High Real-time Performance Research Articles

High-Precision Measurement Method for Small Angles Based on the Defect Spot Mode of the Position-Sensitive Detector

The paper proposes and verifies a small-angle measurement method based on the defect spot mode of the position-sensitive detector (PSD). With the output characteristics of the PSD in the defect spot mode and the size transformation properties of a focused beam, the measurement sensitivity can be significantly improved. Calibration experiments with the piezoelectric transducer (PZT) indicate that compared with the current PSD-based autocollimation method, the proposed method can improve the sensitivity of small-angle measurement by 57 times, and the measurement sensitivity of the proposed method can be further improved by optimizing the system parameters, while the proposed method has the advantages of a simple system and high real-time performance. Therefore, the proposed method is expected to be used in high-precision motion error detection, as well as in shape and position measurement.

Three-dimensional finite element analysis of biological signal feedback in the mechanical properties of sound production

In response to the problems of low reliability and long time consumption in traditional research on sound production, this article used electromyography (EMG) signals as input signals to build a high-precision sound production mechanics model. A Proportional-Integral-Derivative (PID) controller was used to dynamically adjust the model and develop a real-time feedback system. This article established a detailed three-dimensional (3D) finite element model including the vocal cords and throat, defined the nonlinear elastic properties and linear elastic properties of different tissues, and used tetrahedral grid partitioning technology to improve the computational accuracy of the model. Through a EMG sensor, an individual EMG was collected and filtered to remove noise. The processed EMGs were used as input parameters for the finite element model to drive the muscle units in the model. By using a PID controller to receive real-time EMG input, the error was calculated and the model was adjusted, enabling accurate simulation of the mechanical properties of vocal cord vibration under different vocal states and achieving real-time feedback. Considering the complexity of vocal cord vibration driven by biological signals, this article simulated and analyzed the modal characteristics of vocal cord vibration, and analyzed the differences in vocal cord vibration characteristics under different vocal states. The sound pressure distribution and resonance frequency were simulated and analyzed to understand the propagation characteristics of sound. Finally, by comparing and analyzing the simulated data with the actual collected data, it was found that the maximum relative error rate of the model under different sound states was 6.14%, and the overall error rate of the model was relatively low, which verified the reliability of the model. The findings demonstrated that the feedback delays of the model in different sound states were all within 100 milliseconds, indicating that the system had high real-time performance and accuracy, which was promising in practical applications.

Research on the Detection Algorithm of Elderly Falling Behavior Based on AlphaPose Model

This study aims to address the challenge of quickly and accurately detecting high-risk behaviors such as falls in elderly people in hardware environments with limited power consumption and cost. Therefore, this article proposes an optimized behavior anomaly detection algorithm based on the AlphaPose model. The algorithm first improved the human target detection and pose estimation models to achieve faster pedestrian detection and pose joint point inference; Then, using the adjusted AlphaPose model, efficiently obtain the image coordinate data of each joint point in the human body; Finally, by analyzing the relationship between the linear velocities of the human head and hip joints, as well as the changes in the angle between the vertical line of the human center and the X-axis of the image, it is determined whether a fall event has occurred. This study deployed the proposed algorithm on the JetsonNano embedded development board and compared its performance with mainstream fall detection algorithms based on human posture, including YOLOv3+Pose, YOLOv4+Pose, YOLOv5+Pose, trtpose, and NanoDet+Pose. The experimental results show that on the set embedded platform, when the image resolution is 320×240, this algorithm achieves a detection rate of 8.83 frames per second and an accuracy of 0.913, both of which are superior to the compared algorithms. In summary, this algorithm has high real-time performance and accuracy, and can effectively identify the falling behavior of elderly people in a timely manner.

