Real-time System Research Articles

Rapid photo-crosslinking, highly conductive, and anti-freezing acrylamide-based hydrogels applied for ECG sensors

In recent years, ionic conductive hydrogels have attracted significant attention in the wearable sensors field due to their simplified preparation process, cost- effectiveness, and high conductivity. However, a large amount of water in traditional hydrogels inevitably condense into ice at low temperatures and make them lose various properties in their practical applications. Hence, this study proposes a method to rapidly synthesized a water/glycerol binary anti-freezing hydrogel only in 10 s through photo-initiated free-radical polymerization. The introduction of LiCl into the crosslinked network composed of acrylamide (AAm) and poly (ethylene glycol) diacrylate (PEGDA) regulated the ionic conductivity of the hydrogel. The hydrogel exhibits a fracture strain of 1062.1 %, a tensile strength of 31 kPa, and an adhesive strength of 6.49 kPa. The synergistic effects of glycerol and LiCl imparted excellent anti-freezing properties (−60 °C). The three-dimensional network structure of the hydrogel facilitates ion transport, exhibiting superior conductivity (30.25 S/m), maintaining high conductivity capability of 1.46 S/m even at a low temperature of −55 °C. Notably, the hydrogel demonstrates prolonged durability and stability in real-time human motion monitoring applications while concurrently exhibiting remarkable sensitivity as an ECG sensor. Hence, this as-prepared hydrogel can adapt to extreme environments and deliver comprehensive performance as an ECG sensor.

Research on Process Control of Laser-Based Direct Energy Deposition Based on Real-Time Monitoring of Molten Pool

In the process of laser-based direct energy deposition (DED-LB), the quality of the deposited layer will be affected by the process parameters and the external environment, and there are problems such as poor stability and low accuracy. A molten pool monitoring method based on coaxial vision is proposed. Firstly, the molten pool image is captured by a coaxial CCD camera, and the geometric features of the molten pool are accurately extracted by image processing techniques such as grayscale, median filtering noise reduction, and K-means clustering combined with threshold segmentation. The molten pool width is accurately extracted by the Canny operator combined with the minimum boundary rectangle method, and it is used as the feedback of weld pool control. The influence of process parameters on the molten pool was further analyzed. The results show that with an increase in laser power, the width and area of the molten pool increase monotonously, but exceeding the material limit will cause distortion. Increasing the scanning speed will reduce the size of the molten pool. By comparing the molten pool under constant power mode and width control mode, it is found that in width control mode, the melt pool width fluctuates less, and the machining accuracy is improved, validating the effectiveness of the real-time control system.

BLUE SABINO: Development of a BiLateral Upper-Limb Exoskeleton for Simultaneous Assessment of Biomechanical and Neuromuscular Output

Arm and hand function play a critical role in the successful completion of everyday tasks. Lost function due to neurological impairment impacts millions of lives worldwide. Despite improvements in the ability to assess and rehabilitate arm deficits, knowledge about underlying sources of impairment and related sequela remains limited. The comprehensive assessment of function requires the measurement of both biomechanics and neuromuscular contributors to performance during the completion of tasks that often use multiple joints and span three-dimensional workspaces. To our knowledge, the complexity of movement and diversity of measures required are beyond the capabilities of existing assessment systems. To bridge current gaps in assessment capability, a new exoskeleton instrument is developed with comprehensive bilateral assessment in mind. The development of the BiLateral Upper-limb Exoskeleton for Simultaneous Assessment of Biomechanical and Neuromuscular Output (BLUE SABINO) expands on prior iterations toward full-arm assessment during reach-and-grasp tasks through the development of a dual-arm and dual-hand system, with 9 active degrees of freedom per arm and 12 degrees of freedom (six active, six passive) per hand. Joints are powered by electric motors driven by a real-time control system with input from force and force/torque sensors located at all attachment points between the user and exoskeleton. Biosignals from electromyography and electroencephalography can be simultaneously measured to provide insight into neurological performance during unimanual or bimanual tasks involving arm reach and grasp. Design trade-offs achieve near-human performance in exoskeleton speed and strength, with positional measurement at the wrist having an error of less than 2 mm and supporting a range of motion approximately equivalent to the 50th-percentile human. The system adjustability in seat height, shoulder width, arm length, and orthosis width accommodate subjects from approximately the 5th-percentile female to the 95th-percentile male. Integration between precision actuation, human–robot-interaction force-torque sensing, and biosignal acquisition systems successfully provide the simultaneous measurement of human movement and neurological function. The bilateral design enables use with left- or right-side impairments as well as intra-subject performance comparisons. With the resulting instrument, the authors plan to investigate underlying neural and physiological correlates of arm function, impairment, learning, and recovery.

