Development Of Real-time Applications Research Articles

ShaderPerFormer: Platform-independent Context-aware Shader Performance Predictor

The ability to model and predict the execution time of GPU computations is crucial for real-time graphics application development and optimization. While there are many existing methodologies for graphics programmers to provide such estimates, those methods are often vendor-dependent, require the platforms to be tested, or fail to capture the contextual influences among shader instructions. To address this challenge, we propose ShaderPerFormer, a platform-independent, context-aware deep-learning approach to model GPU performance and provide end-to-end performance predictions on a per-shader basis. To provide more accurate predictions, our method contains a separate stage to gather platform-independent shader program trace information. We also provide a dataset consisting of a total of 54,667 fragment shader performance samples on 5 different platforms. Compared to the PILR and SH baseline methods, our approach reduces the average MAPE across five platforms by 8.26% and 25.25%, respectively.

A method for calculating real-time ZTD grid data in Chinese regions based on GNSS ZTD modified ERA5 grid products

The low spatial and temporal resolution and poor accuracy of real-time zenith tropospheric delay (ZTD) have always been one of the main constraints during the development of real-time GNSS applications. This paper presents a method to obtain real-time ZTD grid data in Chinese regions utilizing the ZTD-modified ERA5 grid products from GNSS stations. Initially, the original ZTD fitting function for vertical variation is improved. Then, the real-time ZTD grid data for the Chinese region (RZTDG) could be obtained by correcting the 7-day earlier ERA5 data through the real-time ZTD data from the GNSS stations. In this paper, we analyze the performance of the model under various weather conditions, and the results show that it still keeps a high accuracy even under variable weather conditions. ZTD data from 66 radiosonde stations in China in 2022 are used as reference values to evaluate the accuracy of the RZTDG. Experimental results show that the RMSE (2.3 cm) and MAE (1.8 cm) of the RZTDG are comparable to the ERA5 data of the day (RMSE: 2.4 cm, MAE: 1.8 cm) and significantly better than the accuracy of the HGPT2 (RMSE: 5.1 cm, MAE: 4.1 cm). Meanwhile, the Bias of RZTDG (0.1 cm) is much smaller than the Bias of HGPT2 (−0.9 cm) and the Bias of ERA5 (−0.8 cm), proving that the method in this paper has less deviation from the radiosonde ZTD data. Finally, the convergence times of the float PPP with and without tropospheric constraints are counted for 120 GNSS stations for DOY 11–13, DOY 101–103, and DOY 192–194 in 2022. The results show that the convergence time of the float PPP is reduced by 30% after constraining the ZTD using RZTDG.

Timing and Performance Metrics for TWR-K70F120M Device

Currently, single-board computers (SBCs) are sufficiently powerful to run real-time operating systems (RTOSs) and applications. The purpose of this research was to investigate the timing performance of an NXP TWR-K70F120M device with μClinux OS on concurrently running tasks with real-time features and constraints, and provide new and distinct technical data not yet available in the literature. Towards this goal, a custom-built multithreaded application with specific compute-intensive sorting and matrix operations was developed and applied to obtain measurements in specific timing metrics, including task execution time, thread waiting time, and response time. In this way, this research extends the literature by documenting performance results on specific timing metrics. The performance of this device was additionally benchmarked and validated against commonly used platforms, a Raspberry Pi4 and BeagleBone AI SBCs. The experimental results showed that this device stands well both in terms of timing and efficiency metrics. Execution times were lower than with the other platforms, by approximately 56% in the case of two threads, and by 29% in the case of 32-thread configurations. The outcomes could be of practical value to companies which intend to use such low-cost embedded devices in the development of reliable real-time industrial applications.

Evaluating the Performance of Pre-Trained Convolutional Neural Network for Audio Classification on Embedded Systems for Anomaly Detection in Smart Cities.

