GESTURE CONTROLLED SMART GLOVE WITH WEB APPLICATION

In this paper, we provide an approach for authenticating users in virtual reality (VR) environments by tracking the behavior of users as they perform goal-oriented tasks, such as throwing a ball at a target. With the pervasion of VR in mission-critical applications such as manufacturing, navigation, military training, education, and therapy, validating the identity of users using VR systems is becoming paramount to prevent tampering of the VR environments, and to ensure user safety. Unlike prior work, which uses PIN and pattern based passwords to authenticate users in VR environments, our approach authenticates users based on their natural interactions within the virtual space by matching the 3D trajectory of the dominant hand gesture controller in a display-based head-mounted VR system to a library of trajectories. To handle natural differences in wait times between multiple parts of an action such as picking a ball and throwing it, our matching approach uses a symmetric sum-squared distance between the nearest neighbors across the query and library trajectories. Our work enables seamless authentication without requiring the user to stop their activity and enter specific credentials, and can be used to continually validate the identity of the user. We conduct a pilot study with 14 subjects throwing a ball at a target in VR using the gesture controller and achieve a maximum accuracy of 92.86% by comparing to a library of 10 trajectories per subject, and 90.00% by comparing to 6 trajectories per subject.

In this article, we present a platform that allows for the integration of different applications for the follow-up of patients with chronic diseases. We developed two elements: a mobile and a web application. The mobile application allows the capture and processing of vital signs for patients with high blood pressure (hypertension). This application allows for the patient to store the data obtained, provides historical information and trends of the stored measures, and provides alerts and recommendations according to ranges of measures that were obtained. The web application allows the doctor and patients to obtain updated information of the disease behavior through the measures obtained. We used different biometric devices including an efimomanometer, glucometer, scale, and a thermometer with a wi-fi connection. Through this web application, we also generated information about average measures at a given time, by age, by region, and by a specific date. The developed system was evaluated in a medical center with different types of patients.

Currently, mobile handheld device are being used increasingly for web access. This leads to the issue relating to the ease of use for users, which refers to not user-friendly web design, unorganized content and difficulty of information navigation. Based on literature analysis, design of physical features and functions for mobile devices are unique, as it should emphasize mobility, personality and flexibility features, apart from providing the added values, such as accessibility at anytime and anywhere access. However as most web applications are designed specifically for desktop environments, it is discovered that most web applications design are inappropriate for mobile devices. In other words, web sites design for desktop is unsuitable to be adopted for the mobile devices. Literature had stated that designing mobile web application approach differs from development ordinary web for desktop computer. Adapting the original web content dynamically to deal with the constraints of mobile handheld devices (the screen size and limited system resources and variation of user preferences) becomes very important. Combination of User Centred Design (UCD) and Service Science (SS) approach expected to enhance quality of the mobile web that can meet user satisfaction. As such the purpose of this study is to identify the design of mobile web content that is suitable for users' in which it meet users' values and requirement. The work conducted uses a co-creation approach in which co-creation values are obtained through a series of process. Co-creation value is essential as it is the missing part that is not being incorporated during the design process. As a result a guideline on web mobile design application based on co-creation values is established. The investigations presented in this paper highlight some ways in development of mobile web applications by adopting co-creation value with customer to make it more accessible to mobile devices. A case study conducted at Malaysian Agricultural Research And Development Institute (MARDI) as its web mobile applications is still at its initial. A prototype system based on the proposed guideline is provided as a proof of concept and shows the difference required in web applications for mobile design where values are incorporated.

As location-aware IoT (Internet of Things) devices intend to spread across almost every application, IoT devices are going to require different GNSS (Global Navigation Satellite System) receiver design requirements. To deliver on IoT opportunities, system architects and designers need to look carefully at every aspect of system design and architecture to be able to enable the full potential of the IoT era. IoT devices are equipped with different connectivity sensors that can provide a reliable integrated location-awareness solution. IoT devices are mainly relying on GNSS chip to deliver a service when no other RF signal is available. Hence, GNSS chip is expected to maintain a robust and reliable performance even in harsh environment. The harsh environment includes multipath environment, NLOS (Non-Line of Sight) GNSS signal reception, weak GNSS signal power reception, superimposed motion (e.g., arm swing, floating platform fluctuation …etc.), operation under freezing temperature, and interference. The targeted low-cost system design along with the expected harsh operational environment derived us to face exceptional GNSS signal reception condition. This paper discusses lessons learned, and challenges faced signal acquisition, reacquisition, and tracking for several IoT applications. Different operational modes are also explored to accommodate various use cases scenarios for several IoT applications. These different power consumption operational modes allow the GNSS chip to achieve an Ultra-low power consumption which indeed required for IoT applications. This paper provides an overview about the addressed GNSS chip fabricated in a 28nm FD-SOI process. We demonstrate a system performance and power consumption comparison of this chip along with other state-of-the-art GNSS chip. We compare the positioning performance for this device alongside another state-of-the-art GNSS modules and Novatel’s tightly coupled GNSS/INS (Inertial Navigation System) reference solution at numerous scenarios ranging from walking, running, and driving at several sites under different environment.

