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检索条件"任意字段=Conference on Advanced Sensor Systems and Applications VII"

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An Integrated Arduino Solution for Comprehensive Gas Leak Detection and Response

An Integrated Arduino Solution for Comprehensive Gas Leak De...

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International conference on Signal Processing, Communication, Power and Embedded System (SCOPES)

作者： Rekha R Nair Tina Babu Sumana S G Monish L Department of Computer Science and Engineering Alliance School of Advanced Computing Alliance University Bengaluru India Department of Computer Applications School of Engineering Dayanada Sagar University Bengaluru India Department of AIML School of Engineering Dayanada Sagar University Bengaluru India

ISBN: (数字)9798331506452

ISBN: (纸本)9798331506469

Significant safety dangers are posed by gas leaks in commercial, industrial, and residential environments. In order to improve safety and dependability in the detection and response to gas leaks, this study proposes an Arduino-based gas leak detector and monitoring system. By utilizing carefully placed sensors for early detection and precise identification of gases like methane, propane, and carbon monoxide, the system minimizes false alarms by fusing cutting-edge sensor technology with clever algorithms. At the core are Arduino microcontrollers processing data from gas sensors. Communication modules enable robust remote monitoring and alert notifications through smartphones. The system triggers visual and auditory alarms when gas concentrations exceed thresholds, facilitating prompt response. The system's design incorporates user-friendly interfaces, low maintenance requirements, and cost-effective implementation. With a reliable power source and battery backup, it offers an ad- vanced, accessible solution for gas leak detection and monitoring, enhancing overall safety and mitigating risks across residential, commercial, and industrial settings. Key features include Arduino microcontrollers interfaced with gas sensors like MQ-2, MQ-3, or MQ-7, communication modules (Wi-Fi, Bluetooth, GSM), alarm systems (LEDs, buzzers), and reliable power supply. The system's architecture ensures seamless integration and functionality.

关键词： GSM Microcontrollers Signal processing algorithms sensor systems Real-time systems sensors Maintenance Leak detection Gas detectors Remote monitoring

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GreenSight: IoT-Based Greenhouse Monitoring System

GreenSight: IoT-Based Greenhouse Monitoring System

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International conference on Computer applications and Industrial Electronics (ICCAIE)

作者： Charles Bryan R. Fabian Jerson S. Sumalinog Paul John S. Vicente N. Romasanta Joan Katherine NU Dasmariñas National University Philippines Manila Philippines

ISBN: (数字)9798350348798

ISBN: (纸本)9798350348804

Greenhouse agriculture has emerged as a critical component in addressing global food security challenges, experiencing rapid growth and technological integration in recent years. This paper presents GreenSight, an IoT-based automated greenhouse monitoring system designed to optimize plant growth conditions while enhancing operational efficiency. Leveraging advanced technologies such as the NodeMCU ESP8266 microcontroller and DHT22 sensor, GreenSight facilitates real-time environmental monitoring and control within the greenhouse environment. The system incorporates actuators including SG90 servo motors, 9V DC motors, and mist atomizers to regulate atmospheric conditions tailored to diverse plant species' requirements. Additionally, a user-friendly Android application enables remote access and configuration. Ten (10) experts specializing in information technology and computer engineering assessed the prototype using ISO 9126 evaluation criteria. The Likert Scale was employed for interpretation. According to the evaluation, the project attained an overall mean of 3.32, indicating that it satisfies requirements concerning functionality, reliability, efficiency, usability, maintainability, and portability.

关键词： Cloud computing Microcontrollers Operating systems ISO Standards Green products DC motors Real-time systems

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Smart Low-Cost Sensing Solutions for Enhanced Behind-the-Meter Visibility of Grid Edge Renewable Energy Sources

Smart Low-Cost Sensing Solutions for Enhanced Behind-the-Met...

