Showing papers by "Stephen McLaughlin published in 2023"
TL;DR: In this paper , a fully submerged underwater LiDAR transceiver system based on single-photon detection technologies was demonstrated with the transceiver and target objects immersed in a water tank at a depth of 1.8 meters, with the targets placed at a standoff distance of approximately 3 meters.
Abstract: We demonstrate a fully submerged underwater LiDAR transceiver system based on single-photon detection technologies. The LiDAR imaging system used a silicon single-photon avalanche diode (SPAD) detector array fabricated in complementary metal-oxide semiconductor (CMOS) technology to measure photon time-of-flight using picosecond resolution time-correlated single-photon counting. The SPAD detector array was directly interfaced to a Graphics Processing Unit (GPU) for real-time image reconstruction capability. Experiments were performed with the transceiver system and target objects immersed in a water tank at a depth of 1.8 meters, with the targets placed at a stand-off distance of approximately 3 meters. The transceiver used a picosecond pulsed laser source with a central wavelength of 532 nm, operating at a repetition rate of 20 MHz and average optical power of up to 52 mW, dependent on scattering conditions. Three-dimensional imaging was demonstrated by implementing a joint surface detection and distance estimation algorithm for real-time processing and visualization, which achieved images of stationary targets with up to 7.5 attenuation lengths between the transceiver and the target. The average processing time per frame was approximately 33 ms, allowing real-time three-dimensional video demonstrations of moving targets at ten frames per second at up to 5.5 attenuation lengths between transceiver and target.
1 citations
TL;DR: In this paper , a two-step statistical based approach for real-time image reconstruction applicable to a transmission medium with extreme light scattering conditions is introduced, where the first step is an optional target detection method to select informative pixels which have photons reflected from the target, hence allowing data compression.
Abstract: Single-photon methods are emerging as a key approach to 3D imaging. This paper introduces a two step statistical based approach for real-time image reconstruction applicable to a transmission medium with extreme light scattering conditions. The first step is an optional target detection method to select informative pixels which have photons reflected from the target, hence allowing data compression. The second is a reconstruction algorithm that exploits data statistics and multiscale information to deliver clean depth and reflectivity images together with associated uncertainty maps. Both methods involve independent operations that are implemented in parallel on graphics processing units (GPUs), which enables real-time data processing of moving scenes at more than 50 depth frames per second for an image of $128 \times 128$ pixels. Comparisons with state-of-the-art algorithms on simulated and real underwater data demonstrate the benefit of the proposed framework for target detection, and for fast and robust depth estimation at multiple frames per second.
1 citations
04 Jun 2023
TL;DR: In this article , the authors present a reconstruction algorithm that exploits data statistics and multi-scale information to deliver clean depth and reflectivity images together with associated uncertainty maps, and demonstrate the robust and efficient performance of the proposed method.
Abstract: Time-correlated single-photon technology is emerging as an important approach to 3D Imaging. This paper presents a reconstruction algorithm that exploits data statistics and multi-scale information to deliver clean depth and reflectivity images together with associated uncertainty maps. The statistical method has been implemented to run on graphics processing units (GPUs) that enable real-time reconstruction of moving scenes at more than 1000 depth frames per second on the 32 × 64 pixels real Quantic4x4 SPAD sensor array data. Comparisons with state-of-the-art algorithms on simulated and real data demonstrate the robust and efficient performance of the proposed method.
TL;DR: In this paper , the spectrum efficiency, energy efficiency, and economic efficiency for a heterogeneous cellular architecture that separates the indoor and outdoor scenarios for beyond 5G (B5G) wireless communication systems are studied.
Abstract: In this paper, we study the spectrum efficiency (SE), energy efficiency (EE), and economic efficiency (ECE) for a heterogeneous cellular architecture that separates the indoor and outdoor scenarios for beyond 5G (B5G) wireless communication systems. For outdoor scenarios, massive multiple-input-multiple-output (MIMO) technologies and distributed antenna systems (DASs) at sub-6 GHz frequency bands are used for long-distance communications. For indoor scenarios, millimeter-wave (mmWave) and beamforming communication technologies are deployed at wireless indoor access points (IAPs) to provide high-speed short-range services to indoor users. Mathematical expressions for the system capacity, SE, EE, and ECE are derived using a proposed realistic power consumption model. The results shed light on the fact that the proposed network architecture is able to improve SE and EE by more than three times compared to those conventional network architectures. The analysis of system performance in terms of SE, EE, ECE, and their trade-off results in the observation that the proposed network architecture offers a promising solution for future B5G communication systems.
TL;DR: In this article , a hierarchical Bayesian model for 3D reconstruction and spectral classification of multispectral single-photon LiDAR data is proposed, which promotes spatial correlation between point cloud estimates and uses a coordinate gradient descent algorithm for parameter estimation.
Abstract: 3D single-photon LiDAR imaging has an important role in many applications. However, full deployment of this modality will require the analysis of low signal to noise ratio target returns and very high volume of data. This is particularly evident when imaging through obscurants or in high ambient background light conditions. This paper proposes a multiscale approach for 3D surface detection from the photon timing histogram to permit a significant reduction in data volume. The resulting surfaces are background-free and can be used to infer depth and reflectivity information about the target. We demonstrate this by proposing a hierarchical Bayesian model for 3D reconstruction and spectral classification of multispectral single-photon LiDAR data. The reconstruction method promotes spatial correlation between point-cloud estimates and uses a coordinate gradient descent algorithm for parameter estimation. Results on simulated and real data show the benefits of the proposed target detection and reconstruction approaches when compared to state-of-the-art processing algorithms.