• Journal of the Optical Society of Korea
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• v.18 no.6
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• pp.650-656
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• 2014

We propose a novel active fiber design for selectively generating cylindrical vector beams (CVBs) or cylindrical vector modes (CVMs) which can be applied to conventional fiber lasers. A fiber is designed to have a ring-shaped core refractive index profile which can lead to the best overlap between the active dopant distribution profile and the lowest-order CVM (LCVM) field profile. Therefore, the overlap factor (OVF) of the LCVM becomes even higher than that of the fundamental mode. We emphasize that this condition cannot be satisfied by a conventional step-index core fiber (SICF) but by the ring-doped core fiber (RDCF). Because the lasing threshold is inversely proportional to the OVF, the LCVM can predominantly be stimulated even without going through special procedures to impose extra loss mechanisms to the fundamental mode. We numerically verify that the OVF of the LCVM with the doped ions can significantly exceed that of the fundamental mode if the proposed fiber design is applied. In addition, an RDCF of the proposed fiber design can also operate in a regime containing no higher-order modes besides the LCVM, so that it can selectively and efficiently generate the LCVM without being disrupted by the parasitic lasing of the higher-order modes. We highlight that an optimized RDCF can lead to a >30 % higher OVF ratio than a SICF having the same doped area. The proposed model is expected to be useful for enhancing the efficiency of generating CVBs in an all-fiber format.

• JSTS:Journal of Semiconductor Technology and Science
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• v.17 no.1
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• pp.1-6
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• 2017

This paper presents an optimized adoption of NVM for the storage system of heterogeneous applications. Our analysis shows that a bulk of I/O does not happen on a single storage partition, but it is varied significantly for different application categories. In particular, journaling I/O accounts for a dominant portion of total I/O in DB applications like OLTP, whereas swap I/O accounts for a large portion of I/O in graph visualization applications, and file I/O accounts for a large portion in web browsers and multimedia players. Based on these observations, we argue that maximizing the performance gain with NVM is not obtained by fixing it as a specific storage partition but varied widely for different applications. Specifically, for graph visualization, DB, and multimedia player applications, using NVM as a swap, a journal, and a file system partitions, respectively, performs well. Our optimized adoption of NVM improves the storage performance by 10-61%.

• Journal of Information Processing Systems
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• v.10 no.4
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• pp.491-502
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• 2014

In this paper we present some research results on computing intensive applications using modern high performance architectures and from the perspective of high computational needs. Computing intensive applications are an important family of applications in distributed computing domain. They have been object of study using different distributed computing paradigms and infrastructures. Such applications distinguish for their demanding needs for CPU computing, independently of the amount of data associated with the problem instance. Among computing intensive applications, there are applications based on simulations, aiming to maximize system resources for processing large computations for simulation. In this research work, we consider an application that simulates scheduling and resource allocation in a Grid computing system using Genetic Algorithms. In such application, a rather large number of simulations is needed to extract meaningful statistical results about the behavior of the simulation results. We study the performance of Oracle Grid Engine for such application running in a Cluster of high computing capacities. Several scenarios were generated to measure the response time and queuing time under different workloads and number of nodes in the cluster.

• International Journal of Computer Science & Network Security
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• v.22 no.3
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• pp.189-200
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• 2022

In today's digital world, smartphones and web-based applications have gained remarkable importance throughout the globe. These smart applications are playing a very significant role in maintaining a powerful business. As well as, they are helping a lot to expand these businesses via social networks. Social media networks such as Instagram, Facebook, Twitter, and LinkedIn are playing a prominent role to promote the companies. In the hospitality sector, most of the companies are running their hotel booking systems by utilizing mobile applications and a web-based infrastructure, but usability issues still exist. This study has been conducted specifically to tackle the usability issues of hotel booking systems and the best utilization of social networks to promote the business. TripAdvisor was selected as an authentic source for selecting those systems and two international hotels are selected for this study. The first step is to identify different hotel booking systems. In the second step, the user's satisfaction level was measured for the selected systems by performing the System Usability Scale (SUS, Quick & Dirty) approach. Additionally, by which source (social media or personal relations) they found these hotels. It is found that the SUS rating for both systems is below the acceptable level of usability. The Mean SUS for hotel 1 is found at 55.25 and 51.2 for hotel 2. The third step was to identify the user interface (UI) issues, and heuristic evaluation is performed for this. The experts identified the UI issues on the basis of their experience. The major issues were related to the visibility of system status, error prevention, flexibility and efficiency of use. Depending upon the identified issues, an interactive UI (prototype) for the selected web-based applications was proposed. This prototype is mainly based on the user's perspective. This prototype can be used for improving the UI of the selected systems which is based on the user's perspective. During the process of verifying the satisfaction level, it is revealed that the targeted audience is not able to use these systems efficiently and effectively. The reason behind this is the negligence of usability guidelines throughout the process of design and development of these hotel booking systems. Therefore, it is highly recommended that the usability of these systems should be evaluated and redesigned, based on expert opinions. It has also been observed that the reviews/ feedback of customers has spread a negative impact through social networks.

