Real-time Processing Research Articles

STAM-LSGRU: a spatiotemporal radar echo extrapolation algorithm with edge computing for short-term forecasting

With the advent of Mobile Edge Computing (MEC), shifting data processing from cloud centers to the network edge presents an advanced computational paradigm for addressing latency-sensitive applications. Specifically, in radar systems, the real-time processing and prediction of radar echo data pose significant challenges in dynamic and resource-constrained environments. MEC, by processing data near its source, not only significantly reduces communication latency and enhances bandwidth utilization but also diminishes the necessity of transmitting large volumes of data to the cloud, which is crucial for improving the timeliness and efficiency of radar data processing. To meet this demand, this paper proposes a model that integrates a spatiotemporal Attention Module (STAM) with a Long Short-Term Memory Gated Recurrent Unit (ST-ConvLSGRU) to enhance the accuracy of radar echo prediction while leveraging the advantages of MEC. STAM, by extending the spatiotemporal receptive field of the prediction units, effectively captures key inter-frame motion information, while optimizations to the convolutional structure and loss function further boost the model’s predictive performance. Experimental results demonstrate that our approach significantly improves the accuracy of short-term weather forecasting in a mobile edge computing environment, showcasing an efficient and practical solution for processing radar echo data under dynamic, resource-limited conditions.

Imageable Brachytherapy with Chelator-Free Radiolabeling Hydrogel.

Brachytherapy stands as an essential clinical approach for combating locally advanced tumors. Here, an injectable brachytherapy hydrogel is developed for the treatment of both local and metastatic tumor. Fe-tannins nanoparticles are efficiently and stably radiolabeled with clinical used therapeutic radionuclides (such as 131I, 90Y, 177Lu and 225Ac) without a chelator, and then chemically cross-linked with 4-ArmPEG-SH to form brachytherapy hydrogel. Upon intratumoral administration, magnetic resonance imaging (MRI) signal from ferric ions embedded within the hydrogel directly correlates with the retention dosage of radionuclides, which can real-time monitor radionuclides emitting short-range rays in vivo without penetration limitation during brachytherapy. The hydrogel's design ensures the long-term tumor retention of therapeutic radionuclides, leading to the effective eradication of local tumor. Furthermore, the radiolabeled hydrogel is integrated with an adjuvant to synergize with immune checkpoint blocking therapy, thereby activating potent anti-tumor immune responses and inhibiting metastatic tumor growth. Therefore, this work presents an imageable brachytherapy hydrogel for real-time monitoring therapeutic process, and expands the indications of brachytherapy from treatment of localized tumors to metastatic tumors. This article is protected by copyright. All rights reserved.

Recognizing online video genres using ensemble deep convolutional learning for digital media service management

It's evident that streaming services increasingly seek to automate the generation of film genres, a factor profoundly shaping a film's structure and target audience. Integrating a hybrid convolutional network into service management emerges as a valuable technique for discerning various video formats. This innovative approach not only categorizes video content but also facilitates personalized recommendations, content filtering, and targeted advertising. Given the tendency of films to blend elements from multiple genres, there is a growing demand for a real-time video classification system integrated with social media networks. Leveraging deep learning, we introduce a novel architecture for identifying and categorizing video film genres. Our approach utilizes an ensemble gated recurrent unit (ensGRU) neural network, effectively analyzing motion, spatial information, and temporal relationships. Additionally,w we present a sophisticated deep neural network incorporating the recommended GRU for video genre classification. The adoption of a dual-model strategy allows the network to capture robust video representations, leading to exceptional performance in multi-class movie classification. Evaluations conducted on well-known datasets, such as the LMTD dataset, consistently demonstrate the high performance of the proposed GRU model. This integrated model effectively extracts and learns features related to motion, spatial location, and temporal dynamics. Furthermore, the effectiveness of the proposed technique is validated using an engine block assembly dataset. Following the implementation of the enhanced architecture, the movie genre categorization system exhibits substantial improvements on the LMTD dataset, outperforming advanced models while requiring less computing power. With an impressive F1 score of 0.9102 and an accuracy rate of 94.4%, the recommended model consistently delivers outstanding results. Comparative evaluations underscore the accuracy and effectiveness of our proposed model in accurately identifying and classifying video genres, effectively extracting contextual information from video descriptors. Additionally, by integrating edge processing capabilities, our system achieves optimal real-time video processing and analysis, further enhancing its performance and relevance in dynamic media environments.

