Time-domain analysis of lossy active transmission lines using FDTD method

In this paper, an accurate and efficient method for analysis of a GaAs MESFET including frequency-dependent losses of the electrodes in the time domain is presented. The time domain analysis is obtained based on the fully distributed model using finite-difference time-domain (FDTD) technique, with the assumption of the skin effect losses. The time-domain results are verified using the conventional time-domain to frequency-domain (TDFD) solution technique.     

Lumped-element analysis and design of CMOS distributed amplifiers with image impedance termination

This paper presents a systematic matrix-based lumped-element analysis of CMOS distributed amplifiers (DAs). Since transmission lines (TLs) of the DAs are artificially constructed from a ladder of a finite number of inductors and capacitors, the conventional TL-based analysis of microwave DAs can not be accurately applied to CMOS DAs. The proposed lumped-analysis method is also more intuitive for analog circuit designers than the TL analysis adapted from microwave amplifiers analysis because it provides the performance characteristics of the amplifiers as functions of circuit elements values, and not the TL characteristics. The image impedance technique is used for the design of input/output terminating networks. A new image impudence matrix is defined to accommodate the extension of the theory from two- to four-port networks, and a practical realization of the image impedance matrix is presented using the available circuit elements in CMOS technology. The simulation results clearly indicate an improved voltage gain and a better gain uniformity over the bandwidth of the proposed DA design terminated at its image impedance compared with the amplifier terminated at its nominal TL characteristics impedance.     

Computerized optimization of bridge structures

A unified bridge design optimization theory has been developed by a research team composed of personnel from the College of Engineering, Louisiana State University working in close collaboration with engineers from the Louisiana Department of Highways. The theory, useful in the design of multispan, simply supported highway bridges considers simultaneously the structure and the geometry of the bridge. Soil and terrain conditions, construction costs, shape, and other parameters can be investigated simultaneously with dynamic programming, an optimization technique applicable to serial systems of the type to which these structures belong.A computer program developed and now completely operational provides the bridge design engineer with an efficient, flexible tool for evaluating a multitude of preliminary designs. The average Central Processing Unit time required for a typical bridge is in the order of l min per spanonanIBM360/65configuration.The program generates the lowest cost bridge and all other bridge structures that fall within any specified percentage of the minimum cost (1 or 2 per cent, for example). The range is specified by the user. In addition the program has cycling capabilities for selecting the most economical configuration (number, lengths and types of spans).Costs, soil conditions, geometry, material properties, and types of decks, girders and bents are specified by the designer such that the program can be used as a simulation tool for selecting the most economical structure quickly and efficiently. Constraints can be imposed upon the bridge geometry by specifying locations of bents or span center lines or both, to account for special conditions such as bridge crossings, existing roadways, and other physical obstructions.The optimization program is modular and operates heuristically, that is, it permits full man-machine interaction not only through the input of data but also through structural design module interehangeability allowing the user to incorporate his own procedures as subroutines.The complete program is fully documented with a users manual and a programmers manual.     

Preparation and characterization of various morphologies of SrFe<Subscript>12</Subscript>O<Subscript>19</Subscript> nano-structures: investigation of magnetization and coercivity

Hard magnetic SrFe₁₂O₁₉ (SrFe) nanostructures were synthesized by a facile chemical precipitation procedure. The influence of temperature, concentration and different capping agents on the particle size and morphology of the magnetic nanoparticles was investigated. The synthesized ferrites were characterized by X-ray diffraction pattern, scanning electron microscope, and Fourier transform infrared spectroscopy. Ferromagnetic property of the hexaferrite nanostructures was determined by vibrating sample magnetometer. The results show hard magnetic ferrite with a high coercivity about 2800–4000 Oe and saturation magnetization around 11–14 emu/g were synthesized.     

RLSP: a signal prediction algorithm for energy conservation in wireless sensor networks

In many real world applications of wireless sensor networks, it is enough for the sensors to send just an approximation of their observations. In these networks dual prediction scheme (DPS)—including two predictive models one in the sensor side and its copy in the sink side—is widely used. In DPS, the total data transmission through the network is a function of the model’s prediction power and the size of its free parameters. In this paper, a DPS using a reinforcement learning based signal predictor (RLSP) algorithm is proposed. RLSP learns the environment’s signal and builds the predictive model gradually based on its experiences. At the moment the model gets invalid, RLSP only needs to learn and transmit the environmental data of that moment. As a result, the amount of data transmission in the network and consequently energy consumption is very low. The simulation results on 16 benchmarking signals and comparison with time series-based DPSs confirm these properties of RLSP.     

Decision support framework for risk management on sea ports and terminals using fuzzy set theory and evidential reasoning approach

As sea ports and terminals are valuable assets, in today’s uncertain and complex environment further refinements are needed to assess risks and prioritise protective measures for these critical pieces of logistics infrastructure. The major problem that port professionals (e.g. port risk managers and port auditors) are facing is the lack of an appropriate methodology and evaluation techniques to support their risk management (RM) cycle. Therefore in response to the uncertainties and to provide continuous risk control assurance in port industry, this paper uses fuzzy set theory (FST) to describe and evaluate the associated risk factors within the ports and terminals operations and management (PTOM). An evidential reasoning (ER) approach is employed to synthesise the information produced. These processes constitute a decision support framework that will be used to conduct port-to-port risk evaluations or to assess a whole port’s and terminal’s overall risk level in order to facilitate continuous improvement strategies. The proposed framework along with a generic methodology and a risk evaluation model is tested by a case study. The case study analyses pieces of three Southern Iranian ports by using an illustrative operational risk hierarchy. The sensitivity analysis carried out in this paper prove pieces of the applicability of the proposed methodology and model for risk evaluation of the sea ports and terminals in real situations.     