Due to the spread of Internet of Things (IoT) devices, IoT security management has become important for network administrators. However, security functions are difficult to implement into IoT devices. We thus propose a security system that controls anomaly traffic as a network function. This system is installed in a gateway close to an IoT device. It automatically generates the control rules for each IoT device with user support and improves the accuracy of information sharing between gateways. By distributing the gateway processing, the system prevents processing load and control authority from concentrating at the central server while reducing the cost of using the server. We also present the system's architecture and discuss the evaluation of its performance.

With the introduction of edge analytics, IoT devices are becoming smarter and ready for AI applications. However, any increase in the training data results in a linear increase in the space complexity of the trained Machine Learning (ML) models, which means they cannot be deployed on IoT devices that have limited memory. To alleviate such memory issues, we recently proposed an SRAM-optimized classifier porting, stitching, and efficient deployment method in [3]. This is currently the most resource-friendly approach that enables large classifiers to be comfortably executed on microcontroller unit (MCU) based IoT devices, and perform ultra-fast classifications (1--4x times faster than state-of-the-art libraries) while consuming 0 bytes of SRAM. In this demo, realizing our recent SRAM-optimized approach, we port and execute 7 dataset-trained classifiers on 7 popular MCUs, and report their inference performance. It is apparent from the demo results that realizing our approach makes even the slowest Atmega328P MCU perform faster unit inference than a NVIDIA Jetson Nano GPU and Raspberry Pi 4 CPU.

Through analyzing the principle of micro-current sensor and the shortage of zero-flux micro-current sensor, a new on-line monitoring method of dielectric tangent of loss angle (tandelta) based on passive micro-current sensor technology is presented. This method can not only overcome difficulties existing in the manufacturing sensors of the high precision relative scale difference and the high precision angular difference which is often necessary in on-line monitoring of insulation dielectric tandelta, but also can achieve high precision at low cost. This method combines the balance measurement principle and potential computer difference measuring technology, and it can be easily realized by using micro-controller unit (MCU). It also adopts anti-circumstance electromagnetic interference and intelligent diagnostic techniques in the course of measurement, so it possesses great anti-interference ability under strong electromagnetic interference environment. Both the principle analysis and the on-site test have proved that this measure method can reach good tandelta monitoring precision

This paper designs a new control system architecture of the Internet of things(IoT), which enables controlling the I/O interface of a microcontroller unit(MCU) via smartphone based on the WebUSB technology. Firstly, an I/O control protocol of the MCU is designed based on WebUSB, so as to standardize the function calls of various I/O procedures; Secondly, based on the WebUSB programming interface, the host side of this I/O control protocol is designed and implemented on the browser side to control the MCU; Then, the device side of the I/O control protocol is implemented based on the USB peripheral stack in the MCU to complete the response and execution of control commands; Finally, a hardware function verification platform based on STM32F103 Series MCU is built, which utilizes the hardware interface to connect to a servo steering gear, and uses the USB interface to receive commands from the PC or smartphone. Through browser based software development, it calls the PC or smartphone’s on-device camera to detect the position of a human face, and uses the MCU I/O to control servo rotation so as to realize automatic face tracking.

The most critical capacity of human beings is to interact (i.e., express their emotions, opinions and feelings) with each other. We cannot overstate the use of communications. In particular, it is very difficult for unspeakable people to communicate their meaning to ordinary people, since there are more than 300 sign languages, and it is difficult for an average person to learn and use these languages. As a result, unspeakable people face numerous irritations and frustrations that limit their ability to fulfill their regular tasks. Different psychiatric services are available to the muted population in order to get their speech back, but the expense of these treatments is costly enough that no one can afford them. ere we suggest a smart speech device that allows voiceless people to communicate their message to ordinary people using hand movements and gestures. The system uses hand motion reading with the aid of flex sensors along with a microcontroller (Aurdino) unit.

The keyboard is the most universally supported input method operable by people with disabilities. Yet, many popular websites lack keyboard accessible mechanism, which could cause failures that make the website unusable. In this paper, we present a novel approach for automatically detecting and localizing keyboard accessibility failures in web applications. Our extensive evaluation of our technique on real world web pages showed that our technique was able to detect keyboard failures in web applications with high precision and recall and was able to accurately identify the underlying elements in the web pages that led to the observed problems.

