Optimal Design of Airborne Test System Based on Model Analysis

In the digital era, data is one of the most important assets since it conceals valuable information. Developers of data-intensive systems have new challenges at each level of streaming, storing, and processing large quantities of data in a variety of forms and speeds. Obtaining useful information at the proper time and place is also crucial. Since the value of information is inversely proportional to time, real-time data processing and analytics are receiving more attention. Due to the importance of real-time data processing and analytics, this study focuses on real-time data processing concepts and terminology, popular technologies used in real-time data processing and analytics, popular NoSQL storage technologies used in real-time data processing, and real-time data processing application areas. The purpose of this paper is to provide researchers of real-time analysis and developers of data-intensive systems with a comparative perspective on real-time data processing by highlighting the key characteristics of real-time data processing technologies, NoSQL storage technologies, their application domains, and selected examples from previous studies.

The problems of design, computer simulation, manufacture of the device sample and its characteristics investigation of the acoustooptical scanner based on surface acoustic waves for 2D data processing in real time are discussed. The method is based on waveguide Bragg and radiation acoustooptic interactions with a synthesized optical aperture using a few optical beams. The total information field is divided into subareas, each of which is irradiated using an individual optical beam controlled by the corresponding waveguide acoustooptic Bragg cell and interaction cell. During the work elaboration of unit contraction, computer simulation, fabrication sample on based Y-cut lithium niobate and its characteristics investigated. This unit makes it possible to process in real time 2600times650 scanning points (information bits) representing two-dimensional arrays

The TPCx-BigBench (TPCx-BB) is a TPC Express benchmark, which is designed to measure the performance of big data analytics systems. It contains 30 use cases that simulate big data processing, big data storage, big data analytics, and reporting. We have used this benchmark to evaluate the performance of software and hardware components for big data systems. It has very good coverage on different data types and provides enough scalability to address data size and node scaling problems. We have gained lots of meaningful insights through this benchmark to design analytic systems. In the meantime, we also found we cannot merely rely on TPCx-BB to evaluate and design an end-to-end big data systems. There are some gaps between an analytics system and a real end-to-end system. The whole data flow of a real end-to-end system should include data ingestion, which moves data from where it is originated into a system where it can be stored and analyzed such as Hadoop. Data ingestion may be challenging for businesses at a reasonable speed in order to maintain a competitive advantage. However, TPCx-BB cannot help on performance evaluation of software and hardware for data ingestion. Big data is composed of three dimensions: Volume, Variety, and Velocity. The Velocity refers to the high speed in data processing: real-time or near real-time. With big data technology widely used, real-time and near real-time processing become more popular. There is very strict limitation on bandwidth and latency for real-time processing. TPCx-BB cannot help on performance evaluation of software and hardware for real-time processing. This paper mainly discusses these experiences and lessons in practice using TPCx-BB. Then, we provide some advices to extend TPCx-BB to cover data ingestion and real-time processing. We also share some ideas how to implement TPCx-BB coverage.

In light of the prevailing approach in which data from side-scan sonar (SSS) from Autonomous Underwater Vehicles (AUVs) are primarily processed and visualized post mission, failing to meet the requirements in terms of timeliness for on-the-fly image acquisition, this paper introduces a novel method for real-time processing and superior imaging of navigation strip data from SSS aboard AUVs. Initially, a comprehensive description of the real-time processing sequence is provided, encompassing the integration of multi-source navigation data using Kalman filtering, and high-pass filtering of attitude and heading data to exclude anomalies, as well as the use of bidirectional filtering techniques within and between pings, ensuring real-time quality control of raw data. In addition, this study adopts the semantic segmentation Unet network for automatic real-time tracking of seafloor lines, devises a real-time correction strategy for radial distortion based on historical echo data, and utilizes the alternating direction multiplier method for real-time noise reduction in strip images. With the combined application of these four pivotal techniques, we adeptly address the primary challenges in real-time navigation data processing. In conclusion, marine tests conducted in Bohai Bay substantiate the efficacy of the methodologies delineated in this research, offering a fresh paradigm for real-time processing and superior visualization of SSS navigation strip data on AUVs.

