Browsing by Author "Lie, Donald Y.C. (TTU)"
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Item A Feasibility Study of Remote Non-Contact Vital Signs (NCVS) Monitoring in a Clinic Using a Novel Sensor Realized by Software-Defined Radio (SDR)(2023) Liu, Yang (TTU); Sweeney, Clint (TTU); Mayeda, Jill C. (TTU); Lopez, Jerry (TTU); Lie, Paul E. (TTUHSC); Nguyen, Tam Q. (TTU); Lie, Donald Y.C. (TTU)The COVID-19 outbreak has caused panic around the world as it is highly infectious and has caused about 5 million deaths globally. A robust wireless non-contact vital signs (NCVS) sensor system that can continuously monitor the respiration rate (RR) and heart rate (HR) of patients clinically and remotely with high accuracy can be very attractive to healthcare workers (HCWs), as such a system can not only avoid HCWs’ close contact with people with COVID-19 to reduce the infection rate, but also be used on patients quarantined at home for telemedicine and wireless acute-care. Therefore, we developed a custom Doppler-based NCVS radar sensor system operating at 2.4 GHz using a software-defined radio (SDR) technology, and the novel biosensor system has achieved impressive real-time RR/HR monitoring accuracies within approximately 0.5/3 breath/beat per minute (BPM) on student volunteers tested in our engineering labs. To further test the sensor system’s feasibility for clinical use, we applied and obtained an Internal Review Board (IRB) approval from Texas Tech University Health Sciences Center (TTUHSC) and have used this NCVS monitoring system in a doctor’s clinic at TTUHSC; following testing on 20 actual patients for a small-scale clinical trial, we have found that the system was still able to achieve good NCVS monitoring accuracies within ~0.5/10 BPM across 20 patients of various weight, height and age. These results suggest our custom-designed NCVS monitoring system may be feasible for future clinical use to help combatting COVID-19 and other infectious diseases.Item Broadband Millimeter-Wave 5G Power Amplifier Design in 22 nm CMOS FD-SOI and 40 nm GaN HEMT(2022) Mayeda, Jill (TTU); Lie, Donald Y.C. (TTU); Lopez, Jerry (TTU)Three millimeter-wave (mm-Wave) power amplifiers (PAs) that cover the key 5G FR2 band of 24.25 to 43.5 GHz are designed in two different state-of-the-art device technologies and are presented in this work. First, a single-ended broadband PA that employs a third-order input matching network is designed in a 40 nm GaN/SiC HEMT (High Electron Mobility Transistor) technology. Good agreement between the measurement and post-layout parasitic extracted (PEX) electromagnetic (EM) simulation data is observed, and it achieves a measured 3-dB BW (bandwidth) of 18.0–40.3 GHz and >20% maximum PAE (power-added-efficiency) across the entire 20–44 GHz band. Expanding upon this measured design, a differential broadband GaN PA that utilizes neutralization capacitors is designed, laid out, and EM simulated. Simulation results indicate that this PA achieves 3-dB BW 20.1–44.3 GHz and maximum PAE > 23% across this range. Finally, a broadband mm-Wave differential CMOS PA using a cascode topology with RC feedback and neutralization capacitors is designed in a 22 nm FD-SOI (fully depleted silicon-on-insulator) CMOS technology. This PA achieves an outstanding measured 3-dB BW of 19.1–46.5 GHz and >12.5% maximum PAE across the entire frequency band. This CMOS PA as well as the single-ended GaN PA are tested with 256-QAM-modulated 5G NR signals with an instantaneous signal BW of 50/100/400/9 × 100 MHz at a PAPR (peak-to-average-power ratio) of 8 dB. The data exhibit impressive linearity vs. POUT trade-off and useful insights on CMOS vs. GaN PA linearity degradation against an increasing BW for potential mm-Wave 5G applications.Item Effective Digital Predistortion (DPD) on a Broadband Millimeter-Wave GaN Power Amplifier Using LTE 64-QAM Waveforms(2023) Somasundaram, Gokul (TTU); Mayeda, Jill C. (TTU); Sweeney, Clint (TTU); Lie, Donald Y.C. (TTU); Lopez, Jerry (TTU)We demonstrate in this work effective linearization on a millimeter-wave (mm-Wave) broadband monolithic gallium nitride (GaN) power amplifier (PA) using digital predistortion (DPD). The PA used is a two-stage common-source (CS)/2-stack PA that operates in the mm-Wave 5G FR2 band, and it is linearized with the generalized memory polynomial (GMP) DPD and tested using 4G (4th