Traffic and transportation management data storage terminal based on Internet of Things

Traditional data storage models are inadequate in the face of the growing demand for big data in transportation and transportation management. Its poor horizontal scalability makes it difficult to deal with the growth of massive data; on the other hand, its complex management makes it challenging to achieve unified management and effective resource utilization due to the differences in equipment from different manufacturers. In order to effectively store and manage this huge amount of information, it is urgent to rely on advanced technical tools. In this context, while ensuring data security and simplifying data management, it is also necessary to meet the terminal’s demand for high real-time performance, and these factors jointly promote the continuous attention and improvement of data storage terminal performance. This paper proposes a Hadoop solution based on distributed computing. As a distributed system infrastructure, Hadoop allows users to develop distributed programs without a deep understanding of distributed details, fully using the high-speed computing and storage capabilities of Hadoop clusters, which is especially suitable for big data processing tasks on the Internet of Things (IoT) platform. The experimental results show that for a 10 GB data file, the traditional terminal (Terminal 1) can store 7.8 GB, while the Hadoop-based terminal (Terminal 2) can store 9.9 GB. For 50 GB of data files, Terminal 1 and Terminal 2 can store 40.4 GB and 49.8 GB of data respectively. These results show that Hadoop terminals have significant advantages in processing large-scale data, especially in terms of data storage efficiency, and can use storage resources more effectively to meet the high-performance requirements of traffic and transportation management for data storage terminals.

Development of autonomous navigation system based on neural network and visual servoing for row-crop tracking in vegetable greenhouses

Accurate navigation line localization strategy and robust path tracking control scheme are two key techniques for advancing precision agriculture and enhancing the sustainability of smart farms. In this paper, a superior and cost-effective autonomous navigation system for a visual servoing-based agricultural vehicle in the greenhouse scenario with a distinct layout of two crop rows in each ridge is presented. First, based on image segmentation techniques, a neural network model named DGLNet integrating recognition of crop rows and prediction of the effective area of the navigation line is designed. The linear equation of the navigation line we required is obtained through post-processing operations of linear fitting completed in the effective area. According to experimental results, directly teaching the neural network to learn navigation line localization features utilizes the semantic and spatial information in the feature map to establish a stronger relationship between crop rows and the navigation line, which greatly improves the generalization of the navigation line localization in the case of large-angle yaw scenarios without additional training datasets. In addition, the performance of the network is significantly enhanced by adding an attention mechanism in the backbone based on U-Net. Subsequently, a dataset of cabbage vegetable images covering a wide range of complexities in the greenhouse scenario is created, and is available to support future academic research now. Further, a path tracking control scheme based on point-feature and line-feature is proposed, which can control the agricultural vehicle using the information from camera images without the need to perform explicit localization or maintain a global map. Finally, extensive field experiments have been carried out to show that this autonomous navigation system can preserve the stability of the vehicle and ensure timely convergence of deviations in different complex scenarios, which indicates that this control scheme is reliable and robust. Meanwhile, benefit from the efficiency of this recognition algorithm and the unnecessity of the complete representation of the path in the control scheme, the system has high real-time performance in every scenario.

The real-time shadow detection of the PV module by computer vision based on histogram matching and gamma transformation method

Solar energy plays an important role in renewable energy generation, with the advantages of low pollution, easy installation, and relatively easy access. However, photovoltaic (PV) modules are susceptible to cause localized shading from external factors such as leaves in the canopy, surrounding buildings, etc., which would affect power generation efficiency and even pose safety risks. Existing methods cannot perform well in real-time conditions. This paper proposes a real-time shading monitoring method for the PV module based on computer vision. The gamma transform and histogram matching were adopted to enhance key features and adjust the global gamut strength distribution in the image of the PV module; then the gray-level slicing method finished the segmentation to detect the shadow from the video. All processing can be realized in the real-time monitor camera and the detection results can be displayed on the HMI in PC with high efficiency and low cost. According to tests in the practical complex environment, the method can have enough detection performance and high real-time performance with an accuracy of 0.98, and the F0.5 and F2 values are 0.87 and 0.85, respectively. The metrics of the proposed method are higher than those of the existing Canny detection method, the Random Forest detection method, and the CNN detection method. In addition, the average time required by the proposed method to process a frame is 0.721 s. In addition, the average time required by the method to process an image frame is 0.721 s, which has good real-time performance.

