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Design and Implementation of an IoT Enabled Bionic Arm Gesture Control Robot Using embedded System

Design and Implementation of an IoT Enabled Bionic Arm Gestu...

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Data Intelligence and Cognitive Informatics (ICDICI), International conference on

作者： U. Ramani J Rini Angeline Vinshia T. Manjula G. G. Sreeja S. Abirami M. thilagaraj K. Ramakrishnan College of Engineering Trichy India Dr. Mahalingam College of Engineering and Technology Coimbatore India Hindusthan College of Engineering and Technology Coimbatore India Sri Eshwar College of Engineering Coimbatore India Jai Shriram Engineering College Tirupur India MVJ College of Engineering Banglore India

ISBN: (数字)9798350389609

ISBN: (纸本)9798350389616

Today, human-machine interaction is becoming more common, and new technologies are helping bridge the gap between people and machines. Gestures are important for improving this connection. the goal of gesture recognition is to make it easier to communicate with the digital world, similar to how people interact with each other. Various methods and algorithms have been developed to achieve gesture recognition with help of movement tracking sensors. this project aims to create a system that allows people to control robots with gestures, which is especially helpful for those with mobility challenges. there is a strong need for gesture-based interfaces instead of speech recognition, as speech can be less reliable for controlling robots.

关键词： Wireless communication Wireless sensor networks Tracking Gesture recognition Speech recognition Arms Robot sensing systems User experience Reliability Communication system security

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Zero-Wire: A Deterministic and Low-Latency Wireless Bus through Symbol-Synchronous Transmission of Optical Signals 20

Zero-Wire: A Deterministic and Low-Latency Wireless Bus Thro...

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18th conference on embedded networked sensor systems

作者： Oostvogels, Jonathan Yang, Fan Michiels, Sam Hughes, Danny Katholieke Univ Leuven IMEC DistriNet B-3001 Leuven Belgium

ISBN: (纸本)9781450375900

the performance dichotomy between wired and wireless networks for the Internet of things primarily arises from the inherent complexity and inefficiency of networking abstractions such as routing, medium access control and store-and-forward packet switching. this paper aims to enable a new class of latency-sensitive applications by breaking all three of these abstractions to deliver a performance envelope that resembles that of a wired bus in terms of deterministic latency and throughput. the essence of this approach is a novel networking paradigm for optical wireless communication, referred to as a symbol-synchronous bus, wherein a mesh of nodes concurrently transmit LED-based signals. this paper realises the paradigm within a platform called Zero-Wire and evaluates it on a 25-node testbed under laboratory conditions. Key end-to-end performance measurements on this physical prototype include 19 kbps of contention-agnostic goodput, interface-level latency under 1 ms for two-byte frames across four hops, jitter on the order of 10s of mu s, and a base reliability of 99%. these first results indicate a bright future for the under-explored area of optical wireless mesh networks in delivering ubiquitous connectivity through a simple and low-cost physical layer.

关键词： Internet of things wireless sensor networks cyber-physical systems visible light communication optical wireless communication concurrent transmission synchronous transmission cut-through forwarding bus networks

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milliEgo: Single-chip mmWave Radar Aided Egomotion Estimation via Deep sensor Fusion 20

milliEgo: Single-chip mmWave Radar Aided Egomotion Estimatio...

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18th conference on embedded networked sensor systems

作者： Lu, Chris Xiaoxuan Saputra, Muhamad Risqi U. Zhao, Peijun Almalioglu, Yasin de Gusmao, Pedro P. B. Chen, Changhao Sun, Ke Trigoni, Niki Markham, Andrew Univ Edinburgh Edinburgh Midlothian Scotland Univ Oxford Oxford England Univ Calif San Diego San Diego CA 92103 USA

