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This paper proposes an autonomous emergency braking (AEB) system that takes into account road conditions at an intersection, using vehicle-to-vehicle (V2V) communication. The conventional AEB system considers the friction characteristics of a particular type of road, and thus has a fixed timing for activating the automatic braking without regard to different road conditions. Such an AEB intervention feature has performance limitations in actual road conditions. To overcome these drawbacks, this study proposes a control method that adjusts the automatic brake application time to road conditions. Additionally, collision risks at an intersection were calculated using various road friction coefficients and the V2V-based speed inputs from adjacent vehicles. The efficacy of the proposed AEB system was validated through tests under various scenarios, applying the road friction coefficient and vehicle speed as variables. The tests verified the improved collision avoidance performance of the proposed AEB system’s control method compared to the conventional method.

This research study introduces the operationalization process for the requirements activity of software engineering discipline to build renewable energy projects. It describes a new development approach composed of three phase’s methodology to undertake the operationalization process combined with the development of an operational model. A theoretical implementation is provided in addition to the development of a practical implementation on a standalone photovoltaic system case study. Moreover, the author’s expect that this will help in a better understanding of the principles of software engineering, in the implementation of these principles for the development of renewable energy projects as well as to contribute to build the research knowledge of software engineering foundations.

On some parameters Software cost estimation based on development and effort on the project. Every line of code and every method are important to estimate the cost of project. To develop the software project using well defined scientific principles, methods, statements and functions, though there are many costs estimation models are available to estimate the cost of project, but still there is lack of efficient system to predict the cost estimation based on size of project, functionality of project, methods of project. In the existing system cost estimation is done on the basis of lines of code, resource management, in time process. Because of various reasons in the existing system we have identified the drawbacks. In this paper, cost prediction model is defined based on clusters, the number of clusters will form the centroids. Each centroid has a specific number of clusters. Each cluster will be called as rule. In this paper, results will show the prediction of cost estimation is defined.

E-books have started to transform the entire ecosystem of book publishing industry including the authors, publishers, bookstores, readers, related workers and so on in this smart media era. Digital environment affected the publishing industry tremendously in the Republic of Korea. Main purpose of this study was to reveal the current situation of Korean publishing in comparison with the global publishing market trends. For this study, I researched the Korean publishing market trends and global changes of publishing industry. And I also reviewed current definition of Korean “publishing” and “publications” in the Korean Publishing Industry Promotion Act. As a result, I noticed that the current definition of “publication” and “publishing” in the Republic of Korea is focused on the narrow aspects. Therefore I recommended that we need to reconceptualize the definition of “publishing” more broadly for fostering of Korean publishing industry in the smart media era. Taking this point of view could make participants in this industry think more broadly, strategically and globally. The transformation of the book and publishing expects the transformation of reading. Therefore, it can be possible for us to think about the real value of publishing and publishing industry in the future.

An Unmanned Air Vehicle (UAV) is a collection of functionalities and capabilities required to execute complex missions, with the combination of ground systems (comprising of vehicle and payload control) on ground and Avionics Systems (along with various payloads) on board. The UAV executes various missions using a combination of payloads and their ground control. The mission-based evaluation of the UAV is a challenging task in terms of achieving sufficient test adequacy in generating effective test cases for accomplishment of complex mission roles. The timeliness performance, which is a combination of event-triggered and time-triggered execution of tasks, is the most important category to evaluate an Integrated System’s Mission based performance, in addition to the correctness and consistency of data transactions among the various systems. This paper proposes a framework for an exhaustive mission-based evaluation of timeliness of integrated Avionics Systems of the UAV. The authors introduce various functional mutation operators for achievement of adequate test adequacy for this timeliness performance evaluation.

Now, human progress is on the verge of virtual world with the introduction of YouTube, Wikipedia and Facebook. In this work, we are using the best feature of both OOPs and Procedural Programming in order to achieve the goal of developing high performance web execution model. Client and server are integral part of any web-based model. Here, we are integrating compiler and program in web based model and finally come with high performance web execution model. High performance system means taking less time in compare to other existing traditional models. Many web based service like YouTube and Wikipedia are benefitted by taking advantage of our laziness. We want everything on the go, just in our hand-held computer. This work is also solving the beginners’ laziness problems like setup of Programming Environment such as compilers etc. If our education system is being shifted online, then this may turn out a big setback in learning process. Thus, we are providing compiler on the go so that the learning process is not interrupted by unavailability of resources (Our whole system is client-side) and of course by the laziness of people.

