On modeling approach for embedded real-time software simulation testing

Modeling technology has been introduced into software testing field. However, how to carry through the testing modeling effectively is still a difficulty. Based on combination of simulation modeling technology and embedded real-time software testing method, the process of simulation testing modeling is studied first. And then, the supporting environment of simulation testing modeling is put forward. Furthermore, an approach of embedded real-time software simulation testing modeling including modeling of cross-linked equipments of system under testing （SUT）, test case, testing scheduling, and testing system service is brought forward. Finally, the formalized description and execution system of testing models are given, with which we can realize real-time, closed loop, mad automated system testing for embedded real-time software.

Real-time embedded software testing method based on extended finite state machine

The reliability of real-time embedded software directly determines the reliability of the whole real-time embedded sys- tem, and the effective software testing is an important way to ensure software quality and reliability. Based on the analysis of the characteristics of real-time embedded software, the formal method is introduced into the real-time embedded software testing field and the real-time extended finite state machine （RT-EFSM） model is studied firstly. Then, the time zone division method of real-time embedded system is presented and the definition and description methods of time-constrained transition equivalence class （timeCTEC） are presented. Furthermore, the approaches of the testing sequence and test case generation are put forward. Finally, the proposed method is applied to a typical avionics real- time embedded software testing practice and the examples of the timeCTEC, testing sequences and test cases are given. With the analysis of the testing result, the application verification shows that the proposed method can effectively describe the real-time embedded software state transition characteristics and real-time requirements and play the advantages of the formal methods in accuracy, effectiveness and the automation supporting. Combined with the testing platform, the real-time, closed loop and automated simulation testing for real-time embedded software can be realized effectively.

REAL-TIME SOFTWARE SYSTEM FOR PRODUCTION PROCESS SUPERVISION

The real-time software system for production process supervision is a inte-gral system,including on-line function subsystem for supervision and off-line auxiliarysubsystem for development and diagnosis.This paper introduces a real-time software sys-tem which has been used in a power station for monitoring a large capacity thermal gener-ating unit.The subsystems,environment,performance and development of the system areexplained,and the common problems about real-time software system are described.

Survey on <i>Essential and Accidental Real-Time Issues</i>in Software Engineering

Software product lines have recently been presented as one of the best promis-ing improvements for the efficient software development. Different research works contribute supportive parameters and negotiations regarding the prob-lems of producing a perfect software scheme. Traditional approaches or recy-cling software are not effective to solve the problems concerning software competence. Since fast developments with software engineering in the past few years, studies show that some approaches are getting extensive attention in both industries and universities. This method is categorized as the software product line improvement;that supports reusing of software in big organizations. Different industries are adopting product lines to enhance efficiency and reduce operational expenses by way of emerging product developments. This research paper is formed to offer in-depth study regarding the software engineering issues such as complexity, conformity, changeability, invisibility, time constraints, budget constraints, and security. We have conducted various research surveys by visiting different professional software development organizations and took feedback from the professional software engineers to analyze the real-time problems that they are facing during the development process of software systems. Survey results proved that complexity is a most occurring issue that most software developers face while developing software applications. Moreover, invisibility is the problem that rarely happens according to the survey.

Study of component-ware & application software framework based decision support system development

The view that the traditional method of DSS development is outdated, which results to the diversiform disadvantages of DSS product. Therefore the ideas of application software framework based development to the generation process of DSS is introduced and a modified flow chat of DSS development is proposed. Moreover, a formal description of the DSS software framework and its development is given. The analysis results indicates that not only does the new development flow ensure the DSS development global stability but also improves the software reusability level of the development process.

Telecom-Oriented Embedded Software Support Platform

This paper presents an embedded software platform used in the telecom field.The platform consists of the Virtual Operating System (VOS)layer,core layer, protection layer and module layer.It supports and simplifies upper application software of telecom systems.In addition to basic modules and functions, its instance scheduling model and distributed process communication are detailed in the paper.

Improving Information Technology Infrastructure Library Service Delivery Using an Integrated Lean Six Sigma Framework: A Case Study in a Software Application Support Scenario

Increasing competition in service businesses (eg: Information Technology, Healthcare, Education, Logistics, Distribution, Hospitality Financial and other services) is compelling corporates to adopt business improvement philosophies and methodologies for addressing customer demands for better quality service and organizational demands for improving the profit margins through cost reduction. Many companies around the world have already implemented Information Technology Infrastructure Library (ITIL) as a way to manage and control their Information Technology (IT) Departments more effectively. These companies are now willing to improve their ITIL processes in order to become even more efficient. Lean Six Sigma is a methodology that has been suggested as a means to resolve these problems, reducing costs and improving quality. Its application in the IT Services area is becoming increasingly popular among researchers and academicians and can be used to conduct these improvements. Despite the application of lean six sigma approaches to different contexts in the service sector, the application of Lean Six Sigma approaches in service delivery specific to Information Technology Service Management (ITSM) remains largely untested. The purpose of this paper is to implement Integrated Lean Six Sigma for Software application support services and evaluate the benefits of continual service improvement over ITIL.