ISBN: (纸本)9781450375900

Robust and accurate trajectory estimation of mobile agents such as people and robots is a key requirement for providing spatial awareness for emerging capabilities such as augmented reality or autonomous interaction. Although currently dominated by optical techniques e.g., visual-inertial odometry, these suffer from challenges with scene illumination or featureless surfaces. As an alternative, we propose milliEgo, a novel deep-learning approach to robust egomotion estimation which exploits the capabilities of low-cost mmWave radar. Although mmWave radar has a fundamental advantage over monocular cameras of being metric i.e., providing absolute scale or depth, current single chip solutions have limited and sparse imaging resolution, making existing point-cloud registration techniques brittle. We propose a new architecture that is optimized for solving this challenging pose transformation problem. Secondly, to robustly fuse mmWave pose estimates with additional sensors, e.g. inertial or visual sensors we introduce a mixed attention approach to deep fusion. through extensive experiments, we demonstrate our proposed system is able to achieve 1.3% 3D error drift and generalizes well to unseen environments. We also show that the neural architecture can be made highly efficient and suitable for real-time embedded applications.

关键词： Egomotion Estimation Indoor localization Millimeter wave radar

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Foot Step Power Generation Using Piezoelectric Materials 5

Foot Step Power Generation Using Piezoelectric Materials

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5th IEEE International conference on Computational systems and Information Technology for Sustainable Solutions, CSITSS 2021

作者： Gadgay, Baswaraj Shubhangi, D.C. Abhishek, H. VTU Center for PG Studies Department of VLSI Design Embedded System Kalaburagi India

ISBN: (纸本)9781665406109

As electrical energy utilization is expanding dramatically. Consequently, the need of a secure and financially reasonable force age and dissemination framework requests a specific interest. this paper proposes a model that utilizes human strolling, hopping and running as a well spring of energy and stores it for fundamental use. Such a model is able in demography that of a nation like India which has a particularly immense person on foot populace. this paper represents a technique for gathering this human headway energy with the utilization of piezoelectric sensor and shows an application with the put away energy for example to charge a cell phone safely and also used to glow an LED based on the motion using the passive infrared sensor. the ground response power (GRF) applied from the foot, when changed over to voltage by piezoelectric sensors is sufficiently fit to control up a device. Progressive effort prompts a periodic voltage develop which with appropriate hardware can be utilized to charge a capacity battery. the force created by this method can likewise be utilized in essential application, for example, road lighting, notice sheets, exercise centers and different spaces of public area. It likewise advances efficient power energy and climate agreeable methodology towards energy age. In this paper we have given the essential idea and configuration subtleties of this model and a fundamental execution of the equivalent. © 2021 IEEE.

关键词： Piezoelectricity

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Safety in Distributed sensor Networks: A Literature Review 5

Safety in Distributed Sensor Networks: A Literature Review

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5th International conference on Internet of things, Big Data and Security (IoTBDS)

作者： Altenburg, Tobias Bosse, Sascha Turowski, Klaus Otto von Guericke Univ Fac Comp Sci Very Large Business Applicat Lab Magdeburg Germany

ISBN: (纸本)9789897584268

the connectivity megatrend dominates the current social change. the number of networked devices and the resulting amount of data is constantly increasing worldwide. For this reason, the dependability of computer systems is becoming increasingly relevant. Especially in the context of civil infrastructures, the constant availability of computer systems is of great importance. this paper provides a structured overview of the current literary status of safety in distributed sensor networks. Most approaches from the literature focus on the design phase. By the following connection with the existing dependability theory, the potential for the optimization of dependability could be proven.

关键词： Dependability sensor Network Internet of things Fault Tolerance Fault Prevention

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An Automated Face Mask Detection Using Ensemble Learning Approach

An Automated Face Mask Detection Using Ensemble Learning App...

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Electronics and Sustainable Communication systems (ICESC), 2020 International conference on

作者： B. Kiran Kumar Guggilam Mounika RVS Harish Valeti Nagarjuna Embedded Systems St. Ann's College of Engineering & Technology Chirala Andhra Pradesh India St. Ann's College of Engineering & Technology Chirala Andhra Pradesh India Dept of CSE Kallam Haranadhareddy Institute of Technology Chowdavaram Andhra Pradesh India

