Formulating Particle Swarm Optimization based Generalized Kernel Function for Kernel-Linear Discriminant Analysis

AbstractSelection of kernel function for solving Kernel-Linear Discriminant Analysis (K-LDA) remains unsolved problem. In this commuication, we propose the method to formulate the Generalized Kernel Function (GKF) for K-LDA.The parameters of the GKF are tuned using the Particle Swarm Optimization (PSO) to maximize the discrimination in the higher dimensional space. Experiments are performed on the petal shaped synthetic toy cluster using the proposed GKF and are compared with the results obtained using the standard kernel functions. The experimental results reveals the importance of using the proposed technique

Musical Notes Identification using Digital Signal Processing

AbstractSongs play a vital role in our day to day life. A song contains basically two things, vocal and background music. Where the characteristics of the voice depend on the singer and in case of background music, it involves mixture of different musical instruments like piano, guitar, drum, etc. To extract the characteristic of a song becomes more important for various objectives like learning, teaching, composing. This project takes song as an input, extracts the features and detects and identifies the notes, each with a duration. First the song is recorded and digital signal processing algorithms used to identify the characteristics. The experiment is done with the several piano songs where the notes are already known, and identified notes are compared with original notes until the detection rate goes higher. And then the experiment is done with piano songs with unknown notes with the proposed algorithm

LiDAR-based estimation of bounding box coordinates using Gaussian process regression and particle swarm optimization

Camera-based object tracking systems in a given closed environment lack privacy and confidentiality. In this study, light detection and ranging (LiDAR) was applied to track objects similar to the camera tracking in a closed environment, guaranteeing privacy and confidentiality. The primary objective was to demonstrate the efficacy of the proposed technique through carefully designed experiments conducted using two scenarios. In Scenario I, the study illustrates the capability of the proposed technique to detect the locations of multiple objects positioned on a flat surface, achieved by analyzing LiDAR data collected from several locations within the closed environment. Scenario II demonstrates the effectiveness of the proposed technique in detecting multiple objects using LiDAR data obtained from a single, fixed location. Real-time experiments are conducted with human subjects navigating predefined paths. Three individuals move within an environment, while LiDAR, fixed at the center, dynamically tracks and identifies their locations at multiple instances. Results demonstrate that a single, strategically positioned LiDAR can adeptly detect objects in motion around it. Furthermore, this study provides a comparison of various regression techniques for predicting bounding box coordinates. Gaussian process regression (GPR), combined with particle swarm optimization (PSO) for prediction, achieves the lowest prediction mean square error of all the regression techniques examined at 0.01. Hyperparameter tuning of GPR using PSO significantly minimizes the regression error. Results of the experiment pave the way for its extension to various real-time applications such as crowd management in malls, surveillance systems, and various Internet of Things scenarios

Brain computer interface analysis using wavelet transforms and auto regressive coefficients

The idea of an EEG based BCI is to assist the people unable to communicate their thoughts due to neuromuscular disorders and hence affected by motor disabilities. The BCI helps them acting as an interface between the human mind and the computer. In this paper an offline analysis of the EEG data recorded from the C3 and C4 electrodes pertaining to motor activities was done. The data obtained was preprocessed with techniques like wavelet transform and linear predictive coding was applied to it to determine the auto regressive coefficients which are treated as feature vectors to train an artificial neural network for appropriate classification. The trained net was then subjected to testing of data from 140 random trials that were taken and the accuracy was determined. The efficiency of this approach was found to be 71.5%