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Deep Learning Approaches for Recognizing Daily Human Activities Using Smart Home sensors 8

Deep Learning Approaches for Recognizing Daily Human Activit...

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Joint 8th International conference on Digital Arts, Media and Technology and 6th ECTI Northern Section conference on Electrical, Electronics, Computer and Telecommunications Engineering, ECTI DAMT and NCON 2023

作者： Sanpote, Worrakit Jantawong, Ponnipa Hnoohom, Narit Jitpattanakul, Anuchit Mekruksavanich, Sakorn School of Information and Communication Technology Department of Computer Science University of Phayao Phayao Thailand School of Information and Communication Technology Department of Computer Engineering University of Phayao Phayao Thailand Mahidol University Image Information and Intelligence Laboratory Faculty of Engineering Department of Computer Engineering Nakorn Pathom Thailand King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science Bangkok Thailand

ISBN: (纸本)9798350396546

Nowadays, one of the most important objectives in health-related research is the improvement of the living condition and well-being of people. Smart home systems can provide health protection for residents based on the results of daily activity recognition. Recent advances and developments in sensor technology have increased the need for sensor-compatible goods and services in smart homes. Consequently, the ever-increasing volume of data requires the field of deep learning (DL) for auto-matic human motion recognition. Recent research has modeled spatiotemporal sequences gathered by smart home sensors using long short-term memory networks. In this work, ResNeXt-based models that learn to classify human activities in smart homes were proposed to improve recognition performance. Experiments conducted on Center for Advanced Studies in Adaptive Systems (CASAS) data, a publicly available benchmark dataset, shows that the proposed ResNeXt-based techniques are significantly superior to the existing DL methods and provide better results compared to the existing literature. The ResNeXt model achieved the averaged accuracy over the benchmark method to 84.81%, 93.57%, and 90.38% for the CASAS-Cairo, CASAS-Milan and CASAS-Kyoto3 datasets, respectively. © 2023 IEEE.

关键词： Deep learning

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Uncertainty processing by Tensor Algebra Means in Condition of Movement Along Complex Roads

Uncertainty Processing by Tensor Algebra Means in Condition ...

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2022 Systems of Signals Generating and processing in the Field of on Board Communications, SOSG 2022

作者： Volosova, A.V. Maksimychev, O.I. Ostroukh, A.V. Ismoilov, M.I. Saakyan, I.E. Moscow Russia

ISBN: (纸本)9781665406352

The article is devoted to research related to the processing of uncertainty by means of tensor algebra in the conditions of the road traffic control based on wireless sensor networks determine complex controlling road infrastructure. The organization of traffic on difficult roads is one of the key challenges of the transportation industry. The solution of this problem occurs under conditions of uncertainty, which may appear at different levels of the traffic control process. The article offers a classification of uncertainty in a complex dynamic system. By the example of organizing the interaction of smart controllers in an electronic coupling, the author shows the results of applying the methods of tensor network analysis to obtain the computational base of electronic coupling network interaction of intelligent (smart) vehicle controllers. Examples of processing uncertainty of different types using the apparatus of fuzzy sets and tensor basis are considered. Tensor equations provide efficient processing of big data, obtaining information in-real-time mode, the stability of 'intelligent electronic hitch' system to changes in the topology of the connection of controllers and changes in soft- and hard- components of the connection. The use of tensors in the computational basis of the intelligent electronic hitch makes it possible successful implementation of embedded AI at the level of an intelligent (smart) controller. The results obtained in the article show that the computational basis on the example of the electronic hitch algorithm can be used in smart controller systems of any level of complexity based on wireless sensor networks. © 2022 IEEE.

关键词： Controllers

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Industrial Application Use Cases of LiDAR sensors Beyond Autonomous Driving 4th

Industrial Application Use Cases of LiDAR Sensors Beyond Aut...

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4th International conference on Innovative intelligent Industrial Production and Logistics, IN4PL 2023

作者： Poenicke, Olaf Groneberg, Maik Sopauschke, Daniel Richter, Klaus Treuheit, Nils Fraunhofer Institute IFF Sandtorstr. 22 Magdeburg39106 Germany

ISBN: (纸本)9783031493386

This paper is giving an overview on different industrial application fields for LiDAR sensors beyond the field of autonomous driving. With insights to three specific use cases, different approaches to process the LiDAR point cloud data are described and referring results and findings of the developed applications are summarized. The application fields of the described use cases are the surveillance of industrial process environments of automated fenceless cells, the provision of visual assistance and location information for crane operators and the monitoring of the storage space occupancy in a port terminal. Based on the information from the three use cases and further general LiDAR related background an initial morphological box is drafted to enable the classification of industrial LiDAR use cases. The paper concludes with a brief overview on future work. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Data processing Industrial application LiDAR sensor Point cloud data Transferability

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Consensus-based distributed streaming coupled tensor factorization

Consensus-based distributed streaming coupled tensor factori...

