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Received Signal-to-Noise Ratio Evaluation for Hybrid intelligent Reflecting Array Systems

Received Signal-to-Noise Ratio Evaluation for Hybrid Intelli...

引用

International conference on Electronics, information and Communications (ICEIC)

作者： Taehee Choi Jaehong Kim Jingon Joung School of Electrical and Electronics Engineering Chung-Ang University Seoul South Korea

ISBN: (数字)9798331510756

ISBN: (纸本)9798331510763

This study considers a direction-of-arrival (DOA)-based hybrid intelligent reflecting array (HIRA) phase shift method. Concretely, the HIRA sensors estimate the DOA of the signal transmitted from the user equipment (UE), and the phase shift values of the HIRA reflectors are calculated using the estimated DOA. Here, the DOA estimation accuracy and the HIRA beamforming gain are proportional to the numbers of the sensors and the reflectors, respectively. Thus, there exists a trade-off between the DOA estimation accuracy and the HIRA beamforming gain when the total number of sensors and reflectors is bounded. From the numerical simulation, the received signal-to-noise ratio was evaluated to verify the trade-off of the DOA-based HIRA phase shift method. Also, the optimal number of sensors was numerically demonstrated.

关键词： Phased arrays Direction-of-arrival estimation Accuracy Array signal processing Estimation Numerical simulation sensor systems intelligent sensors Signal to noise ratio sensor arrays

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A toolbox for neuromorphic perception in robotics 8

A toolbox for neuromorphic perception in robotics

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8th International conference on Event-Based Control, Communication and Signal processing (EBCCSP)

作者： Dupeyroux, Julien Stroobants, Stein de Croon, Guido C. H. E. Delft Univ Technol Micro Air Vehicle Lab MAVLab Delft Netherlands Delft Univ Technol Fac Aerosp Engn Micro Air Vehicle Lab Delft Netherlands

ISBN: (数字)9781665453493

ISBN: (纸本)9781665453493

The third generation of artificial intelligence (AI) introduced by neuromorphic computing is revolutionizing the way robots and autonomous systems can sense the world, process the information, and interact with their environment. Research towards fulfilling the promises of high flexibility, energy efficiency, and robustness of neuromorphic systems is widely supported by software tools for simulating spiking neural networks, and hardware integration (neuromorphic processors). Yet, while efforts have been made on neuromorphic vision (event-based cameras), it is worth noting that most of the sensors available for robotics remain inherently incompatible with neuromorphic computing, where information is encoded into spikes. To facilitate the use of traditional sensors, we need to convert the output signals into streams of spikes, i.e., a series of events (+ 1,- 1) along with their corresponding timestamps. In this paper, we propose a review of the coding algorithms from a robotics perspective and further supported by a benchmark to assess their performance. We also introduce a ROS (Robot Operating System) toolbox to encode and decode input signals coming from any type of sensor available on a robot. This initiative is meant to stimulate and facilitate robotic integration of neuromorphic AI, with the opportunity to adapt traditional off-the-shelf sensors to spiking neural nets within one of the most powerful robotic tools, ROS.

关键词： Neuromorphic engineering Neural networks Signal processing algorithms Signal processing Robot sensing systems sensor systems Robustness

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Leveraging Residual Deep Neural networks and Multi-Device sensors for Heterogeneous Activity Recognition

Leveraging Residual Deep Neural Networks and Multi-Device Se...

