Abstract

Abstract
While bottom-up approaches to object recognition are simple to design and implement, they do not yield the same performance as top-down approaches. On the other hand, it is not trivial to obtain a moderate number of plausible hypotheses to be efficiently verified by top-down approaches. To address these shortcomings, we propose a hybrid top-down bottom-up approach to object recognition where a bottom-up procedure that generates a set of hypothesis based on data is combined with a top-down process for evaluating those hypotheses. We use the recognition of rectangular cuboid shaped objects from 3D point cloud data as a benchmark problem for our research. Results obtained using this approach demonstrate promising recognition performances.

Abstract
Model-checking is increasingly popular in the early phases of the software development process. To establish the correctness of a software design one must usually verify both structural and behavioral(or temporal) properties. Unfortunately, most specification languages, and accompanying model-checkers, excel only in analyzing either one or the other kind. This limits their ability to verify dynamic systems with rich configurations: systems whose state space is characterized by rich structural properties, but whose evolution is also expected to satisfy certain temporal properties. To address this problem, we first propose Electrum, an extension of the Alloy specification language with temporal logic operators, where both rich configurations and expressive temporal properties can easily be de fined. Two alternative model-checking techniques are then proposed, one bounded and the other unbounded, to verify systems expressed in this language, namely to verify that every desirable temporal property holds for every possible configuration.

Abstract
Buffer caching mechanisms are paramount to improve the performance of today's massive scale NoSQL databases. In this work, we show that in fact there is a direct and univocal relationship between the resource usage and the cache hit ratio in NoSQL databases. In addition, this relationship can be leveraged to build a mechanism that is able to estimate resource usage of the nodes composing the NoSQL cluster.

Abstract

Abstract
In this paper an overloading prevention of a private customer power distribution transformer (PDT) in an island in Portugal through the means of a new smart electric vehicle (EV) charging scheduler is proposed. The aim of this paper is to assess the repercussion of the penetration of additional power to restore the full level of EV battery state of charge (SoC) on dielectric oil deterioration of the PDT of a private industry client. This will allow EVs to charge while their owners are at work at three different working shifts during the day. In addition, the system is part of an isolated electric grid in a Portuguese Island. A transformer thermal model is utilised in this paper to assess hot-spot temperature by having the information of the load ratio. The data used for the main inputs of the model are the private industry client daily load profile, PDT parameters, the characteristics of the factory and EV parameters. This paper demonstrates that the proposed solution allows avoiding the overloading of the PDT, thus mitigating the loss-of-life, while charging all the EVs plugged-in at the beginning of each working shift.