Parameterized modeling of multiport passive circuit blocks

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 97-99).System level design optimization has recently started drawing the attention of circuit designers. A system level optimizer would search over the entire design space, adjusting the parameters of interest, for optimal performance metrics. These optimizers demand for the availability of parameterized compact dynamical models of all individual modules. The parameters may include geometrical parameters, such as width and spacing for an inductor or design parameters such as center frequency or characteristic impedance in case of distributed transmission line structures. The parameterized models of individual blocks need to be compact and passive since the optimizer would be solving differential equations (time domain integration or periodic steady state methods) to compute the performance metrics. Additionally, these parameterized models would be able to facilitate the job of the designer who could instantiate the models with different parameter value during manual optimization. In this thesis, we have designed and implemented various highly efficient algorithms for the identification of individual and parameterized models for multiport passive structures. The algorithms are based on convex relaxations of the original non-convex problem consisting of modeling multiport devices from frequency response data. Passivity is enforced in the final models by constrained fitting, where the constraints are either Linear Matrix Inequalities or semidefinite constraints. These individual non-parameterized models can be used for system level simulations for fixed parameter values or for building up a parameterized model. In the first algorithm, we identify a collection of first and second order networks to model individual non-parameterized passive blocks. Passivity of the overall model is guaranteed by enforcing passivity on the individual building blocks. In the second algorithm we exploit the property of causal and stable systems for which the real and imaginary parts of the frequency response are related by the Hilbert transform, by minimizing only the mismatch between real parts. Passivity is enforced in the identified model using semidefinte constraints. In this thesis we also propose an algorithm for generating parameterized multiport models of linear systems that the user will be able to instantiate for any parameter value, always obtaining a stable and passive model. Our approach uses constrained optimization to construct a parameterized model that optimally fits a set of given non-parameterized models using polynomial or rational basis. By using optimization, as opposed to interpolation as in the available parameterized modeling techniques, we are capable of guaranteeing global passivity with respect to the parameters, while simultaneously keeping the number of terms describing the model small. The proposed algorithms are supported by various modeling examples including Wilkinson combiners, power and ground distribution grid, on-chip coupled inductors, microstrip patch antenna and parameterized attenuator. The identified models are verified for passivity using the Hamiltonian matrix based eigenvalue test. Several comparisons with existing techniques are also provided, which demonstrate a promising speed up of 40x in some cases and an amazing efficiency, by generating a highly accurate model in the cases where alternative techniques even failed to generate the model.by Zohaib Mahmood.S.M

Resonant body transistors in standard CMOS technology

This work presents Si-based electromechanical resonators fabricated at the transistor level of a standard SOI CMOS technology and realized without the need for any postprocessing or packaging. These so-called Resonant Body Transistors (RBTs) are driven capacitively and sensed by piezoresistively modulating the drain current of a Field Effect Transistor (FET). First generation devices operating at 11.1-11.5 GHz with footprints of 3μm×5μm are demonstrated. These unreleased bulk acoustic resonators are completely buried within the CMOS stack and acoustic energy at resonance is confined using Acoustic Bragg Reflectors (ABRs). The complimentary TCE of Si/SiO[subscript 2] in the resonator and the surrounding ABRs results in a temperature stability TCF of <;3 ppm/K. Comparative behavior of devices is also discussed to analyze the effect of fabrication variations and active sensing.United States. National Security Agency. Trusted Access Program OfficeUnited States. Defense Advanced Research Projects Agency. Leading Edge Access ProgramIBM Researc

Resonant Body Transistors in IBM's 32nm SOI CMOS technology

This work presents an unreleased CMOS-integrated MEMS resonators fabricated at the transistor level of IBM's 32SOI technology and realized without the need for any post-processing or packaging. These Resonant Body Transistors (RBTs) are driven capacitively and sensed piezoresistively using an n-channel Field Effect Transistor (nFET). Acoustic Bragg Reflectors (ABRs) are used to localize acoustic vibrations in these resonators completely buried in the CMOS stack and surrounded by low-k dielectric. Experimental results from the first generation hybrid CMOS-MEMS show RBTs operating at 11.1-11.5 GHz with footprints <; 5μm × 3μm. The response of active resonators is shown to contrast with passive resonators showing no discernible peak. Comparative behavior of devices with design variations is used to demonstrate the effect of ABRs on spurious mode suppression. Temperature stability and TCF compensation due to complimentary materials in the CMOS stack are experimentally verified

Emotion detection from handwriting and drawing samples using an attention-based transformer model