Charge monitoring of test masses in gravitational waves interferometers

The sensitivity of gravitational waves (GWs) detectors is impacted by several sources of noise. One such source is related to the deposition of charge on the test masses (TMs), since its interaction with the surrounding electrical fields introduces an unwanted non-gravitational force on the TM. The charging process is still not completely understood and the charge deposition has historically never been consistently monitored, therefore we propose to implement a real-time monitoring system to overcome these limitations. In particular, in our work we explored 32 candidate sensor locations. Using simulation, we considered more than 100,000 different Gaussian-distributed charge located on either the front or back face of the TM. Utilizing dimensional reduction theories, like the PCA, we established sensor selection criteria to individuate the most relevant sensors. Exploiting Neural Networks (NNs), trained on the simulations, we determined the charge distributions.

Optimized automated blood cells analysis using Enhanced Greywolf Optimization with integrated attention mechanism and YOLOv5

Blood testing is widely regarded as a fundamental diagnostic procedure in healthcare, with blood cell counting being particularly vital for diagnosing conditions and evaluating overall health. Traditional methods employing hemocytometers and diverse laboratory instruments are typically employed to conduct blood counts manually. Using a microscope, manually counting, and examining blood cells takes time and effort. Thus, developing autonomous blood cells detecting and counting cells in this system becomes necessary to help doctors diagnose patients quickly and accurately. The proposed method integrates the YOLOv5 object detection framework with Enhanced Graywolf Optimization (EGWO) and an Attention Mechanism to improve the accuracy and efficiency of RBC detection. The YOLOv5 architecture is optimized for the task through the EGWO algorithm, which fine-tunes the model parameters, while the Attention Mechanism focuses on extracting critical features from blood cell images. The model is trained and validated using the BCCD dataset, employing an 80/20 split for training and validation. Data augmentation techniques, such as random rotations, flips, and color adjustments, are applied to enhance model generalization. Experimental results demonstrate that our method achieves a high accuracy of 99.89 %, outperforming existing models like Faster R-CNN, CNN, and DCNN regarding accuracy, training time, and inference speed. The proposed method's moderate complexity and fast inference time make it suitable for real-time applications in clinical settings. This research provides a robust and efficient solution for automated RBC detection and counting, with potential implications for improving diagnostic processes in hematological analyses. Overall, in practical applications, it is helpful for counting red blood cells from smeared pictures in less than a second.

Object classification through heterogeneous fog with a fast data-driven algorithm using a low-cost single-photon avalanche diode array

This study presents a framework for classifying a wooden mannequin’s poses using a single-photon avalanche diode (SPAD) array in dynamic and heterogeneous fog conditions. The target and fog generator are situated within an enclosed fog chamber. Training datasets are continuously collected by configuring the temporal and spatial resolutions on the sensor's firmware, utilizing a low-cost SPAD array sensor priced below $5, consisting of an embedded SPAD array and diffused VCSEL laser. An extreme learning machine (ELM) is trained for rapid pose classification, as a benchmark against CNN. We quantitatively justify the selection of nodes in the hidden layer to balance the computing speed and accuracy. Results demonstrate that ELM can accurately classify mannequin poses when obscured by dynamic heavy fog to 35 cm away from the sensor, enabling real-time applications in consumer electronics. The proposed ELM achieves 90.65% and 89.58% accuracy in training and testing, respectively. Additionally, we demonstrate the robustness of both ELM and CNN as the fog density increases. Our study also discusses the sensor’s current optical limitations and lays the groundwork for future advancements in sensor technology.

