http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
Ultralow-Power Ku-Band Dual-Feedback Armstrong VCO With a Wide Tuning Range
Tai Nghia Nguyen,Jong-Wook Lee IEEE 2012 IEEE transactions on circuits and systems. a publi Vol.59 No.7
<P>Here, we investigate the design of a Ku-band Armstrong voltage-controlled oscillator (VCO) utilizing dual transformer feedback for low-voltage low-power operation. The primary transformer feedback between the drain and the source of the VCO increases the output voltage swing under low supply voltage. With the secondary transformer feedback, g_m-boosting is obtained by the coupling between the drain and the gate of the Armstrong VCO. Combined with transformer feedback, g_m-boosting further reduces the negative transconductance required for oscillation startup, enabling ultralow-power operation. Thus, the proposed Armstrong VCO with dual feedback operates at 0.4 V with power consumption as low as 600 W. Under this condition, the measured phase noise of the VCO is -100.6 dB/Hz at 1-MHz offset from a 14.1-GHz carrier. The results show that the proposed VCO is suitable for very low-power applications requiring high signal purity.</P>
A K-band CMOS Differential Vackar VCO With the Gate Inductive Feedback
Tai Nghia Nguyen,Jong-Wook Lee IEEE 2012 IEEE transactions on circuits and systems. a publi Vol.59 No.5
<P>This brief presents a K-band differential Vackar voltage-controlled oscillator (VCO) with gate inductive feedback which enhances negative impedance and thus simplifies the startup condition. Simple analysis and simulations examine the transistor loading effect and amplitude stability. Results indicate that the Vackar VCO has improved amplitude stability compared to the Colpitts VCO. The improved amplitude stability is favorable for suppressing amplitude-to-phase noise conversion. The Vackar VCO was implemented in a 0.13- RF CMOS process. The oscillation frequency ranged from 19 to 19.95 GHz. The measured phase noise at 1-MHz offset was 103 dBc/Hz at 19.5 GHz with a figure of merit of 182 dB.</P>
Low Phase Noise Differential Vackar VCO in 0.18 <tex> $\mu{\rm m}$</tex> CMOS Technology
Tai Nghia Nguyen,Jong-Wook Lee IEEE 2010 IEEE microwave and wireless components letters Vol.20 No.2
<P>In this letter, we present the measured performance of a differential Vackar voltage-controlled oscillator (VCO) implemented for the first time in CMOS technology. The Vackar VCO provided good isolation between the LC tank and the loss-compensating active circuit; thus, excellent frequency stability was achieved over the frequency tuning range. The Vackar VCO was implemented using nMOS transistors and an LC tank in a 0.18 μm RF CMOS process. The oscillation frequency ranged from 4.85 to 4.93 GHz. The measured phase noise of the Vackar VCO at 1 MHz offset was -124.9 dB/Hz at 4.9 GHz with a figure-of-merit (FOM) of -188 dBc/Hz.</P>
Nguyen, Tai Nghia,Lee, Jong-Wook Wiley Subscription Services, Inc., A Wiley Company 2010 MICROWAVE AND OPTICAL TECHNOLOGY LETTERS Vol.52 No.8
<P>A low phase noise tuned-input tuned-output (TITO) voltage-controlled oscillator (VCO) using PMOS transistors is presented in this article.The TITO-VCO, implemented using a 0.18 μm RF CMOS process, resulted in a phase noise performance of −118 dBc/Hz at 1 MHz offset from the center frequency of 4.7 GHz. The figure-of-merit of the TITO-VCO was 188 dB. To the author's knowledge, this is the first report on the measured performance of differential TITO-VCO implemented using PMOS transistors. © 2010 Wiley Periodicals, Inc. Microwave Opt Technol Lett 52: 1852–1855, 2010; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.25341</P>
A TabNet - Based System for Water Quality Prediction in Aquaculture
Trong-Nghia Nguyen,김수형(Soo Hyung Kim),도누따이(Nhu-Tai Do),Thai-Thi Ngoc Hong,양형정(Hyung Jeong Yang),이귀상(Guee Sang Lee) 한국스마트미디어학회 2022 스마트미디어저널 Vol.11 No.2
In the context of the evolution of automation and intelligence, deep learning and machine learning algorithms have been widely applied in aquaculture in recent years, providing new opportunities for the digital realization of aquaculture. Especially, water quality management deserves attention thanks to its importance to food organisms. In this study, we proposed an end-to-end deep learning-based TabNet model for water quality prediction. From major indexes of water quality assessment, we applied novel deep learning techniques and machine learning algorithms in innovative fish aquaculture to predict the number of water cells counting. Furthermore, the application of deep learning in aquaculture is outlined, and the obtained results are analyzed. The experiment on in-house data showed an optimistic impact on the application of artificial intelligence in aquaculture, helping to reduce costs and time and increase efficiency in the farming process.