A Sparse Bayesian Deep Learning Approach for Identification of Cascaded Tanks Benchmark

Nonlinear system identification is important with a wide range of applications. The typical approaches for nonlinear system identification include Volterra series models, nonlinear autoregressive with exogenous inputs models, block-structured models, state-space models and neural network models. Among them, neural networks (NN) is an important black-box method thanks to its universal approximation capability and less dependency on prior information. However, there are several challenges associated with NN. The first one lies in the design of a proper neural network structure. A relatively simple network cannot approximate the feature of the system, while a complex model may lead to overfitting. The second lies in the availability of data for some nonlinear systems. For some systems, it is difficult to collect enough data to train a neural network. This raises the challenge that how to train a neural network for system identification with a small dataset. In addition, if the uncertainty of the NN parameter could be obtained, it would be also beneficial for further analysis. In this paper, we propose a sparse Bayesian deep learning approach to address the above problems. Specifically, the Bayesian method can reinforce the regularization on neural networks by introducing introduced sparsity-inducing priors. The Bayesian method can also compute the uncertainty of the NN parameter. An efficient iterative re-weighted algorithm is presented in this paper. We also test the capacity of our method to identify the system on various ratios of the original dataset. The one-step-ahead prediction experiment on Cascaded Tank System shows the effectiveness of our method. Furthermore, we test our algorithm with more challenging simulation experiment on this benchmark, which also outperforms other methods

Multi-Robot Transfer Learning: A Dynamical System Perspective

Multi-robot transfer learning allows a robot to use data generated by a second, similar robot to improve its own behavior. The potential advantages are reducing the time of training and the unavoidable risks that exist during the training phase. Transfer learning algorithms aim to find an optimal transfer map between different robots. In this paper, we investigate, through a theoretical study of single-input single-output (SISO) systems, the properties of such optimal transfer maps. We first show that the optimal transfer learning map is, in general, a dynamic system. The main contribution of the paper is to provide an algorithm for determining the properties of this optimal dynamic map including its order and regressors (i.e., the variables it depends on). The proposed algorithm does not require detailed knowledge of the robots' dynamics, but relies on basic system properties easily obtainable through simple experimental tests. We validate the proposed algorithm experimentally through an example of transfer learning between two different quadrotor platforms. Experimental results show that an optimal dynamic map, with correct properties obtained from our proposed algorithm, achieves 60-70% reduction of transfer learning error compared to the cases when the data is directly transferred or transferred using an optimal static map.Comment: 7 pages, 6 figures, accepted at the 2017 IEEE/RSJ International Conference on Intelligent Robots and System

Filter-Based Fading Channel Modeling

A channel simulator is an essential component in the development and accurate performance evaluation of wireless systems. A key technique for producing statistically accurate fading variates is to shape the flat spectrum of Gaussian variates using digital filters. This paper addresses various challenges when designing real and complex spectrum shaping filters with quantized coefficients for efficient realization of both isotropic and nonisotropic fading channels. An iterative algorithm for designing stable complex infinite impulse response (IIR) filters with fixed-point coefficients is presented. The performance of the proposed filter design algorithm is verified with 16-bit fixed-point simulations of two example fading filters

ESTIMATION OF STRETCH REFLEX CONTRIBUTIONS OF WRIST USING SYSTEM IDENTIFICATION AND QUANTIFICATION OF TREMOR IN PARKINSON'S DISEASE PATIENTS

"The brain's motor control can be studied by characterizing the activity of spinal motor nuclei to brain control, expressed as motor unit activity recordable by surface electrodes". When a specific area is under consideration, the first step in investigation of the motor control system pertinent to it is the system identification of that specific body part or area. The aim of this research is to characterize the working of the brain's motor control system by carrying out system identification of the wrist joint area and quantifying tremor observed in Parkinson's disease patients. We employ the ARMAX system identification technique to gauge the intrinsic and reflexive components of wrist stiffness, in order to facilitate analysis of problems associated with Parkinson's disease. The intrinsic stiffness dynamics comprise majority of the total stiffness in the wrist joint and the reflexive stiffness dynamics contribute to the tremor characteristic commonly found in Parkinson's disease patients. The quantification of PD tremor entails using blind source separation of convolutive mixtures to obtain sources of tremor in patients suffering from movement disorders. The experimental data when treated with blind source separation reveals sources exhibiting the tremor frequency components of 3-8 Hz. System identification of stiffness dynamics and assessment of tremor can reveal the presence of additional abnormal neurological signs and early identification or diagnosis of these symptoms would be very advantageous for clinicians and will be instrumental to pave the way for better treatment of the disease

RF applications in digital signal processing

Ever higher demands for stability, accuracy, reproducibility, and monitoring capability are being placed on Low-Level Radio Frequency (LLRF) systems of particle accelerators. Meanwhile, continuing rapid advances in digital signal processing technology are being exploited to meet these demands, thus leading to development of digital LLRF systems. The rst part of this course will begin by focusing on some of the important building-blocks of RF signal processing including mixer theory and down-conversion, I/Q (amplitude and phase) detection, digital down-conversion (DDC) and decimation, concluding with a survey of I/Q modulators. The second part of the course will introduce basic concepts of feedback systems, including examples of digital cavity eld and phase control, followed by radial loop architectures. Adaptive feed-forward systems used for the suppression of repetitive beam disturbances will be examined. Finally, applications and principles of system identi cation approaches will be summarized

PCT: Point cloud transformer

The irregular domain and lack of ordering make it challenging to design deep neural networks for point cloud processing. This paper presents a novel framework named Point Cloud Transformer(PCT) for point cloud learning. PCT is based on Transformer, which achieves huge success in natural language processing and displays great potential in image processing. It is inherently permutation invariant for processing a sequence of points, making it well-suited for point cloud learning. To better capture local context within the point cloud, we enhance input embedding with the support of farthest point sampling and nearest neighbor search. Extensive experiments demonstrate that the PCT achieves the state-of-the-art performance on shape classification, part segmentation and normal estimation tasks.Comment: 11 pages, 5 figure

Explicit and Iterative LQG Controller Design

This dissertation presents two new LQG controller designs, namely, explicit and iterative designs. For the explicit design, the explicit solutions to the corresponding Riccati equations of controller design for large structures with collocated rate sensors and actuators are derived. Numerically solving the Riccati equations for state feedback and state estimation is no longer required. Since the number of design parameters for either state feedback or state estimation equals the number of controlled modes, the performance of each mode can be easily adjusted. NASA\u27s Spacecraft COntrol Laboratory Experiment (SCOLE) configuration is used to demonstrate the effectiveness of the explicit design. For the iterative design, a closed-loop identification method is developed for identifying an open-loop system and Kalman filter gain when the system is under closed-loop operation. The iterative controller design basically consists of the closed-loop identification and state-feedback redesign cycle. In each cycle, the identification is used to identify the open-loop model and Kalman filter. The identified Kalman filter can be directly used for state estimation so that the noise statistics are no longer needed to be detected. The identified model is then used to redesign the state feedback. The state feedback and the identified Kalman filter are used to form an updated LQG controller for next cycle. This iterative process continues until the updated controller converges. Since the updated model is identified under the previous controller, the effect of the controller on noise statistics is automatically taken into account. NASA\u27s Large-Angle Magnetic Suspension Test Facility (LAMSTF) is used to validate the iterative design