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检索条件"任意字段=5th ACM Conference on Embedded Networked Sensor Systems"

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Mapping method of embedded system based on multi-sensor data fusion for unstructured terrain 5

Mapping method of embedded system based on multi-sensor data...

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5th International Academic Exchange conference on Science and Technology Innovation, IAECST 2023

作者： Wang, Yufei Wang, Zhiming Shanghai University School of Mechatronic Engineering and Automation Shanghai China

ISBN: (纸本)9798350357738

Simultaneous localization and mapping, as the basic methods for autonomous motion of mobile robots, have been widely developed and applied in conventional structured environments. But it is more difficult to collect and process data in unstructured environments. the elevation map provides an efficient, simple and powerful geometric representation of terrain for ground robots. Robots can use this information for navigation, path planning and motion control in uncertain environments. However, a single sensor provides less information about the environment. At the same time, for embedded systems with limited computational power, their real-time performance will be affected. In order to solve the above problems, this paper proposes an improved elevation mapping method based on multi-sensor data fusion. More informative elevation maps are obtained using sensor data from multiple sources, while processing is accelerated by GPU. We demonstrate the optimization performance of the proposed method with simulated data sets and real terrain experiments, and compare the reconstructed elevation map with the real ground, proving that the proposed method can be used for autonomous navigation of mobile robots. © 2023 IEEE.

关键词： embedded systems

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Indoor Place Prediction on Smart Phones 20

Indoor Place Prediction on Smart Phones

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20th acm conference on embedded networked sensor systems (SenSys)

作者： Sen, Pritam Jiang, Xiaopeng Wu, Qiong Talasila, Manoop Hsu, Wen-Ling Borcea, Cristian New Jersey Inst Technol Newark NJ 07102 USA AT&T Res Lab Bedminster NJ USA

ISBN: (纸本)9781450398862

High-accuracy and low-latency indoor place prediction for mobile users is crucial to enable applications for assisted living, emergency services, smart homes, and augmented reality. Previous studies on indoor place prediction use complex infrastructure with multiple visual/wireless anchors or multiple wireless access points. these localization techniques are difficult to deploy, may negatively impact user privacy through location tracking, and their data collection is not suitable for personalized place prediction. To solve these challenges, this paper proposes GoPlaces, a novel app that fuses inertial sensor data with WiFi-RTT estimated distances to predict the future indoor places visited by a user. GoPlaces does not require any infrastructure, except for one cheap off-the-shelf WiFi access point that supports ranging with RTT. In addition, it enables personalized place naming and prediction through its on-the-phone data collection and protects users' location privacy because user's data never leaves the phone. GoPlaces uses an attention-based bidirectional long short-term memory model to detect user's current trajectory, which is then used together with historical information stored in a prediction tree to infer user's future places. We implemented GoPlaces in Android and evaluated it in several indoor spaces. the experimental results demonstrate prediction accuracy as high as 92%, low latency, and low resource consumption on the phones.

关键词： Indoor place prediction sensor fusion WiFi-RTT Time series analysis Deep learning Human mobility Smart phones

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Information-Seeking in Localization and Mission Planning of Multi-Agent systems 5

Information-Seeking in Localization and Mission Planning of ...

引用

IEEE 5th International conference on Multimedia Information Processing and Retrieval (MIPR)

作者： Lite, Kyriakos Rinner, Bernhard Univ Klagenfurt Inst Networked & Embedded Syst Klagenfurt Austria Univ Klagenfurt Ubiquitous Sensing Syst Lab Silicon Austria Labs Klagenfurt Austria

ISBN: (数字)9781665495486

ISBN: (纸本)9781665495486

Real-time and accurate position estimation is critical for various multi-robot applications and serves as a prerequisite for location-based multi-sensor data analysis. However, it is often impeded by energy, sensing, and processing limitations. In this work, we study the problem of information-seeking in localization and navigation in multi-agent systems, which aims to navigate mobile agents while reducing position errors. We formalize information-seeking as reducing spatial uncertainty and introduce an efficient motion controller based on artificial potential fields superimposing attractive, repulsive, and information-seeking forces. We evaluate the effect of information-seeking on localization and mission planning in a simulation study with non-collaborative and collaborative localization approaches.