Drones in Action: A Comprehensive Analysis of Drone-Based Monitoring Technologies

Unmanned aerial vehicles (UAVs), commonly referred to as drones, are extensively employed in various real-time applications, including remote sensing, disaster management and recovery, logistics, military operations, search and rescue, law enforcement, and crowd monitoring and control, owing to their affordability, rapid processing capabilities, and high-resolution imagery. Additionally, drones mitigate risks associated with terrorism, disease spread, temperature fluctuations, crop pests, and criminal activities. Consequently, this paper thoroughly analyzes UAV-based surveillance systems, exploring the opportunities, challenges, techniques, and future trends of drone technology. It covers common image preprocessing methods for drones and highlights notable one- and two-stage deep learning algorithms used for object detection in drone-captured images. The paper also offers a valuable compilation of online datasets containing drone-acquired photographs for researchers. Furthermore, it compares recent UAV-based imaging applications, detailing their purposes, descriptions, findings, and limitations. Lastly, the paper addresses potential future research directions and challenges related to drone usage

OneTrack - An End2End approach to enhance MOT with Transformers

This paper introduces OneTrack, an innovative end-to-end transformer-based model for Multiple Object Tracking (MOT), focusing on enhancing efficiency without significantly compromising accuracy. Addressing the challenges inherent in MOT, such as occlusions, varied object sizes, and motion prediction, OneTrack leverages the power of vision transformers and attention layers, optimizing them for real-time applications. Utilizing a unique Object Sequence Patch Input and a Vision Transformer Encoder, the model simplifies the standard transformer approach by employing only the encoder component, significantly reducing computational costs. This approach is validated using the MOT17 dataset, a benchmark in the field, ensuring a comprehensive evaluation against established metrics like MOTA, HOTA, and IDF1. The experimental results demonstrate OneTrack's capability to outperform other transformer-based models in inference speed, with a marginal trade-off in accuracy metrics. The model's inherent design limitations, such as a maximum of 100 objects per window, are adjustable to suit specific applications, offering flexibility in various scenarios. The conclusion highlights the model's potential as a lightweight solution for MOT tasks, suggesting future work directions that include exploring alternative data representations and encoders, and developing a dedicated loss function to further enhance detection and tracking capabilities. OneTrack presents a promising step towards efficient and effective MOT solutions, catering to the demands of real-time applications.

REAL-TIME PARAMETRIC DIAGNOSTICS OF MARINE DIESEL ENGINES

Using modern high-performance microcontrollers with wireless interfaces, built-in ADC and low overall consumption, it is possible to develop a portable real-time parametric diagnostic system for marine engines. The system is based on the use of modern Android/iOS gadgets that receive information from sensors via Bluetooth and then make the necessary calculations and display charts and data in real time. The system being developed uses a combination of a gas pressure sensor in the working cylinder and a vibroacoustic sensor, which expands the diagnostic capabilities of marine diesel engines under operating conditions. This solution makes it possible to diagnose the fuel injection system, the valves control mechanism, as well as some other engine systems. In order to develop a portable diagnostic system for marine diesel engines, it is first necessary to solve the problem of analytically determining TDC, since such a system does not involve the use of special sensors for this. Parameters of irregular engine operation are considered, which can be calculated in real time using time diagrams of pressure and vibration. Methods for expressly assessing the stability of the functioning of the main engine systems by monitoring and analyzing a number of successive operating cycles are considered. To assess the unevenness of engine operation, a dispersion estimate of deviations of the main parameters is used. For a comprehensive assessment of engine stability in real time, the CII (cycle’s irregularity index) criterion is proposed. The assessment of fuel injection irregularity in the engine cylinder can be carried out by analyzing the phase fronts of the vibration signal of the injector. Coefficients for assessing the irregularity of fuel injection and gas distribution valves timing are also proposed, using dispersion analysis of vibrograms fronts. The data processing methods proposed in the article will make it possible to analyze the stability of operating cycles, as well as to perform optimal tuning of engine systems and monitor the results during operation.

