Shey-Shi Lu

This work proposed a programmable pulsed radio-frequency (PRF) stimulator for trigeminal neuralgia (TN) relief on demand. The implantable stimulator is a miniaturized micro-system which integrates a wireless interface circuit, a sensor interface circuit, a PRF pattern generation circuit and a logic controller. The multifunctional stimulator capable of delivering current/voltage stimulation provides the choice of the biphasic sinusoidal, square and patterned waveform for PRF treatment researches. The external handheld device can wirelessly transmit the parameters of frequency, amplitude, pulse duration and repetition rate of the pulse train to the implanted stimulator. While stimulating, the temperature sensor can monitor the operating temperature. The feedback signal is transmitted in medical implanted communication system (MICS). The micro-system is fabricated in a 0.35 μm CMOS process with a chip size of 3.1 × 2.7 mm ² . The fabricated chip was mounted on a 2.6 × 2.1 cm ² test board for studying the in vivo efficacy of pain relief by PRF. Animal studies of PRF stimulation and commonly-used medication for trigeminal neuralgia are also demonstrated and the presented results prove that PRF stimulation has greater effectiveness on trigeminal neuralgia relief comparing to the medication. The effectiveness period lasts at least 14 days. The results of neural recording show that the PRF stimulation of trigeminal ganglion (TG) attenuated neuron activities without being severely damaged. Pathology also revealed no lesion found on the stimulated area.

For the thermal monitoring of power transformers, contactless infrared technology has advantages of high resolution and fast responding time, and it does not disturb the original thermal profile across the transformer. However, the thermal faults detected by infrared technology can only be visualized on 2-D projection of the region of interest. The precise spatial localization of the thermal faults cannot be evaluated. In this paper, an improved Augmented Reality technology for panoramic visualization of thermal faults in power transformer is presented. Refined power transformer model is made by using 3-D scanning and reconstruction technology. Infrared images for the apparent temperature measurements of power transformers from different point of views and scales are collected, and the temperature matrix for each image are obtained by using a self-developed data extraction algorithm. Further, 2-D to 3-D geometry transformation are applied to do the mapping of temperature data on the meshing of the refined transformer model. Eventually, the thermal profile across the surface of transformer is holographically displayed, and the precise localization of thermal faults on the transformer can be carried out in 3-D space. Research in this paper makes contribution to smarter and efficient condition monitoring of power transformers.

Obstructive sleep apnea (OSA), which is caused by obstructions of the upper airway, is a syndrome with rising prevalence. Mandibular advancement splints (MAS) are oral appliances for potential treatment of OSA. This work proposes a highly-sensitive pressure sensing array integrated with a system-on-chip (SoC) embedded in a MAS. The device aims to measure tongue pressure distribution in order to determine the efficacy of the MAS for treating OSA. The flexible sensing array consists of an interdigital electrode pair array assembled with conductive polymer films and an SoC capable of retrieving/storing data during sleep, and transmitting data for analysis after sleep monitoring. The surfaces of the conductive polymer films were patterned with microdomed structures, which effectively increased the sensitivity and reduced the pressure sensing response time. The measured results also show that the crosstalk effect between the sensing elements of the array was negligible. The sensitivity of the sensing array changed minimally after the device was submerged in water for up to 100 h.

An ultra low-power 16-channel wireless neural recording system-on-a-chip (SoC) is implemented in 0.18μm standard CMOS process. A novel topology of analog front-end (AFE), which combines the auto-zero technique and channel switching method to obtain both low-noise and low-power features, is presented in this work. A time-domain-based modulation approach reduces the power consumption of wireless transmitter dramatically by 80%. A RF energy harvester is also employed in this chip for sustainable use. The input referred noise and average power per channel of AFE are 0.5 μV _rms and 1.345 μW respectively. The whole system consumes only 473 μW while performing the signal recording and wireless transmission. Furthermore, practical experiments of human EEG signal recording are demonstrated to verify the functions of proposed SoC.

Sleep apnea is a serious sleep disorder, and the most common type is obstructive sleep apnea (OSA). Untreated OSA will cause lots of potential health problems. Oral appliance therapy is an effective and popular approach for OSA treatment, but making a perfect fit for each patient is time-consuming and decreases its efficiency considerably. This paper proposes a System-on-a-Chip (SoC) enabled sleep monitoring system in a smart oral appliance, which is capable of intelligently collecting the physiological data about tongue movement through the whole therapy. A tunneling sensor array with an ultra-high sensitivity is incorporated to accurately detect the subtle pressure from the tongue. When the device is placed on the wireless platform, the temporary stored data will be retrieved and wirelessly transmitted to personal computers and cloud storages. The battery will be recharged by harvesting external RF power from the platform. A compact prototype module, whose size is 4.5 × 2.5 × 0.9 cm³, is implemented and embedded inside the oral appliance to demonstrate the tongue movement detection in continuous time frames. The functions of this design are verified by the presented measurement results. This design aims to increase efficiency and make it a total solution for OSA treatment.