关键词： Location awareness Uncertainty Navigation Multimedia systems Mobile agents Information processing Real-time systems

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CycleGAN based Unsupervised Domain Adaptation for Machine Fault Diagnosis 20

CycleGAN based Unsupervised Domain Adaptation for Machine Fa...

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20th acm conference on embedded networked sensor systems (SenSys)

作者： Pattnaik, Naibedya Vemula, Uday Sai Kumar, Kriti Kumar, A. Anil Majumdar, Angshul Chandra, M. Girish Pal, Arpan TCS Res Embedded Devices & Intelligent Syst Bangalore Karnataka India Indian Inst Technol Madras Tamil Nadu India Indraprastha Inst Informat Technol Delhi India

ISBN: (纸本)9781450398862

Fault diagnosis plays a vital role in ensuring the normal operation of the machine and safe production. In recent years, data-driven techniques have gained a lot of popularity for machine fault diagnosis. But most of these techniques assume the training and test data have the same distribution. However, in most practical application scenarios, domain discrepancy can be observed between the training (source) and test (target) data due to different factors like changes in the operating conditions, different sensor locations, etc. Classical approaches fail to address such domain discrepancy, which leads to poor performance. the problem becomes more challenging when the target is completely unlabeled. To address this scenario, domain adaptation techniques are used to transfer the knowledge learned from the labeled source domain to the unlabeled target domain. Recently, adversarial network based domain adaptation has been extensively explored for fault diagnosis. But the adversarial loss alone does not guarantee the translation of the source to the desired target domain (class consistent). Here, we propose to use cycle-consistency loss employing 1D-CycleGAN for learning the source to target mapping for unsupervised adaptation for bearing fault diagnosis. the proposed method is evaluated for two different scenarios, with the source and target from (i) same machine but different working conditions and (ii) different but related machines. Experimental results show that while the proposed method performs comparable to the best-performing benchmark for the first case, it significantly outperforms all the state-of-the-art methods for the challenging second case.

关键词： Domain adaptation Machine fault diagnosis CycleGAN Unsupervised learning

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CIPhy: Causal Intervention with Physical Confounder from IoT sensor Data for Robust Occupant Information Inference 20

CIPhy: Causal Intervention with Physical Confounder from IoT...

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20th acm conference on embedded networked sensor systems (SenSys)

作者： Hu, Zhizhang Yu, Tong Zhang, Ruiyi Pan, Shijia Univ Calif Merced Merced CA 95343 USA Carnegie Mellon Univ Pittsburgh PA USA Duke Univ Durham NC USA

ISBN: (纸本)9781450398862

Occupant information inference with IoT sensor data enables many smart applications, such as patients'/older adults' in-home monitoring. the difficulty of collecting labeled real-world IoT sensor data often leads to reliability and scalability issues for those systems. Extensive prior works (e.g., domain adaptation) focus on the domain shift issues, i.e., the inconsistent data feature and label relationship, and dataset bias is often neglected. Dataset bias is commonly caused by limited and varied accessibility to labeled data for each class, and it is inevitable for real-world datasets. the model trained with a biased dataset fits into the bias, hence cannot further generalize to the testing data for accurate inference. We propose CIPhy, a causal intervention scheme with physical confounders measured from the sensor data to achieve robust occupant information inference. We model the dataset bias as a confounding problem. there exists a confounder directly impacts both data feature and label, and each class's accessibility to labeled data varies when the confounder's condition changes. the model trained with biased data learns a spurious feature-label correlation conditioned on the confounder's condition in the training data. When testing data has a different condition, i.e., confounding shift, this correlation can not be applied. By using the causal intervention, e.g., backdoor adjustment, the confounding shift's negative impact on the data-driven models can be mitigated. the CIPhy decouples the sensor data to measure the confounder, then conducts the causal intervention for a de-biased occupant information inference. We use a public dataset on occupant identification as a case study, to investigate the feasibility of applying causal intervention to resolve the dataset bias issue. From the experiment, CIPhy achieves up to 11.42% identification accuracy improvement compared to baselines given the biased training data and confounding shift.