Environmental Sound Recognition (ESR) plays a crucial role in smart cities by accurately categorizing audio using well-trained Machine Learning (ML) classifiers. This application is particularly valuable for cities that analyzed environmental sounds to gain insight and data. However, deploying deep learning (DL) models on resource-constrained embedded devices, such as Raspberry Pi (RPi) or Tensor Processing Units (TPUs), poses challenges. In this work, an evaluation of an existing pre-trained model for deployment on Raspberry Pi (RPi) and TPU platforms other than a laptop is proposed. We explored the impact of the retraining parameters and compared the sound classification performance across three datasets: ESC-10, BDLib, and Urban Sound. Our results demonstrate the effectiveness of the pre-trained model for transfer learning in embedded systems. On laptops, the accuracy rates reached 96.6% for ESC-10, 100% for BDLib, and 99% for Urban Sound. On RPi, the accuracy rates were 96.4% for ESC-10, 100% for BDLib, and 95.3% for Urban Sound, while on RPi with Coral TPU, the rates were 95.7% for ESC-10, 100% for BDLib and 95.4% for the Urban Sound. Utilizing pre-trained models reduces the computational requirements, enabling faster inference. Leveraging pre-trained models in embedded systems accelerates the development, deployment, and performance of various real-time applications.

MCrutch: A Novel m-Health Approach Supporting Continuity of Care.

This paper reports the architecture of a low-cost smart crutches system for mobile health applications. The prototype is based on a set of sensorized crutches connected to a custom Android application. Crutches were instrumented with a 6-axis inertial measurement unit, a uniaxial load cell, WiFi connectivity, and a microcontroller for data collection and processing. Crutch orientation and applied force were calibrated with a motion capture system and a force platform. Data are processed and visualized in real-time on the Android smartphone and are stored on the local memory for further offline analysis. The prototype's architecture is reported along with the post-calibration accuracy for estimating crutch orientation (5° RMSE in dynamic conditions) and applied force (10 N RMSE). The system is a mobile-health platform enabling the design and development of real-time biofeedback applications and continuity of care scenarios, such as telemonitoring and telerehabilitation.

Development of the control system for LEGO Mindstorms EV3 mobile robot based on MATLAB/Simulink elements

The Mindstorms EV3 robot, developed by LEGO, is one of the popular robots that has been widely used in various fields. Unlike previous versions of mobile LEGO robots, EV3 allows the development of real-time applications for teaching a variety of subjects, as well as for conducting research experiments. The object of research in this case is the Mindstorms EV3 robot connected to MATLAB/Simulink. The design consists of a controller, one color sensor, two servo motors and one support wheel. Each servo motor is built on a DC collector motor with a matching gearbox and has the ability to measure the number of revolutions corresponding. A digital sensor with a sampling frequency of 1 kHz is used as a color sensor, which can determine the color or brightness of light. Despite its popularity, the EV3 robot control system in interaction with the MATLAB/Simulink programming environment is a rather complex solution and therefore requires further research. The scientific part of the research focuses on discovering the regularities of the Mindstorms EV3 control system, developing a control system model, and exploring the potential of MATLAB/Simulink to expand the robot's capabilities. An analysis of the main elements of the control system, such as sensors and servos, was carried out. The graphs of the dependences of the characteristics of the servo drives were built and the efficiency of the robot movement was checked depending on the parameters set in the program. The result of the development of the mobile robot control system was the adjustment of the mobile robot movement regulators along a given trajectory in the form of a drawn line, which allowed estimating the maximum permissible speed of the robot movement. The presented research and development of a control system based on MATLAB/Simulink elements allows using the proposed method to control a mobile robot with high precision, analyze and verify the robot's electromechanical parameters in real time. This control system has a high potential and can practically be integrated into industrial objects of mobile robotics, provided types the sensors and executive mechanisms of the mobile robot match.

Capture of real-time data from electronic health records: scenarios and solutions.

