Impacts of Model Mismatch and Array Scale on Channel Estimation for XL-HRIS-Aided Systems

Extremely large-scale hybrid reconfigurable intelligence surface(XL-HRIS),an improved version of the RIS,can receive the incident signal and enhance communication performance.However,as the RIS size increases,the phase variations of the received signal across the whole array are nonnegligible in the near-field region,and the channel model mismatch,which will decrease the estimation accuracy,must be considered.In this paper,the lower bound(LB)of the estimated parameter is studied and the impacts of the distance and signal-tonoise ratio(SNR)on LB are then evaluated.Moreover,the impacts of the array scale on LB and spectral efficiency(SE)are also studied.Simulation results verify that even in extremely large-scale array systems with infinite SNR,channel model mismatch can still limit estimation accuracy.However,this impact decreases with increasing distance.

A Review for Model Plant Mismatch Measures in Process Monitoring

Model is usually necessary for the design of a control loop. Due to simplification and unknown dynamics, model plant mismatch is inevitable in the control loop. In process monitoring, detection of mismatch and evaluation of its influences are demanded. In this paper several mismatch measures are presented based on different model descriptions. They are categorized into different groups from different perspectives and their potential in detection and diagnosis is evaluated. Two case studies on mixing process and distillation process demonstrate the efficacy of the framework of mismatch monitoring.

Optimizing Control of Bio-dissimilation Process of Glycerol to 1,3-Propanedlol

An iterative optimization strategy is proposed and applied to the steady state optimizing control of the bio-dissimilation process of glycerol to 1,3-propanediol in the presence of model-plant mismatch and input constraints. The scheme is based on the Augmented Integrated System Optimization and Parameter Estimation （AI- SOPE） technique, but a linearization of some performance function in the modified model-based optimization problem of AISOPE is introduced to overcome the difficulty of determining an appropriate penalty parameter. When carrying out the iterative optimization, the penalty coefficient is set to a larger value at the current iteration than at the previous iteration, which can promote the evolution rate of the iterative optimization. Simulation studies illustrate the potential ofthe approach presented for the optimizing control of the bioTdissimilation process of glycerol to 1,3-propanediol. The effects of measurement noise, measured and unmeasured disturbances on the proposed algorithm are also investigated.

Robust Control of Time Delay Systems

This study presents the design of both H∞ Loop Shaping Control （HLSC） and Internal Model Control （IMC） strategies for linear time delay systems. For first order time delay system, a systematic approach for weight selection based on the sensitivity function was proposed, then compared to the internal model control strategy. For both methods, the synthesis was based on the Pade approximation. Two cases are considered for time delay： upper or lower than system time constant. Simulation results for the proposed approaches are acceptable ever in presence of disturbances and model mismatches.

Modeling of current mismatch induced by random dopant fluctuation in nano-MOSFETs

Deviation of threshold voltage and effective mobility due to random dopant fluctuation is proposed.An improved 65 nm average drain current MOS model calledαlaw is utilized after fitting HSPICE simulating data and extracting process parameters.Then,a current mismatch model of nanoscale MOSFETs induced by random dopant fluctuation is presented based on propagation of variation theory.In test conditions,the calculated standard deviation applying this model,compared to 100 times Monte-Carlo simulation data with HSPICE,indicates that the average relative error and relative standard deviation is 0.24%and 0.22%,respectively.The results show that this mismatch model is effective to illustrate the physical mechanism,as well as being simple and accurate.

Low complexity robust adaptive beamforming for general-rank signal model with positive semidefinite constraint

We propose a low complexity robust beamforming method for the general-rank signal model, to combat against mismatches of the desired signal array response and the received signal covariance matrix. The proposed beamformer not only considers the norm bounded uncertainties in the desired and received signal covariance matrices, but also includes an additional positive semidefinite constraint on the desired signal covariance matrix. Based on the worst-case performance optimization criterion, a computationally simple closed-form weight vector is obtained. Simulation results verify the validity and robustness of the proposed beamforming method.

Reduced thermal boundary conductance in GaN-based electronic devices introduced by metal bonding layer

Achieving high interface thermal conductance is one of the biggest challenges in the nanoscale heat transport of GaN-based devices such as light emitting diodes(LEDs),and high electron mobility transistors(HEMTs).In this work,we experimentally measured thermal boundary conductance(TBC)at interfaces between GaN and the substrates with AuSn alloy as a commonly-used adhesive layer by time-domain thermoreflectance(TDTR).We find that the TBCs of GaN/Ti/AuSn/Ti/Si,GaN/Ti/AuSn/Ti/SiC,and GaN/Ti/AuSn/Ti/diamond,are 16.5,14.8,and 13.2 MW·m^(-2)·K^(-1)at room temperature,respectively.Our measured results show that the TBC of GaN/Ti/AuSn/Ti/SiC interface is inferior to the TBC of pristine GaN/SiC interface,due to the large mismatch of phonon modes between AuSn/Ti and substrates,shown as the difference of Debye temperature of two materials.Overall,we measured the TBC at interface between GaN and thermal conductive substrates,and provided a guideline for designing the interface between GaN and substrate at HEMT from a thermal management point of view.

IDENTIFICATION ERROR BOUNDS AND ASYMPTOTIC DISTRIBUTIONS FOR SYSTEMS WITH STRUCTURAL UNCERTAINTIES

This work is concerned with identification of systems that are subject to not only measurement noises, but also structural uncertainties such as unmodeled dynamics, sensor nonlinear mismatch, and observation bins. Identification errors are analyzed for their dependence on these structural uncertainties. Asymptotic distributions of scaled sequences of estimation errors are derived.