Real-time System Research Articles

Development and Experimentation of a Real-Time Greenhouse Positioning System Based on IUKF-UWB

Development and Experimentation of a Real-Time Greenhouse Positioning System Based on IUKF-UWB

TDBAMLA: Temporal and dynamic behavior analysis in Android malware using LSTM and attention mechanisms

The increasing ubiquity of Android devices has precipitated a concomitant surge in sophisticated malware attacks, posing critical challenges to cybersecurity infrastructures worldwide. Existing models have achieved significant strides in malware detection but often suffer from high false-positive rates, lower recall, and computational delays, thus demanding a more efficient and accurate system. Current techniques primarily rely on static features and simplistic learning models, leading to inadequate handling of temporal aspects and dynamic behaviors exhibited by advanced malware. These limitations compromise the detection of modern, evasive malware, and impede real-time analysis. This paper introduces a novel framework for Android malware detection that incorporates Temporal and Dynamic Behavior Analysis using Long Short-Term Memory (LSTM) networks and Attention Mechanisms. We further propose development of an efficient Grey Wolf Optimized (GWO) Decision Trees to find the most salient API call patterns associated with malwares. An Iterative Fuzzy Logic (IFL) layer is also deployed before classification to assess the "trustworthiness" of app metadata samples. For Ongoing Learning, we propose use of Deep Q-Networks (DQNs), which helps the reinforcement learning model to adapt more quickly to changes in the threat landscapes. By focusing on crucial system calls and behavioral characteristics in real-time, our model captures the nuanced temporal patterns often exhibited by advanced malwares. Empirical evaluations demonstrate remarkable improvements across multiple performance metrics. Compared to existing models, our approach enhances the precision of malware identification by 8.5 %, accuracy by 5.5 %, and recall by 4.9 %, while also achieving an 8.3 % improvement in the Area Under the Receiver Operating Characteristic Curve (AUC), with higher specificity and a 4.5 % reduction in identification delay. In malware pre-emption tasks, our model outperforms by improving precision by 4.3 %, accuracy by 3.9 %, recall by 4.9 %, AUC by 3.5 %, and increasing specificity by 2.9 %. These gains make our framework highly applicable for real-time detection systems, cloud-based security solutions, and threat intelligence services, thereby contributing to a safer Android ecosystem.

Multisource data framework for prehospital emergency triage in real-time IoMT-based telemedicine systems

Background and objectiveThe Internet of Medical Things (IoMT) has revolutionized telemedicine by enabling the remote monitoring and management of patient care. Nevertheless, the process of regeneration presents the difficulty of effectively prioritizing the information of emergency patients in light of the extensive amount of data generated by several integrated health care devices. The main goal of this study is to be improving the procedure of prioritizing emergency patients by implementing the Real-time Triage Optimization Framework (RTOF), an innovative method that utilizes diverse data from the Internet of Medical Things (IoMT). MethodsThe study’s methodology utilized a variety of Internet of Medical Things (IoMT) data, such as sensor data and texts derived from electronic medical records. Tier 1 supplies sensor and textual data, and Tier 3 imports textual data from electronic medical records. We employed our methodologies to handle and examine data from a sample of 100,000 patients afflicted with hypertension and heart disease, employing artificial intelligence algorithms. We utilized five machine-learning algorithms to enhance the accuracy of triage. ResultsThe RTOF approach has remarkable efficacy in a simulated telemedicine environment, with a triage accuracy rate of 98%. The Random Forest algorithm exhibited superior performance compared to the other approaches under scrutiny. The performance characteristics attained were an accuracy rate of 98%, a precision rate of 99%, a sensitivity rate of 98%, and a specificity rate of 100%. The findings show a significant improvement compared to the present triage methods. ConclusionsThe efficiency of RTOF surpasses that of existing triage frameworks, showcasing its significant ability to enhance the quality and efficacy of telemedicine solutions. This work showcases substantial enhancements compared to existing triage approaches, while also providing a scalable approach to tackle hospital congestion and optimize resource allocation in real-time. The results of our study emphasize the capacity of RTOF to mitigate hospital overcrowding, expedite medical intervention, and enable the creation of adaptable telemedicine networks. This study highlights potential avenues for further investigation into the integration of the Internet of Medical Things (IoMT) with machine learning to develop cutting-edge medical technologies.

