Search

This document is a foreword for a special issue of the journal Microsystem Technologies. The special issue features selected papers from the 11th International Conference on Computing, Communication and Sensor Network (CCSN) held in September 2022 at Utkal University in Bhubaneswar, Odisha, India. The conference covered various interdisciplinary areas such as computing, communication, sensor networks, and circuit designs. The papers included in this special issue have undergone a rigorous review process and were selected for publication. The foreword expresses the hope that readers will find the papers informative. [Extracted from the article]

The importance of measuring the complexity of shapes can be seen by the wide range of its application such as computer vision, robotics, cognitive studies, eye tracking, and psychology. However, it is very challenging to define an accurate and precise metric to measure the complexity of the shapes. In this paper, we explore different notions of shape complexity, drawing from established work in mathematics, computer science, and computer vision. We integrate results from user studies with quantitative analyses to identify three measures that capture important axes of shape complexity, out of a list of almost 300 measures previously considered in the literature. We then explore the connection between specific measures and the types of complexity that each one can elucidate. Finally, we contribute a dataset of both abstract and meaningful shapes with designated complexity levels both to support our findings and to share with other researchers. [ABSTRACT FROM AUTHOR]

Searching for all occurrences of a pattern in a text is a fundamental problem in computer science with applications in many other fields, like natural language processing, information retrieval and computational biology. Sampled string matching is an efficient approach recently introduced in order to overcome the prohibitive space requirements of an index construction, on the one hand, and drastically reduce searching time for the online solutions, on the other hand. In this paper we present a new algorithm for the sampled string matching problem, based on a characters distance sampling approach. The main idea is to sample the distances between consecutive occurrences of a given pivot character and then to search online the sampled data for any occurrence of the sampled pattern, before verifying the original text. From a theoretical point of view we prove that, under suitable conditions, our solution can achieve both linear worst-case time complexity and optimal average-time complexity. From a practical point of view it turns out that our solution shows a sub-linear behaviour in practice and speeds up online searching by a factor of up to 9, using limited additional space whose amount goes from 11 to 2.8% of the text size, with a gain up to 50% if compared with previous solutions. [ABSTRACT FROM AUTHOR]

Face as a biometric attribute has been extensively studied over the past few decades. Even though, satisfactory results are already achieved in controlled environments, the practicality of face recognition in realistic scenarios is still limited by several challenges, such as, expression, pose, occlusion, etc. Recently, the research direction is concentrating on the prospects of complementing face recognition systems with facial soft biometric traits. The ease of extracting facial soft biometrics under several varying conditions has mainly resulted in the ability of using the traits to, either improve the performance of traditional face recognition systems, or performing recognition solely based on many facial soft biometrics. This paper presents state-of-the-art techniques in facial soft biometrics research by describing the type of traits, feature extraction methods, and the application domains. It indicates the most recent and valuable results attained, while also highlighting some possible future scientific research directions to be investigated. [ABSTRACT FROM AUTHOR]

To predict the performance of an application, it is crucial to consider the performance of the underlying infrastructure. Thus, to yield accurate prediction results, performance-relevant properties and behaviour of the infrastructure have to be integrated into performance models. However, capturing these properties is a cumbersome and error-prone task, as it requires carefully engineered measurements and experiments. Existing approaches for creating infrastructure performance models require manual coding of these experiments, or ignore the detailed properties in the models. The contribution of this paper is the Goal-oriented INfrastructure Performance EXperiments ( Ginpex) approach, which introduces goal-oriented and model-based specification and generation of executable performance experiments for automatically detecting and quantifying performance-relevant infrastructure properties. Ginpex provides a metamodel for experiment specification and comes with predefined experiment templates that provide automated experiment execution on the target platform and also automate the evaluation of the experiment results. We evaluate Ginpex using three case studies, where experiments are executed to quantify various infrastructure properties. [ABSTRACT FROM AUTHOR]

We show that Span (Graph) ∗ , an algebra for open transition systems introduced by Katis, Sabadini and Walters, satisfies a universal property. By itself, this is a justification of the canonicity of this model of concurrency. However, the universal property is itself of interest, being a formal demonstration of the relationship between feedback and state. Indeed, feedback categories, also originally proposed by Katis, Sabadini and Walters, are a weakening of traced monoidal categories, with various applications in computer science. A state bootstrapping technique, which has appeared in several different contexts, yields free such categories. We show that Span (Graph) ∗ arises in this way, being the free feedback category over Span (Set) . Given that the latter can be seen as an algebra of predicates, the algebra of open transition systems thus arises—roughly speaking—as the result of bootstrapping state to that algebra. Finally, we generalize feedback categories endowing state spaces with extra structure: this extends the framework from mere transition systems to automata with initial and final states. [ABSTRACT FROM AUTHOR]

Process networks and data-flow graphs are used to capture data-dependencies in computation-intensive embedded systems. Their simplicity allows the computation of static schedules that reduce the dynamic overhead and increase predictability. The resulting schedule is a total ordering of actor computations and communications. It can therefore become an over-specification of the initial system when several schedules are valid. This is particularly the case for multidimensional data-flow applications. We propose a methodology to avoid such an over-specification. We propose to use logical time to capture explicitly all the valid schedules for a given multi-dimensional data-flow model. Then, we show that the proposed approach allows for a progressive and explicit refinement of computation scheduling that also captures constraints imposed by the environment and the execution platform. All this is achieved by using uml marte concepts and the resulting models can be considered for simulation and analysis with existing tools for early design validation. The whole approach is validated on a typical application devoted to radar signal processing. [ABSTRACT FROM AUTHOR]