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Parameter tuning for a Markov-based multi-sensor system 47

Parameter tuning for a Markov-based multi-sensor system

EUROMICRO Joint conference of 47th Software Engineering and advanced applications conference (SEAA) and 24th Digital systems Design (DSD)

作者： Qiu, Minhao Kryda, Marco Bock, Florian Antesberger, Tobias Straub, Daniel German, Reinhard Friedrich Alexander Univ Erlangen Nuremberg Dept Comp Sci 7 D-91058 Erlangen Germany Tech Univ Munich Engn Risk Anal Grp D-80333 Munich Germany Audi AG D-85045 Ingolstadt Germany

ISBN: (纸本)9781665427050

Multi-sensor systems are the key components of automated driving functions. They enhance the quality of the driving experience and assisting in preventing traffic accidents. Due to the rapid evolution of sensor technologies, sensor data collection errors occur rarely. Nonetheless, according to Safety Of The Intended Functionality (SOTIF), an erroneous interpretation of the sensor data can also cause safety hazards. For example the front-camera may not understand the meaning of a traffic sign. Due to safety concerns it is essential to analyze the system reliability throughout the whole development process. In this work, we present an approach to explore the sensor's features, such as the dependencies between successive sensor detection errors and the correlation between different sensors on the KITTI dataset quantitatively. Besides, we apply the learned parameters to a proven multi-sensor system model, which is based on Discrete-time Markov chains, to estimate the reliability of a hypothetical Stereo camera-LiDAR based sensor system.

关键词： SOTIF Automated driving Reliability analysis Multi-sensor system Discrete-time Markov chain

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Smart Air-Conditioner Monitoring by IOT applications 8

Smart Air-Conditioner Monitoring by IOT Applications

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8th International conference on Electronics, Communication and Aerospace Technology, ICECA 2024

作者： Mari Sekar, B. Dheeraj, M. Abishek, T. Dinesh Kumar, K. Adharsh, R. Dinesh, Tc Sri Ramakrishna Engineering College Department of Eee Coimbatore India

ISBN: (纸本)9798350367904

The main issue faced at these times is that the consumers have to manually remove and change the internal components manually in an air conditioner such as a rotor or compressor because of overheat that may result in the particular air conditioner malfunctioning. An air conditioner is made up of many components, but the major parts doing the heavy lifting of moving the air indoors and outdoors are the evaporator, condenser, expansion valve, and compressor. advanced air conditioner air filtration systems are considered to be the latest innovation in air condition technology which help to improve indoor air quality and reduce allergens, as well as help with cooling efficiency. In this project, when one of the components of an air conditioner is overheated, a coolant system acquires the temperature as input and begins functioning when the temperature of the component exceeds the limit set in the Arduino code. This system helps in reducing the temperature of the component in the air conditioner and guarantees operation for a prolonged period of time. Also, the moisture content because of the refrigerant gas present inside the air conditioner is recorded using a humidity sensor. Both temperature and humidity readings are notified to the administrator with the help of a GSM module. The coolant system for reducing the temperature of the component could be even an exhaust fan used in the central processing unit of a computer. © 2024 IEEE.

关键词： Air conditioning

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ROBUST JOINT ASSOCIATION AND REGISTRATION UNDER LARGE sensor BIAS

ROBUST JOINT ASSOCIATION AND REGISTRATION UNDER LARGE SENSOR...

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IET International Radar conference 2023, IRC 2023

作者： Ni, Na Jiang, Qi Mao, Huafeng Zhang, Jichuan Wang, Rui Hu, Cheng Radar Research Lab School of Information and Electronics Beijing Institute of Technology Beijing China Advanced Technology Research Institute Beijing Institute of Technology Shandong Jinan China Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing China

ISBN: (纸本)9781839539954

Radar observation of group targets has recently received much attention in the flight mechanism research and many other applications. Group targets are usually closely spaced with lots of missed detections and false alarms, making it difficult for spatial reconstruction. Multisensor systems make use of data from multiple radars to provide more accurate measurements and robust tracks than a single radar, playing an important role in the group targets observation. In the data fusion processes, multisensor measurements are associated after transformed into a global coordinate system. However, large sensor bias makes it difficult to associate measurements of closely spaced targets given outliers, and nonideal association further increase the sensor bias estimation error. In this paper, we proposed a robust joint association and registration method to simultaneously acquire sensor bias estimates and association results. A nonlinear least median of squares estimator is used to achieve better accuracy and robustness of sensor bias estimation. Simulation results show the better bias estimation and association performance of the proposed method compared to other algorithms. © The Institution of Engineering & Technology 2023.