A Lightweight Real-Time Recognition Algorithm for Tomato Leaf Disease Based on Improved YOLOv8

To address the real-time detection challenge of deploying deep learning-based tomato leaf disease detection algorithms on embedded devices, an improved tomato leaf disease detection algorithm based on YOLOv8n is proposed in this paper. It is able to achieve the efficient, real-time detection of tomato leaf diseases while maintaining model’s lightweight requirements. The algorithm incorporated the LMSM (lightweight multi-scale module) and ALSA (Attention Lightweight Subsampling Module) to improve the ability to extract lightweight and multi-scale semantic information for the specific characteristics of tomato leaf disease, which include irregular spot size and lush tomato leaves. The head network was redesigned utilizing partial and group convolution along with a parameter-sharing method. Scalable auxiliary bounding box and loss function optimization strategies were introduced to further enhance performance. After undergoing the pruning technique, computation decreased by 61.7%, the model size decreased by 55.6%, and the FPS increased by 44.8%, all while a high level of accuracy was maintained. A detection speed of 19.70FPS on the Jetson Nano was obtained after undergoing TensorRT quantization, showing a 64.85% improvement compared to the initial detection speed. This method met the high real-time performance and small model size requirements for embedded tomato leaf disease detection systems, indirectly reducing the energy consumption of online detection. It provided an effective solution for the online detection of tomato leaf disease.

Enhanced Harmonic Partitioned Scheduling of Periodic Real-Time Tasks Based on Slack Analysis.

The adoption of multiprocessor platforms is growing commonplace in Internet of Things (IoT) applications to handle large volumes of sensor data while maintaining real-time performance at a reasonable cost and with low power consumption. Partitioned scheduling is a competitive approach to ensure the temporal constraints of real-time sensor data processing tasks on multiprocessor platforms. However, the problem of partitioning real-time sensor data processing tasks to individual processors is strongly NP-hard, making it crucial to develop efficient partitioning heuristics to achieve high real-time performance. This paper presents an enhanced harmonic partitioned multiprocessor scheduling method for periodic real-time sensor data processing tasks to improve system utilization over the state of the art. Specifically, we introduce a general harmonic index to effectively quantify the harmonicity of a periodic real-time task set. This index is derived by analyzing the variance between the worst-case slack time and the best-case slack time for the lowest-priority task in the task set. Leveraging this harmonic index, we propose two efficient partitioned scheduling methods to optimize the system utilization via strategically allocating the workload among processors by leveraging the task harmonic relationship. Experiments with randomly synthesized task sets demonstrate that our methods significantly surpass existing approaches in terms of schedulability.

Missile Fault Detection and Localization Based on HBOS and Hierarchical Signed Directed Graph

The rudder surfaces and lifting surfaces of a missile are utilized to acquire aerodynamic forces and moments, adjust the missile’s attitude, and achieve precise strike missions. However, the harsh flying conditions of missiles make the rudder surfaces and lifting surfaces susceptible to faults. In practical scenarios, there is often a scarcity of fault data, and sometimes, it is even difficult to obtain such data. Currently, data-driven fault detection and localization methods heavily rely on fault data, posing challenges for their applicability. To address this issue, this paper proposes an HBOS (Histogram-Based Outlier Score) online fault-detection method based on statistical distribution. This method generates a fault-detection model by fitting the probability distribution of normal data and incorporates an adaptive threshold to achieve real-time fault detection. Furthermore, this paper abstracts the interrelationships between the missile’s flight states and the propagation mechanism of faults into a hierarchical directed graph model. By utilizing bilateral adaptive thresholds, it captures the first fault features of each sub-node and determines the fault propagation effectiveness of each layer node based on the compatibility path principle, thus establishing a fault inference and localization model. The results of semi-physical simulation experiments demonstrate that the proposed algorithm is independent of fault data and exhibits high real-time performance. In multiple sets of simulated tests with randomly parameterized deviations, the fault-detection accuracy exceeds 98% with a false-alarm rate of no more than 0.31%. The fault-localization algorithm achieves an accuracy rate of no less than 97.91%.