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Grid Edge Technologies conference & Exposition (Grid Edge), IEEE PES

作者： Mehrnaz Madadi Subhashish Bhattacharya Yang-Duan Su Paul Ohodnicki G. Subramania J. Pillars T. Monson N. Gurule North Carolina State University Raleigh NC USA University of Pittsburgh Pittsburgh PA USA National Laboratory SNL Albuquerque NM USA

ISBN: (数字)9798350352528

ISBN: (纸本)9798350352535

Behind-the-meter (BTM) visibility poses a signifi-cant challenge for utilities, especially when conducting area load forecasts, as the operations of distributed energy resource (DER) generation and storage significantly influence the net load. The expense of advanced sensors and budget constraints are key barriers to improving visibility at the grid edge. This paper addresses these challenges by introducing three new low-cost non-intrusive sensing solutions. These solutions offer real-time, high-resolution visibility of grid-edge power flow and health metrics, facilitating applications such as volt/var control and state-of-health estimation for BTM components. Each sensor is equipped for wireless data communication via radio frequencies. Experimental results confirm the feasibility and performance of the proposed sensors in measuring target parameters and transmitting data wirelessly to the aggregator. Additionally, the use of existing commercial micro Phasor Measurement Units $(\boldsymbol{\mu} \mathbf{PMUs})$ as data aggregators in distribution feeders is explored, showing comparable real-time monitoring capabilities to traditional sensors at a significantly lower cost.

关键词： Wireless communication Measurement Wireless sensor networks Renewable energy sources Load forecasting Real-time systems Phasor measurement units Distributed power generation Planning Intelligent sensors

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Graphic Design and Manufacturing of Enterprise Art Products Based on Machine Learning and 5G Network

Graphic Design and Manufacturing of Enterprise Art Products ...

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International conference on advanced Computing and Communication systems (ICACCS)

作者： Ankit Punia Centre for Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

The graphical function use the graphic design typography, photography, portraits and illustrations. Groups are considered in the field of visual media and communication design, but are sometimes formally used to refer to "design with description." Graphic designers create and combine code, images, and text to form a visual representation of ideas. They deal with typography, visual art, and page layout techniques to create visual elements. Common applications of graphic design include corporate image design (brands and branding), editorial design (magazines, newspapers, and books), way finding or environmental art design, advertising, web design, communication design, product packaging and logos. However, although there are all kinds of network to entire the product life cycle, including product design, production, and service, it can be found that current research on product lifetime data focuses mainly on concrete products, rather than virtual models. To propose the issues to lead a limited level of capacity, knowledge and product design sustainable development, production and service levels using KNN algorithm. However, physical product details, virtual product data, and attached data to collaborate on physical and virtual products, support product design to be manufactured and serviced. Mobile Edge Computing (MEC) gives cloud computing, storage, and analysis of end-user power closer to the 5G network. Connected vehicles such as increased speeds and reduced idleness, large populations, video streaming, and industrial robots to run novel applications. Machine Learning (ML) is used at the mobile edge of computing to predict changes automatically needed for cultural activities, natural disasters, or to expand network resources needed to build a network and for daily trips. Graphic designers create video contacts that they watch every day, every minute, around the world. All formats were created by designers, including print publications (magazines, newspapers, and pu

关键词： Wireless communication Visualization Wireless sensor networks Art 5G mobile communication Sociology Media

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Automated Health Monitoring: Integrating AI and IoT for Continuous Patient Observation

Automated Health Monitoring: Integrating AI and IoT for Cont...

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IoT Based Control Networks and Intelligent systems (ICICNIS), International conference on

作者： E V N Jyothi Sighakolli Sailaja M. SriRama Lakshmi Reddy T. Sunitha Department of CSE (AI&ML) CMR College of Engineering & Technology Hyderabad Telangana India Department of Computer Science and Engineering Rise krishna sai prakasam group of institutions Ongole A.P India Department of Computer Science and Engineering CMR Institute of Technology Hyderabad Telangana India Department of Computer Science and Engineering (Data Science) Marri Laxman Reddy Institute of Technology and Management Hyderabad Telangana India

