Advanced information processing system: The Army fault tolerant architecture conceptual study. Volume 2: Army fault tolerant architecture design and analysis

Described here is the Army Fault Tolerant Architecture (AFTA) hardware architecture and components and the operating system. The architectural and operational theory of the AFTA Fault Tolerant Data Bus is discussed. The test and maintenance strategy developed for use in fielded AFTA installations is presented. An approach to be used in reducing the probability of AFTA failure due to common mode faults is described. Analytical models for AFTA performance, reliability, availability, life cycle cost, weight, power, and volume are developed. An approach is presented for using VHSIC Hardware Description Language (VHDL) to describe and design AFTA's developmental hardware. A plan is described for verifying and validating key AFTA concepts during the Dem/Val phase. Analytical models and partial mission requirements are used to generate AFTA configurations for the TF/TA/NOE and Ground Vehicle missions

Notes and laboratory reports on “Technology of Structural materials and Material Science” Part 2

“Technology of Structural materials and Material Science” is one of the basic technical disciplines in the syllabus for “Engineering mechanics” field of study. During the implementation of laboratory work considerable attention is given to the educational and experimental work for the study of materials that are used in different branches of an industry; alloy’s properties dependance on the chemical composition; structure, methods of treatment and external environments. The study of the theory and practice of different methods of materials strengthening is to provide a high reliability and longevity of the machine’s details, devices, tools etc. After every practical class in the laboratory, students will fill the laboratory report. The content of the laboratory class corresponds with the syllabus of the course “Material Science” for students of the “Engineering mechanics” field of study. The purpose of this manual is to provide guidelines for the students in preparation for independent laboratory work and to project its results in the laboratory reports

A Decentralised Digital Identity Architecture

Current architectures to validate, certify, and manage identity are based on centralised, top-down approaches that rely on trusted authorities and third-party operators. We approach the problem of digital identity starting from a human rights perspective, with a primary focus on identity systems in the developed world. We assert that individual persons must be allowed to manage their personal information in a multitude of different ways in different contexts and that to do so, each individual must be able to create multiple unrelated identities. Therefore, we first define a set of fundamental constraints that digital identity systems must satisfy to preserve and promote privacy as required for individual autonomy. With these constraints in mind, we then propose a decentralised, standards-based approach, using a combination of distributed ledger technology and thoughtful regulation, to facilitate many-to-many relationships among providers of key services. Our proposal for digital identity differs from others in its approach to trust in that we do not seek to bind credentials to each other or to a mutually trusted authority to achieve strong non-transferability. Because the system does not implicitly encourage its users to maintain a single aggregated identity that can potentially be constrained or reconstructed against their interests, individuals and organisations are free to embrace the system and share in its benefits.Comment: 30 pages, 10 figures, 3 table

Evaluating Security and Usability of Profile Based Challenge Questions Authentication in Online Examinations

© 2014 Ullah et al.; licensee Springer. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited.Student authentication in online learning environments is an increasingly challenging issue due to the inherent absence of physical interaction with online users and potential security threats to online examinations. This study is part of ongoing research on student authentication in online examinations evaluating the potential benefits of using challenge questions. The authors developed a Profile Based Authentication Framework (PBAF), which utilises challenge questions for students’ authentication in online examinations. This paper examines the findings of an empirical study in which 23 participants used the PBAF including an abuse case security analysis of the PBAF approach. The overall usability analysis suggests that the PBAF is efficient, effective and usable. However, specific questions need replacement with suitable alternatives due to usability challenges. The results of the current research study suggest that memorability, clarity of questions, syntactic variation and question relevance can cause usability issues leading to authentication failure. A configurable traffic light system was designed and implemented to improve the usability of challenge questions. The security analysis indicates that the PBAF is resistant to informed guessing in general, however, specific questions were identified with security issues. The security analysis identifies challenge questions with potential risks of informed guessing by friends and colleagues. The study was performed with a small number of participants in a simulation online course and the results need to be verified in a real educational context on a larger sample sizePeer reviewedFinal Published versio

A heuristic-based approach to code-smell detection

Encapsulation and data hiding are central tenets of the object oriented paradigm. Deciding what data and behaviour to form into a class and where to draw the line between its public and private details can make the difference between a class that is an understandable, flexible and reusable abstraction and one which is not. This decision is a difficult one and may easily result in poor encapsulation which can then have serious implications for a number of system qualities. It is often hard to identify such encapsulation problems within large software systems until they cause a maintenance problem (which is usually too late) and attempting to perform such analysis manually can also be tedious and error prone. Two of the common encapsulation problems that can arise as a consequence of this decomposition process are data classes and god classes. Typically, these two problems occur together – data classes are lacking in functionality that has typically been sucked into an over-complicated and domineering god class. This paper describes the architecture of a tool which automatically detects data and god classes that has been developed as a plug-in for the Eclipse IDE. The technique has been evaluated in a controlled study on two large open source systems which compare the tool results to similar work by Marinescu, who employs a metrics-based approach to detecting such features. The study provides some valuable insights into the strengths and weaknesses of the two approache

