arXiv subjects

Robert K. Henderson

Publications and source records attributed to Robert K. Henderson.

16 recordsLinked to original sources

arXiv2026

Real-time diffuse correlation spectroscopy with a chip-based correlator for measuring human cerebral blood flow and brain function

Diffuse correlation spectroscopy (DCS) is a noninvasive optical technique that probes microvascular blood flow in deep tissues. Here, we present and validate a new on-chip hardware correlator for high-speed DCS measurements. The correlator is embedded in a custom-built 512 x 512 single-photon avalanche diode (SPAD) array named ATLAS, which computes intensity autocorrelation functions directly on-chip at a sampling rate of 116 Hz - the fastest DCS acquisition reported to date. Unlike conventional DCS systems that suffer from low light throughput and therefore cannot resolve cardiac pulsations at source-detector separations (rho) beyond 30 mm, our massively parallel on-chip architecture computes autocorrelations within each macropixel, eliminating the data-throughput bottleneck. This enables high-SNR, real-time detection of pulsatile blood flow even at rho = 50 mm on the human forehead. In phantom experiments at rho = 25 mm, ATLAS-DCS achieves a 12-fold improvement in signal-to-noise ratio over a conventional single-channel DCS instrument while operating at 116 Hz. In human subjects, we resolve functional hyperemia during a mental arithmetic task at rho = 30 mm. Furthermore, we integrate ATLAS DCS with a frequency-domain near-infrared spectroscopy (FD-NIRS) module, enabling simultaneous monitoring of blood flow and tissue oxygenation. With this combined system, we can concurrently resolve core hemodynamic parameters. The on-chip parallelized DCS design substantially improves detection speed, depth sensitivity, and real-time capability, paving the way for wearable, high-speed cerebral blood flow monitoring in both clinical and research settings.

physics.ins-det↗

arXiv2026

Single Pixel Image Classification using an Ultrafast Digital Light Projector

Pattern recognition and image classification are essential tasks in machine vision. Autonomous vehicles, for example, require being able to collect the complex information contained in a changing environment and classify it in real time. Here, we experimentally demonstrate image classification at multi-kHz frame rates combining the technique of single pixel imaging (SPI) with a low complexity machine learning model. The use of a microLED-on-CMOS digital light projector for SPI enables ultrafast pattern generation for sub-ms image encoding. We investigate the classification accuracy of our experimental system against the broadly accepted benchmarking task of the MNIST digits classification. We compare the classification performance of two machine learning models: An extreme learning machine (ELM) and a backpropagation trained deep neural network. The complexity of both models is kept low so the overhead added to the inference time is comparable to the image generation time. Crucially, our single pixel image classification approach is based on a spatiotemporal transformation of the information, entirely bypassing the need for image reconstruction. By exploring the performance of our SPI based ELM as binary classifier we demonstrate its potential for efficient anomaly detection in ultrafast imaging scenarios.

cs.CV↗

arXiv2025

An on-chip Pixel Processing Approach with 2.4μs latency for Asynchronous Read-out of SPAD-based dToF Flash LiDARs

We propose a fully asynchronous peak detection approach for SPAD-based direct time-of-flight (dToF) flash LiDAR, enabling pixel-wise event-driven depth acquisition without global synchronization. By allowing pixels to independently report depth once a sufficient signal-to-noise ratio is achieved, the method reduces latency, mitigates motion blur, and increases effective frame rate compared to frame-based systems. The framework is validated under two hardware implementations: an offline 256$\times$128 SPAD array with PC based processing and a real-time FPGA proof-of-concept prototype with 2.4$\upmu$s latency for on-chip integration. Experiments demonstrate robust depth estimation, reflectivity reconstruction, and dynamic event-based representation under both static and dynamic conditions. The results confirm that asynchronous operation reduces redundant background data and computational load, while remaining tunable via simple hyperparameters. These findings establish a foundation for compact, low-latency, event-driven LiDAR architectures suited to robotics, autonomous driving, and consumer applications. In addition, we have derived a semi-closed-form solution for the detection probability of the raw-peak finding based LiDAR systems that could benefit both conventional frame-based and proposed asynchronous LiDAR systems.

