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检索条件"任意字段=International Conference on Sensor Technologies and Applications, SensorComm"

共 9418 条记录，以下是391-400 订阅

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A Feature-based Approach for the Recognition of Image Quality Degradation in Automotive applications 30

A Feature-based Approach for the Recognition of Image Qualit...

引用

30th international conference on Systems, Signals and Image Processing, IWSSIP 2023

作者： Bauer, Florian B-plus Technologies GmbH Deggendorf Germany

ISBN: (纸本)9798350337297

Cameras play a crucial role in modern driver assistance systems and are an essential part of the sensor technology for automated driving. The quality of images captured by in-vehicle cameras highly influences the performance of visual perception systems. This paper presents a feature-based algorithm to detect certain effects that can degrade image quality in automotive applications. The algorithm is based on an intelligent selection of significant features. Due to the small number of features, the algorithm performs well even with small data sets. Experiments with different data sets show that the algorithm can detect soiling adhering to camera lenses and classify different types of image degradation. © 2023 IEEE.

关键词： Image quality

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Deep Generative Model with Isolation Forest (DGM-IF) for Unsupervised Anomaly Detection in Wireless sensor Network and Internet of Things 9

Deep Generative Model with Isolation Forest (DGM-IF) for Uns...

引用

9th international conference on Control, Decision and Information technologies, CoDIT 2023

作者： Zerkouk, Meriem Mihoubi, Miloud Chikhaoui, Belkacem University of Sherbrooke Faculty of Electrical Engineering Mathematics and Computer Science Enschede7500 AE Netherlands University of Téluq Department of Information Technology Montréal Canada

ISBN: (纸本)9798350311402

Anomaly detection is crucial for maintaining the reliability and security of wireless sensor networks and loT systems. Conventional methods require labeled data, often unavailable in these systems, making unsupervised techniques essential. Deep learning has shown promise in unsupervised anomaly detection tasks, including wireless sensor networks and loT applications. The Isolation Forest, an unsupervised anomaly detection algorithm, isolates anomalies based on in-herent properties. Combining deep learning with the Isolation Forest can improve accuracy and effectiveness in detecting anomalies. This paper presents the Deep Generative Model with Isolation Forest (DGM-IF), a novel unsupervised anomaly detection method for wireless sensor networks and loT. DGM-IF integrates deep generative models with the Isolation Forest algorithm to learn a robust representation of normal data and identify anomalies. The model generates synthetic data based on the learned distribution and employs the Isolation Forest to separate deviating data points. The proposed technique is assessed using real-world datasets and benchmarked against cutting-edge methods, proving its efficacy in detecting anomalies. The DGM-IF approach has the potential to significantly enhance the reliability and security of wireless sensor networks and IoT systems by identifying potential threats and attacks. © 2023 IEEE.

关键词： Wireless sensor networks

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Reducing Extended Kalman Filter Sampling Rates for Multi-Rate Fusion through Computationally Efficient Sequential Single sensor Measurement Processing* 8

Reducing Extended Kalman Filter Sampling Rates for Multi-Rat...

引用

8th international conference on Control, Decision and Information technologies (CoDIT)

作者： Tkany, Charlotte Grotjahn, Martin Jonsky, Torben Univ Appl Sci Inst Engn Design Mechatron & Electromobil D-30173 Hannover Germany Lenze SE D-31855 Aerzen Germany

ISBN: (纸本)9781665496070

Real-time, high-rate Extended Kalman Filter (EKF) execution must include considerations of its computational load, which can pose a challenge for the implementation depending on the specific observer rate. While methods for the reduction of the computational load exist, this paper seeks to circumvent the problem, by reducing the EKF sampling rate. This is explored for the case of multi-rate sensor fusion for drive control applications where at least one sensor sampling rate exceeds the control cycle rate. A lower rate EKF is implemented where, in contrast to a single-rate EKF approach, none of the higher rate measurements are neglected, but instead collected and sequentially processed during each EKF execution. Two formulations based on this concept are introduced. The first optimises the estimation error, accepting a significant increase in computational load, while the second seeks the best compromise between estimation error and computational load.