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379952

the COVID-19 pandemic and other infectious diseases are spreading quickly, which has brought attention to how essential face masks are in stopping the spread of respiratory disorders. To ensure compliance with mask-wearing rules, automatic face mask detection devices are becoming increasingly important. the application of Automated Convolutional Neural Networks (ACNN) in conjunction with Internet of things (IoT) devices for an Automated Face Mask Detection is the main emphasis of this research. the suggested method correctly detects people wearing or not wearing face masks in real-time video and picture data by leveraging ACNN, a deep-learning architecture created for image segmentation. A wide range of labeled photos of people wearing and not wearing masks are used to train the FCNN. the model is ideal for real-world applications in public areas, healthcare facilities, and educational institutions due to its high accuracy and efficiency. Real-time monitoring and notifications are made possible by the integration of IoT devices, which improves the operation of the system. In order to collect images and videos data, IoT sensors and cameras are positioned strategically in specific regions. A central processing unit (CPU) that executes the trained FCNN model is connected to these devices. Using email, SMS, or a mobile app, the system instantly notifies the appropriate authorities or persons when it finds someone not donning a face mask.

关键词： COVID-19 Pandemics Streaming media Real-time systems sensor systems sensors Central Processing Unit Safety Internet of things Open source software

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the Research on Sheet Metal Part Recognition Technology Based on Improved Mask R-CNN

The Research on Sheet Metal Part Recognition Technology Base...

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Machine Learning and Computer Application (ICMLCA), International conference on

作者： Mingfei Liu Jun Liu Kecheng Zhou Jian Wang Key Laboratory of Industrial Intelligence Technology on Chemical Process School of Computer Science and Technology Shenyang University of Chemical Technology Shenyang Liaoning Province China Chinese Academy of Sciences Key Laboratory of Networked Control Systems Shenyang China Chinese Academy of Sciences Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China

ISBN: (数字)9798331530334

ISBN: (纸本)9798331530341

the visual recognition of sheet metal parts is a crucial component of industrial automation sorting, particularly under complex lighting and background interference conditions. To enhance the accuracy of sheet metal part recognition, this paper curates a dataset of sheet metal parts acquired by an intelligent light field image sensor and proposes a visual recognition model, PA-CBAM, based on an improved Mask R-CNN. By improving the original feature pyramid FPN and introducing the Convolutional Block Attention Module (CBAM), the model enhances multi-scale feature fusion and the focus on key features: (1) Bottom up paths and shortcut connections have been added between different layers of the traditional FPN feature pyramid, thereby improving multi-scale object detection capability. (2) CBAM further optimizes the model's channel and spatial attention mechanisms, effectively enhancing the capture of edge details and the suppression of background noise. Experimental results show that the proposed PA-CBAM model significantly improves detection and segmentation performance compared to traditional methods, particularly in terms of edge recognition accuracy for sheet metal parts under complex lighting conditions.

关键词： Visualization Accuracy Image edge detection Metals Lighting Object detection Feature extraction Light fields Optimization Sorting

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Battery-less zero-maintenance embedded sensing at the mithræum of circus maximus 20

Battery-less zero-maintenance embedded sensing at the mithr...

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18th acm conference on embedded networked sensor systems, SenSys 2020

作者： Afanasov, Mikhail Bhatti, Naveed Anwar Campagna, Dennis Caslini, Giacomo Centonze, Fabio Massimo Dolui, Koustabh Maioli, Andrea Barone, Erica Alizai, Muhammad Hamad Siddiqui, Junaid Haroon Mottola, Luca Politecnico di Milano Italy RI.SE Sweden Lums Pakistan Air University Pakistan Microsoft Italy Ku Leuven Belgium Credit Suisse Poland