1200: Clinical introduction of a real-time MLC-tracking system for lung and prostate motion

1200: Clinical introduction of a real-time MLC-tracking system for lung and prostate motion

Literature Survey on Revolutionizing Fake Currency Detection: CNN-Based Approach for Indian Rupee Notes

The proliferation of counterfeit currency poses a significant challenge in various economies, including India. To address this issue, several studies have proposed innovative image processing and machine learning techniques for detecting counterfeit coins and banknotes. Leveraging digital image processing, these studies aim to enhance the security measures against counterfeit currency through accurate and efficient recognition systems. Techniques such as preprocessing, segmentation, feature extraction, and clustering are employed to identify fraudulent currency. The use of Support Vector Machines (SVM), k-means clustering, and Convolutional Neural Networks (CNN) facilitates the recognition and classification of genuine and counterfeit currency. Moreover, these studies explore the application of spatial coding, component-based recognition, and deep learning algorithms to improve the accuracy and robustness of counterfeit detection systems. By developing real-time recognition systems capable of identifying counterfeit currency, these research efforts contribute to combating financial fraud and safeguarding economic integrity.

Real-time placental vessel segmentation in fetoscopic laser surgery for Twin-to-Twin Transfusion Syndrome

Twin-to-Twin Transfusion Syndrome (TTTS) is a rare condition that affects about 15% of monochorionic pregnancies, in which identical twins share a single placenta. Fetoscopic laser photocoagulation (FLP) is the standard treatment for TTTS, which significantly improves the survival of fetuses. The aim of FLP is to identify abnormal connections between blood vessels and to laser ablate them in order to equalize blood supply to both fetuses. However, performing fetoscopic surgery is challenging due to limited visibility, a narrow field of view, and significant variability among patients and domains. In order to enhance the visualization of placental vessels during surgery, we propose TTTSNet, a network architecture designed for real-time and accurate placental vessel segmentation. Our network architecture incorporates a novel channel attention module and multi-scale feature fusion module to precisely segment tiny placental vessels. To address the challenges posed by FLP-specific fiberscope and amniotic sac-based artifacts, we employed novel data augmentation techniques. These techniques simulate various artifacts, including laser pointer, amniotic sac particles, and structural and optical fiber artifacts. By incorporating these simulated artifacts during training, our network architecture demonstrated robust generalizability. We trained TTTSNet on a publicly available dataset of 2060 video frames from 18 independent fetoscopic procedures and evaluated it on a multi-center external dataset of 24 in-vivo procedures with a total of 2348 video frames. Our method achieved significant performance improvements compared to state-of-the-art methods, with a mean Intersection over Union of 78.26% for all placental vessels and 73.35% for a subset of tiny placental vessels. Moreover, our method achieved 172 and 152 frames per second on an A100 GPU, and Clara AGX, respectively. This potentially opens the door to real-time application during surgical procedures. The code is publicly available at https://github.com/SanoScience/TTTSNet.

Internet of Things (IoT) Driven Water Management System for Efficient Level Control

Water is an essential and valuable resource in daily life, making its conservation crucial to prevent adverse effects. Storing water for domestic, industrial, agricultural, and other purposes is particularly important. Safe drinking water is increasingly becoming polluted due to the growing population and their demands for urbanization and industrialization. At the household level, some people leave electric water pumps running and either go to work or sleep, forgetting to turn off the pumps when the water container is full. This highlights the need for a reliable and continuous water supply. IoT plays a significant role in environmental monitoring, particularly in disaster management, early warning systems, and environmental data analytics. One major challenge in urban cities is water management, especially with the rapid growth of urbanization, necessitating sustainable urban development plans. To address these issues, we propose a "Water Level Monitoring System" solution. This paper presents an IoT-based water monitoring system for real-time applications. The system's sensors measure the water level in the tank, and the data is sent to a cloud server, allowing users to view it on a remote dashboard. This system can be used efficiently by both homeowners and industrial users, as well as other water utilities.

