New scanning technique for the optical vortex microscope

In the optical vortex microscopy the focused Gaussian beam with optical vortex scans a sample. An optical vortex can be introduced into a laser beam with the use of a special optical element--a vortex lens. When moving the vortex lens, the optical vortex changes its position inside the spot formed by a focused laser beam. This effect can be used as a new precise scanning technique. In this paper, we study the optical vortex behavior at the sample plane. We also estimate if the new scanning technique results in observable effects that could be used for a phase object detection.     

Influence of Variety,Moisture Content,Kernel Size and Applied Current Frequency on the Electric Properties of Wheat Grain

The work aimed at determination of electric properties of wheat grain in dependence on its variety, moisture, geometrical features of kernels and applied current frequency. Wheat grain of 4 Polish winter varieties: Korweta, Juma, Mikon and Kobra from harvest 2001 were used as the material for study. Grain was sized into 3 fractions: (1) > 2,8mm, (2) 2,5–2,8mm, and (3) 2,2–2,5mm. Basic geometrical features were determined for not sorted grain (control sample) and its three fractions by the use of digital image analysis. Electric properties of grain (at 11% and 15% moisture content) have been performed with the Hewlett Packard 4263B meter. Measurements of impedance, resistance, admittance, conductance, as well as equivalent parallel capacitance and equivalent series capacitance were made. Obtained results were subjected to statistical analysis with the use of Statistica? programme. Changes in electric properties of grain significantly depended on all of studied factors. Most of all significant correlations appeared between geometrical features and studied electric properties of grain of 15% moisture. Statistical analysis of the results proved significant linear correlations between electric properties of kernels and their length, perimeter and circularity coefficient R_C2 at higher measurement frequencies.     

Dialkyldithiophosphate Acids (HDDPs) as Effective Lubricants of Sol–Gel Titania Coatings in Technical Dry Friction Conditions

The goal of this study was the investigation of the effectiveness of dialkyldithiophosphate acids (HDDPs) films in improving the tribological properties of thin, sol–gel derived titania coatings. Amorphous, anatase, and rutile titania coatings were obtained using sol–gel dip–coating deposition after treatment at 100, 500, and 1,000 °C, respectively. Titania coatings were then modified from the liquid phase by HDDPs acids having dodecyl-(C₁₂), tetradecyl-(C₁₄), and hexadecyl-(C₁₆) alkyl chains deposited by dip–coating (DC) and Langmuir–Blodgett (LB) methods. The influence of the deposition procedure, the length of the HDDPs alkyl chain and the type of titania substrate on the surface morphology and tribological properties were studied. It was found, using wetting contact angle measurements, that these modifications of titania coatings decrease the surface free energy and increase its hydrophobicity. The surface topography imaged by Atomic force microscopy (AFM), exhibit island-like or agglomerate features for the DC deposition method, while smooth topographies were observed for LB depositions. Tribological tests were conducted by means of a microtribometer operating in the normal load range 30–100 mN. An enhancement of tribological properties was observed upon modification, as compared to unmodified titania.     

A Feedback Transformation of the Planning Problems

This paper proposes a problem transformation method, called feedback transformation method, for solving the planning problems. Some basic notions from the theory of problem transformation are introduced. Two measures of problem complexity are defined. Algebraic properties of the feedback transformation are investigated. Finally, a partial relationship between the problem transformation and decomposition methods is established. Received: January 21, 1994; revised version: May 25, 1998     

Improved dynamic routing algorithms in elastic optical networks

The considered Routing, Modulation Level and Spectrum Allocation (RMLSA) problem covers path length minimization while still meeting the spectrum continuity constraints, non-overlapping spectra constraints, spectrum contiguity constraints, and choice of modulation level. In this problem, the relationship between spectrum connection and bit rate of the incoming request has been taken into account. In this paper, two algorithms have been proposed. The first one is based on a set of shortest paths whose lengths are determined by the number of links in the network. The second one is a modified version of Dijkstra’s algorithm. Modification of this algorithm is based on introduction of weights of the network links depending on the utilization of the links and the introduction of a aggregated spectrum of the path that is being selected. Extensive research has shown that algorithms reject the lower traffic volume as compared to well-known algorithms proposed in literature.     

Designing RBF Networks Using the Agent-Based Population Learning Algorithm

Radial Basis Function Neural Networks (RBFNs) are nowadays quite popular due to their ability to discover and approximate complex nonlinear dependencies within the data under analysis. Performance of the RBF network depends on numerous factors related to its initialization and training. The paper proposes an approach to the radial basis function networks design, where initial parameters of the network, output weights and parameters of the transfer function are set using the proposed agent-based population learning algorithm (PLA). The algorithm is validated experimentally. Advantages and main features of the PLA-based RBF designs are discussed basing on results of the computational experiment.     

Ensemble Online Classifier Based on the One-Class Base Classifiers for Mining Data Streams

The problem addressed in this study concerns mining data streams with concept drift. The goal of the article is to propose and validate a new approach to mining data streams with concept-drift using the ensemble classifier constructed from the one-class base classifiers. It is assumed that base classifiers of the proposed ensemble are induced from incoming chunks of the data stream. Each chunk consists of prototypes and information about whether the class prediction of these instances, carried-out at earlier steps, has been correct. Each data chunk can be updated by using the instance selection technique when new data arrive. When a new data chunk is formed, the ensemble model is also updated on the basis of weights assigned to each one-class classifier. In this article, two well-known instance-based learning algorithms—the CNN and the ENN—have been adopted to solve the one-class classification problems and, consequently, update the proposed classifier ensemble. The proposed approaches have been validated experimentally, and the computational experiment results are shown and discussed. The experiment results prove that the proposed approach using the ensemble classifier constructed from the one-class base classifiers with instance selection for chunk updating can outperform well-known approaches for data streams with concept drift.     

The Influence of Process Parameters on the Low-Cycle Fatigue Properties of 316L Steel Parts Produced by Powder Bed Fusion

Metallurgical and Materials Transactions A - In this paper, the influence of the additive manufacturing (AM), powder bed fusion (PBF) process parameters on the low-cycle fatigue (LCF) properties of...