In recent years, there has been an enormous growth of location-aware devices, such as GPS embedded cell phones, mobile sensors and radio-frequency identification tags. The age of combining sensing, processing and communication in one device, gives rise to a vast number of applications leading to endless possibilities and a realization of mobile Wireless Sensor Network (mWSN) applications. As computing, sensing and communication become more ubiquitous, trajectory privacy becomes a critical piece of information and an important factor for commercial success. While on the move, sensor nodes continuously transmit data streams of sensed values and spatiotemporal information, known as ``trajectory information". If adversaries can intercept this information, they can monitor the trajectory path and capture the location of the source node. This research stems from the recognition that the wide applicability of mWSNs will remain elusive unless a trajectory privacy preservation mechanism is developed. The outcome seeks to lay a firm foundation in the field of trajectory privacy preservation in mWSNs against external and internal trajectory privacy attacks. First, to prevent external attacks, we particularly investigated a context-based trajectory privacy-aware routing protocol to prevent the eavesdropping attack. Traditional shortest-path oriented routing algorithms give adversaries the possibility to locate the target node in a certain area. We designed the novel privacy-aware routing phase and utilized the trajectory dissimilarity between mobile nodes to mislead adversaries about the location where the message started its journey. Second, to detect internal attacks, we developed a software-based attestation solution to detect compromised nodes. We created the dynamic attestation node chain among neighboring nodes to examine the memory checksum of suspicious nodes. The computation time for memory traversal had been improved compared to the previous work. Finally, we revisited the trust issue in trajectory privacy preservation mechanism designs. We used Bayesian game theory to model and analyze cooperative, selfish and malicious nodes' behaviors in trajectory privacy preservation activities.

Discusses the development of WebSAT (Web-based systems administration tool), a computer network tool that allows systems administrators to create and delete accounts, disable and enable existing accounts, manage disk space conveniently, monitor the status of all network printers, and monitor network security. The WebSAT application was implemented using PHP, a server-side embedded scripting language, with a MySQL database.

This project occupies with the creation modulated signal generator in MATLAB. There are explicit basic princips of modulation PSK, M-QAM, princip of system with spread spektrum and princip of access OFDM and MC-CDMA. Next there are explicit basic parametres and possibilities configuration of conversion analog PCI card CompuGen 4302, which serves as D/A convertor of signals created by PC.

L'accompagnement en tant qu'auxiliaire de vie scolaire de Max, adolescent diagnostiqué autiste Asperger, fut le cadre de notre observation et de recherche clinique. Max a l'habitude d'aller toucher les cheveux d'autrui, ce que nous envisageons comme la manipulation d'un objet autistique atypique et une modalité particulière de rencontre de l'autre. À partir des travaux et des références de Francès Tustin, nous proposons une recherche à propos des fondements métapsychologiques de la notion d'objet autistique afin de déterminer quels sont les processus psychiques qui sous-tendent leur apparition. Nous étudions les cheveux en tant qu'éléments corporels symboliques et découvrons leur polysémie. Puis nous comparons la fonction et la manipulation des objets autistiques à celles des objets transitionnels, fétiches et self-objects, ce qui nous permet de penser des modalités spécifiques de relations d'objet. Notre hypothèse de la relation autistique à l'objet envisage l'émergence des objets autistiques en tant que témoins d'une relation à l'autre impossible et évitée, mais néanmoins recherchée et désirée. Cet évitement mènerait à l'intérêt pour un objet matériel. En outre, nous présentons le travail d'élaboration de notre accompagnement et de notre relation. Ainsi, l'élaboration du contre-transfert s'étaye sur une mise en dialogue de la situation d'observation et d'œuvres littéraires. Enfin, nous proposons une extension de la notion d'objet autistique et de la relation autistique à l'objet par une réflexion sur les nouvelles modalités de relation à l'autre que constituent les communications numériques. / The help I provided to Max as a school assistant was our context for the clinical research presented in this thesis. Max is a handicapped teenager diagnosed with Asperger's syndrome. He is used to touch other people's hair, which we consider as an atypical autistic object and a particular mode of meeting others. Based on Francès Tustin's work and research, we offer a study about the metapsychological foundations of the autistic object's concept to define the psychic processes underlying their appearance. We consider hair as a symbolic part of the body and found their polysemy. Then we compare the autistic object's fonction and manipulation with those of transitionnal objects, fetish objects and self-objects, which allows us to think of specific modes of object relations. Our hypothesis about autistic object relations considers the emergence of autistic objects as witnesses of an impossible and avoided relation to another, nevertheless sought and desired. This avoidance would lead to the interest for a material object. Moreover, we present the development of our study, in helping and relationship. Therefore the countertransference elaboration is based on dialogue between our observation situation and literary work. At last, we propose an extension of the autistic object concept and of the autistic object relation by reflecting on the new forms of communication with others, like digital communications.

