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Copyright of Electric Drive is the property of Electric Drive Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The performance of operating systems like computers requires the proper functioning of the computer language interpreter. This interpreter follows various types of programming languages that make the performing of computer programming easy and effective. The application of the programming language of Java helps in processing multiple tasks at once. This research analyzes the novelty of Java-based data communication models in advanced computing services. This performance makes the saving of the resources used for the development of the programming language. All these development includes the performing of the multiple threads communication data processing. These multiple threads help distribute the single processed input in the multi-channel language processing, thus helping the work competition in time. It also reduces the cost of maintaining the programming languages reduced. Thus, the implementing cost of resources required for programming performance is reduced. Therefore, this implementation impacts the programmer to become more indented to use the language transformation process of Java. Moreover, it creates a more effective representation of the audio or visual content represented by a multi-tasking operating system. In this process of development in the language transformation of the operating system, the ability of the operating system for data processing improves. The systematic process of this language transformation helps in systematically transforming multiple programs at once. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Real-time messaging and opinion sharing in social media websites have made them valuable sources of different kinds of information. This source provides the opportunity for doing different kinds of analysis. Sentiment analysis as one of the most important of these analyses gains increasing interests. However, the research in this field is still facing challenges. The mainstream of the sentiment analysis research on social media websites and microblogs just exploits the textual content of the posts. This makes the analysis hard because microblog posts are short and noisy. However, they have lots of contexts which can be exploited for sentiment analysis. In order to use the context as an auxiliary source, some recent papers use reply/retweet to model the context of the target post. We claim that multiple sequential contexts can be used jointly in a unified model. In this article, we propose a context-aware multi-thread hierarchical long short-term memory (MHLSTM) that jointly models different kinds of contexts, such as tweep, hashtag and reply besides the content of the target post. Experimental evaluations on a real-world Twitter data set demonstrate that our proposed model can outperform some strong baseline models by 28.39% in terms of relative error reduction. [ABSTRACT FROM AUTHOR]

With the rapid development of general hardware technology, microcomputers with multi-core CPUs have been widely applied in commercial services and household usage in the last ten years. Multi-core chips could, theoretically, lead to much better performance and computational efficiency than single-core chips. But so far, they have not shown general advantages for users, other than for operating systems and some specialized software. It is not easy to transform traditional single-core-based algorithms into multi-core-, multi-thread-based algorithms that can greatly improve efficiency, because of difficulties in computation and scheduling of hardware kernels, and because some programming languages cannot support multi-core, multi-thread programming. Therefore, a kind of multi-core-, multi-thread-based fast algorithm was designed and coded with Delphi language for the medium- and large-scale traveling salesman problem instances from TSPLIB, which can fully speed up the searching process without loss of quality. Experimental results show that the algorithm proposed can, under the given hardware limitations, take full advantage of multi-core chips and effectively balance the conflict between increasing problem size and computational efficiency and thus acquire satisfactory solutions.

The role of the interconnect network, which connects computing nodes to each other, is important in highperformance computing (HPC) systems. In recent years, the peripheral component interconnect express (PCIe) has become a promising interface as an interconnection network for high-performance and cost-effective HPC systems having the features of non-transparent bridge (NTB) technologies. OpenSHMEM is a programming model for distributed shared memory that supports a partitioned global address space (PGAS). Currently, little work has been done to develop the OpenSHMEM library for PCIe-interconnected HPC systems. This paper introduces a prototype implementation of the OpenSHMEM library through a switchless interconnect network using PCIe NTB to provide a PGAS programming model. In particular, multi-interrupt, multi-thread-based data transfer over the OpenSHMEM shared memory model is applied at the implementation level to reduce the latency and increase the throughput of the switchless ring network system. The implemented OpenSHMEM programming model over the PCIe NTB switchless interconnection network provides a feasible, cost-effective HPC system with a PGAS programming model. [ABSTRACT FROM AUTHOR]

Summary: With the development of genome sequencing technology, the cost of genome sequencing is continuously reducing, while the efficiency is increasing. Therefore, the amount of genomic data has been increasing exponentially, making the transmission and storage of genomic data an enormous challenge. Although many excellent genome compression algorithms have been proposed, an efficient compression algorithm for large collections of FASTA genomes, especially can be used in the distributed system of cloud computing, is still lacking. This article proposes two optimization schemes based on HRCM compression method. One is MtHRCM adopting multi‐thread parallel technology. The other is HadoopHRCM adopting distributed computing parallel technology. Experiments show that the schemes recognizably improve the compression speed of HRCM. Moreover, BSC algorithm instead of PPMD algorithm is used in the new schemes, the compression ratio is improved by 20% compared with HRCM. In addition, our proposed methods also perform well in robustness and scalability. The Java source codes of MtHRCM and HadoopHRCM can be freely downloaded from https://github.com/haicy/MtHRCM and https://github.com/haicy/HadoopHRCM. [ABSTRACT FROM AUTHOR]

