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16th International conference on Signal Processing Systems, ICSPS 2024

作者： Sun, Kangsheng Jin, Biao Cai, Yan Li, Rongmin Jiangsu University of Science and Technology Ocean College Zhenjiang China Suzhou Zadar Vision Technology Co. Ltd. Suzhou China

ISBN: (纸本)9781510689251

Millimeter-wave radar, with its excellent environmental adaptability, high resolution, strong penetration capability, and privacy protection, is widely used in smart homes, elderly care, and security monitoring. However, due to the high sparsity of millimeter-wave point cloud data, it carries less information compared to video or LiDAR, posing significant challenges for precise and rapid human action recognition. This paper proposes a lightweight long-sequence spatiotemporal edge convolution Transformer network (PETer). The model first enhances the data, then separates the spatial and temporal information of the point cloud sequence, utilizes EdgeConv to capture the spatial geometric structure information of single-frame point clouds, and employs a Transformer to extract temporal features from the multi-frame spatial information. To validate the effectiveness of the model, this paper uses the IWR1443-ISK millimeter-wave radar as the detection sensor to capture human actions and generate a 3D point cloud dataset. This dataset covers 12 types of human actions from 7 volunteers, including walking, waving, boxing, and standing. The proposed model achieves an average accuracy of 98.50% on the dataset and 100% accuracy on the MARS human skeleton dataset. Compared to various point cloud networks, the PETer network is the smallest in size, fastest in speed, and highest in accuracy, demonstrating its superior performance in millimeter-wave radar human action recognition tasks. © 2025 SPIE.

关键词： Musculoskeletal system

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Human Activity Recognition Based on radar and Video Surveillance sensor Fusion

Human Activity Recognition Based on Radar and Video Surveill...

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2023 IEEE Ural-Siberian conference on Biomedical Engineering, Radioelectronics and Information technology, USBEREIT 2023

作者： Lobanova, Vera Bezdetnyy, Dmitry Anishchenko, Lesya Bmstu Remote Sensing Laboratory Moscow Russia Bmstu Biomedical Engineering Department Moscow Russia

ISBN: (纸本)9798350336054

Activity monitoring is an actual problem in the prevention of life-threatening conditions. This question is especially important for people over 65 who fall at least once a year. Unstable gait is one of the predictors of falls and the future progression of cognitive diseases. Hence, abnormal gait detection can potentially be a useful tool for the prevention of falls and the diagnosis of cognitive impairment. In this paper, a sensor fusion approach for human activity recognition is presented. radar and video surveillance data of eight volunteers (19-37 years old) were used for training the deep learning model for the binary classification problem (normal/unstable gait). radar data were processed using a wavelet transform, and a pre-trained AlexNet neural network was fitted using a training dataset. Information about skeletal key points was extracted from video frames by means of a BlasePose neural network and used for training the recurrent neural network with long-short-term memory units. Then, a sensor fusion approach at the classification level was applied. The accuracy and Cohen's kappa of the radar-only model were 0.931 and 0.859, respectively. The accuracy and Cohen's kappa of the video-only model were 0.966 and 0.930, respectively. Finally, the accuracy and Cohen's kappa for the sensor fusion-based model were 0.931 and 0.859, respectively. The results of our work can be potentially used for the design of the system for human activity recognition in the smart home technology paradigm. © 2023 IEEE.

关键词： Accident prevention

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Radiometric Resolution Measurement for TerraSAR-X sensor by Realizing Man-Made Homogeneous Regions 2

Radiometric Resolution Measurement for TerraSAR-X Sensor by ...

