Adapa, Bhagyaraja
(2016)
Low Complexity CORDIC based methodologies for BSS.
PhD thesis, Indian Institute of Technology Hyderabad.
Abstract
Our Project aim is to develop a real time chip to process the sensor signals and separating the source signals, which is used in Health care and Wireless Sensor Networks (WSN) like Autism, ECG and disease detection. Autism is a disease which affects the child mental behaviour. So If we analyse the signals form the brain so we can observe the how effectively the disease is cured. So to analyze the Autism we need EEG signals from almost 128 Leads from the scalp of child. In cardiovascular diseases, patient's ECG signal is monitored, which is a combination of ECG, Artefacts and noise.Desired ECG signal is separation from mixed signals is vital for accurate analysis of disease. All these operations has to be done in real-time scenario with patient being monitored all the time. So, there is a need of low-complexity, low-power architecture for this signal separation is needed. Here, we proposed a COordinate Rotation Digital Computer (CORDIC) based engine to separate mixed signals. The proposed algorithm can merge the two key steps of conventional Fast ICA pre-processing and update and is therefore capable of reducing the hardware complexity of the conventional Fast ICA significantly. Hardware implementation can further be simplified due to the recursive nature of the proposed algorithm where the same 2D hardware module can be used as the fundamental core to implement architecture. Architecture has been further improved such that same hardware can be used for any number of input signals by making it reconfigurable. Further, a hybrid architecture has been proposed by combining Fast ICA with cross product.
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