Histogram-less LiDAR through SPAD response linearization

We present a new method to acquire the 3D information from a SPAD-based direct-Time-of-Flight (d-ToF) imaging system which does not require the construction of a histogram of timestamps and can withstand high flux operation regime. The proposed acquisition scheme emulates the behavior of a SPAD detector with no distortion due to dead time, and extracts the Tof information by a simple average operation on the photon timestamps ensuring ease of integration in a dedicated sensor and scalability to large arrays. The method is validated through a comprehensive mathematical analysis, whose predictions are in agreement with a numerical Monte Carlo model of the problem. Finally, we show the validity of the predictions in a real d-ToF measurement setup under challenging background conditions well beyond the typical pile-up limit of 5% detection rate up to a distance of 3.8 m

Numerical Model of SPAD-Based Direct Time-of-Flight Flash LIDAR CMOS Image Sensors

none3We present a Montecarlo simulator developed in Matlab® for the analysis of a Single Photon Avalanche Diode (SPAD)-based Complementary Metal-Oxide Semiconductor (CMOS) flash Light Detection and Ranging (LIDAR) system. The simulation environment has been developed to accurately model the components of a flash LIDAR system, such as illumination source, optics, and the architecture of the designated SPAD-based CMOS image sensor. Together with the modeling of the background noise and target topology, all of the fundamental factors that are involved in a typical LIDAR acquisition system have been included in order to predict the achievable system performance and verified with an existing sensor.noneTontini, Alessandro; Gasparini, Leonardo; Perenzoni, MatteoTontini, Alessandro; Gasparini, Leonardo; Perenzoni, Matte

A SPAD-based linear sensor with in-pixel temporal pattern detection for interference and background rejection with smart readout scheme

In this work, a 1x64 pixel SPAD-based linear sensor for direct time-of-flight (d-ToF) applications with real-time inpixel interference and background rejection is presented. Each pixel is composed by 4 SPADs with passive quenching, a digital logic circuit to exploit photon temporal coincidence with a threshold of up to 3 photons for background rejection, a finite state machine for the detection of temporal laser patterns for the rejection of interfering signals generated by other similar devices and a 16-b time-to-digital converter with 150 ps timing resolution that can be repurposed for intensity measurements. The sensor implements a smart readout scheme capable to output only pixels with meaningful data, i.e., detection events that have been validated by the photon temporal coincidence circuit and/or the laser pattern detection circui

Design and Characterization of a Low-Cost FPGA-Based TDC

We present a field-programmable gate array (FPGA) implementation of a time-to-digital converter (TDC) based on a low-cost, low-area Spartan 6 device. The converter is based on a tapped delay line model. Several implementation details are discussed with a particular focus on critical blocks such as the input stage and thermometer-to-binary decoding techniques. We implemented a tap filtering technique to improve the differential nonlinearity (DNL) of the single delay line while keeping a good LSB value of 25.57 ps with a single-shot precision (SSP) between 0.69 - 1.46 LSB. Measured DNL and integral nonlinearity (INL) lie in the range between -0.90 + 1.23 and -0.43 ÷ 2.96 LSB, respectively. Measured DNL and INL lie in the range between -0.90 ÷ 1.23 and -0.43 ÷ 2.96 LSB, respectively. We then implemented an interpolating TDC to overcome the limitations of a single delay line in terms of linearity and measurement range. The interpolating TDC uses the sliding scale technique, where the time interval to be measured is asynchronous with respect to the FPGA clock, achieving DNL and INL in the range -0.072 ÷ 0.070 and -0.755 ÷ 0.872 LSB. SSP is in the 1.096 ÷ 2.815 range. Moreover, we present a novel comparison between the DNLs obtained with two different methods: statistical code density test and using a finely controlled delay source. Finally, we present the results of a Monte Carlo simulation used to investigate the effects of nonlinear propagation of the signal through the delay line

SPAD-based quantum random number generator with a Nth-order rank algorithm on FPGA

