Development Of Real-time System Research Articles

Leather-Based Shoe Soles for Real-Time Gait Recognition and Automatic Remote Assistance Using Machine Learning.

Real-time monitoring of gait characteristics is crucial for applications in health monitoring, patient rehabilitation feedback, and telemedicine. However, the effective and stable acquisition and automatic analysis of gait information remain significant challenges. In this study, we present a flexible sensor based on a carbon nanotube/graphene composite conductive leather (CGL), which uses collagen fiber with a three-dimensional network structure as the flexible substrate. The CGL-based sensor demonstrates a high dynamic range, with notable pressure responses ranging from 0.6 to 14.5 kPa and high sensitivity (S = 0.2465 kPa-1). We further developed a device incorporating the CGL-based sensor to collect foot characteristic signals from human motion and designed smart sports shoes to facilitate effective human-computer interaction. Machine learning was employed to collect and process gait characteristic information in various states, including standing, sitting, walking, and falling. For real-time monitoring of falls, we optimized the K-Nearest Time Series Classifier (KNTC) algorithm, achieving an accuracy of 0.99 and a prediction time of only 13 ms, which highlights the system's excellent intelligent response capabilities. The system maintained a gait recognition accuracy of 90% across diverse populations, with low false-positive (3.3%) and false-negative (3.3%) rates. This work demonstrates stable gait recognition capabilities and provides valuable methods and insights for plantar behavior monitoring and data analysis, contributing to the development of advanced real-time gait monitoring systems.

Intraoperative navigation in craniofacial surgery.

Craniofacial surgery requires comprehensive anatomical knowledge of the head and neck to ensure patient safety and surgical precision. Over recent decades, there have been significant advancements in imaging techniques and the development of real-time surgical navigation systems. Intraoperative navigation technology aligns surgical instruments with imaging-derived information on patient anatomy, enabling surgeons to closely follow preoperative plans. This system functions as a radiologic map, improving the accuracy of instrument placement and minimizing surgical complications. The introduction of first-generation navigation systems in the early 1990s revolutionized surgical procedures by enabling real-time tracking of instruments using preoperative imaging. Initially utilized in neurosurgery, intraoperative navigation has since become standard practice in otolaryngology, cranio-maxillofacial surgery, and orthopedics. Since the 2000s, second-generation navigation systems have been developed to meet the growing demand for precision across various surgical specialties. The adoption of these systems in craniofacial surgery has been slower, but their use is increasing, particularly in procedures such as foreign body removal, facial bone fracture reconstruction, tumor resection, and craniofacial reconstruction and implantation. In Korea, insurance coverage for navigation in craniofacial surgery began in 2021, and new medical technologies for orbital wall fracture treatment were approved in August 2022. These technologies have only recently become clinically available, but are expected to play an increasingly important role in craniofacial surgery. Intraoperative navigation enhances operative insight, improves target localization, and increases surgical safety. Although these systems have a steep learning curve and initially prolong surgery, efficiency improves with experience. Calibration issues, registration errors, and soft tissue deformation can introduce inaccuracies. Nonetheless, navigation technology is evolving, and the integration of intraoperative computed tomography data holds promise for further enhancements of surgical accuracy. This paper discusses the various types and applications of navigation employed in craniofacial surgery, highlighting their benefits and limitations.

A real-time eye movement-based computer interface for people with disabilities

It is costly to develop systems that enable individuals exposed to Amyotrophic Lateral Sclerosis and similar diseases that directly affect the neuromotor ability to communicate with the outside world. In this study, a budget friendly, high-accuracy, software-based, gaze-controlled, real-time virtual keyboard approach that can enable these people to communicate effectively is proposed. The proposed application requires only a computer and a webcam and has a user-friendly interface that meets the basic daily needs of individuals with disabilities. Since the proposed system does not require an extra action such as blinking, it makes it possible to use computers in advanced stage patients who cannot blink their eyes. The application which uses a deep learning-based facial landmark detector, determines the letters the user focuses on the screen and converts thoughts into text. The part of the screen that the user focuses on is determined with a new selection approach inspired by the K-Nearest Neighbors algorithm. This approach, which does not require blinking, offers high speed and accuracy. In the tests, a typing speed of 23.33 characters per minute is achieved with an accuracy rate of 95.12%. It is anticipated that the study will increase computer accessibility for disabled individuals with limited mobility and contribute to the development of real-time eye tracking systems.