With the smooth upgrade of the access network, multiple tuning-time devices will coexist in the long-reach access network, and the subsequent problems of high round-trip time (RTT) and optical network unit (ONU) tuning delay need to be solved urgently. In this paper, a multi-thread multiple tuning-time devices coexistence (MT-MTDC) bandwidth allocation algorithm is proposed. This algorithm can solve the problems of high RTT and ONU tuning delay in virtual passive optical network (VPON) based on long-reach wavelength division multiplexing/time division multiplexing passive optical network (LR WDM/TDM PON). Firstly, Multi-threaded polling mechanism is introduced into multi-mode coexistence VPON. Next, the number of wavelengths and threads is selected adaptively to ensure high bandwidth utilization. Then, the mechanism of dynamically adjusting the thread window is proposed. The mechanism strengthens the collaboration ability between threads and solves the degradation problem of the multi-thread algorithm. Furthermore, the construction of tuning buffer and the setting of time flag effectively solve the problem of more frequent ONU tuning pressure and ONU transmission conflict caused by multi-threaded polling algorithm. Finally, by comparing with the multi-thread longest-first first-available (MT-LFFA) algorithm and the multi-tuning-time ONU scheduling (MOS) algorithm, the proposed algorithm demonstrates its effectiveness in terms of polling cycle time, average tuning delay, bandwidth utilization and average packet delay.

The Boolean satisfiability (SAT) problem is the key problem in computer theory and application. his paper proposes a parallel SAT solver based on incomplete algorithm on FPGA. The algorithm uses a multi-threaded strategy to reduce the waiting time of related components and improve the efficiency of the solver. In addition, different threads use data storage structures that share addresses and clause information to reduce the resource overhead of on-chip memory. When all data is stored in the FPGA, s on-chip memory, the solver can achieve the best performance. The experimental results show that, compared with the single-threaded solver, the solver proposed in this paper can achieve a speedup of more than 2 times. [ABSTRACT FROM AUTHOR]

Apriori is one technique of data mining association rules that aims to extract correlations between sets of items in the transaction database. The main problem with the Apriori algorithm is the process of scanning databases repeatedly to generate itemset candidates. This research examines the combination of pruning by using the trieapproach and multi-thread implementation in three algorithms to obtain frequent itemset. Trie is a data structure in the form of an ordered tree to store a set of strings where every node in the tree contains the same prefix. The use of a full combination trie (different from frequent pattern (FP) tree using links) allows the implementation of arrays and the hash calculation to achieve the addressing of itemset combination. In this research, the measure to get the address is called Hash-node calculation used to update support value. For these three alternatives, run time processing is analyzed based on the number of itemset combinations and transaction data at a certain minimum support value. The experimental results show that an algorithm thatexploits resource capabilities by applying multi-threadperforms almost seven times betterthanan algorithm implemented in single-thread in calculating hash-node. The fastest run time of the multi-thread approach is 43 minutes with 150-itemset combinations on 100,000 transaction data. [ABSTRACT FROM AUTHOR]

With the rapid development of general hardware technology, microcomputers with multi-core CPUs have been widely applied in commercial services and household usage in the last ten years. Multi-core chips could, theoretically, lead to much better performance and computational efficiency than single-core chips. But so far, they have not shown general advantages for users, other than for operating systems and some specialized software. It is not easy to transform traditional single-core-based algorithms into multi-core-, multi-thread-based algorithms that can greatly improve efficiency, because of difficulties in computation and scheduling of hardware kernels, and because some programming languages cannot support multi-core, multi-thread programming. Therefore, a kind of multi-core-, multi-thread-based fast algorithm was designed and coded with Delphi language for the medium- and large-scale traveling salesman problem instances from TSPLIB, which can fully speed up the searching process without loss of quality. Experimental results show that the algorithm proposed can, under the given hardware limitations, take full advantage of multi-core chips and effectively balance the conflict between increasing problem size and computational efficiency and thus acquire satisfactory solutions. [ABSTRACT FROM AUTHOR]

