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Hardware Implementation of High-performance Classifiers for Edge Gateway of Smart Automobile 1

Hardware Implementation of High-performance Classifiers for ...

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1st International conference on the Paradigm Shifts in Communication, Embedded Systems, Machine Learning and Signal processing, PCEMS 2022

作者： Gaikwad, Nikhil B. Khare, Smith K. Satpute, Nitin Keskar, Avinash G. Indian Institute of Information Technology Design and Manufacturing Electronics and Communication Discipline Jabalpur India Electronics and Communication Department Nagpur India Aarhus University Department of Electrical and Computer Engineering Denmark

ISBN: (纸本)9781665459044

Fog computing is a key solution for internet of things (IoT) applications, which demands operational security, real-time and power efficient intelligent responses, and low bandwidth usage. This paper introduces a novel idea related to an hardware implementation of High-performance classifiers for real-time and low power sensor data analytic on the intelligent edge gateway running on smart automobile. The high-performance classifiers uses an artificial neural network (ANN) to extract conclusive inferences from the raw automotive sensors information. The multiple classifiers are embedded into a re-configurable ANN hardware deign i.e. intellectual property core (IP core) which implemented and tested using field-programmable gate array fabric. In addition, this work studies the effect of the IP cores on the performance of the edge gateway. The implementation of fog/edge computing enables throughput reduction of 96.78% to 98.75% compared with the traditional gateway. The hardware design of the high-performance classifiers IP core requires only 31μ s and power consumption of 124mW for classification. The concept of re-configurable ANN model reduce about 41% to 93% of hardware resources requirement that contributing to reduced system power and cost. © 2022 IEEE.

关键词： Neural networks

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Semantic sensor Network Ontology for Forest Fire Management Decision Support

Semantic Sensor Network Ontology for Forest Fire Management ...

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2025 International conference on intelligent Control, Computing and Communications, IC3 2025

作者： Kirubakaran, S. Hussein, Ramy R. Ramakrishna, Thanneeru Venkata Duraipandi Reddy, Gurram Vijendar Sandeep, C.S. Cmr College of Engineering & Technology Department of Cse Telangana Hyderabad India College of technical engineering The Islamic University Department of computers Techniques engineering Najaf Iraq Saveetha Medical College and Hospital Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Chennai India Sethu Institute of Technology Department of Information Technology Pulloor Tamil Nadu Kariapatti626115 India Gokaraju Rangaraju Institute of Engineering and Technology Department of Information Technology Telangana Hyderabad India Jawaharlal College of Engineering and Technology Department of Ece Kerala Palakkad India

ISBN: (纸本)9798331527495

Forests are essential resources for each country, since they are essential to maintaining environmental equilibrium. The negative effects of deforestation, especially those caused by forest fires, highlight the need of efficient management and monitoring. When determining the risk of a fire, giving warnings, and assigning resources for emergency management, fire weather indices are essential measures. Utilising sensor networks-Semantic sensor networks (SSN) in particular-improves the ability to gather data on a range of meteorological factors, including temperature, relative humidity, and wind speed. processing data streams produced by these sensors poses difficulties for estimating fire weather indicators. This methodology guarantees the system's flexibility in responding to changing circumstances, facilitating reasoning and preserving the open world's generic semantics as established by all participating ontologies. The suggested fire management system contributes to effective forest fire prevention and control by functioning in accordance with the current circumstances © 2025 IEEE.

关键词： Risk management

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Semantic sensor Network Ontology for Forest Fire Management Decision Support

Semantic Sensor Network Ontology for Forest Fire Management ...

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intelligent Control, Computing and Communications (IC3), International conference on

作者： S Kirubakaran Ramy R. Hussein Thanneeru Venkata Ramakrishna Duraipandi Gurram Vijendar Reddy Sandeep C S Department of CSE CMR College of Engineering & Technology Hyderabad Telangana India Department of computers Techniques engineering College of technical engineering The Islamic University Najaf Iraq General Surgery Saveetha Medical College and Hospital Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Department of Information Technology Sethu Institute of Technology Pulloor Kariapatti Tamil Nadu India Department of Information Technology Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad Telangana India Department of ECE Jawaharlal College of Engineering and Technology Palakkad Kerala

ISBN: (数字)9798331527495

ISBN: (纸本)9798331527501

关键词： Temperature measurement Forests Wildfires Web services Wind speed Semantics Humidity Ontologies Cognition Monitoring

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Design of Indoor Occupancy Estimation System with Wireless Brain-Inspired Computing Platform

Design of Indoor Occupancy Estimation System with Wireless B...

