arXiv subjects

Ian Walker

Publications and source records attributed to Ian Walker.

12 recordsLinked to original sources

arXiv2025

Hyper Yoshimura: How a slight tweak on a classical folding pattern unleashes meta-stability for deployable robots

Deployable structures inspired by origami have provided lightweight, compact, and reconfigurable solutions for various robotic and architectural applications. However, creating an integrated structural system that can effectively balance the competing requirements of high packing efficiency, simple deployment, and precise morphing into multiple load-bearing configurations remains a significant challenge. This study introduces a new class of hyper-Yoshimura origami, which exhibits a wide range of kinematically admissible and locally metastable states, including newly discovered symmetric "self-packing" and asymmetric "pop-out" states. This metastability is achieved by breaking a design rule of Yoshimura origami that has been in place for many decades. To this end, this study derives a new set of mathematically rigorous design rules and geometric formulations. Based on this, forward and inverse kinematic strategies are developed to stack hyper-Yoshimura modules into deployable booms that can approximate complex 3D shapes. Finally, this study showcases the potential of hyper-Yoshimura with a meter-scale pop-up cellphone charging station deployed at our university's bus transit station, along with a 3D-printed, scaled prototype of a space crane that can function as an object manipulator, solar tracking device, or high-load-bearing structure. These results establish hyper-Yoshimura as a promising platform for deployable and adaptable robotic systems in both terrestrial and space environments.

cs.RO↗

arXiv2024

"Golden Ratio Yoshimura" for Meta-Stable and Massively Reconfigurable Deployment

Yoshimura origami is a classical folding pattern that has inspired many deployable structure designs. Its applications span from space exploration, kinetic architectures, and soft robots to even everyday household items. However, despite its wide usage, Yoshimura has been fixated on a set of design constraints to ensure its flat-foldability. Through extensive kinematic analysis and prototype tests, this study presents a new Yoshimura that intentionally defies these constraints. Remarkably, one can impart a unique meta-stability by using the Golden Ratio angle to define the triangular facets of a generalized Yoshimura. As a result, when its facets are strategically popped out, a ``Golden Ratio Yoshimura'' boom with $m$ modules can be theoretically reconfigured into $8^m$ geometrically unique and load-bearing shapes. This result not only challenges the existing design norms but also opens up a new avenue to create deployable and versatile structural systems.

cs.RO↗

arXiv2022

A Failure Identification and Recovery Framework for a Planar Reconfigurable Cable Driven Parallel Robot

In cable driven parallel robots (CDPRs), a single cable malfunction usually induces complete failure of the entire robot. However, the lost static workspace (due to failure) can often be recovered through reconfiguration of the cable attachment points on the frame. This capability is introduced by adding kinematic redundancies to the robot in the form of moving linear sliders that are manipulated in a real-time redundancy resolution controller. The presented work combines this controller with an online failure detection framework to develop a complete fault tolerant control scheme for automatic task recovery. This solution provides robustness by combining pose estimation of the end-effector with the failure detection through the application of an Interactive Multiple Model (IMM) algorithm relying only on end-effector information. The failure and pose estimation scheme is then tied into the redundancy resolution approach to produce a seamless automatic task (trajectory) recovery approach for cable failures.

cs.RO↗

arXiv2019

Causality matters in medical imaging

This article discusses how the language of causality can shed new light on the major challenges in machine learning for medical imaging: 1) data scarcity, which is the limited availability of high-quality annotations, and 2) data mismatch, whereby a trained algorithm may fail to generalize in clinical practice. Looking at these challenges through the lens of causality allows decisions about data collection, annotation procedures, and learning strategies to be made (and scrutinized) more transparently. We discuss how causal relationships between images and annotations can not only have profound effects on the performance of predictive models, but may even dictate which learning strategies should be considered in the first place. For example, we conclude that semi-supervision may be unsuitable for image segmentation---one of the possibly surprising insights from our causal analysis, which is illustrated with representative real-world examples of computer-aided diagnosis (skin lesion classification in dermatology) and radiotherapy (automated contouring of tumours). We highlight that being aware of and accounting for the causal relationships in medical imaging data is important for the safe development of machine learning and essential for regulation and responsible reporting. To facilitate this we provide step-by-step recommendations for future studies.

eess.IV↗

arXiv2019

Graph Convolutional Gaussian Processes

We propose a novel Bayesian nonparametric method to learn translation-invariant relationships on non-Euclidean domains. The resulting graph convolutional Gaussian processes can be applied to problems in machine learning for which the input observations are functions with domains on general graphs. The structure of these models allows for high dimensional inputs while retaining expressibility, as is the case with convolutional neural networks. We present applications of graph convolutional Gaussian processes to images and triangular meshes, demonstrating their versatility and effectiveness, comparing favorably to existing methods, despite being relatively simple models.

cs.LG↗

arXiv2019

Controlling Meshes via Curvature: Spin Transformations for Pose-Invariant Shape Processing

We investigate discrete spin transformations, a geometric framework to manipulate surface meshes by controlling mean curvature. Applications include surface fairing -- flowing a mesh onto say, a reference sphere -- and mesh extrusion -- e.g., rebuilding a complex shape from a reference sphere and curvature specification. Because they operate in curvature space, these operations can be conducted very stably across large deformations with no need for remeshing. Spin transformations add to the algorithmic toolbox for pose-invariant shape analysis. Mathematically speaking, mean curvature is a shape invariant and in general fully characterizes closed shapes (together with the metric). Computationally speaking, spin transformations make that relationship explicit. Our work expands on a discrete formulation of spin transformations. Like their smooth counterpart, discrete spin transformations are naturally close to conformal (angle-preserving). This quasi-conformality can nevertheless be relaxed to satisfy the desired trade-off between area distortion and angle preservation. We derive such constraints and propose a formulation in which they can be efficiently incorporated. The approach is showcased on subcortical structures.