eess.IV↗

arXiv2025

Compact Eye Tracking for VR/AR Displays via Deep Learned MicroLED Projection and Single-Pixel Sensing

Fast and accurate eye tracking in a virtual reality or augmented reality headset could lead to better display performance and enable novel methods of user interaction with the system. However, it remains a challenge for a system to combine the required operational speed and accuracy of eye tracking with a technology that has a small enough form factor and weight to be easily integrated into a user-friendly headset. By using small, lightweight hardware comprising a high frame rate microLED array and fast single pixel detector, we report a model eye tracking system based on single pixel tracking and a specially developed set of deep learned illumination patterns. This model system is used to demonstrate eye tracking with an angular accuracy of better than one degree and a measurement rate of up to $3.59 \,$ kHz.

physics.optics↗

arXiv2025

Cerebral blood flow monitoring using a deep learning implementation of the two-layer DCS analytical model with a 512x512 SPAD array

Diffuse correlation spectroscopy (DCS) analyzes the autocorrelation function of photons scattered by red blood cells, enabling non-invasive, continuous measurement of deep tissue blood flow at the bedside. Multi-layer DCS models (two- and three-layer) enhance cerebral blood flow index (CBFi) sensitivity and mitigate interference from extracerebral tissues. However, these models require multiple predefined parameters and are computationally intensive, making them impractical for real-time bedside monitoring. To address this challenge, we integrate a single-photon avalanche diode (SPAD) array with a deep learning (DL)-based approach trained on data generated by the two-layer analytical model. This method bypasses traditional model fitting, enabling real-time CBFi monitoring while minimizing superficial tissue contamination. We first validate our approach using Monte Carlo-simulated test datasets, demonstrating superior accuracy in relative CBFi estimation (5.8% error vs. 19.1% for conventional fitting) and enhanced CBFi sensitivity (87.1% vs. 55.4%). Additionally, our method effectively isolates shallow blood flow changes and 750-fold faster than single-exponential fitting in a realistic scenario. We further evaluate the system in a healthy adult, achieving real-time CBFi monitoring and pulsatile waveform recovery during a brain activity test using a 512 512 SPAD array sensor. These results highlight the potential of our approach for real-time brain activity monitoring.

physics.med-ph↗

arXiv2025

Fiber-based Ultra-High Speed Diffuse Speckle Contrast Analysis System for Deep Blood Flow Sensing Using a Large SPAD Camera

Diffuse speckle contrast analysis (DSCA), also called speckle contrast optical spectroscopy(SCOS), has emerged as a groundbreaking optical imaging technique for tracking dynamic biological processes, including blood flow and tissue perfusion. Recent advancements in single-photon avalanche diode (SPAD) cameras have unlocked exceptional capabilities in sensitivity, time resolution, and high frame rate imaging. Despite this, the application of large-format SPAD arrays in speckle contrast analysis is still relatively uncommon. In this study, we introduce a pioneering use of a large format SPAD camera for DSCA. By harnessing the camera's high temporal resolution and photon detection efficiency, we significantly enhance the accuracy and robustness of speckle contrast measurements. Our experimental results demonstrate the system's remarkable ability to capture rapid temporal variations over a broad field of view, enabling detailed spatiotemporal analysis. Through simulations, phantom experiments, and in vivo studies, we validate the approach's potential for a wide range of biomedical applications, such as cuff occlusion tests and functional tissue monitoring. This work highlights the transformative impact of large SPAD cameras on DSCA, paving the way for new breakthroughs in optical imaging.

