Microwave and Millimeter-wave Concurrent Multiband Low-Noise Amplifiers and Receiver Front-end in SiGe BiCMOS Technology

A fully integrated SiGe BiCMOS concurrent multiband receiver front-end and its building blocks including multiband low-noise amplifiers (LNAs), single-to-differential amplifiers and mixer are presented for various Ku-/K-/Ka-band applications. The proposed concurrent multiband receiver building blocks and receiver front-end achieve the best stopband rejection performances as compared to the existing multiband LNAs and receivers. First, a novel feedback tri-band load composed of two inductor feedback notch filters is proposed to overcome the low Q-factor of integrated passive inductors, and hence it provides superior stopband rejection ratio (SRR). A new 13.5/24/35-GHz concurrent tri-band LNA implementing the feedback tri-band load is presented. The developed tri-band LNA is the first concurrent tri-band LNA operating up to millimeter-wave region. By expanding the operating principle of the feedback tri-band load, a 21.5/36.5-GHz concurrent dual-band LNA with an inductor feedback dual-band load and another 23/36-GHz concurrent dual-band LNA with a new transformer feedback dual-band load are also presented. The latter provides more degrees of freedom for the creation of the stopband and passbands as compared to the former. A 22/36-GHz concurrent dual-band single-to-differential LNA employing a novel single-to-differential transformer feedback dual-band load is presented. The developed LNA is the first true concurrent dual-band single-to-differential amplifier. A novel 24.5/36.5 GHz concurrent dual-band merged single-to-differential LNA and mixer implementing the proposed single-to-differential transformer feedback dual-band load is also presented. With a 21-GHz LO signal, the down-converted dual IF bands are located at 3.5/15.5 GHz for two passband signals at 24.5/36.5 GHz, respectively. The proposed merged LNA and mixer is the first fully integrated concurrent dual-band mixer operating up to millimeter-wave frequencies without using any switching mechanism. Finally, a 24.5/36.5-GHz concurrent dual-band receiver front-end is proposed. It consists of the developed concurrent dual-band LNA using the single-to-single transformer feedback dual-band load and the developed concurrent dual-band merged LNA and mixer employing the single-to-differential transformer feedback dual-band load. The developed concurrent dual-band receiver front-end achieves the highest gain and the best NF performances with the largest SRRs, while operating at highest frequencies up to millimeter-wave region, among the concurrent dual-band receivers reported to date

Microwave and Millimeter-wave Concurrent Multiband Low-Noise Amplifiers and Receiver Front-end in SiGe BiCMOS Technology

A fully integrated SiGe BiCMOS concurrent multiband receiver front-end and its building blocks including multiband low-noise amplifiers (LNAs), single-to-differential amplifiers and mixer are presented for various Ku-/K-/Ka-band applications. The proposed concurrent multiband receiver building blocks and receiver front-end achieve the best stopband rejection performances as compared to the existing multiband LNAs and receivers. First, a novel feedback tri-band load composed of two inductor feedback notch filters is proposed to overcome the low Q-factor of integrated passive inductors, and hence it provides superior stopband rejection ratio (SRR). A new 13.5/24/35-GHz concurrent tri-band LNA implementing the feedback tri-band load is presented. The developed tri-band LNA is the first concurrent tri-band LNA operating up to millimeter-wave region. By expanding the operating principle of the feedback tri-band load, a 21.5/36.5-GHz concurrent dual-band LNA with an inductor feedback dual-band load and another 23/36-GHz concurrent dual-band LNA with a new transformer feedback dual-band load are also presented. The latter provides more degrees of freedom for the creation of the stopband and passbands as compared to the former. A 22/36-GHz concurrent dual-band single-to-differential LNA employing a novel single-to-differential transformer feedback dual-band load is presented. The developed LNA is the first true concurrent dual-band single-to-differential amplifier. A novel 24.5/36.5 GHz concurrent dual-band merged single-to-differential LNA and mixer implementing the proposed single-to-differential transformer feedback dual-band load is also presented. With a 21-GHz LO signal, the down-converted dual IF bands are located at 3.5/15.5 GHz for two passband signals at 24.5/36.5 GHz, respectively. The proposed merged LNA and mixer is the first fully integrated concurrent dual-band mixer operating up to millimeter-wave frequencies without using any switching mechanism. Finally, a 24.5/36.5-GHz concurrent dual-band receiver front-end is proposed. It consists of the developed concurrent dual-band LNA using the single-to-single transformer feedback dual-band load and the developed concurrent dual-band merged LNA and mixer employing the single-to-differential transformer feedback dual-band load. The developed concurrent dual-band receiver front-end achieves the highest gain and the best NF performances with the largest SRRs, while operating at highest frequencies up to millimeter-wave region, among the concurrent dual-band receivers reported to date

