PUF-enhanced offline RFID security and privacy

RFID (Radio Frequency IDentification) based communication solutions have been widely used nowadays for mobile environments such as access control for secure system, ticketing systems for transportation, and sport events. These systems usually depend on readers that are not continuously connected to a secure backend system. Thus, the readers should be able to perform their duties even in offline mode, which generally requires the management by the readers of the susceptible data. The use of RFID may cause several security and privacy issues such as traceability of tag owner, malicious eavesdropping and cloning of tags. Besides, when a reader is compromised by an adversary, the solution to resolve these issues getting worse. In order to handle these issues, several RFID authentication protocols have been recently proposed; but almost none of them provide strong privacy for the tag owner. On the other hand, several frameworks have been proposed to analyze the security and privacy but none of them consider offline RFID system.Motivated by this need, in this paper, we first revisit Vaudenay's model, extend it by considering offline RFID system and introduce the notion of compromise reader attacks. Then, we propose an efficient RFID mutual authentication protocol. Our protocol is based on the use of physically unclonable functions (PUFs) which provide cost-efficient means to the fingerprint chips based on their physical properties. We prove that our protocol provides destructive privacy for tag owner even against reader attacks.     

A survey on coverage and connectivity issues in wireless sensor networks

A wireless sensor network (WSN) is composed of a group of small power-constrained nodes with functions of sensing and communication, which can be scattered over a vast region for the purpose of detecting or monitoring some special events. The first challenge encountered in WSNs is how to cover a monitoring region perfectly. Coverage and connectivity are two of the most fundamental issues in WSNs, which have a great impact on the performance of WSNs. Optimized deployment strategy, sleep scheduling mechanism, and coverage radius cannot only reduce cost, but also extend the network lifetime. In this paper, we classify the coverage problem from different angles, describe the evaluation metrics of coverage control algorithms, analyze the relationship between coverage and connectivity, compare typical simulation tools, and discuss research challenges and existing problems in this area.     

Flexible and robust CAD models for design automation

This paper explores novel methodologies for enabling Multidisciplinary Design Optimization (MDO) of complex engineering products. To realize MDO, Knowledge Based Engineering (KBE) is adopted with the aim of achieving design reuse and automation. The aim of the ongoing research at Linköping University is to shift from manual modeling of disposable geometries to Computer Aided Design (CAD) automation by introducing generic high level geometry templates. Instead of repeatedly modeling similar instances of objects, engineers should be able to create more general models that can represent entire classes of objects. The proposed methodology enables utilization of commercial design tools, hence taking industrial feasibility into consideration. The concept of High Level CAD templates (HLCt) will be proposed and discussed as the building blocks of flexible and robust CAD models, which in turn enables high fidelity geometry in the MDO loop. Furthermore, quantification of the terms flexibility and robustness are presented, providing a means to measure the quality of the geometry models. Finally, application examples are presented in which the outlined framework is evaluated. The applications have been chosen from three ongoing research projects aimed at automating the design of transport aircraft, industrial robots, and micro air vehicles.     

A concept-based information retrieval approach for engineering domain-specific technical documents

Technical documents, which often have complicated structures, are often produced during Architecture/Engineering/Construction (A/E/C) projects and research. Applying information retrieval (IR) techniques directly to long or multi-topic documents often does not lead to satisfactory results. One way to address the problem is to partition each document into several “passages”, and treat each passage as an independent document. In this research, a novel passage partitioning approach is designed. It generates passages according to domain knowledge, which is represented by base domain ontology. Such a passage is herein defined as an OntoPassage. In order to demonstrate the advantage of the OntoPassage partitioning approach, this research implements a concept-based IR system to illustrate the application of such an approach. The research also compares the OntoPassage partitioning approach with several conventional passage partitioning approaches to verify its IR effectiveness. It is shown that, with the proposed OntoPassage approach, IR effectiveness on domain-specific technical reports is as good as conventional passage partitioning approaches. In addition, the OntoPassage approach provides the possibility to display the concepts in each passage, and concept-based IR may thus be implemented.     

