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Architecture for Intensive Care data processing and Visualization in real-time

Procedia Computer Science 2021年 184卷 923-928页

作者： Ricardo Cruz Tiago Guimarães Hugo Peixoto Manuel Filipe Santos Centro Algoritmi University of Minho Braga 4710 Portugal

Clinical data is growing every day. Ergo, to treat, store and publish such data is an emergent task. Furthermore, analysing data in real-time using streaming and processing technologies and methods, in order to obtain quality data, prepared to support decision making is of extreme value. Big data emerged with the introduction of real-time processing, thus revolutionizing traditional technologies and techniques through the ability to deal with the volume, speed and variety of data. Countless studies have been proposed in the healthcare domain in search of solutions that allow the flow of data in real-time. However, the work presented hereby is distinguished by allowing the collection, processing, storage and analysis of Intensive Care Units (ICU) data, both collected in real-time from bedside monitors but also stored in a historical repository. The architecture proposed makes use of current technologies, like Nextgen Connector as message supplier and integrator, Elasticsearch as a search index, Kibana for viewing stored data and Grafana for real-time streaming. This article is part of the ICDS4IM project - Intelligent Clinical Decision Support in Intensive Care Medicine to support the experimentation of data processing techniques and technologies, based in HL7 format and collected in real-time so that it can be made available through Health Information Systems across the healthcare institutions.

关键词： real-time data processing real-time data Visualization Decision Support Systems Big data

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Mitigating impulsive noise in airborne PLC: Introducing the S-SAMP-PV algorithm for MIMO OFDM systems

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SIGNAL processing 2025年 230卷

作者： Yan, Ruowen Li, Qiao Xiong, Huagang Beihang Univ Beijing 100083 Peoples R China

Power Line Communication (PLC) offers an efficient solution for data transmission over electrical power lines, presenting a promising avenue for in-flight communication in More Electrical Aircraft (MEA). A significant challenge in airborne PLC is Impulsive Noise (IN), which hampers transmission reliability. Existing noise mitigation methods, while valuable, face limitations in airborne settings due to computational intensiveness and sub-optimal sparse recovery performance. This paper introduces the Structured Sparsity Adaptive Matching Pursuit with Preliminary partial support estimation and Variable step-size (S-SAMP-PV) algorithm, devised for Multiple-Input-Multiple-Output (MIMO) systems. It uniquely pre-estimates partial support of sparse IN signals, enabling adaptive convergence without prior sparsity knowledge. This methodology substantially reduces computational demands, satisfying stringent real-time requirements of airborne applications. In simulation, the S-SAMP-PV algorithm exhibits marked advantages over traditional algorithms such as Orthogonal Matching Pursuit (OMP). Specifically, it realizes an approximate 81.3% reduction in Normalized Mean Square Error (NMSE) and demonstrates around 37% improvement in computational efficiency relative to OMP. Moreover, its Bit Error Rate (BER) performance at high Signal to Noise Ratio (SNR) approaches the ideal scenario where IN is assumed to be perfectly eliminated. These results emphasize the promise of S-SAMP-PV in elevating the performance of airborne PLC systems by efficient IN mitigation.

关键词： Power Line Communication Impulsive noise S-SAMP-PV algorithm Noise mitigation Sparse recovery real-time data processing

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real-time Optimization Algorithm for Intelligent Logistics Transportation Routes Based on Big data Analysis 24

Real-time Optimization Algorithm for Intelligent Logistics T...

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International Conference on Machine Intelligence and Digital Applications (MIDA)

作者： Wang, Jinyan Digital Business Acad Heilongjiang Vocat Coll Harbin 150086 Heilongjiang Peoples R China

