729 research outputs found
Plasmonic FET Terahertz Spectrometer
We show that Si MOSFETs, AlGaN/GaN HEMTs, AlGaAs/InGaAs HEMTs, and p-diamond
FETs with feature sizes ranging from 20 nm to 130 nm could operate at room
temperature as THz spectrometers in the frequency range from 120 GHz to 9.3 THz
with different subranges corresponding to the transistors with different
features sizes and tunable by the gate bias. The spectrometer uses a
symmetrical FET with interchangeable source and drain with the rectified THz
voltage between the source and drain being proportional to the sine of the
phase shift between the voltages induced by the THz signal between
gate-to-drain and gate-to-source. This phase difference could be created by
using different antennas for the source-to-gate and drain-to gate contacts or
by using a delay line introducing a phase shift or even by manipulating the
impinging angle of the two antennas. The spectrometers are simulated using the
multi-segment unified charge control model implemented in SPICE and ADS and
accounting for the electron inertia effect and the distributed channel
resistances, capacitances and Drude inductances.Comment: 5 pages, 10 figures, submission to IEEE Acces
A deep learning framework based on Koopman operator for data-driven modeling of vehicle dynamics
Autonomous vehicles and driving technologies have received notable attention
in the past decades. In autonomous driving systems, \textcolor{black}{the}
information of vehicle dynamics is required in most cases for designing of
motion planning and control algorithms. However, it is nontrivial for
identifying a global model of vehicle dynamics due to the existence of strong
non-linearity and uncertainty. Many efforts have resorted to machine learning
techniques for building data-driven models, but it may suffer from
interpretability and result in a complex nonlinear representation. In this
paper, we propose a deep learning framework relying on an interpretable Koopman
operator to build a data-driven predictor of the vehicle dynamics. The main
idea is to use the Koopman operator for representing the nonlinear dynamics in
a linear lifted feature space. The approach results in a global model that
integrates the dynamics in both longitudinal and lateral directions. As the
core contribution, we propose a deep learning-based extended dynamic mode
decomposition (Deep EDMD) algorithm to learn a finite approximation of the
Koopman operator. Different from other machine learning-based approaches, deep
neural networks play the role of learning feature representations for EDMD in
the framework of the Koopman operator. Simulation results in a high-fidelity
CarSim environment are reported, which show the capability of the Deep EDMD
approach in multi-step prediction of vehicle dynamics at a wide operating
range. Also, the proposed approach outperforms the EDMD method, the multi-layer
perception (MLP) method, and the Extreme Learning Machines-based EDMD
(ELM-EDMD) method in terms of modeling performance. Finally, we design a linear
MPC with Deep EDMD (DE-MPC) for realizing reference tracking and test the
controller in the CarSim environment.Comment: 12 pages, 10 figures, 1 table, and 2 algorithm
Contextual Bandits with Budgeted Information Reveal
Contextual bandit algorithms are commonly used in digital health to recommend
personalized treatments. However, to ensure the effectiveness of the
treatments, patients are often requested to take actions that have no immediate
benefit to them, which we refer to as pro-treatment actions. In practice,
clinicians have a limited budget to encourage patients to take these actions
and collect additional information. We introduce a novel optimization and
learning algorithm to address this problem. This algorithm effectively combines
the strengths of two algorithmic approaches in a seamless manner, including 1)
an online primal-dual algorithm for deciding the optimal timing to reach out to
patients, and 2) a contextual bandit learning algorithm to deliver personalized
treatment to the patient. We prove that this algorithm admits a sub-linear
regret bound. We illustrate the usefulness of this algorithm on both synthetic
and real-world data
Incorporating nonparametric methods for estimating causal excursion effects in mobile health with zero-inflated count outcomes
In the domain of mobile health, tailoring interventions for real-time
delivery is of paramount importance. Micro-randomized trials have emerged as
the "gold-standard" methodology for developing such interventions. Analyzing
data from these trials provides insights into the efficacy of interventions and
the potential moderation by specific covariates. The "causal excursion effect",
a novel class of causal estimand, addresses these inquiries, backed by current
semiparametric inference techniques. Yet, existing methods mainly focus on
continuous or binary data, leaving count data largely unexplored. The current
work is motivated by the Drink Less micro-randomized trial from the UK, which
focuses on a zero-inflated proximal outcome, the number of screen views in the
subsequent hour following the intervention decision point. In the current
paper, we revisit the concept of causal excursion effects, specifically for
zero-inflated count outcomes, and introduce novel estimation approaches that
incorporate nonparametric techniques. Bidirectional asymptotics are derived for
the proposed estimators. Through extensive simulation studies, we evaluate the
performance of the proposed estimators. As an illustration, we also employ the
proposed methods to the Drink Less trial data.Comment: 37pages,2 figure
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