physics.ins-det↗

arXiv2024

Resolution Limit of Single-Photon LiDAR

Single-photon Light Detection and Ranging (LiDAR) systems are often equipped with an array of detectors for improved spatial resolution and sensing speed. However, given a fixed amount of flux produced by the laser transmitter across the scene, the per-pixel Signal-to-Noise Ratio (SNR) will decrease when more pixels are packed in a unit space. This presents a fundamental trade-off between the spatial resolution of the sensor array and the SNR received at each pixel. Theoretical characterization of this fundamental limit is explored. By deriving the photon arrival statistics and introducing a series of new approximation techniques, the Mean Squared Error (MSE) of the maximum-likelihood estimator of the time delay is derived. The theoretical predictions align well with simulations and real data.

eess.SP↗

arXiv2023

Single-Photon Counting Receivers for Optical Wireless Communications in Future 6G Networks

Optical wireless communication (OWC) offers several complementary advantages to radio-frequency wireless networks such as its massive available spectrum; hence, it is widely anticipated that OWC will assume a pivotal role in the forthcoming sixth generation wireless communication networks. Although significant progress has been achieved in OWC over the past decades, the outage induced by occasionally low received optical power continues to pose a key limiting factor for its deployment. In this work, we discuss the potential role of single-photon counting (SPC) receivers as a promising solution to overcome this limitation. We present an overview of the applications of SPC-based OWC systems in 6G networks, introduce their major performance-limiting factors, propose a performance enhancement framework to tackle these issues, and identify critical areas of open problems for future research.

cs.IT↗

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

A direct time-of-flight image sensor with in-pixel surface detection and dynamic vision

3D flash LIDAR is an alternative to the traditional scanning LIDAR systems, promising precise depth imaging in a compact form factor, and free of moving parts, for applications such as self-driving cars, robotics and augmented reality (AR). Typically implemented using single-photon, direct time-of-flight (dToF) receivers in image sensor format, the operation of the devices can be hindered by the large number of photon events needing to be processed and compressed in outdoor scenarios, limiting frame rates and scalability to larger arrays. We here present a 64x32 pixel (256x128 SPAD) dToF imager that overcomes these limitations by using pixels with embedded histogramming, which lock onto and track the return signal. This reduces the size of output data frames considerably, enabling maximum frame rates in the 10 kFPS range or 100 kFPS for direct depth readings. The sensor offers selective readout of pixels detecting surfaces, or those sensing motion, leading to reduced power consumption and off-chip processing requirements. We demonstrate the application of the sensor in mid-range LIDAR.

cs.CV↗

arXiv2021

Robust super-resolution depth imaging via a multi-feature fusion deep network

Three-dimensional imaging plays an important role in imaging applications where it is necessary to record depth. The number of applications that use depth imaging is increasing rapidly, and examples include self-driving autonomous vehicles and auto-focus assist on smartphone cameras. Light detection and ranging (LIDAR) via single-photon sensitive detector (SPAD) arrays is an emerging technology that enables the acquisition of depth images at high frame rates. However, the spatial resolution of this technology is typically low in comparison to the intensity images recorded by conventional cameras. To increase the native resolution of depth images from a SPAD camera, we develop a deep network built specifically to take advantage of the multiple features that can be extracted from a camera's histogram data. The network is designed for a SPAD camera operating in a dual-mode such that it captures alternate low resolution depth and high resolution intensity images at high frame rates, thus the system does not require any additional sensor to provide intensity images. The network then uses the intensity images and multiple features extracted from downsampled histograms to guide the upsampling of the depth. Our network provides significant image resolution enhancement and image denoising across a wide range of signal-to-noise ratios and photon levels. We apply the network to a range of 3D data, demonstrating denoising and a four-fold resolution enhancement of depth.

eess.IV↗

arXiv2019

High-sensitivity free space optical communications using low size, weight and power hardware

Free space optical communication systems with extremely high detector sensitivities are attractive for various applications with low size, weight and power requirements. For these practical systems, integrated hardware elements with small form factor are needed. Here, we demonstrate a communication link using a CMOS integrated micro-LED and array of single-photon avalanche diodes. These integrated systems provide a data rate of 100 Mb/s at a sensitivity of -55.2 dBm, corresponding to 7.5 detected photons per bit.

