538 research outputs found
Haplotypic polymorphism of the IRG protein family mediates resistance of mice against virulent strains of Toxoplasma gondii
Comparator Design in Sensors for Environmental Monitoring
This paper presents circuit design considerations of comparator in analog-to-digital converters (ADC) applied for a portable, low-cost and high performance nano-sensor chip which can be applied to detect the airborne magnetite pollution nano particulate matter (PM) for environmental monitoring. High-resolution ADC plays a vital important role in high perfor-mance nano-sensor, while high-resolution comparator is a key component in ADC. In this work, some important design issues related to comparators in analog-to-digital converters (ADCs) are discussed, simulation results show that the resolution of the comparator proposed can achieve 5µV , and it is appropriate for high-resolution application
Exploiting smallest error to calibrate non-linearity in SAR ADCs
This paper presents a statistics-optimised organisation technique to achieve better element matching in Successive Approximation Register (SAR) Analog-to-Digital Converter (ADC) in smart sensor systems. We demonstrate the proposed technique ability to achieve a significant improvement of around 23 dB on Spurious Free Dynamic Range (SFDR) of the ADC than the conventional, testing with a capacitor mismatch σu = 0.2% in a 14 bit SAR ADC system. For the static performance, the max root mean square (rms) value of differential nonlinearity (DNL) reduces from 1.63 to 0.20 LSB and the max rms value of integral nonlinearity (INL) reduces from 2.10 to 0.21 LSB. In addition, it is demonstrated that by applying grouping optimisation and strategy optimisation, the performance boosting on SFDR can be effectively achieved. Such great improvement on the resolution of the ADC only requires an off-line pre-processing digital part
Closed-Loop Solvability of Linear Quadratic Mean-Field Type Stackelberg Stochastic Differential Games
This paper is devoted to a Stackelberg stochastic differential game for a
linear mean-field type stochastic differential system with a mean-field type
quadratic cost functional in finite horizon. The coefficients in the state
equation and weighting matrices in the cost functional are all deterministic.
Closed-loop Stackelberg equilibrium strategies are introduced which require to
be independent of initial states. Follower's problem is solved firstly, which
is a stochastic linear quadratic optimal control problem. By converting the
original problem into a new one whose optimal control is known, the closed-loop
optimal strategy of the follower is characterized by two coupled Riccati
equations as well as a linear mean-field type backward stochastic differential
equation. Then the leader turns to solve a stochastic linear quadratic optimal
control problem for a mean-field type forward-backward stochastic differential
equation. Necessary conditions for the existence of closed-loop optimal
strategies for the leader is given by the existence of two coupled Riccati
equations with a linear mean-field type backward stochastic differential
equation. The solvability of Riccati equations of leader's optimization problem
is discussed in the case where the diffusion term of the state equation does
not contain the control process of the follower. Moreover, leader's value
function is expressed via two backward stochastic differential equations and
two Lyapunov equations.Comment: 44 page
An Interdisciplinary Survey on Origin-destination Flows Modeling: Theory and Techniques
Origin-destination~(OD) flow modeling is an extensively researched subject
across multiple disciplines, such as the investigation of travel demand in
transportation and spatial interaction modeling in geography. However,
researchers from different fields tend to employ their own unique research
paradigms and lack interdisciplinary communication, preventing the
cross-fertilization of knowledge and the development of novel solutions to
challenges. This article presents a systematic interdisciplinary survey that
comprehensively and holistically scrutinizes OD flows from utilizing
fundamental theory to studying the mechanism of population mobility and solving
practical problems with engineering techniques, such as computational models.
Specifically, regional economics, urban geography, and sociophysics are adept
at employing theoretical research methods to explore the underlying mechanisms
of OD flows. They have developed three influential theoretical models: the
gravity model, the intervening opportunities model, and the radiation model.
These models specifically focus on examining the fundamental influences of
distance, opportunities, and population on OD flows, respectively. In the
meantime, fields such as transportation, urban planning, and computer science
primarily focus on addressing four practical problems: OD prediction, OD
construction, OD estimation, and OD forecasting. Advanced computational models,
such as deep learning models, have gradually been introduced to address these
problems more effectively. Finally, based on the existing research, this survey
summarizes current challenges and outlines future directions for this topic.
Through this survey, we aim to break down the barriers between disciplines in
OD flow-related research, fostering interdisciplinary perspectives and modes of
thinking.Comment: 49 pages, 6 figure
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