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Finding clusters in high dimensional data is a challenging research problem. Subspace clustering algorithms aim to find clusters in all possible subspaces of the dataset where, a subspace is the subset of dimensions of the data. But exponential increase in the number of subspaces with the dimensionality of data renders most of the algorithms inefficient as well as ineffective. Moreover, these algorithms have ingrained data dependency in the clustering process, thus, parallelization becomes difficult and inefficient. SUBSCALE is a recent subspace clustering algorithm which is scalable with the dimensions and contains independent processing steps which can be exploited through parallelism. In this paper, we aim to leverage, firstly, the computational power of widely available multi-core processors to improve the runtime performance of the SUBSCALE algorithm. The experimental evaluation has shown linear speedup. Secondly, we are developing an approach using graphics processing units (GPUs) for fine-grained data parallelism to accelerate the computation further. First tests of the GPU implementation show very promising results.
Passive hybridization of battery cell and photovoltaic cell: modeling and experimental validation
(2017)
Climate change and resultant scarcity of water are becoming major challenges for countries around the world. With the advent of Wireless Sensor Networks (WSN) in the last decade and a relatively new concept of Internet of Things (IoT), embedded systems developers are now working on designing control and automation systems that are lower in cost and more sustainable than the existing telemetry systems for monitoring. The Indus river basin in Pakistan has one of the world's largest irrigation systems and it is extremely challenging to design a low-cost embedded system for monitoring and control of waterways that can last for decades. In this paper, we present a hardware design and performance evaluation of a smart water metering solution that is IEEE 802.15.4-compliant. The results show that our hardware design is as powerful as the reference design, but allows for additional flexibility both in hardware and in firmware. The indigenously designed solution has a power added efficiency (PAE) of 24.7% that is expected to last for 351 and 814 days for nodes with and without a power amplifier (PA). Similarly, the results show that a broadband communication (434 MHz) over more than 3km can be supported, which is an important stepping stone for designing a complete coverage solution of large-scale waterways.
The need to measure basic aerosol parameters has increased dramatically in the last decade. This is due mainly to their harmful effect on the environment and on public health. Legislation requires that particle emissions and ambient levels, workplace particle concentrations and exposure to them are measured to confirm that the defined limits are met and the public is not exposed to harmful concentrations of aerosols.
Gaps in basic math knowledge are among the biggest obstacles to a successful start in university. Students starting their studies in STEM disciplines display significant diversity, “math anxiety” is a widespread phenomenon, and the transition to a self-determined way of studying presents a huge challenge. Universities offer support measures such as preparatory courses. Over the years, Offenburg University realized that with increased diversity, traditional ways of teaching in front of the class have become inefficient. The majority of the students remained inactive and just listened to the teachers’ explanations and the few active participants’ answers.
Since 2013 our new course concept fosters a shift from teaching to active learning on a large scale, involving several hundred participants of our on-site preparatory math courses. This switch to broad active practicing, however, must go hand in hand with providing individual support for an increasingly diverse student body. Meanwhile students bring along their mobile devices, and the training App TeachMatics serves as a facilitator. The course concept has been very well received by both students and teachers.
The aim of the smart grid is to achieve more efficient, distributed and secure supply of energy over the traditional power grid by using a bidirectional information flow between the grid agents (e.g. generator node, customer). One of the key optimization problems in smart grid is to produce power among generator nodes with a minimum cost while meeting the customer demand, known as Economic Dispatch Problem (EDP). In recent years, many distributed approaches to solve EDP have been proposed. However, protecting the privacy-sensitive data of individual generator nodes has been largely overlooked in the existing solutions. In this work, we show an attack against an existing auction-based EDP protocol considering a non-colluding semi-honest adversary. We briefly introduce our approach to a practical privacy-preserving EDP solution as our work in progress.
Our university carries out various research projects. Among others, the project Schluckspecht is an interdisciplinary work on different ultra-efficient car concepts for international contests. Besides the engineering work, one part of the project deals with real-time data visualization. In order to increase the efficiency of the vehicle, an online monitoring of the runtime parameters is necessary. The driving parameters of the vehicle are transmitted to a processing station via a wireless network connection. We plan to use an augmented reality (AR) application to visualize different data on top of the view of the real car. By utilizing a mobile Android or iOS device a user can interactively view various real-time and statistical data. The car and its components are meant to be augmented by various additional information, whereby that information should appear at the correct position of the components. An engine e.g. could show the current rpm and consumption values. A battery on the other hand could show the current charge level. The goal of this paper is to evaluate different possible approaches, their suitability and to expand our application to other projects at our university.
In the present work, nonlinearities in temperature compensating (TC) SAW devices are investigated. The materials used are LiNbO₃-rot128YX as the substrate and Copper electrodes covered with a SiO₂-layer as the compensating layer. In order to understand the role of these materials for the nonlinearities in such acoustic devices, a FEM simulation model in combination with a perturbation approach is applied. The nonlinear tensor data of the different materials involved in TC-SAW devices have been taken from literature, but were partially modified to fit experimental data by introducing scaling factors. An effective nonlinearity constant is determined by comparison of nonlinear P-matrix simulations to IMD3 measurements of test filters. By employing these constants in nonlinear periodic P-matrix simulations a direct comparison to nonlinear periodic FEM-simulations yields the scaling factors for the material used. Thus, the contribution of different materials to the nonlinear behavior of TC-SAW devices is obtained and the role of metal electrodes is discussed in detail.