Effect of silver nano particles on the fluorescence quantum yield of Rhodamine 6G determined using dual beam thermal lens method

Nano structured noble metals have very important applications in diverse fields as photovoltaics, catalysis, electronic and magnetic devices, etc. Here, we report the application of dual beam thermal lens technique for the determination of the effect of silver sol on the absolute fluorescence quantum yield (FQY) of the laser dye rhodamine 6G. A 532 nm radiation from a diode pumped solid state laser was used as the excitation source. It has been observed that the presence of silver sol decreases the fluorescence quantum efficiency. This is expected to have a very important consequence in enhancing Raman scattering which is an important spectrochemical tool that provides information on molecular structures. We have also observed that the presence of silver sol can enhance the thermal lens signal which makes the detection of the signal easier at any concentration.

Reactive loaded microstrip leaky wave antenna for low cost beam steering applications

A novel fixed frequency beam scanning microstrip leaky wave antenna is reported. The beam scanning at fixed frequency is achieved by reactive loading. Simulation and measured results shows frequency scanability of 80° as well as fixed frequency beam steering of 68° over the −10 dB impedance band of 4.56–5.06 GHz.

Beam shaping of sectoral electromagnetic horn antennas using corner reflector technique

In the present thesis, possibility of beam shaping of sectoral horns and corner reflector systems'has been studied in detail. The experimental results obtained in the above two cases are compared. As far as the flanged sectoral horns are concerned, the special advantage is that the gain is increased without impairing impedance conditions. An intense study on corner reflector antennas shows that the been broadening or focussing will be possible by adjusting parameters involved. Beam tilting by imposing asymmetries is another interesting property of the systems. A comprehensive study of these fields has been presented in Chapter II. Chapter III is exclusively for describing the experimental techniques used in the present investigation. In Chapter IV, experimental results on flanged sectoral horns and corner reflector eyetses are presented. A comparative analysis of the experimental results obtained with flanged sectoral horns and corner reflector systems is presented in the Chapter V. The similarity and close resemblance in each aspects are shown by presenting typical results from these two eysteee. Theoretical aspects of both types of antennas are considered in Chapter VI. Attempts are made for co-ordinating the theoretical aspects and drawing a final conclusion. In Chapter VII. the final conclusion that the flanged sectoral horn may be considered as a corner reflector system has been drawn. The importance of the conclusions and usefulness are pointed out. The scope for further work in these lines has been indicated.

Corrugated flange technique for beam shaping of sectoral electromagnetic horn antennas

The need for improved feed systems for large reflector antennas employed in Radio Astronomy and Satellite tracking spurred the interest in horn antenna research in the 1960's. The major requirements were to reduce spill over, cross-polarisation losses,and to enhance the aperture efficiency to the order of about 75-8O%L The search for such a feed culminated in the corrugated horn. The corrugat1e 1 horn triggered widespread interest and enthusiasm, and a large amount of work(32’34’49’5O’52’53’58’65’75’79)has already been done on this type of antennas. The properties of corrugated surfaces has been investigated in detail. It was strongly felt that the flange technique and the use of corrugated surfaces could be merged together to obtain the advantages of both. This is the idea behind the present work. Corrugations are made on the surface of flange elements. The effect of various corrugation parameters are studied. By varying the flange parameters, a good amount of data is collected and analysed to ascertain the effects of corrugated flanges. The measurements are repeated at various frequencies, in the X— and S-bands. The following parameters of the system were studied: (a) beam shaping (b) gain (c) variation of V.S.U.R. (d) possibility of obtaining circularly polarised radiation from the flanged horn. A theoretical explanation to the effects of corrugated flanges is attempted on the basis of the line-source theory. Even though this theory utilises a simplified model for the calculation of radiation patterns, fairly good agreement between the computed pattern and experimental results are observed.

Formation of elliptically polarized beam from h-plane sectoral horns using corrugated flanges

The flange technique, suggested by Reynolds72 is simple technique to improve antenna characteristics. Using flange technique we can trim the antenna characteristic by suitably adjusting the flange parameters75. Later corrugated flanges87 are used for beam shaping. The important parameters of the corrugated flanges are (a) flange angle, (b) flange width, (c) flange position, (d) conductivity of the flange, (e) amplitude excitation of the flange elements, (f) period of corrugation etc. Compared to a compound horn the flange technique offers great convenience in trimming antenna characteristics. Horns are commonly used as a feed in radar and satellite communications. A large number of work had been done to improve the characteristics of horn antennas. It is an established fact that grooved walls on the inner surface of a horn can improve the antenna characteristics44. Corrugated comb surface can be used for the circular polarization98, tilt of polarization99 etc. This suggests the possibility to combine these two phenomena and to obtain a resultant beam. This thesis presents the result of an investigation to study the possibility of controlling different antenna characteristics like polarization, beam shaping, matching etc, using corrugated flange techniques.