关键词： Estimation error Measurement uncertainty Organizations Observers sensor fusion Real-time systems Computational efficiency

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CSRR Integrated Microwave sensor for Analyte Dielectric Sensing 3

CSRR Integrated Microwave Sensor for Analyte Dielectric Sens...

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3rd IEEE international conference on Mobile Networks and Wireless Communications, ICMNWC 2023

作者： Challa Ram, G. Yuvaraj, S. Venkata Subbarao, M. Sai Sneha, P. National Institute of Technology Department of ECE Andhra Pradesh India Shri Vishnu Engineering College for Women Department of ECE A.P Bhimavaram India

ISBN: (纸本)9798350317022

A microstrip patch antenna with Rectangular Complementary Split Ring Resonator (R-CSRR) for sensing applications is proposed in this paper. In recent years, due to the rapid growth of wireless communication systems and emerging technologies, there has been an increased need for microwave antenna designs that are both compact and efficient. This paper introduces an approach to enhance the performance of microstrip patch antennas through the integration of a RCSRR structure. The proposed design provides an operating frequency of 2.58 GHz and s-parameter magnitude of -21.35 dB. The design can act as a high sensitive microwave sensor to detect the dielectric constants of different analytes. Moreover, a range of machine learning regression algorithms have been utilized to accurately forecast the dielectric constant of the analyte by analyzing the frequency response of the sensor. The performance analysis reveals that polynomial regression exceeds other methods, demonstrating a minimal root mean squared error of 0.2948. These machine learning-driven techniques greatly improve the sensor's performance, making it a valuable asset for the future applications in biochemical sensing. © 2023 IEEE.

关键词： Dielectric sensing Machine Learning Rectangular Complementary Split Ring Resonator (RCSRR) Regression sensor

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CAMJ: Enabling System-Level Energy Modeling and Architectural Exploration for In-sensor Visual Computing 23

CAMJ: Enabling System-Level Energy Modeling and Architectura...

引用

50th Annual international Symposium on Computer Architecture (ISCA)

作者： Ma, Tianrui Feng, Yu Zhang, Xuan Zhu, Yuhao Washington Univ St Louis St Louis MO 63130 USA Univ Rochester Rochester NY 14627 USA

ISBN: (纸本)9798400700958

CMOS Image sensors (CIS) are fundamental to emerging visual computing applications. While conventional CIS are purely imaging devices for capturing images, increasingly CIS integrate processing capabilities such as Deep Neural Network (DNN). Computational CIS expand the architecture design space, but to date no comprehensive energy model exists. This paper proposes CAMJ, a detailed energy modeling framework that provides a component-level energy breakdown for computational CIS and is validated against nine recent CIS chips. We use CamJ to demonstrate three use-cases that explore architectural trade-offs including computing in vs. off CIS, 2D vs. 3D-stacked CIS design, and analog vs. digital processing inside CIS. The code of CAMJ is available at: https://***/horizon-research/CamJ.

关键词： in-sensor computing energy modeling analog modeling

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Forecasting IPL Score Using Machine Learning Techniques

Forecasting IPL Score Using Machine Learning Techniques

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2024 international conference on Artificial Intelligence and Quantum Computation-Based sensor applications, ICAIQSA 2024

作者： Panchal, Riddhi Sakarkar, Gopal Shelke, Nilesh Pimpalkar, Amit MITWPU Department of Computer Science and Applications Maharashtra Pune India Symbiosis Institute of Technology Nagpur Campus Symbiosis International Deemed University Pune India Maharashtra Nagpur India

