Kenji Aono

Noise-Shaping Gradient Descent-Based Online Adaptation Algorithms for Digital Calibration of Analog Circuits

Analog circuits that are calibrated using digital-to-analog converters (DACs) use a digital signal processor-based algorithm for real-time adaptation and programming of system parameters. In this paper, we first show that this conventional framework for adaptation yields suboptimal calibration properties because of artifacts introduced by quantization noise. We then propose a novel online stochastic optimization algorithm called noise-shaping or ΣΔ gradient descent, which can shape the quantization noise out of the frequency regions spanning the parameter adaptation trajectories. As a result, the proposed algorithms demonstrate superior parameter search properties compared to floating-point gradient methods and better convergence properties than conventional quantized gradient-methods. In the second part of this paper, we apply the ΣΔ gradient descent algorithm to two examples of real-time digital calibration: 1) balancing and tracking of bias currents, and 2) frequency calibration of a band-pass Gm-C biquad filter biased in weak inversion. For each of these examples, the circuits have been prototyped in a 0.5- μm complementary metal-oxide-semiconductor process, and we demonstrate that the proposed algorithm is able to find the optimal solution even in the presence of spurious local minima, which are introduced by the nonlinear and non-monotonic response of calibration DACs.

Self-Powered Monitoring of Repeated Head Impacts Using Time-Dilation Energy Measurement Circuit

Due to the current epidemic levels of sport-related concussions (SRC) in the U.S., there is a pressing need for technologies that can facilitate long-term and continuous monitoring of head impacts. Existing helmet-sensor technology is inconsistent, inaccurate, and is not economically or logistically practical for large-scale human studies. In this paper, we present the design of a miniature, battery-less, self-powered sensor that can be embedded inside sport helmets and can continuously monitor and store different spatial and temporal statistics of the helmet impacts. At the core of the proposed sensor is a novel time-dilation circuit that allows measurement of a wide-range of impact energies. In this paper an array of linear piezo-floating-gate (PFG) injectors has been used for self-powered sensing and storage of linear and rotational head-impact statistics. The stored statistics are then retrieved using a plug-and-play reader and has been used for offline data analysis. We report simulation and measurement results validating the functionality of the time-dilation circuit for different levels of impact energies. Also, using prototypes of linear PFG integrated circuits fabricated in a 0.5 μm CMOS process, we demonstrate the functionality of the proposed helmet-sensors using controlled drop tests.

Infrastructural health monitoring using self-powered Internet-of-Things

By incorporating sensing capabilities in passive radio-frequency identification (RFID) tagging technology it is possible to extend the coverage of Internet-of-Things (IoT) to monitor the health of different segments of a large civil infrastructure like pavement highway, buildings or a multi-span bridge. The challenge in this regard is to deliver energy to the RFID sensors that are embedded inside the structures in a manner that they can continuously sense for occurrence of any rare structural events. This paper summarizes some of the progress that has been made to-date in the area of self-powered R FID sensor networks within the concept of IoT. The core sensor uses a self-powering method which directly harvests computational and storage energy from slight strain-variations in the structure. The event signatures can then be stored on a non-volatile memory and remotely retrieved at a later period of time. In this “sense now retrieve later” paradigm, self-powering is only used for continuous sensing and data-logging of essential statistics; whereas, data retrieval and reconfiguration is achieved using a low-cost commercial RFID system. Another advantage of using a commercial RFID system for data retrieval is that the related standards and FCC compliance are well established and the technology can be easily integrated with other IoT network infrastructure.

Monitoring of repeated head impacts using time-dilation based self-powered sensing

Measuring head impacts in helmeted sports is important for prognosticating onset of mild traumatic brain injuries (MTBIs) or concussions. In this paper we present a miniature battery-less, self-powered sensor that can be embedded inside sport helmets and can continuously monitor and log the statistics of different levels of helmet impacts. At the core of the proposed sensor is a novel time-dilation circuit which allows measurement of the high-levels of impact energy. An array of linear floatinggate injector is used for storing the location of the sensor on the helmet and for logging the statistics of helmet impacts which can be retrieved using an external plug-and-play reader. Measured results from prototypes fabricated in a 0.5 μm CMOS process validate the functionality of the sensor when subjected to controlled drop tests.

Exploiting Jump-Resonance Hysteresis in Silicon Auditory Front-Ends for Extracting Speaker Discriminative Formant Trajectories

Jump-resonance is a phenomenon observed in non-linear circuits where the amplitude of the output signal exhibits an abrupt jump when the frequency of the input signal is varied. For Gm-C filters used in the design of analog auditory front-ends (AFEs), jump-resonance is generally considered to be undesirable and several techniques have been proposed in literature to avoid or alleviate this artifact. In this paper we explore the use of jump-resonance based hysteresis in Gm-C band-pass filters for encoding speech formant trajectories. Using prototypes of silicon AFEs fabricated in a 0.5 μm CMOS process, we demonstrate the benefits of the proposed approach for extracting speaker discriminative features. These benefits are validated using speaker recognition experiments where consistent improvements in equal-error-rates (EERs) are achieved using the jump-resonance based features as compared to conventional features.

