Nick Savage
University of Portsmouth
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Publication
Featured researches published by Nick Savage.
vehicular technology conference | 2005
Jürgen Richter; Rafael F. S. Caldeirinha; M.O. Al-Nuaimi; Andy Seville; Neil C. Rogers; Nick Savage
This paper aims to describe the results of a 15 months consortium project to study the effects of microwave and millimetre wave propagation radio signals through vegetation. The aim of the project was to provide a generic model for the determination of propagation loss through vegetation, and was to be achieved by a combination of an extensive campaign of measurements and deterministic modelling. The proposed model is ideally suited to micro- and picocellular radio service planning, and with the aid of a forest database giving dimensions, locations and tree types, the model may be used for macrocellular radio system planning.
Engineering Education | 2011
Nick Savage; Roy Birch; Eleni Noussi
Abstract This paper examines motivational factors affecting higher education (HE) students in the Faculty of Technology at the University of Portsmouth. A reliable identification of motivational factors would usefully inform pedagogical interventions. Students who are more intrinsically motivated may benefit from less prescriptive assignments which offer more freedom to choose from “formative” assessment topics in which they have a greater personal interest. Those who are more extrinsically motivated, where the final “summative” grade is thought of as the most important, may be less influenced by pedagogical styles. The investigatory approaches employed in this study to assess motivation discover different results. While questionnaire responses indicate that students operate both intrinsically and extrinsically, semi-structured interviews found little evidence of the former, with most students indicating that they operate extrinsically.
Iet Information Security | 2016
Gareth Owen; Nick Savage
Tor hidden services allow someone to host a website or other transmission control protocol (TCP) service whilst remaining anonymous to visitors. The collection of all Tor hidden services is often referred to as the ‘darknet’. In this study, the authors describe results from what they believe to be the largest study of Tor hidden services to date. By operating a large number of Tor servers for a period of 6 months, the authors were able to capture data from the Tor distributed hash table to collect the list of hidden services, classify their content and count the number of requests. Approximately 80,000 hidden services were observed in total of which around 45,000 are present at any one point in time. Abuse and Botnet C&C servers were the most frequently requested hidden services although there was a diverse range of services on offer.
International Journal of Antennas and Propagation | 2010
David Ndzi; Nick Savage; Boris Gremont
Extensive studies of the impact of temporal variations induced by people on the characteristics of indoor wideband channels are reported. Singular Value Decomposition Prony algorithm has been used to compute the impulse response from measured channel transfer functions. The high multipath resolution of the algorithm has allowed a detailed assessment of the shapes of individual multipath clusters and their variation in time and space in indoor channels. Large- and small-scale analyses show that there is a significant dependency of the channel response on room size. The presence of people in the channel has been found to induce both signal enhancements and fading with short-term dynamic variations of up to 30 dB, depending on the number of people and their positions within the room. A joint amplitude and time of arrival model has been used to successfully model measured impulse response clusters.
ieee symposium series on computational intelligence | 2016
Nitin Naik; Paul Jenkins; Nick Savage; Vasilios Katos
Big Data security analysis is commonly used for the analysis of large volume security data from an organisational perspective, requiring powerful IT infrastructure and expensive data analysis tools. Therefore, it can be considered to be inaccessible to the vast majority of desktop users and is difficult to apply to their rapidly growing data sets for security analysis. A number of commercial companies offer a desktop-oriented big data security analysis solution; however, most of them are prohibitive to ordinary desktop users with respect to cost and IT processing power. This paper presents an intuitive and inexpensive big data security analysis approach using Computational Intelligence (CI) techniques for Windows desktop users, where the combination of Windows batch programming, EmEditor and R are used for the security analysis. The simulation is performed on a real dataset with more than 10 million observations, which are collected from Windows Firewall logs to demonstrate how a desktop user can gain insight into their abundant and untouched data and extract useful information to prevent their system from current and future security threats. This CI-based big data security analysis approach can also be extended to other types of security logs such as event logs, application logs and web logs.
digital enterprise and information systems | 2011
Funminiyi Olajide; Nick Savage
There have been few investigations into the amount of relevant information that can be recovered from the physical memory of Windows applications. Extraction of user information is vital in today’s digital investigation and forensic investigators find it helpful to gain access to dispersal evidence stored over time in the physical memory of these applications. In this research, we present the quantitative and qualitative results of experiments carried out on the extraction of forensically relevant information from Windows computer systems. This process involves a pattern matching techniques of the original user input and the extracted memory dump strings processes. In conducting this research; we have identified the most commonly used applications on Windows systems, designed a methodology to capture data and processed that data. This research will report the amount of evidence dispersed over time in the physical memory when the application was running and user is not interacting with the system.
international conference on telecommunications | 2014
Kyriakos Ovaliadis; Nick Savage; Vassilios Tsiantos
Energy efficiency is an important issue during the design and the overall performance evaluation of an UWSN system. Clustering sensor nodes have proven to be an effective method to improve the load balancing and scalability of the network while minimizing the systems overall energy consumption. In this paper, a new clustering algorithm is proposed to provide an improved cluster system against cluster-head failures. This study suggests that system CH failures could be further minimized when simultaneously a CH (primary CH) and a vice/backup CH are selected. Thus, when a primary CH fails due to an irreparable fault, a backup CH will take its place and it will operates as a head node. This study proposes two major procedures in order this to be accomplished, the detection failure and the recovery procedures. The first one initially detects any failures that occurred in the network and then reports this information to the relevant nodes to initiate recovery. The recovery procedure actually decides who and when will trigger the recovery function according to the origin of the CH node failure which can be either the energy depletion of the CHs battery or a software/hardware malfunction. The simulation results clearly indicate that there is an improvement in terms of network lifetime expectancy and energy consumption.
trust security and privacy in computing and communications | 2011
Ahmed Al-Khazzar; Nick Savage
Biometric identification systems are being used widely in many applications and security systems all over the world. In this paper, a review of the research in behavioural biometrics is presented and a biometric identification system is proposed which utilises user interactions with virtual worlds for identification. Three game based virtual environments are implemented and three biometric similarity measures namely: distance, Euclidean, and interclass are used to evaluate the system. The proposed system was able to identify users with up to 89 percent of accuracy. The average equal error rates between 29 to 35 percent are achieved. The proposed system can have applications such as identifying users in virtual environments, differentiating humans from machines in online games, and finding users operating multiple accounts in an online system.
Archive | 2002
Neil C. Rogers; Andrew Seville; Jurgen Richter; David Ndzi; Nick Savage; Rafael F. S. Caldeirinha; Anil Kumar Shukla; Miqdad O. Al-Nuaimi; Ken H. Craig; E. Vilar; J. Austin
Radio Science | 2003
Nick Savage; David Ndzi; Andrew Seville; E. Vilar; J. Austin