Self-adaptive systems: A survey of current approaches,research challenges and applications

Self-adaptive software is capable of evaluating and changing its own behavior, whenever the evaluation shows that the software is not accomplishing what it was intended to do, or when better functionality or performance may be possible. The topic of system adaptivity has been widely studied since the mid-60s and, over the past decade, several application areas and technologies relating to self-adaptivity have assumed greater importance. In all these initiatives, software has become the common element that introduces self-adaptability. Thus, the investigation of systematic software engineering approaches is necessary, in order to develop self-adaptive systems that may ideally be applied across multiple domains. The main goal of this study is to review recent progress on self-adaptivity from the standpoint of computer sciences and cybernetics, based on the analysis of state-of-the-art approaches reported in the literature. This review provides an over-arching, integrated view of computer science and software engineering foundations. Moreover, various methods and techniques currently applied in the design of self-adaptive systems are analyzed, as well as some European research initiatives and projects. Finally, the main bottlenecks for the effective application of self-adaptive technology, as well as a set of key research issues on this topic, are precisely identified, in order to overcome current constraints on the effective application of self-adaptivity in its emerging areas of application.     

Convergence of a lowest-order finite element method for the transmission eigenvalue problem

The transmission eigenvalue problem arises in scattering theory. The main difficulty in its analysis is the fact that, depending on the chosen formulation, it leads either to a quadratic eigenvalue problem or to a non-classical mixed problem. In this paper we prove the convergence of a mixed finite element approximation. This approach, which is close to the Ciarlet–Raviart discretization of biharmonic problems, is based on Lagrange finite elements and is one of the less expensive methods in terms of the amount of degrees of freedom. The convergence analysis is based on classical abstract spectral approximation result and the theory of mixed finite element methods for solving the stream function–vorticity formulation of the Stokes problem. Numerical experiments are reported in order to assess the efficiency of the method.     

Diagnosis of lung nodule using Moran’s index and Geary’s coefficient in computerized tomography images

This paper analyzes the application of Moran’s index and Geary’s coefficient to the characterization of lung nodules as malignant or benign in computerized tomography images. The characterization method is based on a process that verifies which combination of measures, from the proposed measures, has been best able to discriminate between the benign and malignant nodules using stepwise discriminant analysis. Then, a linear discriminant analysis procedure was performed using the selected features to evaluate the ability of these in predicting the classification for each nodule. In order to verify this application we also describe tests that were carried out using a sample of 36 nodules: 29 benign and 7 malignant. A leave-one-out procedure was used to provide a less biased estimate of the linear discriminator’s performance. The two analyzed functions and its combinations have provided above 90% of accuracy and a value area under receiver operation characteristic (ROC) curve above 0.85, that indicates a promising potential to be used as nodules signature measures. The preliminary results of this approach are very encouraging in characterizing nodules using the two functions presented.

Global robust stabilization of nonlinear systems subject to input constraints

Our main purpose in this paper is to further address the global stabilization problem for affine systems by means of bounded feedback control functions, taking into account a large class of control value sets: p, r ‐weighted balls ??^m _r (p), with 1<p?∞, defined via p, r ‐weighted gauge functions. Observe that p=∞ is allowed, so that m‐dimensional r ‐hyperboxes ??^m _r (∞)?[?r₁^?,r₁⁺]×???×[?r_m^?,r_m⁺], r_j^±>0 are also considered. Working along the line of Artstein–Sontag's approach, we construct an explicit formula for a one‐parameterized family of continuous feedback controls taking values in ?? _r ^m(p) that globally asymptotically stabilize an affine system, provided an appropriate control Lyapunov function is known. The designed family of controls is suboptimal with respect to the robust stability margin for uncertain systems. The problem of achieving disturbance attenuation for persistent disturbances is also considered. Copyright © 2002 John Wiley & Sons, Ltd.     

