Simultaneous registration and modelling for multi-dimensional functional data

PhD ThesisFunctional data analysis (FDA) has many applications in almost every branch of science, such as engineering, medicine and biology. It aims to cope with the analysis of data in the form of images, curves and shapes. In this thesis, we study the 2D trajectories of hyoid bone movement from X-ray image. Those curves are seen as the observations of multi-dimensional functional data. We rstly develop an all-in-one platform for the data acquisition and preprocessing. However, analyzing the data arises a lot of challenges. In this thesis, we provide solutions to solve some of those challenging problems. We propose one new registration method for handling those raw 2D curves. It basically integrates Generalized Procrusts analysis and self-modelling registration method (GPSM). However, the application reveals that the classi cation followed by registration does not work well. Therefore, we propose two-stage functional models for joint curve registration and classi cation (JCRC). In the rst stage, we use a functional logistic regression model where the aligned curves are estimated from the second stage. The latter uses a nonlinear warping function while modelling the 2D curves, i.e. resolving the misaligned problem and modelling problem simultaneously. This two-stage model takes into account both the scalar variables and the multi-dimensional functional data. For the functional data clustering, we propose mixtures of Gaussian process functional regression with time warping and logistic allocation model, allowing the use of both types of variables and also allowing simultaneous registration and clustering (SRC). A two-level model is introduced. For the data collected from subjects in di erent groups, a Gaussian process functional regression model is used as the rst level model; an allocation model depending on scalar variables is used as the second level model providing further information over the groups. Those three methods, i.e., GPSM, JCRC and SRC are all examined on both simulated data and real data

Learnable Blur Kernel for Single-Image Defocus Deblurring in the Wild

Recent research showed that the dual-pixel sensor has made great progress in defocus map estimation and image defocus deblurring. However, extracting real-time dual-pixel views is troublesome and complex in algorithm deployment. Moreover, the deblurred image generated by the defocus deblurring network lacks high-frequency details, which is unsatisfactory in human perception. To overcome this issue, we propose a novel defocus deblurring method that uses the guidance of the defocus map to implement image deblurring. The proposed method consists of a learnable blur kernel to estimate the defocus map, which is an unsupervised method, and a single-image defocus deblurring generative adversarial network (DefocusGAN) for the first time. The proposed network can learn the deblurring of different regions and recover realistic details. We propose a defocus adversarial loss to guide this training process. Competitive experimental results confirm that with a learnable blur kernel, the generated defocus map can achieve results comparable to supervised methods. In the single-image defocus deblurring task, the proposed method achieves state-of-the-art results, especially significant improvements in perceptual quality, where PSNR reaches 25.56 dB and LPIPS reaches 0.111.Comment: 9 pages, 7 figure

Passive Integrated Sensing and Communication Scheme based on RF Fingerprint Information Extraction for Cell-Free RAN

This paper investigates how to achieve integrated sensing and communication (ISAC) based on a cell-free radio access network (CF-RAN) architecture with a minimum footprint of communication resources. We propose a new passive sensing scheme. The scheme is based on the radio frequency (RF) fingerprint learning of the RF radio unit (RRU) to build an RF fingerprint library of RRUs. The source RRU is identified by comparing the RF fingerprints carried by the signal at the receiver side. The receiver extracts the channel parameters from the signal and estimates the channel environment, thus locating the reflectors in the environment. The proposed scheme can effectively solve the problem of interference between signals in the same time-frequency domain but in different spatial domains when multiple RRUs jointly serve users in CF-RAN architecture. Simulation results show that the proposed passive ISAC scheme can effectively detect reflector location information in the environment without degrading the communication performance.Comment: 11 pages, 6 figures, submitted on 28-Feb-2023, China Communication, Accepted on 14-Sep-202

Relative posture-based kinematic calibration of a 6-RSS parallel robot by optical coordinate measurement machine

In this article, a relative posture-based algorithm is proposed to solve the kinematic calibration problem of a 6-RSS parallel robot using the optical coordinate measurement machine system. In the research, the relative posture of robot is estimated using the detected pose with respect to the sensor frame through several reflectors which are fixed on the robot end-effector. Based on the relative posture, a calibration algorithm is proposed to determine the optimal error parameters of the robot kinematic model and external parameters introduced by the optical sensor. This method considers both the position and orientation variations and does not need the accurate location information of the detection sensor. The simulation results validate the superiority of the algorithm by comparing with the classic implicit calibration method. And the experimental results demonstrate that the proposal relative posture-based algorithm using optical coordinate measurement machine is an implementable and effective method for the parallel robot calibration

Similarity-driven and Task-driven Models for Diversity of Opinion in Crowdsourcing Markets

The recent boom in crowdsourcing has opened up a new avenue for utilizing human intelligence in the realm of data analysis. This innovative approach provides a powerful means for connecting online workers to tasks that cannot effectively be done solely by machines or conducted by professional experts due to cost constraints. Within the field of social science, four elements are required to construct a sound crowd - Diversity of Opinion, Independence, Decentralization and Aggregation. However, while the other three components have already been investigated and implemented in existing crowdsourcing platforms, 'Diversity of Opinion' has not been functionally enabled yet. From a computational point of view, constructing a wise crowd necessitates quantitatively modeling and taking diversity into account. There are usually two paradigms in a crowdsourcing marketplace for worker selection: building a crowd to wait for tasks to come and selecting workers for a given task. We propose similarity-driven and task-driven models for both paradigms. Also, we develop efficient and effective algorithms for recruiting a limited number of workers with optimal diversity in both models. To validate our solutions, we conduct extensive experiments using both synthetic datasets and real data sets.Comment: 32 pages, 10 figure

Observation of momentum-confined in-gap impurity state in Ba0.6_{0.6}K0.4_{0.4}Fe2_2As2_2: evidence for anti-phase s±s_{\pm} pairing

We report the observation by angle-resolved photoemission spectroscopy of an impurity state located inside the superconducting gap of Ba$_{0.6}$K$_{0.4}$Fe$_2$As$_2$ and vanishing above the superconducting critical temperature, for which the spectral weight is confined in momentum space near the Fermi wave vector positions. We demonstrate, supported by theoretical simulations, that this in-gap state originates from weak non-magnetic scattering between bands with opposite sign of the superconducting gap phase. This weak scattering, likely due to off-plane Ba/K disorders, occurs mostly among neighboring Fermi surfaces, suggesting that the superconducting gap phase changes sign within holelike (and electronlike) bands. Our results impose severe restrictions on the models promoted to explain high-temperature superconductivity in these materials.Comment: 8 pages, 5 figures. Accepted for publication in Physical Review