This work utilizes PEDOT:PSS/PANI composite polymer to develop a low-power and low-cost CO ₂ sensing material. With proper material composition, the developed CO ₂ sensing material overcomes a major problem, selectivity to humidity, of traditional conductive polymer sensors. Based on experimental results, it shows a CO ₂ detection range from 1000 ppm to 20000 ppm. It also has an independent response to relative humidity ranging from 55% to 90%. In addition, the developed sensing material has experimentally demonstrated its power consumption of 10 μW. Based on these experimental results, the developed CO ₂ polymer sensor demonstrates a good potential for chemical/environmental applications of Internet-of-Thing technologies.

A linearity improvement technique for elliptic filters in low-supply-voltage transceiver design for 5G (5th generation of mobile technology) communication systems has been proposed. The zero in the transfer function has been placed at the third-harmonic of the intermediate frequency (IF) signals in order to suppress the undesirable components, and thus the total harmonic distortion (THD) can be dramatically reduced. To achieve precise zero control to maximize linearity optimization, the transfer function of the elliptic filter with Tow-Thomas-II implementation has been re-derived, taking into account the operational amplifiers (op-amps) nonidealities. In this paper, a fourth-order low-pass filter, composed of two cascaded Tow-Thomas-II biquads, has been described and simulated to validate the linearity improvement technique, which features a 0.1-1.6 GHz tunable 3-dB bandwidth. In addition, 6 dB THD enhancement can be realized when adopting modified transfer function, and THD can be smaller than -46 dB with -1 dBm input power, corresponding to 1.2 Vpp differential output signal.

A self-sustained multi-sensor platform for indoor environmental monitoring is proposed in this paper. To reduce the cost and power consumption of the sensing platform, in the developed platform, organic materials of PEDOT:PSS and PEDOT:PSS/EB-PANI are used as the sensing films for humidity and CO₂ detection, respectively. Different from traditional gas sensors, these organic sensing films can operate at room temperature without heating processes or infrared transceivers so that the power consumption of the developed humidity and the CO₂ sensors can be as low as 10 μW and 5 μW, respectively. To cooperate with these low-power sensors, a Complementary Metal-Oxide-Semiconductor (CMOS) system-on-chip (SoC) is designed to amplify and to read out multiple sensor signals with low power consumption. The developed SoC includes an analog-front-end interface circuit (AFE), an analog-to-digital convertor (ADC), a digital controller and a power management unit (PMU). Scheduled by the digital controller, the sensing circuits are power gated with a small duty-cycle to reduce the average power consumption to 3.2 μW. The designed PMU converts the power scavenged from a dye sensitized solar cell (DSSC) module into required supply voltages for SoC circuits operation under typical indoor illuminance conditions. To our knowledge, this is the first multiple environmental parameters (Temperature/CO₂/Humidity) sensing platform that demonstrates a true self-powering functionality for long-term operations.

Cardiovascular diseases (CVDs) have become the leading cause of death globally (as reported by the World Health Organization, June 2016). An effective method of preventing CVDs is to measure and monitor blood pressure (BP), which serves as a physiological indicator for cardiovascular systems. A previous research has proposed the use of pulse transit time (PTT) information to compute the BP measure. We propose herein a novel method based on a linear regression model that incorporates static and dynamic PTT features to predict BP measures more accurately. Moreover, the proposed model considers the estimated systolic blood pressure (SBP) when estimating the diastolic blood pressure (DBP). Experimental results first show that the proposed method can predict the BP more accurately than conventional methods, with notably higher correlation scores and lower mean square errors. These results confirm the effectiveness of incorporating dynamic PTT features for accurate BP predictions. Next, our experimental results demonstrate that the proposed method attains a better DBP prediction capability, showing that the estimated SBP provides useful information for the DBP prediction.

A compact low-power VCO in 0.18 μm CMOS technology is presented for V-band applications. Unlike conventional push-push VCOs using huge passive transmission-line-based frequency doublers to rise their output oscillation frequencies, the proposed VCO adopts a more compact transistor-based frequency doubler to reduce chip layout area. Under 1-V supply operation, experimental results show that the output power of VCO ranges from -13.5 dBm to -6.5 dBm over the V band of interest with low power dissipation from 6.3 mW to 16.1 mW excluding the output buffer. The measured phase noise is -93.12 dBc/Hz at 10MHz offset from 55 GHz carrier. This VCO only occupies a small chip layout area of 0.1 mm² excluding pads and bypass capacitors.