A high-level requirements engineering methodology for electronic system-level design

Current electronic systems’ complexity severely limits their validation. Even if development frameworks keep improving and are heavily supported by the industry, methods for hardware/software electronic systems co-design are reaching a major crisis. Although the community is heading towards higher abstraction levels, requirements remain out of the validation scope. We therefore present a requirements engineering methodology that intersects formal, linguistic, and scenario views. Modeling consists in abstracting functionalities’ behaviours in terms of actions, expressed in a semi-formal structured language, later automatically translated in a pure formal notation. Such a mix makes the language accessible to designers and permits automation. Validation is then performed using consistency rules. Finally, an elicitation of missing functionalities is achieved using Boolean algebra.     

Statistical signal processing approaches to fault detection

The parity space approach to fault detection and isolation (FDI) has been developed during the last 20 years, and the focus here is to describe its application to stochastic systems. A mixed model with both stochastic inputs and deterministic disturbances and faults is formulated over a sliding window. Algorithms for detecting and isolating faults on-line and analyzing the probability for correct and incorrect decisions off-line are provided. A major part of the paper is devoted to discussing properties of this model-based approach and generalizations to cases of incomplete model knowledge, and non-linear non-Gaussian models. For this purpose, a simulation example is used throughout the paper for numerical illustrations, and real-life applications for motivations. The final section discusses the reverse problem: fault detection approaches to statistical signal processing. It is motivated by three applications that a simple CUSUM detector in feedback loop with an adaptive filter can mitigate the inherent trade-off between estimation accuracy and tracking speed in linear filters.     

Designing spectral sensitivity curves for use with Artificial Color

Artificial Color uses data from two or more spectrally overlapping sensitivity curves to assign class membership to pixels and ultimately to images. The usefulness of Artificial Color for various scene segmentation tasks has been shown in several recent papers, but those demonstrations all used sensitivity curves not optimized for the particular task, i.e. the R, G, B filters of commercial color cameras. This paper explores means to evolve suitable spectral sensitivity curves suited to any specialized task and illustrates that method with synthetic data chosen to be very hard to discriminate spectrally. Two special cases are illustrated. In one, a single Gaussian curve is used for a dichroic beamsplitter, so that the curve and its complement are used for discrimination. In the other case, two essentially orthogonal curves are utilized for the same task. The single Gaussian curve leads to poorer discrimination but better light efficiency relative to the two curves. Both do quite well on the difficult target problem.     

Unrealistic optimism in internet events

This study assessed the tendency for individuals to be unrealistically optimistic about internet related activities. Ninety-seven participants estimated their chances of experiencing 31 positive and negative internet events compared to the average student at their school. The data indicated that students believed positive internet events were more likely to happen to them and negative events were less likely to happen to them compared to the average student. Heavy internet users reported more optimistic responses than did light users. Perceptions of event characteristics (controllability, desirability, and personal experience) were also significantly correlated with optimistic bias.     

Approximate bisimulation relations for constrained linear systems

In this paper, we define the notion of approximate bisimulation relation between two continuous systems. While exact bisimulation requires that the observations of two systems are and remain identical, approximate bisimulation allows the observations to be different provided the distance between them remains bounded by some parameter called precision. Approximate bisimulation relations are conveniently defined as level sets of a so-called bisimulation function which can be characterized using Lyapunov-like differential inequalities. For a class of constrained linear systems, we develop computationally effective characterizations of bisimulation functions that can be interpreted in terms of linear matrix inequalities and optimal values of static games. We derive a method to evaluate the precision of the approximate bisimulation relation between a constrained linear system and its projection. This method has been implemented in a Matlab toolbox: MATISSE. An example of use of the toolbox in the context of safety verification is shown.     

Understanding life cycle social impacts in manufacturing: A processed-based approach

Developing sustainable products and processes is growing in importance due to increasing regulation, consumer interest, access to information, and competitive forces. In order to adequately evaluate the sustainability of products and processes, there is a need to consider the impacts from all three pillars of sustainability – society, environment, and economics. There are substantial challenges to identifying and understanding the social impacts associated with manufacturing activities. This paper provides a framework for characterizing the social impacts of manufacturing throughout the life cycle of a product or process. Social impacts occur on various scales in manufacturing, from the level of a unit process to the level of the enterprise. Additionally, manufacturing activities impact consumers, communities, and larger political/spatial realms. This paper identifies key characteristics of social impacts associated with manufacturing that should be considered to more effectively address the social dimension of sustainability for products and processes. Examples involving a typical manufacturing process – welding – are presented to illustrate the utility of the framework.     

Machining feature recognition and tool-path generation for 3-axis CNC milling

This study develops an effective method for identifying machining features. While recognizing features, the workpiece is sliced at some assigned positions. The sectional curves of the workpiece faces and slicing plane constitute the feature profiles. Not only the isolated machining features but also the intersecting machining features can be identified by the information from these intersection profiles. Moreover, the recognized machining features can be employed for scheduling the manufacturing sequence. Different kinds of tool paths can be automatically generated for various machining features to improve the cutting efficiency.     

Facial expression recognition system based on rigid and non-rigid motion separation and 3D pose estimation

This study presents a facial expression recognition system which separates the non-rigid facial expression from the rigid head rotation and estimates the 3D rigid head rotation angle in real time. The extracted trajectories of the feature points contain both rigid head motion components and non-rigid facial expression motion components. A 3D virtual face model is used to obtain accurate estimation of the head rotation angle such that the non-rigid motion components can be precisely separated to enhance the facial expression recognition performance. The separation performance of the proposed system is further improved through the use of a restoration mechanism designed to recover feature points lost during large pan rotations. Having separated the rigid and non-rigid motions, hidden Markov models (HMMs) are employed to recognize a prescribed set of facial expressions defined in terms of facial action coding system (FACS) action units (AUs).