Biomedical Signal Processing: EEG and ECG Classification with Discrete Wavelet Transforms, Energy Distribution, and Convolutional Neural Networks

Biomedical signal processing extract meaningful information from physiological signals like electrocardiograms (ECGs), electroencephalograms (EEGs), and electromyograms (EMGs) to diagnose, monitor, and treat medical conditions and diseases such as seizures, cardiomyopathy, and neuromuscular disorders, respectively. Traditional manual physician analysis of electrical recordings is prone to human error as subtle anomolies may not be detected. Recently, advanced deep learning has significantly improved the accuracy of biomedical signal analysis. A multi-modal deep learning model is proposed that utilizes discrete wavelet transforms for signal pre-processing to reduce noise. A multi-modal image fusion and multimodal feature fusion framework is utilized that converts numeric biomedical signals into 2D and 3D images for image processing using Gramian angular fields, recurrency plots, and Markov transition fields. In this paper, deep learning models are applied to ECG, EEG, and human activity signals using actual medical datasets, brain, and heart recordings. The results demonstrate that using a multi-modal approach using wavelet transforms improves the accuracy of disease and disorder classification.

Neural Artistic Style and Color Transfer Using Deep Learning

Neural artistic style transfers and blends the content and style representation of one image with the style of another. This enables artists to create unique innovative visuals and enhances artistic expression in various fields including art, design, and film. Color transfer algorithms are an important in digital image processing by adjusting the color information in a target image based on the colors in the source image. Color transfer enhances images and videos in film and photography, and can aid in image correction. We introduce a methodology that combines neural artistic style with color transfer. The method uses the Kullback-Leibler (KL) divergence to quantitatively evaluate color and luminance histogram matching algorithms including Reinhard global color transfer, iteration distribution transfer (IDT), IDT with regrain, Cholesky, and PCA between the original and neural artistic style transferred image using deep learning. We estimate the color channel kernel densities. Various experiments are performed to evaluate the KL of these algorithms and their color histograms for style to content transfer.

Autonomous Mobile Plant Watering Robot : A Kinematic Approach

Plants need regular and the appropriate amount of watering to thrive and survive. While agricultural robots exist that can spray water on plants and crops such as the , they are expensive and have limited mobility and/or functionality. We introduce a novel autonomous mobile plant watering robot that uses a 6 degree of freedom (DOF) manipulator, connected to a 4 wheel drive alloy chassis, to be able to hold a garden hose, recognize and detect plants, and to water them with the appropriate amount of water by being able to insert a soil humidity/moisture sensor into the soil. The robot uses Jetson Nano and Arduino microcontroller and real sense camera to perform computer vision to detect plants using real-time YOLOv5 with the Pl@ntNet-300K dataset. The robot uses LIDAR for object and collision avoideance and does not need to move on a pre-defined path and can keep track of which plants it has watered. We provide the Denavit-Hartenberg (DH) Table, forward kinematics, differential driving kinematics, and inverse kinematics along with simulation and experiment results