Real-time Processing Requirements Research Articles

Reconfigurable Intelligent Surface Assisted Target Three-Dimensional Localization with 2-D Radar

Battlefield surveillance radar is usually 2-D radar, which cannot realize target three-dimensional localization, leading to poor resolution for the air target in the elevation dimension. Previous researchers have used the Traditional Height Finder Radar (HFR) or multiple 2-D radar networking to estimate the target three-dimensional location. However, all of them face the problems of high cost, poor real-time performance and high requirement of space–time registration. In this paper, Reconfigurable Intelligent Surfaces (RISs) with low cost are introduced into the 2-D radar to realize the target three-dimensional localization. Taking advantage of the wide beam of 2-D radar in the elevation dimension, several Unmanned Aerial Vehicles (UAVs) carrying RISs are set in the receiving beam to form multiple auxiliary measurement channels. In addition, the traditional 2-D radar measurements combined with the auxiliary channel measurements are used to realize the target three-dimensional localization by solving a nonlinear least square problem with a convex optimization method. For the proposed RIS-assisted target three-dimensional localization problem, the Cramer–Rao Lower Bound (CRLB) is derived to measure the target localization accuracy. Simulation results verify the effectiveness of the proposed 3-D localization method, and the influences of the number, the positions and the site errors of the RISs on the localization accuracy are covered.

Key Issues on Integrating 5G into Industrial Systems

Under the auspice of further developing 5G mobile communication technology and integrating it with the latest advancements in the field of Industrial Internet-of-Things, this study conducts in-depth research and detailed analysis on the combination of 5G with industrial systems based on composite structures, communication network architectures, wireless application scenarios, and communication protocols. The status quo, development trend, and necessity of 5G mobile communication technology are explored and its potential in industrial applications is analyzed. Based on the current practical development level of 5G technology, by considering different requirements for bandwidth, real-time performance, and reliability in communication networks of industrial systems, this study proposes three feasible paths for the integration between 5G and industrial systems, including the method to use 5G in place of field buses. Finally, by introducing real-world cases, this study has successfully demonstrated the integration of 5G and industrial systems by extending 5G terminals as field bus gateways. This study provides valuable references for research and practice in related fields.

Detecting Moving Objects in Photometric Images Using 3D Hough Transform

In response to the exponential growth of space debris, an increasing number of observation devices are being used for the observation of moving objects, such as space debris and asteroids, which require further improvements in data-processing capabilities for the detection of moving objects. In this study, we propose a rapid detection algorithm designed for detecting moving objects, leveraging the power of the 3D Hough transform. By the simulated image experiments, our results show that the detection rate increases with the number of continuous images when fully extracting objects. Based on this foundation, the object detection rate is at least 87% regardless of the object number in the image sequence when detecting objects from at least six continuous images. In the observed image experiments, we used source-extractor to extract sources. The results show the method can successfully detect objects with signal-to-noise ratio higher than three from sidereal tracking images and can identify asteroids from asteroid tracking images while maintaining a detection speed that meets the requirements for real-time processing.

Miniaturization Design of High-Integration Unmanned Aerial Vehicle-Borne Video Synthetic Aperture Radar Real-Time Imaging Processing Component

The unmanned aerial vehicle (UAV)-borne video synthetic aperture radar (SAR) possesses the characteristic of having high-continuous-frame-rate imaging, which is conducive to the real-time monitoring of ground-moving targets. The real-time imaging-processing system for UAV-borne video SAR (ViSAR) requires miniaturization, low power consumption, high frame rate, and high-resolution imaging. In order to achieve high-frame-rate real-time imaging on limited payload-carrying platforms, this study proposes a miniaturization design of a high-integration UAV-borne ViSAR real-time imaging-processing component (MRIPC). The proposed design integrates functions such as broadband signal generation, high-speed real-time sampling, and real-time SAR imaging processing on a single-chip FPGA. The parallel access mechanism using multiple sets of high-speed data buffers increases the data access throughput and solves the problem of data access bandwidth. The range-Doppler (RD) algorithm and map-drift (MD) algorithm are optimized using parallel multiplexing, achieving a balance between computing speed and hardware resources. The test results have verified that our proposed component is effective for the real-time processing of 2048 × 2048 single-precision floating-point data points to realize a 5 Hz imaging frame rate and 0.15 m imaging resolution, satisfying the requirements of real-time ViSAR-imaging processing.