Improving preparation in the emergency trauma room: the development and impact of real-time data transfer and dashboard visualization system.

This study examines, with clinical end users, the features of a visualization system in transmitting real-time patient data from the ambulance to the emergency trauma room (ETR) to determine if the real-time data provides the basis for more informed and timely interventions in the ETR before and after patient arrival. We conducted a qualitative in-depth interview study with 32 physicians in six German and Swiss hospitals. A visualization system was developed as prototype to display the transfer of patient data, and it serves as a basis for evaluation by the participating physicians. The prototype demonstrated the potential benefits of improving workflow within the ETR by providing critical patient information in real-time. Physicians highlighted the importance of features such as the ABCDE scheme and vital signs that directly impact patient care. Configurable and mobile versions of the prototype were suggested to meet the specific needs of each clinic or specialist, allowing for the transfer of only essential information. The results highlight on the one hand the potential need for adaptable interfaces in medical communication technologies that balance efficiency with minimizing additional workload for emergency medical services and show that the use of pre-notification systems in communication between ambulance and hospital can be supportive. Further research is recommended to assess practical application and support in clinical practice, including a re-evaluation of the enhanced prototype by professionals.

Applied static analysis and specialization of cross-core syscalls for multi-core AUTOSAR OS

The development of static real-time control systems often follows a closed-world assumption, allowing extensive RTOS-aware whole-program optimization. For single-core systems, previous work could show the high potential of control-flow-aware static system-call tailoring. However, due to an exponential state explosion in the analysis phase, it cannot simply be extended to a multi-core setting, since the core’s relative timing to each other is undetermined. In this work, we present MultiSSE, a multi-core capable and RTOS-aware static whole-system optimization. First, MultiSSE analyzes the system by determining the relative positions of multiple cores only when necessary. For that, it exploits structural control flow and optionally timing information to handle each core separately as much as possible. Based on the analysis result, a synthesis applies lock elision and system-call optimization to generate specialized multi-core real-time systems for AUTOSAR OS. To enable a static prediction of the run-time reduction, we additionally provide cost models for the optimized cross-core system calls and evaluate the approach with synthetic benchmarks and a real-world quadrotor application. MultiSSE was able to optimize or even completely elide costly cross-core system calls and system objects leading to a reduction of up to 14% of a task’s execution time. In this extended version of a conference publication (Entrup et al. 2023), we provide an advanced description, new cost models, and an end-to-end measurement by developing a synthesis complementing the analysis.

Real-time Vertical Air Quality Monitoring System Development for Urban Scale

Real-time Vertical Air Quality Monitoring System Development for Urban Scale

Development of real-time energy monitoring system using IoT base

This paper aims to develop a real-time energy monitoring system based on smart metering to enhance energy efficiency in the single-phase residential sector. Based on the concept of low-cost IoT devices, this intelligent meter system is designed to monitor household energy consumption. It provides real-time information on a graphical Node-RED Dashboard, making it easy for households to track and manage their energy usage. The hardware of the power metering system included the Node MCU ESP8266, the PZEM-004T, and a cloud server built on the Raspberry Pi for storing electricity consumption data by using Node-RED to connect devices via an application programming interface system. This system can help analyze the electricity consumption behavior in the residential sector. It is a guideline for selecting the electricity rate between the TOD and TOU rates. The results found that in the electrical energy measurements of households 1 and 2, the mean deviations were 0.8758% and 0.5523%, respectively. The electricity cost-saving results when changing the electricity tariff from the TOD rate of households 1 and 2 to the TOU rate. It was found that electricity costs can be saved by 0.2807% and 1.0936%, respectively. The most critical variable is electricity consumption during peak periods. In the case study of household 1, if the electricity consumption during the peak period is less than 13%, it will be appropriate to select the Time of Use rate. In household 2, if there is electricity consumption during the peak period, less than 38% would be appropriate to choose the type of electricity user with a TOU rate.

Development and application of multi-parameter real-time acquisition system for intelligent secondary equipment

Abstract A multi-parameter real-time collection and analysis system, including intelligent secondary equipment, data collection gateway, and health management backend, has been constructed to solve the problem of current incomplete monitoring data and inability to providential warning of hidden faults of intelligent secondary equipment in substations. The key feature parameters of the device’s functional modules were extracted, and a data collection gateway was developed to achieve IoT access to feature data. A fault identification and warning model for the secondary equipment functional module considering temperature is provided, which realizes the assessment of equipment health status and ensures the stable and reliable operation of the power grid.

