Shuo-Tsung Chen

Hiding Patients Confidential Datainthe ECG Signal viaa Transform-Domain Quantization Scheme

Watermarking is the most widely used technology in the field of copyright and biological information protection. In this paper, we use quantization based digital watermark encryption technology on the Electrocardiogram (ECG) to protect patient rights and information. Three transform domains, DWT, DCT, and DFT are adopted to implement the quantization based watermarking technique. Although the watermark embedding process is not invertible, the change of the PQRST complexes and amplitude of the ECG signal is very small and so the watermarked data can meet the requirements of physiological diagnostics. In addition, the hidden information can be extracted without knowledge of the original ECG data. In other words, the proposed watermarking scheme is blind. Experimental results verify the efficiency of the proposed scheme.

Implementation of a Big Data Accessing and Processing Platform for Medical Records in Cloud

Big Data analysis has become a key factor of being innovative and competitive. Along with population growth worldwide and the trend aging of population in developed countries, the rate of the national medical care usage has been increasing. Due to the fact that individual medical data are usually scattered in different institutions and their data formats are varied, to integrate those data that continue increasing is challenging. In order to have scalable load capacity for these data platforms, we must build them in good platform architecture. Some issues must be considered in order to use the cloud computing to quickly integrate big medical data into database for easy analyzing, searching, and filtering big data to obtain valuable information.This work builds a cloud storage system with HBase of Hadoop for storing and analyzing big data of medical records and improves the performance of importing data into database. The data of medical records are stored in HBase database platform for big data analysis. This system performs distributed computing on medical records data processing through Hadoop MapReduce programming, and to provide functions, including keyword search, data filtering, and basic statistics for HBase database. This system uses the Put with the single-threaded method and the CompleteBulkload mechanism to import medical data. From the experimental results, we find that when the file size is less than 300MB, the Put with single-threaded method is used and when the file size is larger than 300MB, the CompleteBulkload mechanism is used to improve the performance of data import into database. This system provides a web interface that allows users to search data, filter out meaningful information through the web, and analyze and convert data in suitable forms that will be helpful for medical staff and institutions.

Implementation of Software-Defined Storage Service with Heterogeneous Object Storage Technologies

With the rapid development of information, personal computers are not only popular but also provide cloud services. Cloud Service is a concept that users can upload their requirement via Internet to cloud environment and then receive a response by postprocessing of the cloud environment, for example cloud storage. Software-defined storage (SDS) is a kind of virtualization technology for cloud storage. It uses the software to integrate the resources so as to improve accessibility and usability. There are many different open source projects for SDS in the Internet. This work aims to utilize these open source projects of SDS to improve the integration of the hardware and software resources effectively. In other words, we integrate various SDS open source projects or technologies to implement a cloud system. In the system architecture, we use some open-source software to make the proposed system more compatible and automatically assign a file to an appropriate storage location after users upload files. In addition, a manager can set some parameters to make this system more flexible. We also provide a high usability user interface. The user interface is designed as a web application. According to the concept of cloud services, this interface can be used anywhere and anytime.

Virtual machine management system based on the power saving algorithm in cloud

This work uses the open source codes and PHP web programming to implement a resource management system with power saving method for virtual machines. We propose a system integrated with open source software, such as KVM and Libvirt, to construct a virtual cloud management platform. This system can detect the status of cloud resources via SNMP, calculate the operation efficiency of the overall system, allocate virtual machines through the live migration technology, and turn off extra machines in the cloud to save energy. According to our proposed power saving method, we have constructed a power efficient virtualization management platform in the cloud. Our objective is to provide enterprises or end users with power saving private cloud solutions. In this work we have also built a web page to allow users to easily access and control the cloud virtualization resources, i.e., users can manage virtual machines and monitor the status of resources via the web interface. From analysis of the experimental results of live migration of virtual machines, this work demonstrates that efficient use of hardware resources is realized by the power saving method, and the aim of power saving for cloud computing is achieved.

Cloud City Traffic State Assessment System Using a Novel Architecture of Big Data

Recently, big data are widely applied to different field. This work presents a cloud city traffic state assessment system using a novel architecture of big data. The proposed system provides the real-time bus location and real-time traffic situation, especially the real-time traffic situation nearby, through open data, GPS, GPRS and cloud technologies. With the high-scalability cloud technologies, Hadoop and Spark, the proposed system architecture is first implemented successfully and efficiently. Next, we utilize three clustering methods, DBSCAN, K-Means, and Fuzzy C-Means to find the area of traffic jam in Taichung city and moving average to find the area of traffic jam in Taiwan Boulevard which is the main road in Taichung city. Finally, experimental results show the effectiveness and efficiency of the proposed system services via an advanced web technology. In addition, some experimental results indicate that the computing ability of Spark is better than that of Hadoop.