We present a compact, all-solid-state, low-cost quantum random number generator (QRNG) based on a single-photon avalanche diode (SPAD) and a field programmable gate array (FPGA). A new algorithm for random bit generation is described, ranking the inter-arrival times of a group of $M$ photons detected by the SPAD device, and processed directly on the FPGA. The proposed approach improves the efficiency of generated random bits per detected photon, spanning from 0.5 bits/photon in case of 0 order rank, up to 0.875 bits/photon for second order rank. By extending the algorithm to higher orders, the proposed system approaches the maximum theoretical value of 1.0 bit/photon. The rate of generation of random numbers is limited by the SPAD minimum deadtime, achieving an experimentally proven bit rate of 7.3 Mbps. The standard randomness statistical tests are passed for a wide range of photon fluxes and for all the implemented rank orders with no additional post-processing on the generated sequence

Comparison of background-rejection techniques for SPAD-based LiDAR systems

We present the results of Montecarlo simulations and measurements focusing on the analysis of two techniques aimed at reducing the negative effect of background light in Single Photon Avalanche Diode (SPAD)-based Light Detection And Ranging (LiDAR) systems. The first technique, known as photon coincidence technique, exploits the temporal proximity of multiple detections to reject background light and maximize the detection of photons belonging to the target reflection. The second technique, named Auto-Sensitivity (AS) technique, reduces the photon-detection probability (PDP) if a certain background illumination level is detected, to avoid the risk of saturating SPADs due to intense background level. The two methods are first compared to each other, showing that the photon coincidence technique outperforms the AS technique. Then, the two techniques are operated together, resulting in an increase of the maximum achievable measurement range if the AS technique is applied on top of the photon coincidence technique

Comparative evaluation of background-rejection techniques for SPAD-based LiDAR systems

This paper presents the analysis and comparative evaluation of three background-rejection techniques implemented in CMOS processes for Single Photon Avalanche Diode (SPAD)-based Light Detection and Ranging (LiDAR) systems, using both Monte Carlo simulations and laboratory measurements. The first technique, known as photon coincidence technique, uses the temporal proximity of multiple detections to reject background light and maximize the detection of photons belonging to the reflected laser pulse. With the second technique, named Auto-Sensitivity (AS) technique, background light is rejected by automatically reducing the SPAD photon-detection probability (PDP), in order to avoid sensor saturation due to the intense environment illumination. The third technique we consider is the last-hit detection, which is able to detect and timestamp the last event impinging on the sensor over the acquisition window rather than the first, maximizing the system performance for long distance targets. The photon coincidence technique and AS technique are first compared to each other, showing the photon coincidence technique to outperform the AS technique. Then, the two techniques are applied together, resulting in an increase of the measurement range. Furthermore, a detailed analysis considering three different implementations of the photon coincidence technique is presented, showing pros and cons of each implementation and how the performance is affected. The last set of results focuses on the last-hit detection, which is compared against the standard detection paradigm (first-hit), showing not only an improvement for long distance targets (as expected), but an overall increase in system performance in terms of both success rate and SNR

The sub‐ice structure of Mt. Melbourne Volcanic Field (Northern Victoria Land, Antarctica) uncovered by High‐Resolution Aeromagnetic data

The Mt. Melbourne Volcanic Field is a quiescent volcanic complex located in Northern Victoria Land, Antarctica, mostly covered by ice. Its inner structure has remained largely unknown, due to the paucity of outcrops and the lack of detailed multi-disciplinary investigations. Here we present a novel high-resolution aeromagnetic dataset, revealing strong long-wavelength negative anomalies superimposed by short-wavelength positive ones forming characteristic radial patterns. Automatic lineament detection, through the Hough transform technique applied to the tilt derivative of our magnetic dataset, shows prevailing NW-SE- to NNE-SSW-trending structural features, which combined with the few structural field observations contribute to define the deformation pattern. Pre-existing and novel magnetic property measurements, coupled with available geochronological data, are used to constrain a two-step 3D magnetic inversion. A layer-structured Oldenburg-Parker’s inversion was utilized to model the deep and long-wavelength components of the magnetic field, whereas a linear inversion based on a set of shallower prisms was used to model the short-wavelength components. The final 3D model shows widespread reversely-polarized volcanics, which are locally intruded and superimposed respectively by swarms of normally-polarized dikes and radial lava flows along paleo-valleys. These results support the onset of volcanic activity in the entire field at least in the Matuyama magnetic epoch, i.e., between 2.58 and 0.78 Ma

