Multithreading Technology Research Articles

Automobile Intelligent Vehicle-machine and Human-computer Interaction System based on Big Data

This paper measures the accelerator, brake pedal, clutch, transmission device and steering wheel under different driving conditions in real-time and accurately on the simulation experiment platform. The goal is to conduct human-machine-road system interaction in a virtual simulation of human-vehicle-road systems. Fitting test data establishes the mathematical model of traffic control parameters. In terms of hardware, the distributed architecture of upper and lower computers is utilized. The system communicates point-to-point with the host computer through the RS-232 serial port. The system adopts multi-thread technology and serial communication technology. The simulation system of driving operation is designed with the visual central controller. The system takes 89S52 as the core. The slave program is written in C language. Then, the system establishes a multi-target coordinated obstacle avoidance method based on a multi-sensor information vehicle cooperation collision avoidance method. The multi-vehicle cooperation obstacle avoidance problem is transformed into an optimal control problem under multiple constraints. Simulation analysis shows that the velocity and displacement obtained by the multi-robot collaborative collision avoidance method are in good agreement with the measured values. Compared with the time series algorithm, the output accuracy of the proposed collaborative collision avoidance algorithm is significantly reduced, and the changes in velocity and displacement in the time domain are more stable.

B-LPoET: A middleware lightweight Proof-of-Elapsed Time (PoET) for efficient distributed transaction execution and security on Blockchain using multithreading technology

A private permissioned chain is typically considered a fully protected authorized blockchain. The concept of a permissioned chain has become better single-entity control operation over the network and allows entry only to selected nodes, which most probably supports different small-medium enterprises and government officials. It is one of the prominent features of blockchain technology that maintains transaction integrity and security throughout the deliverance. This paper initially highlights the benefits of a permissioned private chain. Present the current issues of the Proof-of-Elapsed Time consensus mechanism for the distributed applicational environment. In order to make blockchain systems more adaptive for private chains, this paper proposes a middleware lightweight consensus based on PoET, named “B-LPoET”. Objecting to being improved as a lightweight PoET from the predefined PoET in terms of verifying transactions and creating more blocks, to decide the mining right, and participants allows to acknowledge their identities before joining. The design of B-LPoET reduces the cost of multi-node efficiency by establishing a lightweight topology for selecting and waiting time of winning node using multithreading, which affects positively by increasing systems scalability. The experimental results of the B-LPoET illustrate that it performs better as compared with the existing PoET and other state-of-the-art consensus.

Computer Algorithm Design and Linearity Analysis of Its Data Structures

Linearity optimization of data structure is in an important position in the design of computer algorithms, and the study of its optimization measures and strategies can help to promote the research of computer algorithms in depth. This paper introduces the multithreading technology, designs a multithreaded inverted sorted cited table on the basis of the data chain table structure, and prunes and optimizes the data in the table and its distribution repetitions, and puts forward the linearity optimization strategy. And the GaBP parallel computer algorithm for banded linear equations is designed by combining this strategy. The linearity analysis of the optimized data structure reveals that the linearity is significantly improved, and the overall linearity is smooth and reasonably skewed. In the calculation of large-scale numerical values, the running time of the Partition number of 30 is only 3756.9 s. The average linear solution speed is only 0.063 s when solving the satisfiable SAT instances, which is 4.631 s less than that of the Minisat algorithm, and the optimization strategy of the data structure linearity proposed in this paper provides a reference scheme to accelerate the speed of computer algorithms in solving the difficult problems, and the design of the computer parallel algorithm provides a reference scheme to accelerate the speed of computer algorithms in solving the difficult problems. The designed computer parallel algorithm provides an effective method for the field of large-scale numerical processing and SAT problem solving.