关键词： Occupant Information Inference Causal Intervention Dataset Bias

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Noise Level Detection Analysis in Biomedical Signals Based on Capacitive Electrodes for Electric Bicycles 1

引用

5th International conference on Biomedical and Health Informatics (ICBHI)

作者： Stanesic, Antonio Culjak, Ivana Klaic, Luka Sajinovic, Patrik Vrhoci, Ivan Cifrek, Mario Dzapo, Hrvoje Univ Zagreb Fac Elect Engn & Comp Zagreb Croatia

ISBN: (数字)9783031592164

ISBN: (纸本)9783031592157;9783031592164

the rise of cycling as a common form of daily exercise combined with recent advances in wearable systems provides an opportunity for advanced activity monitoring using custom-tailored wireless biomonitoring systems. In this study, a low-power, small-size, lightweight battery-powered wireless bio-medical sensor system using non-contact capacitive electrodes is proposed. Two main and several secondary case studies were evaluated using three test scenarios. the best results were obtained using a specially crafted cycling glove with an embedded body bias graphite electrode.

关键词： Wireless Biomedical sensor System Capacitive Electrode sEMG ECG CST Studio Suite

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Non-acoustic speech sensing system based on flexible piezoelectric 20

Non-acoustic speech sensing system based on flexible piezoel...

引用

20th acm conference on embedded networked sensor systems (SenSys)

作者： Yuan, Shiji Sun, Ying Wang, Shuai Chen, Xinlei Ding, Ying Zheng, Dezhi Fan, Shangchun Beihang Univ Sch Large Aircraft Adv Training Ctr Beijing Peoples R China Beihang Univ Sch Instrumentat & Optoelect Engn Beijing Peoples R China Beihang Univ Sch Comp Sci & Engn Beijing Peoples R China Tsinghua Univ Pengcheng Lab Shenzhen Peoples R China Beihang Univ Res Inst Frontier Sci Beijing Peoples R China

ISBN: (纸本)9781450398862

Speech is one of the most important biological signals to complement human-human and human-computer interaction. Traditional speech datasets were collected by air microphones, but using these datasets in noisy environments such as factories is practically challenging. therefore, speech recognition in noisy environments poses higher requirements. the non-acoustic speech dataset plays a significant role in robust speech recognition under high background noise. Existing datasets suffered from dull sound, low intelligibility and poor recognition accuracy due to hardware and computer technology limitations. this paper presents a non-acoustic speech sensing system based on flexible piezoelectric. the system collected vibration signals from the jaws of six males and five females, and the corpus contained ten different control commands at 90 dB of background noise. the dataset is reliable with high intelligibility and capable of achieving 93.7% recognition accuracy by calculation. With the aforementioned benefits, this dataset is an essential tool for studying human-computer interaction in high-noise environments, analyzing human acoustic properties, and aiding medical rehabilitation.

关键词： speech command data non-acoustic speech flexible piezoelectric sensor

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Multi-Task Learning Based Blind Calibration for Low-Cost Air Quality sensor Deployments 20

Multi-Task Learning Based Blind Calibration for Low-Cost Air...

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20th acm conference on embedded networked sensor systems, SenSys 2022

作者： Li, Guodong Wu, Zhiyuan Liu, Xinyu Wang, Yue Zhang, Lin Tsinghua University China

ISBN: (纸本)9781450398862

Air pollution problem has caught much attention globally. In addition to the national air quality monitoring stations deployed by the government, the number of low-cost air quality sensors increases rapidly as a supplement to support fine-grained monitoring. In-field calibration methods are necessary for these low-cost sensor nodes to assure the data quality. However, it is costly to collect enough reference data after deployment to train the in-field calibration model and many sensors even have no synchronized reference in the real application scenarios. To address the above challenge, we propose a multi-task learning based blind calibraiton method for air quality sensors after deployments. Our method introduces not only the reference data of the target location to formulate calibration task, but also reference measurements collected from highly accurate stations already deployed by the government in other geographical locations to formulate prediction task. To utilize the reference measurements which are not in the same location with our target sensors, e.g., in other cities, we combine the proposed calibration task and prediction task under a multi-task learning scheme. the introduced references in other locations alleviate our few-reference challenge. Furthermore, we elaborate on the choices of different tasks to have better effect of the target calibraiton task. Evaluations on the real-world collected datasets show that our proposed algorithm has better calibraiton effect. © 2022 Owner/Author.