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2nd IEEE International conference on Signal, Information and data Processing, ICSIDP 2024

作者： Lin, Xin Chen, Quan Mao, JingJun Gao, Junying Ai, Shaojie He, Jing Wang, Kaizhi Shanghai Institute of Satellite Engineering Shanghai China Tianjin University School of Future Technology Tianjin China Shanghai Jiao Tong University Department of Electronic Engineering Shanghai China

ISBN: (纸本)9798331515669

A homogeneous region exhibiting a stable and high backscattering coefficient is crucial for the radiometric calibration and performance evaluation of synthetic aperture radar (SAR) systems. However, creating such a region in practice presents significant challenges. This study presents a new method for creating a uniform region with consistent backscattering, defined by a known backscattering coefficient, using specially designed metal grids. This method is applied to evaluate the radiometric resolution of the TerraSAR-X *** proposed man-made grid-structured target, referred to as the metal grid, is constructed from aluminum. The backscattering coefficients of this grid were measured across various incident angles and frequencies in a microwave anechoic chamber. radar cross-section (RCS) measurements confirm the target's strong backscattering properties. Using this designed target, homogeneous regions with high backscattering were created at Shanghai Jiao Tong University, China. Two spaceborne experiments conducted by the TerraSAR-X satellite over the test area were carried out to assess the radiometric resolution of the SAR sensor. The experimental results demonstrate that the radiometric resolution values derived from the TerraSAR-X images closely align with the reference values provided in the product specifications. Additionally, the developed strong backscattering homogeneous region proves to be highly effective and well-suited for SAR radiometric calibration and other related applications. © 2024 IEEE.

关键词： radar cross section

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CLOi-Mapper: Consistent, Lightweight, Robust, and Incremental Mapper With Embedded Systems for Commercial Robot Services

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IEEE ROBOTICS AND AUTOMATION LETTERS 2024年第9期9卷 7541-7548页

作者： Noh, Dongki Lim, Hyungtae Eoh, Gyuho Choi, Duckyu Choi, Jeong-Sik Lim, Hyunjun Baek, Seungmin Myung, Hyun KAIST Korea Adv Inst Sci & Technol Sch Elect Engn Daejeon 34141 South Korea LG Elect CTO Div Adv Robot Lab Seoul 06772 South Korea Tech Univ Korea Dept Mechatron Engn Siheung Si 15073 Gyeonggi Do South Korea

In commercial autonomous service robots with several form factors, simultaneous localization and mapping (SLAM) is an essential technology for providing proper services such as cleaning and guidance. Such robots require SLAM algorithms suitable for specific applications and environments. Hence, several SLAM frameworks have been proposed to address various requirements in the past decade. However, we have encountered challenges in implementing recent innovative frameworks when handling service robots with low-end processors and insufficient sensor data, such as low-resolution 2D LiDAR sensors. Specifically, regarding commercial robots, consistent performance in different hardware configurations and environments is more crucial than the performance dedicated to specific sensors or environments. Therefore, we propose a) a multi-stage approach for global pose estimation in embedded systems;b) a graph generation method with zero constraints for synchronized sensors;and c) a robust and memory-efficient method for long-term pose-graph optimization. As verified in in-home and large-scale indoor environments, the proposed method yields consistent global pose estimation for services in commercial fields. Furthermore, the proposed method exhibits potential commercial viability considering the consistent performance verified via mass production and long-term ($>$5 years) operation.

关键词： sensors Robots Simultaneous localization and mapping Laser radar Service robots visualization Odometry Commercial robots embedded systems global-pose estimation mapping SLAM framework

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Smart Automated Irrigation through IoT technology 5

Smart Automated Irrigation through IoT Technology

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5th International conference on Smart Electronics and Communication, ICOSEC 2024

作者： Hema, R. Subbulakshmi, S. Veera Jyothi, B. Gurulakshmi, R. Vignesh, M. Blessy Queen Mary, M. Bharath Institute of Higher Education & Research Department of Electronics and communication Engineering Chennai India Chaitanya Bharathi Institute of Technology Hyderabad India Erode Sengunthar Engineering College Department of Electronics and communication Engineering Perundurai638057 India Karpagam Institute of Technology Department of Artificial Intelligence and Data Science Coimbatore641105 India Government College of Technology Department of Information Technology Coimbatore India