ISBN: (数字)9798331518097

ISBN: (纸本)9798331518103

Integrating Artificial Intelligence (AI) with the Internet of Things (IoT) has recently become more advantageous for various complex fields like innovative health, intrusion detection in various devices, and continuous monitoring of crops yielding in various farming lands. From the health point of view, it is essential to monitor multiple patients' health according to their abnormal health conditions. The proposed Integrated Health Monitoring System (IHMS) is developed in this work by combining AI-based models such as Recurrent Neural Networks (RNNs) and advanced IoT sensors. The IHMS is mainly used in health care applications such as heart care analysis and other abnormal health conditions like blood pressure, levels of oxygen, and glucose readings, and it transmits this information to AI-powered systems. The IHMS mainly analyzes abnormal patterns, detects anomalies, and provides timely analytical analysis of patient data. Finally, the proposed approach IHMS analyzes several datasets and shows the performance in terms of classification.

关键词： Recurrent neural networks Medical services Sepsis Real-time systems Blood pressure sensor systems Internet of Things Biomedical monitoring Monitoring Wearable sensors

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Event-Driven Frequency Decomposition with Spiking Phase-Locked Loops

Event-Driven Frequency Decomposition with Spiking Phase-Lock...

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Asilomar conference on Signals, systems & Computers

作者： Michele Mastella Hugh Greatorex Tesse Tiemens Madison Cotteret Ole Richter Elisabetta Chicca Bio-Inspired Circuits and Systems (BICS) Lab Zernike Institute for Advanced Materials University of Groningen Netherlands Groningen Cognitive Systems and Materials Center (CogniGron) University of Groningen Netherlands Micro- and Nanoelectronic Systems (MNES) Technische Universität Ilmenau Germany

ISBN: (数字)9798350354058

ISBN: (纸本)9798350354065

Event-driven sensors are highly effective at capturing stimuli in real-world scenarios where signals are sparse in both time and space. Unlike clock-based approaches, these sensors capture data from the environment only upon perceiving a variation in the signal, translating temporal sequences into trains of unary events, often referred to as spikes. However, while the sensor functions in an event-driven manner, subsequent data processing often still relies on clock-based methods. This lack of event-driven processing units undermines the latency, power efficiency and responsiveness benefits of the event-driven approach. In this work, we present a circuit implementation of a spiking phase-locked loop (sPLL), which decomposes event data into frequency components using fully event-driven CMOS circuits. Through experimental measurements on a CMOS chip, we demonstrate that the fabricated sPLL circuit can accurately synchronize with input frequencies and perform frequency decomposition. This approach demonstrates the feasibility of event-driven CMOS circuits for processing and decoding complex event data, offering a promising solution for low-power and low-latency applications that require spectral analysis.

关键词： Frequency estimation Frequency measurement sensors System-on-chip Synchronization Phase locked loops Low latency communication Frequency synchronization Synapses Spectral analysis

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3rd Workshop on Intelligent Data - From Data to Knowledge, DOING 2022, 1st Workshop on Knowledge Graphs Analysis on a Large Scale, K-GALS 2022, 4th Workshop on Modern Approaches in Data Engineering and Information System Design, MADEISD 2022, 2nd Workshop on advanced Data systems Management, Engineering, and Analytics, MegaData 2022, 2nd Workshop on Semantic Web and Ontology Design for Cultural Heritage, SWODCH 2022 and Doctoral Consortium which accompanied 26th European conference on Advances in Databases and Information systems, ADBIS 2022

3rd Workshop on Intelligent Data - From Data to Knowledge, D...

