Deep Neural Network for Robust Speech Recognition With Auxiliary Features From Laser-Doppler Vibrometer Sensor

Recently, the signal captured from a laser Doppler vibrometer (LDV) sensor been used to improve the noise robustness automatic speech recognition (ASR) systems by enhancing the acoustic signal prior to feature extraction. This study proposes another approach in which auxiliary features extracted from the LDV signal are used alongside conventional acoustic features to further improve ASR performance based on the use of a deep neural network (DNN) as the acoustic model. While this approach is promising, the best training data sets for ASR do not include LDV data in parallel with the acoustic signal. Thus, to leverage such existing large-scale speech databases, a regres- sion DNN is designed to map acoustic features to LDV features. This regression DNN is well trained from a limited size parallel signal data set, then used to form pseudo-LDV features from a massive speech data set for parallel training of an ASR system. Our experiments show that both the features from the limited scale LDV data set as well as the massive scale pseudo-LDV features are able to train an ASR system that significantly outperforms one using acoustic features alone, in both quiet and noisy environments

Nondestructive Testing (NDT)

The aim of this book is to collect the newest contributions by eminent authors in the field of NDT-SHM, both at the material and structure scale. It therefore provides novel insight at experimental and numerical levels on the application of NDT to a wide variety of materials (concrete, steel, masonry, composites, etc.) in the field of Civil Engineering and Architecture

Ultra-low-power circuits and systems for wearable and implantable medical devices

Thesis (Ph. D.)--Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2013.Cataloged from PDF version of thesis.Includes bibliographical references (pages 219-231).Advances in circuits, sensors, and energy storage elements have opened up many new possibilities in the health industry. In the area of wearable devices, the miniaturization of electronics has spurred the rapid development of wearable vital signs, activity, and fitness monitors. Maximizing the time between battery recharge places stringent requirements on power consumption by the device. For implantable devices, the situation is exacerbated by the fact that energy storage capacity is limited by volume constraints, and frequent battery replacement via surgery is undesirable. In this case, the design of energy-efficient circuits and systems becomes even more crucial. This thesis explores the design of energy-efficient circuits and systems for two medical applications. The first half of the thesis focuses on the design and implementation of an ultra-low-power, mixed-signal front-end for a wearable ECG monitor in a 0.18pm CMOS process. A mixed-signal architecture together with analog circuit optimizations enable ultra-low-voltage operation at 0.6V which provides power savings through voltage scaling, and ensures compatibility with state-of-the-art DSPs. The fully-integrated front-end consumes just 2.9[mu]W, which is two orders of magnitude lower than commercially available parts. The second half of this thesis focuses on ultra-low-power system design and energy-efficient neural stimulation for a proof-of-concept fully-implantable cochlear implant. First, implantable acoustic sensing is demonstrated by sensing the motion of a human cadaveric middle ear with a piezoelectric sensor. Second, alternate energy-efficient electrical stimulation waveforms are investigated to reduce neural stimulation power when compared to the conventional rectangular waveform. The energy-optimal waveform is analyzed using a computational nerve fiber model, and validated with in-vivo ECAP recordings in the auditory nerve of two cats and with psychophysical tests in two human cochlear implant users. Preliminary human subject testing shows that charge and energy savings of 20-30% and 15-35% respectively are possible with alternative waveforms. A system-on-chip comprising the sensor interface, reconfigurable sound processor, and arbitrary-waveform neural stimulator is implemented in a 0.18[mu]m high-voltage CMOS process to demonstrate the feasibility of this system. The sensor interface and sound processor consume just 12[mu]W of power, representing just 2% of the overall system power which is dominated by stimulation. As a result, the energy savings from using alternative stimulation waveforms transfer directly to the system.by Marcus Yip.Ph.D