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This report describes the development of a computer model for the design of a laser seeker optical system. A laser seeker is a device that detects pulsed laser energy. The computer model is configured to design the seeker optics based on the following performance criteria: Sensitivity to laser energy, which can be related to target acquisition range; optical field of view; and seeker optics cross section area. The design is defined by four variables and a set of fixed parameters, and is configured using computer optimization with both a direct search and a random search being used. A superior design is selected from comparison of many sets of variables based on the value of an objective function made up of some of all of the performance criteria listed above and additional penalty factors applied for design constraint violations. The computer model contains design blocks for the detector, the preamplifier, and the optical elements of the seeker. There is also a computer ray trace routine to evaluate optical performance. The model was run with roar different objective functions, and the resulting seeker designs were analyzed. A detail listing of the computer program is contained in Appendix B.

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Yes / This paper proposes a new solution to improve the performance parameters of on-chip antenna designs on standard CMOS silicon (Si.) technology. The proposed method is based on applying the metasurface technique and exciting the radiating elements through coupled feed mechanism. The on-chip antenna is constructed from three layers comprising Si.-GND-Si. layers, so that the ground (GND) plane is sandwiched between two Si. layers. The silicon and ground-plane layers have thicknesses of 20μm and 5μm, respectively. The 3×3 array consisting of the asterisk-shaped radiating elements has implemented on the top silicon layer by applying the metasurface approach. Three slot lines in the ground-plane are modelled and located directly under the radiating elements. The radiating elements are excited through the slot-lines using an open-circuited microstrip-line constructed on the bottom silicon layer. The proposed method to excite the structure is based on the coupled feeding mechanism. In addition, by the proposed feeding method the on-chip antenna configuration suppresses the substrate losses and surface-waves. The antenna exhibits a large impedance bandwidth of 60GHz from 0.5THz to 0.56THz with an average radiation gain and efficiency of 4.58dBi and 25.37%, respectively. The proposed structure has compact dimensions of 200×200×45μm3. The results shows that, the proposed technique is therefore suitable for on-chip antennas for applications in system-on-chip for terahertz (THz) integrated circuits. / Innovation programme under grant agreement H2020-MSCA-ITN-2016 SECRET-722424; UK Engineering and Physical Sciences Research Council (EPSRC) under grant EP/E0/22936/1.