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3rd Workshop on Intelligent Data - From Data to Knowledge, DOING 2022, 1st Workshop on Knowledge Graphs Analysis on a Large Scale, K-GALS 2022, 4th Workshop on Modern Approaches in Data Engineering and Information System Design, MADEISD 2022, 2nd Workshop on advanced Data systems Management, Engineering, and Analytics, MegaData 2022, 2nd Workshop on Semantic Web and Ontology Design for Cultural Heritage, SWODCH 2022 and Doctoral Consortium which accompanied 26th European conference on Advances in Databases and Information systems, ADBIS 2022

ISBN: (纸本)9783031157424

The proceedings contain 61 papers. The special focus in this conference is on Intelligent Data - From Data to Knowledge. The topics include: Embedding Process Structure in Activities for Process Mapping and Comparison;Bulk Loading of the Secondary Index in LSM-Based Stores for Flash Memory;blink: Lightweight Sample Runs for Cost Optimization of Big Data applications;A RoBERTa Based Approach for Address Validation;empirical Comparison of Semantic Similarity Measures for Technical Question Answering;denoising Architecture for Unsupervised Anomaly Detection in Time-Series;discretizing Numerical Attributes: An Analysis of Human Perceptions;interactive Knowledge Graph Querying Through Examples and Facets;Understanding Misinformation About COVID-19 in WhatsApp Messages;It’s Too Noisy in Here: Using Projection to Improve Differential Privacy on RDF Graphs;modeling and Querying sensor Networks Using Temporal Graph Databases;indexing Continuous Paths in Temporal Graphs;unsupervised Extractive Text Summarization Using Frequency-Based Sentence Clustering;experimental Comparison of Theory-Guided Deep Learning Algorithms;hyper-parameters Tuning of Artificial Neural Networks: An Application in the Field of Recommender systems;exploring Latent Space Using a Non-linear Dimensionality Reduction Algorithm for Style Transfer Application;a Novel Method for University Ranking: Bounded Skyline;QLDT: A Decision Tree Based on Quantum Logic;using Provenance in Data Analytics for Seismology: Challenges and Directions;automatic Inference of Taxonomy Relationships Among Legal Documents;storing Feature Vectors in Relational Image Data Warehouses;automatic Classification of Stigmatizing Articles of Mental Illness: The Case of Portuguese Online Newspapers;a Multiplex Network Framework Based Recommendation systems for Technology Intelligence;relation Extraction from Clinical Cases for a Knowledge Graph;privacy Operators for Semantic Graph Databases as Graph Rewriting;a Collaborative Filtering Appr

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Development of In-Mold Integration of EDA sensors Via Additive Printing

Development of In-Mold Integration of EDA Sensors Via Additi...

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Intersociety conference on Thermal and Thermomechanical Phenomena in Electronic systems (ITHERM)

作者： Pradeep Lall Hyesoo Jang Scott Miller NSF-CAVE3 Electronics Research Center Auburn University Auburn AL NextFlex Manufacturing Institute San Jose CA

ISBN: (数字)9798350364330

ISBN: (纸本)9798350364347

Wire harnesses account for a significant weight of the vehicle. The emergence of advanced driver assistance systems has seen the emergence of a number of safety features, including lane departure warning, driver alertness monitoring, cross-traffic alert, guidance, navigation, and control. Touch surfaces in the automotive platform are presently used for little more than aesthetic appeal. However, the integration of human-machine interfaces on the touch surfaces requires additively printed in-mold electronics. The processes for the printing of sensors in addition to the integration of surface mount electronics for signal processing are not yet known for additively printed conformal electronics. This research focuses on developing robust and reliable PCBs for wearable electro-dermal activity (EDA) biosensors, particularly on substrates that are thermoformable. Electrodermal sensors measure the surface conductance of the skin to determine the state of the autonomous nervous system. The Electrodermal Activity (EDA) sensors are used for their ability to monitor a multitude of biosignals within the human body. These wearables could acquire biosignals with high accuracy, which makes them beneficial in scenarios like assessing a driver’s emotional and medical condition during various driving scenarios. The potential real-world implications of such monitoring are considerable, from safety applications to medical diagnostics. The purpose of using thermoformable substrates was due to their ability to be reshaped to provide unprecedented design flexibility, such as mounting on any curved surface. The other focus was to accurately measure skin conductance and response as biosignals for real-time driver monitoring. The approach will involve using the Voltera additive print platform for the fabrication of EDA sensors along with the signal processing circuits for the measurement of surface conductance. The influence of the sintering time temperature, print speed, print volume, and t

关键词： Temperature measurement Temperature sensors Measurement uncertainty sensor phenomena and characterization sensor systems Biomedical monitoring Wearable devices Surface treatment Monitoring Wearable sensors

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Data Security Analysis of PHY Layer Using Artificial Intelligence and Machine Learning

Data Security Analysis of PHY Layer Using Artificial Intelli...