This paper describes the architecture and implementation of the Tahoma Web browsing system. Key to Tahoma is the browser operating system (BOS), a new trusted software layer on which Web browsers execute. The benefits of this architecture are threefold. First, the BOS runs the client-side component of each Web application (e.g., on-line banking, Web mail) in its own virtual machine. This provides strong isolation between Web services and the user's local resources. Second, Tahoma lets Web publishers limit the scope of their Web applications by specifying which URLs and other resources their browsers are allowed to access. This limits the harm that can be caused by a compromised browser. Third, Tahoma treats Web applications as first-class objects that users explicitly install and manage, giving them explicit knowledge about and control over downloaded content and code. We have implemented a prototype of Tahoma using Linux and the Xen virtual machine monitor. Our security evaluation shows that Tahoma can prevent or contain 87% of the vulnerabilities that have been identified in the widely used Mozilla browser. In addition, our measurements of latency, throughput, and responsiveness demonstrate that users need not sacrifice performance for the benefits of stronger isolation and safety.

A general concept has been developed for application of a software and hardware complex in a virtual reality environment in 3D-digital design and technological preparation of production, implemented by the method of synthesizing an electronic model of a product — the center section of an An-148 short-range aircraft in integration with corporate CAD, PDM, BP systems. The composition of software and hardware used in the development of the methodology corresponding to the level of similar systems of foreign aircraft manufacturers, as well as the composition and architecture of the developed system of virtual reality and the principles of its operation are given. The content of the stages of a 3D-digital design and technological preparation of production in the context of integrated information production systems has been outlined, the center-plane of the An 148 aircraft has been tested in a virtual reality and simulation environment.

The deployment of deep learning models on resource-constrained devices requires the development of new optimisation techniques to effectively exploit the computational and storage capacities of these devices. Thus, the primary objective of this research is to introduce an innovative and efficient approach for fusing convolution (or fully connected), ReLU, and batch normalisation neural network layers into a unified, single-layer structure, alongside a quantisation method for this new fused layer. This approach has been evaluated using the Arduino BLE Sense ARM Cortex-M4 and the Arduino Portenta H7 Lite ARM Cortex-M4 and M7 processors, known for their widespread adoption in various Internet of Things devices. Depending on the microcontroller unit and compilation flag used, the fused layers can reduce the overall execution time by up to 1.53×, and on individual layers it can reach a speedup of 2.95×.

Internet of things (IoT) is an indispensable paradigm in today’s industrial change. IoT interconnects appliances around us through the Internet, and user’s private information is deeply placed inside the network. Nonetheless, security vulnerabilities in IoT have not been addressed appropriately so far due to various reasons. Even in the initial WiFi connection procedures of IoT devices, WiFi credentials can be leaked, making the WiFi access point (AP) a hacking path. We propose a secure connection scheme, termed PUP, that aims to securely and quickly connect an IoT device to the AP in proximity while improving the user experience. PUP shows perfect security performance, enabling connection time within 11 s.

Using low-rank adaptation to fine-tune pre-trained neural network models has attracted widespread attention due to its advantages of low resource requirements, high precision, and no additional inference delay. However, most existing methods are designed for large language models based on the Transformer structure and lack adaptation to convolutional neural networks (CNN) widely used on Internet of Things (IoT) devices. In addition, IoT devices are usually deployed outdoors and collect large amounts of data that are affected by environmental conditions. Providing a highly robust CNN model is a prerequisite for providing high-quality services. To this end, this paper proposes a highly robust seed-filter adaptation method (SeFA) for pre-trained CNNs. SeFA introduces an adaptation branch with the same structure as the backbone network. In the adaptation branch, some filters are first designated seed filters and grouped. Then, additional filters are generated from the grouped seed filters and nonlinear transformation functions (NLFs) with different hyperparameters. The seed filters' parameters are updated during model training, and the NLF hyperparameters are randomly initialized and frozen. Both grouping seed filters and configuring NLFs with non-learnable hyperparameters can improve the model's robustness. This is because grouping seed filters can generate diverse filters on demand without increasing the model's complexity, and the NLFs' rules can regularize the model. It is worth mentioning that the number of fine-tuning parameters of the pre-trained model that can adapt to downstream tasks can be flexibly controlled by specifying the number of seed filters. The key idea of this paper is to propose SeFA with flexible, controllable, learnable parameters, high robustness, and adaptability to pretrained CNN models, thereby facilitating fine-tuning on resource-constrained IoT devices and providing highly robust visual services. Experimental results on the CIFAR-10, CIFAR-10-C, CIFAR-100, CIFAR-100-C, and Icons-50 datasets demonstrate that the proposed SeFA outperforms other state-of-the-art methods. Specifically, on the ResNet-152 model and the CIFAR-10-C dataset, the accuracy of our SeFA is about 7% higher than that of the full fine-tuning method. In addition, we verify the efficiency of SeFA-based models for fine-tuning and inference on the device.