Trend Research on Maritime Autonomous Surface Ships (MASSs) Based on Shipboard Electronics: Focusing on Text Mining and Network Analysis

The growing adoption of electric propulsion systems in Maritime Autonomous Surface Ships (MASSs) necessitates advancements in shipboard electronics for safe, efficient, and reliable operation. These advancements are crucial for tasks such as real-time sensor data processing, control algorithms for autonomous navigation, and robust decision-making capabilities. This study investigates research trends in MASSs, using bibliographic analysis to identify policy and future research directions in this evolving field. We analyze 3363 MASS-related articles from the Web of Science database, employing co-occurrence word analysis and latent Dirichlet allocation (LDA) topic modeling. The findings reveal a rapidly growing field dominated by image recognition research. Keywords such as “datum”, “image”, and “detection” suggest a focus on collecting and analyzing marine data, particularly with deep learning for synthetic aperture radar imagery. LDA confirms this, with “image analysis and classification research” as the leading topic. The study also identifies national and organizational leaders in MASS research. However, research on Arctic routes lags behind that on other areas. This work provides valuable insights for policymakers and researchers, promoting a deeper understanding of MASSs and informing future policy and research agendas regarding the integration of electric propulsion systems within the maritime industry.

A DRL-Based Satellite Service Allocation Method in LEO Satellite Networks

Satellite computing represents a recent computational paradigm in the development of low Earth orbit (LEO) satellites. It aims to augment the capabilities of LEO satellites beyond their current transparent relay functions by enabling real-time processing, thereby providing low-latency computational services to end users. In LEO constellations, a significant deployment of computationally capable satellites is orchestrated to offer enhanced computational resources. Challenges arise in the optimal allocation of terminal services to the most suitable satellite due to overlapping coverage among neighboring satellites, compounded by constraints on satellite energy and computational resources. The satellite service allocation (SSA) problem is recognized as NP-hard, yet assessing allocation methods through results allows for the application of deep reinforcement learning (DRL) to obtain improved solutions, partially addressing the SSA challenge. In this paper, we introduce a satellite computing capability model to quantify satellite computational resources. A DRL model is proposed to address service demands, computational resources, and resolve service allocation conflicts, strategically placing each service on appropriate servers. Through simulation experiments, numerical results demonstrate the superiority of our proposed method over baseline approaches in service allocation and satellite resource utilization, showcasing advancements in this field.

Dual-Frequency, Dual-Mode Reconfigurable Digital Atmospheric Radar Receiver Design

A new dual-frequency, dual-mode reconfigurable digital receiver based on Field-Programmable Gate Array (FPGA) dynamic reconfiguration is proposed, which is based on a common hardware platform of high-bandwidth RF front-end, high-speed data acquisition, and real-time signal processing. The receiver adopts the design of dynamically reconfigurable down-conversion, filter extraction, and matched filtering in the digital domain. In this study, we completed the design and development of the digital receiver, experimental platform construction, and field detection test with hardware and software cooperation. The experimental results show that the receiver achieves full digital reception and signal processing for 53.8 MHz stratosphere–troposphere (ST) detection and 35.0 MHz meteor detection and successfully acquired the number of meteors versus time, the meteor trail, and low-altitude atmospheric radial winds. This dual-frequency, dual-mode reconfigurable digital receiver can be applied to new-generation multifunction integrated radar systems such as dual-frequency ST/meteor radars.

Revolutionizing Supply Chain Management: Real-time Data Processing and Concurrency

In the contemporary business landscape, effective supply chain management (SCM) is paramount for organizations seeking to thrive amidst evolving market dynamics and heightened customer expectations. This research paper presents a pioneering approach to SCM that harnesses cutting-edge technologies, namely Kafka and Akka, to revolutionize data integration and decision-making processes. By leveraging Kafka as a robust distributed event streaming platform and Akka as a versatile toolkit for developing concurrent and distributed applications, our system facilitates seamless communication and coordination across diverse nodes within the supply chain network. This paper elucidates the intricacies of the proposed architecture, detailing the implementation methodology and performance evaluation metrics. Through a comprehensive examination, we demonstrate how our solution enhances supply chain visibility, fosters operational agility, and enables real-time responsiveness to market fluctuations and customer demands. Moreover, practical use cases exemplify the transformative impact of our approach on inventory management optimization, order fulfillment efficiency, and logistics optimization. Furthermore, we delve into the challenges encountered during implementation and deployment, offering insights into potential mitigative strategies. Finally, we outline avenues for future research, exploring emerging trends and opportunities in the realm of SCM empowered by Kafka and Akka technologies.