聚焦可变形注意力的马铃薯芽苗精确无损检测方法

Accurate potato sprout detection is the key to automatic seed potato cutting, which is important for potato quality and yield. In this paper, a lightweight DAS-YOLOv8 model is proposed for the potato sprout detection task. By embedding DAS deformable attention in the feature extraction network and the feature fusion network, the global feature context can be efficiently represented and the attention increased to the relevant pixel image region; then, the C2f_Atten module fusing Shuffle attention is designed based on the C2f module to satisfy the attention to the key feature information of the high-level abstract semantics of the feature extraction network. At the same time, the ghost convolution is introduced to improve the C2f module and convolutional module to realize the decomposition of the redundant features to extract the key features. Verified on the collected potato sprout image data set, the average accuracy of the proposed DAS-YOLOv8 model is 94.25%, and the calculation amount is only 7.66 G. Compared with the YOLOv8n model, the accuracy is 2.13% higher, and the average accuracy is 1.55% higher. In comparison to advanced state-of-the-art (SOTA) target detection algorithms, the method in this paper offers a better balance between comprehensive performance and lightweight model design. The improved and optimized DAS-YOLOv8 model can realize the effective detection of potato sprouts, meet the requirements of real-time processing, and can provide theoretical support for the non-destructive detection of sprouts in automatic seed potato cutting.

Research on Multi-Sensor Data Collection and Mapping Methods

In today's information society, the importance of collecting data through multiple channels continues to increase. This article focuses on the use of drones for large-area forest information collection and tree analysis. In complex environments, there are a series of problems in obtaining tree mapping information with a single sensor, such as single use scenarios, low accuracy, limited viewing angles, and high requirements for computing power. To address these challenges, this paper proposes a Bayesian-based multi-sensor fusion strategy to overcome the limitations of a single sensor by integrating depth camera and lidar data. Experimental results show that the proposed method significantly improves the accuracy of mapping surrounding trees and the ability to collect information. The multi-sensor fusion strategy effectively reduces the problem of repeated data processing and improves the comprehensiveness of collected data. It meets the requirements of real-time performance and accurate collection of surrounding scenes.

Real-Time Batch Optimization for the Stochastic Container Relocation Problem

The container relocation problem (CRP) is an important factor affecting the operation efficiency of container terminal yards, and it has attracted much attention for decades. The CRP during the pickup operations of import containers is still an intractable problem for two reasons: the first is that the solution efficiency of the algorithms developed in the existing literature cannot meet the real-time operation requirements; the second is that the pre-optimized operation plan cannot cope with the changes in the real-time operation scenarios caused by the uncertainty of the arrival time of external trucks. This paper proposes an optimization method for the real-time operation scenario which aims to solve the most reasonable operation plan quickly according to the arrivals of external trucks, in which a dynamic upper bound of the optimal solution is derived based on the dynamic programming model of the import containers’ CRP, and an approximate optimal solution can be obtained by minimizing this dynamic upper bound. A heuristic algorithm based on three relocation rules is developed to implement this method, considering the adjustment of the pickup sequence of the target containers. Numerical experiments show that (1) when the number of a batch of target containers is less than 10 (excluding target containers that can be directly picked up), the method proposed in this paper can solve the problem quickly to meet the demand of optimizing real-time pickup operations; (2) compared with other outstanding algorithms, the quality of the solutions obtained by this method is also improved; and (3) this method can be applied to the most container terminals for optimizing real-time pickup operations.

Design and experiment of an adaptive cruise weeding robot for paddy fields based on improved YOLOv5

Weed control in paddy fields is a critical agronomic practice for enhancing rice yield, in which mechanical weed control is widely used due to its high weed control rate and convenient operation. However, traditional mechanical weed control methods require manual operation, leading to increased operational costs. Therefore, adaptive cruise weeding robots hold significant application promise and market potential. In this paper, an adaptive cruise weeding robot for paddy fields based on improved YOLOv5 is designed to adaptively perform weeding operations. Firstly, a real-time rice seedling recognition method based on MW-YOLOv5s is proposed by establishing a rice seedling dataset under different paddy field environments and growth stages. The model combines the Backbone network structure with MobileViTv3, replacing GIoU_loss with WIoU_loss, which significantly improves the precision and speed of rice seedling recognition. Next, the MW-YOLOv5s was integrated into a paddy weeding machine, and seedling navigation lines were extracted using the least square method. Finally, the control system autonomously operates the weeding machine in real time through feedback control based on the navigation path. The test results showed that the MW-YOLOv5s model exhibits strong recognition performance for rice seedlings under different paddy field backgrounds and growth stages, with a precision of 90.05 % and an mAP of 92.32 %, while the real-time performance reaches 19.51 FPS, which meet the requirements for real-time operation of the weeding machine in paddy fields. The results of the paddy field weed control experiment reveal a weed control rate of 82.4 % and a seedling injury rate of 2.8 %, which meet the agronomic requirements for mechanical weed control in paddy fields. The research findings contribute to the design of intelligent agricultural equipment and promote the practice and application of machine vision in the field of paddy field plant protection.