Causal technological model for predicting void fraction and energy consumption in material extrusion process of polylactic acid

The rapid expansion of Additive Manufacturing (AM) technologies within the framework of Industry 4.0 raises important questions about their sustainability. Specifically, the widespread use of Material Extrusion (MEx) processes for polymeric materials necessitates a thorough evaluation of the possible balance between performance efficiency and sustainability, highlighting the need for further research into optimizing process parameters to ensure high product quality and reduced energy consumption. The present work focuses on Fused Filament Fabrication (FFF) technology, investigating the relationship between sustainability and print quality. A causal technology model is proposed to elucidate this relationship, underlining the impact of process parameters on defect generation and energy use. A real-time monitoring system for energy consumption measurements was employed to create an energy model that accounts for each component of the CreatBot F430 printer. An image analysis procedure was performed on a special configuration of the HIROX RH-2000 digital microscope to determine the void fraction of each sample. An experimental campaign was conducted, producing single-layer samples in polylactic acid (PLA) following a full factorial plan with four process parameters. A void fraction of up to 18 % was observed, with energy consumption per sample ranging between 638 J and 8843 J. Statistical analysis was performed to assess the impact of each process parameter and to determine operational ranges through the Response Surfaces Method. These results showed good agreement with the predicted conditions, demonstrating the model's effectiveness in supporting decision-making processes in technology selection.

Low-Cost Real-Time Localisation for Agricultural Robots in Unstructured Farm Environments

Agricultural robots have demonstrated significant potential in enhancing farm operational efficiency and reducing manual labour. However, unstructured and complex farm environments present challenges to the precise localisation and navigation of robots in real time. Furthermore, the high costs of navigation systems in agricultural robots hinder their widespread adoption in cost-sensitive agricultural sectors. This study compared two localisation methods that use the Error State Kalman Filter (ESKF) to integrate data from wheel odometry, a low-cost inertial measurement unit (IMU), a low-cost real-time kinematic global navigation satellite system (RTK-GNSS) and the LiDAR-Inertial Odometry via Smoothing and Mapping (LIO-SAM) algorithm using a low-cost IMU and RoboSense 16-channel LiDAR sensor. These two methods were tested on unstructured farm environments for the first time in this study. Experiment results show that the ESKF sensor fusion method without a LiDAR sensor could save 36% of the cost compared to the method that used the LIO-SAM algorithm while maintaining high accuracy for farming applications.

Mechanistic studies on bioremediation of dye using Aeromonas veronii immobilized peanut shell biochar

Recalcitrant chemicals in the environment not only present obstacles to living organisms but also contribute to the degradation of natural resources. One contribution to environmental pollution is the discharge of synthetic dyes from the textile sector. This study investigates the combined effect of microbial cells and biochar on eliminating methyl orange (MO) dye. The immobilization of Aeromonas veronii on peanut shell biochar (APSB) was conducted to investigate its efficacy in removing MO dye from water. PSB synthesized by pyrolysis at 300 °C for 120 min showed maximum bacterial immobilization potential. The highest degradation rate of 96.19 % was achieved in APSB within 96 h using MO dye concentration of 100 mg L−1, incubation temperature of 37 °C, pH 7, and biocatalyst dosage of 1g L−1. In comparison, free cells achieved degradation rates of 72.53 % and 61.56 % for PSB. Moreover, the adsorption process was primarily controlled by PSB, with subsequent dye mineralization by A. veronii, as supported by FTIR and LC-MS studies. Moreover, this innovative approach exhibited the reusability of the biocatalyst, giving 76.23 % removal after fifth cycle, suggesting sustainable alternative in dye remediation and potential option for real-time applications.