Advanced Techniques for Real-time Facial Expression Recognition Using Deep Learning

Abstract—Developing systems that can automatically recognize and interpret human emotions from facial expressions is the aim of the quickly expanding field of facial emotion identification and detection research. This technology finds applications in a wide range of areas, including as healthcare, marketing, security, and human-computer interface. Using computer vision and machine learning algorithms, facial emotion recognition systems analyze a face’s features and classify it into numerous emotional categories, including joyful, sorrowful, angry, fearful, and surprised.The three steps in the multi-step process that goes into identifying facial emotions are face detection, facial feature extraction, and emotion categorization. Thanks to recent advances in deep learn- ing, facial emotion detection systems can now identify emotions with high resilience and precision. Further, the development of real-time face expression recognition systems has opened up new avenues for applications such as sentiment analysis, emotional intelligence, and affective computing. This technology could fundamentally alter human-machine interactions and open the way to more compassionate and personalized relationships.A multitude of applications, including virtual assistants, mental health aids, and human-centered technology, will be greatly impacted by the development of systems for identifying and de- tecting facial expressions. Artificial intelligence (AI) technologies that recognize emotions allow people to interact with the digital world in more intelligent and flexible ways. But the complexity of emotion identification lies in the fact that it requires context and geometric elements in addition to facial expressions.

Real-Time Detection of Face Mask Usage Using Convolutional Neural Networks

The widespread adoption of face masks has been a crucial strategy in mitigating the spread of infectious diseases, particularly in communal settings. However, ensuring compliance with mask-wearing directives remains a significant challenge due to inconsistencies in usage and the difficulty in monitoring adherence in real time. This paper addresses these challenges by leveraging advanced deep learning techniques within computer vision to develop a real-time mask detection system. We have designed a sophisticated convolutional neural network (CNN) model, trained on a diverse and comprehensive dataset that includes various environmental conditions and mask-wearing behaviors. Our model demonstrates a high degree of accuracy in detecting proper mask usage, thereby significantly enhancing the ability of organizations and public health authorities to enforce mask-wearing rules effectively. The key contributions of this research include the development of a robust real-time monitoring system that can be integrated into existing surveillance infrastructures to improve public health safety measures during ongoing and future health crises. Furthermore, this study lays the groundwork for future advancements in automated compliance monitoring systems, extending their applicability to other areas of public health and safety.

Applicability of pre-trained CNNs in temperate deforestation detection

ABSTRACT Technological advancements have opened up new possibilities for accurately identifying deforested areas and developing effective data collection strategies. Our paper proposes a practical approach to improve deforestation monitoring using satellite images and Convolutional Neural Networks (CNNs) for image classification. To train the CNNs, we used labeled data primarily sourced from the Amazon basin, while images from the Romanian Carpathian mountains were reserved for testing. Building on this foundation, we evaluated various pre-trained CNN architectures, including AlexNet, VGGNet, ResNet, DenseNet, EfficientNet, GoogLeNet, and Swin Transformer, comparing their performance with a typical CNN architecture. In addition, we refined the performance by testing four ensemble learning methods. We found a decrease of only 10% in accuracy for using the models on data from the temperate area. Our findings contribute to this interdisciplinary field by providing insights into the effectiveness of pre-trained CNNs in classifying satellite images from temperate regions, trained with knowledge from tropical deforestation data. This contribution may open ways for the development of a quasi real-time deforestation monitoring system and serve as a reference for future research in temperate areas.

The Electrostatic Induction Characteristics of SiC/SiC Particles in Aero-Engine Exhaust Gases: A Simulated Experiment and Analysis

This study investigates the electrostatic induction characteristics of silicon carbide-fiber-reinforced silicon carbide (SiC/SiC) particles within aero-engine exhaust gases using a dedicated J20 turbojet engine experimental platform. Our comprehensive experiments explored the electrostatic properties of SiC/SiC particles under varying engine operational states—specifically focusing on different thermal conditions, particle mass concentrations, particle sizes, and exhaust gas velocities compared to those of common engine exhaust constituents like carbon (C) and iron (Fe) particles. The results demonstrate that SiC/SiC particles consistently maintain a stable positive charge across varied temperatures, significantly diverging from the behaviors of carbon (C) and iron (Fe) particles. Additionally, our findings reveal that higher mass concentrations of SiC/SiC particles, smaller particle sizes within a certain range, and greater exhaust gas velocities of the aero-engine all lead to increased particle charge and more pronounced electrostatic induction characteristics. This study highlights the potential of electrostatic sensors for the early detection and diagnosis of failures in aero-engines, offering crucial insights into the development of more resilient real-time aero-engine health monitoring systems.