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International conference on advanced Computing and Communication systems (ICACCS)

作者： Panduraju Pagidimalla T. Venketbabu J. K. Kanimozhi S. Renuka Nandula Anuradha S. Surenderkumar Department of Computer Science and Engineering KLH is Deemed to be a University Hyderabad Telangana India Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai Tamilnadu Computer Science and Applications Vivekanandha College of Arts and Sciences for Women (Autonomous) Namakkal (DT) Department of IT Vasavi College of Engineering Hyderabad India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Hyderabad Telangana Department ECE Sri Eshwar College of Engineering Coimbatore TamilNadu India

Artificial Intelligence (AI) and Machine Learning (ML) plays a critical role in determining the future of wireless networks. The advancements in technology will unleash a significant Internet of Things (IoT) where numerous interconnected processes, people and devices will be served in a simultaneous manner. Open and broadcast wireless channels make wireless networks particularly susceptible to espionage, jamming, and disturbance attacks. Physical Layer (PHY layer) security becomes a major concern since typical secure methods do not operate well in wireless PHY networks. As a starting point, we examine the PHY layer of wireless connections for threats and hazards. When it comes to PHY layer security systems, there are three basic categories: Spatial Domain-Based (SDB), Temporal (TTB), and Frequency (FF). We will weigh the benefits and drawbacks of the most recent safe technology in each area along the way. The methodologies and methods employed in these areas, as well as the open research questions and prospects in this field, are also discussed.

关键词： Wireless sensor networks Power demand Wireless networks Data security Machine learning Physical layer Communication system security

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advanced Ultra Low-Power Deep Learning applications with Neuromorphic Computing

Advanced Ultra Low-Power Deep Learning Applications with Neu...

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IEEE conference on High Performance Extreme Computing (HPEC)

作者： Mark Barnell Courtney Raymond Lisa Loomis Darrek Isereau Daniel Brown Francesca Vidal Steven Smiley Air Force Research Laboratory Information Directorate Rome NY USA Defense Systems & Solutions SRC Inc. North Syracuse NY USA

The latest Intel neuromorphic processor, Loihi 2, provides a breakthrough in Artificial Intelligence (AI) for computing at the edge, where sensor information is collected. The computing architecture does this by leveraging computations at the transistor level in a fashion analogous to the human brain's biological neural networks (vs. a Von Neumann compute architecture). The Loihi 2's high performance, small form factor, and low-power consumption makes it a unique capability that is well suited for use in devices. Our technical approach and findings support extreme computing needs for the internet of things (IoT) and various airborne platforms' applications. The recently released Loihi 2 and the novel research completed on this effort were combined to accelerate development and demonstration of a new concept of operation for machine learning at the edge. This research included the development of spiking neural networks (SNN) on sensor data representative of information sources from a small research platform. Our concept uses the representative sensor data to predict the platform mode through machine learning. Importantly, our technical approach allowed us to rapidly scale from IBM's TrueNorth Corelet framework to the Lava framework, which Intel's Loihi 2 neuromorphic processor utilizes. The use of the Lava framework demonstrates the art-of-the-possible in edge computing by demonstrating capabilities on small airborne platform sensor data and wide extensibility to other domains that can use this neuromorphic compute hardware. In summary, this research included the use of new compute frameworks, novel processing algorithms, and a unique concept of operation. This technical approach resulted in the classification of the platform mode given the sensor information with accuracies up to 97.6%.

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