Electromyography (EMG) is a tool routinely used for a variety of applications in a very large breadth of disciplines. However, this signal is inevitably contaminated by various artifacts originated from different sources. Electrical activity of heart muscles, electrocardiogram (ECG), is one of sources which affects the EMG signals due to the proximity of the collection sites to the heart and ma...

Electroencephalography (EEG) is a portable brain-imaging technique with the advantage of high-temporal resolution that can be used to record electrical activity of the brain. However, it is difficult to analyze EEG signals due to the contamination of ocular artifacts, and which potentially results in misleading conclusions. Also, it is a proven fact that the contamination of ocular artifacts ca...

In recent years, watermarking has become an attractive field in areas like copyright protection, image authentication and biomedical engineering. Many literatures have reported about discrete wavelet transform (DWT) watermarking techniques for data security. However, DWT based watermarking schemes are found to be less robust against image processing attacks. In this paper, an attempt is made to...

Title of Dissertation LEARNING ALGORITHMS FOR AUDIO AND VIDEO PROCESSING INDEPENDENT COMPONENT ANALYSIS AND SUPPORT VECTOR MACHINE BASED APPROACHES Yuan Qi Master of Science Dissertation directed by Professor Rama Chellappa Department of Electrical and Computer Engineering In this thesis we propose two new machine learning schemes a Subband based Independent Component Analysis scheme and a hybr...

Independent component analysis (ICA) is a suitable method for decomposing functional magnetic resonance imaging (fMRI) activity into spatially independent patterns. Practice has revealed that low-pass filtering prior to ICA may improve ICA results by reducing noise and possibly by increasing source smoothness, which may enhance source independence; however, it eliminates useful information in h...

Among all wavelet transform and zero-tree quantization based image coding algorithms, set partitioning in hierarchical trees (SPIHT) is well known for its simplicity and efficiency. But theoretical analysis and experimental results have shown there are still some key points need to be further improved. This paper proposes a coefficient Statistic based Modified SPIHT Lossless Image Compression A...

This paper exploits independent component analysis (ICA) to obtain transform-based compression schemes adapted to specific image classes. This adaptation results from the data-dependent nature of the ICA bases, learnt from training images. Several coder architectures are evaluated and compared, according to both standard (SNR) and perceptual (picture quality scale – PQS) criteria, on two classe...

Problem statement: Because of the distance between the skull and the brain and their different resistivity’s, Electroencephalogram (EEG) recordings on a machine is usually mixed with the activities generated within the area called noise. EEG signals have been used to diagnose major brain diseases such as Epilepsy, narcolepsy and dementia. The presence of these noises however can result in misdi...

The scheme proposed in this paper combines independent components analysis (ICA) with discrete wavelet transform (DWT) and discrete cosine transform (DCT). Firstly, the original image is decomposed by 2-D DWT and the detail sub-bands are reserved. Then, the approximate image is transformed by DCT and embedded with watermark. The watermark is detected through ICA. The simulation results demonstr...