Star Pattern Identification Technique by Modified Grid Algorithm

In this paper a star pattern identification algorithm is addressed. Proposed algorithm is associated in terms of pattern recognition. Each star has related with well-defined pattern that can be determined by the surrounding stars. One of the well-known types related the class is the grid algorithm. The proposed algorithm is based on the algorithm. To enhance the algorithm the modifying methods so called virtual grids and selection multi-reference stars are proposed. This method makes the grid algorithm more accurate and reliable.

Grid algorithm is a classical star identification algorithm based on star pattern. A three-dimensional grid algorithm for all-sky autonomous star identification is proposed, which is associated with the information of star view magnitude. In contrast with traditional grid algorithm that constructs the grid cells on two-dimensional plane (e.g. x-y coordinate plane), the proposed approach makes use of the star view magnitudes of the neighboring stars as the third dimension (e.g. z-axis). A pattern is generated for each of its three-dimensional grid cells that contain neighboring star are 1, and those without are 0. The progress of star identification is to determine which pattern in the database is associated with the particular sensor pattern. Simulation shows that this method can achieve identification rate of 98.0% while the standard deviation of star position error and star view magnitudes are 1 pixel and 0.3Mv respectively. Compared with the traditional grid algorithm, the identification rate is higher, and the average runtime is 50 percent shorter.

Algorithms for speech recognition can be characterized broadly as pattern recognition approaches and acoustic phonetic approaches. To date, the greatest degree of success in speech recognition has been obtained using pattern recognition paradigms. Thus, in this paper, we will be concerned primarily with showing how pattern recognition techniques have been applied to the problems of isolated word (or discrete utterance) recognition, connected word recognition, and continuous speech recognition. We will show that our understanding (and consequently the resulting recognizer performance) is best for the simplest recognition tasks and is considerably less well developed for large scale recognition systems.

Using pattern recognition for estimating cultivar coefficients of a crop simulation model

This article presents the results of the 2-year iroboapp research project that aims at devising path planning algorithms for large grid maps with much faster execution times while tolerating very small slacks with respect to the optimal path. We investigated both exact and heuristic methods. We contributed with the design, analysis, evaluation, implementation and experimentation of several algorithms for grid map path planning for both exact and heuristic methods. We also designed an innovative algorithm called relaxed A-star that has linear complexity with relaxed constraints, which provides near-optimal solutions with an extremely reduced execution time as compared to A-star. We evaluated the performance of the different algorithms and concluded that relaxed A-star is the best path planner as it provides a good trade-off among all the metrics, but we noticed that heuristic methods have good features that can be exploited to improve the solution of the relaxed exact method. This led us to design new hybrid algorithms that combine our relaxed A-star with heuristic methods which improve the solution quality of relaxed A-star at the cost of slightly higher execution time, while remaining much faster than A* for large-scale problems. Finally, we demonstrate how to integrate the relaxed A-star algorithm in the robot operating system as a global path planner and show that it outperforms its default path planner with an execution time 38% faster on average.

The application of pattern recognition to oil and gas prospection is very recent, and results are still sparse. This paper presents an introduction to pattern recognition, a summary of previous applications in seismic processing, and several new pattern recognition approaches. Topics discussed include impluse recognition, horizon correlation, determination of the boundaries and nature of a reservoir, and two-dimensional Fourier (f-k) filtering to attenuate ground roll. To conclude, we examine the outlook for pattern recognition and propose increased use of the theory of fuzzy sets for pattern recognition applications in oil and gas exploration.