A novel image dehazing algorithm for complex natural environments

As an essential preprocessing step in computer vision tasks, image dehazing is preparatory work in many practical applications. To ensure the navigation safety of intelligent ships in complex haze weather, this paper proposes a lightweight real-time dehazing method for the natural environment based on an all-in-one dehazing network. Specifically, a hybrid dilated convolution is used to construct a dehazing network, which effectively expands the receptive field and improves the ability to extract characteristic information without increasing the calculation amount of the model. Secondly, a hybrid attention module is developed to enhance the model's ability to remove haze while retaining more detailed features by giving weights to light haze and heavy haze areas in the image. Finally, considering the subjective perception of human eyes, a mixed loss function is designed to address image dimness and color distortion after dehazing. Through experiments on both synthetic and real-world haze datasets, the proposed method attains ideal dehazing results and has high real-time performance. It can dehaze under different haze conditions, address the critical challenge of degraded visual perception in severe weather conditions of intelligent ship navigation on inland rivers in complex environments, and promote intelligent ships' development.

Introduction to the Special Issue on Next Generation Pervasive Reconfigurable Computing for High Performance Real Time Applications

Introduction to the Special Issue on Next Generation Pervasive Reconfigurable Computing for High Performance Real Time Applications

TROG: a fast and robust scene-matching algorithm for geo-referenced images

ABSTRACT In a GNSS-denied/challenged environment, the scene-matching navigation system (SMNS) is a vital autonomous technology for unmanned aerial vehicles (UAV). This paper proposes a novel template-matching framework for multimodal images since UAV navigation requires precision and real-time performance while utilizing onboard computers with low computational power. Specifically, first, the local descriptor is extracted to form a pixel-wise feature representation of an image. Then, the Fast Fourier Transform is applied to measure the similarity based on the feature representation. In the feature extraction part, a gradient-like pixel-level feature descriptor is designed, which is reconstructed by weighting it according to the gradient-like angles, named the three-dimensional reconstruction oriented gradient-like (TROG) descriptor. An optimized similarity measurement template is introduced in the matching part, which improves the traditional feature-based similarity measurement algorithm defined using Fast Fourier Transform (FFT) in the frequency domain. This strategy eliminates redundant computations during the matching process. To verify the effectiveness of the proposed algorithm, satellite imagery data from Google Earth are used as a reference images, and sensed images (including optical, IR, SAR, and Hyperspectral) are captured by UAV and satellites for image matching to testify to the registration accuracy, robustness, and computational efficiency. The experiment demonstrates that TROG is accurate, robust, and attains high real-time performance, making it applicable to UAV navigation and positioning. Additionally, field-flight experiments evaluate scene-matching navigation under satellite-denied conditions and low computational power conditions for UAVs, demonstrating that our scene-matching navigation system can achieve precise positioning with a positioning error of less than 1.637 m, which is comparable to satellite/inertial navigation systems. The experimental results from outdoor flight experiments highlight the value of our proposed algorithm in engineering applications under satellite denial conditions.

Image reconstruction for electrostatic tomography with deep convolutional neural network

Electrostatic tomography (EST) has the characteristics of being radiation-free and high real-time imaging performance. However, due to its passive measurement method, EST only provides limited input data equal to the number of electrode plates, which results in more serious ill-posed inverse problem. As a result, the imaging accuracy of traditional algorithms is low. To address this problem, an improved deep learning model is proposed for image reconstruction of EST, which is based on a convolutional neural network (CNN). The proposed network is built upon the LeNet-5 deep network model, which incorporates two convolution layers to extract data features and two maximum pooling layers to perform dimension reduction. Furthermore, the LeakyReLU activation function is employed to solve the gradient vanishing problem and accelerate the training process by introducing the non-zero gradients. By constructing an EST model, 8 representative distributions were simulated and 15,930 samples were generated. After 250 iterations, more accurate reconstructed images were generated. To verify the noise immunity of the network model, Gaussian white noises of 20 dB, 30 dB, and 50 dB were added to the test set, and the proposed model could still accurately reconstruct the positions and shapes of the samples. The network is further compared with the traditional algorithms, which reconstructs better results. In addition, the reconstruction results by the proposed model are still relatively accurate for several simulated random samples of different number, shapes and positions. Finally, a 16-electrode sensor array experimental system is designed. The experimental results show that the improved CNN algorithm has a better imaging effect than the traditional algorithms and the CNN algorithm, which is consistent with the simulation results, proving that the model has good generalization ability.