Photocatalytic degradation of azo dye using nano-ZrO<Subscript>2</Subscript>/UV/Persulfate: Response surface modeling and optimization

Dyes have always been considered in the context of recalcitrant organic pollutants in water. The present research has focused on the decolorization of Direct Blue 71 (DB71) using photocatalysis process of nano-ZrO₂/UV/ Persulfate. Response surface method with central composite design was applied to determine the effects of four main factors (time, ZrO₂ dosage, persulfate dosage and pH) on decolorization of DB71. The results indicated that the obtained quadratic model had a high R-squared coefficient based on the analysis of variance (ANOVA). Time had the highest effect (45.5%) on decolorization of DB71. The optimum condition predicted for complete decolorization was pH=7, 0.4 g ZrO₂, 0.75 mM persulfate and 40 min reaction time. Verification experiments confirmed that there was good agreement between the experimental and predicted responses. The studied photocatalytic process could oxidize and destruct the structure of the DB71, and average oxidation state (AOS) significantly increased from ?1.5 to +1.33, indicating the presence of more oxidized by-products and, consequently, improvement of biodegradability. The quenching tests showed that sulfate radical was the major agent in DB71 decolorization. It can be concluded that nano-ZrO₂/ UV/Persulfate is a very effective process for decolorization of colored wastewater.     

Ultracompact two‐way and four‐way SIW/HMSIW power dividers loaded by complementary split‐ring resonators

In this paper, two ultracompact power dividers based on the substrate integrated waveguide (SIW) and half‐mode SIW (HMSIW) technologies loaded by complementary split‐ring resonators (CSRRs) are presented. The presented structures are designed based on the theory of evanescent mode propagation. To obtain a size reduction, the CSRR unit cells are etched on the metallic surface of the SIW and HMSIW structures. First, a two‐way HMSIW power divider is reported. In this circuit, the concept of HMSIW is utilized aiming at a further size reduction in addition to the size reduction by the CSRR unit cells. Then, a four‐way SIW power divider is designed so that the direct coaxial feed is used for the input port and microstrip transmission lines are used for the output ports. Both two‐way and four‐way SIW/HMSIW power dividers at 5.8 GHz covering WLAN are designed, fabricated, and measured. They respectively have 0.18 × 0.21 λg² and 0.38 × 0.21 λg² total size. A fair agreement between simulated and measured results is achieved. The measured insertion losses are 0.5 ± 0.5 and 0.6 ± 0.5 dB for the two‐way and four‐way SIW/HMSIW power dividers, respectively, in the operating band of interest.     

Automating image segmentation verification and validation by learning test oracles

An image segmentation algorithm delineates (an) object(s) of interest in an image. Its output is referred to as a segmentation. Developing these algorithms is a manual, iterative process involving repetitive verification and validation tasks. This process is time-consuming and depends on the availability of experts, who may be a scarce resource (e.g., medical experts). We propose a framework referred to as Image Segmentation Automated Oracle (ISAO) that uses machine learning to construct an oracle, which can then be used to automatically verify the correctness of image segmentations, thus saving substantial resources and making the image segmentation verification and validation task significantly more efficient. The framework also gives informative feedback to the developer as the segmentation algorithm evolves and provides a systematic means of testing different parametric configurations of the algorithm. During the initial learning phase, segmentations from the first few (optimally two) versions of the segmentation algorithm are manually verified by experts. The similarity of successive segmentations of the same images is also measured in various ways. This information is then fed to a machine learning algorithm to construct a classifier that distinguishes between consistent and inconsistent segmentation pairs (as determined by an expert) based on the values of the similarity measures associated with each segmentation pair. Once the accuracy of the classifier is deemed satisfactory to support a consistency determination, the classifier is then used to determine whether the segmentations that are produced by subsequent versions of the algorithm under test, are (in)consistent with already verified segmentations from previous versions. This information is then used to automatically draw conclusions about the correctness of the segmentations. We have successfully applied this approach to 3D segmentations of the cardiac left ventricle obtained from CT scans and have obtained promising results (accuracies of 95%). Even though more experiments are needed to quantify the effectiveness of the approach in real-world applications, ISAO shows promise in increasing the quality and testing efficiency of image segmentation algorithms.     

Learning to rank: new approach with the layered multi-population genetic programming on click-through features

Users’ click-through data is a valuable source of information about the performance of Web search engines, but it is included in few datasets for learning to rank. In this paper, inspired by the click-through data model, a novel approach is proposed for extracting the implicit user feedback from evidence embedded in benchmarking datasets. This process outputs a set of new features, named click-through features. Generated click-through features are used in a layered multi-population genetic programming framework to find the best possible ranking functions. The layered multi-population genetic programming framework is fast and provides more extensive search capability compared to the traditional genetic programming approaches. The performance of the proposed ranking generation framework is investigated both in the presence and in the absence of explicit click-through data in the utilized benchmark datasets. The experimental results show that click-through features can be efficiently extracted in both cases but that more effective ranking functions result when click-through features are generated from benchmark datasets with explicit click-through data. In either case, the most noticeable ranking improvements are achieved at the tops of the provided ranked lists of results, which are highly targeted by the Web users.