High accuracy and a real-time system are priorities in the development of a prosthetic hand. This study aimed to develop and evaluate a real-time embedded time-domain feature extraction and machine learning on a system on chip (SoC) Raspberry platform using a multi-thread algorithm to operate a prosthetic hand device. The contribution of this study is that the implementation of the multi-thread in the pattern recognition improves the accuracy and decreases the computation time in the SoC. In this study, ten healthy volunteers were involved. The EMG signal was collected by using two dry electrodes placed on the wrist flexor and wrist extensor muscles. To reduce the complexity, four time-domain features were applied to extract the EMG signal. Furthermore, these features were used as the input of the machine learning. The machine learning evaluated in this study were k-nearest neighbor (k-NN), Naive Bayes (NB), decision tree (DT), and support vector machine (SVM). In the SoC implementation, the data acquisition, feature extraction, machine learning, and motor control process were implemented using a multi-thread algorithm. After the evaluation, the result showed that the pairing of the MAV feature and machine learning DT resulted in higher accuracy among other combinations (98.41%) with a computation time of ~1 ms. The implementation of the multi-thread algorithm in the pattern recognition system resulted in significant impact on the time processing.

Currently, most monitor devices are developed based on foreign software and hardware. The core technology is controlled by others. So that this paper designs an monitor software which is applied on native software and hardware platform. By means of multi-database storage, the method of insert and query is optimized. A large number of parallel data is realized by using multi-threaded design. Using object-oriented program design method makes the data receiving layer, processing layer and display layer separated. Through testing by the actual environment, the carrying capacity, real-time and reliability of monitor is enhanced. At the same time, the software designed has good scalability and maintainability.

In this paper, we propose a method of analyzing the types and causes of concurrency bugs that can occur in multi-process/multi-thread environments by analyzing the information gathered in the execution environment. Our method reduces the false detection rate by employing a hooking technique that intercepts the software's execution at runtime and reduces the overhead that can occur in the original software due to defect detection methods using optimized data collection. We implemented the proposed method as a tool and demonstrated its effectiveness by applying it to weapon system software that previously had concurrency bugs. In addition, the proposed method's high performance was proven by applying it to software into which faults were injected and comparing the results with those obtained using other tools. [ABSTRACT FROM AUTHOR]

In the traditional video and image processing technology field, researchers often focus on the processing of the image content, especially the accuracy and the speed. However, the geographic information data carried by UAV video and image is often ignored, resulting in the image only containing the scene information and losing the geographic data information after the image processing is completed, so that the user cannot quickly obtain the geographic information of the target of interest from image processing results. In order to process the geographic information efficiently, an image mosaicking and verification algorithm for UAV image with geographic information regards geographic information data as multi-channel double floating-point matrix data, which can be calculated synchronously using matrix processing algorithms. Meanwhile, the accuracy and speed of a large number of image mosaicking tasks can be improved by using image-splicing algorithm based on grouping control with geographic information. The experimental results show that the proposed algorithm can effectively process the UAV image with geographic information, especially in image mosaicking. [ABSTRACT FROM AUTHOR]

Copyright of Experimental Technology & Management is the property of Experimental Technology & Management Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The aesthetic appeal of s-shaped curves in art and landscape design may help explain the prevalence of single-thread meandering channels in river restoration projects where the channel planform is reconfigured. In some restoration projects, the channel planform follows repeating symmetrical bends. To see if freshwater scientists would draw a similar restored planform, we asked scientists to illustrate a restored channel and observed what patterns emerged from the drawings. Most scientists depicted single-thread meanders for the restored channel form with some drawings including large wood in the channel, multi-thread channel forms, and wildlife and human elements. We consider possible explanations for why respondents depicted idealized meanders. We also describe drawings that did not depict the ideal meander but showcased a diversity of channel forms and restored river scenes. While recognizing the role of aesthetics in river restoration, especially in urban settings, designs can be expanded to include disorderly planform designs with non-symmetrical single-thread meanders and multi-threaded channels with forms inspired by local ecology, geomorphology, and social values. [ABSTRACT FROM AUTHOR]

At present, finite element method (FEM) is still the mainstream tool in a range of complex science and engineering applications, including the analysis of structures, heat, fluid, and electromagnetics. However, FEM becomes computationally very intensive when the number of physics equations grows and the mesh is refined. In this paper, we investigated the parallel computing techniques in the nonlinear magnetostatic field solution of an axisymmetrical actuator with permanent magnet by proposing a generalized black-box transmission line method (BB-TLM). A novel black-box circuit model was built to represent complex FEM element data. By means of the transmission line model, each element is isolated and then a series of parallel procedures is considered during the solution stages. The magnetic field distribution and magnetic force of the studied actuator are calculated by the proposed method and the simulation results are compared with COMSOL. Compared with the conventional N-R method, the generalized BB-TLM algorithm greatly reduces the computation time. [ABSTRACT FROM AUTHOR]

This research presented how to use a multi-thread programming technique to improve the performance of a program written by Windows Presentation Foundation (WPF). The Computer Assisted Instruction (CAI) software, named GAME24, was selected to use as a case study. This study composed of two main parts. The first part was about design and modification of the program structure upon the Object Oriented Programing (OOP) approach. The second part was about coding the program using the multi-thread technique which the number of threads were based on the calculated Catalan number. The result showed that the multi-thread programming technique increased the performance of the program 44%-88% compared to the single-thread technique. In addition, it has been found that the number of cores in the CPU also increase the performance of multithreaded program proportionally.

