3D object recognition is performed by using geometric hashing where transformation and scale invariant 3D surface features are utilized. 3D features are extracted from object surfaces after a scale space search where size of each feature is also estimated. Scale space is constructed based on orientation invariant surface curvature values which classify each surface point&#039 / s shape. Extracted features are grouped into triplets and orientation invariant descriptors are defined for each triplet. Each pose of each object is indexed in a hash table using these triplets. For scale invariance matching, cosine similarity is applied for scale variant triple variables. Tests were performed on Stuttgart database where 66 poses of 42 objects are stored in the hash table during training and 258 poses of 42 objects are used during testing. %90.97 recognition rate is achieved.

In the future, more data intensive applications, such as P2P auction networks, P2P job--search networks, P2P multi--player games, will require the capability to respond to more complex queries such as the nearest neighbor queries involving numerous data types. For the problem of answering nearest neighbor queries (NN query) for spatial region data in the P2P environment, a quadtree-based structure probably is a good choice. However, the quadtree stores the data in the leaf nodes, resulting in the load unbalance and expensive cost of any query. The MX--CIF quadtree can solve this problem. The MX--CIF quadtree has three properties: controlling efficiently the height of the tree, reducing load unbalance, and reducing the NNquery scope with controlling the value of the radius. Although the P2P MX--CIF quadtree can do the NN query efficiently, it still has some problems as follows: low accuracy of the nearest neighbor query, the expensive cost of the tree construction, the high search cost of the NN query, and load unbalance. In fact, the index structures for the region data can also work for the point data which can be considered as the degenerated case of the region data. Therefore, the KDB--tree which is a well-known algorithm for the point data can be used to reduce load unbalance, but it has the same problem as the quadtree. The data is stored only in the leaf nodes of the KDB--tree. In this thesis, we propose an Adjustable KDB--tree (AKDB--tree) to improve this situation for the P2P system. The AKDB--tree has five properties: reducing load unbalance, low cost of the tree construction, storing the data in the internal nodes and leaf nodes, high accuracy and low search cost of the NN query. The Chord system is a well--known structured P2P system in which the data search is performed by a hash function, instead of flooding used in most of the unstructured P2P system. Since the Chord system is a hash approach, it is easy to deal with peers joining/exiting. Besides, in order to combine AKDB--tree with the Chord system, we design the IDs of the nodes in the AKDB--tree. Each node is hashed to the Chord system by the ID. The IDs can be used to differentiate the edge node in the AKDB-tree is a vertical edge or a horizontal edge and the relative position of two nodes in the 2D space. And, we can calculate the related edge of a region in the 2D space according to the ID of the region. As discussed above, we make use of the property of IDs to reduce the search cost of the NN query by a wide margin. In our simulation study, we compare our method with the P2P MX--CIF quadtree by considering five performance measures under four different situations of the P2P MX--CIF quadtree. From our simulation results, for the NN query, our AKDB-tree can provide the higher accuracy and lower search cost than the P2P MX--CIF quadtree. For the problem of load, our AKDB-tree is more balance than the P2P MX--CIF quadtree. For the time of the tree construction, our AKDB-tree needs shorter time than the P2P MX--CIF quadtree.

This thesis is concerned with the design and analysis of practical provably-secure encryption schemes. We give several results that include new schemes with attractive tradeoffs between efficiency and security and new techniques for analyzing existing schemes. Our results are divided into three chapters, which we summarize below. The Twin Diffie-Hellman Problem. We describe techniques for analyzing encryption schemes based on the hardness of Diffie-Hellman-type problems. We apply our techniques to several specific cases of encryption, including identity-based encryption, to design a collection of encryption schemes that offer improved tradeoffs between efficiency and evidence for security over similar schemes. In addition to offering quantitative advantages over prior work in this area, our technique also simplifies security proofs for these types of encryption schemes. Our main tool in this chapter is the notion of Twin Diffie-Hellman Problems, which provide an intermediate step for organizing security reductions and reveal very simple variants of known schemes with correspondingly simple, but non-obvious, analyses. Non-Malleable Hash Functions. We consider security proofs for encryption that are carried out in the random oracle model, where one declares that a scheme's hash functions are ``off limits' for an attacker in order to make a proof go through. Such proofs leave some doubt as to the security of the scheme in practice, when attackers are free to exploit weaknesses in the hash functions. A particular concern is that a scheme may be insecure in practice no matter what very strong security properties its real hash functions satisfy. We address this doubt for an encryption scheme of Bellare and Rogaway by showing that, using appropriately strong hash functions, this scheme's hash functions can be partially instantiated in a secure way.

Network Processors are multicore processors capable of processing network packets at wire speeds of multi-Gbps. Due to their high performance and programmability, these processors have become the main computing elements in many demanding network processing equipments like enterprise, edge and core routers. With the ever increasing use of the internet, the processing demands of these routers have also increased. As a result, the number and complexity of the cores in network processors have also increased. Hence, efficiently managing these cores has become very challenging. This dissertation discusses two main issues related to efficient usage of large number of parallel cores in network processors: (1) How to allocate work to the processing cores to optimize performance? (2) How to meet the desired performance requirement power efficiently? This dissertation presents the design of a hash based scheduler to distribute packets to cores. The scheduler exploits multiple dimensions of locality to improve performance while minimizing out of order delivery of packets. This scheduler is designed to work seamlessly when the number of cores allocated to a service is changed. The design of a resource allocator is also presented which allocates different number of cores to services with changing traffic behavior. To improve the power efficiency, a traffic aware power management scheme is presented which exploits variations in traffic rates to save power. The results of simulation studies are presented to evaluate the proposals using real and synthetic network traces. These experiments show that the proposed packet scheduler can improve performance by as much as 40% by improving locality. It is also observed that traffic variations can be exploited to save significant power by turning off the unused cores or by running them at lower frequencies. Improving performance of the individual cores by careful scheduling also helps to reduce the power consumption because the same amount of work can now be done with fewer cores with improved performance. The proposals made in this dissertation show promising improvements over the previous work. Hashing based schedulers have very low overhead and are very suitable for data rates of 100 Gbps and even beyond. / text

In this research work, we propose a facial expression classification method using metric learning-based k-nearest neighbor voting. To achieve accurate classification of a facial expression from frontal face images, we first learn a distance metric structure from training data that characterizes the feature space pattern, then use this metric to retrieve the nearest neighbors from the training dataset, and finally output the classification decision accordingly. An expression is represented as a fusion of face shape and texture. This representation is based on registering a face image with a landmarking shape model and extracting Gabor features from local patches around landmarks. This type of representation achieves robustness and effectiveness by using an ensemble of local patch feature detectors at a global shape level. A naive implementation of the metric learning-based k-nearest neighbor would incur a time complexity proportional to the size of the training dataset, which precludes this method being used with enormous datasets. To scale to potential larger databases, a similar approach to that in [24] is used to achieve an approximate yet efficient ML-based kNN voting based on Locality Sensitive Hashing (LSH). A query example is directly hashed to the bucket of a pre-computed hash table where candidate nearest neighbors can be found, and there is no need to search the entire database for nearest neighbors. Experimental results on the Cohn-Kanade database and the Moving Faces and People database show that both ML-based kNN voting and its LSH approximation outperform the state-of-the-art, demonstrating the superiority and scalability of our method. / text

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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.

Thesis (M.S.)--Rochester Institute of Technology, 2008. / Typescript. Includes bibliographical references (leaf 49).

Thesis (M.S.)--Rochester Institute of Technology, 1987. / Typescript. Includes bibliographical references (leaves 137-144).