Challenges For Real-time Processing Research Articles

Dynamic Object Detection in Surveillance Videos using Temporal Convolutional Networks and Federated Learning in Edge Computing Environments

This research addresses the importance of advancing dynamic object detection in surveillance videos by introducing a novel framework that integrates Temporal Convolutional Networks (TCNs) and Federated Learning (FL) within edge computing environments. This research is motivated by the critical need for real-time threat response, enhanced security measures, and privacy preservation in dynamic surveillance scenarios. Leveraging TCNs, the system captures temporal dependencies, providing a comprehensive understanding of object movements. FL ensures decentralized model training, mitigating privacy concerns associated with centralized approaches. Current challenges in real-time processing, privacy preservation, and adaptability to dynamic environments are addressed through innovative solutions. Model optimization techniques optimize TCN efficiency, ensuring real-time processing. Advanced privacy-preserving mechanisms secure FL, addressing privacy concerns. Transfer learning and data augmentation enhance adaptability to dynamic scenarios. The proposed system not only addresses existing challenges but also contributes to the evolution of surveillance technology. Comprehensive metrics, including accuracy, sensitivity, specificity, and real-time processing metrics, provide a thorough evaluation of the system's performance. This research introduces an approach to dynamic object detection, combining TCN and FL in edge computing environments. Results show accuracy exceeding 97%, sensitivity and specificity at 97% and 98%, and F1 score reaching 96%.

Enhancing Credit Card Fraud Detection: An Ensemble Machine Learning Approach

In the era of digital advancements, the escalation of credit card fraud necessitates the development of robust and efficient fraud detection systems. This paper delves into the application of machine learning models, specifically focusing on ensemble methods, to enhance credit card fraud detection. Through an extensive review of existing literature, we identified limitations in current fraud detection technologies, including issues like data imbalance, concept drift, false positives/negatives, limited generalisability, and challenges in real-time processing. To address some of these shortcomings, we propose a novel ensemble model that integrates a Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Random Forest (RF), Bagging, and Boosting classifiers. This ensemble model tackles the dataset imbalance problem associated with most credit card datasets by implementing under-sampling and the Synthetic Over-sampling Technique (SMOTE) on some machine learning algorithms. The evaluation of the model utilises a dataset comprising transaction records from European credit card holders, providing a realistic scenario for assessment. The methodology of the proposed model encompasses data pre-processing, feature engineering, model selection, and evaluation, with Google Colab computational capabilities facilitating efficient model training and testing. Comparative analysis between the proposed ensemble model, traditional machine learning methods, and individual classifiers reveals the superior performance of the ensemble in mitigating challenges associated with credit card fraud detection. Across accuracy, precision, recall, and F1-score metrics, the ensemble outperforms existing models. This paper underscores the efficacy of ensemble methods as a valuable tool in the battle against fraudulent transactions. The findings presented lay the groundwork for future advancements in the development of more resilient and adaptive fraud detection systems, which will become crucial as credit card fraud techniques continue to evolve.

Enhancing the Performance of XR Environments Using Fog and Cloud Computing

The extended reality (XR) environment demands high-performance computing and data processing capabilities, while requiring continuous technological development to enable a real-time integration between the physical and virtual worlds for user interactions. XR systems have traditionally been deployed in local environments primarily because of the need for the real-time collection of user behavioral patterns. On the other hand, these XR systems face limitations in local deployments, such as latency issues arising from factors, such as network bandwidth and GPU performance. Consequently, several studies have examined cloud-based XR solutions. While offering centralized management advantages, these solutions present bandwidth, data transmission, and real-time processing challenges. Addressing these challenges necessitates reconfiguring the XR environment and adopting new approaches and strategies focusing on network bandwidth and real-time processing optimization. This paper examines the computational complexities, latency issues, and real-time user interaction challenges of XR. A system architecture that leverages edge and fog computing is proposed to overcome these challenges and enhance the XR experience by efficiently processing input data, rendering output content, and minimizing latency for real-time user interactions.

