Prediction-enhanced Routing in Disruption-tolerant Satellite Networks

This thesis introduces a framework for enhancing DTN (Delay-/Disruption-Tolerant Networking) routing in dynamic LEO satellite constellations based on the prediction of contacts. The solution is developed with a clear focus on the requirements imposed by the 'Ring Road' use case, mandating a concept for dynamic contact prediction and its integration into a state-of-the-art routing approach. The resulting system does not restrict possible applications to the 'Ring Road,' but allows for flexible adaptation to further use cases. A thorough evaluation shows that employing proactive routing in concert with a prediction mechanism offers significantly improved performance when compared to alternative opportunistic routing techniques

The presentation and evaluation of a unit on the structure of matter for twelfth-grade college chemistry class

Thesis (M.A.)--Boston University, 195

Centralized wide area damping controller for power system oscillation problems

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.In this paper, three different centralized control designs that vary on complexity are presented to damp inter-area oscillations in large power systems. All the controls are based on phasor measurements. The first two proposed architectures use simple proportional gains that consider availability of measurements from different areas of the system and fulfill different optimization functions. The third controller is based on a more sophisticated Linear Quadratic Gaussian approach which requires access to the state space model of the system under investigation. The novelty of the proposed scheme resides in designing a single control to command the most influence group of machines in the system. To illustrate the effectiveness of the proposed algorithms, simulations results in the IEEE New England model are presented

Online coherency identification and stability condition for large interconnected power systems using an unsupervised data mining technique

Identification of coherent generators and the determination of the stability system condition in large interconnected power system is one of the key steps to carry out different control system strategies to avoid a partial or complete blackout of a power system. However, the oscillatory trends, the larger amount data available and the non-linear dynamic behaviour of the frequency measurements often mislead the appropriate knowledge of the actual coherent groups, making wide-area coherency monitoring a challenging task. This paper presents a novel online unsupervised data mining technique to identify coherent groups, to detect the power system disturbance event and determine status stability condition of the system. The innovative part of the proposed approach resides on combining traditional plain algorithms such as singular value decomposition (SVD) and K -means for clustering together with new concept based on clustering slopes. The proposed combination provides an added value to other applications relying on similar algorithms available in the literature. To validate the effectiveness of the proposed method, two case studies are presented, where data is extracted from the large and comprehensive initial dynamic model of ENTSO-E and the results compared to other alternative methods available in the literature

Asymptotic performance of port-based teleportation

Quantum teleportation is one of the fundamental building blocks of quantum Shannon theory. While ordinary teleportation is simple and efficient, port-based teleportation (PBT) enables applications such as universal programmable quantum processors, instantaneous non-local quantum computation and attacks on position-based quantum cryptography. In this work, we determine the fundamental limit on the performance of PBT: for arbitrary fixed input dimension and a large number $N$ of ports, the error of the optimal protocol is proportional to the inverse square of $N$. We prove this by deriving an achievability bound, obtained by relating the corresponding optimization problem to the lowest Dirichlet eigenvalue of the Laplacian on the ordered simplex. We also give an improved converse bound of matching order in the number of ports. In addition, we determine the leading-order asymptotics of PBT variants defined in terms of maximally entangled resource states. The proofs of these results rely on connecting recently-derived representation-theoretic formulas to random matrix theory. Along the way, we refine a convergence result for the fluctuations of the Schur-Weyl distribution by Johansson, which might be of independent interest.Comment: 68 pages, 4 figures; comments welcome! v2: minor fixes, added plots comparing asymptotic expansions to exact formulas, code available at https://github.com/amsqi/port-base

Learning Models for Following Natural Language Directions in Unknown Environments

Natural language offers an intuitive and flexible means for humans to communicate with the robots that we will increasingly work alongside in our homes and workplaces. Recent advancements have given rise to robots that are able to interpret natural language manipulation and navigation commands, but these methods require a prior map of the robot's environment. In this paper, we propose a novel learning framework that enables robots to successfully follow natural language route directions without any previous knowledge of the environment. The algorithm utilizes spatial and semantic information that the human conveys through the command to learn a distribution over the metric and semantic properties of spatially extended environments. Our method uses this distribution in place of the latent world model and interprets the natural language instruction as a distribution over the intended behavior. A novel belief space planner reasons directly over the map and behavior distributions to solve for a policy using imitation learning. We evaluate our framework on a voice-commandable wheelchair. The results demonstrate that by learning and performing inference over a latent environment model, the algorithm is able to successfully follow natural language route directions within novel, extended environments.Comment: ICRA 201

Quantifying cyanide in water and foodstuff using corrin-based CyanoKit technologies and a smartphone

This paper describes the detection of endogenous cyanide using corrin-based CyanoKit technologies in combination with a smartphone readout device. When applied to the detection of cyanide in water, this method demonstrates high repeatability and discriminative power with a limit of blank of 0.074 ppm and an instrument limit of detection of 0.13 ppm. Quantification of endogenous cyanide in cassava and bitter almond extracts with the smartphone readout is in excellent agreement with independent analyses using traditional spectrophotometric detection. The prototype system objectively detects levels of cyanide with a high granularity at the point-of-need and does not depend on large, heavy and expensive instrumentation. The methodology has the potential to be easily adopted in resource limited situations and low-income countries

Evaluating an online-game intervention to prevent violent extremism

As gaming and gamification play an increasingly important role in recruitment processes and radicalisation, there is an urgent need for evidence-based research in this field. One aspect is the use of games and gamification in prevention work. The article presents a project in which an online-game against extremism was developed and focuses on its evaluation using a pre-post design and a combination of quantitative and qualitative methods. The aim of the game is to educate young people about radicalisation processes in order to increase their resilience. The pre-post comparison showed that young people changed their attitude towards extremist narratives after playing the game: they agreed significantly less with statements that referred to extremist narratives, e.g. legitimising violence or spreading conspiracy theories. When they played the game in the course of a workshop, they also showed lower approval rates for authoritarian attitudes afterwards. The self-assessment of their learnings was consistently high, whereby even greater effects could be observed for those who had played the game in the course of a workshop. Despite some limitations in data collection due to the COVID-19 pandemic, the evaluation provides interesting insights into the impact of the game on the prevention of radicalisation