Ultra-Low Power Circuit Design for Miniaturized IoT Platform

This thesis examines the ultra-low power circuit techniques for mm-scale Internet of Things (IoT) platforms. The IoT devices are known for their small form factors and limited battery capacity and lifespan. So, ultra-low power consumption of always-on blocks is required for the IoT devices that adopt aggressive duty-cycling for high power efficiency and long lifespan. Several problems need to be addressed regarding IoT device designs, such as ultra-low power circuit design techniques for sleep mode and energy-efficient and fast data rate transmission for active mode communication. Therefore, this thesis highlights the ultra-low power always-on systems, focusing on energy efficient optical transmission in order to miniaturize the IoT systems. First, this thesis presents a battery-less sub-nW micro-controller for an always-operating system implemented with a newly proposed logic family. Second, it proposes an always-operating sub-nW light-to-digital converter to measure instant light intensity and cumulative light exposure, which employs the characteristics of this proposed logic family. Third, it presents an ultra-low standby power optical wake-up receiver with ambient light canceling using dual-mode operation. Finally, an energy-efficient low power optical transmitter for an implantable IoT device is suggested. Implications for future research are also provided.PHDElectrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/145862/1/imhotep_1.pd

Neural Feature Predictor and Discriminative Residual Coding for Low-Bitrate Speech Coding

Low and ultra-low-bitrate neural speech coding achieves unprecedented coding gain by generating speech signals from compact speech features. This paper introduces additional coding efficiency in neural speech coding by reducing the temporal redundancy existing in the frame-level feature sequence via a recurrent neural predictor. The prediction can achieve a low-entropy residual representation, which we discriminatively code based on their contribution to the signal reconstruction. The harmonization of feature prediction and discriminative coding results in a dynamic bit allocation algorithm that spends more bits on unpredictable but rare events. As a result, we develop a scalable, lightweight, low-latency, and low-bitrate neural speech coding system. We demonstrate the advantage of the proposed methods using the LPCNet as a neural vocoder. While the proposed method guarantees causality in its prediction, the subjective tests and feature space analysis show that our model achieves superior coding efficiency compared to LPCNet and Lyra V2 in the very low bitrates

Design of a Compact Indirect Slot-Fed Wideband Patch Array with an Air SIW Cavity for a High Directivity in Missile Seeker Applications

This research proposes a compact indirect slot-fed wideband patch array antenna for a missile seeker application. The proposed single antenna consists of three dielectric layers for a radiator, an air substrate-integrated waveguide (SIW) cavity, and an indirect feeding network. The rectangular patch is used as a radiator on the first substrate layer, and the air SIW cavity (ASIWC) is employed to obtain high directivity and low mutual coupling characteristics in the second substrate layer. In the third layer, an indirect feeding structure is used to achieve the wideband characteristics in the Ka-band. The single element is extended to a 4 x 1 linear array with fabrication, and the fabricated array characteristics are measured in a full anechoic chamber. The measured operating fractional frequency bandwidth is 9.2%, and the measured array gain is 11.7 dBi at the bore-sight direction (theta(0) = 0 degrees)

SpecAugment for Sound Event Detection in Domestic Environments using Ensemble of Convolutional Recurrent Neural Networks

In this paper, we present a method to detect sound events in domestic environments using small weakly labeled data, large unlabeled data, and strongly labeled synthetic data as proposed in the Detection and Classification of Acoustic Scenes and Events 2019 Challenge task 4. To solve the problem, we use a convolutional recurrent neural network composed of stacks of convolutional neural networks and bi-directional gated recurrent units. Moreover, we propose various methods such as SpecAugment, event activity detection, multi-median filtering, mean-teacher model, and an ensemble of neural networks to improve performance. By combining the proposed methods, sound event detection performance can be enhanced, compared with the baseline algorithm. Consequently, performance evaluation shows that the proposed method provides detection results of 40.89% for event-based metrics and 66.17% for segment-based metrics. For the evaluation dataset, the performance was 34.4% for event-based metrics and 66.4% for segment-based metrics.12913