Research on Parallel Reading and Drawing Techniques for Chemical Mechanical Polishing Simulation Data Based on Multi-Thread

In advanced integrated circuit manufacturing, the quality of chemical mechanical polishing (CMP) is a key factor affecting chip performance and yield. Designers need to use CMP simulation tools to locate and analyze the defects in the layout after the CMP process. However, the advancement of process nodes and the increase in data volume presents a great challenge to the speed of graphical display of CMP simulation data. To solve this issue, we propose a solution that uses multi-threading technology to optimize both data reading and drawing. In the process of data reading, we employ OpenMP and memory mapping (Mmap) technology to achieve parallel reading of file segmentation and propose a fast-string conversion algorithm based on the properties of simulation data. In the process of data drawing, we propose an adaptive downsampling method for data graphical display that combines multi-threading and double buffering technology to enable the parallel drawing of layouts. The effectiveness of this method is verified by testing CMP simulation data of various scales. Compared to traditional methods, this approach improves reading efficiency by over 8 times and drawing efficiency by more than 10 times. Furthermore, it enhances the smoothness of interaction with the CMP simulation tool.

Dynamic multi-objective path-order planning research in nuclear power plant decommissioning based on NSGA-II

The requirement for nuclear reactor decommissioning multi-objective optimization has grown in recent years. This paper proposes a dynamic total exposure dose estimation method based on the point-kernel method and improves its performance by introducing single-instruction-multiple-data and multi-threading technologies. Then, the dynamic dose estimating model is integrated into the non-dominated sorting genetic algorithm version 2 to obtain the preference-less path-order schemes for multi-objective decision-making. It is applied to a research reactor to validate its capability and analyze the optimized results. Results show the estimated dose conforms to the practically measured value, and the optimized schemes can significantly reduce the cumulative dose and the total path length objective. Besides, a frequency analysis recommendation based on the Pareto front is proposed to provide a non-weighting optimal decision. Hence, we suggest using this method to acquire optimal solutions and make further decisions for decommissioning.

Real-Time Object Localization Using a Fuzzy Controller for a Vision-Based Drone

This study proposes a drone system with visual identification and tracking capabilities to address the issue of limited communication bandwidth for drones. This system can lock onto a target during flight and transmit its simple features to the ground station, thereby reducing communication bandwidth demands. RealFlight is used as the simulation environment to validate the proposed drone algorithm. The core components of the system include DeepSORT and MobileNet lightweight models for target tracking. The designed fuzzy controller enables the system to adjust the drone’s motors, gradually moving the locked target to the center of the frame and maintaining continuous tracking. Additionally, this study introduces channel and spatial reliability tracking (CSRT) switching from multi-object to single-object tracking and multithreading technology to enhance the system’s execution speed. The experimental results demonstrate that the system can accurately adjust the target to the frame’s center within approximately 1.5 s, maintaining precision within ±0.5 degrees. On the Jetson Xavier NX embedded platform, the average frame rate (FPS) for the multi-object tracker was only 1.37, with a standard deviation of 1.05. In contrast, the single-object tracker CSRT exhibited a significant improvement, achieving an average FPS of 9.77 with a standard deviation of 1.86. This study provides an effective solution for visual tracking in drone systems that is efficient and conserves communication bandwidth. The validation of the embedded platform highlighted its practicality and performance.

A Multithreaded Algorithm for the Computation of Sample Entropy

Many popular entropy definitions for signals, including approximate and sample entropy, are based on the idea of embedding the time series into an m-dimensional space, aiming to detect complex, deeper and more informative relationships among samples. However, for both approximate and sample entropy, the high computational cost is a severe limitation. Especially when large amounts of data are processed, or when parameter tuning is employed premising a large number of executions, the necessity of fast computation algorithms becomes urgent. In the past, our research team proposed fast algorithms for sample, approximate and bubble entropy. In the general case, the bucket-assisted algorithm was the one presenting the lowest execution times. In this paper, we exploit the opportunities given by the multithreading technology to further reduce the computation time. Without special requirements in hardware, since today even our cost-effective home computers support multithreading, the computation of entropy definitions can be significantly accelerated. The aim of this paper is threefold: (a) to extend the bucket-assisted algorithm for multithreaded processors, (b) to present updated execution times for the bucket-assisted algorithm since the achievements in hardware and compiler technology affect both execution times and gain, and (c) to provide a Python library which wraps fast C implementations capable of running in parallel on multithreaded processors.