• International Journal of Computer Science & Network Security
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• v.22 no.3
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• pp.173-182
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• 2022

Healthcare organizations are overwhelmingly embracing smart value-based care strategies, which focuses on providing superior treatment at a significantly lower cost and quality of service (QoS). For these purposes, fifth generation (5G) of mobile service provides an innumerable improvement that clearly outperforms previous generations e.g., 3G and 4G. However, as with most advancements, 5G is projected to introduce new challenges, prompting the community to think about what comes next. This research was conducted to examine the most recent smart 5G technology applications and the solutions they provide to the healthcare industry. Finally, the paper discusses how the upcoming 6G technology has the potential to transform the future of healthcare sector even beyond the current 5G systems.

• Proceedings of the Korea Contents Association Conference
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• 2007.11a
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• pp.833-836
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• 2007

Recently, variety Web applications which is used Web2.0 are being developed and serviced. In this paper, we implemented Schedule Management Program for the individual and group based on Ajax applications, which apply the MVC design pattern with Java Servlet in order to complement Schedule Management Program which is developed with server-script languages. Also, we improved utilization of applications to provide various additional service through mash-ups built by combing APIs.

• Journal of the Korea Society of Computer and Information
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• v.23 no.10
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• pp.1-9
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• 2018

Modern graphics processing units (GPUs) have become one of the most attractive platforms in exploiting high thread level parallelism with the support of new programming tools such as CUDA and OpenCL. Recent GPUs has applied cache hierarchy to support irregular memory access patterns; however, L1 data cache (L1D) exhibits poor efficiency in the GPU. This paper shows that the L1D does not always positively affect the applications in terms of performance and energy efficiency for the GPU. The performance of the GPU is even harmed by using the L1D for lots of applications. Our proposed technique exploits the characteristics of the currently-executed applications to predict the performance impact of the L1D on the GPU and then decides whether to continuously use the cache for the application or not. Our experimental results show that the proposed technique improves the GPU performance by 9.4% and saves up to 52.1% of the power consumption in the L1D.

• International Journal of Computer Science & Network Security
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• v.23 no.10
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• pp.81-88
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• 2023

In the present scenario, enormous amounts of data are produced every second. These data also contain private information from sources including media platforms, the banking sector, finance, healthcare, and criminal histories. Data mining is a method for looking through and analyzing massive volumes of data to find usable information. Preserving personal data during data mining has become difficult, thus privacy-preserving data mining (PPDM) is used to do so. Data perturbation is one of the several tactics used by the PPDM data privacy protection mechanism. In Perturbation, datasets are perturbed in order to preserve personal information. Both data accuracy and data privacy are addressed by it. This paper will explore and compare several perturbation strategies that may be used to protect data privacy. For this experiment, two perturbation techniques based on random projection and principal component analysis were used. These techniques include Improved Random Projection Perturbation (IRPP) and Enhanced Principal Component Analysis based Technique (EPCAT). The Naive Bayes classification algorithm is used for data mining approaches. These methods are employed to assess the precision, run time, and accuracy of the experimental results. The best perturbation method in the Nave-Bayes classification is determined to be a random projection-based technique (IRPP) for both the cardiovascular and hypothyroid datasets.

• International Journal of Computer Science & Network Security
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• v.22 no.5
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• pp.103-114
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• 2022

The Internet of Things (IoT) is one of the fastest technologies that are used in various applications and fields. The concept of IoT will not only be limited to the fields of scientific and technical life but will also gradually spread to become an essential part of our daily life and routine. Before, IoT was a complex term unknown to many, but soon it will become something common. IoT is a natural and indispensable routine in which smart devices and sensors are connected wirelessly or wired over the Internet to exchange and process data. With all the benefits and advantages offered by the IoT, it does not face many security and privacy challenges because the current traditional security protocols are not suitable for IoT technologies. In this paper, we presented a comprehensive survey of the latest studies from 2018 to 2021 related to the security of the IoT and the use of machine learning (ML) and deep learning and their applications in addressing security and privacy in the IoT. A description was initially presented, followed by a comprehensive overview of the IoT and its applications and the basic important safety requirements of confidentiality, integrity, and availability and its application in the IoT. Then we reviewed the attacks and challenges facing the IoT. We also focused on ML and its applications in addressing the security problem on the IoT.

• International Journal of Computer Science & Network Security
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• v.23 no.10
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• pp.49-56
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• 2023

Computer architecture serves as a link between application requirements and underlying technology capabilities such as technical, mathematical, medical, and business applications' computational and storage demands are constantly increasing. Machine learning these days grown and used in many fields and it performed better than traditional computing in applications that need to be implemented by using mathematical algorithms. A mathematical algorithm requires more extensive and quicker calculations, higher computer architecture specification, and takes longer execution time. Therefore, there is a need to improve the use of computer hardware such as CPU, memory, etc. optimization has a main role to reduce the execution time and improve the utilization of computer recourses. And for the importance of execution time in implementing machine learning supervised module linear regression, in this paper we focus on optimizing machine learning algorithms, for this purpose we write a (Diabetes prediction program) and applying on it a Practical Swarm Optimization (PSO) to reduce the execution time and improve the utilization of computer resources. Finally, a massive improvement in execution time were observed.