A two-dimensional monotonical logical grid algorithm

ObjectivesRecently, pattern recognition approaches have been used to classify patterns of brain activity elicited by sensory or cognitive processes. In the clinical context, these approaches have been mainly applied to classify groups of individuals based on structural magnetic resonance imaging (MRI) data. Only a few studies have applied similar methods to functional MRI (fMRI) data.MethodsWe used a novel analytic framework to examine the extent to which unipolar and bipolar depressed individuals differed on discrimination between patterns of neural activity for happy and neutral faces. We used data from 18 currently depressed individuals with bipolar I disorder (BD) and 18 currently depressed individuals with recurrent unipolar depression (UD), matched on depression severity, age, and illness duration, and 18 age- and gender ratio-matched healthy comparison subjects (HC). fMRI data were analyzed using a general linear model and Gaussian process classifiers.ResultsThe accuracy for discriminating between patterns of neural activity for happy versus neutral faces overall was lower in both patient groups relative to HC. The predictive probabilities for intense and mild happy faces were higher in HC than in BD, and for mild happy faces were higher in HC than UD (all p < 0.001). Interestingly, the predictive probability for intense happy faces was significantly higher in UD than BD (p = 0.03).ConclusionsThese results indicate that patterns of whole-brain neural activity to intense happy faces were significantly less distinct from those for neutral faces in BD than in either HC or UD. These findings indicate that pattern recognition approaches can be used to identify abnormal brain activity patterns in patient populations and have promising clinical utility as techniques that can help to discriminate between patients with different psychiatric illnesses.

The finite-difference method (FDM) algorithm with staggered grid greatly improved the computational efficiency of solving differential equations in two dimensional simulation of GPR, but the strict Courant-Friedrich-Levy (CFL) condition must be satisfied. In three dimensional simulation of GPR, a maximum time-step size will lead to a serious numerical dispersion, while a minimum time-step size will increase the computation time with higher cost. The rotated staggered grid (RSG) algorithm achieves a balance between computational efficiency and numerical stability in three dimensional simulations, as it solves the linear correlation of field value along the diagonal nodes, even if a significantly larger time-step size is chosen, and it can achieve a numerical calculation accuracy as high as the conventional algorithm. The difference scheme of the three dimensional RSG was deduced together with the corresponding updated equations, and a numerical tunnel model with typical hidden disease was presented. The RSG and conventional algorithms were used in the three dimensional simulation. Meanwhile, the comparison of computational efficiency and numerical stability was analyzed, and the calculation result of this model has shown that the RSG algorithm has a quality of higher precision with numerical stability even at a large time-step sizes, which can further facilitate the interpretation of GPR data of typical hidden disease in tunnels.

Artificial Intelligence (AI) has been used extensively to solve different types of pattern recognition problems such as disease diagnosis and classification problems. Due to poor performance in recognition of patterns, several methods have been used. Among them genetic algorithm (GA) have shown better performance than other algorithms. Microbial Genetic Algorithm (MGA) or Bacterial Genetic Algorithm is one of the newer branch of GA. MGA follows the evaluation procedure of microbial which gives better results in pattern recognition. In this paper, microbial genetic algorithm with neural network approach for fitness calculation has been developed and it is used for performance analysis of different pattern recognition problems. Proposed algorithm is named as Microbial Neuro-Genetic Algorithm (MNGA). Advantages of MNGA over simple genetic algorithm (SGA) have also been discussed. XOR, Breast Cancer, Diabetes, Heart Diseases, Glass and Card classification problems are taken from UCI machine learning repository dataset as sample problems for performance analysis which shows that this method provides good performance for different types of problems and thus reduces the need for different types of methods for different types of problems.

In this paper, a pattern recognition approach is developed to identify the driver steering behavior characteristics. The detailed process is divided into three parts, feature parameters extracting, clustering process and identification model building. The driving simulator experiments are designed to obtain the primitive data, and the feature parameters are extractedto profile each driver steering manipulation sample. After that,all the driver steering manipulation samples are clustered with the aid of K-means and Gaussian mixture model (GMM). Based on the samples and the corresponding cluster labels, two identification models of driver steering behavior characteristics are built with two typical pattern recognition methods respectively, which are BP Artificial neural network (BP_ANN) and Support vector machine (SVM). The result shows that the BP_ANN model has higheridentificationaccuracyand can realize the pattern recognition of driver’s driving habits.

