Word Blending and Other Formal Models of Bio-operations

As part of ongoing efforts to view biological processes as computations, several formal models of DNA-based processes have been proposed and studied in the formal language literature. In this thesis, we survey some classical formal language word and language operations, as well as several bio-operations, and we propose a new operation inspired by a DNA recombination lab protocol known as Cross-pairing Polymerase Chain Reaction, or XPCR. More precisely, we define and study a word operation called word blending which models a special case of XPCR, where two words x w p and q w y sharing a non-empty overlap part w generate the word x w y. Properties of word blending that we study include closure properties of the Chomsky families of languages under this operation and its iterated version, existence of solution to equations involving this operation, and its state complexity

Smart attitude control system for small satellites

The attitude control system is one of the most important systems for satellites, which is essential for the satellite's detumbling, pointing, and orbital maneuver. The conventional attitude control system consists of magnetorquers, reaction wheels, and thrusters. Among these actuators, magnetorquers are widely used for satellite detumbling and attitude control, especially for small satellites and CubeSats. It consumes zero propellant compared with thrusters and has a high chance of survival compared with the reaction wheel as it does not contain any moving parts, which makes them last longer in harsh environments. Conventional magnetorquers utilize air or soft magnetic materials, e.g., iron and alloys, as core, and the magnetic field is generated by feeding the electric current to the wrapped solenoid. Due to the power limit of the small satellites, the magnetic field strength is strictly limited, and the continuous current supply results in massive energy consumption for detumbling and other attitude adjustment missions. The long copper wire of the solenoid will also result in high resistance and generate significant heat. To improve the current design and overcome the proposed drawbacks, a novel electro-permanent magnetorquer has been designed and developed in this thesis as one actuator of the attitude control system. Unlike conventional magnetorquers, the electro-permanent magnetorquer utilizes hard magnetic materials as the core, which can maintain the magnetization when the external magnetic field is removed, to generate the required magnetic field. A special driving circuit is designed to generate the desired dipole moment for the magnetorquer, and the components used for the circuit are carefully selected. The experiments show that the electro-permanent magnetorquer can generate 1.287 Am2 dipole moment in either direction. The magnetorquer works in pulse mode to adjust the dipole moment, and it requires around 0.75 J energy maximum per pulse. A single-axis detumbling experiment has been conducted using only one torque rod on the air-bearing table inside an in-house manufactured Helmholtz cage. The experiment results show that the magnetorquer can detumble the air bearing table with 0.0612 kgm2 moment of inertia from an initial speed of around 27°/s to zero within 800s, and total energy of 82.92 J was consumed for the detumbling experiment. A single torque rod single-axis pointing experiment has been conducted with a sliding mode controller on the same platform. The results show that a single torque rod can achieve the target angle and maintain the error discrepancy within the ±0.4° boundary under a specific system configuration. A micro air-fed magnetoplasmadynamic thruster has been designed and tested as another attitude control system actuator. The thruster is a miniaturized electric propulsion system based on the conventional full-scale magnetoplasmadynamic thruster that operates at hundreds of kilowatts. The thruster is designed and tested using normal air as the propellant under the pulse operation mode on a calibrated micro-force measurement thruster stand. The experiments revealed that the thruster could generate a 34.534 µNs impulse bit with an average power input of 1.857 ± 0.0679 W and thrust to power ratio of 8.266 µN/W. The specific impulse is calculated to be 2319 s with a thruster efficiency of 9.402%, which is quite competitive compared with other solid-state and liquid-fed pulse-mode thrusters. This paper presents the design and test results for the thruster under a low power level, as well as an analysis of its problems and limitations with corresponding future research and optimization directions noted at the end. The electro-permanent magnetorquer as a payload of the CUAVA-2 satellite mission has been introduced in this thesis. The design considerations and adjustment based on the requirement of the CUAVA-2 has been introduced in detail. A simple sliding mode controller has been developed to achieve three-axis attitude control using both electro-permanent magnetorquer and the micro air-fed magnetoplasmadynamic thruster. The controller's performance has been tested using MATLAB-based simulation with the experimentally obtained performance parameters and some assumptions. The results show that the smart attitude control system can achieve ±0.005° pointing error discrepancy with the help of both actuators

Score-based Generative Modeling Through Backward Stochastic Differential Equations: Inversion and Generation

The proposed BSDE-based diffusion model represents a novel approach to diffusion modeling, which extends the application of stochastic differential equations (SDEs) in machine learning. Unlike traditional SDE-based diffusion models, our model can determine the initial conditions necessary to reach a desired terminal distribution by adapting an existing score function. We demonstrate the theoretical guarantees of the model, the benefits of using Lipschitz networks for score matching, and its potential applications in various areas such as diffusion inversion, conditional diffusion, and uncertainty quantification. Our work represents a contribution to the field of score-based generative learning and offers a promising direction for solving real-world problems.Comment: Preliminary Preprin