Developing an efficient and robust lightweight graphic user interface (GUI) for industry process monitoring is always a challenging task. Current implementation methods for embedded GUI are with the matters of real-time processing and ergonomics performance. To address the issue, an embedded lightweight GUI component library design method based on quasar technology embedded (Qt/E) is proposed. First, an entity-relationship (E-R) model for the GUI library is developed to define the functional framework and data coupling relations. Second, a cross-compilation environment is constructed, and the Qt/E shared library files are tailored to satisfy the requirements of embedded target systems. Third, by using the signal-slot communication interfaces, a message mapping mechanism that does not require a call-back pointer is developed, and the context switching performance is improved. According to the multi-thread method, the parallel task processing capabilities for data collection, calculation, and display are enhanced, and the real-time performance and robustness are guaranteed. Finally, the human-computer interaction process is optimized by a scrolling page method, and the ergonomics performance is verified by the industrial psychology methods. Two numerical cases and five industrial experiments show that the proposed method can increase real-time read-write correction ratios by more than 26% and 29%, compared with Windows-CE-GUI and Android-GUI, respectively. The component library can be tailored to 900 KB and supports 12 hardware platforms. The average session switch time can be controlled within 0.6 s and six key indexes for ergonomics are verified by different industrial applications. [ABSTRACT FROM AUTHOR]

Massive trajectory data contains wealth useful information and knowledge. Spectral clustering, which has been shown to be effective in finding clusters, becomes an important clustering approaches in the trajectory data mining. However, the traditional spectral clustering lacks the temporal expansion on the algorithm and limited in its applicability to large-scale problems due to its high computational complexity. This paper presents a parallel spatiotemporal spectral clustering based on multiple acceleration solutions to make the algorithm more effective and efficient, the performance is proved due to the experiment carried out on the massive taxi trajectory dataset in Wuhan city, China. [ABSTRACT FROM AUTHOR]

Finite-element method (FEM) has been widely used in industrial applications for many years. With the increase of finite-element model complexity and the number of elements, computation burden has become a serious issue and requires more attention. In this paper, the transmission line method (TLM) is adopted to the solution of FEM-based nonlinear axisymmetric magnetostatic problem and an equivalent finite-element network’s circuit model is given. Several methods were investigated to speed up the computation, such as Newton preconditioners, simplification of nonlinear equation solution and parallel computation. Compared with traditional Newton FEM iteration, our results show that the proposed method has more obvious advantages on computational efficiency at the same calculation accuracy. The method is applicable and valuable in the characteristic analysis and design of the magnetic device with a cylindrical structure. [ABSTRACT FROM AUTHOR]

This paper proposed multi-thread latent-friendship recommendation algorithm based on community detection (MTFRCD) due to the low running speed of a method for latent-friendship recommendation based on community detection (FRCD). Firstly, MTFRCD figured out kernel sub-networks on network topological diagram by multi-thread technology. Secondly, it regarded kernel sub-networks as seed nodes，the algorithm detected every community structure on network topological diagram by multi-thread paralleling label propagation. At the end，it recommended latent-friendships in all communities. The experiment on artificial network shows MTFRCD maintains performance and elevates running speed notably comparing to traditional method FRCD. Therefore, MTFRCD can apply to the real social network(Scholat. com) to complete latent-friendship recommendation. According to recommendation assessment，MTFRCD is able to achieve a good recommendation. [ABSTRACT FROM AUTHOR]