Enhanced query performance for stored streaming data through structured streaming within spark SQL

Traditional database systems like relational databases can store data which are structured with predefined schema, but in the case of bigdata, the data comes in different formats or are collected from diverse sources. The distributed databases like not only spark querying language (NoSQL) repositories are often used in relation to bigdata analytics, but a continual updating is required in business because of the streaming data that comes from stock trading, online activities of website visitors, and from the mobile applications in real time. It will not have to delay, for some report to show up, to assess and analyse the current situation, to move forward with the next business choice. Apache Spark’s structured streaming offer capabilities for handling streaming data in a batch processing mode with faster responses compared to MongoDB which is a document-based NoSQL database. This study completes similar queries to evaluate Spark SQL and NoSQL database performance, focusing on the upsides of Spark SQL over NoSQL databases in streaming data exploration. The queries are completed with streaming data stored in a batch mode.

Fault diagnosis and intelligent maintenance of industry 4.0 power system based on internet of things technology and thermal energy optimization

The application of Internet of Things technology provides a new opportunity for the fault diagnosis and maintenance of power system. This study aims to explore how to improve the fault diagnosis ability of power system through Internet of Things technology, and realize the efficient utilization of heat energy, so as to achieve the goal of intelligent maintenance. Based on the analysis of current power system operation status, this paper determines the key role of thermal energy management in improving system operation efficiency and reducing energy consumption. It then uses iot sensors and data analytics to monitor heat flow and loss in the power system in real time, identifying potential points of failure through big data. In order to realize intelligent maintenance, this paper designs a fault prediction model based on thermal energy optimization, combined with machine learning algorithm, to further improve the accuracy of fault diagnosis. The experimental results show that the fault diagnosis method combined with the Internet of Things technology can significantly reduce the fault incidence and optimize the efficiency of heat energy use. By applying this model, the overall operating cost of the power system is reduced and the maintenance efficiency is improved.

Design and Development of a Real-Time Monitoring System for Highway Road Surface Anomalies

The recent unprecedented increase in highway networks in India has increased the need to maintain safe and smooth roads. The increasing number of road surface anomalies such as cracks, surface irregularities, and anomalies can lead to vehicle damage and accidents. This research work presents the design and development of a real-time monitoring system for detecting anomalies in highway road surfaces. The proposed system utilizes the machine vision model integrated with cameras, sensors, and edge computing to provide timely and accurate alerts for the drivers and also to the road maintenance authorities. The proposed solution is designed specially to work under different environmental conditions and enable large-scale deployment. The output generated from the proposed model is visual feedback of the detected anomalies and its severity analysis, enabling quick road maintenance actions.

Ultrasonically assisted fabrication of electrochemical platform for tinidazole detection

Based on sonochemistry, green synthesis methods play an important role in the development of nanomaterials. In this work, a novel chitosan modified MnMoO4/g-C3N4 (MnMoO4/g-C3N4/CHIT) was developed using ultrasonic cell disruptor (500 W, 30 kHz) for ultra-sensitive electrochemical detection of tinidazole (TNZ) in the environment. The morphology and surface properties of the synthesized MnMoO4/g-C3N4/CHIT electrode were characterized using X-ray diffraction (XRD), fourier transform infrared spectroscopy (FT-IR), scanning electron microscope (SEM) and transmission electron microscope (TEM). Cyclic voltammetry (CV) and differential pulse voltammetry (DPV) techniques were utilized to assess the electrochemical performance of TNZ. The results indicate that the electrochemical detection performance of TNZ is highly efficient, with a detection limit (LOD) of 3.78 nM, sensitivity of 1.320 µA·µM−1·cm−2, and a detection range of 0.1–200 μM. Additionally, the prepared electrode exhibits excellent selectivity, desirable anti-interference capability, and decent stability. MnMoO4/g-C3N4/CHIT can be successfully employed to detect TNZ in both the Songhua River and tap water, achieving good recovery rates within the range of 93.0 % to 106.6 %. Consequently, MnMoO4/g-C3N4/CHIT’s simple synthesis might provide a new electrode for the sensitive, repeatable, and selective measurement of TNZ in real-time applications. Using the MnMoO4/g-C3N4/CHIT electrode can effectively monitor and detect the concentration of TNZ in environmental water, guiding the sewage treatment process and reducing the pollution level of antibiotics in the water environment.