ISBN: (纸本)9798331504403

This work presents the development and implementation of an IoT-based automatic irrigation system designed to optimize water usage in agricultural settings. The system integrates a Wireless sensor Network (WSN) with the Blynk platform to enable real-time monitoring and control of irrigation parameters, including soil moisture, water level, and ambient light. Using an Arduino UNO microcontroller and an ESP-8266 Wi-Fi module, the system collects and processes sensor data to automate irrigation based on predefined thresholds. The Blynk platform facilitates remote data visualization and system control through customizable dashboards, allowing for both automatic and manual adjustments. The proposed system demonstrates a cost-effective solution for improving irrigation efficiency and reducing water waste, with implications for sustainable agricultural practices. © 2024 IEEE.

关键词： Soil moisture

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Research on environmental sensor data analysis based on real-time data warehouse

Research on environmental sensor data analysis based on real...

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2024 International conference on Cloud Computing, Performance Computing, and Deep Learning, CCPCDL 2024

作者： Lv, Taizhi Zou, Yujuan Wang, Yaowei Wu, Enze College of Information Engineering Jiangsu Maritime Institute Nanjing211170 China Nanjing Haizhirui Information Technology Co. Ltd. Nanjing210001 China

ISBN: (纸本)9781510683044

Environmental sensors play a crucial role in modern society. They provide strong support for environmental protection, resource management, and sustainable development by monitoring and perceiving various parameters in real-time. With the development of big data and artificial intelligence technologies, intelligent management of environmental data has become possible. To enhance the level of intelligence in environmental monitoring, this paper studies the performance of environmental sensor data operations in data warehouses such as Hive, ClickHouse, and Doris, and constructs an environmental sensor data analysis system based on a real-time data warehouse. The system employs Flink for real-time collection of environmental data, ClickHouse for data storage, Spark for data analysis, and front-end and back-end technologies for data visualization. The system achieves automatic collection, processing, and analysis of environmental data, thereby improving the efficiency and accuracy of environmental monitoring. © 2024 SPIE.

关键词： Environmental monitoring

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radar-Image Fusion Transformer for Object Detection with Uncertainty Estimation 26

Radar-Image Fusion Transformer for Object Detection with Unc...

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IEEE 26th International conference on Intelligent Transportation Systems (ITSC)

作者： Zhao, Yang Wang, Xiao Cheng, Hong Univ Elect Sci & Technol China Sch Automat Engn Chengdu Sichuan Peoples R China

ISBN: (纸本)9798350399462

Multi-sensor fusion is currently the main way of autonomous driving perception. Among them, radar has been paid more and more attention by researchers due to its low cost and strong anti- interference. The fusion perception method of radar and image has gradually become a hot research field. Nevertheless, the high-noise data of radar brings the challenge of uncertain perception results. In order to solve these problems, this paper proposes a Transformer-based 2D object detection algorithm with the fusion of radar and images, which can effectively detect objects. And for the problem of uncertainty in perception results, a method for modeling regression and classification uncertainty is proposed. We validate the proposed method on the NuScenes dataset. Experimental results show that our method achieves higher detection performance compared to the baseline model and can estimate the uncertainty of the proposed algorithm in real traffic scenarios.

关键词： Image fusion

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LoRa Enhanced Real-Time Earthquake Monitoring System for Constructions Using IoT and Cloud 7

LoRa Enhanced Real-Time Earthquake Monitoring System for Con...

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7th International conference on Electronics, Communication and Aerospace technology, ICECA 2023

作者： Teresa, V.V. Thamaraimanalan, T. Jeevanantham, S. Balaji, A. Gokulanand, H. Jeevanprasath, N. Sri Eshwar College of Engineering Department of ECE Coimbatore India