关键词： Radar measurement

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A Novel system for Tracking and Screening COVID Patients

A Novel system for Tracking and Screening COVID Patients

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2022 International conference on advanced Computing Technologies and applications, ICACTA 2022

作者： Sasikala Devi, S. Arulkumar, V. Kumar, Saroj Priyanka, D. PPG College of Arts and Science College Department of Computer Science Coimbatore India Sri Krishna College of Engineering and Technology Department of Computer Science and Business Systems Tamilnadu India Chandigarh University Computer Science and Engineering Gharuan India Institute of IT Saveetha School of Engineering Department of Knowledge Engineering Chennai India

ISBN: (纸本)9781665495158

The wandering of older persons with Covid is one of the several behavioral difficulties that they experience, and it is the source of the most anxiety for their caregivers. Using a novel mobile phone-based safety assistance system, we have been able to relay information about a wandering individual's whereabouts to others who are near that person. The wearable sensor is made up of a GSM module, an Arduino UNO, and a GPS module. It is possible to determine the position of the wandering individual after they have beyond a particular meter. When the old person leaves the area, the GSM transmits the position of the wandering elderly person, which is determined with the aid of GPS. The server computer sends an SMS to the caregiver to notify him or her of the situation. The caregiver may keep track of the wandering person's whereabouts on a map by connecting to the Internet. © 2022 IEEE.

关键词： Global system for mobile communications

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Low-Cost Weather Monitoring System with Intelligent Design and XGBoost Regression

Low-Cost Weather Monitoring System with Intelligent Design a...

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2023 International conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

作者： Vishnu Vilashini, S. Kishore, S. Nair, Rekha. R. Babu, Tina Bhadani, Anant Thanushree, G.R. Dixit, Anuj Kumar Dayananda Sagar University School of Engineering Department of Computer Applications Bengaluru India Nitte Institute of Professional Education Department of Computer Applications Karnataka India Allaince University Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350313413

Weather monitoring is a critical aspect of weather forecasting and analysis, providing essential data for decision-making in various sectors. However, traditional weather monitoring systems face limitations such as high costs, limited accuracy, and inadequate coverage. The proposed system addresses these challenges by utilizing the Internet of Things (IoT) technology and NodeMCU, a low-cost microcontroller. The novelty of this work lies in the implementation of a cost-effective weather monitoring system that accurately forecasts upcoming weather conditions. Notably, the XGBoost Regression model emerges as the top performer, exhibiting exceptional accuracy with a mean absolute error (MAE) of 0.0213 indicating high accuracy in weather prediction. This innovative approach not only provides an affordable solution but also demonstrates the system's advanced capability to predict future weather patterns. Utilizing a NodeMCU board, real-time sensor data is gathered and transmitted to ThingSpeak's cloud server for analysis. This methodology has the potential for future applications in industries such as agriculture, transportation, and disaster management. © 2023 IEEE.

关键词： NodeMCU Real-time monitoring Recursive feature elimination ThingSpeak Weather prediction XGBoost Regression

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Deep Learning Based Low Light Enhancements for advanced Driver-Assistance systems at Night 17

Deep Learning Based Low Light Enhancements for Advanced Driv...