cs.CG↗

arXiv2018

Semi-Supervised Learning via Compact Latent Space Clustering

We present a novel cost function for semi-supervised learning of neural networks that encourages compact clustering of the latent space to facilitate separation. The key idea is to dynamically create a graph over embeddings of labeled and unlabeled samples of a training batch to capture underlying structure in feature space, and use label propagation to estimate its high and low density regions. We then devise a cost function based on Markov chains on the graph that regularizes the latent space to form a single compact cluster per class, while avoiding to disturb existing clusters during optimization. We evaluate our approach on three benchmarks and compare to state-of-the art with promising results. Our approach combines the benefits of graph-based regularization with efficient, inductive inference, does not require modifications to a network architecture, and can thus be easily applied to existing networks to enable an effective use of unlabeled data.

cs.LG↗

arXiv2017

An Energy Minimization Approach to 3D Non-Rigid Deformable Surface Estimation Using RGBD Data

We propose an algorithm that uses energy mini- mization to estimate the current configuration of a non-rigid object. Our approach utilizes an RGBD image to calculate corresponding SURF features, depth, and boundary informa- tion. We do not use predetermined features, thus enabling our system to operate on unmodified objects. Our approach relies on a 3D nonlinear energy minimization framework to solve for the configuration using a semi-implicit scheme. Results show various scenarios of dynamic posters and shirts in different configurations to illustrate the performance of the method. In particular, we show that our method is able to estimate the configuration of a textureless nonrigid object with no correspondences available.

cs.CV↗

arXiv2015

A passive THz video camera based on lumped element kinetic inductance detectors

We have developed a passive 350 GHz (850 {\mu}m) video-camera to demonstrate lumped element kinetic inductance detectors (LEKIDs) -- designed originally for far-infrared astronomy -- as an option for general purpose terrestrial terahertz imaging applications. The camera currently operates at a quasi-video frame rate of 2 Hz with a noise equivalent temperature difference per frame of $\sim$0.1 K, which is close to the background limit. The 152 element superconducting LEKID array is fabricated from a simple 40 nm aluminum film on a silicon dielectric substrate and is read out through a single microwave feedline with a cryogenic low noise amplifier and room temperature frequency domain multiplexing electronics.

physics.ins-det↗

arXiv2006

SCUBA-2: a 10,000 pixel submillimeter camera for the James Clerk Maxwell Telescope

SCUBA-2 is an innovative 10,000 pixel submillimeter camera due to be delivered to the James Clerk Maxwell Telescope in late 2006. The camera is expected to revolutionize submillimeter astronomy in terms of the ability to carry out wide-field surveys to unprecedented depths addressing key questions relating to the origins of galaxies, stars and planets. This paper presents an update on the project with particular emphasis on the laboratory commissioning of the instrument. The assembly and integration will be described as well as the measured thermal performance of the instrument. A summary of the performance results will be presented from the TES bolometer arrays, which come complete with in-focal plane SQUID amplifiers and multiplexed readouts, and are cooled to 100mK by a liquid cryogen-free dilution refrigerator. Considerable emphasis has also been placed on the operating modes of the instrument and the common-user aspect of the user interface and data reduction pipeline. These areas will also be described in the paper.

astro-ph↗

arXiv2004

An update on the SCUBA-2 project

SCUBA-2, which replaces SCUBA (the Submillimeter Common User Bolometer Array) on the James Clerk Maxwell Telescope (JCMT) in 2006, is a large-format bolometer array for submillimeter astronomy. Unlike previous detectors which have used discrete bolometers, SCUBA-2 has two dc-coupled, monolithic, filled arrays with a total of ~10,000 bolometers. It will offer simultaneous imaging of a 50 sq-arcmin field of view at wavelengths of 850 and 450 microns. SCUBA-2 is expected to have a huge impact on the study of galaxy formation and evolution in the early Universe as well as star and planet formation in our own Galaxy. Mapping the sky to the same S/N up to 1000 times faster than SCUBA, it will also act as a pathfinder for the new submillimeter interferometers such as ALMA. SCUBA-2's absorber-coupled pixels use superconducting transition edge sensors operating at 120 mK for performance limited by the sky background photon noise. The monolithic silicon detector arrays are deep-etched by the Bosch process to isolate the pixels on silicon nitride membranes. Electrical connections are made through indium bump bonds to a SQUID time-domain multiplexer (MUX). We give an overview of the SCUBA-2 system and an update on its status, and describe some of the technological innovations that make this unique instrument possible.

astro-ph↗

arXiv1995

A Merger Origin for X-Structures in S0 Galaxies

Using numerical simulation, we study the response of a disk galaxy to a merger involving a low-mass satellite companion. During a prograde satellite accretion, the disk galaxy forms a strong bar in response to the perturbation of the companion. After the accretion event is over, the bar buckles vertically due to a bending instability, sending disk material well out of the disk plane. The material forms into an X-shaped feature when seen edge-on, morphologically similar to X-structures observed in several S0/Sa galaxies. The mechanism described here unites previously suggested accretion and bar instability hypotheses for forming X-structures into a self-consistent scenario for merger-induced formation and evolution of S0 galaxies. To complement these models, we also present observations of the peculiar S0 galaxy Hickson 87a. The thick disk, isophotal warping, and strong X-structure described in the merger model are all evident in Hickson 87a, suggesting this galaxy may be an excellent example of such merger-induced galaxy evolution.

astro-ph↗