CMOS Integration of High Performance Quantum Dot Lasers For Silicon Photonics

Integration of III-V components on Si substrates is required for realizing the promise of Silicon Photonic systems. Specifically, the direct bandgap of many III-V materials is required for light sources, efficient modulators and photodetectors. Several different approaches have been taken to integrate III-V lasers into the silicon photonic platform, such as wafer bonding, direct growth, butt coupling, etc. Here, we have devised a novel laser design that overcomes the above limitations. In our approach, we use InAs quantum dot (QD) lasers monolithically integrated with silicon waveguides and other Si photonic passive components. Due to their unique structures, the QD lasers have been proven by several groups to have the combination of high temperature stability, large modulation bandwidth and low power consumption compared with their quantum well counterparts, which makes it an ideal candidate for Si photonic applications. The first section of this dissertation introduces the theory and novelty of QD lasers, the DC and RF characterization methods of QD lasers are also discussed. The second section is focused on the growth of QD gain chip which a broadband gain chip based on QD inhomogeneous broadening properties was demonstrated. In third section, the lasers devices are built on Si substrate by Pd wafer bonding technology. Firstly, a ridge waveguide QD laser is demonstrated in this section which have better heat dissipation and lower threshold current compared to the unbonded lasers. In section four, a on Si comb laser is also developed. Due to inhomogeneous broadening and ultrafast carrier dynamics, InAs quantum dots have key advantages that make them well suited for Mode-locked lasers (MLLs). In section five, a passively mode-locked InAs quantum dots laser on Si is achieved at a repetition rate of ~7.3 GHz under appropriate bias conditions. In section six, a butt-joint integration configuration based on QD lasers and silicon photonics ring resonator is tested by using to translation stage. In order to achieve the on chip butt-joint integration, an on chip laser facet was created in section seven. A novel facet etching method is developed by using Br-ion beam assist etching (Br-IBAE). In section eight, a Pd-GaAs butt-joint integration platform was proposed, a hybrid tunable QD laser which consist of a QD SOA gain chip butt joint coupled with a passive Si3N4 photonic integrated circuit is proof of concept by using an external booster SOA coupled with a Si3N4 ring reflector feedback circuit. The final section summarized the work discussed in this thesis and also discussed some future approaches by using QD lasers integrated with silicon photonics integrated circuits based on the Pd-GaAs wafer bonding butt-joint coupled platform

Deep Recurrent Generative Decoder for Abstractive Text Summarization

We propose a new framework for abstractive text summarization based on a sequence-to-sequence oriented encoder-decoder model equipped with a deep recurrent generative decoder (DRGN). Latent structure information implied in the target summaries is learned based on a recurrent latent random model for improving the summarization quality. Neural variational inference is employed to address the intractable posterior inference for the recurrent latent variables. Abstractive summaries are generated based on both the generative latent variables and the discriminative deterministic states. Extensive experiments on some benchmark datasets in different languages show that DRGN achieves improvements over the state-of-the-art methods.Comment: 10 pages, EMNLP 201

Fully Automated Segmentation of the Left Ventricle in Magnetic Resonance Images

Automatic and robust segmentation of the left ventricle (LV) in magnetic resonance images (MRI) has remained challenging for many decades. With the great success of deep learning in object detection and classification, the research focus of LV segmentation has changed to convolutional neural network (CNN) in recent years. However, LV segmentation is a pixel-level classification problem and its categories are intractable compared to object detection and classification. Although lots of CNN based methods have been proposed for LV segmentation, no robust and reproducible results are achieved yet. In this paper, we try to reproduce the CNN based LV segmentation methods with their disclosed codes and trained CNN models. Not surprisingly, the reproduced results are significantly worse than their claimed accuracies. We also proposed a fully automated LV segmentation method based on slope difference distribution (SDD) threshold selection to compare with the reproduced CNN methods. The proposed method achieved 95.44% DICE score on the test set of automated cardiac diagnosis challenge (ACDC) while the two compared CNN methods achieved 90.28% and 87.13% DICE scores. Our achieved accuracy is also higher than the best accuracy reported in the published literatures. The MATLAB codes of our proposed method are freely available on line

Upper Limb Movement Recognition utilising EEG and EMG Signals for Rehabilitative Robotics

Upper limb movement classification, which maps input signals to the target activities, is a key building block in the control of rehabilitative robotics. Classifiers are trained for the rehabilitative system to comprehend the desires of the patient whose upper limbs do not function properly. Electromyography (EMG) signals and Electroencephalography (EEG) signals are used widely for upper limb movement classification. By analysing the classification results of the real-time EEG and EMG signals, the system can understand the intention of the user and predict the events that one would like to carry out. Accordingly, it will provide external help to the user. However, the noise in the real-time EEG and EMG data collection process contaminates the effectiveness of the data, which undermines classification performance. Moreover, not all patients process strong EMG signals due to muscle damage and neuromuscular disorder. To address these issues, this paper explores different feature extraction techniques and machine learning and deep learning models for EEG and EMG signals classification and proposes a novel decision-level multisensor fusion technique to integrate EEG signals with EMG signals. This system retrieves effective information from both sources to understand and predict the desire of the user, and thus aid. By testing out the proposed technique on a publicly available WAY-EEG-GAL dataset, which contains EEG and EMG signals that were recorded simultaneously, we manage to conclude the feasibility and effectiveness of the novel system.Comment: 20 pages, 11 figures, 2 tables; Thesis for Undergraduate Research Project in Computing, NUS; Accepted by Future of Information and Communication Conference 2023, San Francisc

Quasi-symplectic Langevin Variational Autoencoder

Variational autoencoder (VAE) is a very popular and well-investigated generative model vastly used in neural learning research. To leverage VAE in practical tasks dealing with a massive dataset of large dimensions it is required to deal with the difficulty of building low variance evidence lower bounds (ELBO). Markov ChainMonte Carlo (MCMC) is one of the effective approaches to tighten the ELBO for approximating the posterior distribution. Hamiltonian Variational Autoencoder(HVAE) is an effective MCMC inspired approach for constructing a low-variance ELBO which is also amenable to the reparameterization trick. In this work, we propose a Quasi-symplectic Langevin Variational autoencoder (Langevin-VAE) by incorporating the gradients information in the inference process through the Langevin dynamic. We show the effectiveness of the proposed approach by toy and real-world examples