluate the 45 different fixed windowing in noncontrast cranial computer tomography (NCCT) images without computer tomography perfusion (CTp). The 15 latest deep learning models are performed on all interested windows to classify between the hyperacute or acute phases of ischemic stroke and normal brain. The experiments can provide the reference fixed windowing value to optimize the deep learning model and help clinicians to choose the appropriate fixed windowing values.

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