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17th IEEE International conference on Industrial and Information systems, ICIIS 2023

作者： Ranasinghe, Pamudu Muthukuda, Dilshan Morapitiya, Primash Dissanayake, Maheshi B Lakmal, H.K.I.S. University of Peradeniya Dept. Electrical & Electronic Eng. Peradeniya Sri Lanka

ISBN: (纸本)9798350323634

In the modern automobile industry, full/partial autonomous driving has become a common phenomenon. These systems heavily rely on sensors, both visual and non-visual, such as cameras and LIDAR, to interpret their surroundings and aid the decision-making process. Recently, visual-only sensing devices, mainly cameras, have emerged as the primary sensor for obtaining an immersive feeling in autonomous driving. However, the performance of such systems is heavily constrained by fluctuating light intensity and shadows. The primary goal of the presented research is to develop an effective low-light image enhancement system that improves the perception and inter-pretability of images taken in low-light driving environments. The developed image enhancement architecture demonstrates remarkable capabilities, surpassing existing models in terms of real-time suitability, PSNR (Peak Signal-to-Noise Ratio), and SSIM (Structural Similarity Index) values. Additionally, the model has been specifically designed to have low resource usage, making it an ideal solution for deployment on low-end devices. With significantly lower inference time, the model is exceptionally well-suited for real-time operation in autonomous driving applications. Furthermore, this architecture provides an efficient and effective solution not only for autonomous driving but also for a range of applications in video/image processing and computer vision, such as surveillance systems, medical imaging, astronomy, underwater imaging, and the mining industry. Finally, the relatively short training time makes it an attractive option for researchers and developers. Overall, this architecture represents a significant advancement in low-light image enhancement and autonomous vehicles. A demo of the system and the source code of the proposed LLE_UNET can be found on https://***/pamudu123ILow_Light_Image_Enhancement. © 2023 IEEE.

关键词： Image enhancement

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Enhancing Sustainability: Leveraging sensor Technology in Smart Bins for Real-Time Data Analysis 8th

Enhancing Sustainability: Leveraging Sensor Technology in Sm...

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8th International conference on Smart City applications, SCA 2023

作者： Idrissi, A. Benabbou, R. Benhra, J. Haji, M. El University Hassan II Casablanca Morocco

ISBN: (纸本)9783031538230

The escalating challenges posed by urbanization and rapid population growth are exacerbating the global waste crisis. The World Health Organization underscores the risks of inadequate waste management, as it contributes to the spread of diseases. To address these multifaceted issues, our research is driven by two primary objectives. Firstly, we are dedicated to designing cutting-edge smart garbage bins equipped with nine sensors, including waste level sensors to monitor bin capacity, Humidity sensors to measure moisture content, Temperature sensors for climate insights, Carbon dioxide (CO2) sensors for air quality assessment, Methane (CH4) sensors to detect potential hazards, and ambient temperature sensors to gauge environmental conditions. Secondly, our research establishes a technological backbone for real-time monitoring and informed decision-making through the seamless integration of ThingSpeak and WiFi ESP8266 technologies. In addition, we will explore three usage scenarios for this intelligent waste bin. The initial scenario (Day 1) entails the collection of sensor data throughout the first day, with measurements taken every 15 min. The second scenario (Day 5) involves the collection of sensor data on the fifth day, also at a 15-min interval. Finally, the third scenario involves continuous monitoring of the smart bin over the course of one week. In essence, our research represents a pioneering effort to harness the potential of smart bins and advanced data analysis techniques. It aspires to revolutionize waste management by introducing data-driven approaches that promise not only to improve the efficiency of waste collection and treatment but also to advance the cause of cleaner, more sustainable urban environments. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Carbon dioxide

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Radiation Testing of a Multiprocessor Macrosynchronized Lockstep Architecture With FreeRTOS

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IEEE TRANSACTIONS ON NUCLEAR SCIENCE 2022年第3期69卷 462-469页

作者： Aviles, Pablo M. Lindoso, Almudena Belloch, Jose A. Garcia-Valderas, Mario Morilla, Yolanda Entrena, Luis Univ Carlos III Madrid Dept Elect Technol Leganes 28911 Spain Univ Seville CSIC JA Ctr Nacl Aceleradores CNA Seville 41092 Spain

Nowadays, high-performance microprocessors are demanded in many fields, including those with high-reliability requirements. Commercial microprocessors present a good tradeoff between cost, size, and performance, albeit they must be adapted to satisfy the reliability requirements when they are used in harsh environments. This work presents a high-end multiprocessor hardened with macrosynchronized lockstep and additional protections. A commercial dual-core advanced RISC Machine (ARM) cortex A9 has been used as a case study and a complete hardened system has been developed. Evaluation of the proposed hardened system has been accomplished with exhaustive fault injection campaigns and proton irradiation. The hardening approach has been accomplished for both baremetal applications and operating system (OS)-based. The hardened system has demonstrated high reliability in all performed experiments with error coverage up to 99.3% in the irradiation experiments. Experimental irradiation results demonstrate a cross-sectional reduction of two orders of magnitude.