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International conference on information Fusion

作者： Liangliang Li Lin Gao Luigi Chisci Ping Wei Huaguo Zhang Alfonso Farina School of Information and Communication Engineering University of Electronic Science and Technology of China Dipartimento di Ingegneria dell’Informazione (DINFO) Università degli Studi di Firenze Selex ES (retired)

ISBN: (数字)9781737749769

ISBN: (纸本)9798350371420

This paper discusses the problem of streaming coupled tensor factorization based on sensor networks, where each sensor observes only some features of the targets, and the measurements from sensors are provided in a streaming tensor fashion. Moreover, the observed features of different sensors might overlap (i.e., coupled tensor), and there is no central processing unit to collect all sensor data. Then, in our work, the canonical polyadic (CP) decomposition is exploited to perform local tensor decomposition based on the measurements of each sensor, and average consensus (AC) for diffusing information throughout the network. The proposed method is verified via simulations.

关键词： Tensors Heuristic algorithms Simulation Loading Central processing Unit Load modeling

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Poster Abstract: UarLogger: Logging Measurements from UWB and AR sensors on iOS Devices

Poster Abstract: UarLogger: Logging Measurements from UWB an...

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International Symposium on information processing in sensor networks (IPSN)

作者： Yuyang Zhang Xu Weng KV Ling Nanyang Technological University Singapore

ISBN: (数字)9798350362015

ISBN: (纸本)9798350362022

The multi-user Augmented Reality (AR) is powered by shared mapping and localization obtained from Visual Inertial Odometry (VIO) using AR sensors, including cameras and Inertial Measurement Units (IMU). However, VIO is vulnerable to sparse environment features, low lighting conditions, and dynamic motions. The Ultra-Wideband (UWB) transceiver, as a radio-sensing modality robust to visual and dynamic defects, has been considered as a formfitting patch on VIO to flatter multi-user AR. Nevertheless, like other wireless sensors, UWB suffers from noise and interference. Therefore, how to fuse UWB and VIO for multi-user AR is a promising but challenging research direction. To facilitate this process, we designed and released a tool, UarLogger, to log the relative location measurements from UWB and AR sensors mounted on iOS devices, as well as context-related data. We provide two examples–environmental condition evaluation and sensor fusion–to demonstrate its usefulness and showcase how it can boost the development of new algorithms with daily devices in hand.

关键词： Wireless communication Visualization Wireless sensor networks Measurement units Dynamics Noise Transceivers

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Dynamic Graph Learning with Long and Short-Term for Multivariate Time Series Anomaly Detection 12

Dynamic Graph Learning with Long and Short-Term for Multivar...

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12th IEEE International conference on intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, IDAACS 2023

作者： Tian, Yuyin Gao, Rong Yan, Lingyu Liu, Donghua Ye, Zhiwei School of Computer Science Hubei University of Technology Wuhan430068 China Nanjing University State Key Laboratory for Novel Software Technology Nanjing210023 China China Waterborne Transport Research Institue Information Center Beijing100080 China

ISBN: (纸本)9798350358056

Existing multivariate time series anomaly detection methods utilize graph neural networks to model inter-sensor dependencies and achieve satisfactory results. However, these methods often employ a predefined or self-learning static adjacency matrix for graph information aggregation, disregarding the dynamic characteristics of inter-sensor dependencies in the real world, which change over time and are observable at different time scales as dynamic changing graph structure information. Furthermore, existing techniques struggle to capture long-term temporal contextual relationships. To address these issues, we propose a Dynamic Graph Learning with Long and Short-term time model (DGL-LS) for multivariate time series anomaly detection. The network integrates a dynamic graph structure learning method among sensors in multi-time mode and long- and short-term temporal context information to enhance the prediction of multivariate time series anomalies. We propose a strategy for learning dynamic graphs that aggregates sensor node representations at various time intervals, and then uses an attention-based gating unit to evolve the node dynamic characterization and construct a series of adjacency matrices that describe the dynamic changes in graph structure over time. Moreover, we formulate a long-short time convolution to capture the long-term and short-term temporal dependencies within the time series. We demonstrate the superiority of our approach over other state-of-the-art methods by conducting extensive experiments on three publicly available datasets. © 2023 IEEE.

关键词： Anomaly Detection Graph Learning Multivariate Time Series Time Convolution

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ST-HCSS: Deep Spatio-Temporal Hypergraph Convolutional Neural Network for Soft Sensing

ST-HCSS: Deep Spatio-Temporal Hypergraph Convolutional Neura...