引用

International conference on Telecommunications and Signal processing (TSP)

作者： Sakorn Mekruksavanich Wikanda Phaphan Anuchit Jitpattanakul Department of Computer Engineering School of Information and Communication Technology University of Phayao Phayao Thailand Department of Applied Statistics Faculty of Applied Science Research Group in Statistical Learning and Inference King Mongkut's University of Technology North Bangkok Bangkok Thailand Department of Mathematics Faculty of Applied Science Intelligent and Nonlinear Dynamic Innovations Research Center King Mongkut's University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9798350365597

ISBN: (纸本)9798350365603

This study introduces a novel approach to identifying human activities using wearable sensors, particularly smart-phones and smartwatches. By leveraging deep learning neural networks and data from the HHAR dataset, which includes accelerometer and gyroscope data from individuals engaged in various activities, our method, centered around the HAR-Res NeXt model, accurately detects six activities. Utilizing residual connections and multi-kernel blocks, our approach effectively captures temporal and spatial relationships in sensor data. Experimental results demonstrate superior performance to standard machine learning algorithms and other deep learning approaches for human activity recognition. HAR-ResNeXt achieves high accuracy rates, particularly in classifying smartphone sensor data, underscoring its adaptability across diverse scenarios. Comparative analysis reveals the effectiveness of smartphone sensors and emphasizes the importance of multi-modal sensor fusion for accurate activity detection.

关键词： Deep learning Accuracy Machine learning algorithms Multimodal sensors Signal processing Telecommunications Gyroscopes

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Application of sensors in Flexible Process Control of intelligent Manufacturing

Application of Sensors in Flexible Process Control of Intell...

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Youth Academic Annual conference of Chinese Association of Automation (YAC)

作者： Jianxiong Qiu Zhaoshui He Cheng Yan Hailong Zhou Guangdong University of Technology Guangzhou China School of Automation Guangdong University of Technology Key Laboratory of Information Processing and System Integration of IoT Ministry of Education Guangzhou China Huizhou Metal Cutting Processing Unit Intelligent Manufacturing Engineering Technology Research Center Huizhou China Tianjin Bonuo Intelligent Robot Technology Co. Ltd. Tianjin China

ISBN: (数字)9798350379228

ISBN: (纸本)9798350390780

In order to carry out the intelligent production of typical chemical products for the application of order-based flexible production system, based on intelligent measurement and control technology, a new generation of information technology such as industrial Internet, intelligent management and control, data visualization, and informatization is integrated. In this paper, we study the establishment of an order-based flexible production process with definable configuration according to the intelligent processing mode of process automation. After selecting the sensor reasonably, Configuration of the industrial intelligent detection system, System signal processing and digitalization, Industrial information network construction and debugging, Programming operation and debugging of intelligent measurement and control systems, Production tasks such as visualization and remote monitoring of the production process. The study found,The sensor plays a vital role in the control system control of the DCS -based controller, Liquid level sensors and flow/weight sensors and other key sensors can monitor various parameters in the production process in real time, provide accurate data support for the control system to ensure the accuracy and stability of the ingredient system. And realize the flexible ingredients controlled by program control, and then make the feeding at the same time to improve production efficiency. This research provides a strong technical guarantee for actual production, and provides a feasible implementation method for our intelligent manufacturing process industrial control.

关键词： Temperature measurement Temperature sensors Accuracy Data visualization Process control Production Control systems

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9th Annual International conference on information Management and Big Data, SIMBig 2022

9th Annual International Conference on Information Managemen...

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9th Annual International conference on information Management and Big Data, SIMBig 2022

ISBN: (纸本)9783031354441

The proceedings contain 19 papers. The special focus in this conference is on information Management and Big Data. The topics include: Peak Anomaly Detection from Environmental sensor-Generated Watershed Time Series Data;gas sensors and Machine Learning for Quality Evaluation of Grape Spirits (Pisco);optimal Layer Selection on Deep Convolutional Neural networks Using Backward Freezing and Binary Search;profiling Public Service Accessibility Based on the Public Transport Infrastructure;multiple Scale Comparative Analysis of Classical, Dynamic and intelligent Edge Detection Schemes;soil Organic Carbon Prediction Using Digital Color sensor in Peru;ideation of Computational Thinking Programs by Assembling Code Snippets from the Web;smart Doorbell with Telegram Notification for Multifamily Dwellings;performance Analysis of Machine Learning for Food Fraud Prediction;user-Agnostic Model for Prediction of Retweets Based on Social Neighborhood information;segmentation and Classification of Pages for Digitized Documents of the Public Prosecutor’s Office;a Semantic Query Engine for Knowledge Rich Legal Digital Libraries;getting Quechua Closer to Final Users Through Knowledge Graphs;using Features Based on Elongation to Enhance Sentiment Analysis;image Generation from Sketches and Text-Guided Attribute Edition;ADRAS: Airborne Disease Risk Assessment System for Closed Environments;predictive Sentiment Analysis Model Regarding the Variation of the Dollar Exchange Rate.

