Application of Asynchronous Transfer Mode (Atm) technology to Picture Archiving and Communication Systems (Pacs): A survey

Broadband Integrated Services Digital Network (R-ISDN) provides a range of narrowband and broad-band services for voice, video, and multimedia. Asynchronous Transfer Mode (ATM) has been selected by the standards bodies as the transfer mode for implementing B-ISDN; The ability to digitize images has lead to the prospect of reducing the physical space requirements, material costs, and manual labor of traditional film handling tasks in hospitals. The system which handles the acquisition, storage, and transmission of medical images is called a Picture Archiving and Communication System (PACS). The transmission system will directly impact the speed of image transfer. Today the most common transmission means used by acquisition and display station products is Ethernet. However, when considering network media, it is important to consider what the long term needs will be. Although ATM is a new standard, it is showing signs of becoming the next logical step to meet the needs of high speed networks; This thesis is a survey on ATM, and PACS. All the concepts involved in developing a PACS are presented in an orderly manner. It presents the recent developments in ATM, its applicability to PACS and the issues to be resolved for realising an ATM-based complete PACS. This work will be useful in providing the latest information, for any future research on ATM-based networks, and PACS

Jmas: A Java-based Mobile Actor System for Heterogeneous Distributed Parallel Computing

Computer Scienc

Network traffic classification : from theory to practice

Since its inception until today, the Internet has been in constant transformation. The analysis and monitoring of data networks try to shed some light on this huge black box of interconnected computers. In particular, the classification of the network traffic has become crucial for understanding the Internet. During the last years, the research community has proposed many solutions to accurately identify and classify the network traffic. However, the continuous evolution of Internet applications and their techniques to avoid detection make their identification a very challenging task, which is far from being completely solved. This thesis addresses the network traffic classification problem from a more practical point of view, filling the gap between the real-world requirements from the network industry, and the research carried out. The first block of this thesis aims to facilitate the deployment of existing techniques in production networks. To achieve this goal, we study the viability of using NetFlow as input in our classification technique, a monitoring protocol already implemented in most routers. Since the application of packet sampling has become almost mandatory in large networks, we also study its impact on the classification and propose a method to improve the accuracy in this scenario. Our results show that it is possible to achieve high accuracy with both sampled and unsampled NetFlow data, despite the limited information provided by NetFlow. Once the classification solution is deployed it is important to maintain its accuracy over time. Current network traffic classification techniques have to be regularly updated to adapt them to traffic changes. The second block of this thesis focuses on this issue with the goal of automatically maintaining the classification solution without human intervention. Using the knowledge of the first block, we propose a classification solution that combines several techniques only using Sampled NetFlow as input for the classification. Then, we show that classification models suffer from temporal and spatial obsolescence and, therefore, we design an autonomic retraining system that is able to automatically update the models and keep the classifier accurate along time. Going one step further, we introduce next the use of stream-based Machine Learning techniques for network traffic classification. In particular, we propose a classification solution based on Hoeffding Adaptive Trees. Apart from the features of stream-based techniques (i.e., process an instance at a time and inspect it only once, with a predefined amount of memory and a bounded amount of time), our technique is able to automatically adapt to the changes in the traffic by using only NetFlow data as input for the classification. The third block of this thesis aims to be a first step towards the impartial validation of state-of-the-art classification techniques. The wide range of techniques, datasets, and ground-truth generators make the comparison of different traffic classifiers a very difficult task. To achieve this goal we evaluate the reliability of different Deep Packet Inspection-based techniques (DPI) commonly used in the literature for ground-truth generation. The results we obtain show that some well-known DPI techniques present several limitations that make them not recommendable as a ground-truth generator in their current state. In addition, we publish some of the datasets used in our evaluations to address the lack of publicly available datasets and make the comparison and validation of existing techniques easier

Factors Influencing the Adoption of New Information Technology in College and University Foodservices

The purpose of this study was to identify determinants for the adoption of new information technology (IT) applications in the context of college and university foodservices. A total of 207 voting delegates of the National Association of College and University Food Services (NACUFS) participated in this study. The approach to empirically test the relationships in the research model and the hypotheses was a self-administered cross-sectional survey methodology. The questionnaire was developed through literature review, focus group findings, and from questionnaires utilized in similar studies. With respect to the factors affecting the adoption behavior, the study examined a series of relationships that previously had either been posited with little proof or had created ambiguity in response to the foodservice professionals' actual needs and perceptions. Importance-Performance Analysis (IPA), factor analysis with VARIMAX rotation, multiple regression analysis, and one-way analysis of variance (ANOVA) were used to test research hypotheses. Findings and Conclusions: This study investigated the technology adoption behavior related to individual perceptions and organizational environments. First, this study identified a demonstrated relatively considerable gap between the perceived importance and satisfaction level of selected information technology applications. In particular, this study showed that individual perceptions of technology, technology motivations, and technology inhibitors played a significant role in forming the college and university foodservice administrators' behavioral intentions to adopt new information technologies. The results of a series of one-way ANOVA indicated that there were significant differences in the technology dimensions among foodservice administrators with different demographic profiles and technology characteristics.School of Hotel and Restaurant Administratio