A near real-time carbon accounting framework for the decarbonization of maritime transport

The recently proposed marine greenhouse gas (GHG) emission pricing mechanism has pressured shipping companies and regulators to adopt effective methods for the real-time monitoring of carbon emissions. This study proposes a Near Real-Time (NRT) carbon accounting framework that leverages machine learning models to enable carbon emission tracking at a 15-minute time interval. The framework incorporates critical factors, such as ship navigation characteristics, weather, and sea conditions to achieve accurate carbon accounting. We validate the framework’s efficacy through a case study of four mega-container ships of varying sizes and navigation scenarios. Our results show a maximum cumulative error of 5.83% for all ship navigation scenarios, even without critical data, and during the most extended voyages of their respective services. The proposed framework provides a new perspective on the decarbonization application of ship energy efficiency prediction research. By integrating it with a cloud-computing platform, shipping companies can enhance their voyage planning and route adjustment to optimize operational efficiency and reduce carbon footprints. Using this framework, international maritime transport regulators can develop an early warning system for carbon emissions to coordinate and improve environmental sustainability practices in the shipping industry.

Distance-Based Localization in Wireless Sensor Network Using Exponential Grey Prediction Model

Localization in wireless sensor network is the key for many applications in which location of occurrence of event is very important. Distance based localization techniques have proved to be more accurate and feasible for the location calculation of the sensor nodes. Moreover, Received Signal Strength Indicator (RSSI) based techniques are well adopted by the researcher to calculate the distance between the sensor nodes. The optimized value of RSSI may estimate the location more precisely. In this paper, Exponential Grey prediction model and weighted predicted RSSI values are utilized to provide estimated values of RSSI which are then optimized using an objective function. The decreasing term used in the differential equation of the Grey prediction model provides better prediction accuracy which if optimized provides efficient result. The conventional Grey prediction model is limited by the sample space and scope of applications. The Exponential Grey prediction model used in the proposed algorithm removes those drawbacks and can be applied in real time applications with larger sample space. The objective function used in the proposed algorithm uses weighting factor to be applied to Grey predicted RSSI and weighted predicted RSSI to estimate the RSSI value more accurately. The objective function calculates more near values of RSSI to localize the node more accurately. The simulation results obtained are compared with the latest techniques of RSSI calculation like weighted centroid method, RSSI quantization, TMA and FRBW algorithm. The results show that the proposed algorithm outperform the existing methods for RSSI estimation.

Binding-induced activation of CRISPR-Cas12a using PAM-engineered toehold switch and DNA tile architectures for enhanced detection of thrombin

This study presents a novel biosensor that integrates CRISPR-Cas12a technology with DNA tile-based architectures to enhance thrombin detection. Utilizing PAM-engineered toehold switches, the biosensor dynamically activates the CRISPR-Cas12a system upon thrombin recognition, significantly improving both sensitivity and specificity. The label-free electrochemical detection method offers rapid, cost-effective analysis, making it ideal for real-time clinical applications. The biosensor achieved an impressive detection limit of 103 aM, surpassing traditional methods and setting a new standard for thrombin assays. The biosensor's robust performance in complex biological samples, such as diluted human serum, demonstrates its reliability for clinical diagnostics. This is particularly important for the early diagnosis and management of thrombotic and coagulation-related disorders. By combining advanced genetic engineering with traditional electrochemical techniques, the biosensor not only optimizes detection but also expands its potential applications across various biomedical fields. Overall, this biosensor provides a powerful and innovative tool for thrombin detection, offering enhanced diagnostic accuracy, faster patient management, and potential cost savings in healthcare. Its superior performance and adaptability highlight its promise for broader adoption in clinical diagnostics and other biosensing applications.

