Parikshit Kishor Singh

Implementing Edge Detection for Detecting Neurons from Brain to Identify Emotions

Edges of an image are considered a type of crucial information that can be extracted by applying detectors with different methodology. Edge detection is a basic and important subject in computer vision and image processing In this Paper we discuss several Digital Image Processing Techniques applied in edge feature extraction. Firstly, Linear filtering of Image is done is used to remove noises from the image collected. Secondly, some edge detection operators such as Sobel, Log edge detection, canny edge detection are analyzed and then according to the simulation results, the advantages and disadvantages of these edge detection operators are compared. It is shown that the canny operator can obtain better edge feature. Finally, Edge detection is applied to identify neurons in Brain. After this the Neurons are classified and feature vector will be calculated. KeywordsFilters, Sobel, Canny, Log, Distortion, Edge Detection Introduction (Heading 1)

Comparative study of neural network architectures for rainfall prediction

Majority of Indian farmers depend on rainfall for agriculture. Thus, in an agrarian country like India, rainfall prediction becomes very important. This paper presents comparative study of neural network architectures namely Back Propagation Neural Network (BPNN), Generalized Regression Neural Network (GRNN) and Radial Basis function Neural Network (RBNN) to predict rainfall in Thanjavur district of southern province TamilNadu, India. The different models are trained using the training data set and have been tested for accuracy on available test data. MATLAB has been used for model development. After training all networks and testing them we found that RBNN gives best result for prediction.

Self-tuned fuzzy logic control of a pH neutralization process

On-line implementation of self-tuning mechanism based adaptive fuzzy logic control of a pH neutralization process which takes care of steady state error and time taken to reach steady state under varying operating conditions has been presented in this paper. The pH neutralization system is Armfield pH Sensor Accessory (PCT42) in conjunction with Process Vessel Accessory (PCT41) and Multifunction Process Control Teaching System (PCT40). The proposed adaptive scheme updates the normalized universe of discourse of output fuzzy membership functions with varying scaling factors based on error and change of error values. The speed of response of the adaptive controller is taken care by use of coarse control technique whereas amount of deviation under steady state is accounted with the help of fine control technique. The performance of adaptive scheme is tested for pH control at equivalence point. LabVIEW software is used for online communication, control and display.

Neural Control of Neutralization Process using Fuzzy Inference System based Lookup Table

Over a number of years, pH control of neutralization process is recognized as a benchmark for modeling and control of nonlinear processes. This paper first describes dynamic modeling of pH neutralization process. Thereafter fuzzy logic based pH control scheme for neutralization process is developed. Further, a two-dimensional (2-D) lookup table is generated based on defuzzification mechanism of fuzzy inference system (FIS). Finally, using this lookup table, a neural network control for pH neutralization process is developed. Performances of fuzzy logic based control and lookup table based neural network control for servo and regulatory operations are compared based on integral square error (ISE) and integral absolute error (IAE) criterions. Results indicate that lookup table based neural network control performs better than fuzzy logic based control. General Terms Nonlinear process control, fuzzy logic control, neural network control.

Optimized adaptive neuro-fuzzy inference system for pH control

pH control plays an important role in many modern industrial plants due to strict environment regulations. This paper presents fuzzy logic based pH control scheme for neutralization process in which genetic algorithm is used to optimize the various membership functions of fuzzy inference system. Further, using this optimized fuzzy inference system, adaptive neuro-fuzzy inference system for pH neutralization process is developed. Performances of both control schemes are compared for servo and regulatory operations. Results indicate that adaptive neuro-fuzzy inference system based control uses fewer rules as compared to optimized fuzzy logic based control.

Morphology of open clusters NGC 1857 and Czernik 20 using clustering algorithms

The morphology and cluster membership of the Galactic open clusters - Czernik 20 and NGC 1857 were analyzed using two different clustering algorithms. We present the maiden use of density-based spatial clustering of applications with noise (DBSCAN) to determine open cluster morphology from spatial distribution. The region of analysis has also been spatially classified using a statistical membership determination algorithm. We utilized near infrared (NIR) data for a suitably large region around the clusters from the United Kingdom Infrared Deep Sky Survey Galactic Plane Survey star catalogue database, and also from the Two Micron All Sky Survey star catalogue database. The densest regions of the cluster morphologies (1 for Czernik 20 and 2 for NGC 1857) thus identified were analyzed with a K-band extinction map and color-magnitude diagrams (CMDs). To address significant discrepancy in known distance and reddening parameters, we carried out field decontamination of these CMDs and subsequent isochrone fitting of the cleaned CMDs to obtain reliable distance and reddening parameters for the clusters (Czernik 20: D = 2900 pc; E(J-K) = 0.33; NGC 1857: D = 2400 pc; E(J-K) = 0.18-0.19). The isochrones were also used to convert the luminosity functions for the densest regions of Czernik 20 and NGC 1857 into mass function, to derive their slopes. Additionally, a previously unknown over-density consistent with that of a star cluster is identified in the region of analysis.

Differential evolution based optimal fuzzy logic control of pH neutralization process

Differential evolution (DE) is a member of evolutionary algorithm family which has gained popularity due to its conceptual simplicity and better convergence. This paper presents fuzzy logic based pH control scheme for neutralization process in which DE is used to optimize the input and output membership functions of fuzzy inference system (FIS). The fitness function for optimization is integral of squared errors (ISE). DE is able to converge and find optimal global solution over narrow as well as wide search spaces. Finally the controller performance has been evaluated for servo and regulatory operations.