generation) long-term-evolution (LTE) 64-QAM (quadrature amplitude modulation) modulated signals with a PAPR (peak-to-average power ratio) of 8 dB. Measurement results after implementing GMP DPD indicate considerable broadband improvement in the adjacent channel leakage power ratio (ACLR) of 16.9 dB/17.3 dB/16.5 dB/15.1 dB at 24 GHz/28 GHz/37 GHz/39 GHz, respectively, with a common average POUT of 15 dBm using a 100 MHz LTE 64-QAM input signal. At a fixed frequency of 28 GHz, the GaN PA after GMP DPD achieved signal bandwidth-dependent ACLR improvement and root-mean-square (rms) EVM (error vector magnitude) reduction using 20 MHz/40 MHz/80 MHz/100 MHz LTE 64-QAM waveforms with a common average POUT of 15 dBm. The GaN PA thus achieved very good linearization results compared to that in other state-of-the-art mm-Wave PA DPD studies in the literature, suggesting that GMP DPD should be rather effective for linearizing mm-Wave 5G broadband GaN PAs to improve POUT, Linear.Item Epileptic seizure detection and prediction based on continuous cerebral blood flow monitoring - A review(2015) Tewolde, Senay (TTU); Oommen, Kalarickal; Lie, Donald Y.C. (TTU); Zhang, Yuanlin (TTU); Chyu, Ming Chien (TTU)Epilepsy is the third most common neurological illness, affecting 1% of the world's population. Despite advances in medicine, about 25 to 30% of the patients do not respond to or cannot tolerate the severe side effects of medical treatment, and surgery is not an option for the majority of patients with epilepsy. The objective of this article is to review the current state of research on seizure detection based on cerebral blood flow (CBF) data acquired by thermal diffusion flowmetry (TDF), and CBF-based seizure prediction. A discussion is provided on the applications, advantages, and disadvantages of TDF in detecting and localizing seizure foci, as well as its role in seizure prediction. Also presented are an overview of the present challenges and possible future research directions (along with methodological guidelines) of the CBF-based seizure detection and prediction methods.Item Real-time classification of patients with balance disorders vs. normal subjects using a low-cost small wireless wearable gait sensor(2016) Nukala, Bhargava Teja (TTU); Nakano, Taro (TTU); Rodriguez, Amanda; Tsay, Jerry (TTU); Lopez, Jerry (TTU); Nguyen, Tam Q. (TTU); Zupancic, Steven (TTUHSC); Lie, Donald Y.C. (TTU)Gait analysis using wearable wireless sensors can be an economical, convenient and effective way to provide diagnostic and clinical information for various health-related issues. In this work, our custom designed low-cost wireless gait analysis sensor that contains a basic inertial measurement unit (IMU) was used to collect the gait data for four patients diagnosed with balance disorders and additionally three normal subjects, each performing the Dynamic Gait Index (DGI) tests while wearing the custom wireless gait analysis sensor (WGAS). The small WGAS includes a tri-axial accelerometer integrated circuit (IC), two gyroscopes ICs and a Texas Instruments (TI) MSP430 microcontroller and is worn by each subject at the T4 position during the DGI tests. The raw gait data are wirelessly transmitted from the WGAS to a near-by PC for real-time gait data collection and analysis. In order to perform successful classification of patients vs. normal subjects, we used several different classification algorithms, such as the back propagation artificial neural network (BP-ANN), support vector machine (SVM), k-nearest neighbors (KNN) and binary decision trees (BDT), based on features extracted from the raw gait data of the gyroscopes and accelerometers. When the range was used as the input feature, the overall classification accuracy obtained is 100% with BP-ANN, 98% with SVM, 96% with KNN and 94% using BDT. Similar high classification accuracy results were also achieved when the standard deviation or other values were used as input features to these classifiers. These results show that gait data collected from our very low-cost wearable wireless gait sensor can effectively differentiate patients with balance disorders from normal subjects in real time using various classifiers, the success of which may eventually lead to accurate and objective diagnosis of abnormal human gaits and their underlying etiologies in the future, as more patient data are being collected.