Real-Time Fault Diagnosis of Permanent Magnet Synchronous Motor and Drive System

Permanent Magnet Synchronous Motors (PMSMs) have gained massive popularity in industrial applications such as electric vehicles, robotic systems, and offshore industries due to their merits of efficiency, power density, and controllability. PMSMs working in such applications are constantly exposed to electrical, thermal, and mechanical stresses, resulting in different faults such as electrical, mechanical, and magnetic faults. These faults may lead to efficiency reduction, excessive heat, and even catastrophic system breakdown if not diagnosed in time. Therefore, developing methods for real-time condition monitoring and detection of faults at early stages can substantially lower maintenance costs, downtime of the system, and productivity loss. In this dissertation, condition monitoring and detection of the three most common faults in PMSMs and drive systems, namely inter-turn short circuit, demagnetization, and sensor faults are studied. First, modeling and detection of inter-turn short circuit fault is investigated by proposing one FEM-based model, and one analytical model. In these two models, efforts are made to extract either fault indicators or adjustments for being used in combination with more complex detection methods. Subsequently, a systematic fault diagnosis of PMSM and drive system containing multiple faults based on structural analysis is presented. After implementing structural analysis and obtaining the redundant part of the PMSM and drive system, several sequential residuals are designed and implemented based on the fault terms that appear in each of the redundant sets to detect and isolate the studied faults which are applied at different time intervals. Finally, real-time detection of faults in PMSMs and drive systems by using a powerful statistical signal-processing detector such as generalized likelihood ratio test is investigated. By using generalized likelihood ratio test, a threshold was obtained based on choosing the probability of a false alarm and the probability of detection for each detector based on which decision was made to indicate the presence of the studied faults. To improve the detection and recovery delay time, a recursive cumulative GLRT with an adaptive threshold algorithm is implemented. As a result, a more processed fault indicator is achieved by this recursive algorithm that is compared to an arbitrary threshold, and a decision is made in real-time performance. The experimental results show that the statistical detector is able to efficiently detect all the unexpected faults in the presence of unknown noise and without experiencing any false alarm, proving the effectiveness of this diagnostic approach.publishedVersio

Aspect structure of compilers

Compilers are among the most widely-studied pieces of software; and, modularizing these valuable artifacts is a recurring theme in research. However, modularization of cross-cutting concerns in compilers is not yet well explored. Even today, implementation of one compiler concern scatters across and tangles with the implementation of several other concerns, thereby leading to a mismatch between different compiler modules and the operations they represent. Essentially, current compiler implementations fail to explicitly identify the control dependencies of different phases, and separately characterize the actions to execute during those phases. As a result, information about their program-execution path remains non-intuitive: it stays hidden within the program structure and cuts-across several phase implementations. Consequently, this makes compiler designs and artifacts difficult to comprehend, maintain and reuse. Such limitations occur primarily as a result of the inability of mainstream object-oriented languages, such as Java, to organize the cross-cutting concerns into clean modular units. This thesis demonstrates how such modularity-issues in compilers can be addressed with the help of a relatively new, yet powerful programming paradigm called aspect-oriented programming

A Framework for Uncertain Cloud Data Security and Recovery Based on Hybrid Multi-User Medical Decision Learning Patterns

Machine learning has been supporting real-time cloud based medical computing systems. However, most of the computing servers are independent of data security and recovery scheme in multiple virtual machines due to high computing cost and time. Also, this cloud based medical applications require static security parameters for cloud data security. Cloud based medical applications require multiple servers to store medical records or machine learning patterns for decision making. Due to high Uncertain computational memory and time, these cloud systems require an efficient data security framework to provide strong data access control among the multiple users. In this work, a hybrid cloud data security framework is developed to improve the data security on the large machine learning patterns in real-time cloud computing environment. This work is implemented in two phases’ i.e. data replication phase and multi-user data access security phase. Initially, machine decision patterns are replicated among the multiple servers for Uncertain data recovering phase. In the multi-access cloud data security framework, a hybrid multi-access key based data encryption and decryption model is implemented on the large machine learning medical patterns for data recovery and security process. Experimental results proved that the present two-phase data recovering, and security framework has better computational efficiency than the conventional approaches on large medical decision patterns