Managing the E-commerce Data Deluge through Text Analytics and Web Management (Overview of Amazon.com)

Today, more than 80% of the big data handled in the e-commerce industry is text and unstructured data. Text analytics is an automated process for analyzing text and extracting useful information from it. It can discover trends and relationships in data. Web analytics is the collection, processing, and analysis of data in order to draw conclusions to optimize usability on a website. Web analytics can be used to improve the usability of a site by analyzing user behavior patterns such as time spent on the site, abandonment rates, most frequently accessed products, click-through rates, etc. It can also help analyze the interests of different user demographics, as it tracks granular details such as user demographics, age and gender, geography, and devices used as data. In order to obtain UpToDate information, the business can utilize business intelligence for real-time data processing, then they can practice stream analysis to analyse continuous flow of data. For instance, the business can collect instant information in Twitter or other social media and analyse it by using social media analysis. For website management, business can practice web analysis to analyse the customer’s behaviours. Tracking the customer’s activity, page view and conversion rate is important for business to analyse how to improve the website performance. Text analytics of comments received on Amazon can be used to group text data and produce results in terms of word frequency distribution and sentiment analysis. Text analytics could be used for decision making, improving service quality, and developing new business models.

High-Speed Spatial–Temporal Saliency Model: A Novel Detection Method for Infrared Small Moving Targets Based on a Vectorized Guided Filter

Infrared (IR) imaging-based detection systems are of vital significance in the domains of early warning and security, necessitating a high level of precision and efficiency in infrared small moving target detection. IR targets often appear dim and small relative to the background and are easily buried by noise and difficult to detect. A novel high-speed spatial–temporal saliency model (HS-STSM) based on a guided filter (GF) is proposed, which innovatively introduces GF into IR target detection to extract the local anisotropy saliency in the spatial domain, and substantially suppresses the background region as well as the bright clutter false alarms present in the background. Moreover, the proposed model extracts the motion saliency of the target in the temporal domain through vectorization of IR image sequences. Additionally, the proposed model significantly improves the detection efficiency through a vectorized filtering process and effectively suppresses edge components in the background by integrating a prior weight. Experiments conducted on five real infrared image sequences demonstrate the superior performance of the model compared to existing algorithms in terms of the detection rate, noise suppression, real-time processing, and robustness to the background.

Interface Enables Faster Surface Reconstruction in a Heterostructured CuSey/NiSex Electrocatalyst for Realizing Urea Oxidation.

Creating affordable electrocatalysts and understanding the real-time catalytic process of the urea oxidation reaction (UOR) are crucial for advancing urea-based technologies. Herein, a Cu-Ni based selenide electrocatalyst (CuSey/NiSex/NF) was created using a hydrothermal technique and selenization treatment, featuring a heterogeneous interface rich in Cu2-xSe, Cu3Se2, Ni3Se4, and NiSe2. This catalyst demonstrated outstanding urea electrooxidation performance, achieving 10 mA cm-2 with just 1.31 V and sustaining stability for 96 h. Through in-situ Raman spectroscopy and ex-situ characterizations, it is discovered that NiOOH is formed through surface reconstruction in the UOR process, with high-valence Ni serving as the key site for effective urea oxidation. Moreover, the electrochemical analysis revealed that CuSey had dual effects. An analysis of XPS and electrochemical tests revealed that electron transfer from CuSey to NiSex within the CuSey/NiSex/NF heterostructure enhanced the UOR kinetics of the catalyst. Additionally, according to the in-situ Raman spectroscopy findings, the existence of CuSey facilitates a easier and faster surface reconstruction of NiSex, leading to the creation of additional active sites for urea oxidation. More significantly, this work provides an excellent "precatalyst" for highly efficient UOR, along with an in-depth understanding of the mechanism behind the structural changes in electrocatalysts and the discovery of their true active sites.

