Textile products are present in almost every aspect of human life. With the introduction of electronic textiles (e-textiles), textile products have become capable of converting various physiological and environmental ...
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Textile products are present in almost every aspect of human life. With the introduction of electronic textiles (e-textiles), textile products have become capable of converting various physiological and environmental stimuli into electrical signals, many of which are of vital importance to humans. Therefore, these products require real-time (low-latency) and robust computing systems. However, due to comfort considerations, they cannot accommodate powerful computing resources. In this study, a novel fog computing-based framework (FogETex) is proposed to meet the needs of e-textile applications. FogETex is a Platform-as-a-Service model that is cross-platform supported, scalable, and operates in real time. This framework encompasses end-to-end integration of the system, including Textile-based Internet of Things (T-IoT) device, fog devices, and the cloud. Fog devices consist of a broker that manages the fog node and a worker that handles incoming computation requests. Sensor data is transmitted to the fog node through a mobile application, and system architecture can be monitored through developed user interfaces. Resource usage from broker devices is monitored in real time to prevent worker devices from experiencing overload. For the system case study, a deep-learning-based gait phase analysis application using textile-based capacitive sensors is employed. FogETex was evaluated in terms of time characteristics, resource usage, and network bandwidth usage using a mock client to determine the ideal system performance and an actual client to conduct real-world tests. The fog devices outperformed the cloud system in these metrics. Besides being developed primarily for e-textile applications, the FogETex framework can accommodate other IoT devices as well.
This research addresses a significant gap in power system protection methodologies by developing a dedicated simulation environment that supports the communication of protection relays via the IEC 61850 protocol. Curr...
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This research addresses a significant gap in power system protection methodologies by developing a dedicated simulation environment that supports the communication of protection relays via the IEC 61850 protocol. Current studies have focused on hardware-in-the-loop approaches, but there is a lack of research in the software-in-the-loop domain. This limitation means that manufacturer-independent simulations cannot be performed, restricting testing to the capabilities provided by the manufacturer. By introducing a relay capable of communicating within a Simulink-modeled power system through socket programming, this study harnesses the capabilities of the IEC 61850 GOOSE protocol and sampled values. This work aims to eliminate the manufacturer dependencies present in hardware-in-the-loop approaches, thereby enabling the independent development of new protection and control strategies in an academic context. Furthermore, by facilitating advanced communication strategies through a detailed simulation framework, this research contributes to the broader field of electrical engineering by offering a robust tool for developing, testing, and validating new relay communication techniques and protection schemes. This approach not only fills a critical gap in simulation capabilities but also paves the way for future advancements in power system protection and management regarding the IEC 61850 protocol.
Light Detection and Ranging (LiDAR) sensor play a vital role in the fields like environment perception for an autonomous vehicle, surveying and many other fields. LiDAR emits an enormous amount of data, which is diffi...
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ISBN:
(纸本)9798350339024
Light Detection and Ranging (LiDAR) sensor play a vital role in the fields like environment perception for an autonomous vehicle, surveying and many other fields. LiDAR emits an enormous amount of data, which is difficult to transmit over a wireless medium. The need for LiDAR data transmission arises specifically in the Intelligent Transportation System (ITS) and surveying. It is advantageous to transmit LiDAR (mounted on a car) data over a wireless medium and receive the same by another vehicle or Road Side Units (RSUs) or control stations of a tesbed setup. Similarly, visualizing LiDAR data remotely while data acquisition by LiDAR mounted on Unmanned Aerial Vehicles (UAVs) could simplify the process of surveying. This paper presents a client-server-based LiDAR data streaming system using socket programming. In this system, the server transmits LiDAR point-cloud percept data, and the client captures and visualizes the streaming data over a WiFi medium. The server and the client are both equipped with the Robot Operating System(ROS). An extensive analysis of data transfer on two popular protocols, TCP and UDP, has also been presented. The data transmission and reception on ROS platform was successfully performed with a tolerable latency.
In automotive system design, the estimation of the distance is a crucial task, with respect to the autonomous vehicle perception module. The range sensors like LiDAR, RADAR, Ultrasonic sensors, etc. have widely been u...
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This paper presents a remote network database system based on image-visual communication technology. Access to remote databases is realized based on network programming core Windows sockets and client/server mode. SQL...
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Hand gestures are a relatively new way for humans to communicate with computers. The goal of gesture recognition is to bridge the physical and digital worlds. Hand gestures make it much easier to communicate our inten...
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ISBN:
(纸本)9783031055447;9783031055430
Hand gestures are a relatively new way for humans to communicate with computers. The goal of gesture recognition is to bridge the physical and digital worlds. Hand gestures make it much easier to communicate our intentions and ideas to the computer. There are numerous methods for a computer to recognize a hand gesture, one of which is image recognition. The use of a Convolutional Neural Network (CNN) allows for the detection of human gestures. However, training a CNN necessitates a massive dataset of human gesture images. In this paper, we employ Google MediaPipe, a Machine Learning (ML) pipeline that combines Palm Detection and Hand Landmark Models, to develop a simple hand tracking method to control a Robot Operating System (ROS) based surveillance car with socket programming. The study demonstrates control of a ROS car's steering direction and speed. Hand-gesture-controlled surveillance vehicles could aid in the improvement of security systems.