physics.app-ph↗

arXiv2018

Long-range depth imaging using a single-photon detector array and non-local data fusion

The ability to measure and record high-resolution depth images at long stand-off distances is important for a wide range of applications, including connected and automotive vehicles, defense and security, and agriculture and mining. In LIDAR (light detection and ranging) applications, single-photon sensitive detection is an emerging approach, offering high sensitivity to light and picosecond temporal resolution, and consequently excellent surface-to-surface resolution. The use of large format CMOS single-photon detector arrays provides high spatial resolution and allows the timing information to be acquired simultaneously across many pixels. In this work, we combine state-of-the-art single-photon detector array technology with non-local data fusion to generate high resolution three-dimensional depth information of long-range targets. The system is based on a visible pulsed illumination system at 670~nm and a 240~$\times$ 320 pixel array sensor, achieving sub-centimeter precision in all three spatial dimensions at a distance of 150 meters. The non-local data fusion combines information from an optical image with sparse sampling of the single-photon array data, providing accurate depth information at low signature regions of the target.

eess.IV↗

arXiv2018

High-speed dual color fluorescence lifetime endomicroscopy for highly-multiplexed pulmonary diagnostic applications and detection of labeled bacteria

We present a dual color laser scanning endomicroscope capable of fluorescence lifetime endomicroscopy at one frame per second (FPS). The scanning system uses a coherent imaging fiber with 30,000 cores. High-speed lifetime imaging is achieved by distributing the signal over an array of 1024 parallel single-photon avalanche photo--diode detectors (SPADs), minimizing detection dead time maximizing the number of photons detected per excitation pulse without photon pile--up to achieve the high frame rate. Dual color fluorescence imaging is achieved by temporally shifting the dual excitation lasers, with respect to each other, to separate the two spectrally distinct fluorescent decays. Combining the temporal encoding, to provide spectral separation, with lifetime measurements we show a one FPS, multi-channel endomicroscopy platform for clinical applications and diagnosis. We demonstrate the potential of the system by imaging smartprobe-labeled bacteria in ex vivo samples of human lung using lifetime to differentiate bacterial fluorescence from the strong background lung auto--fluorescence which was used to provide structural information.

physics.med-ph↗

arXiv2016

Multiplexed Single-Mode Wavelength-to-Time Mapping of Multimode Light

We demonstrate that photonic lanterns based on tapered multicore fibres provide an efficient way to couple multimode states of light to a two-dimensional array of Single-Photon Avalanche Detectors (SPADs), each of which has its own Time-to-Digital Converter (TDC) for Time-Correlated Single-Photon-Counting (TCSPC). Exploiting this capability, we demonstrate multiplexed single-mode wavelength-to-time conversion of multimode states of light using a multicore-fibre photonic lantern with 121 single-mode cores, coupled in a one-to-one fashion to 121 SPADS on a 32 $\times$ 32 pixel CMOS SPAD array. The application of photonic lanterns for coupling multimode light to SPAD arrays in this manner may find wide-ranging applications in areas such as Raman spectroscopy, coherent LIDAR and quantum optics.

physics.optics↗

arXiv2016

Smart-aggregation imaging for single molecule localization with SPAD cameras

SPAD cameras offer single photon detection sensitivity, high frame rates and zero readout noise. They are a core technology for widefield FLIM, but have further potential in ultra-fast imaging applications. However, in practice sensitivity falls behind that of EMCCD and sCMOS devices due to the lower photon detection efficiency of SPAD arrays. This paper considers the application of a binary SPAD camera to the capture of blinking molecules as part of a superresolution (dSTORM/PALM) experiment. Simulation and experimental studies demonstrate that the effective sensitivity of the camera can be improved significantly by aggregation of signal only binary frames. The simulations also indicate that with future advances in SPAD camera technology, SPAD devices could in time outperform existing scientific cameras when capturing fast temporal dynamics.

physics.ins-det↗