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

Performance Improvement of Fuzzy and Neuro Fuzzy Systems: Prediction of Learning Disabilities in School-age Children

Learning Disability (LD) is a classification including several disorders in which a child has difficulty in learning in a typical manner, usually caused by an unknown factor or factors. LD affects about 15% of children enrolled in schools. The prediction of learning disability is a complicated task since the identification of LD from diverse features or signs is a complicated problem. There is no cure for learning disabilities and they are life-long. The problems of children with specific learning disabilities have been a cause of concern to parents and teachers for some time. The aim of this paper is to develop a new algorithm for imputing missing values and to determine the significance of the missing value imputation method and dimensionality reduction method in the performance of fuzzy and neuro fuzzy classifiers with specific emphasis on prediction of learning disabilities in school age children. In the basic assessment method for prediction of LD, checklists are generally used and the data cases thus collected fully depends on the mood of children and may have also contain redundant as well as missing values. Therefore, in this study, we are proposing a new algorithm, viz. the correlation based new algorithm for imputing the missing values and Principal Component Analysis (PCA) for reducing the irrelevant attributes. After the study, it is found that, the preprocessing methods applied by us improves the quality of data and thereby increases the accuracy of the classifiers. The system is implemented in Math works Software Mat Lab 7.10. The results obtained from this study have illustrated that the developed missing value imputation method is very good contribution in prediction system and is capable of improving the performance of a classifier.

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

An Improved Design of Combinational Digital Circuits with Multiplexers using Genetic Algorithm

This paper presents a new approach to the design of combinational digital circuits with multiplexers using Evolutionary techniques. Genetic Algorithm (GA) is used as the optimization tool. Several circuits are synthesized with this method and compared with two design techniques such as standard implementation of logic functions using multiplexers and implementation using Shannon’s decomposition technique using GA. With the proposed method complexity of the circuit and the associated delay can be reduced significantly

Polarization and correlation phenomena in the radiative electron capture by bare highly-charged ions

In dieser Arbeit wird die Wechselwirkung zwischen einem Photon und einem Elektron im starken Coulombfeld eines Atomkerns am Beispiel des radiativen Elektroneneinfangs beim Stoß hochgeladener Teilchen untersucht. In den letzten Jahren wurde dieser Ladungsaustauschprozess insbesondere für relativistische Ion–Atom–Stöße sowohl experimentell als auch theoretisch ausführlich erforscht. In Zentrum standen dabei haupsächlich die totalen und differentiellen Wirkungsquerschnitte. In neuerer Zeit werden vermehrt Spin– und Polarisationseffekte sowie Korrelationseffekte bei diesen Stoßprozessen diskutiert. Man erwartet, dass diese sehr empfindlich auf relativistische Effekte im Stoß reagieren und man deshalb eine hervorragende Methode zu deren Bestimmung erhält. Darüber hinaus könnten diese Messungen auch indirekt dazu führen, dass man die Polarisation des Ionenstrahls bestimmen kann. Damit würden sich neue experimentelle Möglichkeiten sowohl in der Atom– als auch der Kernphysik ergeben. In dieser Dissertation werden zunächst diese ersten Untersuchungen zu den Spin–, Polarisations– und Korrelationseffekten systematisch zusammengefasst. Die Dichtematrixtheorie liefert hierzu die geeignete Methode. Mit dieser Methode werden dann die allgemeinen Gleichungen für die Zweistufen–Rekombination hergeleitet. In diesem Prozess wird ein Elektron zunächst radiativ in einen angeregten Zustand eingefangen, der dann im zweiten Schritt unter Emission des zweiten (charakteristischen) Photons in den Grundzustand übergeht. Diese Gleichungen können natürlich auf beliebige Mehrstufen– sowie Einstufen–Prozesse erweitert werden. Im direkten Elektroneneinfang in den Grundzustand wurde die ”lineare” Polarisation der Rekombinationsphotonen untersucht. Es wurde gezeigt, dass man damit eine Möglichkeit zur Bestimmung der Polarisation der Teilchen im Eingangskanal des Schwerionenstoßes hat. Rechnungen zur Rekombination bei nackten U92+ Projektilen zeigen z. B., dass die Spinpolarisation der einfallenden Elektronen zu einer Drehung der linearen Polarisation der emittierten Photonen aus der Streuebene heraus führt. Diese Polarisationdrehung kann mit neu entwickelten orts– und polarisationsempfindlichen Festkörperdetektoren gemessen werden. Damit erhält man eine Methode zur Messung der Polarisation der einfallenden Elektronen und des Ionenstrahls. Die K–Schalen–Rekombination ist ein einfaches Beispiel eines Ein–Stufen–Prozesses. Das am besten bekannte Beispiel der Zwei–Stufen–Rekombination ist der Elektroneneinfang in den 2p3/2–Zustand des nackten Ions und anschließendem Lyman–1–Zerfall (2p3/2 ! 1s1/2). Im Rahmen der Dichte–Matrix–Theorie wurden sowohl die Winkelverteilung als auch die lineare Polarisation der charakteristischen Photonen untersucht. Beide (messbaren) Größen werden beträchtlich durch die Interferenz des E1–Kanals (elektrischer Dipol) mit dem viel schwächeren M2–Kanal (magnetischer Quadrupol) beeinflusst. Für die Winkelverteilung des Lyman–1 Zerfalls im Wasserstoff–ähnlichen Uran führt diese E1–M2–Mischung zu einem 30%–Effekt. Die Berücksichtigung dieser Interferenz behebt die bisher vorhandene Diskrepanz von Theorie und Experiment beim Alignment des 2p3/2–Zustands. Neben diesen Ein–Teichen–Querschnitten (Messung des Einfangphotons oder des charakteristischen Photons) wurde auch die Korrelation zwischen den beiden berechnet. Diese Korrelationen sollten in X–X–Koinzidenz–Messungen beobbachtbar sein. Der Schwerpunkt dieser Untersuchungen lag bei der Photon–Photon–Winkelkorrelation, die experimentell am einfachsten zu messen ist. In dieser Arbeit wurden ausführliche Berechnungen der koinzidenten X–X–Winkelverteilungen beim Elektroneneinfang in den 2p3/2–Zustand des nackten Uranions und beim anschließenden Lyman–1–Übergang durchgeführt. Wie bereits erwähnt, hängt die Winkelverteilung des charakteristischen Photons nicht nur vom Winkel des Rekombinationsphotons, sondern auch stark von der Spin–Polarisation der einfallenden Teilchen ab. Damit eröffnet sich eine zweite Möglichkeit zur Messung der Polaristion des einfallenden Ionenstrahls bzw. der einfallenden Elektronen.

Relativistic many-electron SCF correlation diagram for superheavy quasimolecules: Pb - Pb

We present the first relativistic many-electron SCF correlation diagram for a superheavy quasimolecule: Pb - Pb. The discussion shows a large number of quantitative as well as qualitative differences as compared with the known one-electron correlation diagram.

Kr - Kr correlation diagram and its use in heavy-ion collisions

A realistic self-consistent charge correlation diagram calculation of the Kr{^2+} - Kr{^2+} system has been performed. We get excellent agreement for the 4(3/2)_u level with an experimentally observed MO level at large distances. Possible reasons for discrepancies between experiment and theory at small distances are discussed.