© 2024 The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY), https://creativecommons.org/licenses/by/4.0/Emotion detection (ED) involves the identification and understanding of an individual’s emotional state through various cues such as facial expressions, voice tones, physiological changes, and behavioral patterns. In this context, behavioral analysis is employed to observe actions and behaviors for emotional interpretation. This work specifically employs behavioral metrics like drawing and handwriting to determine a person’s emotional state, recognizing these actions as physical functions integrating motor and cognitive processes. The study proposes an attention-based transformer model as an innovative approach to identify emotions from handwriting and drawing samples, thereby advancing the capabilities of ED into the domains of fine motor skills and artistic expression. The initial data obtained provides a set of points that correspond to the handwriting or drawing strokes. Each stroke point is subsequently delivered to the attention-based transformer model, which embeds it into a high-dimensional vector space. The model builds a prediction about the emotional state of the person who generated the sample by integrating the most important components and patterns in the input sequence using self-attentional processes. The proposed approach possesses a distinct advantage in its enhanced capacity to capture long-range correlations compared to conventional recurrent neural networks (RNN). This characteristic makes it particularly well-suited for the precise identification of emotions from samples of handwriting and drawings, signifying a notable advancement in the field of emotion detection. The proposed method produced cutting-edge outcomes of 92.64% on the benchmark dataset known as EMOTHAW (Emotion Recognition via Handwriting and Drawing).Peer reviewe

SmartSIM - a virtual reality simulator for laparoscopy training using a generic physics engine

International audienceVirtual reality (VR) training simulators have started playing a vital role in enhancing surgical skills, such as hand–eye coordination in laparoscopy, and practicing surgical scenarios that cannot be easily created using physical models. We describe a new VR simulator for basic training in lapa-roscopy, i.e. SmartSIM, which has been developed using a generic open‐source physics engine called the simulation open framework architecture (SOFA). This paper describes the systems perspective of SmartSIM including design details of both hardware and software components, while highlighting the critical design decisions. Some of the distinguishing features of SmartSIM include: (i) an easy‐to‐fabricate custom‐built hardware interface; (ii) use of a generic physics engine to facilitate wider accessibility of our work and flexibility in terms of using various graph-ical modelling algorithms and their implementations; and (iii) an intelligent and smart evaluation mechanism that facilitates unsupervised and independent learning

Automatic design tool for robust radio frequency decoupling matrices in magnetic resonance imaging

Thesis: S.M., Massachusetts Institute of Technology, Computation for Design and Optimization Program, 2015.Cataloged from PDF version of thesis.Includes bibliographical references (pages 43-45).In this thesis we study the design of robust decoupling matrices for coupled transmit radio frequency arrays used in magnetic resonance imaging (MRI). In a coupled parallel transmit array, because of the coupling itself, the power delivered to a channel is typically partially re-distributed to other channels. This power must then be dissipated in circulators resulting into a significant reduction in the power efficiency of the overall system. In this thesis, we propose an automated approach to design a robust decoupling matrix interfaced between the RF amplifiers and the coils. The decoupling matrix is optimized to ensure all forward power is delivered to the load. The decoupling condition dictates that the admittance matrix seen by power amplifiers with 50 Ohms output impedance is a diagonal matrix with matching 1 (or 0.02 Siemens) at the diagonal. Our tool computes the values of the decoupling matrix via a non linear optimization and generate a physical realization using reactive elements such as inductors and capacitors. The methods presented in this thesis scale to any arbitrary number of channels and can be readily applied to other coupled systems such as antenna arrays. Furthermore our tool computes parameterized dynamical models and performs sensitivity analysis with respect to patient head-size and head-position for MRI coils.by Zohaib Mahmood.S.M

