Youngchun Kim

Multi-Channel Sparse Data Conversion With a Single Analog-to-Digital Converter

We address the problem of performing simultaneous analog-to-digital (A/D) conversion on multi-channel signals using a single A/D converter (ADC). Assuming that each input has an unknown sparse representation in known dictionaries, we find that multi-channel information can be sampled with a single ADC. The proposed ADC architecture consists of a mixed signal block and a digital signal processing (DSP) block. The channel inputs are sampled by switched-capacitor-based sample-and-hold circuits, and then mixed using sequences of plus or minus ones, leading to no bandwidth expansion. The resulting discrete-time signals are converted to digital sequences by a single ADC or quantizer. At the DSP block, each channel is separated from the digitized mixture through various separation algorithms that are widely used in compressive sensing. For this, we study several techniques for separating the mixture of the channel inputs into the sample number of digital sequences corresponding to each channel. We show that with an ideal ADC, perfect reconstruction of the signals is possible if the input signals are sufficiently sparse. We also show simulation results with a 16-bit ADC model, and the reconstruction is possible up to the accuracy of the ADCs.

A single SAR ADC converting multi-channel sparse signals

This paper presents a simple but high performance architecture for multi-channel analog-to-digital conversion. Based on compressive sensing, only one SAR ADC is needed to convert multi-channel sparse inputs, leading to significant analog power saving and hardware saving. Moreover, it helps avoid problems occurring in conventional multi-channel ADCs such as timing skew, offset mismatch, and gain mismatch. A 12-bit SAR ADC converting 4-channel sparse signals simultaneously is designed in 130nm CMOS process. The design reaches a SNDR of 66.3dB and consumes an average power of 58μW at the sampling frequency of 1MHz. The L1 minimization method is chosen to reconstruct the input signals. The single-tone and multi-tone inputs can be reconstructed with a minimum precision of 68dB and 55dB THD, respectively.

PowerTrain: A learning-based calibration of McPAT power models

As research on improving energy efficiency becomes prevalent, the necessity of a tool to accurately estimate power is increasing. Among various tools proposed, McPAT has gained some popularity due to its easy-to-use analytical power models. However, McPATs prediction has several limitations. Although under- or over-estimated power from unmodeled and mis-modeled parts offset each other, it still incorporates errors in each block. Moreover, the lack of awareness to the implementation details exacerbates the prediction inaccuracies. To alleviate this problem, we propose a new methodology to train McPAT towards precise processor power prediction using power measurements from real hardware. This calibration enables McPATs power to fit to the target processor power. Once we adjusted the power consumption of each block to best match those in the target processor, our trained McPAT delivered more precise power estimation. We calibrated the outputs of McPAT against a Cortex-A15 within a Samsung Exynos 5422 SoC. We observe that our methodology successfully reduces the errors, particularly for workloads with fluctuating power behaviors. The results show that the mean percentage error and the mean percentage absolute error of the calibrated power against real hardware are 2.04 percent and 4.37 percent, respectively.

Ultra-low power multi-channel data conversion with a single SAR ADC for mobile sensing applications

Based on the recently emerging compressive sensing theory, the paper proposes an ultra-low power multichannel data conversion system whose architecture is almost as simple as a single SAR ADC. The proposed architecture is capable of simultaneously converting multi-channel sparse signals while running at the Nyquist rate of only one channel. A chip is fabricated in a 0.13μm CMOS process. Operating at 1MS/s, the SAR ADC itself achieves a 66dB SNDR and a 25fJ/step FoM at 0.8V. Using convex optimization methods, 4-channel 500kHz-bandwidth signals can be reconstructed with a 66dB peak SNDR and a 41% max occupancy, leading to an effective FoM per channel of 6.25 fJ/step.

An efficient detection on capacitive touch screens using bandwidth expansion

This paper presents a new efficient detection method on capacitive touch screens by mixing multi-channel inputs and expanding the sensing bandwidth. Multi-channel touch signals can be combined with a modulation scheme which allows an overlap in spectral domain. By expanding the sensing bandwidth, the overlapped region can be minimized which leads to more accurate detection. The touch-detection problem is formulated as an event detection problem which decides touch-input status under Gaussian noise. We provide the numerical analysis of the proposed scheme, and show the relation between the detection performance and the bandwidth expansion rates. The simulation result confirms that slight increase in sensing bandwidth yields an accurate touch detection using the proposed sensing and detection schemes.

Low power detection on capacitive touch screens

This paper presents a novel, power efficient and low-latency method for detecting touch locations on capacitive touch screens. In capacitive touch screens, only a few touch sensors experience detectable change corresponding to the touch locations. We show that power consumption can be reduced by monitoring fewer number of samples than the standard method to sense changes in capacitances and their locations. The proposed method uses group testing for touch sensing and location detection. We also propose a decoding method based on Tikhonov regularization, and provide a closed form of the numerical solution. To verify the proposed method, we compare detection performance and power consumption with those of traditional approach. The results show that the proposed alternative leads to equivalent detection performance with significant reduction in power consumption and sensing latency.

