This thesis explores what patterns, if any, exist to differentiate non-malware from malware, given only a sequence of raw bytes composing either a received file or a fixed-length initial segment of a received file. If any such patterns are found, their effectiveness as filtering criteria is investigated.

The need for an effective clustering algorithm for Vehicle Ad Hoc Networks (VANETs) is motivated by the recent research in cluster-based MAC and routing schemes. VANETs are highly dynamic and have harsh channel conditions, thus a suitable clustering algorithm must be robust to channel error and must consider node mobility during cluster formation. This work presents a novel, mobility-based clustering scheme for Vehicle Ad hoc Networks, which forms clusters using the Affinity Propagation algorithm in a distributed manner. This proposed algorithm considers node mobility during cluster formation and produces clusters with high stability. Cluster performance was measured in terms of average cluster head duration, average cluster member duration, average rate of cluster head change, and average number of clusters. The proposed algorithm is also robust to channel error and exhibits reasonable overhead. Simulation results confirm the superior performance, when compared to other mobility-based clustering techniques.

The need for an effective clustering algorithm for Vehicle Ad Hoc Networks (VANETs) is motivated by the recent research in cluster-based MAC and routing schemes. VANETs are highly dynamic and have harsh channel conditions, thus a suitable clustering algorithm must be robust to channel error and must consider node mobility during cluster formation. This work presents a novel, mobility-based clustering scheme for Vehicle Ad hoc Networks, which forms clusters using the Affinity Propagation algorithm in a distributed manner. This proposed algorithm considers node mobility during cluster formation and produces clusters with high stability. Cluster performance was measured in terms of average cluster head duration, average cluster member duration, average rate of cluster head change, and average number of clusters. The proposed algorithm is also robust to channel error and exhibits reasonable overhead. Simulation results confirm the superior performance, when compared to other mobility-based clustering techniques.

車載網路受到各方廣泛討論，激發出許多新的議題，由於車載網路的通訊品質不穩定，速度快、節點多，封包傳送不易，因此許多人都採用分群式架構增進效能，以集中式管理群組，避免封包被重複傳送，降低封包碰撞的機會。然而，現有的分群機制只能用在即時方面的應用，在檔案傳輸方面效能不足。本篇論文擬改善C. Shea等人[1]所提出的分群機制File-based Affinity Propagation Cluster, FAPC，建立兼具動態性和檔案相關性的叢集架構，並且提出改善失去叢集管理員的重建機制，以提升分群的穩定性及吞吐量（throughput）。最後，我們以模擬證明所提出的方法優於C. Shea [1]的方法，以query hit ratio、retrieve file ratio、average number of clusters及average cluster head duration為效能指標，觀察在不同時間、車輛數目及車輛速度時效能表現。 / Vehicular Ad-hoc Network (VANET) has been widely discussed and many issues have been proposed. Due to VANET’s unstable quality, varying speed, lots of mobility nodes, it’s not easy to deliver packets. Thus many researchers suggested using cluster architecture to enhance performance. Because of the central management, we can avoid duplication of packets in the same cluster and decrease the probability of packet collision. However, we find most of the cluster architectures are suitable for real-time applications, but not for file transfer. In this research, we improve C. Shea’s [1] method by adding file-similarity by classifying into groups and reselecting cluster head, when the group of nodes have not cluster head. This cluster architecture can enhance stability and throughput. Finally, we use simulation to prove that our method outperforms Chen’s [1] cluster method in terms of query hit ratio, retrieve file ratio, average number of clusters and average cluster head duration.

As the demand of indoor Location-Based Services (LBSs) increases, there is a growing interest in developing an accurate indoor positioning and tracking system on mobile devices. The core location determination problem can be reformulated as a sparse natured problem and thus can be solved by applying the Compressive Sensing (CS) theory. This thesis proposes a compact received signal strength (RSS) based real-time indoor positioning and tracking systems using CS theory that can be implemented on personal digital assistants (PDAs) and smartphones, which are both limited in processing power and memory compared to laptops. The proposed tracking system, together with a simple navigation module is implemented on Windows Mobile-operated smart devices and their performance in different experimental sites are evaluated. Experimental results show that the proposed system is a lightweight real-time algorithm that performs better than other traditional fingerprinting methods in terms of accuracy under constraints of limited processing and memory resources.

Gesture Recognition provides an efficient human-computer interaction for interactive and intelligent computing. In this work, we address the problem of gesture recognition using the theory of random projection and by formulating the recognition problem as an $\ell_1$-minimization problem. The gesture recognition uses a single 3-axis accelerometer for data acquisition and comprises two main stages: a training stage and a testing stage. For training, the system employs dynamic time warping as well as affinity propagation to create exemplars for each gesture while for testing, the system projects all candidate traces and also the unknown trace onto the same lower dimensional subspace for recognition. A dictionary of 18 gestures is defined and a database of over 3,700 traces is created from 7 subjects on which the system is tested and evaluated. Simulation results reveal a superior performance, in terms of accuracy and computational complexity, compared to other systems in the literature.