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Annual IEEE Systems conference

作者： Ryuji Nagazawa Haruto Ota Kien Nguyen Hiroo Sekiya Won-Joo Hwang Hiroyuki Torikai Mikio Hasegawa Department of Informatics Graduate School of Informatics Chiba University Chiba Japan Department of Biomedical Convergence Engineering Pusan National University Yangsan-si Korea Graduate School of Science and Engineering Hosei University Tokyo Japan Department of Electrical Engineering Tokyo University of Science Tokyo Japan

ISBN: (数字)9798331508180

ISBN: (纸本)9798331508197

The estimation of indoor occupancy rates has become increasingly important, especially for accident prevention at events and providing critical rescue information during disasters. However, conventional systems require significant effort and cost for installation, such as the need for dedicated servers and internet connectivity, making them difficult to deploy conveniently. This study proposes the design of an indoor occupancy estimation system based on the Wireless Brain-Inspired Computing (WiBIC) platform. By equipping each IoT device with neuron functionality, environmental data is compressed into spike signals directly at the source. These signals are processed using intelligent information processing techniques based on the principles of brain-inspired computing, enabling the estimation of indoor occupancy. The WiBIC platform's serverless architecture and low-power characteristics allow for easy and efficient deployment of the system in various indoor environments. The proposed system further emphasizes energy efficiency by incorporating FPGA implementation and the Asynchronous Pulse Code Multiple Access (APCMA) protocol. Experimental evaluations were conducted using data collected from illuminance sensors, human detection sensor, and current sensors to estimate occupancy rates. The results show that the system achieves practical accuracy, validating its effectiveness. The accuracy achieved is sufficient for practical applications, demonstrating the effectiveness of the proposed system.

关键词： Wireless communication Performance evaluation Wireless sensor networks Accuracy Neurons Estimation information processing sensor phenomena and characterization Internet of Things Servers

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CalibDNN: Multimodal sensor Calibration for Perception Using Deep Neural networks 30

CalibDNN: Multimodal Sensor Calibration for Perception Using...

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conference on Signal processing, sensor/information Fusion, and Target Recognition XXX

作者： Zhao, Ganning Hu, Jiesi You, Suya Kuo, C-C Jay Univ Southern Calif 3551 Trousdale Pkwy Los Angeles CA 90089 USA US Army Res Lab 12025 E Waterfront Dr Los Angeles CA 90094 USA

ISBN: (数字)9781510643505

ISBN: (纸本)9781510643505

Current perception systems often carry multimodal imagers and sensors such as 2D cameras and 3D LiDAR sensors. To fuse and utilize the data for downstream perception tasks, robust and accurate calibration of the multimodal sensor data is essential. We propose a novel deep learning-driven technique (CalibDNN) for accurate calibration among multimodal sensor, specifically LiDAR-Camera pairs. The key innovation of the proposed work is that it does not require any specific calibration targets or hardware assistants, and the entire processing is fully automatic with a single model and single iteration. Results comparison among different methods and extensive experiments on different datasets demonstrates the state-of-the-art performance.

关键词： Multimodal sensor calibration sensor fusion Deep neural network intelligent perception

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Target Detection and Re-Identification Techniques in Cross-Domain Multidimensional Environments

Target Detection and Re-Identification Techniques in Cross-D...

