One-Way Functions Using Algorithmic and Classical Information Theories

We prove several results relating injective one-way functions, time-bounded conditional Kolmogorov complexity, and time-bounded conditional entropy. First we establish a connection between injective, strong and weak one-way functions and the expected value of the polynomial time-bounded Kolmogorov complexity, denoted here by?E(K ^t (x|f(x))). These results are in both directions. More precisely, conditions on?E(K ^t (x|f(x))) that imply that?f is a weak one-way function, and properties of?E(K ^t (x|f(x))) that are implied by the fact that?f is a strong one-way function. In particular, we prove a separation result: based on the concept of time-bounded Kolmogorov complexity, we find an interval in which every function?f is a necessarily weak but not a strong one-way function. Then we propose an individual approach to injective one-way functions based on Kolmogorov complexity, defining Kolmogorov one-way functions and prove some relationships between the new proposal and the classical definition of one-way functions, showing that a Kolmogorov one-way function is also a deterministic one-way function. A relationship between Kolmogorov one-way functions and the conjecture of polynomial time symmetry of information is also proved. Finally, we relate?E(K ^t (x|f(x))) and two forms of time-bounded entropy, the unpredictable entropy?H ^unp, in which ??one-wayness?? of a function can be easily expressed, and the Yao⁺ entropy, a measure based on compression/decompression schema in which only the decompressor is restricted to be time-bounded.     

Non-Pauli observables for CWS codes

It is known that nonadditive quantum codes can have higher code dimensions than stabilizer codes for the same length and minimum distance. The class of codeword stabilized codes (CWS) provides tools to obtain new nonadditive quantum codes by reducing the problem to finding nonlinear classical codes. In this work, we establish some results on the kind of non-Pauli operators that can be used as observables in the decoding scheme of CWS codes and propose a procedure to obtain those observables.     

Refractive indices of layers and optical simulations of Cu(In,Ga)Se2 solar cells

Cu(In,Ga)Se₂-based solar cells have reached efficiencies close to 23%. Further knowledge-driven improvements require accurate determination of the material properties. Here, we present refractive indices for all layers in Cu(In,Ga)Se₂ solar cells with high efficiency. The optical bandgap of Cu(In,Ga)Se₂ does not depend on the Cu content in the explored composition range, while the absorption coefficient value is primarily determined by the Cu content. An expression for the absorption spectrum is proposed, with Ga and Cu compositions as parameters. This set of parameters allows accurate device simulations to understand remaining absorption and carrier collection losses and develop strategies to improve performances.     

On the design of multi‐rate tracking controllers: Application to rotorcraft guidance and control

This paper presents a new methodology for the design and implementation of gain‐scheduled controllers for multi‐rate systems. The proposed methodology provides a natural way to address the integrated guidance and control problem for autonomous vehicles when the outputs are sampled at different instants of time. A controller structure is first proposed for the regulation of non‐square multi‐rate systems with more measured outputs than inputs. Based on this structure, an implementation for a gain‐scheduled controller is obtained that satisfies an important property known as the linearization property. The implementation resembles the velocity implementation for single‐rate systems. The method is then applied to the problem of steering an autonomous rotorcraft along a predefined trajectory defined in terms of space and time coordinates. By considering a convenient error vector to describe the vehicle's dynamics, the trajectory tracking problem is reduced to that of regulating the error variables to zero. Because of the periodic multi‐rate nature of the onboard sensor suite, the controller synthesis is dealt with under the scope of linear periodic systems theory. Simulation results obtained with a full non‐linear rotorcraft dynamic model are presented and discussed. Copyright © 2010 John Wiley & Sons, Ltd.     

On fluidization of discrete event models: observation and control of continuous Petri nets

As a preliminary overview, this work provides first a broad tutorial on the fluidization of discrete event dynamic models, an efficient technique for dealing with the classical state explosion problem. Even if named as continuous or fluid, the relaxed models obtained are frequently hybrid in a technical sense. Thus, there is plenty of room for using discrete, hybrid and continuous model techniques for logical verification, performance evaluation and control studies. Moreover, the possibilities for transferring concepts and techniques from one modeling paradigm to others are very significant, so there is much space for synergy. As a central modeling paradigm for parallel and synchronized discrete event systems, Petri nets (PNs) are then considered in much more detail. In this sense, this paper is somewhat complementary to David and Alla (2010). Our presentation of fluid views or approximations of PNs has sometimes a flavor of a survey, but also introduces some new ideas or techniques. Among the aspects that distinguish the adopted approach are: the focus on the relationships between discrete and continuous PN models, both for untimed, i.e., fully non-deterministic abstractions, and timed versions; the use of structure theory of (discrete) PNs, algebraic and graph based concepts and results; and the bridge to Automatic Control Theory. After discussing observability and controllability issues, the most technical part in this work, the paper concludes with some remarks and possible directions for future research.     