The integration of real-time data (RTD) in the electronic health records (EHRs) is transforming the healthcare of tomorrow. In this work, the common scenarios of capturing RTD in the healthcare from EHRs are studied and the approaches and tools to implement real-time solutions are investigated. Delivering RTD by representational state transfer (REST) application programming interfaces (APIs) is usually accomplished through a Publish-Subscribe approach. Common technologies and protocols used for implementing subscriptions are REST hooks and WebSockets. Polling is a straightforward mechanism for obtaining updates; nevertheless, it may not be the most efficient or scalable solution. In such cases, other approaches are often preferred. Database triggers and reverse proxies can be useful in RTD scenarios; however, they should be designed carefully to avoid performance bottlenecks and potential issues. The implementation of subscriptions through REST hooks and WebSocket notifications using a Fast Healthcare Interoperability Resources (FHIR) REST API, as well as the design of a reverse proxy and database triggers is described. Reference implementations of the solutions are provided in a GitHub repository. For the reverse proxy implementation, the Go language (Golang) was used, which is specialized for the development of server-side networking applications. For FHIR servers a python script is provided to create a sample Subscription resource to send RTD when a new Observation resource for specific patient id is created. The sample WebSocket client is written using the "websocket-client" python library. The sample RTD endpoint is created using the "Flask" framework. For database triggers a sample structured query language (SQL) query for Postgres to create a trigger when an INSERT or UPDATE operation is executed on the FHIR resource table is available. Furthermore, a use case clinical example, where the main actors are the healthcare providers (hospitals, physician private practices, general practitioners and medical laboratories), health information networks and the patient are drawn. The RTD flow and exchange is shown in detail and how it could improve healthcare. Capturing RTD is undoubtedly vital for health professionals and successful digital healthcare. The topic remains unexplored especially in the context of EHRs. In our work for the first time the common scenarios and problems are investigated. Furthermore, solutions and reference implementations are provided which could support and contribute to the development of real-time applications.

Comparison of Client-Server Solutions in the Development of Massively Multiplayer Online Games on Unity

This paper presents a critique of the traditional approach used to create a multiplayer game in the Unity real-time interactive application development system, especially in the case of a large number of concurrent users. As a hypothesis, an alternative option, which is not common, but which solves many of the problems of the previous approach, is proposed. Two client-server solutions have been compared for developing multiplayer online games in Unity, and the advantages of both approaches have been described for different cases. A game development architecture using a more up-to-date method is proposed: instead of the Mirror library, a standard toolkit for Unity development, microservices written in Golang are used. We present solid proofs of the preference of the alternative approach, the main advantage of which is the support of modern architecture providing high-speed communication between microservices, supported tests on messaging on different platforms.
 The test results confirm the hypothesis put forth, and we can conclude that the Unity bundle with Golang is more effective for multiplayer video games.
 The article also contains basic methods for debugging multi-threaded application in Golang bundled with Unity game development system and suggests a technological method that allows to get a fast way of data transfer between the client and the server.

Adaptive symbol-by-symbol decision feedback threshold detection receiver to suppress oceanic turbulence scintillation effect

In this paper, an adaptive symbol-by-symbol decision feedback (DFB) threshold detection receiver is proposed that can suppress the scintillation effect of oceanic turbulence in underwater wireless optical communication (UWOC) system. The DFB receiver has a simple structure and can estimate the instantaneous channel state information (CSI) through the previously detected signal after adding pilot-symbols to the transmission data header. It thereby adaptively adjusts the decision threshold of the UWOC system receiver. The DFB receiver is independent of perfect CSI (PCSI). The channel model and transmitted signal power information are not essential for it. Moreover, the channel bandwidth can accommodate the inserted pilot-symbols. Using the gamma–gamma oceanic turbulence model, the performance of the DFB receiver under intensity fluctuation scintillation index σI2=0.15 and σI2=1.60 with different signal-to-noise ratios (SNRs) γ and different pilot-symbol lengths L was simulated. The results showed that the performance of the proposed DFB receiver was highly similar to the performance of the ideal receiver with PCSI known in advance. When σI2=1.60, γ=33dB, L=10, the bit error rate performance of the decision threshold TDFB of the DFB receiver was the same as that of the decision threshold TPCSI of the PCSI receiver, and the SNR gain loss was 0 dB. Furthermore, a wavelet denoising method was designed to improve the DFB receiver performance. When the sampling frequency of the DFB receiver was 25 MHz, σI2=1.60, γ=24dB, L=10, the proposed wavelet denoising preprocessing could obtain a SNR gain of 2.1 dB. These findings suggest that the proposed DFB receiver can promote the development of real-time applications of UWOC system.