An Alternative Understanding of the Amoralist

From the Washington University Senior Honors Thesis Abstracts (WUSHTA), 2017. Published by the Office of Undergraduate Research. Joy Zalis Kiefer, Director of Undergraduate Research and Associate Dean in the College of Arts & Sciences; Lindsey Paunovich, Editor; Helen Human, Programs Manager and Assistant Dean in the College of Arts and Sciences Mentor: Julia Drive

BridgeNets: Student-Teacher Transfer Learning Based on Recursive Neural Networks and its Application to Distant Speech Recognition

Despite the remarkable progress achieved on automatic speech recognition, recognizing far-field speeches mixed with various noise sources is still a challenging task. In this paper, we introduce novel student-teacher transfer learning, BridgeNet which can provide a solution to improve distant speech recognition. There are two key features in BridgeNet. First, BridgeNet extends traditional student-teacher frameworks by providing multiple hints from a teacher network. Hints are not limited to the soft labels from a teacher network. Teacher's intermediate feature representations can better guide a student network to learn how to denoise or dereverberate noisy input. Second, the proposed recursive architecture in the BridgeNet can iteratively improve denoising and recognition performance. The experimental results of BridgeNet showed significant improvements in tackling the distant speech recognition problem, where it achieved up to 13.24% relative WER reductions on AMI corpus compared to a baseline neural network without teacher's hints.Comment: Accepted to 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2018

WiseMove: A Framework for Safe Deep Reinforcement Learning for Autonomous Driving

Machine learning can provide efficient solutions to the complex problems encountered in autonomous driving, but ensuring their safety remains a challenge. A number of authors have attempted to address this issue, but there are few publicly-available tools to adequately explore the trade-offs between functionality, scalability, and safety. We thus present WiseMove, a software framework to investigate safe deep reinforcement learning in the context of motion planning for autonomous driving. WiseMove adopts a modular learning architecture that suits our current research questions and can be adapted to new technologies and new questions. We present the details of WiseMove, demonstrate its use on a common traffic scenario, and describe how we use it in our ongoing safe learning research

An Empirical Study of the Factors Influencing Use of Social Network Service

Social network service (SNS) has become a popular media for social interaction and information exchange. However, it is not clear why people use SNS. In addition, human behaviour researches related to SNS are not sufficient despite of its prominence. Hence, the goal of this paper is to explain why people use SNS. First, we empirically examined what individual’s characteristics affect the use of SNS. To examine these characteristics, we developed a structural model on the basis of three popular theories; Technology Acceptance Model (TAM), Network Externality, and Innovation Diffusion Theory (IDT). Also, to achieve our goal, we applied Structural Equation Modeling (SEM) approach to evaluate the effect of each construct. The finding shows that Perceived Usefulness (PU), Perceived Ease of Use (PEU), Members (M), and Compatibility (C) have a positive significant effect on the Actual Use (AU) of SNS. Moreover, we identified the differences between Facebook users and Twitter users. From the control variable (Facebook and Twitter users) analysis, we concluded that PU and M are important factors to Facebook users, while PEU and C are prominent to Twitter users

Determinants of Users' Intention to Purchase Probability-Based Items in Mobile Social Network Games: A Case of South Korea

The goal of this paper was to identify factors that influence the purchase of probability-based items (PBIs) in mobile social network games (MSNGs). This paper introduces an extended research model based on the technology acceptance model. Statistical results from a survey of MSNG users find that factors that influence the purchase of PBIs include perceived enjoyment, perceived usefulness, perceived number of users, and perceived number of friends which are factors attributed to mobile game and social network characteristics, and also perceived desire for jackpot that is one of the major features of PBIs. We also analyzed the research model by gender to provide MSNG companies with a reference that may guide the development of PBI strategies that are targeted at genders. Males responded differently than females to some factors.11Ysciescopu