Algorithms for passive dynamical modeling and passive circuit realizations

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2015.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (pages 163-174).The design of modern electronic systems is based on extensive numerical simulations, aimed at predicting the overall system performance and compliance since early design stages. Such simulations rely on accurate dynamical models. Linear passive components are described by their frequency response in the form of admittance, impedance or scattering parameters which are obtained by physical measurements or electromagnetic field simulations. Numerical dynamical models for these components are constructed by a fitting to frequency response samples. In order to guarantee stable system level simulations, the dynamical models of the passive components need to preserve the passivity property (or inability to generate power), in addition to being causal and stable. A direct formulation results into a non-convex nonlinear optimization problem which is difficult to solve. In this thesis, we propose multiple algorithms that fit linear passive multiport dynamical models to given frequency response samples. The algorithms are based on convex relaxations of the original non-convex problem. The proposed techniques improve accuracy and computational complexity compared to the existing approaches. Compared to sub-optimal schemes based on singular value or Hamiltonian eigenvalue perturbation, we are able to guarantee convergence to the optimal solution within the given relaxation. Compared to convex formulations based on direct Bounded-Real (or Positive-Real) Lemma constraints, we are able to reduce both memory and time requirements by orders of magnitude. We show how these models can be extended to include geometrical and design parameters. We have applied our passive modeling algorithms and developed new strategies to realize passive multiport circuits to decouple multichannel radio frequency (RF) arrays, specifically for magnetic resonance imaging (MRI) applications. In a coupled parallel transmit array, because of the coupling, the power delivered to a channel is partially distributed to other channels and is dissipated in the circulators. This dissipated power causes a significant reduction in the power efficiency of the overall system. In this work, we propose an automated eigen-decomposition based approach to designing a passive decoupling matrix interfaced between the RF amplifiers and the coils. The decoupling matrix, implemented via hybrid couplers and reactive elements, is optimized to ensure that all forward power is delivered to the load. The results show that our decoupling matrix achieves nearly ideal decoupling. The methods presented in this work scale to any arbitrary number of channels and can be readily applied to other coupled systems such as antenna arrays.by Zohaib Mahmood.Ph. D

Circuit synthesizable guaranteed passive modeling for multiport structures

In this paper we present a highly efficient algorithm to automatically generate circuit synthesizable dynamical models for passive multiport structures. The algorithm is based on a natural convex relaxation of the original nonconvex problem of modeling multiport devices from frequency response data, subject to global passivity constraints. The algorithm identifies a collection of first and second order passive networks interconnected in either series or parallel fashion. Passive models for several multiport structures, including Wilkinson type combiners, power and ground distribution grids and coupled on-chip inductors are provided to corroborate the theoretical development and show efficacy of the implemented algorithm. To demonstrate the practical usage of our algorithm, the identified models are also interfaced with commercial simulators and used to perform time domain simulations while being connected to highly nonlinear power amplifiers.United States. Defense Advanced Research Projects AgencySemiconductor Research Corporation. Center for Circuits and Systems SolutionsFocus Center Research Program. Focus Center for Circuit & System Solutions. Semiconductor Research Corporation. Interconnect Focus Cente

Enhancing Time Series Forecasting With an Optimized Binary Gravitational Search Algorithm for Echo State Networks

The echo state network (ESN) is a cutting-edge reservoir computing technique designed to handle time-dependent data, making it highly effective for addressing time series prediction tasks. ESN inherits the more precise design of standard neural networks and the relatively simple learning process and has a strong computing capacity for solving nonlinear problems. It can disseminate low-dimensional information cues to high-dimensional areas enabling extracting data. However, this study has proven that not all reservoir output dimensions directly impact model generalization. This study desires to enhance the ESN model&#x2019;s generalization abilities by decreasing the redundant reservoir output feature. A remarkable hybrid model is proposed that optimizes the ESN output association through feature selection. This model is called the binary improved gravitational search algorithm (BIGSA) echo state network (BIGSA-ESN). BIGSA&#x2019;s feature selection approach complements the ESN output connection architecture. In this study, evaluation was performed using root mean square error (RMSE). The experimental findings on the Lorenz and Mackey-Glass benchmark time-series datasets demonstrate that the proposed technique outperforms conventional evolutionary methods. Moreover, empirical findings on predicting a significant water quality parameter from the wastewater treatment process (WWTP) dataset demonstrate that the proposed ensemble of BIGSA models performs very well in real-world scenarios

Fundamentals and Design-Led Synthesis of Emulsion-Templated Porous Materials for Environmental Applications

Emulsion templating is at the forefront of producing a wide array of porous materials that offers interconnected porous structure, easy permeability, homogeneous flow-through, high diffusion rates, convective mass transfer, and direct accessibility to interact with atoms/ions/molecules throughout the exterior and interior of the bulk. These interesting features together with easily available ingredients, facile preparation methods, flexible pore-size tuning protocols, controlled surface modification strategies, good physicochemical and dimensional stability, lightweight, convenient processing and subsequent recovery, superior pollutants remediation/monitoring performance, and decent recyclability underscore the benchmark potential of the emulsion-templated porous materials in large-scale practical environmental applications. To this end, many research breakthroughs in emulsion templating technique witnessed by the recent achievements have been widely unfolded and currently being extensively explored to address many of the environmental challenges. Taking into account the burgeoning progress of the emulsion-templated porous materials in the environmental field, this review article provides a conceptual overview of emulsions and emulsion templating technique, sums up the general procedures to design and fabricate many state-of-the-art emulsion-templated porous materials, and presents a critical overview of their marked momentum in adsorption, separation, disinfection, catalysis/degradation, capture, and sensing of the inorganic, organic and biological contaminants in water and air