An Improved YOLOv7-Based Model for Real-Time Meter Reading with PConv and Attention Mechanisms.

With the increasing complexity of the grid meter dial, precise feature extraction is becoming more and more difficult. Many automatic recognition solutions have been proposed for grid meter readings. However, traditional inspection methods cannot guarantee detection accuracy in complex environments. So, deep-learning methods are combined with grid meter recognition. Existing recognition systems that utilize segmentation models exhibit very high computation. It is challenging to ensure high real-time performance in edge computing devices. Therefore, an improved meter recognition model based on YOLOv7 is proposed in this paper. Partial convolution (PConv) is introduced into YOLOv7 to create a lighter network. Different PConv introduction locations on the base module have been used in order to find the optimal approach for reducing the parameters and floating point of operations (FLOPs). Meanwhile, the dynamic head (DyHead) module is utilized to enhance the attention mechanism for the YOLOv7 model. It can improve the detection accuracy of striped objects. As a result, this paper achieves mAP50val of 97.87% and mAP50:90val of 62.4% with only 5.37 M parameters. The improved model's inference speed can reach 108 frames per second (FPS). It enables detection accuracy that can reach ±0.1 degrees in the grid meter.

Replica fault-tolerant scheduling with time guarantee under energy constraint in fog computing

Fog computing offers stronger local computing power and reduces data transmission load, making it an ideal solution to meet the energy-saving and efficient requirements of intelligent connected vehicle applications. As intelligent networked vehicles and vehicle–road collaboration technologies advance rapidly, optimizing scheduling under fog computing architecture has become a prominent research area. However, existing studies primarily concentrate on parallel task scheduling with low energy consumption or high real-time performance, failing to address the requirement for high reliability in intelligent networked vehicle scenarios. To achieve time and reliability optimization for vehicular applications in fog computing architecture, this paper proposes a fog computing task scheduling algorithm and explores its extension using replication techniques. Subsequently, the algorithm underwent evaluation utilizing randomly generated directed acyclic graph models as well as real-life automotive application instances. The experimental findings indicate that in comparison to existing methods, the proposed algorithm exhibits a notable improvement in reliability while ensuring time optimization, thereby demonstrating a distinct level of advancement and practicality.

Real-Time Recognition Algorithm of Small Target for UAV Infrared Detection

Unmanned Aerial Vehicle (UAV) infrared detection has problems such as weak and small targets, complex backgrounds, and poor real-time detection performance. It is difficult for general target detection algorithms to achieve the requirements of a high detection rate, low missed detection rate, and high real-time performance. In order to solve these problems, this paper proposes an improved small target detection method based on Picodet. First, to address the problem of poor real-time performance, an improved lightweight LCNet network was introduced as the backbone network for feature extraction. Secondly, in order to solve the problems of high false detection rate and missed detection rate due to weak targets, the Squeeze-and-Excitation module was added and the feature pyramid structure was improved. Experimental results obtained on the HIT-UAV public dataset show that the improved detection model’s real-time frame rate increased by 31 fps and the average accuracy (MAP) increased by 7%, which proves the effectiveness of this method for UAV infrared small target detection.

Indoor Navigation in Facilities with Repetitive Structures.

Most facilities are structured in a repetitive manner. In this paper, we propose an algorithm and its partial implementation for a cellular guide in such facilities without GPS use. The complete system is based on iBeacons-like components, which operate on BLE technology, and their integration into a navigation application. We assume that the user's location is determined with sufficient accuracy. Our main goal revolves around leveraging the repetitive structure of the given facility to optimize navigation in terms of storage requirements, energy efficiency in the cellular device, algorithmic complexity, and other aspects. To the best of our knowledge, there is no prior experience in addressing this specific aim. In order to provide high performance in real time, we rely on optimal saving and the use of pre-calculated and stored navigation sub-routes. Our implementation seamlessly integrates iBeacon communications, a pre-defined indoor map, diverse data structures for efficient information storage, and a user interface, all working cohesively under a single supervision. Each module can be considered, developed, and improved independently. The approach is mainly directed to places, such as passenger ships, hotels, colleges, and so on. Because of the fact that there are "replicated" parts on different floors, stored once and used for multiple routes, we reduce the amount of information that must be stored, thus helping to reduce memory usage and as a result, yielding a better running time and energy consumption.

