Low-Noise Energy-Efficient Sensor Interface Circuits

Today, the Internet of Things (IoT) refers to a concept of connecting any devices on network where environmental data around us is collected by sensors and shared across platforms. The IoT devices often have small form factors and limited battery capacity; they call for low-power, low-noise sensor interface circuits to achieve high resolution and long battery life. This dissertation focuses on CMOS sensor interface circuit techniques for a MEMS capacitive pressure sensor, thermopile array, and capacitive microphone. Ambient pressure is measured in the form of capacitance. This work propose two capacitance-to-digital converters (CDC): a dual-slope CDC employs an energy efficient charge subtraction and dual comparator scheme; an incremental zoom-in CDC largely reduces oversampling ratio by using 9b zoom-in SAR, significantly improving conversion energy. An infrared gesture recognition system-on-chip is then proposed. A hand emits infrared radiation, and it forms an image on a thermopile array. The signal is amplified by a low-noise instrumentation chopper amplifier, filtered by a low-power 30Hz LPF to remove out-band noise including the chopper frequency and its harmonics, and digitized by an ADC. Finally, a motion history image based DSP analyzes the waveform to detect specific hand gestures. Lastly, a microphone preamplifier represents one key challenge in enabling voice interfaces, which are expected to play a dominant role in future IoT devices. A newly proposed switched-bias preamplifier uses switched-MOSFET to reduce 1/f noise inherently.PHDElectrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/137061/1/chaseoh_1.pd

The Gestural Control of Audio Processing

Gesture enabled devices have become so ubiquitous in recent years that commands such as ‘pinch to zoom-in on an image’ are part of most people’s gestural vocabulary. Despite this, gestural interfaces have been used sparingly within the audio industry. The aim of this research project is to evaluate the effectiveness of a gestural interface for the control of audio processing. In particular, the ability of a gestural system to streamline workflow and rationalise the number of control parameters, thus reducing the complexity of Human Computer Interaction (HCI). A literature review of gestural technology explores the ways in which it can improve HCI, before focussing on areas of implementation in audio systems. Case studies of previous research projects were conducted to evaluate the benefits and pitfalls of gestural control over audio. The findings from these studies concluded that the scope of this project should be limited to two-dimensional gestural control. An elicitation of gestural preferences was performed to identify expert-user’s gestural associations. This data was used to compile a taxonomy of gestures and their most widely-intuitive parameter mappings. A novel interface was then produced using a popular tablet-computer. This facilitated the control of equalisation, compression and gating. Objective testing determined the performance of the gestural interface in comparison to traditional WIMP (Windows, Icons, Menus, Pointer) techniques, thus producing a benchmark for the system under test. Further testing is carried out to observe the effects of graphic user interfaces (GUIs) in a gestural system, in particular the suitability of skeuomorphic (knobs and faders) designs in modern DAWs (Digital Audio Workstations). A novel visualisation method, deemed more suitable for gestural interaction, is proposed and tested. Semantic descriptors are explored as a means of further improving the speed and usability of gestural interfaces, through the simultaneous control of multiple parameters. This rationalisation of control moves towards the implementation of gestural shortcuts and ‘continuous pre-sets’