Design space exploration of reliable networked embedded systems

In this paper, a new methodology is presented for topology optimization of networked embedded systems as they occur in automotive and avionic systems as well as wireless sensor networks. By introducing a model which is (1) suitable for heterogeneous networks with different communication bandwidths, (2) modeling of routing restrictions, and (3) flexible binding of tasks onto processors, current design issues of networked embedded systems can be investigated. On the basis of this model, the presented methodology firstly allocates the required resources which can be communication links as well as computational nodes and secondly binds the functionality onto the nodes and the data dependencies onto the links such that no routing restrictions will be violated or capacities on communication links will be exceeded. Due to the often error-prone communication in networks, we allow for routing each data dependency over multiple routes in the networks. With this strategy, our methodology is able to increase the reliability of the entire system. This reliability analysis is based on Binary Decision Diagrams (BDDs) and is integrated in our multi-objective design space exploration. By applying Evolutionary Algorithms, we are able to consider multiple objectives simultaneously during the optimization process and allow for a subsequent unbiased decision making. An experimental evaluation as well as a demonstration of a case study from the field of automotive electronics will show the applicability of the presented approach.     

Influence of lips on thin-walled sections

Turning the free edge of an unstiffened flange inwards or outwards to form a ‘lip’, can substantially improve the local buckling resistance of a member. The lip is the most common type of edge stiffener used in cold-rolled, thin-walled sections. In this paper the behaviour of plate elements of thin-walled sections stiffened by compound lips (i.e. lips which are folded twice to form ‘lips on the lips’) is examined both theoretically and experimentally. An outline of a series of tests on compound edge-stiffened thin-walled sections of various geometries is given and some load-end compression displacement paths are compared with the theoretical predictions. Reasonably good agreement is obtained between the experimental and theoretical results. Comparisons of the theoretical predictions with experimental results of other researchers are also presented in this paper.     

Machine-learning paradigms for selecting ecologically significant input variables

Harmful algal blooms, which are considered a serious environmental problem nowadays, occur in coastal waters in many parts of the world. They cause acute ecological damage and ensuing economic losses, due to fish kills and shellfish poisoning as well as public health threats posed by toxic blooms. Recently, data-driven models including machine-learning (ML) techniques have been employed to mimic dynamics of algal blooms. One of the most important steps in the application of a ML technique is the selection of significant model input variables. In the present paper, we use two extensively used ML techniques, artificial neural networks (ANN) and genetic programming (GP) for selecting the significant input variables. The efficacy of these techniques is first demonstrated on a test problem with known dependence and then they are applied to a real-world case study of water quality data from Tolo Harbour, Hong Kong. These ML techniques overcome some of the limitations of the currently used techniques for input variable selection, a review of which is also presented. The interpretation of the weights of the trained ANN and the GP evolved equations demonstrate their ability to identify the ecologically significant variables precisely. The significant variables suggested by the ML techniques also indicate chlorophyll-a (Chl-a) itself to be the most significant input in predicting the algal blooms, suggesting an auto-regressive nature or persistence in the algal bloom dynamics, which may be related to the long flushing time in the semi-enclosed coastal waters. The study also confirms the previous understanding that the algal blooms in coastal waters of Hong Kong often occur with a life cycle of the order of 1–2 weeks.     

PPREM: Privacy Preserving REvocation Mechanism for Vehicular Ad Hoc Networks

One of the critical security issues of Vehicular Ad Hoc Networks (VANETs) is the revocation of misbehaving vehicles. While essential, revocation checking can leak potentially sensitive information. Road Side Units (RSUs) receiving the certificate status queries could infer the identity of the vehicles posing the query. An important loss of privacy results from the RSUs ability to tie the checking vehicle with the query's target. We propose a Privacy Preserving Revocation mechanism (PPREM) based on a universal one-way accumulator. PPREM provides explicit, concise, authenticated and unforgeable information about the revocation status of each certificate while preserving the users' privacy.     

Ehrenfeucht-Fraïssé games in finite set theory

In this article, with Ehrenfeucht-Fraïssé games we prove that Δ₁≠Δ₀ on BFR, which implies Δ≠Δ₀ on BFR, and thus solve an open problem raised by Albert Atserias in his dissertation (Δ₀,Δ₁,Δ are fragments of first order logic and BFR is a class of finite sets which in essence is equivalent to a class of finite pure arithmetic structures with built-in BIT predicate).     

CDZoom: a human-like sequential zoom agent for efficient change detection in large scenes

Neural Computing and Applications - High-resolution (HR) remote sensing images provide rich information for human activities. However, processing entire HR images is time-consuming, and many...