HV-YOLOv8 by HDPconv: Better lightweight detectors for small object detection

Accurately identifying and localising small objects within images or videos is a critical challenge in the field of computer vision. It is mostly applied in scenarios that require high real-time performance, such as pedestrian detection and autonomous driving scenarios. These tiny targets generally include small objects at long distances, or objects appearing in low-resolution images, due to which it becomes exceptionally difficult to extract effective feature information. Since YOLOv8 with its large downsampling multiplier leads to deeper feature maps that make it difficult to detect tiny objects, we find that the use of residual structures in the convolution module can enhance the accuracy of small object detection. However, this undoubtedly increases the computational cost, so we lightened the convolution module to make it more suitable for practical applications and named it Halved Deep Pointwise Convolution (HDPConv). A cross-level partial module Variety of View Group Shuffle Cross Stage Partial Network (VOV-GSCSP) is also utilised, using a rational architecture as well as multi-scale information fusion, to ensure that the overall model is lightweight while obtaining rich gradient flows. On this basis, we propose a new network lightweight model HV-YOLOv8. In multiple sets of comparative experiments on two datasets (containing several state-of-the-art solutions as well as classical ones), we demonstrate the superiority of HV-YOLOv8, in particular, the accuracy is improved by 1.4% compared to YOLOv8, while the number of parameters and the amount of computation are drastically reduced.

Application of Deep Learning in Drone Image Detection

As an important research direction in the field of computer vision, image detection is a technology that can localize and identify the image and can accurately discriminate the features of the task image, and its application to UAVs has been a hot topic in recent years. The field of algorithms of deep learning applied to image detection is progressing rapidly, however, there are still many advantages and disadvantages between various algorithms, and the application of UAV image detection focuses on different. Therefore, the research topic of this article is to compare the advantages and disadvantages of various algorithms of image detection and conduct a technical analysis of improved algorithms applied to drones. The research method of this paper is as follows: firstly, introducing the model of each algorithm, analyzing the principle, and then comparing the advantages and disadvantages of detection speed, detection accuracy, and finally choosing improved algorithms that are suitable for drone Image detection. the improved algorithms suitable for UAV image detection are selected. In terms of accuracy, Faster R-CNN and YOLO perform better compared to R-CNN and Fast R-CNN. In terms of speed, YOLO has a higher processing speed and is suitable for application scenarios that require high real-time performance. Faster R-CNN is suitable for scenarios that require high accuracy but relatively low-speed requirements. SSD performs outstandingly in handling multi-scale targets. Therefore, in the field of UAV image detection, choosing the appropriate deep learning model can balance the needs of accuracy and speed according to the application scenarios.

Evaluation on Data Transmission in Wireless Sensor Networks Based on Computer Technology

Traditional network data transmission (DT) has certain limitations in terms of processing, storage, and communication capabilities. Moreover, DT is easily affected by the network environment, which can reduce the real-time performance of DT. In severe cases, network transmission failures, DT interruptions, and other issues may even occur. As a new network, wireless sensor networks (WSN) have been widely used in military, industrial, agricultural, medical and other fields. In this paper, WSN was regarded as an important research method, and in-depth research was carried out around the real-time and energy consumption (for the convenience of the following text, the abbreviation for energy consumption is EC) of WSN DT. This paper focused on the issues of real-time performance, EC, and real-time transmission. It balanced EC and improved real-time performance through the low energy adaptive clustering hierarchy (LEACH) protocol and the power-efficient gathering in sensor information systems (PEGASIS) protocol. The experimental results showed that the real-time rates of LEACH under single reaction nodes were 45.5% and 48.6%, respectively. The lowest and highest real-time rates of PEGASIS were 86.2% and 89.5%, respectively. The real-time rate of PEGASIS was much higher than that of LEACH, proving that the proposed PEGASIS had high real-time performance and reduced DT delay.