Feature extraction and neural network-based multi-peak analysis on time-correlated LiDAR histograms

Time correlated single photon counting (TCSPC) is a statistical method to generate time-correlated histograms (TC-Hists), which are based on the time-of-flight (TOF) information measured by photon detectors such as single-photon avalanche diodes. With restricted measurements per histogram and the presence of high background light, it is challenging to obtain the target distance in a TC-Hist. In order to improve the data processing robustness under these conditions, the concept of machine learning is applied to the TC-Hist. Using the neural network-based multi-peak analysis (NNMPA), introduced by us, including a physics-guided feature extraction, a neural network multi-classifier, and a distance recovery process, the analysis is focused on a small amount of critical features in the TC-Hist. Based on these features, possible target distances with correlated certainty values are inferred. Furthermore, two optimization approaches regarding the learning ability and real-time performance are discussed. In particular, variants of the NNMPA are evaluated on both synthetic and real datasets. The proposed method not only has higher robustness in allocating the coarse position (±5 %) of the target distance in harsh conditions, but also is faster than the classical digital processing with an average-filter. Thus, it can be applied to improve the system robustness, especially in the case of high background light and middle-range detections.

Improvement of Accuracy and Precision of the LiDAR System Working in High Background Light Conditions

Background light noise is one of the major challenges in the design of Light Detection and Ranging (LiDAR) systems. In this paper, we build a single-beam LiDAR module to investigate the effect of light intensity on the accuracy/precision and success rate of measurements in environments with strong background noises. The proposed LiDAR system includes the laser signal emitter and receiver system, the signal processing embedded platform, and the computer for remote control. In this study, two well-known time-of-flight (ToF) estimation methods, which are peak detection and cross-correlation (CC), were applied and compared. In the meanwhile, we exploited the cross-correlation technique combined with the reduced parabolic interpolation (CCP) algorithm to improve the accuracy and precision of the LiDAR system, with the analog-to-digital converter (ADC) having a limited resolution of 125 mega samples per second (Msps). The results show that the CC and CCP methods achieved a higher success rate than the peak method, which is 12.3% in the case of applying emitted pulses 10 µs/frame and 8.6% with 20 µs/frame. In addition, the CCP method has the highest accuracy/precision in the three methods reaching 7.4 cm/10 cm and has a significant improvement over the ADC’s resolution of 1.2 m. This work shows our contribution in building a LiDAR system with low cost and high performance, accuracy, and precision.

Block-Greedy and CNN Based Underwater Image Dehazing for Novel Depth Estimation and Optimal Ambient Light

A lack of adequate consideration of underwater image enhancement gives room for more research into the field. The global background light has not been adequately addressed amid the presence of backscattering. This paper presents a technique based on pixel differences between global and local patches in scene depth estimation. The pixel variance is based on green and red, green and blue, and red and blue channels besides the absolute mean intensity functions. The global background light is extracted based on a moving average of the impact of suspended light and the brightest pixels within the image color channels. We introduce the block-greedy algorithm in a novel Convolutional Neural Network (CNN) proposed to normalize different color channels’ attenuation ratios and select regions with the lowest variance. We address the discontinuity associated with underwater images by transforming both local and global pixel values. We minimize energy in the proposed CNN via a novel Markov random field to smooth edges and improve the final underwater image features. A comparison of the performance of the proposed technique against existing state-of-the-art algorithms using entropy, Underwater Color Image Quality Evaluation (UCIQE), Underwater Image Quality Measure (UIQM), Underwater Image Colorfulness Measure (UICM), and Underwater Image Sharpness Measure (UISM) indicate better performance of the proposed approach in terms of average and consistency. As it concerns to averagely, UICM has higher values in the technique than the reference methods, which explainsits higher color balance. The μ values of UCIQE, UISM, and UICM of the proposed method supersede those of the existing techniques. The proposed noted a percent improvement of 0.4%, 4.8%, 9.7%, 5.1% and 7.2% in entropy, UCIQE, UIQM, UICM and UISM respectively compared to the best existing techniques. Consequently, dehazed images have sharp, colorful, and clear features in most images when compared to those resulting from the existing state-of-the-art methods. Stable σ values explain the consistency in visual analysis in terms of sharpness of color and clarity of features in most of the proposed image results when compared with reference methods. Our own assessment shows that only weakness of the proposed technique is that it only applies to underwater images. Future research could seek to establish edge strengthening without color saturation enhancement.

