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.     

Fault diagnosis of networked control systems

Networked control systems (NCS) are feedback systems closed through data networks. NCS have many advantages compared with traditional systems; however, the network-induced delay and other characteristics of data networks may degrade the performance of feedback systems designed without taking the network into account. Supported by the National Nature Science Foundation of China, we studied the fault diagnosis and fault-tolerant control theory for NCS in recent years. This paper summarizes our main ideas and results on fault diagnosis of NCS, including the fundamentals of fault diagnosis for NCS with information-scheduling, fault diagnosis approaches based on the simplified time-delay system models, and the quasi T-S fuzzy model and fault diagnosis for linear and nonlinear NCS with long delay.     

“Wanted: Some Black Long Distance [Writers]”: Blackboard Flava-Flavin and other AfroDigital experiences in the classroom

This essay examines how students of African descent at a predominantly black college on the East Coast digitally perform their ethnic identities and rhetorics in a freshman composition course. The essay begins by showing how multiple uses of signifying frame students’ Blackboard discussions where they use a type of trickster motif to enact their agreements, disagreements, challenges, and questions, very much akin to Flava Flav's initial cultural role as part of the Rap/activist group, Public Enemy. Students’ online writing groups are then examined by focusing on one particular group, the “Black Long Distance Writers,” whose title signifies and signals the work of the African American writer and activist, John Oliver Killens, most notably, his seminal 1973 essay, “Wanted: Some Black Long Distance Runners.” The understandings of these “Black Long Distance Writers” bear the most powerful definition of literacy and computer-based writing instruction because their framework is not contingent upon making digitally divided minorities more technologically advanced and better at one type of English, its culture of power, or its academic discourses. Instead, these students experience rhetoric and writing as a way to alter the ways that knowledge is constructed for them and about them, “revocabularizing” the academy and its technologies. Such freshman writers are re-envisioned in this kind of cyberspace as constructors of and co-participants in black intellectual and rhetorical traditions … now AfroDigitized.     

Weathering wikis: Net-based learning meets political science in a South African university

Wikis represent flexible tools functioning as open-ended environments for collaboration while also offering process and group writing support. Here we focus on a project to innovate the use of wikis for collaborative writing within student groups in a final-year undergraduate political science course. The primary questions guiding our research were in what ways could wikis assist collaborative learning in an undergraduate course in political science and how we could support educators’ in the effective use of wikis? Curiously, wikis may serve as a mediating artifact for collaborative writing even among students who are reluctant to post online drafts. The paper raises questions concerning the nature and limits of lecturer and tutor power to deliver transformative educational innovations in relation to the capacity of students to embrace, comply with, or resist such innovation. In analysing the negotiation of the use of wikis in the course by and among the lecturer, tutors, and students, we draw on two principles in activity theory, which Yrjö Engeström argued are central to his model of expansive learning: multi-voicedness and contradictions [Engeström, Yrjö. (1987). Learning by expanding: An activity-theoretical approach to developmental research. Helsinki: Orienta-Konsultit; Engeström, Yrjö. (2001). Expansive learning at work: Toward an activity theoretical reconceptualization. Journal of Education and Work14(1), 133-156.]. We add a third principle, transparency, to more fully capture what we observed.     

Estimation of skew angles for scanned documents based on piecewise covering by parallelograms

We propose a fast and robust skew estimation method for scanned documents that estimates skew angles based on piecewise covering of objects, such as textlines, figures, forms, or tables. The method first divides a document image into a number of non-overlapping slabs in which each object is covered by parallelograms. It then estimates the skew angle based on these parallelograms or, equivalently, their complementary regions. Putting our method to a systematic test and comparing it with some alternatives, we find that it yields favorable results in terms of accuracy, sensitivity to non-textual objects, effectiveness in dealing with documents of unspecified reading order, and computational efficiency. Some work is also conducted to find an effective way to further shorten its computation time at the expense of an extremely small loss of accuracy.     

An investigation of the modified direction feature for cursive character recognition

This paper describes and analyses the performance of a novel feature extraction technique for the recognition of segmented/cursive characters that may be used in the context of a segmentation-based handwritten word recognition system. The modified direction feature (MDF) extraction technique builds upon the direction feature (DF) technique proposed previously that extracts direction information from the structure of character contours. This principal was extended so that the direction information is integrated with a technique for detecting transitions between background and foreground pixels in the character image.In order to improve on the DF extraction technique, a number of modifications were undertaken. With a view to describe the character contour more effectively, a re-design of the direction number determination technique was performed. Also, an additional global feature was introduced to improve the recognition accuracy for those characters that were most frequently confused with patterns of similar appearance. MDF was tested using a neural network-based classifier and compared to the DF and transition feature (TF) extraction techniques. MDF outperformed both DF and TF techniques using a benchmark dataset and compared favourably with the top results in the literature. A recognition accuracy of above 89% is reported on characters from the CEDAR dataset.     

A survey of skin-color modeling and detection methods

Skin detection plays an important role in a wide range of image processing applications ranging from face detection, face tracking, gesture analysis, content-based image retrieval systems and to various human computer interaction domains. Recently, skin detection methodologies based on skin-color information as a cue has gained much attention as skin-color provides computationally effective yet, robust information against rotations, scaling and partial occlusions. Skin detection using color information can be a challenging task as the skin appearance in images is affected by various factors such as illumination, background, camera characteristics, and ethnicity. Numerous techniques are presented in literature for skin detection using color. In this paper, we provide a critical up-to-date review of the various skin modeling and classification strategies based on color information in the visual spectrum. The review is divided into three different categories: first, we present the various color spaces used for skin modeling and detection. Second, we present different skin modeling and classification approaches. However, many of these works are limited in performance due to real-world conditions such as illumination and viewing conditions. To cope up with the rapidly changing illumination conditions, illumination adaptation techniques are applied along with skin-color detection. Third, we present various approaches that use skin-color constancy and dynamic adaptation techniques to improve the skin detection performance in dynamically changing illumination and environmental conditions. Wherever available, we also indicate the various factors under which the skin detection techniques perform well.     

Optimizing resources in model selection for support vector machine

Tuning support vector machine (SVM) hyperparameters is an important step in achieving a high-performance learning machine. It is usually done by minimizing an estimate of generalization error based on the bounds of the leave-one-out (LOO) such as radius-margin bound and on the performance measures such as generalized approximate cross-validation (GACV), empirical error, etc. These usual automatic methods used to tune the hyperparameters require an inversion of the Gram-Schmidt matrix or a resolution of an extra-quadratic programming problem. In the case of a large data set these methods require the addition of huge amounts of memory and a long CPU time to the already significant resources used in SVM training. In this paper, we propose a fast method based on an approximation of the gradient of the empirical error, along with incremental learning, which reduces the resources required both in terms of processing time and of storage space. We tested our method on several benchmarks, which produced promising results confirming our approach. Furthermore, it is worth noting that the gain time increases when the data set is large.     

A semi-supervised regression model for mixed numerical and categorical variables

In this paper, we develop a semi-supervised regression algorithm to analyze data sets which contain both categorical and numerical attributes. This algorithm partitions the data sets into several clusters and at the same time fits a multivariate regression model to each cluster. This framework allows one to incorporate both multivariate regression models for numerical variables (supervised learning methods) and k-mode clustering algorithms for categorical variables (unsupervised learning methods). The estimates of regression models and k-mode parameters can be obtained simultaneously by minimizing a function which is the weighted sum of the least-square errors in the multivariate regression models and the dissimilarity measures among the categorical variables. Both synthetic and real data sets are presented to demonstrate the effectiveness of the proposed method.