Although the identification of humans by fingerprints is a well–known technique in practice, the identification of an audio sample by means of a technique called audio fingerprinting is still under development. Audio fingerprinting can be used to identify different types of audio samples of which music and advertisements are the two most frequently encountered. Different audio fingerprinting techniques to identify audio samples appear seldom in the literature and direct comparisons of the techniques are not always available In this dissertation, the two audio fingerprinting techniques of Avery Wang and Haitsma and Kalker are compared in terms of accuracy, speed, versatility and scalability, with the goal of modifying the algorithms for optimal advertisement identification applications. To start the background of audio fingerprinting is summarised and different algorithms for audio fingerprinting are reviewed. Problems, issues to be addressed and research methodology are discussed. The research question is formulated as follows : “Can audio fingerprinting be applied successfully to advertisement monitoring, and if so, which existing audio fingerprinting algorithm is most suitable as a basis for a generic algorithm and how should the original algorithm be changed for this purpose?” The research question is followed by literature regarding the background of audio fingerprinting and different audio fingerprinting algorithms. Next, the importance of audio fingerprinting in the engineering field is motivated by the technical aspects related to audio fingerprinting. The technical aspects are not always necessary or part of the algorithm, but in most cases, the algorithms are pre–processed, filtered and downsampled. Other aspects include identifying unique features and storing them, on which each algorithm’s techniques differ. More detail on Haitsma and Kalker’s, Avery Wang’s and Microsoft’s RARE algorithms are then presented. Next, the desired interface for advertisement identification Graphical User Interface (GUI) is presented. Different solution architectures for advertisement identification are discussed. A design is presented and implemented which focuses on advertisement identification and helps with the validation process of the algorithm. The implementation is followed by the experimental setup and tests. Finally, the dissertation ends with results and comparisons, which verified and validated the algorithm and thus affirmed the first part of the research question. A short summary of the contribution made in the dissertation is given, followed by conclusions and recommendations for future work. / Thesis (M.Ing. (Computer and Electronical Engineering))--North-West University, Potchefstroom Campus, 2012.

Although the identification of humans by fingerprints is a well–known technique in practice, the identification of an audio sample by means of a technique called audio fingerprinting is still under development. Audio fingerprinting can be used to identify different types of audio samples of which music and advertisements are the two most frequently encountered. Different audio fingerprinting techniques to identify audio samples appear seldom in the literature and direct comparisons of the techniques are not always available In this dissertation, the two audio fingerprinting techniques of Avery Wang and Haitsma and Kalker are compared in terms of accuracy, speed, versatility and scalability, with the goal of modifying the algorithms for optimal advertisement identification applications. To start the background of audio fingerprinting is summarised and different algorithms for audio fingerprinting are reviewed. Problems, issues to be addressed and research methodology are discussed. The research question is formulated as follows : “Can audio fingerprinting be applied successfully to advertisement monitoring, and if so, which existing audio fingerprinting algorithm is most suitable as a basis for a generic algorithm and how should the original algorithm be changed for this purpose?” The research question is followed by literature regarding the background of audio fingerprinting and different audio fingerprinting algorithms. Next, the importance of audio fingerprinting in the engineering field is motivated by the technical aspects related to audio fingerprinting. The technical aspects are not always necessary or part of the algorithm, but in most cases, the algorithms are pre–processed, filtered and downsampled. Other aspects include identifying unique features and storing them, on which each algorithm’s techniques differ. More detail on Haitsma and Kalker’s, Avery Wang’s and Microsoft’s RARE algorithms are then presented. Next, the desired interface for advertisement identification Graphical User Interface (GUI) is presented. Different solution architectures for advertisement identification are discussed. A design is presented and implemented which focuses on advertisement identification and helps with the validation process of the algorithm. The implementation is followed by the experimental setup and tests. Finally, the dissertation ends with results and comparisons, which verified and validated the algorithm and thus affirmed the first part of the research question. A short summary of the contribution made in the dissertation is given, followed by conclusions and recommendations for future work. / Thesis (M.Ing. (Computer and Electronical Engineering))--North-West University, Potchefstroom Campus, 2012.

The use of electroencephalogram (EEG), an electrophysiological monitoring method for recording the brain activity, for authentication has attracted the interest of researchers for over a decade. In addition to exhibiting qualities of biometric-based authentication, they are revocable, impossible to mimic, and resistant to coercion attacks. However, EEG signals carry a wealth of information about an individual and can reveal private information about the user. This brings significant privacy issues to EEG-based authentication systems as they have access to raw EEG signals. This thesis proposes a privacy-preserving EEG-based authentication system that preserves the privacy of the user by not revealing the raw EEG signals while allowing the system to authenticate the user accurately. In that, perceptual hashing is utilized and instead of raw EEG signals, their perceptually hashed values are used in the authentication process. In addition to describing the authentication process, algorithms to compute the perceptual hash are developed based on two feature extraction techniques. Experimental results show that an authentication system using perceptual hashing can achieve performance comparable to a system that has access to raw EEG signals if enough EEG channels are used in the process. This thesis also presents a security analysis to show that perceptual hashing can prevent information leakage.

<p dir="ltr">Asset traceability systems support sustainable value creation. Use case scenarios include the transition from a linear to a circular economy (CE) and legislative initiatives in Europe and North America. Traceability systems are needed to consistently link physical assets with the corresponding digital life cycle data. However, there is a lack of system architectures for consistent asset life cycle traceability. Therefore, the work proposes a traceability system architecture using digital product passports (DPPs) and fingerprint (FP) technology. By providing asset related data, DPPs increase the transparency across value chain partners. The system architecture uses the Asset Administration Shell (AAS) to create interoperable and standardized DPPs. Besides, consistent product identification (ID) and unique (single occurrence) identifiers are a prerequisite for effective traceability systems. Using natural markers to identify assets can enhance consistent asset traceability in sustainable supply chains. When using FP technology, the inherent surface structure of an asset is captured by an imaging system and then compressed into a digital asset fingerprint. Since assets are not artificially marked, the work investigates the use of Bounding Symbols (BSs) to locate an asset’s fingerprint Region of Interest (ROI). Furthermore, four fingerprint creation algorithms are compared and evaluated regarding their feasibility for asset life cycle traceability. The research validates the proposed system architecture in an experimental setup by using aluminum raw castings (medallions) as the investigated asset type. Key findings include the successful identification of 80 medallions with a 100% success rate. The related fingerprint information was stored in a DPP as an AAS submodel.</p>