Mixed-signal VLSI robust time-frequency feature extraction

Robust feature extraction is critical to pattern recognition and intelligent systems. The problem of extracting reliable features from real-world signals is challenging because these signals are embedded in non-stationary noise; real-time operation requires fast adaptation; and mobile operation requires miniature hardware with low-power consumption. The need for robust real-time, and low-power feature extraction solutions calls for an integrative approach that combines adaptive signal processing, machine learning, and micropower VLSI design.; This dissertation presents time-frequency feature extraction algorithms, inspired by biological models of auditory processing, for robust feature extraction. The algorithms map efficiently onto massive parallel VLSI hardware to provide real-time operation. Analog models of computation are combined with digital programmability and adaptation, leading to significant savings in silicon area and power compared to purely analog or digital approaches. A digitally programmable operational transconductance amplifier (OTA) offers more than three decades transconductance gain and wide linear range. Fully differential continuous-time OTA-C filters provide high dynamic range, high power supply noise rejection, and programmable filter parameters. Fabrication-induced mismatch in the analog implementation is compensated through calibration using a generalized linear modeling and optimization scheme.; My research has resulted in a reconfigurable biomimetic mixed-signal VLSI feature extraction frontend for sonar and acoustic classification. The reconfigurable system is integrated on a single 3mm x 3mm chip in 0.5um CMOS technology. It features a 32-channel filter bank, with reconfigurable filter topology and programmable filter parameters. The chip consumes 9mW power for a total of 96 integrated biquad filters.; My work further led to application of the frontend architecture to robust speech and speaker recognition, sonar signal processing for under water target detection, and statistical analysis of speckle field for adaptive laser communication and adaptive optics. Experiments with robust speech and speaker recognition demonstrated the noise robustness of the extracted features under the auditory perception model, outperforming MFCC feature extraction algorithms. Combining ultrasonic time-frequency features with support machine learning algorithms, we showed detection of buried underwater targets from sonar backscatter of single-ping sonar signals. In adaptive optics experiments, time-frequency analysis of speckle field signals indicated to be an effective laser beam quality metric for adaptive target-in-the-loop control.