Abstract
Nowadays applications produce infinite streams of data distributed across wide sensor networks. In this work we study the problem of continuously maintain a cluster structure over the data points generated by the entire network. Usual techniques operate by forwarding and concentrating the entire data in a central server, processing it as a multivariate stream. In this paper, we propose DGClust, a new distributed algorithm which reduces both the dimensionality and the communication burdens, by allowing each local sensor to keep an online discretization of its data stream, which operates with constant update time and (almost) fixed space. Each new data point triggers a cell in this univariate grid, reflecting the current state of the data stream at the local site. Whenever a local site changes its state, it notifies the central server about the new state it is in. This way, at each point in time, the central site has the global multivariate state of the entire network. To avoid monitoring all possible states, which is exponential in the number of sensors, the central site keeps a small list of counters of the most frequent global states. Finally, a simple adaptive partitional clustering algorithm is applied to the frequent states central points in order to provide an anytime definition of the clusters centers. The approach is evaluated in the context of distributed sensor networks, focusing on three outcomes: loss to real centroids, communication prevention, and processing reduction. The experimental work on synthetic data supports our proposal, presenting robustness to a high number of sensors, and the application to real data from physiological sensors exposes the aforementioned advantages of the system.

Abstract
This paper presents one compact printed monopole antenna with one chip inductor embedded on the radiation elements. By introducing the chip inductor, the resonant frequency of the original antenna can be decreased by more than 37% while there is no significant influence on its radiation characteristics including radiation efficiency and antenna gain. Meanwhile, after adding the chip inductor, the higher mode of the original antenna can also be brought down to a lower frequency, which makes the proposed antenna resonate at the dual frequency band and simultaneously have a compact size. This antenna has a C-shaped geometry and its parameters including the location of the chip inductor are optimized by doing parametrical studies in Ansoft HFSS. The measurement and simulation results show that the proposed C-shaped monopole antenna can operate at 2.55-2.65GHz and 5.1-5.3GHz with peak gain of 2.2 and 4.7dB, respectively. Moreover, according to the simulation results, the radiation efficiency of this antenna at both bands is around 90%.

Abstract
This paper analyzes the application of the Information Theoretic (IT) Mean Shift algorithm for modes finding in order to provide the classification of Electricity Customer Load Patterns. The impact of the algorithm parameters is discussed and then clustering indices are used in order to make a comparison with the classical methods available. Results show a good capability of the modes found in capturing the data structure, aggregating similar load patterns and identifying the uncommon patterns (outliers). © 2011 IEEE.

Abstract
Rationale Complex clinical scenarios involving a high degree of uncertainty frequently lead to a poor agreement over diagnosis and management. However, inconsistent results can be found with the most widely used measures of agreement for continuous variables - the limits of agreement and the intraclass correlation coefficient. Aims and objectives We aim to improve the interpretation of agreement studies using continues variables. Methods and results Evaluation of agreement may be improved by complexity analysis and by categorization of variables, followed by the use of the proportions of agreement. Conclusions The average never characterizes a complex phenomenon and the methods used to access agreement in continuous variables are based on the mean. For future agreement studies, involving complex continuous variables, we recommend a complexity and categorical analysis.

Abstract
The proliferation of complex service systems raises new challenges for service design and requires new methods. Multilevel Service Design (MSD) is presented as a new interdisciplinary method for designing complex service systems. MSD synthesizes contributions from new service development, interaction design, and the emerging field of service design. MSD enables integrated development of service offerings at three hierarchical levels: (a) Designing the firm's service concept with the customer value constellation of service offerings for the value constellation experience; (b) Designing the firm's service system, comprising its architecture and navigation, for the service experience; and (c) Designing each service encounter with the Service Experience Blueprint for the service encounter experience. Applications of the MSD method are described for designing a new retail grocery service and for redesigning a bank service. MSD contributes an interdisciplinary service design method that accommodates the cocreative nature of customer experiences and enables experience integration from the design of the service concept through the design of the service system and service encounter.

Abstract
Context-aware mobile applications can benefit from context inference adaptation based on run-time operating conditions, such as battery life or sensor availability. Developing applications with such adaptable behavior, however, is notoriously cumbersome, as developers need to deal with low-level system interfacing and programming issues. In this paper we describe a domain-specific language (DSL) and a middleware infrastructure to support the specification, deployment and maintenance of run-time adaptable context inference processes. We illustrate the benefits of our approach via a case study, highlighting the new abstractions that facilitate the specification of adaptable behavior using different algorithms and the corresponding varying parameter settings, with a specific goal of minimizing the energy while maintaing acceptable end-application performance and accuracy. © 2011 IEEE.