Internet broadcasting provides television-like services on the Internet. Therefore, it can be accessed from anywhere in the world, unlike TV broadcast services, which are limited by the range of the antenna. Because most people in developed countries can access the Internet, the popularity of Internet broadcasting is rapidly increasing. Streaming servers are the most essential part of Internet broadcasting systems because these are the servers that stream the multimedia content constituting the playlist. The playlist, in streaming server jargon, is the list of programs, where a program is a basic unit of the stream, such as a song, an episode of a television series, or a movie. Streaming servers provide environments in which users can easily edit the playlist. Therefore, management of streaming servers in an Internet broadcasting system is very important. This paper introduces our design and implementation of a streaming server management system for Internet broadcasting systems.

Humans psychological and behavioral understanding often lead to make natural decision which accurately identifies and remembers the faces which are highly appreciated or criticized by themselves in comparing to the normal viewed faces, in terms of beauty, ugliness or unique appearance. It happens due to human psychology of being biased towards the salient face in the process of face recognition and identification. This paper attempts a novel method to measure, how our attention is more restricted towards some particular faces in the crowd. This restricted attention is strongly guided by the relative visual saliency of these faces. In this paper, normalized relative visual saliency (NVRS) of the faces is evaluated using their intensity values modulated with respective spatial distance. Experiment has been carried out on test image dataset via bottom up approach. The experimental results are found to be encouraging and accuracy has also been measured exhibiting efficacy of the proposed approach.

Individual data might not be thought of as that important for business purposes. However, Big Data analytics use cases are increasing, because individual data can become a valuable data aggregate from which any hidden information can be found, once it’s collected into large volumes. Big Data. Known as one of conventional Big Data analytics technologies, Hadoop is a widely accepted technology to analyze structured/ unstructured Big Data to date. However, Hadoop has a high possibility for response time latency with larger data because of batch processing systems, which makes it difficult to do real time analysis for massive amounts of high speed event data under the current business environment and market conditions. In this paper, open source CEP (Complex Event Processing)-based technologies are used as an alternative for rapidly changing business, thereby developing the real time analytics system that enables us to analyze over thousands of event streams per second on a real time basis without latency, in order to be applicable to medical institution ERP systems.

In this paper, we proposed a system to generate randomly relocated multiple choice questions using ready-made arrays instead of random functions in order to reduce the time to make test set. Before making the test set, such as, multiple choice questions consisting of questions, their correct answers and their distractors, first predefined multiple choice questions sets are chosen and then the selected questions are relocated randomly using a ready-made array. Finally, multiple choices for each question are relocated using another ready-made array. The relocated answers for each question are sent to a correct answer array to check applicant’s answers later. Applicants take the tests to choose answers for multiple choice questions to evaluate applicants’ abilities. Thus, the method to make randomly relocated tests set using arrays is executed faster than one using random functions.

In this paper, the dependency of attributes in information systems has been used to define a finer quasi-discrete topology in information systems. This topology gives the characterization of roughness properties of sets. It is shown that every rough subset in the finer quasi-discrete topology is rough in the coarser quasi-discrete topology.

Recently, cloud computing platforms are available to provide convenient infrastructures such that cloud applications could conduct cloud and data-intensive computing. As the number of users and data increases, distribution and concurrency in systems is becoming the norm, not the exception. Data is replicated in geographically distinct datacentres, and large numbers of user requests are concurrently processed within a single replica. Consequently, most present new consistency models, which seek to provide stronger guarantees whilst preserving performance, through making certain assumptions on the operations executed or the data accessed. We discuss that this approach is often flawed. Recent years have seen a paradigm shift: distribution and concurrency have become prevalent. It is unrealistic to expect that a unique system view could, or even should exist at any given time. Yet, the literature still equates divergent views to inconsistency. Secondly, current distributed systems often rely on a definition of consistency that is too narrow. Consistency is an application-centric property. It should not be reduced to freshness. Thirdly, there exists a multiplicity of consistency definitions, each relying on subtly different assumptions and definitions of consistency. As a result, it is almost impossible to compare the guarantees provided by each system.

Software designers are motivated to utilize off-the-shelf software components for rapid application development. Such applications are expected to have high reliability as a result of deploying trusted components. This paper introduces Halstead’s software science to predict the fault before testing phase for component based system. Halstead’s software science is used to predict the faults for individual component and based on this faults reliability of different component is measured so that only reliable component will be reused.