OMR Sheet Evaluation using Image Processing

Optical Mark Recognition (OMR) technology has revolutionized the grading and assessment processes in educational institutions, surveys, and various other fields. OMR sheets, designed with predefined bubbles or checkboxes, are scanned and processed to extract relevant data. This paper presents a comprehensive review of the methodologies and advancements in OMR sheet evaluation using image processing techniques. The review begins with an overview of traditional OMR systems and their limitations, such as susceptibility to errors due to variations in scanning quality, paper orientation, and noise interference. Subsequently, it delves into the evolution of image processing algorithms tailored for OMR sheet evaluation. Several key components of OMR sheet evaluation are discussed, including image pre-processing techniques for enhancing readability, segmentation methods for isolating individual marks, feature extraction algorithms for capturing relevant data, and classification techniques for accurate identification of marked responses. The review highlights recent trends and innovations in OMR sheet evaluation, such as the integration of machine learning and deep learning algorithms for improved accuracy and robustness. Additionally, it addresses challenges such as handling skewed or distorted images, multi-page OMR sheets, and real-time processing requirements. Furthermore, the paper discusses benchmark datasets and evaluation metrics commonly used to assess the performance of OMR systems. It also examines practical considerations such as scalability, cost-effectiveness, and usability in diverse settings..

Autonomous traffic sign detection for self-driving car system using convolutional neural network algorithm

The accurate detection of traffic signs is a critical component of self-driving systems, enabling safe and efficient navigation. In the literature, various methods have been investigated for traffic sign detection, among which deep learning-based approaches have demonstrated superior performance compared to other techniques. This paper justifies the widespread adoption of deep learning due to its ability to provide highly accurate results. However, the current research challenge lies in addressing the need for high accuracy rates and real-time processing requirements. In this study, we propose a convolutional neural network based on the YOLOv8 algorithm to overcome the aforementioned research challenge. Our approach involves generating a custom dataset with diverse traffic sign images, followed by conducting training, validation, and testing sets to ensure the robustness and generalization of the model. Experimental results and performance evaluation demonstrate the effectiveness of the proposed method. Extensive experiments show that our model achieved remarkable accuracy rates in traffic sign detection, meeting the real-time requirements of the input data.

Dynamic encryption method for MQTT communication

Internet of vehicles (IoV) system is mixed-criticality, and different functions have different requirements for information security and real-time performance. Thesis aims to design a comprehensive dynamic encryption algorithm to ensure the function of the IoV system while taking into account the security level required by different critical functions. The algorithm can flexibly coordinate the security requirements between different functions, improve the efficiency and reliability of the system to the greatest extent, and ensure information security. In order to verify the performance of the algorithm, thesis uses Pycharm platform to carry out simulation experiments through python programming and verifies the feasibility of the algorithm.

Advancing Ultrasonic Defect Detection in High-Speed Wheels via UT-YOLO.

In the context of defect detection in high-speed railway train wheels, particularly in ultrasonic-testing B-scan images characterized by their small size and complexity, the need for a robust solution is paramount. The proposed algorithm, UT-YOLO, was meticulously designed to address the specific challenges presented by these images. UT-YOLO enhances its learning capacity, accuracy in detecting small targets, and overall processing speed by adopting optimized convolutional layers, a special layer design, and an attention mechanism. This algorithm exhibits superior performance on high-speed railway wheel UT datasets, indicating its potential. Crucially, UT-YOLO meets real-time processing requirements, positioning it as a practical solution for the dynamic and high-speed environment of railway inspections. In experimental evaluations, UT-YOLO exhibited good performance in best recall, mAP@0.5 and mAP@0.5:0.95 increased by 37%, 36%, and 43%, respectively; and its speed also met the needs of real-time performance. Moreover, an ultrasonic defect detection data set based on real wheels was created, and this research has been applied in actual scenarios and has helped to greatly improve manual detection efficiency.