Recent advances in large graph-based models have shown great performance in a variety of tasks, including node classification, link prediction, and influence modeling. However, these graph-based models struggle to capture high-order relations and interactions among entities effectively, leading them to underperform in many real-world scenarios. This thesis focuses on analyzing networks using hypergraphs for detection, classification, and prediction methods in social media-related problems. In particular, we study four specific applications with four proposed novel methods: detecting topic-specific influential users and tweets via hypergraphs; detecting spatiotemporal, topic-specific, influential users and tweets using hypergraphs; augmenting data in hypergraphs to mitigate class imbalance issues; and introducing a novel hypergraph convolutional network model designed for the multiclass classification of mental health advice in Arabic tweets. For the first method, existing solutions for influential user detection did not consider topics that could produce incorrect results and inadequate performance in that task. The proposed contributions of our work include: 1) Developing a hypergraph framework that detects influential users and tweets. 2) Proposing an effective topic modeling method for short texts. 3) Performing extensive experiments to demonstrate the efficacy of our proposed framework. For the second method, we extend the first method by incorporating spatiotemporal information into our solution. Existing influencer detection methods do not consider spatiotemporal influencers in social media, although influence can be greatly affected by geolocation and time. The contributions of our work for this task include: 1) Proposing a hypergraph framework that spatiotemporally detects influential users and tweets. 2) Developing an effective topic modeling method for short texts that geographically provides the topic distribution. 3) Designing a spatiotemporal topic-specific influencer user ranking algorithm. 4) Performing extensive experiments to demonstrate the efficacy of our proposed framework. For the third method, we address the challenge of bot detection on social media platform X, where there's an inherent imbalance between genuine users and bots, a key factor leading to biased classifiers. Our approach leverages the rich structure of hypergraphs to represent X users and their interactions, providing a novel foundation for effective bot detection. The contributions of our work include: 1) Introducing a hypergraph representation of the X platform, where user accounts are nodes and their interactions form hyperedges, capturing the intricate relationships between users. 2) Developing HyperSMOTE to generate synthetic bot accounts within the hypergraph, ensuring a balanced training dataset while preserving the hypergraph's structure and semantics. 3) Designing a hypergraph neural network specifically for bot detection, utilizing node and hyperedge information for accurate classification. 4) Conducting comprehensive experiments to validate the effectiveness of our methods, particularly in scenarios with pronounced class imbalances. For the fourth method, we introduce a Hypergraph Convolutional Network model for classifying mental health advice in Arabic tweets. Our model distinguishes between valid and misleading advice, leveraging high-order word relations in short texts through hypergraph structures. Our extensive experiments demonstrate its effectiveness over existing methods. The key contributions of our work include: 1) Developing a hypergraph-based model for short text multiclass classification, capturing complex word relationships through hypergraph convolution. 2) Defining four types of hyperedges to encapsulate local and global contexts and semantic similarities in our dataset. 3) Conducting comprehensive experiments in which the proposed model outperforms several baseline models in classifying Arabic tweets, demonstrating its superiority. For the fifth method, we extended our previous Hypergraph Convolutional Network (HCN) model to be tailored for sarcasm detection across multiple low-resource languages. Our model excels in interpreting the subtle and context-dependent nature of sarcasm in short texts by exploiting the power of hypergraph structures to capture complex, high-order relationships among words. Through the construction of three hyperedge types, our model navigates the intricate semantic and sentiment differences that characterize sarcastic expressions. The key contributions of our research are as follows: 1) A hypergraph-based model was adapted for the task of sarcasm detection in five short low-resource language texts, allowing the model to capture semantic relationships and contextual cues through advanced hypergraph convolution techniques. 2) Introducing a comprehensive framework for constructing hyperedges, incorporating short text, semantic similarity, and sentiment discrepancy hyperedges, which together enrich the model's ability to understand and detect sarcasm across diverse linguistic contexts. 3) The extensive evaluations reveal that the proposed hypergraph model significantly outperforms a range of established baseline methods in the domain of multilingual sarcasm detection, establishing new benchmarks for accuracy and generalizability in detecting sarcasm within low-resource languages. / Doctor of Philosophy / In the digital era, social media platforms are not just tools for communication but vast networks where billions of messages, opinions, and pieces of advice are exchanged every day. Navigating through this massive data to identify influential content, detect misleading information, or understand subtle expressions like sarcasm presents a significant challenge. Traditional methods often struggle to grasp the complex relationships and nuances embedded within the data. This dissertation introduces innovative approaches using hypergraphs—a type of network representation that captures complex interactions more effectively than traditional network models. The research presented explores six distinct applications of hypergraphs in social media analysis, each addressing a unique challenge: 1) The identification of influential users and content specific to certain topics, extending beyond general influence to understand context-driven impact. 2) The incorporation of time and location to detect influential content, recognizing that relevance can significantly vary by these factors. 3) Addressing the issue of imbalanced data in bot detection, where genuine user interactions are overwhelmed by automated accounts, through novel data augmentation techniques. 4) Classifying mental health advice in Arabic tweets to differentiate between valid and misleading information is crucial, given the subject's sensitivity. 5) Detecting sarcasm in low-resource languages is particularly challenging due to its subtle and context-dependent nature. 6) Predicting metro passenger ridership at each metro station is challenging due to the constantly evolving nature of the network and passengers going in and out of stations. This work contributes to the field by demonstrating the capability of hypergraphs to provide more fine-grained and context-aware analyses of social media content. Through extensive experimentation, it showcases the effectiveness of these methods in improving detection, classification, and prediction tasks. The findings not only advance our technical understanding and capabilities in social media analysis but also have practical implications for enhancing the reliability and usefulness of information disseminated on these platforms.

The object of this paper is to present the properties and some of the applications of the Elliptic Integrals.

Mémoire numérisé par la Direction des bibliothèques de l'Université de Montréal.

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