Development of smart application for house condition survey

Information based technologies and devices have proven their effectiveness in almost all the aspects of life. In near past it was hard to believe that, building systems and devices can be controlled through phone. However, IT has made it possible and today it is difficulty to think of a normal life without IT. Mobile application is another marvel of IT technology that allows people to carry all essential businesses through various types of mobile applications. Easiness, secure and resourceful are three factors that moves the masses of population to switch overuse of mobile technology. This study has been planned to develop a mobile application to conduct house condition survey. Methodology of study is planned on three core factors i.e. literature review, a data of building defects, and application development process. An inclusive database of low rise building defects (secondary data) was acquired from from earlier research of first author. The acquired secondary data from Chohan, A.H (2007) has surfaced the various types of building defects and their implications in low rise RCC construction/buildings. Data was validated through participation at site and 10 major segments of building defects were identified. Through review of literature research validity was established, application development process was accomplished and various IoT models and similar application were identified. In application development process, study has set the hierarchy of building defects, construed app. development process in domain of building surveying, validate the data, and developed a conceptual plan of smart survey. In addition, defined the development process of mobile application. In section 3, this research has explained the development of mobile application through three major steps i.e. classifying data, setting defects hierarchy and application development. Study has succeeded to surface a prototype framework of smart “Smart Condition Survey” (SCS) which can be practically used for development of real-time mobile application.

Container-Based Fast Service Migration Method for Mobile Edge Computing

Mobile edge computing (MEC) provides users with low-latency, high-bandwidth, and high-reliability services by migrating the computing power of the cloud computing center to the edge of the network. It is thus being considered an effective solution for the contradiction between the limited computing capabilities of Internet of Things (IoT) devices and the rapid development of delay-sensitive real-time applications. In this study, we propose and design a container union file system based on the differencing hard disk and dynamic loading strategy to address the excessively long migration time caused by the bundling transmission of the file system and container images during container-based service migration. The proposed method involves designing a mechanism based on a remote dynamic loading strategy to avoid the downloading of all container images, thereby reducing the long preparation time before which stateless migration can begin. Furthermore, in view of the excessive latency of the edge service during the stateful migration process, a strategy for avoiding the transmission of the underlying file system and container images is designed to optimize the service interruption time and service quality degradation time. Experiments show that the proposed method and strategy can effectively reduce the migration time of container-based services.

Intelligent Channel Allocation for Age of Information Optimization in Internet of Medical Things

Along with the development of realtime applications, the freshness of information becomes significant because the overdue information is worthless and useless and even harmful to the right judgement of system. Therefore, The Age of Information (AoI) used for marking the freshness of information is proposed. In Internet of Medical Things (IoMT), which is derived from the requirement of Internet of Thins (IoT) in medicine, high freshness of medical information should be guaranteed. In this paper, we introduce the AoI of medical information when allocating channels for users in IoMT. Due to the advantages of Deep Q-learning Network (DQN) applied in resource management in wireless network, we propose a novel DQN-based Channel Allocation (DQCA) algorithm to provide the strategy for channel allocation under the optimization of the system cost considering the AoI and energy consumption of coordinator nodes. Unlike the traditional centralized channel allocation methods, the DQCA algorithm is distributed as each user performs the DQN process separately. The simulation results show that our proposed DQCA algorithm is superior to Greedy algorithm and Q-learning algorithm in terms of the average AoI, average energy consumption and system cost.

WITHDRAWN: Development and deployment of real-time cloud applications on red hat OpenShift and IBM bluemix

Most computing services, hosted by a number of cloud service providers, are available on demand nowadays. There are a range of cloud computing realms and core fields that have been under scrutiny for a long time due to rising digital infrastructure demands and applications. The cloud service providers bill based on the space or service offered to the customer to get access to cloud data. It is not always possible in research and development to provide the actual cloud technology to conduct the experiments. It is not possible for any research scholar, educator or scientist to employ cloud providers at all times and then perform their algorithms or implementations. For research, production and testing purposes, the real time cloud platforms can be taken by the researchers with free tier. In this research manuscript, the inclusion of IBM Bluemix and Red Hat OpenShift clouds are done for the development and effectual deployment of web apps on cloud. Such services can be used by the researchers and academicians for high performance web applications on cloud with real time logs and analytics on assorted parameters.

