The solution growth route and characterization of electrochromic tungsten oxide thin films

Electrochromic tungsten oxide thin films were prepared by using an aqueous solution of Na₂WO₄·2H₂O and dimethyl sulfate. Various techniques were used for the characterization of the films such as X-ray diffraction, cyclic voltammetry, SEM analysis and VIS-spectroscopy. The thin film durability was tested in an aqueous solution of LiClO₄ (0.1 mol/dm³) for about 7000 cycles followed by cyclic voltammetry. No significant changes in the cyclic voltammograms were found, thus proving the high durability of the films.The optical transmittance spectra of coloured and bleached states showed significant change in the transmittance, which makes these films favorable for electrochromic devices.     

Dynamic Virtual Resource Allocation in Virtualized multi-RAT Cellular Networks

The legacy wireless systems are designed to exploit static configuration and deployment, and cannot handle the discrepancies of the spatio-temporal traffic demand. Cloud RAN (C-RAN) is a novel flexible radio technology that utilizes the virtualization concepts and can efficiently address the static deployment of conventional wireless systems. The C-RAN also leverages high radio network flexibility by introducing the network function virtualization approach to wireless networks. This paper presents a novel C-RAN platform that virtualizes and operates with full GSM and LTE systems. The presented platform is solely based on open-source and off the shelf solutions, providing easy implementation, low cost and high scalability. The paper also introduces a novel dynamic resource allocation algorithm that facilitates the C-RAN’s optimal performance in dynamic scenarios. The proposed algorithm is analyzed and validated on the presented C-RAN platform. The results of the performance analysis clearly show the advantages of the proposed dynamic resource allocation algorithm. Moreover, they prove the applicability of the C-RAN platform for variety of different scenarios.     

Inductive process modeling

In this paper, we pose a novel research problem for machine learning that involves constructing a process model from continuous data. We claim that casting learned knowledge in terms of processes with associated equations is desirable for scientific and engineering domains, where such notations are commonly used. We also argue that existing induction methods are not well suited to this task, although some techniques hold partial solutions. In response, we describe an approach to learning process models from time-series data and illustrate its behavior in three domains. In closing, we describe open issues in process model induction and encourage other researchers to tackle this important problem. Editor: David Page.     

Handling three-winding transformers and loads in short circuit analysis by the admittance summation method

The admittance summation method is efficient in short circuit analysis of radial and weakly meshed networks. It is especially powerful if all node loads can be represented as any combination of constant impedance and constant current load component. However, in the existing literature one cannot find any explanation how to handle power transformer in that approach. For that reason, the corresponding equations have been developed in this paper along with a procedure for development of a three-winding transformer admittance matrix. In addition, a procedure for handling an arbitrary load was proposed. A large number of tests were performed and results were compared with that obtained by the previously published method. As an illustration, some of the results for single fault currents are presented. It can be concluded that the proposed approach makes the admittance summation method capable to take into account all network elements in a proper way.     

Combining Classifiers with Meta Decision Trees

The paper introduces meta decision trees (MDTs), a novel method for combining multiple classifiers. Instead of giving a prediction, MDT leaves specify which classifier should be used to obtain a prediction. We present an algorithm for learning MDTs based on the C4.5 algorithm for learning ordinary decision trees (ODTs). An extensive experimental evaluation of the new algorithm is performed on twenty-one data sets, combining classifiers generated by five learning algorithms: two algorithms for learning decision trees, a rule learning algorithm, a nearest neighbor algorithm and a naive Bayes algorithm. In terms of performance, stacking with MDTs combines classifiers better than voting and stacking with ODTs. In addition, the MDTs are much more concise than the ODTs and are thus a step towards comprehensible combination of multiple classifiers. MDTs also perform better than several other approaches to stacking.     

Presentation of dermatological images on the Internet

In this paper, we focused on selected problems of integrating and presenting medical images organised in a World Wide Web (WWW) database. To solve these problems we developed a prototype of a bilingual (Slovenian and English) WWW database of medical images for the field of dermatology. This dermatology database includes a graphic interface with four modes of access: (1) browsing, (2) searching, (3) comparison of images, and (4) self-testing. The quantity and quality of requests to this WWW database was estimated with log file analysis. There was a steady increase in the number of users and volume of data transferred from the dermatology WWW database.     

Deterministic predictive models for DC voltage reference sourcecontrol

In this paper, we use predictive models for voltage correction in a high-precision DC voltage reference source (DCVRS) based on Zener diodes. Voltage reference elements, which compose the DCVRS, are improved by implementing a control loop with built-in predictors. Thus, the sensitivity of the system is reduced and, thereby, the stability of the DCVRS is improved. The predictive abilities of two different paradigms, neural-network-based predictors and difference equation predictors obtained by equation discovery system LAGRAMGE, are compared     

Repetitive interpolation: A robust algorithm for DTM generation from Aerial Laser Scanner Data in forested terrain

We present a new algorithm for digital terrain model (DTM) generation from an airborne laser scanning point cloud, called repetitive interpolation (REIN). It is especially applicable in steep, forested areas where other filtering algorithms typically have problems distinguishing between ground returns and off-ground points reflected in the vegetation. REIN can produce a DTM either in a vector grid or in a TIN data structure. REIN is applied after an initial filtering, which involves removal of all negative outliers and removal of many, but not necessarily all, off-ground points by some existing filtering algorithm. REIN makes use of the redundancy in the initially filtered point cloud (FPC) in order to mitigate the effect of the residual off-ground points. Multiple independent random samples are taken from the initial FPC. From each sample, ground elevation estimates are interpolated at individual DTM locations. Because the lower bounds of the distributions of the elevation estimates at each DTM location are almost insensitive to positive outliers, the true ground elevations can be approximated by adding the global mean offset to the lower bounds, which is estimated from the data. The random sampling makes REIN unique among the methods of filtering airborne laser data. While other filters behave deterministically, always generating a filter error in special situations, in REIN, because of its random aspects, these errors do not occur in each sample, and typically cancel out in the final computation of DTM elevations. Reduction of processing time by parallelization of REIN is possible. REIN was tested in a test area of 2 hectares, encompassing steep relief covered by mixed forest. An Optech ALTM 1020 lidar was used, with a flying height of 260-300 m above the ground, the beam divergence was 0.3 mrad, and the obtained point cloud density for the last returns was 8.5 m^− 2. A DTM grid was generated with 1 m horizontal resolution. The root mean square elevation error of the DTM ranged between ± 0.16 m and ± 0.37 m, depending on REIN sampling rate and number of samples taken, the lowest value achieved with 4 samples and using a 23% sampling rate. The paper also gives a short overview on existing filtering algorithms.