Maximized nanojunctions in Pd/SnO2 nanoparticles for ultrasensitive and rapid H2 detection

H2 energy has gained massive attraction as a promising candidate for future energy sources. However, the explosive properties of H2 arouse significant safety concerns, emphasizing the need for the development of real-time H2 detection systems. This study presents the fabrication of an ultrasensitive and selective H2 gas sensor using Pd/SnO2 nanoparticles (NPs) synthesized through the utilization of Pluronic F-127. Pluronic F-127 improves the dispersion and regulates the particle size of Pd NPs. Pd/SnO2 NPs, which are comprised of numerous nanojunctions, exhibit H2 response of 27,190 and a response time of 3 s when exposed to 50 ppm of H2 at 100 °C. The practical applicability of Pd/SnO2 NPs was demonstrated by detecting H2 generated from water-splitting cells, exhibiting promising features for H2 energy industries. Overall, the synthetic method of this study proposes advanced strategies for development of sensing materials with maximized gas sensing performance.

A Real-Time Parallel Programming Approach for Rust

The development of real-time systems is one of the areas with the highest relevance in computer science, and the number of critical systems has increased significantly. These systems considers several applications running concurrently, and inside each of those applications code might be parallelized to improve their performance and control the priority of each parallelizable task. Several efforts have been done in different programming languages to provide real-time systems with parallel programming models, whether by code extensions or annotations, or with specific features in the actual language core.

CPU 병렬 처리를 이용한 SPH 유체와 단일보드 컴퓨터의 실시간 상호작용 시스템 개발

CPU 병렬 처리를 이용한 SPH 유체와 단일보드 컴퓨터의 실시간 상호작용 시스템 개발

Real-time biological early-warning system based on freshwater mussels’ valvometry data

Abstract. Quantifying the effects of external climatic and anthropogenic stressors on aquatic ecosystems is an important task for scientific purposes and management progress in the field of water resources. In this study, we propose an innovative use of biotic communities as real-time indicators, which offers a promising solution to directly quantify the impact of these external stressors on the aquatic ecosystem health. Specifically, we investigated the influence of natural river floods on riverine biotic communities using freshwater mussels (FMs) as reliable biosensors. Using the valvometry technique, we monitored the valve gaping of FMs and analysed both the amplitude and frequency. The valve movement of the FMs was tracked by installing a magnet on one valve and a Hall effect sensor on the other valve. The magnetic field between the magnet and the sensor was recorded using an Arduino board, and its changes over time were normalised to give the opening percentage of the FMs (how open the mussels were). The recorded data were then analysed using continuous wavelet transform (CWT) analysis to study the time-dependent frequency of the signals. The experiments were carried out both in a laboratory flume and in the Paglia River (Italy). The laboratory experiments were conducted with FMs in two configurations: freely moving on the bed and immobilised on vertical rods. Testing of the immobilised configuration was necessary because the same configuration was used in the field in order to prevent FMs from packing against the downstream wall of the protection cage during floods or from breaking their connection wires. These experiments allowed us to verify that immobilised mussels show similar responses to abrupt changes in flow conditions as free mussels. Moreover, immobilised mussels produced more neat and interpretable signals than free-moving mussels due to the reduced number of features resulting from movement constraints. We then analysed the response of 13 immobilised mussels under real river conditions during a flood on 31 March 2022. The FMs in the field showed a rapid and significant change in valve gap frequency as the flood escalated, confirming the general behaviour observed in the laboratory in the presence of an abrupt increase in the flow. These results highlight the effectiveness of using FMs as biosensors for the timely detection of environmental stressors related to natural floods and emphasise the utility of CWT as a powerful signal-processing tool for the analysis of valvometry data. The study proposes the integration of FM valvometry and CWT for the development of operational real-time biological early-warning systems (BEWSs) with the aim of monitoring and protecting aquatic ecosystems. Future research should focus on extending the investigation of the responsiveness of FMs to specific stressors (e.g. turbidity, temperature, and chemicals) and on testing the applications of the proposed BEWSs to quantify the impact of both natural stressors (e.g. heat waves and droughts) and anthropogenic stressors (e.g. hydropeaking, reservoir flushing, and chemical contamination).