An efficient hybrid approach for forecasting real-time stock market indices

The stock market’s volatility, noise, and information overload necessitate efficient prediction methods. Forecasting index prices in this environment is complex due to the non-linear and non-stationary nature of time series data generated from the stock market. Machine learning and deep learning have emerged as powerful tools for identifying financial data patterns and generating predictions based on historical trends. However, updating these models in real-time is crucial for accurate predictions. Deep learning models require extensive computational resources and careful hyperparameter optimization, while incremental learning models struggle to balance stability and adaptability. This paper proposes a novel hybrid bidirectional-LSTM (H.BLSTM) model that combines incremental learning and deep learning techniques for real-time index price prediction, addressing these scalability and memory challenges. The method utilizes both univariate time series derived from historical index prices and multivariate time series incorporating technical indicators. Implementation within a real-time trading system demonstrates the method’s effectiveness in achieving more accurate price forecasts for major stock indices globally through extensive experimentation. The proposed model achieved an average mean absolute percentage error of 0.001 across nine stock indices, significantly outperforming traditional models. It has an average forecasting delay of 2 s, making it suitable for real-time trading applications.

A Linear Regression Approach for Best Scanline Determination in the Object to Image Space Transformation Using Pushbroom Images.

The use of linear array pushbroom images presents a new challenge in photogrammetric applications when it comes to transforming object coordinates to image coordinates. To address this issue, the Best Scanline Search/Determination (BSS/BSD) field focuses on obtaining the Exterior Orientation Parameters (EOPs) of each individual scanline. Current solutions are often impractical for real-time tasks due to their high time requirements and complexities. This is because they are based on the Collinearity Equation (CE) in an iterative procedure for each ground point. This study aims to develop a novel BSD framework that does not need repetitive usage of the CE with a lower computational complexity. The Linear Regression Model (LRM) forms the basis of the proposed BSD approach and uses Simulated Control Points (SCOPs) and Simulated Check Points (SCPs). The proposed method is comprised of two main steps: the training phase and the test phase. The SCOPs are used to calculate the unknown parameters of the LR model during the training phase. Then, the SCPs are used to evaluate the accuracy and execution time of the method through the test phase. The evaluation of the proposed method was conducted using ten various pushbroom images, 5 million SCPs, and a limited number of SCOPs. The Root Mean Square Error (RMSE) was found to be in the order of ten to the power of negative nine (pixel), indicating very high accuracy. Furthermore, the proposed approach is more robust than the previous well-known BSS/BSD methods when handling various pushbroom images, making it suitable for practical and real-time applications due to its high speed, which only requires 2-3 s of time.

Efficient motion estimation and discrete cosine transform implementation using the graphics processing units.

Motion Estimation (ME) and the two-dimensional (2D) discrete cosine transform (2D-DCT) are both computationally expensive parts of HEVC standard, therefore real-time performance of the HEVC may not be free from glitches. To address this issue, this study deploys the graphics processing units (GPUs) to perform the ME and 2D-DCT tasks. In this concern, authors probed into four levels of parallelism (i.e., frame, macroblock, search area, and sum of the absolute difference (SAD) levels) existing in ME. For comparative analysis, authors involved full search (FS), test zone search (TZS) of HEVC, and hierarchical diamond search (EHDS) ME algorithms. Similarly, two levels of parallelism (i.e., macroblock and sub-macroblock) are also explored in 2D-DCT. Notably, the least computationally complex multithreaded Loeffler DCT algorithm is utilized for computing 2D-DCT. Experimental results show that ME processing task corresponding to 25 frames, with each frame of size (3840×2160) pixels, is accomplished in 0.15 seconds on the NVIDIA GeForce GTX 1080, whereas the 2D-DCT task along with the image reconstruction and differencing corresponding to 25 frames took 0.1 seconds. Collectively, both ME and 2D-DCT tasks are processed in 0.25 seconds, which still leaves enough room for the encoder's remaining parts to be executed within one second. Due to this enhancement, the resultant encoder can safely be used in real-time applications.

Develop an RS-485 Protocol for Arduino Boards Applied To Networked Real Time Control Systems

The Arduino microprocessor boards such as Mega 2560, UNO R3, Leonardo, Micro, and Nano are simple and low-cost tools for real-time measurement and control applications. These Arduino boards cannot be used in distributed systems because they lack the networking capabilities to transfer data across units. In this study, an RS-485 protocol for Arduino boards that operate in Master-Slave networks was developed. Network operations could be carried out independently on the main thread program, and devices in the network could react quickly to information received. This was made possible by the asynchronous serial communication feature and a high-speed timer provided in Arduino boards. The networks designed in this study were applied to an electric vehicle model with all-wheel drive and all-wheel steering capabilities for supermaneuverability as well as a saltwater intrusion early warning system installed in a river entry. The results showed that highly reliable and stable network operations could be achieved, thus extending the usage of popular Arduino boards for networked real-time applications.