Analysing neurobiological models using communicating automata

Two important issues in computational modelling in cognitive neuroscience are: first, how to formally describe neuronal networks (i.e. biologically plausible models of the central nervous system), and second, how to analyse complex models, in particular, their dynamics and capacity to learn. We make progress towards these goals by presenting a communicating automata perspective on neuronal networks. Specifically, we describe neuronal networks and their biological mechanisms using Data-rich Communicating Automata, which extend classic automata theory with rich data types and communication. We use two case studies to illustrate our approach. In the first case study, we model a number of learning frameworks, which vary in respect of their biological detail, for instance the Backpropagation (BP) and the Generalized Recirculation (GeneRec) learning algorithms. We then used the SPIN model checker to investigate a number of behavioral properties of the neural learning algorithms. SPIN is a well-known model checker for reactive distributed systems, which has been successfully applied to many non-trivial problems. The verification results show that the biologically plausible GeneRec learning is less stable than BP learning. In the second case study, we presented a large scale (cognitive-level) neuronal network, which models an attentional spotlight mechanism in the visual system. A set of properties of this model was verified using Uppaal, a popular real-time model checker. The results show that the asynchronous processing supported by concurrency theory is not only a more biologically plausible way to model neural systems, but also provides a better performance in cognitive modelling of the brain than conventional artificial neural networks that use synchronous updates. Finally, we compared our approach with several other related theories that apply formal methods to cognitive modelling. In addition, the practical implications of the approach are discussed in the context of neuronal network based controllers.     

Error Analysis for hp-FEM Semi-Lagrangian Second Order BDF Method for Convection-Dominated Diffusion Problems

We present in this paper an analysis of a semi-Lagrangian second order Backward Difference Formula combined with hp-finite element method to calculate the numerical solution of convection diffusion equations in ℝ². Using mesh dependent norms, we prove that the a priori error estimate has two components: one corresponds to the approximation of the exact solution along the characteristic curves, which is O(Dt²+h^m+1(1+\frac\mathopen|logh|Dt))O(\Delta t^{2}+h^{m+1}(1+\frac{\mathopen{|}\log h|}{\Delta t})); and the second, which is O(Dt^p+|| [(u)\vec]-[(u)\vec]_h||_L^￥)O(\Delta t^{p}+\| \vec{u}-\vec{u}_{h}\|_{L^{\infty}}), represents the error committed in the calculation of the characteristic curves. Here, m is the degree of the polynomials in the finite element space, [(u)\vec]\vec{u} is the velocity vector, [(u)\vec]_h\vec{u}_{h} is the finite element approximation of [(u)\vec]\vec{u} and p denotes the order of the method employed to calculate the characteristics curves. Numerical examples support the validity of our estimates.     

Beyond network simulators: Fostering novel distributed applications and protocols through extendible design

Simulation has been of paramount importance to the development of novel Internet protocols. Such an approach typically focuses on one of three domains: wireless and other link-layer technologies, routing protocols, and transport-layer mechanisms and protocols. Existing techniques can tackle well simulation at layers 2, 3 and 4 of the TCP/IP architecture, but are not flexible enough to appropriately deal with application-layer protocols. These require simulators that support the modeling of networks and components with different levels of abstraction. Simmcast is an object-oriented framework that focuses on the necessary flexibility for application-layer protocol research. A simulation can be developed by the simple extension of building blocks that closely resemble components of a real network such as hosts, links and routers. The internal complexity of these components, however, is hidden from the user, so he/she can focus on the implementation of the desired protocol characteristics. This paper describes the flexible simulation architecture proposed and instantiated through Simmcast, and draws lessons from our experience in designing, implementing and deploying it. We also present framework instances used to evaluate application-layer protocols, exemplifying how different kinds of simulations can be developed with Simmcast.     

A socially inspired energy feedback technology: challenges in a developing scenario

Raising awareness of the environmental impact of energy generation and consumption has been a recent concern of contemporary society worldwide. Underlying the awareness of energy consumption is an intricate network of perception and social interaction that can be mediated by technology. In this paper we argue that issues regarding energy, environment and technology are very much situated and involve tensions of sociocultural nature. This exploratory investigation addresses the subject by introducing the design of a Socially-inspired Energy Eco-Feedback Technology (SEET), which is composed of an interactive system to trigger and mediate collective savings and a tangible device as a public feedback. Results of an evaluation situated in the context of a school in a socially disadvantaged area in Brazil are discussed, shedding light on the sociocultural aspects related to the subject. The role of the SEET to motivate energy awareness collectively among the social group is assessed, as well as the design characteristics that contributed to that. Outcomes bring to light social aspects and dynamics that would hardly have been predicted, evidencing critical factors related to a socially inspired design approach in the energy awareness domain.     

A combinatorial approach to the generalized Baker–Campbell–Hausdorff–Dynkin formula

The aim of this paper is to obtain an explicit expression, instead of using a recursive method, for the nth term coefficient of the generalized Baker–Campbell–Hausdorff–Dynkin (gBCHD) formula. The gBCHD formula has been applied to control theory, specially to nonholonomic motion planning.