Multi-objective assembly job shop scheduling using genetic algorithm and tabu search

Assembly job shop scheduling problem (AJSP) is one of the most complicated combinatorial optimization problem that involves simultaneously scheduling the processing and assembly operations of complex structured products. The problem becomes even more complicated if a combination of two or more optimization criteria is considered. This thesis addresses an assembly job shop scheduling problem with multiple objectives. The objectives considered are to simultaneously minimizing makespan and total tardiness. In this thesis, two approaches viz., weighted approach and Pareto approach are used for solving the problem. However, it is quite difficult to achieve an optimal solution to this problem with traditional optimization approaches owing to the high computational complexity. Two metaheuristic techniques namely, genetic algorithm and tabu search are investigated in this thesis for solving the multiobjective assembly job shop scheduling problems. Three algorithms based on the two metaheuristic techniques for weighted approach and Pareto approach are proposed for the multi-objective assembly job shop scheduling problem (MOAJSP). A new pairing mechanism is developed for crossover operation in genetic algorithm which leads to improved solutions and faster convergence. The performances of the proposed algorithms are evaluated through a set of test problems and the results are reported. The results reveal that the proposed algorithms based on weighted approach are feasible and effective for solving MOAJSP instances according to the weight assigned to each objective criterion and the proposed algorithms based on Pareto approach are capable of producing a number of good Pareto optimal scheduling plans for MOAJSP instances.

Fixed Point Decimal Multiplication Using RPS Algorithm

Decimal multiplication is an integral part of financial, commercial, and internet-based computations. A novel design for single digit decimal multiplication that reduces the critical path delay and area for an iterative multiplier is proposed in this research. The partial products are generated using single digit multipliers, and are accumulated based on a novel RPS algorithm. This design uses n single digit multipliers for an n × n multiplication. The latency for the multiplication of two n-digit Binary Coded Decimal (BCD) operands is (n + 1) cycles and a new multiplication can begin every n cycle. The accumulation of final partial products and the first iteration of partial product generation for next set of inputs are done simultaneously. This iterative decimal multiplier offers low latency and high throughput, and can be extended for decimal floating-point multiplication.

A Novel Decision Tree Algorithm for Numeric Datasets - C 4.5*Stat

Decision trees are very powerful tools for classification in data mining tasks that involves different types of attributes. When coming to handling numeric data sets, usually they are converted first to categorical types and then classified using information gain concepts. Information gain is a very popular and useful concept which tells you, whether any benefit occurs after splitting with a given attribute as far as information content is concerned. But this process is computationally intensive for large data sets. Also popular decision tree algorithms like ID3 cannot handle numeric data sets. This paper proposes statistical variance as an alternative to information gain as well as statistical mean to split attributes in completely numerical data sets. The new algorithm has been proved to be competent with respect to its information gain counterpart C4.5 and competent with many existing decision tree algorithms against the standard UCI benchmarking datasets using the ANOVA test in statistics. The specific advantages of this proposed new algorithm are that it avoids the computational overhead of information gain computation for large data sets with many attributes, as well as it avoids the conversion to categorical data from huge numeric data sets which also is a time consuming task. So as a summary, huge numeric datasets can be directly submitted to this algorithm without any attribute mappings or information gain computations. It also blends the two closely related fields statistics and data mining

Parallel Genetic Algorithm for Document Image Compression Optimization

This work proposes a parallel genetic algorithm for compressing scanned document images. A fitness function is designed with Hausdorff distance which determines the terminating condition. The algorithm helps to locate the text lines. A greater compression ratio has achieved with lesser distortion

A Reinforcement Learning Algorithm to Economic Dispatch Considering Transmission Losses

Reinforcement Learning (RL) refers to a class of learning algorithms in which learning system learns which action to take in different situations by using a scalar evaluation received from the environment on performing an action. RL has been successfully applied to many multi stage decision making problem (MDP) where in each stage the learning systems decides which action has to be taken. Economic Dispatch (ED) problem is an important scheduling problem in power systems, which decides the amount of generation to be allocated to each generating unit so that the total cost of generation is minimized without violating system constraints. In this paper we formulate economic dispatch problem as a multi stage decision making problem. In this paper, we also develop RL based algorithm to solve the ED problem. The performance of our algorithm is compared with other recent methods. The main advantage of our method is it can learn the schedule for all possible demands simultaneously.