ISBN: (纸本)9798400718144

In this article, based on big data analysis, the real-time optimization algorithm of intelligent logistics transportation routes is studied for the problems of dataset limitation, lack of real-time performance and single optimization project in traditional logistics transportation routes. This article integrates GPS tracking, traffic monitoring, and weather report data through Talend to remove duplicates, fill in missing values, and handle outliers. time series, traffic flow, and weather feature engineering are constructed to enhance understanding of periodicity and variability. Apache Kafka is utilized to process data in real time and support vector machine algorithms are combined to predict traffic congestion and accidents. Genetic algorithms are used to dynamically generate and optimize transportation routes, and the planning is adjusted through real-time monitoring and response. Finally, the Pareto optimal solution algorithm is used for multi-objective optimization to ensure efficient operation of logistics transportation. In the experiment, the algorithm presented in this article shows significant advantages in transportation time and cost in different cases. In Case 1, the transportation time of the algorithm in this article is only 2.1 hours, and the cost is $45;in the real-time data processing capability test, the response time of the algorithm in this article is the shortest, with 8.7 seconds, 12.1 seconds, and 21.6 seconds, respectively. Meanwhile, the robustness and user satisfaction of the algorithm in this article achieve good results. The algorithm in this article effectively improves the efficiency of logistics transportation and provides reference for similar research.

关键词： Intelligent Logistics Route Optimization Big data Analysis real-time data processing Genetic Algorithm

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real-time intelligent big data processing:technology, platform, and applications

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Science China(Information Sciences) 2019年第8期62卷 102-113页

作者： Tongya ZHENG Gang CHEN Xinyu WANG Chun CHEN Xingen WANG Sihui LUO College of Computer Science and Technology Zhejiang University

Human beings keep exploring the physical space using information means. Only recently, with the rapid development of information technologies and the increasing accumulation of data, human beings can learn more about the unknown world with data-driven methods. Given data timeliness, there is a growing awareness of the importance of real-time data. There are two categories of technologies accounting for data processing: batching big data and streaming processing, which have not been integrated well. Thus,we propose an innovative incremental processing technology named after Stream Cube to process both big data and stream data. Also, we implement a real-time intelligent data processing system, which is based on real-time acquisition, real-time processing, real-time analysis, and real-time decision-making. The real-time intelligent data processing technology system is equipped with a batching big data platform, data analysis tools, and machine learning models. Based on our applications and analysis, the real-time intelligent data processing system is a crucial solution to the problems of the national society and economy.

关键词： batching big data streaming processing technology real-time data processing incremental computation intelligent data processing system

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Domain-Specific Manufacturing Analytics Framework: An Integrated Architecture with Retrieval-Augmented Generation and Ollama-Based Models for Manufacturing Execution Systems Environments

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PROCESSES 2025年第3期13卷 670-670页

作者： Choi, Hangseo Jeong, Jongpil Sungkyunkwan Univ Dept Smart Factory Convergence 2066 Seobu Ro Suwon 16419 Gyeonggi Do South Korea

To support data-driven decision-making in a Manufacturing Execution System (MES) environment, a system that can quickly and accurately analyze a wide range of production, quality, asset, and material information must be deployed. However, existing MES data management approaches rely on predefined queries or report templates that lack flexibility and limit real-time decision support. In this paper, we proposes a domain-specific Retrieval-Augmented Generation (RAG) architecture that extends LangChain's capabilities with Manufacturing Execution System (MES)-specific components and the Ollama-based Local Large Language Model (LLM). The proposed architecture addresses unique MES requirements including real-time sensor data processing, complex manufacturing workflows, and domain-specific knowledge integration. It implements a three-layer structure: an application layer using FastAPI for high-performance asynchronous processing, an LLM layer for natural language understanding, and a data storage layer combining MariaDB, Redis, and Weaviate for efficient data management. The system effectively handles MES-specific challenges such as schema relationships, temporal data processing, and security concerns without exposing sensitive factory data. This is an industry-specific, customized approach focusing on problem-solving in manufacturing sites, going beyond simple text-based RAG. The proposed architecture considers the specificity of data sources, real-time and high-availability requirements, the reflection of domain knowledge and workflows, compliance with security and quality control regulations, and direct interoperability with MES systems. The architecture can be further enhanced through integration with various manufacturing systems, an advanced LLM, and distributed processing frameworks while maintaining its core focus on MES domain specialization.

