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Real time system is a system in which the computation not only depends on producing a correct output, but the output should be delivered within predetermine timing constraint called deadline. As humans, we handle scheduling issues every day. For example, a student must accomplish homework by the appropriate due date, a professor must complete the rough draft of a paper by the submission date, etc., where we have only one task to accomplish, meeting that deadline probably would be very simple. Our lives, however, contain many tasks that have deadlines tax forms, car inspections, meetings, classes, etc. Thus, we must use some sort of scheduling technique to “juggle” our various tasks, so that they all are completed by their appropriate deadlines. As a result, many scholars develop different static and dynamic scheduling algorithms, which are best suited for both uniprocessor and multiprocessors. This paper work will focus on proposing a new dynamic scheduling algorithm. Because, it is found that the well-known uniprocessor dynamic scheduling (EDF (earlier deadline first) and LLF (least slack time first)) algorithms have many context switching and many probability of overhead occurrence (deadline-tie and laxity-tie respectively) which will degrade the performance of the whole system. The paper proposes a dynamic scheduling algorithm that has low context switching and totally reduce the probability of overhead occurrence by combining the deadline and slack to give priority for each task in the ready queue.

Our world is increasingly driven by sophisticated networks of advanced computing technology, and the basic operation of everyday society is becoming increasingly vulnerable to those networks shortcomings. The implementation and upkeep of a strong network defense is a substantial challenge, beset not only by economic disincentives, but also by an inherent logistical bias that grants advantage to attackers.Network Security Attacks and Countermeasures discusses the security and optimization of computer networks for use in a variety of disciplines and fields. Touching on such matters as mobile and VPN security, IP spoofing, and intrusion detection, this edited collection emboldens the efforts of researchers, academics, and network administrators working in both the public and private sectors.This edited compilation includes chapters covering topics such as attacks and countermeasures, mobile wireless networking, intrusion detection systems, next-generation firewalls, and more.

As Internet and its technologies are improving with rapid pace, there are security threats growing with same pace. The malicious software such as worm is causing such threats to IT systems linked to information super highway. The worms are capable of replicating themselves and infect systems over network. Their traffic propagation can be detected by employing anti worm or virus software. However, there is a new type of worm that can camouflage itself so as to prevent anti worm software from identifying it. The difference between normal worm's traffic and C-worm's traffic can't be found when time domain is considered. However, in terms of frequency definitely it can be differentiated. Based on this hypothesis, this paper presents novel schemes such as PSD and SFM that are capable of differentiating the traffic of C-worm from background traffic. The empirical results revealed that our schemes are effecting in detecting camouflaging worms effectively besides identifying normal worms. Keywords - Traffic propagation, worm, camouflaging worm, time domain, and frequency domain. I. Introduction Worm is a word with broad meaning. It refers to any program which is malicious in nature. Such program could be a VIRUS, worm etc. They have common features which are also there with biological virus. The common features include that they replicate themselves and also propagate from one machine to another machine. The means of propagation is only through infected storage media and also networks of all kinds including those without wire. Active worms continuously strive to propagate themselves to other systems and make them insecure. This is a problem which has been around ever since the world came across malicious programs for the first time. Some worms include Slammer (2), Sasser (3) and Code-Red (1). Some worms will work together by forming bonnets and cause more damage to IT systems. The attacks made by such worms include DDoS; attack to obtain sensitive information; destroying data (5) and also put forth unwanted materials such as advertisements. Many such worms are commonly known as malware (malicious software). This includes virus as well. The virus could be boot sector virus, file virus, love virus, time bomb virus, Trojan virus and so on. There is enough evidence in the history that some people have made it their business to create malware and also solutions to prevent them. This is major problem in the world of computers. This man made evil will continue posing threats to IT systems and also cause the businesses to loose confidential information and thus loosing confidence and profits in the business (4), (6). Researchers also predicting the possibility of malicious programs such as bonnets to collaborate and cause more security threats to IT world. Such collaborated bonnets is known as super bots (7). As there were reports of worms causing major damage to IT systems, the past few years saw significant research in the area of worms. Worm detection and prevention is an essential task required by all systems involved in IT. Thus the presence of anti-worm software is felt and the same is done through research. The process of identifying the worms by observing their scan traffic much anti-worm software succeed in detecting and also preventing any damage to IT systems. The emergence of Internet and also other networking facilities and communication systems paved way for the increase of threats caused by worms. Studying different kinds of worms and their impact on the IT systems and also prevention techniques are to be given paramount importance. When a worm infects a system, it will propagate its traffic in the system to cause damage to its data. It also strives to propagate the traffic to other systems though infected storage media and networks to other systems in the real world. They keep on identifying IP addresses of systems in the world and infect them though the ways known to them. The common way they follow is generating scan traffic in the time domain and frequency domain. Thus they make all the systems attacked by worms vulnerable to security threats. There is a possibility of loosing companies' sensitive information that leads to collapse of business or losing in revenues in large scale. The patterns of the worms (2), (8), (9) are increasing day by day. The more patterns of worm propagation is known, the more possibility to detect and prevent them. The assumption of all software in the world that is sued to combat worms is that the worms generate scan traffic and try to replicate themselves and infect systems in the same network and remote networks. The patterns are generally having same characteristics so as to enable anti-worm to detect them. However, a new class of worm has come into existence. This new worm is capable of hiding its presence.