Parallel Optimization for Large Scale Interferometric Synthetic Aperture Radar Data Processing

Interferometric synthetic aperture radar (InSAR) has developed rapidly over the past years and is considered as an important method for surface deformation monitoring, benefiting from growing data quantities and improving data quality. However, the handing of SAR big data poses significant challenges for related algorithms and pipeline, particularly in large-scale SAR data processing. In addition, InSAR algorithms are highly complex, and their task dependencies are intricate. There is a lack of efficient optimization models and task scheduling for InSAR pipeline. In this paper, we design parallel time-series InSAR processing models based on multi-thread technology for high efficiency in processing InSAR big data. These models concentrate on parallelizing critical algorithms that have high complexity, with a focus on deconstructing two computationally intensive algorithms through loop unrolling. Our parallel models have shown a significant improvement of 10–20 times in performance. We have also developed a parallel optimization tool, Simultaneous Task Automatic Runtime (STAR), which utilizes a data flow optimization strategy with thread pool technology to address the problem of low CPU utilization resulting from multiple modules and task dependencies in the InSAR processing pipeline. STAR provides a data-driven pipeline and enables concurrent execution of multiple tasks, with greater flexibility to keep the CPU busy and further improve CPU utilization through predetermined task flow. Additionally, a supercomputing-based system has been constructed for processing massive InSAR scientific big data and providing technical support for nationwide surface deformation measurement, in accordance with the framework of time series InSAR data processing. Using this system, we processed InSAR data with the volumes of 500 TB and 700 TB in 5 and 7 days, respectively. Finally we generated two maps of land surface deformation all over China.

Real-Time Identification of Cyanobacteria Blooms in Lakeshore Zone Using Camera and Semantic Segmentation: A Case Study of Lake Chaohu (Eastern China)

The surface water in the lakeshore zone is the primary area where cyanobacteria bloom floats intensively. In lake water environment monitoring, it has become pressing to accurately identify the distribution and accumulation coverage area of cyanobacteria blooms in the surface water of the lakeshore zone. This study proposes a real-time and dynamic monitoring technology for cyanobacteria blooms in surface water using a shore-based camera monitoring network. The specific work is as follows: Chaohu Lake, a large eutrophic lake in China, is selected as the research object. The multithreading technology is used to dynamically obtain the hourly video images of 43 cameras around Chaohu Lake. The semantic segmentation method is used to identify the cyanobacteria blooms in the video images, calculate the coverage of cyanobacteria blooms, and draw the spatial distribution map of cyanobacteria blooms in the lakeshore zone of Chaohu Lake. To improve the accuracy of cyanobacteria blooms recognition, we use the ResNet-50 network to integrate three semantic segmentation models, namely FCN, U-net, and DeeplabV3+. By comparing the cyanobacteria blooms results identified by the three methods, it is found that the boundary of the cyanobacteria blooms results identified by DeeplabV3+(ResNet-50) is clear, which is more consistent with the real spatial information of the distribution of cyanobacteria blooms and is more suitable for monitoring the hourly dynamic changes of cyanobacteria blooms in the Chaohu Lake lakeshore zone. The results demonstrated that the time requirement of monitoring cyanobacteria blooms in real time on an hourly basis could be met by utilizing technology that uses multiple threads. The OA (Overall Accuracy), MPA (Mean Pixel Accuracy), IOU (Intersection Over Union) of cyanobacteria blooms, and the IOU of water values of the DeeplabV3+(ResNet-50) were the highest, which were 0.83, 0.82, 0.71, and 0.74, and the RMSE between the predicted and real cyanobacterial blooms coverage of 43 cameras was 6.65%. The above values show that DeeplabV3+(ResNet-50) is this technology’s most suitable semantic segmentation model. This technique can provide technical support for the scientific development of a cyanobacteria blooms management plan in the lakeshore zone of Chaohu Lake by calculating the coverage area of cyanobacteria blooms and drawing the spatial distribution map of cyanobacteria blooms in the lakeshore zone.

Modeling of crowd groups with an extended social field model

A social field model is extended by adding supports of simulations of crowd group related dynamics such as group cohesion, back-tracking, etc. A computational implementation combining heterogeneous computing and multi-threading technologies is developed to integrate the proposed extension into a heterogeneous computing framework. Hence modeling of group related dynamics can be accomplished in a very efficient manner. In the section of numerical experiments, the extended model is firstly validated with a set of practical data. Results show that the simulated evacuation time matches the practical one quite well. Then a study of fundamental diagrams is expanded with considerations of crowd groups. It is shown that the impact of crowd groups mainly happens in the regime of low densities and would become insignificant in the regime of high densities.