The Role of High Performance Computing and Communication for Real-Time Biofeedback in Sport

This paper studies the main technological challenges of real-time biofeedback in sport. We identified communication and processing as two main possible obstacles for high performance real-time biofeedback systems. We give special attention to the role of high performance computing with some details on possible usage of DataFlow computing paradigm. Motion tracking systems, in connection with the biomechanical biofeedback, help in accelerating motor learning. Requirements about various parameters important in real-time biofeedback applications are discussed. Inertial sensor tracking system accuracy is tested in comparison with a high performance optical tracking system. Special focus is given on feedback loop delays. Real-time sensor signal acquisitions and real-time processing challenges, in connection with biomechanical biofeedback, are presented. Despite the fact that local processing requires less energy consumption than remote processing, many other limitations, most often the insufficient local processing power, can lead to distributed system as the only possible option. A multiuser signal processing in football match is recognised as an example for high performance application that needs high-speed communication and high performance remote computing. DataFlow computing is found as a good choice for real-time biofeedback systems with large data streams.

Special issue on architectures and techniques for real-time processing of remotely sensed images

Advances in sensor technology are revolutionizing the way remotely sensed images are collected, managed and processed. The incorporation of latest generation sensors to airborne and satellite platforms is currently producing a nearly continual stream of high-dimensional image data [6], and this explosion in the amount of collected information has rapidly created new processing challenges [4]. In particular, many current and future applications of remote sensing in Earth science, space science, and soon in planetary exploration science, require real or near realtime processing capabilities. Relevant examples include monitoring of natural disasters, such as earthquakes and floods, military applications, tracking of man-induced hazards, such as wild land and forest fires, oil spills and other types of biological agents. Most of these applications require timely responses for swift decisions, which depend on realtime performance of algorithm analysis. Owing to the diversity and large dimensionality of the image data sets provided by observation instruments in Earth and planetary applications, there are no commonly accepted real-time image processing architectures and techniques in the context of remote sensing missions. In future years, remote sensing imaging spectrometers [2] such as hyperspectraland ultraspectral imagers [1] will continue increasing their spatial and spectral resolutions (imagers with thousands of narrow spectral bands are currently under development), thus producing a nearly continual stream of high-dimensional image data [5] and increasing the possibility of exploitation of the data with regards to multi-spectral imagers [3]. Such technological advances are not only expected in optical remote sensing instruments, but also in radar and other types of remote sensing systems. Such explosion in the amount, size and dimensionality of the information collected on a daily basis presents new challenges for real-time processing in certain remote sensing applications, which demand for quick response in practical exploitation. This special issue on ‘‘Architectures and techniques for real-time processing of remotely sensed images’’ is intended to present the state of the art and the most recent developments in the incorporation of (near) real-time image processing techniques and specialized architectures in the context of remote sensing applications. This special issue covers different techniques and architectures for (near) real-time image processing in remote sensing. The techniques addressed in the special issue deal with relevant problems in Earth observation, such as damage mapping in case of earthquakes, flood monitoring, target and anomaly detection for military applications, cloud cover assessment or precision agriculture. The set of implementation strategies discussed in the special issue is also extensive, including parallel and distributed platforms, grid computing and multi-core environments, and specialized architectures for real-time on-board processing such as field programmable gate arrays (FPGAs) or commodity graphics processing units (GPUs). Combined these topics are expected to provide an excellent snapshot of the state of the art in those areas, and to offer a thoughtful perspective of the potential and emerging challenges of applying real-time image processing architectures and algorithms to realistic remote sensing problems. The special issue comprises eight articles contributed by authors with extensive experience in the field of efficient A. J. Plaza (&) Department Technology of Computers and Communications, Escuela Politecnica, University of Extremadura, Avenida de la Universidad s/n, 10071 Caceres, Spain e-mail: aplaza@unex.es URL: http://www.umbc.edu/rssipl/people/aplaza