Abstract

Abstract

Abstract
Complexity in manufacturing systems appears under a variety of aspects, namely product, processes and operations and systems. Considering that the manufacturing environment is rapidly and constantly changing, with higher levels of customization and complexity, there is higher demand for flexibility and adaptability from companies. In this context, it seems essential to explore new approaches that can support decision-makers to take better decisions concerning the action plans that they need to launch to achieve the expected strategic and operational performance and alignment goals. Companies should become able to analyse their performance drivers, understand their meaning and the feedback loops that affect them. Therefore, decision makers can look into the future, and act even before these causes affect the transformation systems efficiency and effectiveness. This paper presents an approach oriented to multi-performance measurement in complex manufacturing environments. With this approach it is expected to overcome the gap between the operational and strategic layers of a manufacturing system, in order to reduce time when measuring performance and reacting to unexpected behaviours, as well as reduce errors when taking decisions. Moreover, it is expected to decrease the time necessary to calculate an indicator or to introduce a new one into performance management process, reducing the operational costs.

Abstract
Cryptography is an inherently interdisciplinary area and the development of high-quality cryptographic software is a time-consuming task drawing on skills from mathematics, computer science and electrical engineering, only achievable by highly skilled programmers. The challenge is to map high-level cryptographic specifications phrased using mathematical abstractions into efficient implementations at the level of C or assembly that can be deployed on a target computational platform, whilst adhering to the specification both in terms of correctness and security. The High Assurance Software Laboratory at INESC-TEC maintains a domain-specific toolchain for the specification, implementation and verification of cryptographic software centred on CAO, a cryptography analyses and operations-aware language.

Abstract
Attribute grammars are a suitable formalism to express complex software language analysis and manipulation algorithms, which rely on multiple traversals of the underlying syntax tree. Attribute grammars have been extended with mechanisms such as reference, higher order and circular attributes. Such extensions provide a powerful modular mechanism and allow the specification of complex computations. This paper studies an elegant and simple, zipper-based embedding of attribute grammars and their extensions as first class citizens. In this setting, language specifications are defined as a set of independent, off-the-shelf components that can easily be composed into a powerful, executable language processor. Techniques to describe automatic bidirectional transformations between grammars in this setting are also described. Several real examples of language specification and processing programs have been implemented.

Abstract
Object detection in images is a computing demanding task which usually needs to deal with the detection of different classes of objects, and thus requiring variations and adaptations easily provided by software solutions. Object detection algorithms are being part of real-time smarter embedded systems, such as automotive, medical, robotics and security systems. In most embedded systems, efficient implementations of object oriented algorithms need to provide high performance, low power consumption, and programmability to allow greater development flexibility. The Histogram of Oriented Gradients (HOG) is one of the most widely used algorithms for object detection in images. In this paper, we show our work towards mapping the HOG algorithm to an FPGA-based system consisting of multiple Nios II softcore processors and bearing in mind high-performance and programmability issues. We show how to reduce 19x the algorithms execution time by source to source transformations and specially avoiding redundant processing. Furthermore, we show how the use of pipelining processing using three Nios II processors allows a speedup of 49x compared to the embedded baseline application.