Single-metalens-assisted polarization imaging and edge detection for target recognition

Simultaneous capture of various light information, including polarization and edge information of the objects, has consistently been a fundamental concern within the field of target recognition. However, these tasks are typically accompanied by bulky optical components and active illumination methods, which significantly restricts their use in compact and lightweight applications. Here, we demonstrate a metalens-assisted imaging system that can simultaneously achieve polarization imaging and optoelectronic edge detection in a single shot with low consumption. The dielectric metalens is designed to achieve polarization imaging by dispersing the input polarized light into two orthogonal components, resulting in optoelectronic isotropic edge detection of two-dimensional images after digital post-processing. Compared with the algorithmic methods using a convolution kernel, the proposed system has a much lower computational complexity. The work presented in this study demonstrates the potential applications in machine vision and paves the way for the development of compact target recognition and real-time image processing systems.

Development of Real-Time Unmanned Aerial Vehicle Urban Object Detection System with Federated Learning

In this paper, an urban object detection system via unmanned aerial vehicles (UAVs) is developed to collect real-time traffic information, which can be further utilized in many applications such as traffic monitoring and urban traffic management. The system includes an object detection algorithm, deep learning model training, and deployment on a real UAV. For the object detection algorithm, the Mobilenet-SSD model is applied owing to its lightweight and efficiency, which make it suitable for real-time applications on an onboard microprocessor. For model training, federated learning (FL) is used to protect privacy and increase efficiency with parallel computing. Last, the FL-trained object detection model is deployed on a real UAV for real-time performance testing. The experimental results show that the object detection algorithm can reach a speed of 18 frames per second with good detection performance, which shows the real-time computation ability of a resource-limited edge device and also validates the effectiveness of the developed system.

Development of real-time brain-computer interface control system for robot

Electroencephalogram (EEG)-based brain-computer interfaces (BCI) have been considered a prevailing non-invasive method for collecting human biomedical signals by attaching electrodes to the scalp. However, it is difficult to detect and use these signals to control an online BCI robot in a real environment owing to environmental noise. In this study, a novel state recognition model is proposed to determine and improve EEG signal states. First, a Long Short-Term Memory Convolutional Neural Network (LSTM-CNN) was designed to extract EEG features along the time sequence. During this process, errors caused by the randomness of the mind or external environmental factors may be generated. Thus, an actor-critic based decision-making model was proposed to correct these errors. The model consists of two networks that can be used to predict the final signal state based on both the current signal state probability and past signal state probabilities. Subsequently, a hybrid BCI real-time control system application is proposed to control a BCI robot. The Unicorn Hybrid Black EEG device was used to acquire brain signals. A data transmission system was constructed using OpenViBE to transfer data. An EEG classification system was built to classify the BCI commands. In this experiment, EEG data from five subjects were collected to train and test the performance and reliability of the proposed control system. The system records the time spent by the robot and the moving distance. Experimental results were provided to demonstrate the feasibility of the real-time control system. Compared to similar BCI studies, the proposed hybrid BCI real-time control system can accurately classify seven BCI commands in a more reliable and precise manner. Overall, the offline testing accuracy was 87.20%. When we apply the proposed system to control a BCI robot in a real environment, the average online control accuracy is 93.12%, and the mean information transmission rate is 67.07 bits/min, which is better than those of some state-of-the-art control systems. This shows that the proposed hybrid BCI real-time control system demonstrated higher reliability, which can be used in practical BCI control applications.

Development and implementation of real-time staff performance monitoring and evaluation system based on Global Positioning System (GPS)

This work presents the development and implementation of a Real-Time Staff Performance Monitoring and Evaluation System (RT-SPMES) using Global Positioning System (GPS) technology. In line with the dynamism of the workplace, due to rapid changes in technology, existing staff performance appraisal systems have fallen short of expectations due to their manual nature. Hence, the need arises for a more robust approach to monitoring and evaluating employee work performance. To achieve this, this work carried out an exhaustive review of existing performance evaluation methods within the academic and a conceptual architecture was developed. A real-time monitoring and evaluation system was developed, using PHP programming language, Hyper Text Markup Language and Cascading Styles Sheet. The system is hosted on Azure Cloud infrastructure. Through sufficient testing of the developed system developed in this work, it was asserted effectively monitors employee performance in real-time, paving the way for an objective appraisal process devoid of bias and manipulation.