Chaos-based audio encryption: Efficacy of 2D and 3D hyperchaotic systems

Secure communication in the digital age is necessary; securing audio data becomes very critical since this is normally transmitted across susceptible networks. Traditional approaches to encryption, like Advanced Encryption Standard and Rivest–Shamir–Adleman, are pretty solid but mostly too computationally intensive for real-time audio applications. This paper presents a new audio encryption scheme using chaotic systems, characterized by high sensitivity to initial conditions, pseudo-randomness, and determinism. These properties make chaotic systems ideal for generating keys for cryptographic purposes. Indeed, complex keys that would be nearly impossible to reverse-engineer without exact initial parameters can be obtained with them. This methodology first digitizes the audio signal and subsequently encrypts each sample according to chaotic sequences from multiple systems, like 2D Memristive Hyperchaotic Maps and 3D Hyperchaotic Quadratic Maps. The encryption processes enormously increase the randomness of the encrypted audio and blind the original data, demonstrating the strength of the chaotic systems in securing audio information.Simulation of this encryption approach confirms its effectuality in providing strong security for audio data while maintaining efficiency, thus being suitable for real-time applications. The developed encryption schemes show high resistance to differential attacks, while the quality of the encrypted audio remains acceptable after transformation. This result can demonstrate chaotic systems’ potential to provide a secure and efficient solution for audio communications, which has broad applications in telecommunications, military communications, and confidential conferencing systems. This work, described in the paper, thus satisfies the growing demand for robust security in digital communications and opens up avenues of future research toward chaotic parameter optimization and an extension to other forms of data, enhancing the security features in all types of digital communication channels.

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.

A Review of Negligible Factors Inimical to Building Failures in Nigeria: Architectural View

Building failures pose significant risks to public safety, property, and the environment, making it imperative to identify and address the negligible factors contributing to such failures in Nigeria. This review examines the architectural perspective of building failures in Nigeria, focusing on factors that play a role in mitigating risks and promoting safer construction practices. Through an analysis of recent literature and empirical evidence, the study highlights key findings, conclusions, and recommendations aimed at enhancing construction safety standards nationwide. The findings of the review reveal the multifaceted nature of building failures, influenced by regulatory frameworks, construction materials, skilled labor, community engagement, technological integration, economic incentives, and environmental considerations. Strengthening regulatory enforcement, promoting the use of high-quality construction materials, and investing in skilled labor development emerge as critical strategies for mitigating risks and improving construction safety outcomes. Community engagement initiatives and technological innovations, such as building information modeling (BIM) and real-time monitoring systems, are also identified as effective measures for enhancing transparency and accountability in construction projects. Based on the findings, the study concludes that addressing the negligible factors inimical to building failures requires a holistic approach, encompassing regulatory reforms, capacity building, and technological advancements. Furthermore, the study emphasizes the importance of fostering collaboration between government agencies, industry stakeholders, and local communities to promote construction safety and sustainable development goals. The recommendations proposed include enhancing regulatory enforcement mechanisms, promoting the use of high-quality construction materials, and investing in skilled labor development programs. Additionally, leveraging technological innovations and fostering community engagement initiatives are identified as key strategies for enhancing construction safety standards and fostering sustainable development in Nigeria. Overall, this review provides valuable insights and actionable recommendations for policymakers, construction professionals, and community stakeholders seeking to address the negligible factors contributing to building failures in Nigeria. By implementing the recommendations outlined in this study, stakeholders can work collaboratively to create safer, more resilient built environments that promote the well-being of communities and support sustainable development goals.

Representation and Processing of Temporal Cases in Real-Time Intelligent Systems

Representation and Processing of Temporal Cases in Real-Time Intelligent Systems