Purpose: The study sought to explore the impact of edge computing on real-time data processing. Methodology: The study adopted a desktop research methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library. Findings: The findings reveal that there exists a contextual and methodological gap relating to the impact of edge computing on real-time data processing. Preliminary empirical review reveled that edge computing significantly reduced latency and enhanced efficiency in real-time data processing across various industries by bringing computational resources closer to data sources. It highlighted the technology's ability to handle large volumes of IoT-generated data, improve security by localizing data processing, and drive innovation and economic growth through new applications and services. Edge computing's decentralized approach proved essential for reliable and robust data handling, particularly in critical sectors like healthcare and finance, ultimately solidifying its importance in the digital transformation landscape. Unique Contribution to Theory, Practice and Policy: The Diffusion of Innovations Theory, Resource-Based View (RBV) and Sociotechnical Systems Theory may be used to anchor future studies on edge computing on real-time data processing. The study recommended expanding theoretical frameworks to include the unique aspects of edge computing, investing in robust edge infrastructure, and developing standardized protocols and best practices. It emphasized the need for government incentives and supportive regulatory frameworks to promote adoption, and suggested that academic institutions incorporate edge computing into curricula. Additionally, the study called for ongoing research to address emerging challenges and opportunities, ensuring continuous advancement and effective implementation of edge computing technologies.

In order that the CNC machine tool has to achieve very high process accuracy, the bed structure has to have a reasonable construction to meet this strict technical demand. The bed structure of machine tool as a important component is analyzed in order to be clarified about its vibration mechanisms. In this paper, the dynamic character of the bed structure of HTM40100 horizontal mill-turn compound machine center is researched by the finite elements method (FEM) in the circumstance of ANSYS, and modal analysis theory is introduced and applied. Bed structure is modeled as flexible body by FEM. The harmonic response and the modal results is given by calculation and running of the model in ANSYS. The bed structure of machine meets the technical demands well enough after those analyses.

Recent advances in BazookaSPECT: Real-time data processing and the development of a gamma-ray microscope

Real-time big data processing is a crucial need of meteorology today. As a matter of fact, the weather is a determining factor in decision making in different areas such as air or sea transport, so acquiring knowledge of the environment in real time is a consequential question. A promising approach for big data treatment in real time is based on the concept of complex events processing CEP). The CEP aims to identify significant events in a cloud of simple and individually detectable events in real time. The meteorological variables, constituting simple event's spaces, offer significant flow of data to be processed using CEP technologies. This real-time data processing is done by phase of their integration with weather systems. This paper discusses the big data's meteorological aspects, and shows the architectural model for integration of weather data and its processing in real time.

In order to meet the tomato color sorting system with the requirements of multi-channel data acquisition and real-time processing, [ ]firstly, a multi-channel color digital acquisition and processing technique program based on FPGA were proposed . Then, a multi-channel data acquisition and real-time parallel processing system was built. The RGB color sensors were slected as detection components and the FPGA chip EP1C3T144C8 was slected as a core of data acquisition and processing. Meanwhile, ports of signal acquisition and the solenoid valve control were adopted a two-way analog switch HC4051 for saving I/O ports of FPGA. Thirdly, under the modular design and module reuse principles, we developed all modules with VHDL Language in QuartusII development environment. Finally, RTL and time sequence simulation for the system were done. The results showed that the connections between the modules accord with control logics. [²»´o¶O accorded ¹ýE¥E½] The performance of system time sequencev are good, which can meet with the requirements of real-time processing of tomato color sorting. On these bases, we also conducted a tomato sorting test verification. The results showed that the average accuracy of sorting is 98.70% and coefficient of variation is 0.01. This results can meet requirements of production.

Major advances in telecommunications and the Internet of Things have given rise to numerous smart city scenarios in which smart services are provided. What was once a dream for the future has now become reality. However, the need to provide these smart services quickly, efficiently, in an interoperable manner and in real time is a cutting-edge technological challenge. Although some software architectures offer solutions in this area, these are often limited in terms of reusability and maintenance by independent modules —involving the need for system downtime when maintaining or evolving, as well as by a lack of standards in terms of the interoperability of their interface. In this paper, we propose a fully reusable microservice architecture, standardized through the use of the Web of things paradigm, and with high efficiency in real-time data processing, supported by complex event processing techniques. To illustrate the proposal, we present a fully reusable implementation of the microservices necessary for the deployment of the architecture in the field of air quality monitoring and alerting in smart ports. The performance evaluation of this architecture shows excellent results.