An evaluation research of constant false alarm rate algorithm for the application of underwater vehicles management system based on USBL sonar principles in shallow water

The paper presents an overview of underwater traffic management systems utilizing sonar principles. Based on the evaluation of the current situation both domestically and internationally, the research team has proposed the development of a underwater signal processing algorithm based on an adaptive threshold in the Ultra-short Baseline System (USBL). The theoretical results were implemented on FPGA hardware, not only enhancing signal detection capabilities but also meeting real-time signal processing requirements when deployed in complex underwater conditions in various shallow water regions of Vietnam. The research's future direction indicates substantial potential in the practical application of hydroacoustic signal processing algorithms when executing hardware implementations.

QoS-aware task offloading and resource allocation optimization in vehicular edge computing networks via MADDPG

To meet the stringent requirements for real-time performance of computing tasks on the Internet of Vehicles (IoV) scenario, the Mobile Edge Computing (MEC) technique is introduced to combine edge servers with vehicles that have storage and computing resources, thereby reducing latency. However, successfully accessing the channel and completing the offloading computation within the specified time remains a big challenge in scenarios with multitasking vehicles and multibase stations equipped with multiple channels. To address this problem, we propose the Multi-Agent Deep Deterministic Policy Gradient-based Offloading and Resource Allocation (MADDPG-RA) algorithm. First, a sub-optimal offloading strategy is determined using the MADDPG algorithm to minimize the sum of system latency and energy consumption. Specifically, this strategy determines whether each vehicle should perform local or offload computation, which MEC server to choose, and which channel to access if offloaded. Based on the above, a closed form expression for the optimal computational resource allocation for MEC is derived using Lagrange multipliers. The simulation results demonstrate that the proposed MADDPG-RA algorithm can effectively reduce the total system latency and energy consumption compared to the existing algorithms.

A Staged Real-Time Ground Segmentation Algorithm of 3D LiDAR Point Cloud

Ground segmentation is a crucial task in the field of 3D LiDAR perception for autonomous driving. It is commonly used as a preprocessing step for tasks such as object detection and road extraction. However, the existing ground segmentation algorithms often struggle to meet the requirements of robustness and real-time performance due to significant variations in ground slopes and flatness across different scenes, as well as the influence of objects such as grass, flowerbeds, and trees in the environment. To address these challenges, this paper proposes a staged real-time ground segmentation algorithm. The proposed algorithm not only achieves high real-time performance but also exhibits improved robustness. Based on a concentric zone model, the algorithm filters out reflected noise points and vertical non-ground points in the first stage, improving the validity of the fitted ground plane. In the second stage, the algorithm effectively addresses the issue of undersegmentation of ground points through three steps: ground plane fitting, ground plane validity judgment, and ground plane repair. The experimental results on the SemanticKITTI dataset demonstrate that the proposed algorithm outperforms the existing methods in terms of segmentation results.

Nonlinear Model Predictive Control for a Dynamic Positioning Ship Based on the Laguerre Function

In this paper, we present a novel nonlinear model predictive control (NMPC) algorithm based on the Laguerre function for dynamic positioning ships to solve the problems of input saturation, unknown time-varying disturbances, and heavy computation. The nonlinear model of a dynamic positioning ship is presented as a linear model, transformed from a standard affine nonlinear state-space model by precise feedback linearization. The environmental disturbance is overcome using an integrator. The time cost of the proposed nonlinear control algorithm is decreased by inducing the Laguerre function to describe the feedback-linearization system input increments. The Laguerre function reduces the matrix dimensions of the nonlinear optimization problem. The simulation results for a DP supply vessel showed that the novel algorithm maintained the effective control performance of the original nonlinear model predictive control algorithm and had a reduced computation load to satisfy the requirements of real-time operation.

Real-time millimeter wave holography with an arrayed detector.