关键词： Air quality

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A Large-Scale Multi-sensor Dataset for Mapping 5

A Large-Scale Multi-Sensor Dataset for Mapping

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5th IEEE International conference on Power, Electronics and Computer Applications, ICPECA 2025

作者： Zou, Yujie Zhang, Hanwen Yan, Zhewen Cheng, Hui Sun Yat-sen University Guangzhou China

ISBN: (纸本)9798331533694

In recent years, advancements and applications of computer vision techniques such as neural radiance fields (NeRF) and Gaussian splatting have set new expectations of mapping and 3D reconstruction tasks. Achieving high-accuracy outdoor mapping using radiance field methods requires the integration of robust localization method and carefully designed multi-sensor perception systems. In this paper, we introduce a multimodal dataset tailored for high-precision mapping of large-scale outdoor scenes. To achieve this, we developed an aerial data acquisition platform equipped with a GPS-synchronized solid-state LiDAR, industrial cameras, and inertial measurement units. After comprehensive intrinsic and extrinsic calibration, we collected 3D reconstruction datasets focusing on building exteriors across scenes within a university campus. the raw data, along with documentation on synchronization, calibration procedures, and platform design, will be progressively released on the project's Github repository: https://***/lilrookie76/radiance-dataset. © 2025 IEEE.

关键词： Mapping

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Capricorn: Towards Real-time Rich Scene Analysis Using RF-Vision sensor Fusion 20

Capricorn: Towards Real-time Rich Scene Analysis Using RF-Vi...

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20th acm conference on embedded networked sensor systems (SenSys)

作者： Wang, Ziqi Sarker, Ankur Wu, Jason Hua, Derek Dong, Gaofeng Singh, Akash Deep Srivastava, Mani Univ Calif Los Angeles Elect & Comp Engn Dept Los Angeles CA 90095 USA

ISBN: (纸本)9781450398862

Video scene analysis is a well-investigated area where researchers have devoted efforts to detect and classify people and objects in the scene. However, real-life scenes are more complex: the intrinsic states of the objects (e.g., machine operating states or human vital signals) are often overlooked by vision-based scene analysis. Recent work has proposed a radio frequency (RF) sensing technique, wireless vibrometry, that employs wireless signals to sense subtle vibrations from the objects and infer their internal states. We envision that the combination of video scene analysis with wireless vibrometry form a more comprehensive understanding of the scene, namely "rich scene analysis". However, the RF sensors used in wireless vibrometry only provide time series, and it is challenging to associate these time series data with multiple real-world objects. We propose a real-time RF-vision sensor fusion system, Capricorn, that efficiently builds a cross-modal correspondence between visual pixels and RF time series to better understand the complex natures of a scene. the vision sensors in Capricorn model the surrounding environment in 3D and obtain the distances of different objects. In the RF domain, the distance is proportional to the signal time-of-flight (ToF), and we can leverage the ToF to separate the RF time series corresponding to each object. the RF-vision sensor fusion in Capricorn brings multiple benefits. the vision sensors provide environmental contexts to guide the processing of RF data, which helps us select the most appropriate algorithms and models. Meanwhile, the RF sensor yields additional information that is originally invisible to vision sensors, providing insight into objects' intrinsic states. Our extensive evaluations show that Capricorn real-timely monitors multiple appliances' operating status with an accuracy of 97%+ and recovers vital signals like respirations from multiple people. A video (https: //***/b-5nav3Fi78) demonstrates the capabi

关键词： Time series

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