Security is always a big problem in IoT (internet of things),when it comes to IoT, there must have cloud computing because many devices in IoT are small embedded devices and they don’t always have enough power to finish some complex calculations. Then, they need to take advantage of a third party system especially cloud at present to finish some operations, but the cloud is not safe enough now, in which some important and private information may be leaked, then people introduce homomorphic encryption which can do calculation on encrypted data. To meet the modern needs for random calculations in which the operation can have random times’ addition and multiplication, researchers are trying to make fully homomorphic encryption practical. So in my thesis, I would choose one fully homomorphic encryption scheme to implement a detailed IoT scenario using some IoT devices such as laptop and raspberry pi. Then I would use performance measurements such as response time calculations to do the performance evaluation such as effectiveness and scalability for this technique. Finally, I find some relationship between different parameters and response time, and also effectiveness, scalability in results and conclusion part.

This thesis initiates the study of the MAX-CUT problem in fully dynamic graphs. Given a graph $G=(V,E)$, we present the first fully dynamic algorithms to maintain a $\frac{1}{2}$-approximate cut in sublinear update time under edge insertions and deletions to $G$. Our results include the following deterministic algorithms: i) an $O(\Delta)$ \textit{worst-case} update time algorithm, where $\Delta$ denotes the maximum degree of $G$ and ii) an $O(m^{1/2})$ amortized update time algorithm where $m$ denotes the maximum number of edges in $G$ during any sequence of updates. \\ \indent We also give the following randomized algorithms when edge updates come from an oblivious adversary: i) a $\tilde{O}(n^{2/3})$ update time algorithm\footnote{Throughout this thesis, $\tilde{O}$ hides a $O(\text{polylog}(n))$ factor.} to maintain a $\frac{1}{2}$-approximate cut, and ii) a $\min\{\tilde{O}(n^{2/3}), \tilde{O}(\frac{n^{{3/2}+2c_0}}{m^{1/2}})\}$ worst case update time algorithm which maintains a $(\frac{1}{2}-o(1))$-approximate cut for any constant $c_0>0$ with high probability. The latter algorithm is obtained by designing a fully dynamic algorithm to maintain a sparse subgraph with sublinear (in $n$) maximum degree which approximates all large cuts in $G$ with high probability. / Graduate

<p>To correctly reproduce the appearance of different objects in computer graphics applications, numerous lighting models have been proposed over the past several decades. These models are among the most important components in the modern graphics pipeline since they decide the final pixel color shown in the generated images. More physically valid parameters and functions have been introduced into recent models. These parameters expanded the range of materials that can be represented and made virtual scenes more realistic, but they also made the lighting models more complex and dependent on measured data.</p> <p>Artificial neural networks, or neural networks are famous for their ability to deal with complex data and to approximate arbitrary functions. They have been adopted by many data-driven approaches for computer graphics and proven to be effective. Furthermore, neural networks have also been used by the artists for creative works and proven to have the ability of supporting creation of visual effects, animation and computational arts. Therefore, it is reasonable to consider artificial neural networks as potential tools for representing lighting models. Since shaders are used for general-purpose computing, neural networks can be further combined with modern graphics pipeline using shader implementation. </p> <p>In this research, the possibilities of shader-based neural networks to be used as an alternative to traditional lighting models are explored. Fully connected neural networks are implemented in fragment shader to reproduce lighting results in the graphics pipeline, and trained in compute shaders. Implemented networks are proved to be able to approximate mathematical lighting models. In this thesis, experiments are described to prove the ability of shader-based neural networks, to explore the proper network architecture and settings for different lighting models. Further explorations of possibilities of manually editing parameters are also described. Mean-square errors and runtime are taken as measurements of success to evaluate the experiments. Rendered images are also reported for visual comparison and evaluation.</p>

In a wireless system, the frequency-reconfigurable RF components are highly desired because one such component can replace multiple RF components to reduce the size, cost, and weight. Typically, the reconfigurable RF components are realized using capacitive varactors, PIN diodes, or MEMS switches. Most of these RF switches are expensive, rigid, and need tedious soldering steps, which are not suitable for futuristic flexible and wearable applications. Therefore, there is a need to have a solution for low cost, flexible, and easy to integrate RF switches. All the above-mentioned issues can be alleviated if these switches can be simply printed at the place of interest. In this work, we have demonstrated vanadium dioxide (VO2) based RF switches that have been realized through additive manufacturing technologies (inkjet printing and screen printing), which dramatically brings the cost down to a few cents. Also, no soldering or additional attachment step is required as the switch can be simply printed on the RF component. The printed VO2 switches are configured in two types (shunt configuration and series configuration) where both types have been characterized with two activation mechanisms (thermal activation and electrical activation) up to 40 GHz. The measured insertion loss of 1-3 dB, isolation of 20-30 dB, and switching speed of 400 ns are comparable to other non-printed and expensive RF switches. As an application for the printed VO2 switches, a fully printed frequency reconfigurable filter has also been designed in this work. An open-ended dual-mode resonator with meandered loadings has been co-designed with the VO2 switches, resulting in a compact filter with decent insertion loss of 2.6 dB at both switchable frequency bands (4 GHz and 3.75 GHz). Moreover, the filter is flexible and highly immune to the bending effect, which is essential for wearable applications. Finally, a multi-parameter (switch thickness, width, length, temperature) model has been established using a customized artificial neural network (ANN) to achieve a faster simulation speed. The optimized model’s average error and correlation coefficient are only 0.0003 and 0.9905, respectively, which both indicate the model’s high accuracy.