Design and implementation of virtual fitting system based on gesture recognition and clothing transfer algorithm

Virtual fitting can bring fast and convenient fitting experience for people. The two core problems of virtual fitting system are human–computer interaction and clothing simulation. Interaction is an important factor in determining the fitting experience. The previous virtual fitting products usually use the mouse and keyboard interaction, and users rarely have a good sense of substitution and interaction. While, the method of using multiple cameras to take user images from different angles and then carry out posture recognition has the defect of low recognition accuracy. In view of clothing simulation and human–computer interaction of virtual fitting system, in order to achieve better customer immersion experience, this paper implemented a real-time interactive virtual fitting system based on Microsoft Kinect motion sensing device, and proposed a gesture determination algorithm based on finger recognition and an image transfer algorithm based on skeleton information matching. Using OpenNI development library and multi-threading technology, we have developed a motion-sensing capture module and a complete real-time virtual fitting system, and the system test results show that it has a good user experience.

Language Inference Using Elman Networks with Evolutionary Training

In this paper, a novel Elman-type recurrent neural network (RNN) is presented for the binary classification of arbitrary symbol sequences, and a novel training method, including both evolutionary and local search methods, is evaluated using sequence databases from a wide range of scientific areas. An efficient, publicly available, software tool is implemented in C++, accelerating significantly (more than 40 times) the RNN weights estimation process using both simd and multi-thread technology. The experimental results, in all databases, with the hybrid training method show improvements in a range of 2% to 25% compared with the standard genetic algorithm.

Computer Security Issues and Legal System Based on Cloud Computing.

To effectively improve the security and accuracy of computer information storage, a computer security problem and legal system based on cloud computing are proposed. Firstly, this article details the evolution of cloud computing, its characteristics, architecture, and application status of cloud computing in detail. Second, we discussed security strategies to ensure the confidentiality and integrity of cloud computing information, focuses on the data encryption technology of cloud data security, and designs and implements the data backup and recovery system based on the cloud platform. The core layers of the system are the system layer and data operation layer. The system uses multithreading technology based on epoll and thread pool to improve the efficiency of data transmission. At the same time, the basic visual page is realized, and users can use the page to create a convenient operating system. Finally, the system is built in the laboratory environment and tested as a whole. The test results show that through the performance comparison with the current commonly used systems, it is found that the system in this paper has a certain improvement in data transmission rate, but the utilization rate of node CPU is as high as 40%, which leads to certain requirements for node CPU performance. Therefore, the system meets the functional requirements proposed in the design. Compared to the existing system, its performance has been found to meet the actual requirements of use, proving that the system is accessible and efficient.

GPU-Based Real-Time Software Coincidence Processing for Digital PET System

In some modern digital positron emission tomography (PET) systems, the coincidence pairs are extracted by software coincidence processing (SCP) for reconstruction. The SCP is typically implemented on central processing units (CPUs) and then it is accelerated by CPU multithreading technology. However, the more detection modules a PET system has, the more CPU threads are used in acquisition and the fewer threads are available in coincidence processing when the number of threads is fixed. This phenomenon results in reduced processing performance of CPU-based SCP, which limits the application of SCP. In this article, we propose low-cost GPU-based real-time SCP (GPU-SCP) methods to solve the limited CPU thread problem. The proposed processing architecture simplifies the management of threads between acquisition and coincidence processing, leading to the decouple of acquisition and coincidence, accelerates the coincidence processing by GPU multiple threads, and finally realizes the online coincidence processing in high-sensitivity digital PET systems with 20 basic detection modules (BDMs). To evaluate the performance of the proposed GPU-based SCP approaches, we adapted them to the home-made PET systems with different architectures. The speedup experimental results show that the proposed sorting-based GPU-SCP achieves up to ~15 times average speedup on GTX1070 compared with serial CPU algorithms, which is comparable to the parallel CPU algorithm with 30 threads. Besides, the proposed combination-based GPU-SCP is superior to the sorting-based GPU-SCP for a specific system architecture. Besides, the image quality experiments indicate the reconstruction images processed by GPU-SCPs are almost the same as the ground truth (differences ~1% in the image domain).