PSFHSP-Net: an efficient lightweight network for identifying pubic symphysis-fetal head standard plane from intrapartum ultrasound images

AbstractThe accurate selection of the ultrasound plane for the fetal head and pubic symphysis is critical for precisely measuring the angle of progression. The traditional method depends heavily on sonographers manually selecting the imaging plane. This process is not only time-intensive and laborious but also prone to variability based on the clinicians’ expertise. Consequently, there is a significant need for an automated method driven by artificial intelligence. To enhance the efficiency and accuracy of identifying the pubic symphysis-fetal head standard plane (PSFHSP), we proposed a streamlined neural network, PSFHSP-Net, based on a modified version of ResNet-18. This network comprises a single convolutional layer and three residual blocks designed to mitigate noise interference and bolster feature extraction capabilities. The model’s adaptability was further refined by expanding the shared feature layer into task-specific layers. We assessed its performance against both traditional heavyweight and other lightweight models by evaluating metrics such as F1-score, accuracy (ACC), recall, precision, area under the ROC curve (AUC), model parameter count, and frames per second (FPS). The PSFHSP-Net recorded an ACC of 0.8995, an F1-score of 0.9075, a recall of 0.9191, and a precision of 0.9022. This model surpassed other heavyweight and lightweight models in these metrics. Notably, it featured the smallest model size (1.48 MB) and the highest processing speed (65.7909 FPS), meeting the real-time processing criterion of over 24 images per second. While the AUC of our model was 0.930, slightly lower than that of ResNet34 (0.935), it showed a marked improvement over ResNet-18 in testing, with increases in ACC and F1-score of 0.0435 and 0.0306, respectively. However, precision saw a slight decrease from 0.9184 to 0.9022, a reduction of 0.0162. Despite these trade-offs, the compression of the model significantly reduced its size from 42.64 to 1.48 MB and increased its inference speed by 4.4753 to 65.7909 FPS. The results confirm that the PSFHSP-Net is capable of swiftly and effectively identifying the PSFHSP, thereby facilitating accurate measurements of the angle of progression. This development represents a significant advancement in automating fetal imaging analysis, promising enhanced consistency and reduced operator dependency in clinical settings. Graphical abstract

An in-depth evaluation of deep learning-enabled adaptive approaches for detecting obstacles using sensor-fused data in autonomous vehicles

This paper delivers an exhaustive analysis of the fusion of multi-sensor technologies, including traditional sensors such as cameras, Light Detection and Ranging(LiDAR), Radio Detection and Ranging(RADAR), and ultrasonic sensors, with Artificial Intelligence(AI) powered methodologies in obstacle detection for Autonomous Vehicles(AVs). With the growing momentum in AVs adoption, a heightened need exists for versatile and resilient obstacle detection systems. Our research delves into study of literatures, where proposed approaches assimilate data from this diverse sensor suite, integrated through Deep Learning(DL) techniques, to refine AV performance. Recent advancements and prevailing challenges within the domain are thoroughly examined, with particular focus on the integration of sensor fusion techniques, the facilitation of real-time processing via edge and fog computing, and the implementation of advanced artificial intelligence architectures, including Convolutional Neural Networks(CNNs), Recurrent Neural Networks(RNNs), and Generative Adversarial Networks(GANs), to enhance data interpretation efficacy. In conclusion, the paper underscores the critical contribution of multi-sensor arrays and deep learning in enhancing the safety and reliability of autonomous vehicles, offering significant perspectives for future research and technological progress.

Improvement of Student Interaction Analysis in Online Education Platforms Through Interactive Mobile Technology and Machine Learning Integration

The emergence of online education platforms, driven by interactive mobile technology, has significantly reshaped traditional educational paradigms and underscored the critical need for advanced analysis and improvement of student interactions. Effective analysis of student interaction is crucial for enhancing teaching quality and optimizing the learning experience in these digitally enriched environments. Traditional analysis frameworks often face challenges such as inaccuracies in anomaly detection and inefficiencies in data handling, particularly when handling extensive datasets typical of online platforms. This study introduces a novel approach to enhancing student interaction analysis systems by leveraging the synergy between machine learning and advanced interactive mobile technologies. Initially, the study proposes an advanced anomaly detection method tailored for identifying irregular student interactions. This method utilizes a blend of machine learning algorithms and the real-time data processing capabilities of mobile technology. Furthermore, to address the complexities of data transmission in mobile-based online education ecosystems, a state-ofthe- art congestion control algorithm has been developed. This algorithm optimizes data flow, significantly enhancing transmission stability and efficiency. The integration of interactive mobile technology with machine learning offers a robust and dynamic framework for analyzing student interactions, thereby facilitating a more engaging and effective online educational experience. This research contributes to the advancement of online education quality and efficiency by emphasizing the role of interactive mobile technology in shaping future learning environments.