ISBN: (纸本)9798331517953

The IPL, which stands for Indian Premier League, has become one of the most widespread and fought over Twenty20 cricketers in the world, which borrows the interest of its audience with its ever-changing and chaotic nature. In this research paper, the researchers aim to employ various machine learning techniques to predict the cricket scores of an IPL match, recording valuable insights aimed at enhancing the understanding of the sport and providing useful insights for the sporting teams and the fans as well as the analysts. The work aims to develop and test computational intelligence models appropriate for estimating IPL match total scores using modern technologies such as machine learning and artificial intelligence based on historical match scores, player statistics and match context. Among the methods used were ensemble techniques, such as neural and regression networks, to investigate complex patterns within the dataset and increase accuracy in predictive performance. As part of modelling, we harnessed a dataset that covers several seasons of the IPL that includes performance indicators of players, match characteristics, and necessary background information like the weather and venue of the game. Models are evaluated using the same dataset, but the target for prediction was never seen during training, thus ensuring that the model was accurate, recall, precision, and overall performance were optimal during training. Standard metrics such as MAE-Mean Absolute Error, MSE-Mean Square Error and RMSE Root Mean Squared Error measure the predictability of models. Lastly, we compared the algorithms to identify those that better predict IPL cricket scores. Knowledge derived from our research is rich since it hails from cricket match result factors highlighting the need to consider a player's form, teamwork, and extra influences from the weather. From its ability to demonstrate the viability and effectiveness of machine learning to predict scores in the game of cricket's IP

关键词： Mean square error

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Sustainable Printed Electrodes for Energy Harvesting from Urine to Power IoT sensor Nodes in Smart Diapers

Sustainable Printed Electrodes for Energy Harvesting from Ur...

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17th international Joint conference on Biomedical Engineering Systems and technologies, BIOSTEC 2024

作者： Tanweer, Muhammad Sepponen, Raimo Tanzer, I. Oguz Halonen, Kari Department of Electronics and Nanoengineering Aalto University Espoo02150 Finland Department of Automation and Electrical Engineering Aalto University Espoo02150 Finland

The expansion of Internet of Things (IoT) devices is rapidly increasing across various aspects of life, notably in wearable healthcare. With billions of already deployed IoT sensor nodes, this figure is anticipated to escalate into the hundreds of billions in coming years. One of the most significant challenges is how to economically power these devices by adopting sustainable and environment-friendly solutions as the conventional power sources are inadequate to meet the demands of this vast IoT ecosystem. In recent years, innovative approaches have emerged to design energy-optimized electronic systems, opening a pathway for applications based on energy harvesting. In this study, a novel energy harvesting solution is proposed by developing sustainable and disposable harvesting electrodes, leveraging the capabilities of printed electronics technology. These electrodes are engineered to harvest energy from human urine, a readily available resource, to power the energy-efficient wearable IoT sensor nodes of smart diapers. A comprehensive characterization of these harvesting electrodes is conducted using pseudo-urine as an electrolyte within a controlled laboratory environment. The results demonstrate great promise for the development of self-powered IoT sensor nodes of smart diapers, with the capacity for overnight operations lasting up to 12 hours. © 2024 by SCITEPRESS–Science and Technology Publications, Lda.

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An IoT Integrated sensor technologies for the Enhancement of Hospital Waste Segregation and Management

An IoT Integrated Sensor Technologies for the Enhancement of...

引用

2023 IEEE international conference on Innovative Data Communication technologies and Application, ICIDCA 2023

作者： Selvakarthi, D. Sivabalaselvamani, D. Wafiq, M. Ahamed Aruna, G. Gokulnath, M. Kongu Engineering College Department of Electronics and Instrumentation Engineering Tamilnadu Erode638060 India Kongu Engineering College Department of Computer Applications Tamilnadu Erode638060 India