SLAM 3D Digital Terrain Mapping with SqueezeNet Driven by Road Traffic Data

In order to improve the efficiency of dynamic visualization of large-scale road traffic data in a web environment, this paper proposes a SLAM 3D digital terrain map visualization method using SqueezeNet in a web environment. We propose a hierarchical organization method of the road network, taking into account road attributes and drawing roads at different view heights; a multiroad merging method based on the line segment indexing feature of WebGL (web graphics library) technology, and optimizing the scene by combining view rejection and multithreading technology. The prototype system was developed and case studies were carried out using the national road network data as an example. The experimental results show that the frame rate of large-scale road traffic data visualization in the network environment is above 40 frames/second, which is 20–30 frames/second higher than that of Baidu’s ECharts GL visualization method.

Detection of Impurity Rate of Machine-Picked Cotton Based on Improved Canny Operator

Aiming at the real-time detection of the impurity rate in machine-picked cotton processing, a detection method for the impurity rate in machine-picked cotton was proposed based on an improved Canny operator. According to the characteristics of different saturations between cotton and impurities, the impurities were separated by extracting the image S channel. Due to problems existing in the traditional Canny operator’s edge detection, the Gaussian filter was replaced by employing mean filtering and nonlocal mean denoising, which could effectively remove the noise in the image. A YOLO V5 neural network was used to classify and identify the impurities after segmentation, and the densities of various impurities were measured. The volume–weight (V–W) model was established to solve the impurity rate based on mass. Compared with a single thread, the data processing time was shortened by 43.65%, and the frame rate was effectively improved by using multithread technology. By solving the average value of the impurity rate, the anti-interference performance of the algorithm was enhanced, and has the characteristics of real-time detection and stability. This method solved the problems of low speed, poor real-time detection, and ease of interference, and can be used to guide the cotton production process.

An optimized XGBoost method for predicting reservoir porosity using petrophysical logs

To overcome the deficiencies of current porosity prediction methods, the XGBoost algorithm is introduced to construct a model for porosity prediction, and the obtained model is optimized by the grid search method and genetic algorithm. First, the optimal values of the three integer hyperparameters and the ranges of optimal values for the five floating-point hyperparameters of the XGBoost algorithm are determined by the grid search method. Then, the optimal values of the five floating-point hyperparameters of the XGBoost algorithm are determined with the genetic algorithm based on the determined value ranges. In this way, the model for porosity prediction based on the XGBoost algorithm and optimized by the grid search method and genetic algorithm (GS-GA-XGBoost) is constructed, and it has eight hyperparameters with determined optimal values. Compared with other porosity prediction methods, our method solves the problems such as the strong subjectivity and poor generalizability of conventional logging interpretation methods and the insufficient generalization performance of machine learning methods in previous porosity prediction studies, and the accuracy of the constructed model for porosity prediction is also greatly improved. Specifically, the RMSE, MAE and MAPE generated by GS-GA-XGBoost on the test set are 0.527946, 0.155880 and 0.020500 respectively, while those generated by linear regression (LR), support vector regression (SVR), random forest (RF), the XGBoost algorithm with default parameters (XGBoost) and the XGBoost algorithm optimized only by the grid search method (GS-XGBoost) on the test set are 3.535521, 2.801047, 0.375713, and 2.695310, 2.002280, 0.283582, and 1.015801, 0.638878, 0.085942, and 2.781069, 1.860557, 0.293334, and 1.380065, 0.979419, 0.128486, respectively. Finally, the multithread technology is introduced to improve the computational speed of GS-GA-XGBoost. GS-GA-XGBoost provides some technical references for the construction of porosity prediction models for oil fields in Northern Shaanxi, China, and other regions.

Design and ARM-Based Implementation of Bitstream-Oriented Chaotic Encryption Scheme for H.264/AVC Video.