Short-Term Load Forecast Of A Low Loadfactor Power System For Optimization Of Merit Order Dispatch Using Adaptive Learning Algorithm

Short term load forecasting is one of the key inputs to optimize the management of power system. Almost 60-65% of revenue expenditure of a distribution company is against power purchase. Cost of power depends on source of power. Hence any optimization strategy involves optimization in scheduling power from various sources. As the scheduling involves many technical and commercial considerations and constraints, the efficiency in scheduling depends on the accuracy of load forecast. Load forecasting is a topic much visited in research world and a number of papers using different techniques are already presented. The accuracy of forecast for the purpose of merit order dispatch decisions depends on the extent of the permissible variation in generation limits. For a system with low load factor, the peak and the off peak trough are prominent and the forecast should be able to identify these points to more accuracy rather than minimizing the error in the energy content. In this paper an attempt is made to apply Artificial Neural Network (ANN) with supervised learning based approach to make short term load forecasting for a power system with comparatively low load factor. Such power systems are usual in tropical areas with concentrated rainy season for a considerable period of the year

ECG Noise Removal using GA tuned Sign-Data Least Mean Square Algorithm

Adaptive filter is a primary method to filter Electrocardiogram (ECG), because it does not need the signal statistical characteristics. In this paper, an adaptive filtering technique for denoising the ECG based on Genetic Algorithm (GA) tuned Sign-Data Least Mean Square (SD-LMS) algorithm is proposed. This technique minimizes the mean-squared error between the primary input, which is a noisy ECG, and a reference input which can be either noise that is correlated in some way with the noise in the primary input or a signal that is correlated only with ECG in the primary input. Noise is used as the reference signal in this work. The algorithm was applied to the records from the MIT -BIH Arrhythmia database for removing the baseline wander and 60Hz power line interference. The proposed algorithm gave an average signal to noise ratio improvement of 10.75 dB for baseline wander and 24.26 dB for power line interference which is better than the previous reported works

A Multi-Objective Antenna Placement Genetic Algorithm for Matched Array Synthesis on Complex Platforms

A Multi-Objective Antenna Placement Genetic Algorithm (MO-APGA) has been proposed for the synthesis of matched antenna arrays on complex platforms. The total number of antennas required, their position on the platform, location of loads, loading circuit parameters, decoupling and matching network topology, matching network parameters and feed network parameters are optimized simultaneously. The optimization goal was to provide a given minimum gain, specific gain discrimination between the main and back lobes and broadband performance. This algorithm is developed based on the non-dominated sorting genetic algorithm (NSGA-II) and Minimum Spanning Tree (MST) technique for producing diverse solutions when the number of objectives is increased beyond two. The proposed method is validated through the design of a wideband airborne SAR

Genetic algorithm based indicator sequence method for exon prediction

Considerable research effort has been devoted in predicting the exon regions of genes. The binary indicator (BI), Electron ion interaction pseudo potential (EIIP), Filter method are some of the methods. All these methods make use of the period three behavior of the exon region. Even though the method suggested in this paper is similar to above mentioned methods , it introduces a set of sequences for mapping the nucleotides selected by applying genetic algorithm and found to be more promising

A Faster 2D Technique for the design of Combinational Digital Circuits Using Genetic Algorithm

Combinational digital circuits can be evolved automatically using Genetic Algorithms (GA). Until recently this technique used linear chromosomes and and one dimensional crossover and mutation operators. In this paper, a new method for representing combinational digital circuits as 2 Dimensional (2D) chromosomes and suitable 2D crossover and mutation techniques has been proposed. By using this method, the convergence speed of GA can be increased significantly compared to the conventional methods. Moreover, the 2D representation and crossover operation provides the designer with better visualization of the evolved circuits. In addition to this, a technique to display automatically the evolved circuits has been developed with the help of MATLAB