The flourish of mobile communications is driven by the increasing number of subscribers and rapid advance of electronic devices. This brings up many multimedia context-aware services, among which real time locating sy...
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The flourish of mobile communications is driven by the increasing number of subscribers and rapid advance of electronic devices. This brings up many multimedia context-aware services, among which real time locating system (RTLS) has become necessary in many applications. To locate a mobile station (MS), RTLS could apply conventional fingerprinting algorithm using received signal strength indicator (RSSI), which allows a MS to collect RSSI data from beacons sent by access points (APs). However, this method does not work for iPhones because of the lack of open access to Apple application programming interface (API) to obtain RSSI values. This paper proposes an alternative approach, under which APs in the monitoring mode are used to collect RSSI values for semi-beacon packets sent from a MS. We implement the packet capture library, which enables us to obtain RSSI values of semi-beacon packets sent by an iPhone. With a region-based k-nearest neighbor (kNN) localization algorithm, we successfully locate an iPhone user in indoor environment. In addition, experimental results show that the proposed approach outperforms the conventional RSSI fingerprinting approach.
SMARTLAM is a European funded research project, intended to develop a modular manufacturing platform and capability database, for three-dimensional integration/manufacturing of component parts constructed from laminat...
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ISBN:
(纸本)9783030304409;9783030304393
SMARTLAM is a European funded research project, intended to develop a modular manufacturing platform and capability database, for three-dimensional integration/manufacturing of component parts constructed from laminated polymer films. To enable communication/interaction between SMARTLAM integrated manufacturing modules and the capability database, a software toolkit has to be developed, which must comply with the modularity concept of the SMARTLAM manufacturing system, and with the capability to integrate changes in the manufacturing platform, e.g. adding a new manufacturing module. This paper presents the implementation of the SMARTLAM software toolkit, i.e. the software and graphical user interface designed and developed to conform to these requirements, which allows for automated process chain selection while enabling the operator to load and modify the selected process chains and to alter parameters' values. The developed software toolkit was tested and it was found able to compile and execute complete manufacturing recipes with the given equipment and it was directly linked to the SMARTLAM database. In addition to its ability to exchange data between different manufacturing modules, it gives the users the opportunity to modify the manufacturing processes in real time. The main result of the overall software development is a fully functional and a commercially applicable solution for the design, planning, configuration and coordination of a modular manufacturing platform for micro-devices with minimum cost/time of the identified process chain. As a validation example, a flexible lighting device demonstrator (DLED) was manufactured using the SMARTLAM platform, and the software toolkit was successfully utilized to visualize and select the appropriate production process chain.
In India, the agricultural industry has seen a boom in recent years, demanding an increased inclusion of automation in it. An important aspect of this agro-automation is grading and classification of agricultural prod...
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ISBN:
(纸本)9781538680759
In India, the agricultural industry has seen a boom in recent years, demanding an increased inclusion of automation in it. An important aspect of this agro-automation is grading and classification of agricultural produce. These labor intensive tasks can be automated by use of Computer Vision and Machine Learning. This paper focuses on developing a standalone system capable of classifying 3 types of fruit and taking apple as test case of grading. The fruit types include apple, orange, pear and lemon. Further, apples have been graded into four grades, Grade 1 being the best quality apple and Grade 4 consisting of the spoilt ones. Input is given in the form of fruit image. The involved methodology is dataset formation, preprocessing, software as well as hardware implementations and classification. Preprocessing consists of background removal and segmentation techniques in order to extract fruit area. Deep Convolutional Neural Network has been chosen for the real time implementation of system and applied on fruit 360 dataset. For that purpose, the Inception V3 model is trained using the transfer training approach, thus enabling it to distinguish fruit images. The results after experimentation show that the Top 5 accuracy on the dataset used is 90% and the Top 1 accuracy is 85% which targets accuracy limitation of previous attempts.
Envisaging the tools required for effective management of the future grid, Phasor Measurement Units (PMU) are being installed throughout the Indian power grid in large scale. With large scale renewable energy integrat...
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ISBN:
(纸本)9781538644911
Envisaging the tools required for effective management of the future grid, Phasor Measurement Units (PMU) are being installed throughout the Indian power grid in large scale. With large scale renewable energy integration happening throughout the grid, monitoring of grid dynamics is of paramount importance. Various on-line and offline applications are being developed to utilize these high resolution time stamped PMU data. These applications will provide better situational awareness and help the system operators to mange and take decisions with much better confidence. The paper describes the formulation and implementation of Real Time Linear State Estimator which gets the real time PMU data on a moving-window basis and gives best possible estimates of the states. This paper also discusses the issues faced at Southern Regional Load Despatch Centre (SRLDC) in running custom applications that require continuous data from PMUs and it explains how a socket program was used to obtain continuous data stream in a format that can be readily used by real time network applications.
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