For a decision table, it is firstly proved that the effective pair is equivalent to improved discernibility matrix to guarantee that the attribute reduction based on discernibility matrix can be calculated by effective pair. To get the attribute reduction more quickly, a parallel radix sort model is proposed. Then a simple decision table and effective pair are obtained. Taking the length of distinguishable or indistinguishable elements string among the effective pair as heuristic information, an attribute reduction algorithm based on parallel Logical OR is proposed. Finally, the examples and experiments are shown to verify the effectiveness and feasibility of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Video synopsis is one of the popular research topics in the field of digital video and has broad application prospects. Current research of it focuses on the methods of generating video synopsis or studying to utilize optimization algorithms such as fuzzy theory, minimum sparse reconstruction, and genetic algorithm to optimize its computing steps. This paper mainly studies the object-based video synopsis technology in distributed environment. We propose an effective video synopsis algorithm and a distributed processing model to accelerate the computing speed of video synopsis. The algorithm is proposed for studies of surveillance videos, which focuses on several key algorithmic steps, for instance, initialization of original video resources, background modeling, moving object detecting, and nonlinear rearrangement. These steps can be performed in parallel. In order to obtain good video synopsis effect and fast computing speed, some optimization methods are applied to these steps. With the aim of employing much more computing resources, we propose a distributed processing model, which splits the original video file into multiple segments and distributes them to different computing nodes to improve the computing performance by leveraging the multi-core and multi-thread capabilities of CPU. Experimental results show that the proposed distributed model can significantly improve the computing speed of video synopsis. [ABSTRACT FROM AUTHOR]

In this paper, we present a sparse memory allocation data structure for sequential and parallel data mining. We explored three algorithms utilizing the proposed data structure: MASP-tree, apriori-TID, and FP-growth. We modified the data structure of apriori-TID and FP-growth algorithms to reduce memory allocation cost. Five data sets are used for comparison. The results show that the modified apriori-TID has a higher speed-up than the modified FP-growth when the proposed data structure is used. A maximum speed-up of 3.42 is observed when MASP algorithm is tested. [ABSTRACT FROM AUTHOR]

To cope with extremely large-scale logistic optimization for strategic planning and real time operational optimizations as well, in this paper, we have proposed an extended algorithm of our hybrid method so that it becomes available for parallel computing. We have also developed a novel algorithm of particle swarm optimization (PSO) associated with binary decision variables. It is quite effective for finding the optimum opening distribution centers in three-echelon logistic network by parallel computing. Eventually, we have implemented the procedure in the parallel algorithm deployed as a multi-population based approach using multi-thread programming technique. Taking two topologies belonging to a coarse grain parallelism, we compared their effects on the performance of the algorithm through large scale logistics optimization. Finally, we confirmed that the proposed method can bring about high performance for the parallel computing that is suitable for the present goal and circumstance through numerical experiments.

Several dynamic bandwidth allocation (DBA) algorithms have been proposed for long-reach passive optical networks (LR-PONs). The gains in the current protocols, however, are incremental and limited to a specific range of loads. Using an analytical framework, we first examine the main sources of performance degradation specific to LR-PONs. From this, we conclude that there is a large scope for improvement in the performance by using an optimal cycle length, which is not influenced by reach, and which polls users according to their reported load while providing opportunities for pre-granting bandwidth at the optical network units. The fraction of bandwidth that is pre-granted should be decreased with load. To tailor these optimal design concepts in a DBA algorithm, we propose Synergized-Adaptive Multi-GATE polling with Void-filling (S-AMGAV), which reduces both the delay bound and packet loss rate by at least 50% when compared to the state-of-the-art DBAs like interleaved polling with adaptive cycle time (IPACT), IPACT with grant estimation (IPACT-GE), multi-thread polling (MTP), double phase polling (DPP), gate driven (GD), and AMGAV. S-AMGAV also maintains simplicity of the protocol while removing bandwidth inefficiencies prevailing in the existing protocols. We verify the usefulness of the algorithm through both analytical modeling and simulations. [ABSTRACT FROM PUBLISHER]

Due to next-generation sequencing (NGS) technology, genome sequencing is able to process much more data at low cost. In NGS data analysis, the mapping of sequences into a reference genome takes the largest amount of time to process. Although the Burrows-Wheeler Aligner (BWA) tool is one of the most widely used open-source software tools to align read sequences, it is still limited in that it does not fully support multi-thread mechanisms during the alignment steps. In this paper, we propose a BWA-MT tool, evolved from BWA but supporting multi-thread computation, designed to fully utilize the underlying multi-core architecture of computing resources. By using multi-thread computation, BWA-MT can significantly shorten the time needed to generate an alignment for single-end read sequences. Meanwhile, it generates an identical Sequence Alignment Map (SAM) result file as BWA. To evaluate BWA-MT, we use an evaluation system equipped with twelve cores and 32 GB memory. As a workload, we used the hg19 human genome reference sequence and various numbers of read sequences from 1M to 40M. In our evaluation, BWA-MT displays up to 3.7 times faster performance and generates an identical SAM result file to BWA. Although the increased speed might be dependent on computing resources, we confirm that BWA-MT is highly efficient and effective. [ABSTRACT FROM AUTHOR]