arXiv subjects

Stirling Scholes

Publications and source records attributed to Stirling Scholes.

10 recordsLinked to original sources

arXiv2026

Field validation of GNSS-independent positioning enhancement using a wearable ultra-stable quantum magnetometer

Increasing the resilience of positioning systems that currently rely on Global Navigation Satellite System (GNSS) signals can be achieved by incorporating stable and sensitive measurements of the permanent crustal anomalies in the Earth's magnetic field. We have realised this concept using an in-house-developed, wearable, Free-Induction-Decay Optically Pumped Magnetometer (FID-OPM) to carry out precise and stable measurements of the geomagnetic field in a walking trial. We present an end-to-end validation, including qualification of FID-OPM performance, alongside quantification of improvement in accuracy when data from this sensor is added to a dead-reckoning estimation of position. Using our wearable sensor system we achieve a Beckmann-distributed radial positioning error of 2.24 m over a route exceeding 500 m in length and spanning approximately 360 s.

physics.atom-ph↗

arXiv2026

Spatial dealiasing of classical geomagnetic survey data through use of a microfabricated wearable quantum magnetometer

Geomagnetic surveys provide insight into the subsurface for a range of applications, from fundamental understanding of geological processes, to mineral exploration and locating unexploded ordnance. A persistent challenge in performing such geomagnetic surveys is the joint problem of anthropogenic noise rejection and spatial aliasing, where the limited bandwidth (< 10 Hz) of traditional surveying instruments introduces artefacts into the surveyed field. Optically Pumped Magnetometers (OPMs) exploit quantum mechanical effects to achieve highly sensitive and stable magnetic field measurements at comparatively high bandwidths. Recent advances in manufacturing have enabled OPMs to be packaged in compact and lightweight systems (approx. 1kg), that are ideal for geomagnetic surveying. Here, we show how an OPM can directly contribute to the reduction of spatial aliasing in traditional PPM data. We carry both a PPM and OPM over a 20 km long transect across the Highland Boundary Fault (HBF) in Scotland. We leverage the continuous acquisition of the OPM sampling at 90 Hz, equivalent to every approx. 1 cm at walking pace (1 m/s) versus every approx. 200 m for our PPM (which had to be stationary for measurements) to reject magnetic noise and identify new small-scale (< 200 m) geological structures. Further, we discuss the logistical advantages of the hybrid survey in terms of portability, survey delivery, data density, and data quality.

physics.atom-ph↗

arXiv2026

Orthogonalised Self-Guided Quantum Tomography: Insights from Single-Pixel Imaging

We introduce the concept of self-guided imaging (SGI) as a linear analogue of self-guided quantum tomography (SGQT). We show that SGI is mathematically equivalent to single-pixel imaging (SPI). Taking inspiration from orthogonalised ghost imaging, a recent advance in SPI, we introduce orthogonalised SGQT. This requires no additional experimental overhead and leads to faster and more accurate final convergence, as we demonstrate numerically (fidelity $95.2\% \rightarrow 99.17\%$) and experimentally (fidelity $92.1\% \rightarrow 95.3\%$). This work suggests that further routines from SPI and SGQT can be interchanged to optimise measurements and convergence.

quant-ph↗

arXiv2022

Video super-resolution for single-photon LIDAR

3D Time-of-Flight (ToF) image sensors are used widely in applications such as self-driving cars, Augmented Reality (AR) and robotics. When implemented with Single-Photon Avalanche Diodes (SPADs), compact, array format sensors can be made that offer accurate depth maps over long distances, without the need for mechanical scanning. However, array sizes tend to be small, leading to low lateral resolution, which combined with low Signal-to-Noise Ratio (SNR) levels under high ambient illumination, may lead to difficulties in scene interpretation. In this paper, we use synthetic depth sequences to train a 3D Convolutional Neural Network (CNN) for denoising and upscaling (x4) depth data. Experimental results, based on synthetic as well as real ToF data, are used to demonstrate the effectiveness of the scheme. With GPU acceleration, frames are processed at >30 frames per second, making the approach suitable for low-latency imaging, as required for obstacle avoidance.

eess.IV↗

arXiv2022

Simulating single-photon detector array sensors for depth imaging

Single-Photon Avalanche Detector (SPAD) arrays are a rapidly emerging technology. These multi-pixel sensors have single-photon sensitivities and pico-second temporal resolutions thus they can rapidly generate depth images with millimeter precision. Such sensors are a key enabling technology for future autonomous systems as they provide guidance and situational awareness. However, to fully exploit the capabilities of SPAD array sensors, it is crucial to establish the quality of depth images they are able to generate in a wide range of scenarios. Given a particular optical system and a finite image acquisition time, what is the best-case depth resolution and what are realistic images generated by SPAD arrays? In this work, we establish a robust yet simple numerical procedure that rapidly establishes the fundamental limits to depth imaging with SPAD arrays under real world conditions. Our approach accurately generates realistic depth images in a wide range of scenarios, allowing the performance of an optical depth imaging system to be established without the need for costly and laborious field testing. This procedure has applications in object detection and tracking for autonomous systems and could be easily extended to systems for underwater imaging or for imaging around corners.