关键词： Microprocessors Program processors Synchronization Software Reliability Operating systems Redundancy advanced RISC Machine (ARM) fault tolerance FreeRTOS lockstep microprocessors proton soft error

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2021 IEEE 23rd International conference on High Performance Computing and Communications, 7th International conference on Data Science and systems, 19th International conference on Smart City and 7th International conference on Dependability in sensor, Cloud and Big Data systems and applications, HPCC-DSS-SmartCity-DependSys 2021

2021 IEEE 23rd International Conference on High Performance ...

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23rd IEEE International conference on High Performance Computing and Communications, 7th IEEE International conference on Data Science and systems, 19th IEEE International conference on Smart City and 7th IEEE International conference on Dependability in sensor, Cloud and Big Data systems and applications, HPCC-DSS-SmartCity-DependSys 2021

ISBN: (纸本)9781665494571

The proceedings contain 343 papers. The topics discussed include: three-level compact caching for search engines based on solid state drives;a fine-grained volley gesture recognition method with direction independence;universal adversarial attack against 3d object tracking;efficient hardware redo logging for secure persistent memory;a cost-efficient metadata scheme for high-performance deduplication systems;high speed true random number generator controlled by logistic map;characterization and implication of edge WebAssembly runtimes;frend for edge servers: reduce server number! keeping service quality!;on-the-fly servers placement for online multiplayer games in the fog;advanced architecture design of high-radix router based on chiplet integration and IP reusability;embrace the conflicts: exploring the integration of single port memory in systolic array-based accelerators;visual sensitivity aware rate adaptation for video streaming via deep reinforcement learning;semi-supervised federated learning with non-IID data: algorithm and system design;on-demand intelligent routing algorithms for the deterministic networks;and distributed service placement in ultra-dense edge computing: a game-theoretical approach.

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AMSER: Adaptive Multimodal Sensing for Energy Efficient and Resilient eHealth systems

AMSER: Adaptive Multimodal Sensing for Energy Efficient and ...

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25th Design, Automation and Test in Europe conference and Exhibition (DATE)

作者： Naeini, Emad Kasaeyan Shahhosseini, Sina Kanduri, Anil Liljeberg, Pasi Rahmani, Amir M. Dutt, Nikil Univ Calif Irvine Dept CS Irvine CA 92717 USA Univ Turku Dept Comp Turku Finland

ISBN: (纸本)9783981926361

eHealth systems deliver critical digital healthcare and wellness services for users by continuously monitoring physiological and contextual data. eHealth applications use multi-modal machine learning kernels to analyze data from different sensor modalities and automate decision-making. Noisy inputs and motion artifacts during sensory data acquisition affect the i) prediction accuracy and resilience of eHealth services and ii) energy efficiency in processing garbage data. Monitoring raw sensory inputs to identify and drop data and features from noisy modalities can improve prediction accuracy and energy efficiency. We propose a closed-loop monitoring and control framework for multi-modal eHealth applications, AMSER, that can mitigate garbage-in garbage-out by i) monitoring input modalities, ii) analyzing raw input to selectively drop noisy data and features, and iii) choosing appropriate machine learning models that fit the configured data and feature vector - to improve prediction accuracy and energy efficiency. We evaluate our AMSER approach using multi-modal eHealth applications of pain assessment and stress monitoring over different levels and types of noisy components incurred via different sensor modalities. Our approach achieves up to 22% improvement in prediction accuracy and 5.6x energy consumption reduction in the sensing phase against the state-of-the-art multi-modal monitoring application.

关键词： Pain Machine learning Predictive models Energy efficiency Electronic healthcare Noise measurement Resource management

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