Prompt electron emission and collisional ionization of ambient gas during pulsed laser ablation of silver

A silver target kept under partial vacuum conditions was irradiated with focused nanosecond pulses at 1:06 mm from a Nd:YAG laser. The electron emission monitored with a Langmuir probe shows a clear twin-peak distribution. The first peak which is very sharp has only a small delay and it indicates prompt electron emission with energy as much as 60 5 eV. Also the prompt electron emission shows a temporal profile with a width that is same as that for the laser pulse whereas the second peak is broader, covers several microseconds, and represents the low-energy electrons (2 0:5 eV) associated with the laser-induced silver plasma as revealed by time-of-flight measurements. It has been found that prompt electrons ejected from the target collisionally excite and ionize ambient gas molecules. Clearly resolved rotational structure is observed in the emission spectra of ambient nitrogen molecules. Combined with time-resolved spectroscopy, the prompt electrons can be used as excitation sources for various collisional excitation–relaxation experiments. The electron density corresponding to the first peak is estimated to be of the order of 1017 cm?--3 and it is found that the density increases as a function of distance away from the target. Dependence of probe current on laser intensity shows plasma shielding at high laser intensities.

Twin peak distribution of electron emission profile and impact ionization of ambient molecules during laser ablation of silver target

Laser-induced plasma generated from a silver target under partial vacuum conditions using the fundamental output of nanosecond duration from a pulsed Nd:yttrium aluminum garnet laser is studied using a Langmuir probe. The time of flight measurements show a clear twin peak distribution in the temporal profile of electron emission. The first peak has almost the same duration as the laser pulse while the second lasts for several microseconds. The prompt electrons are energetic enough ('60 eV) to ionize the ambient gas molecules or atoms. The use of prompt electron pulses as sources for electron impact excitation is demonstrated by taking nitrogen, carbon dioxide, and argon as ambient gases.

Nonlinear optical absorption in silver nanosol

Nonlinear optical absorption in silver nanosol was investigated at selected wavelengths (456 nm, 477 nm and 532 nm) using open aperture Z-scan technique. It was observed that nature of nonlinear absorption is sensitively dependent on input fluence as well as on excitation wavelength. Besides, the present sample was found to exhibit reverse saturable absorption (RSA) and saturable absorption (SA) at these wavelengths depending on excitation fluence. RSA is attributed to enhanced absorption resulting from photochemical changes. SA observed for fluence values lower and higher than those corresponding to RSA are, respectively, attributed to plasmon bleach and saturation of RSA.

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.

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.

Recording multiple holographic gratings in silver-doped photopolymer using peristrophic multiplexing

Plane-wave transmission gratings were stored in the same location of silver- doped photopolymer ¯lm using peristrophic multiplexing techniques. Constant and vari- able exposure scheduling methods were adopted for storing gratings in the ¯lm using He{Ne laser (632.8 nm). The role of recording geometry on the dynamic range of the ma- terial was studied by comparing the results obtained from both techniques. Peristrophic multiplexing with rotation of the ¯lm in a plane normal to the bisector of the incident beams resulted in better homogenization of di®raction e±ciencies and larger M/# value.

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

Size-dependent surface plasmon resonance in silver silica nanocomposites

Silver silica nanocomposites were obtained by the sol–gel technique using tetraethyl orthosilicate (TEOS) and silver nitrate (AgNO3) as precursors. The silver nitrate concentration was varied for obtaining composites with different nanoparticle sizes. The structural and microstructural properties were determined by x-ray diffractometry (XRD), Fourier transform infrared spectroscopy (FTIR) and transmission electron microscopy (TEM). X-ray photoelectron spectroscopic (XPS) studies were done for determining the chemical states of silver in the silica matrix. For the lowest AgNO3 concentration, monodispersed and spherical Ag crystallites, with an average diameter of 5 nm, were obtained. Grain growth and an increase in size distribution was observed for higher concentrations. The occurrence of surface plasmon resonance (SPR) bands and their evolution in the size range 5–10 nm is studied. For decreasing nanoparticle size, a redshift and broadening of the plasmon-related absorption peak was observed. The observed redshift and broadening of the SPR band was explained using modified Mie scattering theory

Sequential interactions of Silver-silica nanocomposite (Ag-SiO2NC) with cell wall, metabolism and genetic stability of Psedomonas aeruginosa, a multiple antibiotic resistant bacterium

The study was carried out to understand the effect of silver-silica nanocomposite (Ag-SiO2NC) on the cell wall integrity, metabolism and genetic stability of Pseudomonas aeruginosa, a multiple drugresistant bacterium. Bacterial sensitivity towards antibiotics and Ag-SiO2NC was studied using standard disc diffusion and death rate assay, respectively. The effect of Ag-SiO2NC on cell wall integrity was monitored using SDS assay and fatty acid profile analysis while the effect on metabolism and genetic stability was assayed microscopically, using CTC viability staining and comet assay, respectively. P. aeruginosa was found to be resistant to β-lactamase, glycopeptidase, sulfonamide, quinolones, nitrofurantoin and macrolides classes of antibiotics. Complete mortality of the bacterium was achieved with 80 μgml-1 concentration of Ag-SiO2NC. The cell wall integrity reduced with increasing time and reached a plateau of 70 % in 110 min. Changes were also noticed in the proportion of fatty acids after the treatment. Inside the cytoplasm, a complete inhibition of electron transport system was achieved with 100 μgml-1 Ag-SiO2NC, followed by DNA breakage. The study thus demonstrates that Ag-SiO2NC invades the cytoplasm of the multiple drug-resistant P. aeruginosa by impinging upon the cell wall integrity and kills the cells by interfering with electron transport chain and the genetic stability

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