Robertas Damaševičius

Application of UML for hardware design based on design process model

We address a problem of reusing and customizing soft IP components by introducing a concept of design process - a series of common, well-defined and well-proven domain-specific actions and methods performed to achieve a certain design aim. We especially examine system-level design processes that are aimed at designing a hardware system by integrating soft IPs at a high level of abstraction. We combine this concept with object-oriented hardware design using UML and metaprogramming paradigm for describing generation of domain code.

Application of design patterns for hardware design

Design patterns, which encapsulate common solutions to the recurring design problems, have contributed to the increased reuse, quality and productivity in software design. We argue that hardware design patterns could be used for customizing and integrating the Intellectual Property (IP) components into System-on-Chip designs. We formulate the role of design patterns in HW design, and describe their implementation using metaprogramming. We propose a Wrapper design pattern for adapting the behavior of the soft IPs, and demonstrate its application to the communication interface synthesis.

Consumer-grade EEG devices: are they usable for control tasks?

We present the evaluation of two well-known, low-cost consumer-grade EEG devices: the Emotiv EPOC and the Neurosky MindWave. Problems with using the consumer-grade EEG devices (BCI illiteracy, poor technical characteristics, and adverse EEG artefacts) are discussed. The experimental evaluation of the devices, performed with 10 subjects asked to perform concentration/relaxation and blinking recognition tasks, is given. The results of statistical analysis show that both devices exhibit high variability and non-normality of attention and meditation data, which makes each of them difficult to use as an input to control tasks. BCI illiteracy may be a significant problem, as well as setting up of the proper environment of the experiment. The results of blinking recognition show that using the Neurosky device means recognition accuracy is less than 50%, while the Emotiv device has achieved a recognition accuracy of more than 75%; for tasks that require concentration and relaxation of subjects, the Emotiv EPOC device has performed better (as measured by the recognition accuracy) by ∼9%. Therefore, the Emotiv EPOC device may be more suitable for control tasks using the attention/meditation level or eye blinking than the Neurosky MindWave device.

Open PROMOL: An Experimental Language for Target Program Modification

We present a new experimental scripting language Open PROMOL developed for: 1) delivering flexible means for representing wide range modifications of a target program, and 2) supporting white-box reuse for well-understood domains, such as hardware design. We evaluate the role of scripting and program modification in the domain. We describe the syntax and semantics of the basic PROMOL functions. We discuss the capabilities of the language to perform program modifications by widening, narrowing and isolating functionality. Examples of program modification in VHDL and other languages are delivered.

An Application of Differential Evolution to Positioning Queueing Systems

In this paper, we present positioning of the queueing system by the use of Differential Evolution algorithm. Positioned system is a H 3/GI/M/1/N-type queueing model with exponentially distributed service and vacation. In the following sections of this article we discuss the possibility of positioning of the selected system in various common scenarios of operation, which are modeled with the independent 3-order hyper exponential input stream of packets and exponential service time distribution. The research results on positioning are presented and discussed to show potential benefits of applied optimization method.

Meta-Programming and Model-Driven Meta-Program Development

Meta-Programming and Model-Driven Meta-Program Development: Principles, Processes and Techniques presents an overall analysis of meta-programming, focusing on insights of meta-programming techniques, heterogeneous meta-program development processes in the context of model-driven, feature-based and transformative approaches. The fundamental concepts of meta-programming are still not thoroughly understood, in this well organized book divided into three parts the authors help to address this. Chapters include: Taxonomy of fundamental concepts of meta-programming; Concept of structural heterogeneous meta-programming based on the original meta-language; Model-driven concept and feature-based modeling to the development process of meta-programs; Equivalent meta-program transformations and metrics to evaluate complexity of feature-based models and meta-programs; Variety of academic research case studies within different application domains to experimentally verify the soundness of the investigated approaches. Both authors are professors at Kaunas University of Technology with 15 years research and teaching experience in the field. Meta-Programming and Model-Driven Meta-Program Development: Principles, Processes and Techniques is aimed at post-graduates in computer science and software engineering and researchers and program system developers wishing to extend their knowledge in this rapidly evolving sector of science and technology.

Structural analysis of regulatory DNA sequences using grammar inference and Support Vector Machine

Regulatory DNA sequences such as promoters or splicing sites control gene expression and are important for successful gene prediction. Such sequences can be recognized by certain patterns or motifs that are conserved within a species. These patterns have many exceptions which makes the structural analysis of regulatory sequences a complex problem. Grammar rules can be used for describing the structure of regulatory sequences; however, the manual derivation of such rules is not trivial. In this paper, stochastic L-grammar rules are derived automatically from positive examples and counterexamples of regulatory sequences using genetic programming techniques. The fitness of grammar rules is evaluated using a Support Vector Machine (SVM) classifier. SVM is trained on known sequences to obtain a discriminating function which serves for evaluating a candidate grammar ruleset by determining the percentage of generated sequences that are classified correctly. The combination of SVM and grammar rule inference can mitigate the lack of structural insight in machine learning approaches such as SVM.

Metaprogramming techniques for designing embedded components for ambient intelligence

Design for Ambient Intelligence (AmI) requires development and adoption of novel domain analysis methods and design methodologies. Our approach is based on domain analysis methods adopted from software engineering, Genetic Embedded Component Model (GECM) and metaprogramming (MPG). A novelty of our approach is that we apply MPG systematically in order to deal with a vast quantity, diversity and heterogeneity of embedded components, manage variability and raise the level of abstraction in embedded system design, as well as achieve higher flexibility, reusability and customizability for AmI-oriented design. We discuss applicability of the MPG techniques for designing embedded components (ECs) for AmI and provide three case studies.

Human Activity Recognition in AAL Environments Using Random Projections

Automatic human activity recognition systems aim to capture the state of the user and its environment by exploiting heterogeneous sensors attached to the subjects body and permit continuous monitoring of numerous physiological signals reflecting the state of human actions. Successful identification of human activities can be immensely useful in healthcare applications for Ambient Assisted Living (AAL), for automatic and intelligent activity monitoring systems developed for elderly and disabled people. In this paper, we propose the method for activity recognition and subject identification based on random projections from high-dimensional feature space to low-dimensional projection space, where the classes are separated using the Jaccard distance between probability density functions of projected data. Two HAR domain tasks are considered: activity identification and subject identification. The experimental results using the proposed method with Human Activity Dataset (HAD) data are presented.

A Prototype SSVEP Based Real Time BCI Gaming System

Although brain-computer interface technology is mainly designed with disabled people in mind, it can also be beneficial to healthy subjects, for example, in gaming or virtual reality systems. In this paper we discuss the typical architecture, paradigms, requirements, and limitations of electroencephalogram-based gaming systems. We have developed a prototype three-class brain-computer interface system, based on the steady state visually evoked potentials paradigm and the Emotiv EPOC headset. An online target shooting game, implemented in the OpenViBE environment, has been used for user feedback. The system utilizes wave atom transform for feature extraction, achieving an average accuracy of 78.2% using linear discriminant analysis classifier, 79.3% using support vector machine classifier with a linear kernel, and 80.5% using a support vector machine classifier with a radial basis function kernel.