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Highly Efficient Data Gathering with Tendency Prediction based on Position information of Event in Wireless sensor networks

Highly Efficient Data Gathering with Tendency Prediction bas...

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Asia-Pacific-Signal-and-information-processing-Association Annual Summit and conference (APSIPA ASC)

作者： Takyu, Osamu Sugimoto, Ryota Shinshu Univ Nagano Japan

ISBN: (纸本)9789881476890

Environmental monitoring with wireless sensor networks is attracting much attentions. If the event giving the impact to dramatically change the environment occurs, the suitable sensors should be requested to send the sensing information for the highly accurate environmental monitoring. This paper proposes the sensor selection scheme based on the location information of event. In the proposed scheme, the spatial tendency of sensing information is predicted from the location information of event and then the suitable sensors are selected.

关键词： Wireless sensor networks

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Research on intelligent Spectral Soil Detection System Based on Computer Artificial Intelligence Technology

Research on Intelligent Spectral Soil Detection System Based...

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2023 IEEE International conference on Image processing and Computer Applications, ICIPCA 2023

作者： Yuan, Meng Chen, Tianqing Xia, Longfei Wei, Jing Shaanxi Land Engineering Construction Group Co. Ltd. Xi'an China Institute of Land Engineering and Technology Shaanxi Land Engineering Construction Group Co. Ltd. Xi'an China Key Laboratory of Degraded and Unused Land Consolidation Engineering The Ministry of Natural Resources Xi'an China Shaanxi Construction Land Engineering Quality Inspection Co. Ltd. Xi'an China

ISBN: (纸本)9798350314670

Combining near infrared spectrum analysis and wireless communication technology, a design scheme of near infrared intelligent spectrum soil detection system based on 2.4GHz radio frequency technology and GPRS communication technology was proposed. Using low power CC2530 chip as the core, the hardware circuit and software flow of coordinator node and sensor node are designed and developed. Power amplifier CC2591 is added to the sensor node to realize longdistance transmission of soil information in farmland wireless network. Farmland soil temperature and humidity parameters are periodically collected by STH11 temperature and humidity sensors and sent to the coordinator via wireless transmission. BPNN was used to train the soil organic carbon detection model after pretreatment, and GA was used to extract the spectral feature band. Finally, experiments show that the detection system has the characteristics of simple structure, easy expansion, high reliability and high sensitivity., the optimal detection model R2 was 0.984. © 2023 IEEE.

关键词： Soils

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Event-based Multisensor Fusion with Correlated Estimates

Event-based Multisensor Fusion with Correlated Estimates

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International conference on information Fusion

作者： Eva Julia Schmitt Benjamin Noack Autonomous Multisensor Systems Group Institute for Intelligent Cooperating Systems Otto von Guericke University Magdeburg Germany

ISBN: (数字)9781737749769

ISBN: (纸本)9798350371420

Many automation tasks require to fuse information that is acquired by distributed sensors and passed through a wireless network across multiple nodes. The growing number of connected sensors and agents increases the burden on the communications network and the energy consumption. Further challenges in information fusion arise from correlated data shared between nodes. To mitigate the negative effects, an efficient multi-sensor fusion approach is presented in this paper. A system design that uses stochastic event-based instead of periodic transmissions is proposed based on two different algorithms, the augmented state approach and fast covariance intersection. Furthermore, two different network topologies are investigated and a methodology to handle correlations among both finite impulse response and recursive estimates is developed. Together, the results represent a wide range of network topologies and possible correlation structures and give insights into the estimation performance and network utilization.