Hand Gesture Recognition System Using Deep Learning

Hand Gesture Recognition Systems have undergone significant advancements, ushering in a new era of human-computer interaction. This paper offers a thorough examination of the current state of the art in hand gesture recognition, addressing both the notable progress achieved and the persistent challenges. By leveraging state-of-the-art technologies such as computer vision and deep learning, the paper explores the methodologies employed in data collection, preprocessing, and the implementation of various algorithms. The research delves into the complexities of popular hand gesture datasets, emphasizing their role in training and testing models. A critical analysis of different algorithms and models, including Hidden Markov Models, Support Vector Machines, and Neural Networks, is presented. The paper scrutinizes their strengths and limitations, providing insights into the delicate balance between accuracy and real-time processing. Furthermore, it investigates the diverse applications of hand gesture recognition, spanning from enriching human-computer interaction to its pivotal role in virtual reality, gaming, and robotics. Despite these advancements, challenges persist, such as occlusion, varying lighting conditions, and the imperative for real-time processing. The hardware utilized in hand gesture recognition systems, including depth sensors, RGB-D cameras, and wearable devices, is examined. Evaluation metrics, such as accuracy, precision, recall, and the F1 score, are employed to evaluate system performance.

MOUSE AND KEYBOARD MOTION SENSING USING HAND SIGNS

The project presents a way for Human- Computer Interaction (HCI) that employs a web camera or an integrated camera to control the movement of the indicator instead to the current methods, which typically involve manually entering buttons or altering the positions of a physical mouse. Instead, it can perform the functions that a physical computer mouse does, and it does it through camera and technology for computer vision to manage different mouse events. The Virtual Mouse color recognition application constantly acquires real-time image data and processes it using several filtering and conversion phases. The application utilizes an image processing technique to obtain the coordinates of the desired colors that have emerged within the frames holding a list of color combinations, each of them correlates to a different mouse function. The program will run the mouse function and transform it to an actual mouse function which will be able to applied on the user’s PC if the current color combination passes. The main aim of the project is to virtually interface with the system in virtually every manner imaginable. Keywords: Gesture Recognition, Human Computer Interactions, Graphical User Interface, Open-Source Computer Vision.

Gas Level and Leakage Detection with Automatic Booking

Gas level and leakage detection is crucial for ensuring safety in various environments such as homes, industries, and laboratories. In this paper, we present a novel approach utilizing embedded C language, MQ2 sensor, ESP32 microcontroller, buzzer, and alert messaging system for efficient gas monitoring and detection. The proposed system employs the MQ2 sensor, capable of detecting various gases including LPG, methane, and carbon monoxide, interfaced with an ESP32 microcontroller for real-time data processing and analysis. Embedded C programming language is utilized for firmware development to facilitate efficient resource management and enhance system performance. Upon detecting abnormal gas levels or leakage, the system triggers an audible alarm through the buzzer and simultaneously sends alert messages to designated recipients via messaging protocols such as SMS. This enables timely response and preventive measures to mitigate potential hazards associated with gas leakage incidents. Additionally, an automatic gas booking feature is integrated, where upon gas depletion, the system automatically sends an SMS to the designated gas provider, facilitating seamless replenishment of gas supply.. Experimental results demonstrate the effectiveness and reliability of the proposed system in accurately detecting gas levels and identifying leakages in different environments. The system's low-cost implementation and versatility make it suitable for deployment in various applications, contributing to enhanced safety standards and risk mitigation strategies

In-line imaging and recognition of flip chip fabrication defects by real-time photoacoustic remote sensing system