Optimal configuration of Energy-Efficient Ethernet

The IEEE 802.3az standard provides a new low power mode that Ethernet network interfaces can use to save energy when there is no traffic to transmit. Simultaneously with the final standard approval, several algorithms were proposed to govern the physical interface state transition between the normal active mode and the new low power mode. In fact, the standard leaves this sleeping algorithm unspecified to spur competition among different vendors and achieve the greatest energy savings. In this paper, we try to bring some light to the most well known sleeping algorithms, providing mathematical models for the expected energy savings and the average packet delay inflicted on outgoing traffic. We will then use the models to derive optimum configuration parameters for them under given efficiency constraints.     

Supply chain trust diagnosis (SCTD) using inductive case-based reasoning ensemble (ICBRE): The case of general competence trust diagnosis

General competence trust among supply chain partners, referring to the trust that a partner holds the general ability of fulfilling contracts, is a critical factor to ensure effective cooperation in a supply chain, especially in the current financial crisis. The method of supply chain trust diagnosis (SCTD) is to evaluate whether or not a partner holds such competence. This research devotes to an early investigation on diagnosing competence trust of supply chain with the method of inductive case-based reasoning ensemble (ICBRE). The so-called supply chain trust diagnosis with inductive case-based reasoning ensemble consists of five levels, that is, information level, the level of ratios of general competence states, the level of inductive case-based reasoning, ensemble level, and diagnosis result level. Knowledge for diagnosing competence trust, which composes of a case base, is hidden in data represented by ratios of general competence states. Inductive approach is combined with randomness to construct diverse and good member methods of inductive case-based reasoning. Finally, simple voting is used to integrate outputs of member inductive case-based reasoning methods in order to produce the final diagnosis on whether or not a partner holds the general ability of fulfilling contracts. We statistically validated results of the method of supply chain trust diagnosis with inductive case-based reasoning ensemble by comparing them with those of multivariate discriminant analysis, logistic regression, single Euclidean case-based reasoning, and single inductive case-based reasoning. The results indicate that the method of supply chain trust diagnosis with inductive case-based reasoning ensemble significantly improves predictive capability of case-based reasoning in this problem and outperforms all the comparative models by group decision of several decision-making agents and non-strict assumptions like statistical methods.     

Evolutionary Generalized Radial Basis Function neural networks for improving prediction accuracy in gene classification using feature selection

Radial Basis Function Neural Networks (RBFNNs) have been successfully employed in several function approximation and pattern recognition problems. The use of different RBFs in RBFNN has been reported in the literature and here the study centres on the use of the Generalized Radial Basis Function Neural Networks (GRBFNNs). An interesting property of the GRBF is that it can continuously and smoothly reproduce different RBFs by changing a real parameter τ. In addition, the mixed use of different RBF shapes in only one RBFNN is allowed. Generalized Radial Basis Function (GRBF) is based on Generalized Gaussian Distribution (GGD), which adds a shape parameter, τ, to standard Gaussian Distribution. Moreover, this paper describes a hybrid approach, Hybrid Algorithm (HA), which combines evolutionary and gradient-based learning methods to estimate the architecture, weights and node topology of GRBFNN classifiers. The feasibility and benefits of the approach are demonstrated by means of six gene microarray classification problems taken from bioinformatic and biomedical domains. Three filters were applied: Fast Correlation-Based Filter (FCBF), Best Incremental Ranked Subset (BIRS), and Best Agglomerative Ranked Subset (BARS); this was done in order to identify salient expression genes from among the thousands of genes in microarray data that can directly contribute to determining the class membership of each pattern. After different gene subsets were obtained, the proposed methodology was performed using the selected gene subsets as new input variables. The results confirm that the GRBFNN classifier leads to a promising improvement in accuracy.     

Strengthening of nickel -base superalloys for nuclear heat exchanger application

Strengthening mechanisms of nickel-base superalloys have been discussed with the background of the Japanese research and development activities in this field. As candidates for materials of intermediate heat exchangers which will be used for a future programme of nuclear steelmaking systems, two kinds of alloys have successfully been developed in Japan. The designs of these alloys have been reviewed from metallurgical aspects including their composition and creep properties. In addition to the conventional methods to strengthen these alloys, such as solid solution hardening or particle precipitation hardening, a grain-boundary precipitation strengthening due to tungsten-rich ₂ phase in the Ni-Cr-W system, would be expected as a further advanced method.     

Granulometric Analysis of Powder Materials Used for Producing Samples by Method of Selective Laser Melting

Metallurgist - The paper discusses the aspects of granulometric and morphological analysis of powders used in the technology of selective laser melting. Studies have been carried out to obtain...