<div>Sorghum, which is grown internationally as a cereal crop that is robust to heat, drought, and disease, has numerous applications for food, forage, and biofuels. When monitoring the growth stages of sorghum, or phenotyping specific traits for plant breeding, it is important to identify and monitor the panicles in the field due to their impact relative to grain production. Several studies have focused on detecting panicles based on data acquired by RGB and multispectral remote sensing technologies. However, few experiments have included hyperspectral data because of its high dimensionality and computational requirements, even though the data provide abundant spectral information. Relative to analysis approaches, machine learning, and specifically deep learning models have the potential of accommodating the complexity of these data. In order to detect panicles in the field with different physical characteristics, such as colors and shapes, very high spectral and spatial resolution hyperspectral data were collected with a wheeled-based platform, processed, and analyzed with multiple extensions of the VGG-16 Fully Convolutional Network (FCN) semantic segmentation model.</div><div><br></div><div>In order to have correct positioning, orthorectification experiments were also conducted in the study to obtain the proper positioning of the image data acquired by the pushbroom hyperspectral camera at near range. The scale of the DSM derived from LiDAR that was used for orthorectification of the hyperspectral data was determined to be a critical issue, and the application of the Savitzky-Golay filter to the original DSM data was shown to contribute to the improved quality of the orthorectified imagery.</div><div><br></div><div>Three tuned versions of the VGG-16 FCN Deep Learning architecture were modified to accommodate the hyperspectral data: PCA&FCN, 2D-FCN, and 3D-FCN. It was concluded that all the three models can detect the late season panicles included in this study, but the end-to-end models performed better in terms of precision, recall, and the F-score metrics . Future work should focus on improving annotation strategies and the model architecture to detect different panicle varieties and to separate overlapping panicles based on an adequate quantities of training data acquired during the flowering stage.</div>

El texto completo de este trabajo no está disponible en el Repositorio Académico UPC por restricciones de la casa editorial donde ha sido publicado. / The fully actuated signal control detects the pedestrian density using sensors and, according to that, it prioritizes pedestrians crossing. One major problem, worldwide, is using fixed time traffic light as a traffic regulator at intersections with high pedestrian and vehicular volume. Lima is no exception, continuing to use this kind of traffic lights completely harms pedestrian accessibility, it increases their waiting and crossing times, it also affects road safety and service levels at the structures. The proposal on this article is to design a fully actuated signal control using logical controls that are able to perceive the pedestrian density on the refuge islands, making everything more accessible. In order to do this, a study to identify the pedestrian and vehicle volume was conducted on the Lima Panamerican highway. There was a total of 7506 pedestrians during rush hour, proving there is a large amount of people at the intersection at that time. Thereby, by using the VisVap module of the Vissim, the study managed to simulate and validate the priority control required. All in all, the results showed a remarkable improvement, the pedestrian crossing time was reduced by 6.84% and the service level of the intersection went from E to D.

京都大学 / 0048 / 新制・課程博士 / 博士(情報学) / 甲第20511号 / 情博第639号 / 新制||情||111(附属図書館) / 京都大学大学院情報学研究科社会情報学専攻 / (主査)教授 石田 亨, 教授 中村 佳正, 教授 岡部 寿男, 岡本 龍明 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM

No description available.

The fully Bayesian estimation via the use of Markov chain Monte Carlo (MCMC) techniques has become popular for estimating item response theory (IRT) models. The current development of MCMC includes two major algorithms: Gibbs sampling and the No-U-Turn sampler (NUTS). While the former has been used with fitting various IRT models, the latter is relatively new, calling for the research to compare it with other algorithms. The purpose of the present study is to evaluate the performances of these two emerging MCMC algorithms in estimating two two-parameter logistic (2PL) IRT models, namely, the 2PL unidimensional model and the 2PL multi-unidimensional model under various test situations. Through investigating the accuracy and bias in estimating the model parameters given different test lengths, sample sizes, prior specifications, and/or correlations for these models, the key motivation is to provide researchers and practitioners with general guidelines when it comes to estimating a UIRT model and a multi-unidimensional IRT model. The results from the present study suggest that NUTS is equally effective as Gibbs sampling at parameter estimation under most conditions for the 2PL IRT models. Findings also shed light on the use of the two MCMC algorithms with more complex IRT models.

We are concerned with the so-called asymptotic Plateau problem in hyperbolic space. That is, to prove the existence of hypersurfaces in hyperbolic space whose principal curvatures satisfy a general curvature relation and has a precribed asymptotic boundary at infinity. In this thesis, by following the method of Bo Guan, Joel Spruck and their collaborators, we solve the problem with the aid of an additional assumption. In particular, our result applies to hypersurfaces whose principal curvatures lie in the k-th Garding cone and has constant (k,k-1) curvature quotient. / Thesis / Master of Science (MSc)