ISBN: (纸本)9798350340600

Robust earthquake monitoring systems are essential in seismically active areas such as construction sites to ensures the construction site safety, protect people, property and ongoing operations. Traditional techniques for earthquake monitoring are costly and it cannot be able to deliver real-time data sometimes. This proposed system provides a solution to overcome those problems. Here, LoRa is used for wide rage data transmission of earthquake data, IoT used to enhance the sensor integration. This system is designed to provide efficient, cost-effective, and real-time earthquake monitoring of seismic activities on construction sites. Details about the seismic events like ground vibrations and structural movements are obtained with the help of seismic sensors like Accelerometer, Gyroscope, Tilt sensor, Vibration sensor, Piezoelectric sensor, etc. In order to prioritize the people and construction site safety, an effective monitoring and communication systems can be implemented by integrating new technologies like LoRaWAN, IoT, Cloud based data analysis and visualization, real-time, cost-effective monitoring. With this capability, it can able to implement immediate, proactive security measures for both employees and infrastructure. This system interfaces with a cloud-based platform to simplify data management, analysis and visualization. Additionally, the platform offers straightforward dashboards and reporting capabilities. data-driven decision- making and improved risk management are possible by harnessing the power of data analytics, which construction stakeholders can use to gain useful insights into the behavior of structures and the condition of the ground. It provides a practical and affordable method for the collection, transmission and analysis of real-time data, thereby increasing safety and reducing the impact of seismic events on building projects. © 2023 IEEE.

关键词： data visualization

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Resilient Sparse Array radar with the Aid of Deep Learning 97

Resilient Sparse Array Radar with the Aid of Deep Learning

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97th IEEE Vehicular technology conference (VTC-Spring)

作者： Ahmed, Aya Mostafa Thanthrige, Udaya S. K. P. Miriya Sezgin, Aydin Gini, Fulvio Ruhr Univ Bochum Bochum Germany Univ Moratuwa Moratuwa Sri Lanka Univ Pisa Pisa Italy

ISBN: (纸本)9798350311143

In this paper, we address the problem of direction of arrival (DOA) estimation for multiple targets in the presence of sensor failures in a sparse array. Generally, sparse arrays are known with very high-resolution capabilities, where N physical sensors can resolve up to O(N-2) uncorrelated sources. However, among the many configurations introduced in the literature, the arrays that provide the largest hole-free co-array are the most susceptible to sensor failures. We propose here two machine learning (ML) methods to mitigate the effect of sensor failures and maintain the DOA estimation performance and resolution. The first method enhances the conventional spatial smoothing using deep neural network (DNN), while the second one is an end-to-end data-driven method. Numerical results show that both approaches can significantly improve the performance of MRA with two failed sensors. The data-driven method can maintain the performance of the array with no failures at high signal-tonoise ratio (SNR). Moreover, both approaches can even perform better than the original array at low SNR thanks to the denoising effect of the proposed DNN.

关键词： DNN Resilience sensor failures Sparse arrays sensing DOA

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SolarClub: Supporting Renewable Energy Communities through an Interactive Coordination System 24

SolarClub: Supporting Renewable Energy Communities through a...

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ACM CHI conference on Human Factors in Computing Sytems (CHI)

作者： Panagiotidou, Georgia Costanza, Enrico Potapov, Kyrill Nkatha, Sonia Fell, Michael J. Samanani, Farhan Knox, Hannah Kings Coll London London England UCL London England

ISBN: (纸本)9798400703300

Energy communities are a key focus for governments around the world in support of more sustainable energy practices. However, interactive systems for supporting energy communities to coordinate around renewable energy resources are still lacking. We present SolarClub, a demand-shifting visualization system that supported households in coordinating their energy usage by booking energy-hungry activities when solar energy was available. We deployed SolarClub with four groups of neighbors (N=15) for a month. SolarClub successfully enabled neighbors to coordinate, even when some of those participating households were less flexible. While participants reported that SolarClub did not foster a feeling of community, it helped them empathize with their neighbors. Our findings demonstrate the potential of sensor- and visualization-based technology to help understand the relation between everyday practices and resources consumption, beyond individual eco-feedback. This work thus contributes to the development of a next generation of practices and technologies that support collective action for environmental sustainability.

关键词： energy communities coordination data visualization demand-shifting sensors

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