In the field of robotics, the two wheel mobile manipulator has the potential to perform wide range of applications. Balancing of the system becomes a bothering task due to the highly unstable nature of the system. Dynamics of wheeled mobile manipulator are derived by the basics of energy balance equation by approximating double inverted pendulum dynamics. Control method for balancing the two wheeled mobile manipulator using H-infinity controller (H∞) is discussed in the work. H-infinity control methods are developed to design systems which have the desired dynamic behavior and are robust with respect to perturbations. Settling time required to achieve stability of the system and maximum deviation from desired value using linear quadratic regulator (LQR) controller and H-infinity controller are compared. Designing and simulations are carried out to check the validity of the proposed method and the results prove the effectiveness of the proposed method.

The benefits of six-sigma quality standard implementation are realized by the manufacturing sector very well and most of the cases reaped astounding benefits. This paper presents an extensive review on the services, six-sigma, and study the Critical Success Factors, Critical to Quality Characteristics and Key Performance Indicators in medium scale hospitals in Tamilnadu, India. Can health care approach the near perfection of Six Sigma in actual practice? . General Electric has begun to apply the same Six Sigma methods that worked to improve its manufacturing processes to its other, more service-oriented businesses. We believe, however, that asking this question must not be an excuse for failing to embark on the journey. Health care now frequently produces defects at rates as high as 500,000 per million––as exemplified in failures to recognize and treat clinical depression (Wells et al. 1989) or control hypertension (Udvarhelyi et al. 1991). Enough examples of improvement exist to conclude that we can do much better. In order to improve health service quality focus on health service process is necessary. Six-sigma is a philosophy which also concentrates on the improvement of process. So, six-sigma if properly applied can be useful for health services. The wider applicability of six-sigma depends on identification of key performance indicators (KPIs) for different types of health service processes. A case study is conducted in a medium scale hospital to identify, analyze and rank the critical success factors for Six sigma implementation available in literature, critical to quality characteristics (CTQs) ,KPIs and hurdles in the hospitals which affect possible quality improvements. This study will be helpful to both practitioners and researchers.

Nowadays, Computer Networks tend to be large and diverse systems that communicate using wide selection of protocols. This complexity made the desire for more sophisticated tools to watch and troubleshoot network traffic. Network traffic monitoring is can be quite a network diagnostic tool that monitors local area networks and provides a graphical display of network statistics. While collecting information through the network's data stream, network monitor displays the source address of the computer that sent a frame onto the network, the destination address of the computer that received the frame and also the protocols employed to send the frame. The procedure through which network monitor collects this information is known as Capturing. Automatically, the network monitor gathers statistics on all of the frames it detects on the network into a capture buffer that is a reserved storage area in memory. To capture statistics on merely a specific subset of frames, we could discriminate these frames by designing a capture filter. To work with network monitor, the computer will need to have a network card that supports promiscuous mode. This paper targets on the usage of packet capturing library Jpcap for monitoring, analyzing and reporting Distributed Denial of Service (DDoS) Attacks.

Distributed Denial of Service (DDoS) attacks in the internet disrupts the availability of a service or resource. Massive Distributed DoS attacks have the potential to severely decrease backbone availability and can virtually detach a network from the Internet. Because of the seriousness of the problem many defense mechanisms have been proposed to combat these attacks. This paper presents a Multi-layered probabilistic model for DDoS attacks and evaluated the performance of the system. Furthermore, important features of each attack and defense system category are described and advantages and disadvantages of each proposed scheme are outlined. The goal of the paper is to place some order into the existing attack and defense mechanisms, so that a better understanding of DDoS attacks can be achieved and subsequently more efficient and effective algorithms, techniques and procedures to combat these attacks may be developed.