Microscopic defects in flip chips, originating from manufacturing, significantly affect performance and longevity. Post-fabrication sampling methods ensure product functionality but lack in-line defect monitoring to enhance chip yield and lifespan in real-time. This study introduces a photoacoustic remote sensing (PARS) system for in-line imaging and defect recognition during flip-chip fabrication. We first propose a real-time PARS imaging method based on continuous acquisition combined with parallel processing image reconstruction to achieve real-time imaging during the scanning of flip-chip samples, reducing reconstruction time from an average of approximately 1134 ms to 38 ms. Subsequently, we propose improved YOLOv7 with space-to-depth block (IYOLOv7-SPD), an enhanced deep learning defect recognition method, for accurate in-line recognition and localization of microscopic defects during the PARS real-time imaging process. The experimental results validate the viability of the proposed system for enhancing the lifespan and yield of flip-chip products in chip manufacturing facilities.

MSFGQ: Design of an efficient Multiparametric model for improving sub-field scheduling performance via novel GA & Q-Learning optimizations

This paper presents the design of a novel multiparametric model aimed at improving sub-field scheduling performance for lithographic processes. The proposed model incorporates various parameters such as sub-field locations, conflict analysis, critical dimensions, delay, current, voltage, dose, and depth of current for optimization of scheduling operations. To achieve this, we have utilized both Genetic Algorithm (GA) and Q-learning algorithms to optimize the scheduling performance in real-time lithographic processes. The need for this work stems from the increasing demand for high precision lithographic processes, which require efficient scheduling operations to achieve optimal results. The proposed model has been tested on real-time lithographic processes, and the results have been evaluated in terms of critical dimensions, scheduling performance, and scheduling efficiency. The results show that the proposed model has reduced critical dimensions by 8.5%, improved scheduling performance by 10.5%, and increased scheduling efficiency by 8.3% . These results demonstrate the efficacy of the proposed model in improving sub-field scheduling performance in lithographic processes. Based on the results it can be observed that this work presents a novel multiparametric model that utilizes GA and Q-learning algorithms to improve sub-field scheduling performance in lithographic processes.

TARGET RECOGINITION AND DETECTION USING DEEP LEARING TECHNIQUE

Object detection is a fundamental computer vision function for identifying and locating things in images or videos. Unlike image classification, object detection not only classifies the items in an image but also locates them inside the picture by constructing a bounding box around each one. Defence objects include a wide range of key assets needed for military and security activities. Tanks, armoured personnel carriers (APCs), military trucks, planes, helicopters, naval boats, and unmanned ground vehicles (UGVs) are among the types of vehicles included. Detection of these vehicles is critical for tracking troop movements, analysing battlefield dynamics, and maintaining operational preparedness. The modern defence scenario demands improved surveillance and security measures to successfully counter growing threats. This work suggests using the You Only Look Once (YOLO) method for real-time identification of numerous defence items. The project's goal is to improve security by properly detecting a variety of items, including cars, individuals, equipment, and structures, with a single deep learning model. Methodologically, a broad dataset containing photos and videos of defence items in various locations is collected and preprocessed. The YOLO algorithm is then trained on this dataset, with parameters optimised for high accuracy and recall rates. Performance evaluation include thorough testing on various datasets to determine accuracy, speed, and resilience. KEYWORDS: Target recognition, Detection, YOLO algorithm, Military applications, Flying objects, Vehicles, Sensor integration, Real-time processing, Threat detection, Situational awareness.

Ham or hamster? Eye-tracking evidence of a clear speech benefit for word segmentation in quiet and in noise

ABSTRACT This study examined whether intelligibility-enhancing hyperarticulated clear speaking styles improve word segmentation during real-time speech processing in quiet and in noise. English-speaking listeners heard clearly and conversationally spoken sentences in which the target (e.g. ham) was temporarily ambiguous with a competitor (e.g. hamster) across a word boundary (e.g. ham starting) while their eye fixations to target and competitor images were recorded. Relative to conversational speech, clear speech led listeners to fixate the target image over the competitor image to a greater degree, indicating facilitation of word segmentation. Such facilitation emerged in quiet and in noise even before disambiguating segmental information (e.g. /ɑ/ in starting) was available. A parallel clear speech benefit was not found when the disyllabic word (e.g. hamster) was the target. The findings suggest that improved word segmentation partly underlies the well-documented clear speech perceptual and cognitive benefits and may arise from the enhancements of multiple word boundary cues.