SESAME-S: Semantic Smart Home System for Energy Efficiency

As the urgent need for efficient and sustainable energy usage becomes ever more apparent, interest in Smart Homes is on the rise. The SESAME-S project (SEmantic SmArt Metering – Services for Energy Efficient Houses) uses semantically linked data to actively assist end-consumers in making well-informed decisions and controlling their energy consumption. By integrating smart metering and home automation functionality, SESAME-S works to effectively address the potential mass market of end-consumers with an easily customizable solution that can be widely implemented in domestic or business environments, with expected savings of over 20?% from the total energy bill. The developed system is a basis for conceptualizing, demonstrating, and evaluating a variety of innovative end-consumer services and their user interface paradigms. In this paper, we present the SESAME-S system as a whole and discuss the semantically enabled services, demonstrating that such systems may have broad acceptance in the future. The data obtained through such systems will be invaluable for future global energy-efficiency strategies and businesses.     

LMI-based H ∞-optimal control with transients

In this article, the worst-case norm of the regulated output over all exogenous signals and initial states as a performance measure of the system is characterised in terms of linear matrix inequalities (LMIs). Optimal time-invariant state- and output-feedback controllers are synthesised as minimising this performance measure. The essential role in this synthesis plays a weighting matrix reflecting the relative importance of the uncertainty in the initial state contrary to the uncertainty in the exogenous signal. H _∞-optimal control with transients is shown to be actually a trade-off between H _∞-control, being optimal under unknown exogenous disturbances and zero initial state, and γ-control, being optimal under zero exogenous signal and unknown initial conditions, if and only if the weighting matrix satisfies a fundamental inequality. If this inequality is met, the performance measure is achieved and the explicit formulae for the worst-case disturbance and initial state are provided. If this inequality fails, the performance measure coincides with the H _∞-norm and the trade-off gets broken.     

A combined technique for amplifier oriented small‐signal noise model extraction

Based on the earlier experimental investigation of the existing GaAs pHEMT small‐signal modeling approaches and their applicability to different manufacturing processes, a combined automatic small‐signal noise model extraction technique, suitable for design of low‐noise and buffer amplifiers is proposed. The technique is based on the usage of measured S‐parameters of passive test structures and S‐parameters of the transistor in cold modes. Expressions are given for extraction of the intrinsic parameters of an equivalent circuit using linear regression. It is shown that the application of the proposed method allows extracting a small‐signal GaAs pHEMT model both in the probe‐tip reference planes and at on‐wafer calibration planes. The moving average algorithm was applied for preprocessing the results of measurements of the 50 Ohm noise figure during extraction of the noise model. The results of S‐parameters and noise figure simulation agree well with the measurements. The new technique was implemented as a plugin in a commercial EDA tool and enables to derive a ready‐to use small‐signal noise model from measured S‐parameters and 50 Ohm noise figure of a 0.15 μm GaAs pHEMT.     

Accuracy estimate and optimization techniques for SimRank computation

The measure of similarity between objects is a very useful tool in many areas of computer science, including information retrieval. SimRank is a simple and intuitive measure of this kind, based on a graph-theoretic model. SimRank is typically computed iteratively, in the spirit of PageRank. However, existing work on SimRank lacks accuracy estimation of iterative computation and has discouraging time complexity. In this paper, we present a technique to estimate the accuracy of computing SimRank iteratively. This technique provides a way to find out the number of iterations required to achieve a desired accuracy when computing SimRank. We also present optimization techniques that improve the computational complexity of the iterative algorithm from O(n ⁴) in the worst case to min(O(nl), O(n ³/ log₂ n)), with n denoting the number of objects, and l denoting the number object-to-object relationships. We also introduce a threshold sieving heuristic and its accuracy estimation that further improves the efficiency of the method. As a practical illustration of our techniques, we computed SimRank scores on a subset of English Wikipedia corpus, consisting of the complete set of articles and category links.     

Survey on hierarchical routing schemes in?��flat�� distributed hash tables

The first generation of DHT designs offered a completely flat structure of the key space that is randomly partitioned among participating DHT nodes. That has certain advantages, for example, even distribution of workload among nodes. On the other hand, grouping keys under a single authority or achieving latency guarantees for queries is difficult. To address these shortcomings, various kinds of hierarchy have been proposed over recent years. The last generation is hierarchical DHTs (HDHTs) where nodes are organized onto layers; each next layer consists of supernodes for the previous layer. In this survey paper, we thoroughly go over the evolution of DHTs from pure flat to pre-hierarchical. Our focus is on hierarchical schemes in DHT routing. We argue that their application is not restricted within HDHT designs. We sequentially built a set of design principles; each provides a base for hierarchical routing schemes. In the extreme case, application these principles leads to HDHT designs.     

Generalized Transfer Subspace Learning Through Low-Rank Constraint

It is expensive to obtain labeled real-world visual data for use in training of supervised algorithms. Therefore, it is valuable to leverage existing databases of labeled data. However, the data in the source databases is often obtained under conditions that differ from those in the new task. Transfer learning provides techniques for transferring learned knowledge from a source domain to a target domain by finding a mapping between them. In this paper, we discuss a method for projecting both source and target data to a generalized subspace where each target sample can be represented by some combination of source samples. By employing a low-rank constraint during this transfer, the structure of source and target domains are preserved. This approach has three benefits. First, good alignment between the domains is ensured through the use of only relevant data in some subspace of the source domain in reconstructing the data in the target domain. Second, the discriminative power of the source domain is naturally passed on to the target domain. Third, noisy information will be filtered out during knowledge transfer. Extensive experiments on synthetic data, and important computer vision problems such as face recognition application and visual domain adaptation for object recognition demonstrate the superiority of the proposed approach over the existing, well-established methods.     

Linear color segmentation and its implementation

A framework for color image segmentation is presented, which combines color histogram analysis and region merging approach. Its main goal is to segment an image at material boundaries (i.e., discontinuities of reflectance properties) while ignoring spatial color inhomogeneities of uniformly pigmented (colored) objects, caused by accidents of illumination and viewing geometry. Theoretical examination of light spectrum transformations upon light reflection from material surfaces and upon interaction with a sensor system shows that in a wide variety of viewed scenes (even containing interreflections and highlight areas) uniformly pigmented objects are projected to the color space of the sensor as planar, linear, or point-like clusters, depending on lighting and viewing conditions and object geometry. To detect such clusters in the color space, three methods are suggested: Generalized Hough Transform method, gradient descent method, and eigenvectors method. A framework algorithm of color segmentation based on region merging approach is developed, which can use any of these methods. Testing this algorithm with both artificially generated and real images shows quite reliable results.