Gesture Recognition provides an efficient human-computer interaction for interactive and intelligent computing. In this work, we address the problem of gesture recognition using the theory of random projection and by formulating the recognition problem as an $\ell_1$-minimization problem. The gesture recognition uses a single 3-axis accelerometer for data acquisition and comprises two main stages: a training stage and a testing stage. For training, the system employs dynamic time warping as well as affinity propagation to create exemplars for each gesture while for testing, the system projects all candidate traces and also the unknown trace onto the same lower dimensional subspace for recognition. A dictionary of 18 gestures is defined and a database of over 3,700 traces is created from 7 subjects on which the system is tested and evaluated. Simulation results reveal a superior performance, in terms of accuracy and computational complexity, compared to other systems in the literature.

As the demand of indoor Location-Based Services (LBSs) increases, there is a growing interest in developing an accurate indoor positioning and tracking system on mobile devices. The core location determination problem can be reformulated as a sparse natured problem and thus can be solved by applying the Compressive Sensing (CS) theory. This thesis proposes a compact received signal strength (RSS) based real-time indoor positioning and tracking systems using CS theory that can be implemented on personal digital assistants (PDAs) and smartphones, which are both limited in processing power and memory compared to laptops. The proposed tracking system, together with a simple navigation module is implemented on Windows Mobile-operated smart devices and their performance in different experimental sites are evaluated. Experimental results show that the proposed system is a lightweight real-time algorithm that performs better than other traditional fingerprinting methods in terms of accuracy under constraints of limited processing and memory resources.

This work is motivated from error-correcting codes in the brain. To counteract the effect of representation noise, a large number of neurons participate in encoding even low-dimensional variables. In many brain areas, the mean firing rates of neurons as a function of represented variable, called the tuning curve, have unimodal shape centered at different values, defining a unary code. This dissertation focuses on a new type of neural code where neurons have periodic tuning curves, with a diversity of periods. Neurons that exhibit this tuning are grid cells of the entorhinal cortex, which represent self-location in two-dimensional space. First, we investigate mutual information between such multi-scale codes and the coded variable as a function of tuning curve width. For decoding, we consider maximum likelihood (ML) and plausible neural network (NN) based models. For unary neural codes, Fisher information increases with narrower tuning, regardless of the decoding method. By contrast, for the multi-scale neural code, the optimal tuning curve width depends on the decoding method. While narrow tuning is optimal for ML decoding, a finite width, matched to statistics of the noise, is optimal with a NN decoder. This finding may explain why actual neural tuning curves have relatively wide tuning. Next, motivated by the observation that multi-scale codes involve non-trivial decoding, we examine a decoding algorithm based on belief propagation (BP) because BP promises certain gains in decoding efficiency. The decoding problem is first formulated as a subset selection problem on a graph and then approximately solved by BP. Even though the graph has many cycles, BP converges to a fixed point after few iterations. The mean square error of BP approaches to that of ML at high signal-to-noise ratios. Finally, using the multi-scale code, we propose a joint source-channel coding scheme that allows separate senders to transmit complementary information over additive Gaussian noise channels without cooperation. The receiver decodes one sender's codeword using the other as side information and achieves a lower distortion using the same number of transmissions. The proposed scheme offers a new framework to design distributed joint source-channel codes for continuous variables. / text

Le Québec est une immense province à l’intérieur de laquelle existe une grande diversité de conditions bioclimatiques et où les perturbations anthropiques et naturelles du couvert végétal sont nombreuses. À l’échelle provinciale, ces multiples facteurs interagissent pour sculpter la composition et la distribution des paysages. Les objectifs généraux de cette recherche visaient à explorer et comprendre la distribution spatiale des patrons des paysages du Québec, de même qu’à caractériser les patrons observés à partir d’images satellitaires. Pour ce faire, les patrons des paysages ont été quantifiés avec un ensemble complet d’indices calculés à partir d’une cartographie de la couverture végétale. Plusieurs approches ont été développées et appliquées pour interpréter les valeurs d’indices sur de vastes étendues et pour cartographier la distribution des patrons des paysages québécois. Les résultats ont révélé que les patrons de la végétation prédits par le Ministère des Ressources naturelles du Québec divergent des patrons de la couverture végétale observée. Ce mémoire dresse un portrait des paysages québécois et les synthétise de manière innovatrice, en plus de démontrer le potentiel d’utilisation des indices comme attributs biogéographiques à l’échelle nationale. / Quebec is a vast province in which bioclimatic conditions, land-uses and land-cover changes are highly diverse. At this scale, multiple drivers interact to have an impact on the composition and configuration of landscape patterns. The main objectives of this research were to explore and better understand the spatial distribution of landscape patterns across Quebec, and to characterize observed patterns as seen from satellite. To achieve these objectives, we quantified landscape patterns with an extensive set of metrics measured from a categorical land-cover map. We developed and applied several approaches to interpret metric values across large areas, and to map the distribution of Quebec landscape patterns. Results revealed that ecological subzones developed by the Ministère des Ressources naturelles et de la Faune were substantially inconsistent with observed land-cover patterns. This master thesis portrays and synthesizes Quebec landscapes in an innovative way, highlighting the considerable potential of use of landscape metrics for broad-scale biogeographic mapping.