关键词： manufacturing execution system (MES) retrieval-augmented generation (RAG) MES domain-specific RAG local large language model (LLM) real-time data processing FastAPI

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Enhancing Intelligent Transport Systems Through Decentralized Security Frameworks in Vehicle-to-Everything Networks

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WORLD ELECTRIC VEHICLE JOURNAL 2025年第1期16卷

作者： Tariq, Usman Ahanger, Tariq Ahamed Prince Sattam Bin Abdulaziz Univ Coll Business Adm Management Informat Syst Dept Al Kharj 16278 Saudi Arabia

Vehicle Ad hoc Networks (VANETs) play an essential role in intelligent transportation systems (ITSs) by improving road safety and traffic management through robust decentralized communication between vehicles and infrastructure. Yet, decentralization introduces security vulnerabilities, including spoofing, tampering, and denial-of-service attacks, which can compromise the reliability and safety of vehicular communications. Traditional centralized security mechanisms are often inadequate in providing the real-time response and scalability required by such dispersed networks. This research promotes a shift toward distributed and real-time technologies, including blockchain and secure multi-party computation, to enhance communication integrity and privacy, ultimately strengthening system resilience by eliminating single points of failure. A core aspect of this study is the novel D-CASBR framework, which integrates three essential components. First, it employs hybrid machine learning methods, such as ElasticNet and Gradient Boosting, to facilitate real-time anomaly detection, identifying unusual activities as they occur. Second, it utilizes a consortium blockchain to provide secure and transparent information exchange among authorized participants. Third, it implements a fog-enabled reputation system that uses distributed fog computing to effectively manage trust within the network. This comprehensive approach addresses latency issues found in conventional systems while significantly improving the reliability and efficacy of threat detection, achieving 95 percent anomaly detection accuracy with minimal false positives. The result is a substantial advancement in securing vehicular networks.

关键词： vehicular ad hoc networks (VANETs) decentralized intrusion detection systems (D-IDSs) interconnectivity privacy preserving hybrid machine learning fog computing minority class imbalance real-time data processing

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GC-YOLOv9: Innovative smart city traffic monitoring solution

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ALEXANDRIA ENGINEERING JOURNAL 2024年 106卷 277-287页

作者： An, Ru Zhang, Xiaochun Sun, Maopeng Wang, Gang Shenzhen Univ Coll Civil & Transportat Engn Shenzhen 518060 Peoples R China Shenzhen Urban Transport Planning Ctr Co Ltd Shenzhen 518057 Peoples R China Shenzhen Univ Res Inst Smart Cities Sch Architecture & Urban Planning Shenzhen 518060 Peoples R China NingboTech Univ Sch Comp & Data Engn Ningbo 315100 Peoples R China Imperial Coll London Dept Bioengn London SW7 2AZ England

In urban smart city environments, traffic hazards can lead to catastrophic outcomes, including significant property losses and severe threats to public safety. Conventional traffic monitoring systems are limited in terms of accuracy and speed, presenting significant challenges for real-time traffic surveillance. To tackle these challenges, this paper introduces the GC-YOLOv9 algorithm. Specifically, we have enhanced the YOLOv9 model by incorporating Ghost Convolution, markedly improving the model's perceptual abilities and detection accuracy. Furthermore, this study designed an integrated smart city framework that includes layers for service applications, the Internet of Things, edge processing, and data centers. By deploying the enhanced YOLOv9 model within this framework, our method achieved mAP@0.5 scores of 77.15 and 74.95 on the BDD100K and Cityscapes datasets, respectively, surpassing existing technologies. Additionally, the potential applications of this method in public area fire safety management, forest fire monitoring, and intelligent security systems further underscore its significant value in improving the safety and efficiency of smart cities.

关键词： Smart cities Traffic monitoring YOLOv9 Internet of Things real-time data processing Ghost convolution

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High-Performance Computing and Parallel Algorithms for Urban Water Demand Forecasting

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ALGORITHMS 2025年第4期18卷 182-182页

作者： Myllis, Georgios Tsimpiris, Alkiviadis Aggelopoulos, Stamatios Vrana, Vasiliki G. Int Hellen Univ Dept Comp Informat & Telecommun Engn Serres 62124 Greece Int Hellen Univ Dept Agr Thessaloniki 57001 Greece Int Hellen Univ Dept Business Adm Serres 62124 Greece