eess.IV↗

arXiv2022

Real-time, low-cost multi-person 3D pose estimation

The process of tracking human anatomy in computer vision is referred to pose estimation, and it is used in fields ranging from gaming to surveillance. Three-dimensional pose estimation traditionally requires advanced equipment, such as multiple linked intensity cameras or high-resolution time-of-flight cameras to produce depth images. However, there are applications, e.g.~consumer electronics, where significant constraints are placed on the size, power consumption, weight and cost of the usable technology. Here, we demonstrate that computational imaging methods can achieve accurate pose estimation and overcome the apparent limitations of time-of-flight sensors designed for much simpler tasks. The sensor we use is already widely integrated in consumer-grade mobile devices, and despite its low spatial resolution, only 4$\times$4 pixels, our proposed Pixels2Pose system transforms its data into accurate depth maps and 3D pose data of multiple people up to a distance of 3 m from the sensor. We are able to generate depth maps at a resolution of 32$\times$32 and 3D localization of a body parts with an error of only $\approx$10 cm at a frame rate of 7 fps. This work opens up promising real-life applications in scenarios that were previously restricted by the advanced hardware requirements and cost of time-of-flight technology.

cs.CV↗

arXiv2021

DronePose: The identification, segmentation, and orientation detection of drones via neural networks

The growing ubiquity of drones has raised concerns over the ability of traditional air-space monitoring technologies to accurately characterise such vehicles. Here, we present a CNN using a decision tree and ensemble structure to fully characterise drones in flight. Our system determines the drone type, orientation (in terms of pitch, roll, and yaw), and performs segmentation to classify different body parts (engines, body, and camera). We also provide a computer model for the rapid generation of large quantities of accurately labelled photo-realistic training data and demonstrate that this data is of sufficient fidelity to allow the system to accurately characterise real drones in flight. Our network will provide a valuable tool in the image processing chain where it may build upon existing drone detection technologies to provide complete drone characterisation over wide areas.

cs.CV↗

arXiv2020

Lossless reshaping of structured light

Structured light concerns the control of light in its spatial degrees of freedom (amplitude, phase and polarization), and has proven instrumental in many applications. The creation of structured light usually involves the conversion of a Gaussian mode to a desired structure in a single step, while the detection is often the reverse process, both fundamentally lossy or imperfect. Here we show how to ideally reshape structured light in a lossless manner in a simple two-step process. We outline the core theoretical arguments, and demonstrate reshaping of arbitrary structured light patterns, in the process highlighting when the technique is applicable and when not, and how best to implement it. This work will be a useful addition to the structured light toolkit, and particularly relevant to those wishing to use the spatial modes of light as a basis in classical and quantum communication.

physics.optics↗

arXiv2020

Improving the beam quality factor ($M^2$) by phase-only reshaping of structured light

Laser brightness is crucial in many optical processes, and is optimised by high power, high beam quality (low $M^2$) beams. Here we show how to improve the laser beam quality factor (reducing the $M^2$) of arbitrary structured light fields in a lossless manner using continuous phase-only elements, thus allowing for the increase in brightness by a simple linear optical transformation. We demonstrate the principle with four high $M^2$ initial beams, converting each to a Gaussian ($M^2 \approx 1$) with a dramatic increase in brightness of $>10 \times$. This work puts a new perspective on the old debate of improving laser beam quality with binary diffractive optics, while providing a practical approach to enhancing laser brightness for arbitrary input beams.

physics.optics↗

arXiv2016

High-bit-rate quantum key distribution with entangled internal degrees of freedom of photons

Quantum communication over long distances is integral to information security and has been demonstrated in free space and fibre with two-dimensional polarisation states of light. Although increased bit rates can be achieved using high-dimensional encoding with spatial modes of light, the efficient detection of high-dimensional states remains a challenge to realise the full benefit of the increased state space. Here we exploit the entanglement between spatial modes and polarization to realise a four-dimensional quantum key distribution (QKD) protocol. We introduce a detection scheme which employs only static elements, allowing for the detection of all basis modes in a high-dimensional space deterministically. As a result we are able to realise the full potential of our high-dimensional state space, demonstrating efficient QKD at high secure key and sift rates, with the highest capacity-to-dimension reported to date. This work opens the possibility to increase the dimensionality of the state-space indefinitely while still maintaining deterministic detection and will be invaluable for long distance 'secure and fas' data transfer.

quant-ph↗