The implementation of a cloud city traffic state assessment system using a novel big data architecture

In order to store and analyze the increasing data in recent years, big data techniques are applied to many fields such as healthcare, manufacturing, telecommunications, retail, energy, transportation, automotive, security, environment, etc. This work implements a city traffic state assessment system in cloud using a novel big data architecture. The proposed system provides the real-time busses location and real-time traffic state, especially the real-time traffic state nearby, through open data, cloud computing, bid data technology, clustering methods, and irregular moving average. With the high-scalability cloud technologies, Hadoop and Spark, the proposed system architecture is first implemented successfully and efficiently. Next, we utilize irregular moving average and clustering methods to find the area of traffic jam. Finally, three important experiments are performed. The first experiment indicates that the computing ability of Spark is better than that of Hadoop. The second experiment applies Spark to process bus location data under different number of executors. In the last experiment, we apply irregular moving average and clustering methods to efficiently find the area of traffic jam in Taiwan Boulevard which is the main road in Taichung city. Based on these experimental results, the provided system services are present via an advanced web technology.

iGEMS: A Cloud Green Energy Management System in Data Center

Today the growing demand for reducing the power is not limited to household electricity saving. For businesses, it is the more important issue to effectively reduce the cost of electricity and the excess consumption under the huge electricity. In order to achieve energy saving and energy requires, the development of energy monitoring systems to obtain information related to consumption is necessary. Accordingly, this work proposes a cloud green energy management system. Because of the data size and the computational efficiency of data analysis, we add the big data technology and cloud computing to upgrade the system performance. By building cloud infrastructure and distributed storage cluster, we adopt the open source, Hadoop, to implement the two main functions: storage and computation. Based on these two functions, the proposed system speeds up the analysis and processing of big data by using Hadoop MapReduce to access HBase. The systemic risk is thus reduced too. Both real-time data and historical data are analyzed to obtain electricity consumption behavior for real-time warning and early warning. Moreover, carbon reduction and environmental protection are also considered in the analysis. Finally, a virtualized user-interface is designed to show the proposed system functions and analysis results. The experimental results indicate the performance of the proposed system.

Improvement of Workload Balancing Using Parallel Loop Self-Scheduling on Intel Xeon Phi

In this paper, we will examine how to improve workload balancing on a computing cluster by a parallel loop self-scheduling scheme. We use hybrid MPI and OpenMP parallel programming in C language. The block partition loop is according to the performance weighting of compute nodes. This study implements parallel loop self-scheduling use Xeon Phi, with its characteristics to improve workload balancing between heterogeneous nodes. The parallel loop self-scheduling is composed of the static and dynamic allocation. A weighting algorithm is adopted in the static part while the well-known loop self-scheduling scheme is adopted in the dynamic part. In recent years, Intel promotes its new product Xeon Phi coprocessor, which is similar to the x86 architecture coprocessor. It has about 60 cores and can be regarded as a single computing node, with the computing power that cannot be ignored. In our experiment, we will use a plurality of computing nodes. We compute four applications, i.e., Matrix multiplication, sparse matrix multiplication, Mandelbrot set computation, and the circuit satisfiability problem. Our results will show how to do the weight allocation and how to choose a scheduling scheme to achieve the best performance in the parallel loop self-scheduling.

Optimizing PSNR for Image Watermarking Using Summation Quantization on DWT Coefficients

This study presents an optimization-based image watermarking scheme that applies summation quantization technique to multi-coefficients of discrete wavelet transform (DWT). Peak signal-to-noise ratio (PSNR) and bit error ratio (BER) are commonly performance indexes in measuring the quality and robustness of an image watermarking scheme. To optimize the tradeoff between PSNR and BER, we minimize the difference between original and watermarked frequency coefficients. First, PSNR is expressed as a performance index using matrix form. Then, an optimized-quality functional that relates the performance index to the summation quantization technique is obtained. Finally, the Lagrange Principle is utilized to obtain the optimal solution. The optimal solution is applied to watermarking. Experimental results show that the watermarked image can keep high PSNR and achieve better BER even when the number of coefficients for embedding a watermark bit increases.

Implementation of Network Traffic Monitor System with SDN

Open Flow, an essential technology in Network functions virtualization (NFV) implementation, enables software-dened networking (SDN) to develop from a simple concept and divides traditional switch into two parts: a data plane and a control plane. Because software-dened rules are used to control traffic, the concept of NFV was developed. Numerous virtualization studies on NFV have investigated conventional networking hardware, such as firewalls, load-balancers, routers, and managed switches. In this paper, we describe virtualizing a basic switch and implementing a network traffic monitor system. The virtualized switch is used to replace a conventional managed switch and monitor traffic without the need for port mirroring hardware. In addition, Open Flow is incorporated to manage networking. Cloud computing is increasingly prevalent, and technology is advancing. The proposed system in this paper can be implemented in any networking environment.