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6th International conference on intelligent Computing and Signal processing (ICSP)

作者： Yahan Wang Jin Zhu Xudong Liu Zifeng Qiu Huihui Bai Gang Wang The 54th Research Institute of CETC Shijiazhuang China Beijing Jiaotong University Beijing China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

Target detection and re-identification technology in cross-domain multi-dimensional environments aims to detect and re-identify pedestrians and vehicles for videos captured by UAVs and surveillance cameras in multiple dimensions. Pedestrians and vehicles captured by UAVs have lower resolution and more partial view, and can utilize all the target's feature information; pedestrians and vehicles captured by surveillance cameras have higher resolution and can utilize the target's license plate and face information. Firstly, we use an anchor-free target detection algorithm for multi-scale weak features to locate the area where pedestrians and vehicles are located, next, we use the re-identification model to transform the input image area into feature vectors and use the distance of feature vectors to measure the similarity of appearance of different targets. Through multi-task learning and information fusion-based target re-identification and localization, multiple sensors are fused using Bayesian networks to finally complete the accurate representation of target re-identification and localization information.

关键词： Location awareness Pedestrians Surveillance Signal processing algorithms Object detection sensor fusion Feature extraction Multitasking Cameras Vectors

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Distributed data-driven engine framework facing CPS brain-inspired intelligent transportation 23

Distributed data-driven engine framework facing CPS brain-in...

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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

作者： Yang, Zhengzhuang Xu, Mingyue Zhao, Kaidi Yang, Chao Zhi, Huiyu Zhang, Xihou Chen, Zhuoran Qiu, Song Han, Dingding School of Information Science and Technology Fudan University Shanghai China School of Communication and Electronic Engineering East China Normal University Shanghai China

ISBN: (纸本)9781665494571

Single data source, isolated data islands and low information utilization in intelligent transportation lead to the poor inversion of the cyber space to the physical space including Incomplete data integration, high interdependency of systems and poor anti-control ability. In this paper the data-driven method is adopted to optimize the W parameter in Webster method to reduce the waiting time of vehicles to realize the traffic congestion. In particular, a multi-source heterogeneous data processing method and a unified modeling of the multi-dimensional and multi-layer networks have been introduced. By data-driven engine, we have implemented both massive storage and fast processing system. We have also build a service system which can rapidly realize data integration and services delivery as well as be directly called by researchers from the database through network requests to reduce the data reprocessing. This paper also produces the data interaction process of the data engine framework in traffic management and control, and proposes a solution for the use of massive city data. © 2021 IEEE.

关键词： Smart cities Soft sensors Transportation Data integration Process control Distributed databases Data models

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Reconfigurable intelligent Surface-Assisted Wireless Powered Sensing and Communication networks

Reconfigurable Intelligent Surface-Assisted Wireless Powered...

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Ubiquitous Communication (Ucom), International conference on

作者： Zhengyu Zhu Kaixuan Guo Zheng Chu De Mi Yuanwei Liu Sami Muhaidat School of Electrical and Information Engineering Zhengzhou University Zhengzhou China Department of Electrical and Electronic Engineering University of Nottingham Ningbo China Ningbo China School of Computing and Digital Technology Birmingham City University Birmingham U.K. School of Electronic Engineering and Computer Science Queen Mary University of London London U.K. Department of Electrical Engineering and Computer Science Khalifa University Abu Dhabi UAE

ISBN: (数字)9798350374216

ISBN: (纸本)9798350374223

A novel idea of wireless powered sensing and communication networks (WPSCN) is proposed. Specifically, a multi-antenna transmitter utilizes a radar signal for sensing targets while enabling multiple Internet of Things (IoT) devices to har-vest energy from the signal, each of which employs the collected energy to upload information to an access point (AP). We deploy a reconfigurable intelligent surface (RIS) to assist sensing and improve wireless energy transfer (WET) and wireless information transfer (WIT). This paper aims to maximize the weighted sum of the communication throughput and the beampattern gain by jointly designing the transmitter beamforming, transmission time scheduling and RIS phase shifts. We initially derive a closed-form expression of the optimal RIS phase shifts in the WIT phase. Then, an alternating optimization (AO) algorithm is proposed to solve the tradeoff problem. Additionally, we introduce a low-complexity AO algorithm that derives the optimal solutions for transmitter beamforming, transmission time scheduling, and sensing/WET phase shift. Simulation results demonstrate the effectiveness of the proposed algorithms.