ISBN: (纸本)9781450375900

We present the design and evaluation of a 3.5-year embedded sensing deployment at the Mithræum of Circus Maximus, a UNESCO-protected underground archaeological site in Rome (Italy). Unique to our work is the use of energy harvesting through thermal and kinetic energy sources. the extreme scarcity and erratic availability of energy, however, pose great challenges in system software, embedded hardware, and energy management. We tackle them by testing, for the first time in a multi-year deployment, existing solutions in intermittent computing, low-power hardware, and energy harvesting. through three major design iterations, we find that these solutions operate as isolated silos and lack integration into a complete system, performing suboptimally. In contrast, we demonstrate the efficient performance of a hardware/software co-design featuring accurate energy management and capturing the coupling between energy sources and sensed quantities. Installing a battery-operated system alongside also allows us to perform a comparative study of energy harvesting in a demanding setting. Albeit the latter reduces energy availability and thus lowers the data yield to about 22% of that provided by batteries, our system provides a comparable level of insight into environmental conditions and structural health of the site. Further, unlike existing energy-harvesting deployments that are limited to a few months of operation in the best cases, our system runs with zero maintenance since almost 2 years, including 3 months of site inaccessibility due to a COVID19 lockdown. © 2020 acm.

关键词： Energy harvesting

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Activity recognition through intermittent distributed processing by energy harvesting PIR sensors: Demo abstract 20

Activity recognition through intermittent distributed proces...

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18th acm conference on embedded networked sensor systems, SenSys 2020

作者： Misaki, Shinya Stirapongsasuti, Sopicha Matsui, Tomokazu Suwa, Hirohiko Yasumoto, Keiichi Nara Institute of Science and Technology Ikoma Nara Japan

ISBN: (纸本)9781450375900

As an increasing demand for human activity monitoring in many smart services such as elderly monitoring, there is a keen need of an activities of daily living (ADL) recognition system. which can be easily deployed in ordinary homes and does not require periodic maintenance such as battery replacement for long time. In this paper, we propose an ADL recognition system which can run continuously without feeding power from outlets by intermittent sensing and distributed processing of energy harvesting (EH) sensor modules. Specifically, we have designed and developed an EH sensor node composed of (i) a micro-controller board with an analog PIR sensor which senses human activity as analog signals and form a BLE mesh network with other sensor nodes and (ii) an energy harvest module with solar panels and a rechargeable battery. We have also implemented a simple distributed random forest (RF) classifier consisting of multiple RF classifiers trained independently and running on different nodes which exchange the classification results with each other via BLE and make a final decision based on majority vote. through experiments with five sensor nodes deployed in our smart home testbed, the distributed RF classifier classified the collected data of up to five different activities with average accuracy of over 90%. © 2020 acm.

关键词： sensor nodes

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Smart Wearable embedded systems for Human Activity Recognition (HAR) Using Edge Computing

Smart Wearable Embedded Systems for Human Activity Recogniti...

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Electronics and Sustainable Communication systems (ICESC), 2020 International conference on

作者： Kalyan Dusarlapudi Myla ChethanSimha RaviTeja Kasukurthi Tanniru Swetha Menon Epuri Sai Shashank Kunda Sai Srinivas EEE Department Koneru Lakshmaiah Education Foundation Vaddeswaram Guntur India CSE Honors Department Koneru Lakshmaiah Education Foundation Vaddeswaram Guntur India

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379952

the medical industry is currently utilizing a significant number of automated tools. the hardware is examined and used on both healthy individuals and those who have Covid-19 to capture breathing data, and a correlation has been found between them. A person's gait can be determined. this study also uses Kodular app, ESP32 module and ADXL335 Accelerometer sensor as an apparatus for detecting the changes. there are no complicated components or wiring needed for the ADXL335 accelerometer circuit, making it simple. It has a direct connection to an Arduino. the spread of these wearable gadgets increases the variety of their applications. With the development of wearable technologies that can process and store enormous amounts of health data, continuous human body monitoring is becoming more common. there are numerous technologies that enable the capture of movement among them. Accelerometers are the most frequently cited because they are already found in many devices, use little power, and cost little, theyhave been studied by researchers. and produce outstanding results. the fall detection system can be used in elderly fall detection system, monitoring the hospital patients, wheelchair person falls, etc. Finding anomalies in the large datasets that can be utilized to calculate the respiration rate is the study's aim. the designed system can be further used in many other applications where a fall need to be detected or where a fall is needed.

关键词： Accelerometers Wiring Industries Wheelchairs Human activity recognition Biomedical monitoring Fall detection Wearable devices Older adults Monitoring

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