Intelligent and Scalable Air Quality Monitoring With 5G Edge

Air pollution introduces a major challenge for societies, where it leads to the premature deaths of millions of people each year globally. Massive deployment of air quality sensing devices and data analysis for the resultant data will pave the way for the development of real-time intelligent applications and services, e.g., minimization of exposure to poor air quality either on an individual or city scale. 5G and edge computing supports dense deployments of sensors at high resolution with ubiquitous connectivity, high bandwidth, high-speed gigabit connections, and ultralow latency analysis. This article conceptualizes AI-powered scalable air quality monitoring and presents two systems of calibrating low-cost air quality sensors and the image processing of pictures captured by hyperspectral cameras to better detect air quality. We develop and deploy different AI algorithms in these two systems on a 5G edge testbed and perform a detailed analytics regarding to 1) the performance of AI algorithms and 2) the required communication and computation resources.

Cybersecurity framework of hybrid watermarking and selective encryption for secure HEVC communication

Recently, the digital multimedia cybersecurity has become a research topic of interest due to the fast development of real-time multimedia applications over public networks such as the Internet. Therefore, this research paper introduces an efficient cybersecurity framework for protecting the high-efficiency video coding (HEVC) frames. The suggested selective cybersecurity HEVC framework employs a robust hybrid technique based on watermarking and selective encryption for maintaining confidentiality and achieving copyright protection of the transmitted HEVC information. The watermarking method employs the Homomorphic transform and singular value decomposition in the discrete wavelet transform to increase the immunity of watermarked HEVC streams to attacks. Moreover, the selective encryption technique uses the Chaotic logistic map for encrypting the motion vector difference and the discrete cosine transform sign bits to provide the feature of HEVC format compliance with low encryption overhead cost. An extensive security investigation is carried out for the proposed selective HEVC cybersecurity framework. The obtained experimental outcomes ensure and validate the effectiveness of the selective HEVC cybersecurity framework for HEVC sequences transmission.

A Novel Real-Time MATLAB/Simulink/LEGO EV3 Platform for Academic Use in Robotics and Computer Science.

Over the last years, mobile robot platforms are having a key role in education worldwide. Among others, LEGO Robots and MATLAB/Simulink are being used mainly in universities to improve the teaching experience. Most LEGO systems used in the literature are based on NXT, as the EV3 version is relatively recent. In contrast to the previous versions, the EV3 allows the development of real-time applications for teaching a wide variety of subjects as well as conducting research experiments. The goal of the research presented in this paper was to develop and validate a novel real-time educational platform based on the MATLAB/Simulink package and the LEGO EV3 brick for academic use in the fields of robotics and computer science. The proposed framework is tested here in different university teaching situations and several case studies are presented in the form of interactive projects developed by students. Without loss of generality, the platform is used for testing different robot path planning algorithms. Classical algorithms like rapidly-exploring random trees or artificial potential fields, developed by robotics researchers, are tested by bachelor students, since the code is freely available on the Internet. Furthermore, recent path planning algorithms developed by the authors are also tested in the platform with the aim of detecting the limits of its applicability. The restrictions and advantages of the proposed platform are discussed in order to enlighten future educational applications.

Android-based object recognition application for visually impaired

Detecting objects in real-time and converting them into an audio output was a challenging task. Recent advancement in computer vision has allowed the development of various real-time object detection applications. This paper describes a simple android app that would help the visually impaired people in understanding their surroundings. The information about the surrounding environment was captured through a phone’s camera where real-time object recognition through tensorflow’s object detection API was done. The detected objects were then converted into an audio output by using android’s text-to-speech library. Tensorflow lite made the offline processing of complex algorithms simple. The overall accuracy of the proposed system was found to be approximately 90%.