Artificial intelligence in gastrointestinal endoscopy for inflammatory bowel disease: a systematic review and new horizons

Introduction: Since the advent of artificial intelligence (AI) in clinical studies, luminal gastrointestinal endoscopy has made great progress, especially in the detection and characterization of neoplastic and preneoplastic lesions. Several studies have recently shown the potential of AI-driven endoscopy for the investigation of inflammatory bowel disease (IBD). This systematic review provides an overview of the current position and future potential of AI in IBD endoscopy. Methods: A systematic search was carried out in PubMed and Scopus up to 2 December 2020 using the following search terms: artificial intelligence, machine learning, computer-aided, inflammatory bowel disease, ulcerative colitis (UC), Crohn’s disease (CD). All studies on human digestive endoscopy were included. A qualitative analysis and a narrative description were performed for each selected record according to the Joanna Briggs Institute methodologies and the PRISMA statement. Results: Of 398 identified records, 18 were ultimately included. Two-thirds of these (12/18) were published in 2020 and most were cross-sectional studies (15/18). No relevant bias at the study level was reported, although the risk of publication bias across studies cannot be ruled out at this early stage. Eleven records dealt with UC, five with CD and two with both. Most of the AI systems involved convolutional neural network, random forest and deep neural network architecture. Most studies focused on capsule endoscopy readings in CD ( n  = 5) and on the AI-assisted assessment of mucosal activity in UC ( n  = 10) for automated endoscopic scoring or real-time prediction of histological disease. Discussion: AI-assisted endoscopy in IBD is a rapidly evolving research field with promising technical results and additional benefits when tested in an experimental clinical scenario. External validation studies being conducted in large and prospective cohorts in real-life clinical scenarios will help confirm the added value of AI in assessing UC mucosal activity and in CD capsule reading. Plain language summary Artificial intelligence for inflammatory bowel disease endoscopy Artificial intelligence (AI) is a promising technology in many areas of medicine. In recent years, AI-assisted endoscopy has been introduced into several research fields, including inflammatory bowel disease (IBD) endoscopy, with promising applications that have the potential to revolutionize clinical practice and gastrointestinal endoscopy. We have performed the first systematic review of AI and its application in the field of IBD and endoscopy. A formal process of paper selection and analysis resulted in the assessment of 18 records. Most of these (12/18) were published in 2020 and were cross-sectional studies (15/18). No relevant biases were reported. All studies showed positive results concerning the novel technology evaluated, so the risk of publication bias cannot be ruled out at this early stage. Eleven records dealt with UC, five with CD and two with both. Most studies focused on capsule endoscopy reading in CD patients ( n  = 5) and on AI-assisted assessment of mucosal activity in UC patients ( n  = 10) for automated endoscopic scoring and real-time prediction of histological disease. We found that AI-assisted endoscopy in IBD is a rapidly growing research field. All studies indicated promising technical results. When tested in an experimental clinical scenario, AI-assisted endoscopy showed it could potentially improve the management of patients with IBD. Confirmatory evidence from real-life clinical scenarios should be obtained to verify the added value of AI-assisted IBD endoscopy in assessing UC mucosal activity and in CD capsule reading

A monolithic SPAD-based random number generator for cryptographic application

A compact quantum random number generator based on an array of Single Photon Avalanche Diode (SP AD) is presented here. As the main feature, the proposed chip has the capability to generate random numbers without the use of an external source of light. In the present approach, SP AD devices are used as emitters and as detectors. An embedded logic allows distinguishing dark events from events coming from the photon emission. The extracted random bit sequence shows a quite uniform distribution and after being post-processed by means of an integrated circuit, used to maximize the entropy of the system, is able to pass the AIS31 test. The average bit rate is 400 kbps