AQUAM——A Decision Support Software for Fish Farm Management

farm is presented.The software called AQUAM is dedicated to fresh water fish farms.Its aim is to make an efficient management of resources through planning,monitoring,analysis and decision support.Successful planning and management requires the integration of data related to ponds,fish species,fish growth,water and energy and economic analysis.AQUAM computes farm budgets relating various costs and returns in order to determine short and long term profitability.A simulation of the profit,as a function of the fish holding density,is performed with AQUAM.The data used in the simulation are from a fish farm of semi-intensive type,located in the region Danube Delta,at village Jurilovca,Tulcea county,Romania.The fish species that were taken into account were carp and sanger.

Online Fashion Retailer Bonobos Selects Gerber Technology's YuniquePLM^(TM) Industry-Tailored Software to Support Rapid Growth and Fast-Paced Product Innovation

Tolland,Connecticut,USA-Gerber Technology,a business unit of Gerber Scientific, Inc.(NYSE:GRB) and a world leader in automated CAD/CAM and PLM solutions for the apparel and flexible materials industry,announces that fast-growing internet retailer Bonobos,Inc. has selected the YuniquePLM product

Runtime environment for reflective real-time component

Reflective real-time component model is a special component model, which can identify timing constraint characteristics of component and support dynamic design-time amendment of real-time component according to users＇ requirements. The reflective real-time component runtime environment is a bearing space and reflective infrastructure for this special component model. It consists of three parts and manages the lifecycle and various relevant services of reflective real-time component. In this paper its mechanism and relevant key techniques in design and realization are formally specified with the communicating sequential processing （CSP） and the extended timed communicating sequential processing （TCSP）. Finally a prototype is established. Experimental study shows that this runtime environment can introduce a relevant reflective infrastructure guaranteeing dynamic and real-time features of software component.

Predicting the fault-proneness of class hierarchy in object-oriented software using a layered kernel

A novel kernel learning method for object-oriented （00） software fault prediction is proposed in this paper. With this method, each set of classes that has inheritance relation named class hierarchy, is treated as an elemental software model. A layered kernel is introduced to handle the tree data structure corresponding to the class hierarchy models. This method was vali- dated using both an artificial dataset and a case of industrial software from the optical communication field. Preliminary experi- ments showed that our approach is very effective in learning structured data and outperforms the traditional support vector learning methods in accurately and correctly predicting the fault-prone class hierarchy model in real-life OO software.

Windows and Fieldbus Based Software Computer Numerical Control System

Computer numerical control(CNC)system is the base of modern digital and intelligent manufacturing technolo- gy.And opened its architecture and constituted based on PC and Windows operating system(OS)is the main trend of CNC sys- tem.However,even if the highest system priority is used in user mode,real-time capability of Windows(2000,NT,XP)for applications is not guaranteed.By using a device driver,which is running in kernel mode,the real time performance of Windows can be enhanced greatly.The acknowledgment performance of Windows to peripheral interrupts was evaluated.Harmonized with an intelligent real-time serial communication bus(RTSB),strict real-time performance can be achieved in Windows platform. An opened architecture software CNC system which is hardware independence is proposed based on PC and RTSB.A numerical control real time kernel(NCRTK),which is implemented as a device driver on Windows,is used to perform the NC tasks.Tasks are divided into real-time and non real-time.Real-time task is running in kernel mode and non real-time task is running in user mode.Data are exchanged between kernel and user mode by DMA and Windows Messages.

SET-MRTS:An Empirical Experiment Tool for Real-Time Scheduling and Synchronization

In the real-time scheduling theory,schedulability and synchronization analyses are used to evaluate scheduling algorithms and real-time locking protocols,respectively,and the empirical synthesis experiment is one of the major methods to compare the performance of such analyses.However,since many sophisticated techniques have been adopted to improve the analytical accuracy,the implementation of such analyses and experiments is often time-consuming.This paper proposes a schedulability experiment toolkit for multiprocessor real-time systems(SET-MRTS),which provides a framework with infrastructures to implement the schedulability and synchronization analyses and the deployment of empirical synthesis experiments.Besides,with well-designed peripheral components for the input and output,experiments can be conducted easily and flexibly on SET-MRTS.This demonstration further proves the effectiveness of SET-MRTS in both functionality and availability.

Alternative Approach for Studying Contractility of the Isolated Muscle Preparations in Real-Time Mode

The real-time control over the observed parameters is known to be necessary during the experiments with biological objects. The use of approaches, such as real-time operating systems, often requires significant material or human resources (operating time, the special administration and programming skills and etc.). In this paper we propose an alternative approach for the implementation of real-time mode in the experiments on muscle preparations (papillary muscles, trabeculae, walls of vessels) based on an external input-output cards with built-in analog-to-digital/digital-to-analog converters and signal processor. As a result, we developed a hardware-software complex for studying the mechanical properties of biological materials (muscles) in real-time mode. This solution has a convenient and easy-to-use user interface, and requires a minimum of computing resources and minimal cost of electricity.