Web is a collection of inter-related files on one or more Web servers. Web mining is one of the mining technologies, which applies data mining techniques in large amount of web data to improve the web services. Wide Web provides every internet citizen with access to an abundance of information, but it becomes increasingly difficult to identify the relevant pieces of information. Research in web mining tries to address this problem by applying techniques from data mining and machine learning to Web data and documents. The Web Mining is an application of Data Mining. Without the internet, life would have been almost impossible. The data available on the web is so voluminous and heterogeneous that it becomes an essential factor to mine this available data to make it presentable, useful, and pertinent to a particular problem. Web mining deals with extracting these interesting patterns and developing useful abstracts from diversified sources. The present paper deals with a preliminary discussion of WEB mining, few key computer science contributions in the field of web mining and outlines some promising areas of future research.

Drought affects hundreds of millions of people in the developing world and causes serious disruption of social and economic activities. Preparedness is better than relief and information is the backbone of drought preparedness. In this paper we report the results from a study in the use of GIS-derived products to assess micro-level drought vulnerability, taking a cluster of 17 villages in the South Central India as the study area. An internet-connected rural information center, linked to village knowledge centers, played a key role in testing the utility of this product, and the associated local-level predictions for seasonal rainfall.

Tuberculosis is a chronic systemic granulomatous disease which rarely affects the oral cavity. Oral lesions can be either primary or secondary to systemic tuberculosis, the former being rare. This is a never-before reported case of primary tuberculosis presenting as a localized diffuse gingival enlargement in an 11-year-old Indian female patient. The diagnosis was reached through identification of positive histopathological features, Tuberculin test results, presence of anti-tubercular antibodies confirmed by a polymerase chain reaction. In view of the recent increase in the incidence of tuberculosis and the prevalence of the same, it is reasonable to include tuberculosis in the differential diagnosis of gingival enlargements. This is essential to avoid any serious complications for both the clinician and patient due to a delay in the diagnosis of such a rare but plausible oral condition.

With the advent of effective drug therapy, tuberculous lesions of the oral cavity have become so rare that they are frequently forgotten. Primary gingival tuberculosis is extremely rare and usually manifests as ulcer. We report the first case of primary tuberculosis manifesting as gingival enlargement, which was the only presenting sign of tuberculosis. Diagnosis was based on histopathology (hematoxin and eosin staining), complete blood count, polymerase chain reaction assay and immunologic investigation with the detection of antibodies against Mycobacterium tuberculosis. The possibility of gingival enlargement due to drugs, leukemia, fungus and sarcoidosis was ruled out. Antituberculous therapy over 6 months was followed by surgical excision of the residual enlargement under local anesthesia. After 1-year follow-up there was no recurrence of the disease. This case emphasizes the need for dentists to include tuberculosis in the differential diagnosis of gingival enlargement so that they may play a role in its early detection.

Big Data is a relatively recent phenomenon, but has already shown its potential to drastically alter the relationships between businesses, individuals, and governments. The issues surrounding privacy of the online users (Mayer-Shoenberger, Cukier 2013) and the overall ethical challenges involved (Schroeder, 2014) make big data a topical issue, especially in the aftermath of the Snowden revelations. Many organisations now control vast amounts of raw data, and those industry players with the resources to mine that data to create new information have a significant advantage in the big data market. The use of predictive analytics in processing information tracked across different platforms to identify trends in the behaviour of individuals further adds value to big data (Fotopoulou, 2014) and makes it an important asset for any commercial entity. This rapid commodification of personal data has given rise to a new approach with regard its legal protection in the era of big data: a shift from the traditional privacy protection regime to a wider protection under property law is considered by scholars as an appropriate legal response to the phenomenon of monetisation of personal data, once seen through the lens of big data (Victor, 2013).\ud \ud The aim of this chapter is to identify the legal grounds for the ownership of big data: who legally owns the petabytes and exabytes of information created daily? Does this belong to the users, the data analysts or to the data brokers and various infomediaries? The chapter presents a succinct overview of the legal ownership of big data by examining the key players in control of the information at each stage of the processing of big data. It then moves on to describe the current legislative framework with regard to data protection and concludes in additional techno-legal solutions offered to complement the law of big data in this respect, with a particular focus on the European context .