关键词： Wireless sensor networks Correlation Network topology Fuses Wireless networks Stars sensor fusion Topology intelligent sensors System analysis and design

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Sign Language Recognition of Autistic Children using Wearable sensors Signals Features Selection 7

Sign Language Recognition of Autistic Children using Wearabl...

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7th International conference on Advanced Communication Technologies and Networking, CommNet 2024

作者： Ullah, Farman Isa, Mohamed Alhammadi, Ahmed Abdulraheim Alyassi, Abdalla Ahmed Ali Yaqoob, Shumayla Jadoon, Ihtesham College of Information Technology United Arab Emirates University Al-Ain Abu Dhabi17551 United Arab Emirates

ISBN: (纸本)9798350367027

Children with autism spectrum disorder (ASD) often face difficulties and challenges in social interactions, communication, and eye contact due to neurological disorders. This neurological disorder causes children to have a lack of social interaction, sensory processing issues, repetitive behaviours, trouble adapting to change, and executive functioning problems. This article proposes the autistic children's gesture recognition framework using wearable sensors feature selection-based machine learning techniques. The system records and analyzes individuals' gestures, providing valuable data for understanding their behaviour. A dataset of 2 4 gestures from 10 children with ASD was collected, and various machine learning models, such as KNN-Manhattan, KNN-Euclidean, neural networks, and random forests, were trained using time-and frequency-domain features extracted from the sensor data. Cross-validation techniques were applied to evaluate the models' accuracy, with most classifiers achieving around 8 8% and the highest accuracy of about 9 1. 5%. This approach demonstrates the potential of wearable technology and machine learning in improving the understanding and support of individuals with ASD. © 2024 IEEE.

关键词： Diseases

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Effective information Gathering in a Three-Dimensional Setting Through the Unmanned Aerial Vehicles and Wireless sensor networks 1

Effective Information Gathering in a Three-Dimensional Setti...

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2023 1st DMIHER International conference on Artificial Intelligence in Education and Industry 4.0, IDICAIEI 2023

作者： Gohatre, Umakant Bhaskar Patil, Venkat P. Wadekar, Smita Y. Smt. Indira Gandhi College of Engineering Department of Computer Science and Engineering Mumbai India

ISBN: (纸本)9798350338423

In order to improve data gathering and monitoring, wireless sensor networks (WSNs) and unmanned aerial vehicles (UAVs) have been employed together recently. This strengthens the design of the WSN, where UAVs serve as aerial base stations linking wireless sensors in remote areas of surveillance networks. In order to tackle the clustering problem of the WSN and determine the best site for the UAV to prolong its usable life, this optimization research employs two algorithms: (1) Particle Swarm Optimization (PSO) and (2) Genetic Algorithm (GA). WSNs can operate in multimedia networks thanks to the mobility, computing power, and battery life of these mobile devices. While offering a comprehensive assessment of nearly all scenarios involving the usage of UAVs and UGVs, this paper focuses mainly on the employment of UAVs for data collecting in WSNs. Drones or unmanned aerial vehicles (UGVs) can collect data from stationary sensor nodes in the monitoring fields. Unmanned aerial vehicles (UAVs) can collect data alone or in conjunction with other UAVs. New technology known as unmanned aerial vehicles (UAVs) has showed promise in raising economic efficiency, enhancing urban intelligence, and enhancing people's quality of life. Due to its applications in a multitude of domains, such as public health, environmental protection, disaster management, security, agriculture, transportation, and communication, unmanned aerial vehicles (UAVs) have grown in popularity. Industry 4.0's B5G applications are inspired by intelligent, networked things. Consequently, wherever in the world a large number of things need to collaborate and be monitored in real time, the Internet of Things (IoT) is crucial for making this possible © 2023 IEEE.

关键词： Internet of things

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