Exergy and Energy Analysis of Bifacial PV Module Performance on a Cloudy Day in Saudi Arabia

Bifacial solar modules, capable of harvesting sunlight from both sides, present a promising pathway for sustainable energy generation. This study examines the performance of bifacial modules on a cloudy day through comparative exergy and energy analyses. The analysis considers both the quality and quantity of energy produced by bifacial and monofacial modules. Conducted at Qassim University in Buraydah City, Saudi Arabia, the study recorded measurements during two intervals on a cloudy day using a real-time photovoltaic measurement system. Module performance was evaluated concerning energy yield, exergy yield, and solar irradiance, factoring in ambient and module temperatures. The results demonstrate that bifacial modules outperformed monofacial modules in electrical energy and output exergy. In the first period, the bifacial modules showed a 9.5% higher exergy efficiency and a 7% greater energy efficiency compared to the monofacial modules. During the second period, the bifacial modules achieved a 4.5% higher exergy efficiency and a 3.5% increased energy efficiency over the monofacial modules. These findings contribute to global sustainability efforts by reducing fossil fuel dependence and optimizing bifacial PV module design and operation for enhanced energy and exergy efficiency, even in cloudy conditions. The study’s implications for sustainable development and energy policies underscore the essential role of advanced PV technologies in achieving sustainable energy goals.

Towards maximum efficiency of an open-cathode PEM fuel cell system: A comparative experimental demonstration

Despite its high energy density and zero-carbon emissions, the path to large commercialization for Proton Exchange Membrane Fuel Cell (PEMFC) systems faces challenges related to cost reduction, efficiency enhancement, and durability improvement. One key strategy to address these hurdles is the development of reliable control algorithms that ensure operation at the Maximum Efficiency Point (MEP) of PEMFC systems. This optimization is essential for achieving high efficiency, a crucial factor for PEMFC technology commercial viability. This work explores the experimental design, implementation, and evaluation of Maximum Efficiency Point Tracking (MEPT) control algorithms for a PEMFC system. The investigated methods span across five distinct categories: conventional, data-driven, metaheuristics, online model identification, and filter-based approaches. To assess these methods, a real-time small-scale PEMFC system test bench was built. The setup consists of 200 W open-cathode PEMFC, industrial step-up DC–DC converter, programmable load and a cost-effective measurement and monitoring unit. Merits and demerits of each method are then discussed based on different performance metrics such as tracking speed, hydrogen consumption and power stress. Comparative analysis of the results reveals that, while all the algorithms achieved a maximum electrical efficiency of about 48%, data-driven methods exhibited a superior trade-off between performance metrics. This ultimately leads to improved PEMFC durability.

Empowering Collaborative Learning: Innovative Integration of Real-Time Communication Into Hololens-Based Learning System

This study primarily aims to investigate the effectiveness of the HoloLens-based learning system, RTCHoloLS, in enhancing collaborative learning skills in undergraduate photography courses. It provides empirical evidence showing how RTC-HoloLS addresses pedagogical challenges and improves collaborative learning outcomes, offering a holistic approach to photography education. Although Augmented Reality (AR) technology helps students theoretically understand camera operations in photography courses, it lacks in promoting collaborative learning skills during hands-on practice. To address this limitation, this study introduces the RTC-HoloLS, a HoloLens-based learning system with Real-Time Communication, and compares it to the conventional video streaming-based HoloLS (CVSHoloLS). The results indicate that RTC-HoloLS significantly enhances collaborative learning skills compared to CVS-HoloLS, highlighting the importance of integrating Real-Time Communication technologies in AR learning environments. Additionally, RTC-HoloLS was equally effective for both male and female students, whereas CVS-HoloLS showed greater benefits for male students. This suggests the need to consider gender differences in design and development to ensure the learning system accommodates all students. The findings have significant implications for instructional technology design and implementation, suggesting that educational institutions should prioritize real-time communication features in AR learning tools to enhance collaboration and interaction.