Design, Development and Evaluation of an Intelligent Animal Repelling System for Crop Protection Based on Embedded Edge-AI

In recent years, edge computing has become an essential technology for real-time application development by moving processing and storage capabilities close to end devices, thereby reducing latency, improving response time and ensuring secure data exchange. In this work, we focus on a Smart Agriculture application that aims to protect crops from ungulate attacks, and therefore to significantly reduce production losses, through the creation of virtual fences that take advantage of computer vision and ultrasound emission. Starting with an innovative device capable of generating ultrasound to drive away ungulates and thus protect crops from their attack, this work provides a comprehensive description of the design, development and assessment of an intelligent animal repulsion system that allows to detect and recognize the ungulates as well as generate ultrasonic signals tailored to each species of the ungulate. Taking into account the constraints coming from the rural environment in terms of energy supply and network connectivity, the proposed system is based on IoT platforms that provide a satisfactory compromise between performance, cost and energy consumption. More specifically, in this work, we deployed and evaluated various edge computing devices (Raspberry Pi, with or without a neural compute stick, and NVIDIA Jetson Nano) running real-time object detector (YOLO and Tiny-YOLO) with custom-trained models to identify the most suitable animal recognition HW/SW platform to be integrated with the ultrasound generator. Experimental results show the feasibility of the intelligent animal repelling system through the deployment of the animal detectors on power efficient edge computing devices without compromising the mean average precision and also satisfying real-time requirements. In addition, for each HW/SW platform, the experimental study provides a cost/performance analysis, as well as measurements of the average and peak CPU temperature. Best practices are also discussed and lastly, this article discusses how the combined technology used can help farmers and agronomists in their decision making and management process.

A Lightweight Secure and Energy-Efficient Fog-Based Routing Protocol for Constraint Sensors Network

The Wireless Sensor Network (WSN) has seen rapid growth in the development of real-time applications due to its ease of management and cost-effective attributes. However, the balance between optimization of network lifetime and load distribution between sensor nodes is a critical matter for the development of energy-efficient routing solutions. Recently, many solutions have been proposed for constraint-based networks using the cloud paradigm. However, they achieve network scalability with the additional cost of routing overheads and network latency. Moreover, the sensors’ data is transmitted towards application users over the uncertain medium, which leads to compromised data security and its integrity. Therefore, this work proposes a light-weight secure and energy-efficient fog-based routing (SEFR) protocol to minimize data latency and increase energy management. It exploits the Quality of Service (QoS) factors and facilitates time-sensitive applications with network edges. Moreover, the proposed protocol protects real-time data based on two levels of cryptographic security primitives. In the first level, a lightweight data confidentiality scheme is proposed between the cluster heads and fog nodes, and in the second level, a high-performance asymmetric encryption scheme is proposed among fog and cloud layers. The analysis of simulation-based experiments has proven the significant outcomes of the proposed protocol compared to existing solutions in terms of routing, security, and network management.

COTS-Based Architectural Framework for Reliable Real-Time Control Applications in Manufacturing

The challenge of keeping the development and implementation of real-time control systems reliable and efficient and at the same time, low-cost and low-energy, is getting harder. This is because system designers and developers are faced with the dependability, inflexibility and often high-cost of specialized or custom-built hardware and software components. This research attempts to tackle issues such as the reliability and efficiency of real-time control systems and advance further the current state-of-the-art. For this purpose, a strong emphasis is placed on finding novel efficient solutions based on standardized and commercially available off-the-shelf hardware/software components. In this direction, this research applies credible and feasible methodologies (e.g., model-based design, component-based design, formal verification, real-time scheduling, prototyping, and validation) in an innovative enhanced way. As an important outcome, a versatile integrative design approach and architectural framework (VIDAF) is proposed, which supports the development and implementation of reliable real-time control systems and applications using commercial off-the-shelf (COTS) components. The feasibility and applicability of the proposed system’s architecture are evaluated and validated through a system application in embedded real-time control in manufacturing. The research outcomes are expected to have a positive impact on emerging areas such as the Industrial Internet of Things (IIoT).