Moth Flame Optimization Based FCNN for Prediction of Bugs in Software

The software engineering technique makes it possible to create high-quality software.One of the most significant qualities of good software is that it is devoid of bugs.One of the most time-consuming and costly software proce-dures isfinding andfixing bugs.Although it is impossible to eradicate all bugs,it is feasible to reduce the number of bugs and their negative effects.To broaden the scope of bug prediction techniques and increase software quality,numerous causes of software problems must be identified,and successful bug prediction models must be implemented.This study employs a hybrid of Faster Convolution Neural Network and the Moth Flame Optimization(MFO)algorithm to forecast the number of bugs in software based on the program data itself,such as the line quantity in codes,methods characteristics,and other essential software aspects.Here,the MFO method is used to train the neural network to identify optimal weights.The proposed MFO-FCNN technique is compared with existing methods such as AdaBoost(AB),Random Forest(RF),K-Nearest Neighbour(KNN),K-Means Clustering(KMC),Support Vector Machine(SVM)and Bagging Clas-sifier(BC)are examples of machine learning(ML)techniques.The assessment method revealed that machine learning techniques may be employed successfully and through a high level of accuracy.The obtained data revealed that the proposed strategy outperforms the traditional approach.

Resource Efficient Hardware Implementation for Real-Time Traffic Sign Recognition

Traffic sign recognition (TSR, or Road Sign Recognition, RSR) is one of the Advanced Driver Assistance System (ADAS) devices in modern cars. To concern the most important issues, which are real-time and resource efficiency, we propose a high efficiency hardware implementation for TSR. We divide the TSR procedure into two stages, detection and recognition. In the detection stage, under the assumption that most German traffic signs have red or blue colors with circle, triangle or rectangle shapes, we use Normalized RGB color transform and Single-Pass Connected Component Labeling (CCL) to find the potential traffic signs efficiently. For Single-Pass CCL, our contribution is to eliminate the “merge-stack” operations by recording connected relations of region in the scan phase and updating the labels in the iterating phase. In the recognition stage, the Histogram of Oriented Gradient (HOG) is used to generate the descriptor of the signs, and we classify the signs with Support Vector Machine (SVM). In the HOG module, we analyze the required minimum bits under different recognition rate. The proposed method achieves 96.61% detection rate and 90.85% recognition rate while testing with the GTSDB dataset. Our hardware implementation reduces the storage of CCL and simplifies the HOG computation. Main CCL storage size is reduced by 20% comparing to the most advanced design under typical condition. By using TSMC 90 nm technology, the proposed design operates at 105 MHz clock rate and processes in 135 fps with the image size of 1360 × 800. The chip size is about 1 mm2 and the power consumption is close to 8 mW. Therefore, this work is resource efficient and achieves real-time requirement.

Approximate entropy and support vector machines for electroencephalogram signal classification

The automatic detection and identification of electroencephalogram waves play an important role in the prediction, diagnosis and treatment of epileptic seizures. In this study, a nonlinear dynamics index–approximate entropy and a support vector machine that has strong generalization ability were applied to classify electroencephalogram signals at epileptic interictal and ictal periods. Our aim was to verify whether approximate entropy waves can be effectively applied to the automatic real-time detection of epilepsy in the electroencephalogram, and to explore its generalization ability as a classifier trained using a nonlinear dynamics index. Four patients presenting with partial epileptic seizures were included in this study. They were all diagnosed with neocortex localized epilepsy and epileptic foci were clearly observed by electroencephalogram. The electroencephalogram data form the four involved patients were segmented and the characteristic values of each segment, that is, the approximate entropy, were extracted. The support vector machine classifier was constructed with the approximate entropy extracted from one epileptic case, and then electroencephalogram waves of the other three cases were classified, reaching a 93.33% accuracy rate. Our findings suggest that the use of approximate entropy allows the automatic real-time detection of electroencephalogram data in epileptic cases. The combination of approximate entropy and support vector machines shows good generalization ability for the classification of electroencephalogram signals for epilepsy.

Computer System Reliability Allocation Method and Supporting Tool

This paper presents a computer system reliability allocation method that is based on the theory of statistic and Markovian chain,which can be used to allocate reliability to subsystem, to hybrid system and software modules. A relevant supporting tool built by us is introduced.

Intelligent decision support platform of new energy vehicles

New energy vehicles(NEVs) are gaining wider acceptance as the transportation sector is developing more environmentally friendly and sustainable technology. To solve problems of complex application scenarios and multi-sources heterogenous data for new energy vehicles and weak platform scalability,the framework of an intelligent decision support platform is proposed in this paper. The principle of software and hardware system is introduced. Hadoop is adopted as the software system architecture of the platform. Master-standby redundancy and dual-line redundancy ensure the reliability of the hardware system. In addition, the applications on the intelligent decision support platform in usage patterns recognition, energy consumption, battery state of health and battery safety analysis are also described.