In this article, a novel hybrid multi-objective bacterial colony chemotaxis (HMOBCC) algorithm is proposed to solve multi-objective optimization problems. A mechanism of particle swarm optimization is introduced to multi-objective bacterial colony chemotaxis (MOBCC) algorithm to improve the performance of MOBCC algorithm. Also, three other techniques, including dynamic reverse learning operator, external archive multiplying operator and adaptive diversity maintenance operator, are further applied to improve the diversity and convergence of the algorithm. The proposed algorithm is validated using 12 benchmark problems, and three performance measures are implemented for 5 benchmark problems to compare its performance with existing popular algorithms such as MOBCC, multi-objective bacterial colony chemotaxis based on grid algorithm, non-dominated sorting genetic algorithm (NSGA-II) and multi-objective evolutionary algorithm based on decomposition. The results show that the proposed HMOBCC is very effective against existing algorithms. The graphical abstract of this study.

The article presents an application of Hidden Markov Models (HMMs) for pattern recognition on genome sequences. We apply HMM for identifying genes encoding the Variant Surface Glycoprotein (VSG) in the genomes of Trypanosoma brucei (T. brucei) and other African trypanosomes. These are parasitic protozoa causative agents of sleeping sickness and several diseases in domestic and wild animals. These parasites have a peculiar strategy to evade the host's immune system that consists in periodically changing their predominant cellular surface protein (VSG). The motivation for using patterns recognition methods to identify these genes, instead of traditional homology based ones, is that the levels of sequence identity (amino acid and DNA sequence) amongst these genes is often below of what is considered reliable in these methods. Among pattern recognition approaches, HMM are particularly suitable to tackle this problem because they can handle more naturally the determination of gene edges. We evaluate the performance of the model using different number of states in the Markov model, as well as several performance metrics. The model is applied using public genomic data. Our empirical results show that the VSG genes on T. brucei can be safely identified (high sensitivity and low rate of false positives) using HMM.

The winner-take-all (WTA) mechanism based on the inhibitory dynamics of vertical-cavity surface-emitting laser with an embedded saturable absorber (VCSEL-SA) neurons is proposed for the first time. The WTA mechanism is shown numerically in a photonic spiking neural network (SNN). The effect of bias current on WTA time window is analyzed based on the proposed SNN. Moreover, a pattern recognition approach is presented numerically based on the WTA mechanism in all-optical neural network consisting of VCSEL-SA neurons. For the pattern recognition, the robustness of noisy inputs is examined. The effects of bias current, strength of inhibition and weight matrix on the speed of pattern recognition are analyzed carefully. Furthermore, the max-pooling operation is implemented numerically in the all-optical VCSEL-SA neural network model based on the WTA mechanism for the first time. The results hold great promise for the development of energy-efficient and high-speed photonic spiking neural network.

The standard method of deriving cloud fraction from space simply finds the fraction of total image pixels that contains some cloud. The sensitivity of this method to sensor resolution has been examined by Shenk and Salomonson [1972], assuming the cloudy pixels are detected perfectly. Their experiment is reexamined to show that the sensitivity is much more complex than they predicted. We derive the upper and lower bounds of the true cloud fraction, At, given the standard method estimate, Ae, to show that the range of possible values for At can be, in general, very wide. By including the fraction of apparent cloud edge and cloud interior pixels, the bounds can be reduced and improvements to the standard method can be obtained. However, this improvement is also resolution limited by the misidentification of partially cloudy pixels as cloud interior rather than cloud edge. A potentially better technique for estimating the true cloud fraction is therefore explored using a pattern recognition approach. A nearest neighbor classification rule is used in two sets of experiments: one using 684 simulated cloud fields as a training set, the other using 370 cloud fields based on Advanced Very High Resolution Radiometer (AVHRR) measurements. Given the underlying distribution of At, Ae overestimates At with an overall average bias of 32% and standard error of 11% for the simulated training set, and a bias of 35% and a standard error of 3% for the AVHRR training set. The pattern recognition estimate, Ap, is essentially unbiased and has a standard error of 12% for both training sets. The relevance of these training sets to new scenes and the importance of imperfect cloud detection have yet to be investigated, but the pattern recognition technique shows considerable potential advantage over the standard technique in providing unbiased estimates of cloud fraction that are less sensitive to the effects of sensor resolution.