Designing vulnerability testing tools for web services: approach,components, and tools

This paper proposes a generic approach for designing vulnerability testing tools for web services, which includes the definition of the testing procedure and the tool components. Based on the proposed approach, we present the design of three innovative testing tools that implement three complementary techniques (improved penetration testing, attack signatures and interface monitoring, and runtime anomaly detection) for detecting injection vulnerabilities, thus offering an extensive support for different scenarios. A case study has been designed to demonstrate the tools for the particular case of SQL Injection vulnerabilities. The experimental evaluation demonstrates that the tools can effectively be used in different scenarios and that they outperform well-known commercial tools by achieving higher detection coverage and lower false-positive rates.     

Fault detection and isolation in robotic manipulators via neural networks: A comparison among three architectures for residual analysis

In this article we discuss artificial neural networks‐based fault detection and isolation (FDI) applications for robotic manipulators. The artificial neural networks (ANNs) are used for both residual generation and residual analysis. A multilayer perceptron (MLP) is employed to reproduce the dynamics of the robotic manipulator. Its outputs are compared with actual position and velocity measurements, generating the so‐called residual vector. The residuals, when properly analyzed, provides an indication of the status of the robot (normal or faulty operation). Three ANNs architectures are employed in the residual analysis. The first is a radial basis function network (RBFN) which uses the residuals of position and velocity to perform fault identification. The second is again an RBFN, except that it uses only the velocity residuals. The third is an MLP which also performs fault identification utilizing only the velocity residuals. The MLP is trained with the classical back‐propagation algorithm and the RBFN is trained with a Kohonen self‐organizing map (KSOM). We validate the concepts discussed in a thorough simulation study of a Puma 560 and with experimental results with a 3‐joint planar manipulator. © 2001 John Wiley & Sons, Inc.     

Urinary proteomic analysis of chronic allograft nephropathy

The pathogenesis of progressive renal allograft injury, which is termed chronic allograft nephropathy (CAN), remains obscure and is currently defined by histology. Prospective protocol-biopsy trials have demonstrated that clinical and standard laboratory tests are insufficiently sensitive indicators of the development and progression of CAN. The study aim was to determine if CAN could be characterized by urinary proteomic data and identify the proteins associated with disease. The urinary proteome of 75 renal transplant recipients and 20 healthy volunteers was analyzed using surface enhanced laser desorption and ionization MS. Patients could be classified into subgroups with normal histology and Banff CAN grades 2-3 with a sensitivity of 86% and a specificity of 92% by applying the classification algorithm Adaboost to urinary proteomic data. Several urinary proteins associated with advanced CAN were identified including α1-microglobulin, β2-microglobulin, prealbumin, and endorepellin, the antiangiogenic C-terminal fragment of perlecan. Increased urinary endorepellin was confirmed by ELISA and increased tissue expression of the endorepellin/perlecan ratio by immunofluoresence analysis of renal biopsies. In conclusion, analysis of urinary proteomic data has further characterized the more severe CAN grades and identified urinary endorepellin, as a potential biomarker of advanced CAN.     

A co-evolutionary differential evolution algorithm for solving min–max optimization problems implemented on GPU using C-CUDA

Several areas of knowledge are being benefited with the reduction of the computing time by using the technology of graphics processing units (GPU) and the compute unified device architecture (CUDA) platform. In case of evolutionary algorithms, which are inherently parallel, this technology may be advantageous for running experiments demanding high computing time. In this paper, we provide an implementation of a co-evolutionary differential evolution (DE) algorithm in C-CUDA for solving min–max problems. The algorithm was tested on a suite of well-known benchmark optimization problems and the computing time has been compared with the same algorithm implemented in C. Results demonstrate that the computing time can significantly be reduced and scalability is improved using C-CUDA. As far as we know, this is the first implementation of a co-evolutionary DE algorithm in C-CUDA.     

Influence of diameter in the Raman spectra of aligned multi-walled carbon nanotubes

Aligned multi-walled carbon nanotubes (MWCNTs) were produced with different diameters, according to the thickness of Ni catalyst layer, by microwave plasmas enhanced chemical vapor deposition, in N₂/H₂/CH₄ environment. Raman spectroscopy, performed both on top and lateral surfaces, revealed that the relative intensity of the G′ band and the G and G′ bandwidths have high sensitivity to tube diameter distribution and also to structural variations along the tube axis. The D band present a distinct behavior: I_D/I_G is sensitive to structural organization, particularly along tube axis, while D bandwidth is sensitive to tube diameter distribution. This result may indicate D bandwidth as a parameter to correlate to the diameter of the aligned MWCNTs.