Millimeter and terahertz wave imaging has emerged as a powerful tool for applications such as security screening, biomedical imaging, and material analysis. However, intensity images alone are often insufficient for detecting variations in the dielectric constant of a sample, and extraction of material properties without additional phase information requires extensive prior knowledge of the sample. Digital holography provides a means for intensity-only detectors to reconstruct both amplitude and phase images. Here we utilize a commercially available source and detector array, both operating at room temperature, to perform digital holography in real-time for the first time in the mm-wave band (at 290 GHz). We compare the off-axis and phase-shifting approaches to digital holography and discuss their trade-offs and practical challenges in this regime. Owing to the low pixel count, we find phase-shifting holography to be the most practical and high fidelity approach for such commercial mm-wave cameras even under real-time operational requirements.

Scalable Multi-Hierarchy Embedded Platform for Neural Population Simulations.

Brain-inspired structured neural circuits are the cornerstones of both computational and perceived intelligence. Real-time simulations of large-scale high-dimensional neural populations with complex nonlinearities pose a significant challenge. Taking advantage of distributed computations using embedded multi-cores, we propose an ARM-based scalable multi-hierarchy parallel computing platform (EmPaas) for neural population simulations. EmPaas is constructed using 340 ARM Cortex-M4 microprocessors to achieve high-speed and high-accuracy parallel computing. The tree-two-dimensional grid-like hybrid topology completes the overall construction, reducing communication strain and power consumption. As an instance of embedded computing, the optimized model for a biologically plausible basal ganglia-thalamus (BG-TH) network is deployed into this platform to verify the performance. At an operating frequency of 168 MHz, the BG-TH network consisting of 4000 Izhikevich neurons is simulated in the platform for 3000 ms with a power consumption of 56.565 mW per core and an actual time of 2748.57 ms, which shows the parallel computing approach significantly improves computational efficiency. EmPaas can meet the requirement of real-time performance with the maximum amount of 2000 Izhikevich neurons loaded in each Extended Community Unit (ECUnit), which provides a new practical method for research in large-scale brain network simulation and brain-inspired computing.

KDP-Net: An Efficient Semantic Segmentation Network for Emergency Landing of Unmanned Aerial Vehicles

As the application of UAVs becomes more and more widespread, accidents such as accidental injuries to personnel, property damage, and loss and destruction of UAVs due to accidental UAV crashes also occur in daily use scenarios. To reduce the occurrence of such accidents, UAVs need to have the ability to autonomously choose a safe area to land in an accidental situation, and the key lies in realizing on-board real-time semantic segmentation processing. In this paper, we propose an efficient semantic segmentation method called KDP-Net for characteristics such as large feature scale changes and high real-time processing requirements during the emergency landing process. The proposed KDP module can effectively improve the accuracy and performance of the semantic segmentation backbone network; the proposed Bilateral Segmentation Network improves the extraction accuracy and processing speed of important feature categories in the training phase; and the proposed edge extraction module improves the classification accuracy of fine features. The experimental results on the UDD6 and SDD show that the processing speed of this method reaches 85.25 fps and 108.11 fps while the mIoU reaches 76.9% and 67.14%, respectively. The processing speed reaches 53.72 fps and 38.79 fps when measured on Jetson Orin, which can meet the requirements of airborne real-time segmentation for emergency landing.

Smart Log Analyzer for Anomaly Detection in Distributed Systems

Abstract—This research work outlines a novel approach to fortify the stability and security of distributed systems through the implementation of an AI-enabled smart log analyzer. The escalating proliferation of distributed systems has led to an unprecedented surge in the volume of generated log data, which holds crucial insights into system performance, security, and dependability. However, the substantial challenges associated with managing this data—such as its overwhelming volume, diverse nature, and real-time processing requirements—have posed significant hurdles. The proposed AI-enabled smart log analyzer, detailed in this report, harnesses the power of advanced machine learning and natural language processing techniques to address these challenges effectively. The methodology is struc- tured into three fundamental phases, namely, Data Preprocessing, Anomaly Detection, and Clustering. The Data Preprocessing phase encompasses the collection, parsing, filtering, and feature extraction of log data. Anomaly Detection integrates machine learning models to discern various anomalies, encompassing irregular access patterns, log flooding, error messages, suspicious content, and outliers in resource requests. The Clustering phase categorizes log entries into meaningful groups based on attributes such as log level, component, event, error code, and resource usage, facilitating a comprehensive understanding of system behavior. This holistic approach holds the promise of significantly enhancing system stability and security. Index Terms—Anomaly Detection, Clustering, Data Prepro- cessing, Distributed Systems, Log analyzer, System security, System stability