A novel QRS complex detection on ECG with motion artifact during exercise

We present a novel QRS complex detection scheme from ECG with motion artifact. The algorithm relies on subspace learning and template matching. QRS complex detection during exercise is a challenging problem because multiple artifacts affect the ECG measurement. Motion artifact is considered to be the main disturbance added to the measurement during exercise. To deal with the problem, we train a dictionary to represent motion artifact using information from a tri-axis accelerometer, and then remove the artifact contribution from noisy ECG measurements. We select the GCC-PHAT filter for efficient QRS detection on the denoised ECG measurements. We show that the proposed algorithm has appreciably higher motion artifact reduction capability and lower computational complexity than competing algorithms. It is therefore a preferred alternative for implementation in mobile health monitoring systems.

Detection of Self-Stimulatory Behaviors of Children with Autism Using Wearable and Environmental Sensors

Autism is one of the five pervasive development disorders that may cause severe impairment to a child. Depending on the degree of the symptoms, autism may cause severe impairments in ones social life such as social interaction and communication with other individuals. They may also face challenges in learning, concentrating, sensation and interacting with their surroundings. According to the Center for Disease Control (CDC), 1 in 150 8-year old children in many areas in the United States were diagnosed with autism. It is also known from recent studies that with early diagnosis we can intervene earlier which allows better assistance and treatment. Therefore, it is critical to have an objective assessment tool to assist diagnosis and for management. We have developed an affordable, reliable system that provides evidence based tools for assessment of children with autism. This system can detect various repetitive behavioral patterns often seen in children with autism and enables long term monitoring of repetitive behaviors. Therefore, it can be used to assist doctors, therapists, caregivers and parents with diagnosis and treatment of children with autism. This system incorporates 2 different sensor platforms which include environmental and wearable sensors. The system consists of a 3-axis accelerometer, small microcontroller and a Bluetooth module to transmit data to a base station such as a PC for analysis. We have customized this wearable device to integrate these modules which can be worn by a child. The environmental sensor configuration is composed of a microphone which records the acoustic data of the subject within the room. Using this sensor system, we are able to achieve the necessary information for assessment and therapy in autism research. We have analyzed the 3-axis accelerometer and acoustic data with an intelligent machine learning algorithm. The algorithm extracts time-domain and frequency domain features from the accelerometer data and applies statistical learning techniques to detect repetitive behavioral patterns. For acoustic data, we used sparse signal representation techniques to detect repetitive patterns that indicate vocalization behaviors. We have achieved an average of 89% in classification accuracy for detecting behavioral patterns. Based on the real data collected from children with autism, we were able to detect and recognize four self-stimulatory behaviors of children with autism. In one instance in which a subject had a tantrum, using the correlation between the hand flapping ratio and vocalization intensity, we were able to predict this extreme behavior. Our study opens an application in which devices could be used in a classroom environment to predict extreme behaviors in order that the stress of children with autism could be diverted accordingly so that their actions would be more socially agreeable.

A nonuniform quantization scheme for high speed SAR ADC architecture

We introduce a new signal sampling scheme which allows high quality signal conversion to overcome the constraint of effective number of bits in high speed signal acquisition. The proposed scheme is based on the popular successive approximation register (SAR) and employs compressive sensing technique to increase the resolution of a SAR analog-to-digital converter (ADC) architecture. We present signal acquisition and recovery model which provides better performance in signal acquisition. The sampled signal shows higher resolution after recovery than conventional compressive sensing based sampling schemes. Circuit level architecture is discussed to implement the proposed scheme using the SAR ADC architecture. Simulation result shows that the proposed nonuniform quantization strategy can be a way to overcome the sampling rate-resolution limitation which is a challenging problem in SAR ADC design even with the most advanced technology.

A Fully Passive Compressive Sensing SAR ADC for Low-Power Wireless Sensors

The compressive sensing (CS) theory states that the sparsity of a signal can be exploited to reduce the analog-to-digital converter (ADC) conversion rate and save power. However, most previous CS frameworks require dedicated analog CS encoders built by power-hungry active amplifiers, which limit the overall power saving. Differently, this paper proposes a fully passive switched-capacitor-based CS framework that directly embeds CS into a successive-approximation-register (SAR) ADC. The proposed CS-SAR ADC can operate in two modes: the Nyquist mode and the CS mode. In the CS mode, the CS-SAR ADC quantizes the input once every four-time sampling, reducing the conversion rate and the circuit power by four times compared to the Nyquist mode. A prototype chip is fabricated in a 0.13-<inline-formula> <tex-math notation=LaTeX>