关键词： Wireless communication Wireless sensor networks Transmitters Array signal processing Simulation Reconfigurable intelligent surfaces Throughput sensors Communication networks Optimization

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Air Condition Management for Smart Living Area networks

Air Condition Management for Smart Living Area Networks

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Communication, Computing and Energy Efficient Technologies (I3CEET), International conference on

作者： Krishna Kant Dixit Prakash Garia Choriev Myasar Mundher Adnan Mahima Sharma Vinay Kandpal Department of Electrical Engineering GLA University School of Management Graphic Era Hill University Uttarakhand India Department of Physics and Chemistry Tashkent Institute of Irrigation and Agricultural Mechanization Engineers" National Research University Tashkent Uzbekistan Western Caspian University Scientific Researcher Baku Azerbaijan Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Computers Techniques Engineering College of Technical Engineering The Islamic University of Al Diwaniyah Al Diwaniyah Iraq Department of Computer Application Chandigarh Engineering College Chandigarh Group of Colleges Mohali Punjab India Department of Management Studies Graphic Era Deemed to be University Dehradun India

ISBN: (数字)9798331541583

ISBN: (纸本)9798331541590

With the development of affordable sensors, real-time geospatial assessment of the quality of indoor air is now possible. It might be difficult to choose among the many low-cost sensors (LCSs) that are available. Our goal is to combine the knowledge from several study domains to convert every sensor into a whole sensing network, therefore establishing IAQ as a crucial element of smart homes. Modern the field of home automation for networked indoor air quality LCSs-based IAQ control and monitoring is the main topic of this study. To transform conventional homes into smart homes, critical procedures such developing models for forecasting, processing information, sensor selection, and distribution methods are essential. We have delineated the advantages and constraints of air pollution LCSs for spatial mapping of IAQ by means of an extensive synthesis of data. Prior to deployment, it is advised to assess the effectiveness of LCSs in a controlled laboratory environment for quality assurance and control. On the other hand, a sensor network requires constant calibration or the use of statistical methods while it is in operation, particularly for long-term monitoring. The position and penetration height of occupants inside their dwellings should be taken into consideration while adjusting the deployment height of cameras for spatiotemporal mapping. Appropriate processing information technologies are required to manage vast volumes of multivariate data in order to automate both pre- and post-processing operations and provide more scalable, dependable, and adaptable solutions. The paper also emphasizes how machine learning methods may be used to forecast spatiotemporal IAQ in LCS systems with networks.

关键词： Atmospheric modeling Smart homes Machine learning Transforms Predictive models Spatiotemporal phenomena Optical sensors intelligent sensors Monitoring Indoor air quality

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External Interaction Estimation of 6-PSS Parallel Robots with Embodied Mechanical Intelligence

External Interaction Estimation of 6-PSS Parallel Robots wit...

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IEEE/RSJ International conference on intelligent Robots and Systems (IROS)

作者： Jingyuan Xia Zecai Lin Xiaojie Ai Guangjun Yu Anzhu Gao Department of Automation Institute of Medical Robotics Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education Shanghai P. R. China Second Affiliated Hospital The Chinese University of Hong Kong Shenzhen P.R. China

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Traditional interaction perception of parallel robots relies on a six-dimensional force sensor for contact sensing at their distal end. However, the sensor body occupies the space of moving platform and also increases the load on the robot actuations. To enable both minimization and embodied intelligence, this paper proposes an external interaction estimation method with embodied mechanical intelligence by embedding two single-axis force sensors in each leg of 6-PSS parallel robot. The method uses a backward propagation neural network optimized by sparrow search algorithm, and it can simultaneously estimate the external force and its position using information from multiple single-axis force sensors and the encoder of driving motor. The experimental platform is established to collect the data and train the network. The result shows that the force estimation mean error is 2.4% and the position estimation error is 2.9%. A demonstration with a virtual display interface showing the reconstructed parallel robot pose, and the interaction force and its pose using the proposed estimation method, indicates the effectiveness of the proposed interaction method with embodied mechanical intelligence for 6-PSS parallel robot

关键词： Robot motion Legged locomotion Parallel robots Force Neural networks Estimation Robot sensing systems Minimization Force sensors Payloads

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