This paper explores the application of parallel algorithms and high-performance computing (HPC) in the processing and forecasting of large-scale water demand data. Building upon prior work, which identified the need for more robust and scalable forecasting models, this study integrates parallel computing frameworks such as Apache Spark for distributed data processing, Message Passing Interface (MPI) for fine-grained parallel execution, and CUDA-enabled GPUs for deep learning acceleration. These advancements significantly improve model training and deployment speed, enabling near-real-time data processing. Apache Spark's in-memory computing and distributed data handling optimize data preprocessing and model execution, while MPI provides enhanced control over custom parallel algorithms, ensuring high performance in complex simulations. By leveraging these techniques, urban water utilities can implement scalable, efficient, and reliable forecasting solutions critical for sustainable water resource management in increasingly complex environments. Additionally, expanding these models to larger datasets and diverse regional contexts will be essential for validating their robustness and applicability in different urban settings. Addressing these challenges will help bridge the gap between theoretical advancements and practical implementation, ensuring that HPC-driven forecasting models provide actionable insights for real-world water management decision-making.

关键词： High-Performance Computing (HPC) parallel algorithms water demand forecasting Apache Spark Message Passing Interface (MPI) machine learning time-series analysis real-time data processing urban water management computational efficiency deep learning models data imputation scalability anomaly detection distributed computing frameworks

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Enhancing the data processing speed of a deep-learning-based three-dimensional single molecule localization algorithm (FD-DeepLoc) with a combination of feature compression and pipeline programming

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JOURNAL OF INNOVATIVE OPTICAL HEALTH SCIENCES 2025年第2期18卷

作者： Guo, Shuhao Lin, Jiaxun Zhang, Yingjun Huang, Zhen-Li Hainan Univ Sch Biomed Engn Key Lab Biomed Engn Hainan Prov Sanya 572025 Peoples R China

Three-dimensional (3D) single molecule localization microscopy (SMLM) plays an important role in biomedical applications, but its data processing is very complicated. Deep learning is a potential tool to solve this problem. As the state of art 3D super-resolution localization algorithm based on deep learning, FD-DeepLoc algorithm reported recently still has a gap with the expected goal of online image processing, even though it has greatly improved the data processing throughput. In this paper, a new algorithm Lite-FD-DeepLoc is developed on the basis of FD-DeepLoc algorithm to meet the online image processing requirements of 3D SMLM. This new algorithm uses the feature compression method to reduce the parameters of the model, and combines it with pipeline programming to accelerate the inference process of the deep learning model. The simulated data processing results show that the image processing speed of Lite-FD-DeepLoc is about twice as fast as that of FD-DeepLoc with a slight decrease in localization accuracy, which can realize real-time processing of 256x256 pixels size images. The results of biological experimental data processing imply that Lite-FD-DeepLoc can successfully analyze the data based on astigmatism and saddle point engineering, and the global resolution of the reconstructed image is equivalent to or even better than FD-DeepLoc algorithm.

关键词： real-time data processing feature compression pipeline programming

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ELECTROENCEPHALOGRAM (EEG) BASED EMOTIONAL LIGHTING DESIGN USING DEEP-LEARNING FOR A USER-CENTRIC APPROACH 29th

ELECTROENCEPHALOGRAM (EEG) BASED EMOTIONAL LIGHTING DESIGN U...

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29th International Conference of the Association-for-Computer-Aided-Architectural-Design-Research-in-Asia (CAADRIA)

作者： Kang, Se Yeon Cho, Ju Eun Jun, Han Jong Hanyang Univ Seoul South Korea

ISBN: (纸本)9789887891833

This study proposes a methodology for using artificial intelligence (AI) and biometrics in spatial design. The research mainly applies a gated recurrent unit (GRU) model, a recurrent neural network (RNN), to analyze electroencephalogram (EEG) data and dynamically adjust lighting according to the user's emotional state. This study suggests an illumination adjustment system that modifies lighting according to the user's emotional state using the proposed method. Integration of EEG data can overcome the limitations of lighting systems. It can effectively target individual emotional responses. The GRU model represents a significant improvement in lighting design by addressing both cognitive and emotional user needs. The model's effectiveness in processing real-time data and adapting through incremental learning was evaluated. The model has shown a significant impact on emotional architecture and spatial design, with a focus on individual experience.

关键词： Gated Recurrent Unit EEG EEG data Analysis User-Centric Design Emotional Lighting real-time data processing Affective Computing BCI BMI

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