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International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Hwa Hui Tew Fan Ding Gaoxuan Li Junn Yong Loo Chee-Ming Ting Ze Yang Ding Chee Pin Tan School of Information Technology Monash University Malaysia School of Engineering Monash University Malaysia

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Higher-order sensor networks are more accurate in characterizing the nonlinear dynamics of sensory time-series data in modern industrial settings by allowing multi-node connections beyond simple pairwise graph edges. In light of this, we propose a deep spatio-temporal hypergraph convolutional neural network for soft sensing (ST-HCSS). In particular, our proposed framework is able to construct and leverage a higher-order graph (hypergraph) to model the complex multi-interactions between sensor nodes in the absence of prior structural knowledge. To capture rich spatio-temporal relationships underlying sensor data, our proposed ST-HCSS incorporates stacked gated temporal and hypergraph convolution layers to effectively aggregate and update hypergraph information across time and nodes. Our results validate the superiority of ST-HCSS compared to existing state-of-the-art soft sensors, and demonstrates that the learned hypergraph feature representations aligns well with the sensor data correlations. The code is available at https://***/htew0001/***

关键词： Correlation Codes Convolution Soft sensors Aggregates Logic gates Feature extraction Nonlinear dynamical systems Convolutional neural networks Speech processing

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Multi-objective CH selection with Scheduling based energy efficient secure transmission in WSN using Siamese graph convolutional Mantis Search attention network

Multi-objective CH selection with Scheduling based energy ef...

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2025 International conference on Medical Imaging, Electronic Imaging, information Technologies, and sensors, MIEITS 2025

作者： Kumar, Ananth M.S. Dept. of ECE C.M.R.Institute of Technology Bengaluru India

ISBN: (纸本)9781510691704

In Wireless sensor networks (WSNs), energy efficiency, secure data transmission, and reduced latency are critical for enhancing network lifetime and reliability. Previously several optimization algorithms were used for optimal cluster head selection, but they do not provide sufficient results and still there are challenges in secure data transmission. To overcome these challenges this work is motivated. In this manuscript, a Multi-objective CH selection with Scheduling based energy efficient secure transmission in WSN using Siamese graph convolutional Mantis Search attention network (EST-SGCMSAN) is proposed. Initially, the cluster head is selected using the novel multi-objective CH selection strategy using the Red Piranha Alpine Skiing Optimization algorithm (RPASOA), designed to minimize energy usage and reduce transmission delay. Furthermore, an advanced sleep scheduling mechanism with duty cycling is introduced, leveraging a Siamese Graph Convolutional Mantis Search Attention Network (SGCMSAN) for reliable and energy-efficient scheduling. To address the security challenges inherent in sensor nodes, which often face constraints in processing power, storage, and energy, an AES-based signature generation approach utilizing White Box Cryptography (WBC) is implemented. This method ensures secure data transmission while maintaining low computational overhead. The proposed system aims to significantly extend the network lifetime of WSNs through optimized CH selection, efficient scheduling, and robust security measures. The experimental simulations are done with the help of python platform. The results show that the introduced approach performs better than previous approaches in a number of performance measures, Higher throughput 98%, higher packet delivery ratio 0.993%, less energy consumption 0.40mJ. © 2025 SPIE.

关键词： AES-based signature generation Mantis Search Red Piranha Alpine Skiing Optimization algorithm Siamese graph convolutional Attention Network White Box Cryptography (WBC) Wireless sensor networks (WSNs)

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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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Ewe Health Monitoring Using IoT simulator

Ewe Health Monitoring Using IoT simulator

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2022 IEEE International conference on Data Science and information System, ICDSIS 2022

作者： Tanveer Baig, Z. Shastry, Chandrasekar Dept. of Ece Bangalore India

ISBN: (数字)9781665498012

ISBN: (纸本)9781665498012

Government initiatives encouraging technological advances and national start-up organizations and digitalization clearly indicate that it is time to expand the use of Internet of Thing (IoT) for livestock in India. Because many farmers rely on sheep and goat livestock, the entire process of remotely monitoring the health of these domestic animals can be achieved by integrating IoT into existing systems or developing IoT systems. Designing and developing a wireless sensor network (WSN) with an intelligent IoT platform with the Bevywise MQTT simulator. We can monitor physiological parameters of any domestic animal using virtual sensors which will help us to reach our goals. Bevywise IoT MQTT platform runs on publish / subscribe based protocols. The Bevywise Message Queuing Telemetry Transport (MQTT) simulated sensor can be configured to publish messages to specific brokers, including subscribers and brokers, on a regular basis. The broker will send you messages about the health of these domestic animals. © 2022 IEEE.

关键词： Wireless sensor networks

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