Abstract
The development of ubiquitous computing (ubicomp) environments raises several challenges in terms of their evaluation. Ubicomp virtual reality prototyping tools enable users to experience the system to be developed and are of great help to face those challenges, as they support developers in assessing the consequences of a design decision in the early phases of development. Given the situated nature of ubicomp environments, a particular issue to consider is the level of realism provided by the prototypes. This work presents a case study where two ubicomp prototypes, featuring different levels of immersion (desktop-based versus CAVE-based), were developed and compared. The goal was to determine the cost/benefits relation of both solutions, which provided better user experience results, and whether or not simpler solutions provide the same user experience results as more elaborate one.

Abstract
Tensor analysis is a powerful tool for multiway problems in data mining, signal processing, pattern recognition and many other areas. Nowadays, the most important challenges in tensor analysis are efficiency and adaptability. Still, the majority of techniques are not scalable or not applicable in streaming settings. One of the promising frameworks that simultaneously addresses these two issues is Incremental Tensor Analysis (ITA) that includes three variants called Dynamic Tensor Analysis (DTA), Streaming Tensor Analysis (STA) and Window-based Tensor Analysis (WTA). However, ITA restricts the tensor's growth only in time, which is a huge constraint in scalability and adaptability of other modes. We propose a new approach called multi-aspect-streaming tensor analysis (MASTA) that relaxes this constraint and allows the tensor to concurrently evolve through all modes. The new approach, which is developed for analysis-only purposes, instead of relying on expensive linear algebra techniques is founded on the histogram approximation concept. This consequently brought simplicity, adaptability, efficiency and flexibility to the tensor analysis task. The empirical evaluation on various data sets from several domains reveals that MASTA is a potential technique with a competitive value against ITA algorithms.

Abstract
We address the problem of providing users, namely non specialists, with out-of-the-box, programmable, Wireless Sensor-Actuator Networks (WSN). The idea is that users get a package containing a gateway and an undetermined number of nodes, pre-configured to work as a self-organized wireless mesh. Each node comes with two pre-installed components: a small operating system and a virtual machine. The user can then use a simple, domain-specific, programming language to implement periodic tasks that are compiled into byte-code, and can be sent to the nodes for execution. At the nodes, the operating system manages a task table and schedules non-preemptive tasks for execution using the virtual machine. No subtle hardware or software configuration is required from the user as these details are abstracted away by the virtual machine. We developed a full specification for a data-layer that follows the aforementioned guidelines and implemented a complete prototype, integrated in our own Publish/Subscribe middleware called SONAR. In this paper we report the first results of using the prototype as compared to using the low level programming tools provided with the hardware. We measure a small increase in both resource consumption and processing overhead suggesting that this data-layer can be used effectively in WSN, even in cases where nodes have very limited hardware resources.

Abstract

Abstract
The Global Positioning System (GPS) provides geolocation to a considerable number of applications in domains such as agriculture, commerce, transportation and tourism. Operational factors such as signal noise or the lack of direct vision from the receiver to the satellites, reduce the GPS geolocation accuracy. Urban canyons are a good example of an environment where continuous GPS signal reception may fail. For some applications, the lack of geolocation accuracy, even if happening for a short period of time, may lead to undesired results. For instance, consider the damages caused by the failure of the geolocation system in a city tour-bus transportation that shows location-sensitive data (historical/cultural data, publicity) in its screens as it passes by a location. This work presents an innovative approach for keeping geolocation accurate in mobile systems that rely mostly on GPS, by using computer vision to help providing geolocation data when the GPS signal becomes temporarily low or even unavailable. Captured frames of the landscape surrounding the mobile system are analysed in real-time by a computer vision algorithm, trying to match it with a set of geo-referenced images in a preconfigured database. When a match is found, it is assumed that the mobile system current location is close to the GPS location of the corresponding matched point. We tested this approach several times, in a real world scenario, and the results achieved evidence that geolocation can effectively be improved for scenarios where GPS signal stops being available.

Abstract
Lung cancer is mainly diagnosed by the identification of malignant nodules in the lung parenchyma. For that purpose, the identification of all the possible structures that could be suspicious of lung nodules became a crucial task in any lung cancer computer aided diagnosis (CAD) system. In this paper, a new approach for lung nodule candidate identification is proposed. This method uses a 3D medialness Hessian-based filtering to identify round shape structures that could be identified as nodules. This technique, that demonstrated its accuracy in lung vesselness extraction, provides clearer candidates than other approaches, providing less response in the presence of noise artifacts and returns a better continuity in vessels, mostly responsible for false positives. That way, they will be better distinguishable from the nodules in posterior analysis. This approach was validated in 120 scans from the LIDC/IDRI image database. They include 212 nodules with diameters in the range 3 mm to 30 mm. The results demonstrate that our approach is capable of identifying most of the nodules and include less false positives than other approaches, facilitating a posterior task for false positive removal.