Campbell Wilson

Vibration Spectrum Imaging: A Novel Bearing Fault Classification Approach

Incipient fault detection in low signal-to-noise ratio (SNR) conditions requires robust features for accurate condition-based machine health monitoring. Accurate fault classification is positively linked to the quality of features of the faults. Therefore, there is a need to enhance the quality of the features before classification. This paper presents a novel vibration spectrum imaging (VSI) feature enhancement procedure for low SNR conditions. An artificial neural network (ANN) has been used as a fault classifier using these enhanced features of the faults. The normalized amplitudes of spectral contents of the quasi-stationary time vibration signals are transformed into spectral images. A 2-D averaging filter and binary image conversion, with appropriate threshold selection, are used to filter and enhance the images for the training and testing of the ANN classifier. The proposed novel VSI augments and provides the visual representation of the characteristic vibration spectral features in an image form. This provides enhanced spectral images for ANN training and thus leads to a highly robust fault classifier.

Dynamic Key Cryptography and Applications

In modern security models, cryptography plays a fundamental role in protecting data integrity and confidentiality in information systems. However, cryptography itself is subject to cryptanalysis attacks. To reduce the cryptanalysis attack risk, a dynamic key theory is presented and analyzed in this paper. Because these dynamic keys are one-time used symmetric cryptographic keys, they can significantly improve the security of cryptographic systems. The dynamic key theory generation scheme and key update mechanism are formally analyzed to demonstrate balance between security and performance. The theory can be applied to enhance the security and performance of cryptographic systems, especially those used in wireless networks communication. Two case studies using the proposed dynamic key theory are also described and analyzed to illustrate the power of the theory.

Feature re-weighting in content-based image retrieval

Relevance Feedback (RF) is a useful technique in reducing semantic gap which is a bottleneck in Content-Based Image Retrieval (CBIR). One of the classical approaches to implement RF is feature re-weighting where weights in the similarity measure are modified using feedback samples as returned by the user. The main issues in RF are learning the system parameters from feedback samples and the high-dimensional feature space. We addressed the second problem in our previous work, here, we focus on the first problem. In this paper, we investigated different weight update schemes and compared the retrieval results. We proposed a new feature re-weighting method which we tested on three different image databases of size varying between 2000 and 8365, and having number of categories between 10 and 98. The experimental results with scope values of 20 and 100 demonstrated the superiority of our method in terms of retrieval accuracy.

PPINA – a forensic investigation protocol for privacy enhancing technologies

Although privacy is often seen as an essential right for internet users, the provision of anonymity can also provide the ultimate cover for malicious users. Privacy Enhancing Technologies (PETs) should not only hide the identity of legitimate users but also provide means by which evidence of malicious activity can be gathered. This paper proposes a forensic investigation technique, which can be embedded in the framework of existing PETs , thereby adding network forensic functionality to the PET. This approach introduces a new dimension to the implementation of Privacy Enhancing Technologies, which enhances their viability in the global network environment.

Unitary anomaly detection for ubiquitous safety in machine health monitoring

Safety has always been of vital concern in both industrial and home applications. Ensuring safety often requires certain quantifications regarding the inclusive behavior of the system under observation in order to determine deviations from normal behavior. In machine health monitoring, the vibration signal is of great importance for such measurements because it includes abundant information from several machine parts and surroundings that can influence machine behavior. This paper proposes a unitary anomaly detection technique (UAD) that, upon observation of abnormal behavior in the vibration signal, can trigger an alarm with an adjustable threshold in order to meet different safety requirements. The normalized amplitude of spectral contents of the quasi stationary time vibration signal are divided into frequency bins, and the summed amplitudes frequencies over bin are used as features. From a training set consisting of normal vibration signals, Gaussian distribution models are obtained for each feature, which are then used for anomaly detection.

A general inference network based architecture for multimedia information retrieval

Bayesian inference networks have found application in probabilistic information retrieval in the context of textual documents. The paper outlines an architecture whereby an inference network retrieval engine can be applied to multimedia retrieval. Initially, the system was developed to support the content based retrieval of images, however extensions are described so that retrieval of more general multimedia data may be performed.

Formal Verification of a Secure Mobile Banking Protocol

Current mobile banking protocols simply are not as well guarded as their Internet counterparts during the transactions between a mobile device and a financial institution. Recently, many mobile banking protocols using public-key cryptography have been proposed. However, they are designed to provide a basic protection for traditional flow of payment data as they only rely on basic identification and verification mechanisms, which is vulnerable to attack and increase the user’s risk. In this paper we propose a new secure mobile banking protocol that provides strong authentication mechanisms. These mechanisms rely on highly usable advanced multifactor authentication technologies i.e. (biometrics and smart cards). The proposed mobile banking protocol not only achieves a completely secure protection for the involved parties and their financial transactions but also minimizes the computational operations and the communication passes between them. An analysis and a proof of the proposed protocol security properties will be provided within this paper.

Monte-Carlo Filesystem Search - A crawl strategy for digital forensics

Criminal investigations invariably involve the triage or cursory examination of relevant electronic media for evidentiary value. Legislative restrictions and operational considerations can result in investigators having minimal time and resources to establish such relevance, particularly in situations where a person is in custody and awaiting interview. Traditional uninformed search methods can be slow, and informed search techniques are very sensitive to the search heuristics quality. This research introduces Monte-Carlo Filesystem Search, an efficient crawl strategy designed to assist investigators by identifying known materials of interest in minimum time, particularly in bandwidth constrained environments. This is achieved by leveraging random selection with non-binary scoring to ensure robustness. The algorithm is then expanded with the integration of domain knowledge. A rigorous and extensive training and testing regime conducted using electronic media seized during investigations into online child exploitation proves the efficacy of this approach.

Use of Self-Organizing Maps for texture feature selection in content-based image retrieval

The ldquosemantic gaprdquo observed in content-based image retrieval (CBIR) has become a highly active research topic in last twenty years, and it is widely accepted that domain specification is one of the most effective methods of addressing this problem. However, along with the challenge of making a CBIR system specific to a particular domain comes the challenge of making those features object dependent. independent component analysis (ICA) is a powerful tool for detecting underlying texture features in images. However, features detected in this way often contain groups of features which are essentially shifted or rotated versions of each other. Thus, a method of dimensionality reduction that takes this self-similarity into account is required. In this paper, we proposed a self-organizing map (SOM) based clustering method to reduce the dimensionality of feature space. This method comprises two phases: clustering as well as representative selection. The result of the implementation confirms this method offers effective CBIR dimensionality reduction when using the ICA method of texture feature extraction.

Towards criteria for visual layout of instructional multimedia interfaces

This article investigates the use of 11 refined Gestalt laws as the layout rules for instructional multimedia screen design by way of the redesign of the interface of a multimedia computer application and the subsequent evaluation of this redesigned interface. These layout rules are suitable for designing interfaces for complex learning materials in order to achieve more effective learning environments. To test the usefulness of these layout methodologies in visual screen design, they were applied to the redesign of an instructional multimedia application. Three different user groups evaluated the new screen design by comparing the designs. The evaluation results were overwhelmingly positive. Overall, 85% of the evaluators rated positively both the new design and the value of applying the 11 layout rules to improve the learning. This set of layout rules may also be successfully applied to design many different instructional multimedia applications.