In this paper, to take advantage of personalized black box service, we did design and implement a combination service using a user terminal device and smart phone application, cloud server. This proposed system is to collect and analyze the individual's status information, it is possible to recognize a dangerous situations and hazards for users to inform the guardian or decided particular government department. To recognize dangerous situations, we are using various electronic sensors like magnetic gyro, GPS, short circuit sensor, camera. Also, to predict future risk situations, we use a face detection method that based on digital image processing. So, it can be guaranteed to be secure before the crime incident.

In recent years, most of text classification researches have used a term-based feature approach, but it has problems that those are language dependent and require a large number of data-set for analysis and learning the classification model. This study proposes a SNS message type classification system combining language independent and dependent features that can be used in short message for type classification in social network service environments and verifies the effectiveness of this system.

Tracking of Quality of Service (QoS) parameters in real-time within a cloud computing system is a major challenge as it involves monitoring each every resource parameter. Such resources may constitute virtual machines, storage devices, network equipment, allied appliances, etc. Examination of physical resources being used to host the virtualized systems is often required on a continuous basis. This is important as virtualized systems also share physical resources among themselves. To monitor resources and services, we introduce a new monitoring tool named Billboard Manager Cloud Monitoring Tool (BMCMT) running on our proposed Billboard Manager system. Through this system, a cloud system administrator is apprised of the status of virtual machines that are running within the cloud system. The Billboard Manager keeps a continuous watch on all the parameters of every virtual machine and physical resource registered with it. At preset intervals, the proposed BMCMT would automatically send reports to the administrators. Every cloud system administrator is provided web login user identification. Should an administrator find it necessary to carry out any activity pertaining to any resource, using the constantly Billboard Manager refreshed hyperlinks on the web page the administrator can access the appropriate management console for further action.

In this paper, a spatial domain image deinterlacing method is presented. The goal of this method is to achieve high-quality vertically upsampled images while spending reasonable computational time. We propose directional edge detection approach to recognize edge directions with their own orientations. Assumed and designed number of classified orientations is 11 and each direction has 2 samples. All 22 pixels are categorized into 5 areas, according to the distance from the center pixel. The weighted average approach is applied to restore the missing pixel in a certain window. The employed weights are evaluated by similarity assessment technique. Simulation results show that the proposed method is able to outperform conventional methods in terms of objective (PSNR, SSIM, and consumed time) and subjective metrics.

Today, searching repetitive patterns on data flows is very important. By data flow we mean a type of data which is constantly produced in a very fast and unlimited manner. As a kind of these data we can name the report of clicks in computer networks. A repetitive pattern is a pattern which is available in a significant number of transactions. Finding repetitive patterns in data flows is a new and arguable issue in data mining as data is received in form of fast and continuous flow. Unlike static databases, flow mining faces a lot of problems including single review, requiring unlimited memory and high rate of input data. A common way of searching repetitive patterns is the excess check of data which requires to be saved in memory. In addition, according to the features of data flows i.e. unlimited and fast production, it is not possible to save them in memory and hence techniques are needed which are able to process them online and find repetitive patterns. One of the most popular relative techniques is using sliding windows. It’s advantage is reduction of the consumed memory and increase in search speed. In this paper, a new vertical display and an algorithm based on pins, called DBP-BA, are proposed to find repetitive patters in data flows. Since this new display without any additional task has a compact form, the proposed algorithm has a better performance than similar ones in terms of consumed memory and processing time. On the other hand, experiments support this matter.

Tracking objects in real time and exact tracking have long been challenging area in the field of computer vision. This paper describes a modified Continuously Adaptive Mean Shift (CAMshift) algorithm based on the Hue Saturation Value (HSV) color model for tracking an object in real time. The existing CAMshift can detect precisely when an object has a simple color. However, it has some disadvantages. When a CAMshift algorithm tracks an object, it does not consider direction and velocity, and it is difficult to track an object that has various colors and when the background contains color(s) similar to those of an object. In such case, CAMshift is worse than Meanshift. To overcome these disadvantages, a Kalman filter is used with CAMshift, as a Kalman filter can obtain direction vectors and it can make CAMshift exact, even under the aforementioned conditions.

Software requirements completeness is the one of the key elements for successful software development. Incomplete requirements can cause one to misunderstand requirements and eventually build a wrong system as a developer interprets them in an ad-hoc manner. In order to handle this issue, the use case pattern is suggested, which is a set of commonly discovered scenarios in the use case specification. However, identifying the use case pattern is not systematic and comprehensive without providing any algorithmic or statistical rationale. This paper proposes an automatic approach to identifying the use case flow pattern from various use case specification. In this paper, we gathered 83 use case specifications from eight industrial systems, and presented two use case flow patterns identified from the use case specification.