Chapter 3 Extra-galactic Gamma-Ray Sources

Extra-galactic gamma-ray sources, such as gamma-ray bursts, active galactic nuclei, starburst galaxies, are interesting and important targets for LHAASO observations. In this chapter, the prospects of detecting these sources with LHAASO and their physical implications are studied. The upgrade plan for the Water Cherenkov Detector Array (WCDA), which aims to enhance the detectability of relatively lower energy photons, is also presented. In addition, a study on constraining the extragalactic background light with LHAASO observation of blazars is presented.

High Dynamic Range Photocurrent Sensory Circuit with a Multi-Transistor Background Light Cancellation Loop for Photoplethysmography Sensing

In this article, we present a new photocurrent sensory circuit with a three-transistor background light cancellation. We describe our innovative photocurrent sensor-based blood pressure measuring device using a resistor-based current-to-voltage converter with a background light cancellation (BLC) loop. The photocurrent sensor is implemented using 0.35 μm standard CMOS technology and has zero average power consumption. The post-layout simulation for the photocurrent sensor shows a 1.3 MΩ transimpedance gain, a referred input noise current of 11 pA, and can reject a DC photocurrent up to 200 μA. This high DC rejection has been achieved due to the newly proposed multi-transistor BLC loop integrated with the sensor.

Noise rejection through an improved quantum illumination protocol

Quantum illumination protocols can be implemented to improve imaging performance in the low photon flux regime even in the presence of both background light and sensor noise. However, the extent to which this noise can be rejected is limited by the rate of accidental correlations resulting from the detection of photon or noise events that are not quantum-correlated. Here we present an improved protocol that rejects up to $$\gtrsim 99.9\%$$ ≳ 99.9 % of background light and sensor noise in the low photon flux regime, improving upon our previous results by an order of magnitude. This improvement, which requires no information regarding the scene or noise statistics, will enable extremely low light quantum imaging techniques to be applied in environments previously thought difficult and be an important addition to the development of covert imaging, quantum microscopes, and quantum LIDAR.

Effect of axion-like particles on the spectrum of the extragalactic gamma-ray background

Axion-like particles (ALPs) provide a feasible explanation for the observed lower TeV opacity of the Universe. If the anomaly TeV transparency is caused by ALPs, then the fluxes of distant extragalactic sources will be enhanced at photon energies beyond TeV, resulting in an enhancement of the observed extragalactic gamma-ray background (EGB) spectrum. In this work, we have investigated the ALP modulation on the EGB spectrum at TeV energies. Our results show that in the most optimistic case, the existence of ALPs can cause the EGB spectrum to greatly deviate from the prediction of a pure extragalactic-background-light (EBL) absorption scenario. The deviation occurs at approximately ≳1 TeV, and the current EGB measurements by Fermi-LAT cannot identify such an effect. We also find that most of the sensitive ALP parameters have been ruled out by existing constraints, leaving only a small region of unrestricted parameters that can be probed using the EGB effect investigated in this work. Observations from forthcoming very-high-energy instruments like LHAASO and CTA may be beneficial for the study of this effect.

STUDY OF BLAZAR ACTIVITY IN 10 YEAR FERMI-LAT DATA AND IMPLICATIONS FOR TEV NEUTRINO EXPECTATIONS

Blazars are the most active extragalactic gamma-ray sources. They show sporadic bursts of activity, lasting from hours to months. In this work we present a 10-year analysis of a sample of bright sources detected by Fermi-LAT (100 MeV - 300 GeV). Using 2-week binned light curves (LC) we estimate the duty cycle (DC): fraction of time that the source spends in an active state. The objects show different DC values, with an average of 22.74% and 23.08% when considering (or not) the extragalactic background light ( EBL). Additionally, we study the so-called “blazar sequence” trend for the sample of selected blazars in the ten years of data. This analysis constrains a possible counterpart of sub-PeV neutrino emission during the quiescent states, leaving open the possibility to explain the observed IceCube signal during the flaring states.