In actual application scenarios of the real-time video confidential communication, encrypted videos must meet three performance indicators: security, real-time, and format compatibility. To satisfy these requirements, an improved bitstream-oriented encryption (BOE) method based chaotic encryption for H.264/AVC video is proposed. Meanwhile, an ARM-embedded remote real-time video confidential communication system is built for experimental verification in this paper. Firstly, a 4-D self-synchronous chaotic stream cipher algorithm with cosine anti-controllers (4-D SCSCA-CAC) is designed to enhance the security. The algorithm solves the security loopholes of existing self-synchronous chaotic stream cipher algorithms applied to the actual video confidential communication, which can effectively resist the combinational effect of the chosen-ciphertext attack and the divide-and-conquer attack. Secondly, syntax elements of the H.264 bitstream are analyzed in real-time. Motion vector difference (MVD) coefficients and direct-current (DC) components in Residual syntax element are extracted through the Exponential-Golomb decoding operation and entropy decoding operation based on the context-based adaptive variable length coding (CAVLC) mode, respectively. Thirdly, the DC components and MVD coefficients are encrypted by the 4-D SCSCA-CAC, and the encrypted syntax elements are re-encoded to replace the syntax elements of the original H.264 bitstream, keeping the format compatibility. Besides, hardware codecs and multi-core multi-threading technology are employed to improve the real-time performance of the hardware system. Finally, experimental results show that the proposed scheme, with the advantage of high efficiency and flexibility, can fulfill the requirement of security, real-time, and format compatibility simultaneously.

THE RUNTIME ANALYSIS OF COMPUTATION OF MODULAR EXPONENTIATION

Context. Providing the problem of fast calculation of the modular exponentiation requires the development of effective algorithmic methods using the latest information technologies. Fast computations of the modular exponentiation are extremely necessary for efficient computations in theoretical-numerical transforms, for provide high crypto capability of information data and in many other applications.
 Objective – the runtime analysis of software functions for computation of modular exponentiation of the developed programs based on parallel organization of computation with using multithreading.
 Method. Modular exponentiation is implemented using a 2k-ary sliding window algorithm, where k is chosen according to the size of the exponent. Parallelization of computation consists in using the calculation of the remainders of numbers raised to the power of 2i modulo, and their further parallel multiplications modulo.
 Results. Comparison of the runtimes of three variants of functions for computing the modular exponentiation is performed. In the algorithm of parallel organization of computation with using multithreading provide faster computation of modular exponentiation for exponent values larger than 1K binary digits compared to the function of modular exponentiation of the MPIR library. The MPIR library with an integer data type with the number of binary digits from 256 to 2048 bits is used to develop an algorithm for computing the modular exponentiation with using multithreading.
 Conclusions. In the work has been considered and analysed the developed software implementation of the computation of modular exponentiation on universal computer systems. One of the ways to implement the speedup of computing modular exponentiation is developing algorithms that can use multithreading technology on multi-cores microprocessors. The multithreading software implementation of modular exponentiation with increasing from 1024 the number of binary digit of exponent shows an improvement of computation time with comparison with the function of modular exponentiation of the MPIR library.

Software Design of a Data Acquisition System for the Hydraulic Fracturing Experiment of Rock at Tongkuangyu Copper Mine

To cooperate with the research on the fragmentation pre-conditioning technology of large hard rock in natural caving mining, we perform a large flow hydraulic fracturing test in Tongkuangyu Copper Mine to investigate the relationship between the occurrence and expansion of fractures in ore bodies and the pressure and flow of water injection. Five sensors of four different types are used in the test. Each sensor uses independent acquisition software. The operation is cumbersome and complex, and data synchronization is difficult and can be easily lost. Therefore, an integrated data acquisition software is needed. The pressure sensor used in the test adopts a non-standard communication protocol, which cannot be used in secondary development by configuration software. Thus, a primary development under the Windows platform using C++ is adopted. The data acquisition module is developed and deployed in background service mode and automatically runs after the acquisition computer OS started. Each communication link establishes an independent data read thread, which reads and analyses the data stream according to the protocol and pushes it to the data processing thread. Such a thread saves the data and provides data service to the data display unit through a TCP/IP service port. All threads in the data acquisition module are synchronized by event signals, and the data read threads have high priority in them to avoid data loss. Multiple data display units are allowed to be connected to the data acquisition module at the same time, which is conducive to the coordination among the field staff. The data display unit shows the field pressure, flow, temperature, and water level. Key data are also developed using multi-thread technology to keep a friendly operation. Such data can be deployed in any computer connected with the acquisition module to assist the staff in performing any operation in the test. Through the field work in a complex environment, this software for data acquisition system can manage types of sensors, with flexible configuration, reliable operation, and convenient expansion and provide further support for the field investigation work.