A Systematic Review on Deep Fake Image Generation, Detection Techniques, Ethical Implications, and Overcoming Challenges

Deepfake technology, rooted in sophisticated machine learning techniques, utilizes deep neural networks to create highly realistic fake content such as videos, audio recordings, and images. This technology has rapidly evolved due to advancements in deep learning models, computational power, and data availability. The ethical implications, social impact, misuse, and legal frameworks surrounding Deepfake technology have been extensively studied. Detection techniques using deep learning approaches have been developed to combat the challenges posed by Deepfake content. Recommendations for future research include enhancing detection techniques, integrating explainable AI, and exploring real-time detection systems. Industry and policy implications emphasize the need for robust detection technologies, comprehensive legal frameworks, and collaborative efforts to address ethical concerns and regulate Deepfake content. This systematic review explores the landscape of deep fake image generation, detection techniques and challenges, in addition to ethical considerations. By synthesizing existing research, we aim to provide insights into deep-fake technology's advancements, limitations, and societal implications. This review underscores the urgent need for interdisciplinary collaboration and robust frameworks to address the multifaceted issues surrounding deep fakes in the digital age.

Optimized Feature Selection for DDoS Attack Recognition and Mitigation in SD-VANETs

Vehicular Ad-Hoc Networks (VANETs) are pivotal to the advancement of intelligent transportation systems (ITS), enhancing safety and efficiency on the road through secure communication networks. However, the integrity of these systems is severely threatened by Distributed Denial-of-Service (DDoS) attacks, which can disrupt the transmission of safety-critical messages and put lives at risk. This research paper focuses on developing robust detection methods and countermeasures to mitigate the impact of DDoS attacks in VANETs. Utilizing a combination of statistical analysis and machine learning techniques (i.e., Autoencoder with Long Short-Term Memory (LSTM), and Clustering with Classification), the study introduces innovative approaches for real-time anomaly detection and system resilience enhancement. Emulation results confirm the effectiveness of the proposed methods in identifying and countering DDoS threats, significantly improving (i.e., 94 percent anomaly detection rate) the security posture of a high mobility-aware ad hoc network. This research not only contributes to the ongoing efforts to secure VANETs against DDoS attacks but also lays the groundwork for more resilient intelligent transportation systems architectures.

MONDEP: A unified SpatioTemporal MONitoring Framework for National DEPression Forecasting

Depression has become a prevalent mental disorder that significantly affects a person's emotions, behaviors, physical health, ability to perform daily tasks, and ability to maintain healthy relationships. Untreated depression can escalate the risk of suicide, making the situation even worse. Despite an abundance of models previously proposed for forecasting depression, the issue of foretelling the overall number of patients at each administrative level remains under-investigated. Therefore, in this paper, we propose a simple but effective SpatioTemporal Monitoring Framework for National Depression Forecasting (MONDEP). In particular, we analyze national depression statistics data in Thailand as a case study and create prediction models for a real-time depression forecasting system using machine learning and deep learning approaches. In order to forecast the prevalence of depression at various administrative levels, we use the hierarchical structure of depression aggregation. The proposed framework consists of three modules: Data Pre-processing to extract and pre-process the raw data, Exploratory Data Analysis (EDA) to visualize and analyze the data to get insight, and Model Training and Testing to predict future depression cases. The objective of our research is to construct a comprehensive MONDEP framework that utilizes machine learning and deep learning to predict depression profiles at the district and national levels using multivariate time series across various administrative levels. Our study illustrates the considerable association between a spatial-temporal component and demonstrates how depression profiles may be represented by employing lower administrative-level data to estimate the general level of mental health across the nation. Additionally, the best performance across all criteria is obtained when a deep learning model is used to exploit multivariate time series, showing a 13% improvement in MAE measure compared to the SARIMAX baseline. We believe the proposed framework could be used as a point of reference for decision-making regarding the management of depression and has the potential to be incredibly helpful for policymakers in successfully managing mental health services on time.