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This paper describes the Sweaty II humanoid adult size robot trying to qualify for the RoboCup 2018 adult size humanoid competition. Sweaty came 2nd in RoboCup 2017 adult size league. The main characteristics of Sweaty are described in the Team Description Paper 2017. The improvements that have been made or are planned to be implemented for RoboCup 2018 are described in this paper.
Covert- and side-channels as well as techniques to establish them in cloud computing are in focus of research for quite some time. However, not many concrete mitigation methods have been developed and even less have been adapted and concretely implemented by cloud providers. Thus, we recently conceptually proposed C 3 -Sched a CPU scheduling based approach to mitigate L2 cache covert-channels. Instead of flushing the cache on every context switch, we schedule trusted virtual machines to create noise which prevents potential covert-channels. Additionally, our approach aims on preserving performance by utilizing existing instead of artificial workload while reducing covert-channel related cache flushes to cases where not enough noise has been achieved. In this work we evaluate cache covert-channel mitigation and performance impact of our integration of C 3 -Sched in the XEN credit scheduler. Moreover, we compare it to naive solutions and more competitive approaches.
In a Semi-autonomic cloud auditing architecture we weaved in privacy enhancing mechanisms [15] by applying the public key version of the Somewhat homomorphic encryption (SHE) scheme from [4]. It turns out that the performance of the SHE can be significantly improved by carefully deriving relevant crypto parameters from the concrete cloud auditing use cases for which the scheme serves as a privacy enhancing approach. We provide a generic algorithm for finding good SHE parameters with respect to a given use case scenario by analyzing and taking into consideration security, correctness and performance of the scheme. Also, to show the relevance of our proposed algorithms we apply it to two predominant cloud auditing use cases.
In this paper, we present a frame synchronization method which consists of the non-orthogonal superposition of a synchronization sequence and the data. We derive the optimum detection criterion and compare it to the classical sequential concatenation of synchronization and data sequences. Computer simulations confirm the benefits of the non-orthogonal allocation for the case of short frames, which makes this technique particularly suited for the increasingly important regime of low latency and ultra- reliable communication.
This paper deals with the detection and segmentation of clouds on high-dynamic-range (HDR) images of the sky as well as the calculation of the position of the sun at any time of the year. In order to predict the movement of clouds and the radiation of the sun for a short period of time, the clouds thickness and position have to be known as precisely as possible. Consequently, the segmentation algorithm has to provide satisfactory results regardless of different weather, illumination and climatic conditions. The principle of the segmentation is based on the classification of each pixel as a cloud or as a sky. This classification is usually based on threshold methods, since these are relatively fast to implement and show a low computational burden. In order to predict if and when the sun will be covered by clouds, the position of the sun on the images has to be determined. For this purpose, the zenith and azimuth angles of the sun are determined and converted into XY coordinates.
Solar irradiance prediction is vital for the power management and the cost reduction when integrating solar energy. The study is towards a ground image based solar irradiance prediction which is highly dependent on the cloud coverage. The sky images are collected by using ground based sky imager (fisheye lens). In this work, different algorithms for cloud detection being a preparation step for their segmentation are compared.
The fisheye camera has been widely studied in the field of ground based sky imagery and robot vision since it can capture a wide view of the scene at one time. However, serious image distortion is a major drawback hindering its wider use. To remedy this, this paperproposes a lens calibration and distortion correction method for detecting clouds and forecasting solar radiation. Finally, the radial distortion of the fisheye image can be corrected by incorporating the estimated calibration parameters. Experimental results validate the effectiveness of the proposed method.