Modern society places an increasingly high demand on data transmission. Much of that data transmission takes place through communication over the frequency spectrum. The channels on the spectrum are limited resources. Researchers realize that at certain times of day some channels are overloaded, while others are not being fully utilized. A spectrum management system may be beneficial to remedy this efficiency issue. One of the proposed systems, Cognitive Radio Network (CRN), has progressed over the years thanks to studies on a wide range of subjects, including geolocation, data throughput rate, and channel handoff selection algorithm, which provide fundamental support for the spectrum management system. To move CRN technology forward, in this thesis we propose a physical, scalable testbed for some of the extant CRN methodologies. This testbed integrates IEEE standards, FCC guidelines, and other TV band regulations to emulate CRN in real time. With careful component selections, we include sufficient operational functionalities in the system, while at the same time making sure it remains affordable. We evaluate the technical feasibility of the testbed by studying several simple CRN logics. When comparing a system with a selection table implemented to those with naive selection methods, there is more than a 60 percent improvement in the overall performance.

With unmanned aerial vehicles (UAVs), also known as drones, becoming readily available and affordable, applications for these devices have grown immensely. One type of application is the use of drones to fly over large areas and detect desired entities. For example, a swarm of drones could detect marine creatures near the surface of the ocean and provide users the location and type of animal found. However, even with the reduction in cost of drone technology, such applications result costly due to the use of custom hardware with built-in advanced capabilities. Therefore, the focus of this thesis is to compile an easily customizable, low-cost drone design with the necessary hardware for autonomous behavior, swarm coordination, and on-board object detection capabilities. Additionally, this thesis outlines the necessary network architecture to handle the interconnection and bandwidth requirements of the drone swarm. The drone on-board system uses a PixHawk 4 flight controller to handle flight mechanics, a Raspberry Pi 4 as a companion computer for general-purpose computing power, and a NVIDIA Jetson Nano Developer Kit to perform object detection in real-time. The implemented network follows the 802.11s standard for multi-hop communications with the HWMP routing protocol. This topology allows drones to forward packets through the network, significantly extending the flight range of the swarm. Our experiments show that the selected hardware and implemented network can provide direct point-to-point communications at a range of up to 1000 feet, with extended range possible through message forwarding. The network also provides sufficient bandwidth for bandwidth intensive data such as live video streams. With an expected flight time of about 17 minutes, the proposed design offers a low-cost drone swarm solution for mid-range aerial surveillance applications.

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In this exploratory thesis project, I consider what emerging approaches we can take as social scientists to showcase and critically engage self-representations of diasporic individuals, who often lack visibility and legibility within the dominant cultural archive. Filmmaking as a social research practice can provide rich audiovisual data, physical and social access to materials for nonacademics, and opportunities to document and share subjects' comments and settings without the limitations of transcription. This is especially salient in the emerging media landscape of Web 2.0, where digital communications technology applications (such as Facebook, Skype, and Snapchat) are accessible by a global audience, and can act as tools for cultural identity production by diasporic individuals. This project documents the experiences of several first- and second-generation Bangladeshi American immigrants in relation to digital communications technology advances within the past decade, for the purposes of collecting and sharing stories of diasporic individuals, offering a venue for self-expression through empathetic interviewing and collaborative oral history methods, and contributing to the American cultural archive in the context of emerging media and academic landscapes. The full project is comprised of this text document, alongside a short documentary film containing portions of audiovisual data from interviews which can be found at https://www.youtube.com/watch?v=Oh9puazpdrw. / Master of Science

Anomaly detection in Internet of Things (IoT) systems has become an increasingly popular field of research as the number of IoT devices proliferate year over year. Recent research often relies on machine learning algorithms to classify sensor readings directly. However, this approach leads to solutions being non-portable and unable to be applied to varying IoT platform infrastructure, as they are trained with sensor data specific to one configuration. Moreover, sensors generate varying amounts of non-standard data which complicates model training and limits generalization. This research focuses on addressing these problems in three ways a) the creation of an IoT Testbed which is configurable and parameterizable for dataset generation, b) the usage of system performance metrics as the dataset for training the anomaly classifier which ensures a fixed dataset size, and c) the application of Symbolic Aggregate Approximation (SAX) to encode patterns in system performance metrics which allows our trained Long Short-Term Memory (LSTM) model to classify anomalies agnostic to the underlying system configuration. Our devised IoT Testbed provides a lightweight setup for data generation which directly reflects some of the most pertinent components of Industry 4.0 pipelines including a MQTT Broker, Apache Kafka, and Apache Cassandra. Additionally, our proposed solution provides improved portability over state-of-the-art models while standardizing the required training data. Results demonstrate the effectiveness of utilizing symbolized performance metrics as we were able to achieve accuracies of 95.87%, 87.33%, and 87.47% for three different IoT system configurations. The latter two accuracies represent the model’s ability to be generalized to datasets generated from differing system configurations.

Artificial intelligence (AI) continues to gain traction and is increasingly reshaping the media and marketing communications field. While significant research has been conducted on the impact of AI in other fields, there is little empirical evidence on how AI is affecting the customer journey. The present study explored both organizations’ current use of AI tools and how customer perceptions about AI affect AI usage and adoption through the lens of diffusion of innovation theory. The research was conducted using mixed-method qualitative research. In-depth interviews and a case study content analysis were conducted to collect and analyze the data. The results suggest that consumer perceptions about AI impact levels of adoption when AI is recognized, but there appears to be cognitive dissonance regarding what constitutes AI and complicit acceptance of some of its benefits. Companies in different geographical locations have different levels of AI adoption along the diffusion of innovation stages.