ISBN: (纸本)9798350397208

Hospitals create an enormous volume of potentially hazardous trash. Currently, bandages, plastic papers, wasted pus, syringes, glucose drip bottles, other dangerous medical wastes, and containers are separated by hand, resulting in long-term health consequences such as tuberculosis, cancer, and infectious diseases. Rag pickers are currently responsible for the majority of waste separation. In this proposed system, waste will be detected using the Deep Learning technique;a model will be trained to distinguish various forms of medical waste. With the use of a flip attachment to a motor, waste will be easily segregated to the waste bin. There are two flips that open depending on the sort of trash detected, and the garbage is divided into compartments having four specific chambers. IoT-based web applications can be used to update the waste level, with respective times and dates. As a result, the overflowing of waste in the bin can be avoided. Here, medical waste is quickly and properly segregated to reduce manual labor and stop diseases from spreading throughout hospitals © 2023 IEEE.

关键词： Deep learning

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Multi-sensor Fusion for the Security Surveillance of Public Areas

Multi-Sensor Fusion for the Security Surveillance of Public ...

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2023 international conference on Images, Signals, and Computing, ICISC 2023

作者： Litzenberger, Martin Hubner, Michael Kohn, Bernhard Wohlleben, Kilian AIT Austrian Institute of Technology GmbH Giefinggasse 4 Vienna1210 Austria

ISBN: (数字)9781510668171

ISBN: (纸本)9781510668164

Increasing security awareness in the public sector are leading to a more and more widespread use of surveillance applications. Although the available technologies like video processing are already well advanced, they still suffer from high false alarm rates when used under realistic conditions. We present a method for sensor fusion based on probability density maps and a rule engine. The system was tested in a public area using the combination of audio localization, audio classification and video detection using 79 simulated scenarios and 44 hours of sample data recorded over a period of several weeks. The false positive rate decreased by 60% and the event localization rate increased by 25% with the fusion approach compared to the detection performance of individual techniques. © 2023 SPIE.

关键词： sensor data fusion

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2nd NIELIT's international conference on Communication, Electronics and Digital technologies, NICEDT 2024

2nd NIELIT's International Conference on Communication, Elec...

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2nd NIELIT's international conference on Communication, Electronics and Digital technologies, NICEDT 2024

ISBN: (纸本)9789819736003

The proceedings contain 79 papers. The special focus in this conference is on Communication, Electronics and Digital technologies. The topics include: Design and Development of Parallel Beamformer Model for RTL Verification;early Detection of Oral Cancer Using Image Processing and Computational Techniques;loRa Enabled IoT sensor Framework for Monitoring Urban Flood in Guwahati City;study and Implementation of Smart Positioner Prototype for Industrial applications;tactile Internet: A Next Gen IoT Technology;a Deep Learning Approach for Hardware Trojan Detection in Netlist of Integrated Circuits with Graph Neural Networks;a Comparative Study of Jpeg and Similar Compression Standards for Wireless Communication;iot-Based Transport Monitoring System for Pharmaceutical Industry;non-intrusive Remote Apiculture Monitoring System;design and Analysis of Enhanced Bandwidth Slotted Logarithmic Spiral Patch Antenna for 5G Communication;IoT Based Real-Time Automatic Number Plate Detection Using OpenCV;prompt Engineering for the Automated Generation of Weaving Pattern;blood Disease Detection System Based on Haemogram Report Using Decision Tree Algorithm;Development of SPR sensor-Based System for Copper Ion Detection in Water;generation of YuBraj Twist and YuBrajTwest Weaves;maintenance and Performance of Solar Water Pump in a Rural Agricultural Community: A Case Study;integrative Analysis of Cancer Gene Expression Using Bio-Inspired Algorithms and Machine Learning: Identification of Key Genes;Potential of AI in Pharma: Bridge the Gap Between Data and Therapeutics;A PC-Based Ultrasound Color Doppler Performance Improvement Using Intel® IPPs;Deep Reach Centrality: An Innovative Network Centrality Metric Grounded in Distance and Degree, with Its Performance Analysis Applied to the SARS-CoV-2 Protein–protein Interaction Network.

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