Abstract
There have been several authors asserting that conceptual query languages (CQLs) perform better for querying purposes than logical query languages such as SQL. This paper proposes a query mapping algorithm for the FConQuer system. FConQuer is a framework based on object-role modeling (ORM) schemas, which allow the end-user to formulate conceptual queries through the FConQuer language. Our mapping algorithm allows the FConQuer system to process conceptual queries based on ORM schemas. More precisely, our algorithm maps FConQuer queries to OQL. © 2010 IEEE.

Abstract
This paper presents the use of LALP to implement typical industrial application kernels, ADPCM Encoder and Decoder, in FPGAs. LALP is a domain specific language and its compilation framework aims to the direct mapping of algorithms originally described in a high-level language onto FPGAs. In particular, LALP focuses on loop pipelining, a key technique for the design of hardware accelerators. While the language syntax resembles C, it contains certain constructs that allow programmer interventions to enforce or relax data dependences as needed, and so optimize the performance of the generated hardware. We present experimental results showing significant performance gains using this approach, while still keeping the language syntax and semantics close to popular high level software languages, a desirable feature when considering time to market constraints. We believe the performance gains observed for the ADPCM implementation can be extended to other industrial applications relying on algorithms spending most of their execution time on loop structures, such signal and image processing.

Abstract
Reconfigurable computing platforms offer the promise of substantially accelerating computations through the concurrent nature of hardware structures and the ability of these architectures for hardware customization. Effectively programming such reconfigurable architectures, however, is an extremely cumbersome and error-prone process, as it requires programmers to assume the role of hardware designers while mastering hardware description languages, thus limiting the acceptance and dissemination of this promising technology. To address this problem, researchers have developed numerous approaches at both the programming languages as well as the compilation levels, to offer high-level programming abstractions that would allow programmers to easily map applications to reconfigurable architectures. This survey describes the major research efforts on compilation techniques for reconfigurable computing architectures. The survey focuses on efforts that map computations written in imperative programming languages to reconfigurable architectures and identifies the main compilation and synthesis techniques used in this mapping.

Abstract
The processing capabilities of mobile devices coupled with portable and wearable sensors provide the basis for new context-aware services and applications tailored to the user environment and daily activities. In this article, we describe the approach developed within the UPCASE project, which makes use of sensors available in the mobile device as well as sensors externally connected via Bluetooth to provide user contexts. We describe the system architecture from sensor data acquisition to feature extraction, context inference and the publication of context information in web-centered servers that support well-known social networking services. In the current prototype, context inference is based on decision trees to learn and to identify contexts dynamically at run-time, but the middleware allows the integration of different inference engines if necessary. Experimental results in a real-world setting suggest that the proposed solution is a promising approach to provide user context to local mobile applications as well as to network-level applications such as social networking services.

Abstract
Embedded systems are considered one of the areas with more potential for future innovations. Two embedded fields that will most certainly take a primary role in future innovations are mobile robotics and mobile computing. Mobile robots and smartphones are growing in number and functionalities, becoming a presence in our daily life. In this paper, we study the current feasibility of a smartphone to execute navigation algorithms and provide autonomous control, e.g., for a mobile robot. We tested four navigation problems: Mapping, Localization, Simultaneous Localization and Mapping, and Path Planning. We selected representative algorithms for the navigation problems, developed them in J2ME, and performed tests on the field. Results show the current mobile Java capacity for executing computationally demanding algorithms and reveal the real possibility of using smartphones for autonomous navigation.

Abstract
The ubiquity of communication devices such as smartphones has led to the emergence of context-aware services that are able to respond to specific user activities or contexts. These services allow communication providers to develop new, added-value services for a wide range of applications such as social networking, elderly care and near-emergency early warning systems. At the core of these services is the ability to detect specific physical settings or the context a user is in, using either internal or external sensors. For example, using built-in accelerometers, it is possible to determine whether a user is walking or running at a specific time of day. By correlating this knowledge with GPS data, it is possible to provide specific information services to users with similar daily routines. This article presents a survey of the techniques for extracting this activity information from raw accelerometer data. The techniques that can be implemented in mobile devices range from classical signal processing techniques such as FFT to contemporary string-based methods. We present experimental results to compare and evaluate the accuracy of the various techniques using real data sets collected from daily activities.