Dynamic Modeling of Human Gait Using a Model Predictive Control Approach

This dissertation aims to develop a dynamic model of human gait, especially the working principle of the central nervous system (CNS), using a novel predictive approach. Based on daily experience, it should be straightforward to understand the CNS controls human gait based on predictive control. However, a thorough human gait model using the predictive approach have not yet been explored. This dissertation aims to fill this gap. The development of such a predictive model can assist the developing of lower limb prostheses and orthoses which typically follows a trial and error approach. With the development of the predictive model, lower limb prostheses might be virtually tested so that their performance can be predicted qualitatively, future cost can be reduced, and the risks can be minimized. The model developed in this dissertation includes two parts: a plant model which represents the forward dynamics of human gait and a controller which represents the CNS. The plant model is a seven-segment six-joint model which has nine degrees of freedom. The plant model is validated using data collected from able-bodied human subjects. The experimental moment profile of each joint is input to the model; the kinematic output of the model is consistent with the experimental kinematics which verifies the fidelity of the plant model. The developed predictive human gait model is first validated by simulating able-bodied human gait. The simulation results show that the controller is able to simulate the kinematic output close to experimental data. The developed model was then validated by simulating variable speed able-bodied human gait. The simulation results showed the dynamic characteristics of variable speed gait could be qualitatively predicted by the developed model. Finally the gait of a unilateral transtibial amputee wearing passive prosthetic ankle joint is simulated to verify its ability to qualitatively predict the dynamic characteristics of pathological gait. This dissertation opens the door for modeling human gait from predictive control perspective. With the development of such a model, future prosthetic and orthotic designers can greatly reduce cost, avoid risk, and save time by using the virtual design and testing of prostheses and orthoses

Powered Transtibial Prosthetic Device Control System Design, Implementation and Testing

A powered lower limb prosthesis, which consists of a four bar mechanism, a torsional spring and a brushed DC motor, was previously designed and fabricated. To regulate the motor power input, a two level controller was proposed and built. The control algorithm includes a higher level finite state controller and lower level PID controllers. To implement the control system, a digital signal processor (DSP) control board and MATLAB Simulink were used to realize the higher level control and a DC motor controller was used to realize the lower level PID control. Sensors were selected to provide the required feedback. The entire control system was implemented on a convenient to carry backpack. Amputee subject testing was performed to obtain some experimental verification of the design. The results showed that the control system performed consistently with the designed control algorithm and did assist in the amputee’s walking. Compared to a currently available powered prosthesis, this control is simple in structure and able to mimic the nonlinear behavior of the ankle closely

Learned Image Compression with Mixed Transformer-CNN Architectures

Learned image compression (LIC) methods have exhibited promising progress and superior rate-distortion performance compared with classical image compression standards. Most existing LIC methods are Convolutional Neural Networks-based (CNN-based) or Transformer-based, which have different advantages. Exploiting both advantages is a point worth exploring, which has two challenges: 1) how to effectively fuse the two methods? 2) how to achieve higher performance with a suitable complexity? In this paper, we propose an efficient parallel Transformer-CNN Mixture (TCM) block with a controllable complexity to incorporate the local modeling ability of CNN and the non-local modeling ability of transformers to improve the overall architecture of image compression models. Besides, inspired by the recent progress of entropy estimation models and attention modules, we propose a channel-wise entropy model with parameter-efficient swin-transformer-based attention (SWAtten) modules by using channel squeezing. Experimental results demonstrate our proposed method achieves state-of-the-art rate-distortion performances on three different resolution datasets (i.e., Kodak, Tecnick, CLIC Professional Validation) compared to existing LIC methods. The code is at https://github.com/jmliu206/LIC_TCM.Comment: Accepted by CVPR2023 (Highlight

Optical rotation of heavy hole spins by non-Abelian geometrical means

A non-Abelian geometric method is proposed for rotating of heavy hole spins in a singly positive charged quantum dot in Voigt geometry. The key ingredient is the delay-dependent non-Abelian geometric phase, which is produced by the nonadiabatic transition between the two degenerate dark states. We demonstrate, by controlling the pump, the Stokes and the driving fields, that the rotations about $y$- and $z$-axes with arbitrary angles can be realized with high fidelity. Fast initialization and heavy hole spin state readout are also possible.Comment: 7 pages, 6 figure

Prevalence of human herpesvirus 8 infection in systemic lupus erythematosus

<p>Abstract</p> <p>Background</p> <p>For decades, scientists have tried to understand the environmental factors involved in the development of systemic lupus erythematosus (SLE), in which viral infections was included. Previous studies have identified Epstein-Barr virus (EBV) to incite SLE. Human herpesvirus 8 (HHV-8), another member of the gammaherpesvirus family, shares a lot in common with EBV. The characteristics of HHV-8 make it a well-suited candidate to trigger SLE.</p> <p>Results</p> <p>In the present study, serum samples from patients (n = 108) with diagnosed SLE and matched controls (n = 122) were collected, and the prevalence of HHV-8 was compared by a virus-specific nested PCR and a whole virus enzyme-linked immunoassay (EIA). There was significant difference in the prevalence of HHV-8 DNA between SLE patients and healthy controls (11 of 107 vs 1 of 122, <it>p </it>= 0.001); significant difference was also found in the detection of HHV-8 antibodies (19 of 107 vs 2 of 122, <it>p </it>< 0.001).</p> <p>We also detected the antibodies to Epstein-Barr virus viral capsid antigen (EBV-VCA) and Epstein-Barr nuclear antigen-1 (EBNA-1). Both patients and controls showed high seroprevalence with no significant difference (106 of 107 vs 119 of 122, <it>p </it>= 0.625).</p> <p>Conclusion</p> <p>Our finding indicated that there might be an association between HHV-8 and the development of SLE.</p

A Unified Contraction Analysis of a Class of Distributed Algorithms for Composite Optimization

We study distributed composite optimization over networks: agents minimize the sum of a smooth (strongly) convex function, the agents' sum-utility, plus a non-smooth (extended-valued) convex one. We propose a general algorithmic framework for such a class of problems and provide a unified convergence analysis leveraging the theory of operator splitting. Our results unify several approaches proposed in the literature of distributed optimization for special instances of our formulation. Distinguishing features of our scheme are: (i) when the agents' functions are strongly convex, the algorithm converges at a linear rate, whose dependencies on the agents' functions and the network topology are decoupled, matching the typical rates of centralized optimization; (ii) the step-size does not depend on the network parameters but only on the optimization ones; and (iii) the algorithm can adjust the ratio between the number of communications and computations to achieve the same rate of the centralized proximal gradient scheme (in terms of computations). This is the first time that a distributed algorithm applicable to composite optimization enjoys such properties.Comment: To appear in the Proc. of the 2019 IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP 19