Gen-2 RFID compatible, zero down-time, programmable mechanical strain-monitors and mechanical impact detectors

A key challenge in structural health monitoring (SHM) sensors embedded inside civil structures is that elec- tronics need to operate continuously such that mechanical events of interest can be detected and appropriately analyzed. Continuous operation however requires a continuous source of energy which cannot be guaranteed using conventional energy scavenging techniques. The paper describes a hybrid energy scavenging SHM sensor which experiences zero down-time in monitoring mechanical events of interest. At the core of the proposed sensor is an analog floating-gate storage technology that can be precisely programmed at nano-watt and pico- watt power levels. This facilitates self-powered, non-volatile data logging of the mechanical events of interest by scavenging energy directly from the mechanical events itself. Remote retrieval of the stored data is achieved using a commercial off-the-shelf Gen-2 radio-frequency identification (RFID) reader which periodically reads an electronic product code (EPC) that encapsulates the sensor data. The Gen-2 interface also facilitates in simultaneous remote access to multiple sensors and also facilitates in determining the range and orientation of the sensor. The architecture of the sensor is based on a token-ring topology which enables sensor channels to be dynamically added or deleted through software control.

Exploiting jump-resonance hysteresis in silicon cochlea for formant trajectory encoding

Jump resonance is a phenomenon observed in nonlinear circuits where the output exhibits abrupt jumps when the frequency of the input signal is varied. In literature, several methods have been proposed for modeling and predicting of jump-resonance, which has led to circuit designs that are optimized to avoid this non-linear phenomenon. In this paper we propose exploiting jump-resonance based hystresis, observed in silicon cochlea, for encoding frequency and specifically formant trajectories in speech signal. Using experimental prototypes fabricated in a 0.5μm CMOS process, we show that the features extracted from a jump-resonance based silicon cochlea are more discriminative for speech based biometrics as compared to features extracted from a conventional silicon cochlea.

Quasi-self-powered Infrastructural Internet of Things: The Mackinac Bridge Case Study

Autonomous, continuous and long-term monitoring systems are required to prognosticate failures in civil infrastructures due to material fatigue or extreme events like earthquakes. While current battery-powered wireless sensors can evaluate the condition of the structure at a given instant of time, they require frequent replacement of batteries due to the need for continuous or frequent sampling. On the other hand, self-powered sensors can continuously monitor the structural condition without the need for any maintenance; however, the scarcity of harvested power limits the range at which the sensors could be wirelessly interrogated. In this paper, we propose a quasi-self-powered sensor that combines the benefits of self-powered sensing and with the benefits of battery-powered wireless transmission. By optimizing both of the functionalities, a complete sensor system can be designed that can continuously operate between the structures maintenance life-cycles and can be wirelessly interrogated at distances that obviates the need for taking the structure out-of-service. As a case study, in this paper we present the design considerations involved in prototyping quasi-self-powered sensors for deployment on the Mackinac Bridge in northern Michigan, with a target operational life span greater than 20 years.

A Coupled Network of Growth Transform Neurons for Spike-Encoded Auditory Feature Extraction

This paper builds upon our previously reported growth transform based optimization framework to present a novel spiking neuron model and demonstrate its application for spike-based auditory signal processing. Unlike conventional neuromorphic approaches, the proposed Growth Transform (GT) neuron model is tightly coupled to a system objective function, which results in network dynamics that are always stable and interpretable; and the process of spike generation and population dynamics is the result of minimizing an energy functional. We then extend the model to include axonal propagation delays in a manner that the optimized solution of the system or network objective function remains unaffected. The paper characterizes the model for different types of stimuli, and explores how changing different aspects of the cost function can reproduce known single neuron dynamics. We then investigate the properties of a coupled GT neural network that can generate spike-encoded auditory features corresponding to the output of a gammatone filterbank. We show that the discriminatory information is not only encoded in the traditional spike-rates and interspike-interval statistics, but is also encoded in the subthreshold response of GT neurons for inputs that are not strong enough to elicit spikes. We also demonstrate that while different forms of coupling between the neurons could produce compact and energy-efficient representation of the auditory features, the classification performance for a speaker recognition task remains invariant across different types of coupling. Thus, we believe that the proposed GT neuron model provides a flexible neuromorphic framework to systematically design large-scale spiking neural networks with stable and interpretable dynamics.

Infrasonic scene fingerprinting for authenticating speaker location

Ambient infrasound with frequency ranges well below 20 Hz is known to carry robust navigation cues that can be exploited to authenticate the location of a speaker. Unfortunately, many of the mobile devices like smartphones have been optimized to work in the human auditory range, thereby suppressing information in the infrasonic region. In this paper, we show that these ultra-low frequency cues can still be extracted from a standard smartphone recording by using acceleration-based cepstral features. To validate our claim, we have collected smartphone recordings from more than 30 different scenes and used the cues for scene fingerprinting. We report scene recognition rates in excess of 90% and a feature set analysis reveals the importance of the infrasonic signatures towards achieving the state-of-the-art recognition performance.