The ability to interpret spatiotemporal data streams in real-time is critical for a range of systems. However, processing vast amounts of spatiotemporal data out of several sources, such as online traffic, social platforms, sensor networks, and other sources, is a considerable challenge. The major goal of this study is to create a framework for processing and analyzing spatiotemporal data from multiple sources with irregular shapes so that researchers can focus on data analysis instead of worrying about the data sources' structure. We introduced a novel spatiotemporal data paradigm for true-real-time stream processing, which enables high-speed and low-latency real-time data processing, with these considerations in mind. A comparison of two state-of-the-art real-time process architectures was offered, as well as a full review of the various open-source technologies for real-time data stream processing, and their system topologies were also presented. Hence, this study proposed a brand-new framework that integrates Apache Kafka for spatiotemporal data ingestion, Apache flink for true real-time processing of spatiotemporal stream data, as well as machine learning for real-time predictions, and Apache Cassandra at the storage layer for distributed storage in real-time. The proposed framework was compared with others from the literature using the following features: Scalability (Sc), prediction tools (PT), data analytics (DA), multiple event types (MET), data storage (DS), Real-time (Rt), and performance evaluation (PE) stream processing (SP), and our proposed framework provided the ability to handle all of this task.

With the digitalization and intellectualization of substations, the amount of state data collected by intelligent components is becoming larger and larger. Traditional data mining technology cannot meet the requirements of real-time data processing and application speed. For a typical smart substation, a fault diagnosis method based on improved association rule mining algorithm is proposed for transformer near-zone fault analysis. Firstly, an improved association rule data mining algorithm based on rough set is designed to extract information from massive data and diagnose faults. Then genetic algorithm is applied to improve association rule data mining algorithm to speed up data processing. Finally, simulation analysis is carried out to demonstrate the effectiveness and rapidity of the proposed method. The results show that the intelligent substation fault diagnosis method based on improved association rule mining algorithm has fast and powerful reduction ability in data processing. It can extract useful data from intelligent substation components to accurately judge fault information, reduce data scale and improve fault processing speed.

The long-standing von Neumann architecture, while foundational to modern computing, intrinsically suffers from data-transfer inefficiency, which imposes severe limits on speed and energy efficiency in artificial intelligence, machine learning, and real-time data processing. Inspired by the remarkable energy efficiency of the human brain, neuromorphic computing seeks to emulate neural architectures through hardware capable of adaptive learning. Although early complementary metal-oxide-semiconductor (CMOS)-based neuromorphic systems captured basic synaptic behaviors, their narrow dynamic ranges and high operating voltages impede their application prospects. Van der Waals (vdW) materials, particularly tellurium (Te) and Selenium (Se), have recently emerged as promising platforms for next-generation neuromorphic devices due to their intriguing electronic and optoelectronic properties including considerable carrier mobilities, broadband photoresponse, and strong coupling between electrical, optical, and mechanical stimuli. These unique properties offer a direct physical analogy to biological synapses, thereby enabling neuromorphic computing. This Perspective introduces the fundamentals of synaptic behavior and neuromorphic computing and highlights the distinctive properties of Te/Se nanomaterials for synaptic devices. Then we discuss critical advances in Te/Se-based memristors, heterostructures, and electronic/optoelectronic synaptic transistors. In the end, we conclude this perspective with a discussion on the remaining challenges and future opportunities in this evolving field.

- An Unmanned Aerial Vehicle (UAV), commonly known as a drone, is an aircraft that operates without a human pilot onboard and is controlled either remotely or autonomously. UAVs serve various applications, from defence to civilian purposes, and are categorized into fixed-wing, rotary-wing, hybrid, nano, tactical, target, and commercial drones. For this project, we selected a quadcopter due to its versatility, stability, and efficiency, making it ideal for applications like surveillance, aerial photography, emergency response, and environmental monitoring. The prototype is modeled in Fusion 360 for precise design and analyzed using ANSYS to assess structural integrity and durability under varying load conditions. The UAV frame is fabricated using lightweight materials such as carbon fiber and integrates key components like brushless DC motors, LiPo batteries, GPS modules, and a Pixhawk flight controller. Advanced features such as real-time data acquisition, autonomous flight capabilities, and efficient payload handling enhance its functionality. Additionally, 3D printing is used for custom parts, ensuring an optimized weight-to-strength ratio. To validate performance, the UAV undergoes simulation of flight dynamics and control systems using SolidWorks, followed by extensive ground and flight testing. The results demonstrate reliable performance in real-world scenarios such as medical supply delivery, environmental condition monitoring, and disaster relief efforts. By leveraging artificial intelligence, real-time data processing, and robust communication networks, this project contributes to the advancement of UAV technology, providing a cost-effective, efficient, and sustainable solution for public services and community needs. Key Words: Unmanned Aerial Vehicle (UAV), Quadcopter, Prototype Development, Structural Integrity, Environmental Monitoring.

Real-time big data processing for anomaly detection: A Survey