scholarly journals Modeling and Analysis of a Direct Time-of-Flight Sensor Architecture for LiDAR Applications

Sensors ◽  
2019 ◽  
Vol 19 (24) ◽  
pp. 5464 ◽  
Author(s):  
Preethi Padmanabhan ◽  
Chao Zhang ◽  
Edoardo Charbon
Keyword(s):  
Background Noise ◽  
3D Imaging ◽  
Dynamic Range ◽  
Time Of Flight ◽  
Operating Conditions ◽  
Exposure Limits ◽  
Depth Sensing ◽  
Wide Dynamic Range ◽  
High Background ◽  
Time Gating

Direct time-of-flight (DTOF) is a prominent depth sensing method in light detection and ranging (LiDAR) applications. Single-photon avalanche diode (SPAD) arrays integrated in DTOF sensors have demonstrated excellent ranging and 3D imaging capabilities, making them promising candidates for LiDARs. However, high background noise due to solar exposure limits their performance and degrades the signal-to-background noise ratio (SBR). Noise-filtering techniques based on coincidence detection and time-gating have been implemented to mitigate this challenge but 3D imaging of a wide dynamic range scene is an ongoing issue. In this paper, we propose a coincidence-based DTOF sensor architecture to address the aforementioned challenges. The architecture is analyzed using a probabilistic model and simulation. A flash LiDAR setup is simulated with typical operating conditions of a wide angle field-of-view (FOV = 40 ° ) in a 50 klux ambient light assumption. Single-point ranging simulations are obtained for distances up to 150 m using the DTOF model. An activity-dependent coincidence is proposed as a way to improve imaging of wide dynamic range targets. An example scene with targets ranging between 8–60% reflectivity is used to simulate the proposed method. The model predicts that a single threshold cannot yield an accurate reconstruction and a higher (lower) reflective target requires a higher (lower) coincidence threshold. Further, a pixel-clustering scheme is introduced, capable of providing multiple simultaneous timing information as a means to enhance throughput and reduce timing uncertainty. Example scenes are reconstructed to distinguish up to 4 distinct target peaks simulated with a resolution of 500 ps. Alternatively, a time-gating mode is simulated where in the DTOF sensor performs target-selective ranging. Simulation results show reconstruction of a 10% reflective target at 20 m in the presence of a retro-reflective equivalent with a 60% reflectivity at 5 m within the same FOV.

scholarly journals Automatic Modulation Classification Based on Deep Feature Fusion for High Noise Level and Large Dynamic Input

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2117
Author(s):  
Hui Han ◽  
Zhiyuan Ren ◽  
Lin Li ◽  
Zhigang Zhu
Keyword(s):  
Neural Network ◽  
Frequency Domain ◽  
Noise Level ◽  
Background Noise ◽  
Dynamic Range ◽  
Feature Fusion ◽  
Superior Performance ◽  
Modulation Classification ◽  
High Background ◽  
Automatic Modulation Classification

Automatic modulation classification (AMC) is playing an increasingly important role in spectrum monitoring and cognitive radio. As communication and electronic technologies develop, the electromagnetic environment becomes increasingly complex. The high background noise level and large dynamic input have become the key problems for AMC. This paper proposes a feature fusion scheme based on deep learning, which attempts to fuse features from different domains of the input signal to obtain a more stable and efficient representation of the signal modulation types. We consider the complementarity among features that can be used to suppress the influence of the background noise interference and large dynamic range of the received (intercepted) signals. Specifically, the time-series signals are transformed into the frequency domain by Fast Fourier transform (FFT) and Welch power spectrum analysis, followed by the convolutional neural network (CNN) and stacked auto-encoder (SAE), respectively, for detailed and stable frequency-domain feature representations. Considering the complementary information in the time domain, the instantaneous amplitude (phase) statistics and higher-order cumulants (HOC) are extracted as the statistical features for fusion. Based on the fused features, a probabilistic neural network (PNN) is designed for automatic modulation classification. The simulation results demonstrate the superior performance of the proposed method. It is worth noting that the classification accuracy can reach 99.8% in the case when signal-to-noise ratio (SNR) is 0 dB.

A wide dynamic range receiver channel for a pulsed time-of-flight laser radar

2001 ◽  
Vol 36 (8) ◽  
pp. 1228-1238 ◽  
Author(s):  
T. Ruotsalainen ◽  
P. Palojarvi ◽  
J. Kostamovaara
Keyword(s):  
Dynamic Range ◽  
Time Of Flight ◽  
Laser Radar ◽  
Wide Dynamic Range

Video Light Microscopy

1997 ◽  
Vol 3 (S2) ◽  
pp. 369-370
Author(s):  
K. R. Spring ◽  
B. Herman ◽  
E. D. Salmon
Keyword(s):  
Light Microscopy ◽  
Dynamic Range ◽  
Biological Significance ◽  
High Gain ◽  
Wide Dynamic Range ◽  
High Background ◽  
Low Contrast ◽  
Video Cameras ◽  
Electronic Imaging ◽  
Contrast Microscopy

This tutorial emphasizes the fundamentals of the use of light microscopy in combination with electronic imaging. The goal of the tutorial is to enable the attendees to make informed choices both about the modes of imaging best suited to their application and about the most suitable components to be employed. The didactic portion of the program will consist of five presentations, each dealing with different aspects of modern imaging systems and their applications to problems of biological significance. A laboratory demonstration session, organized to complement the lectures will follow.The morning session will begin will a brief review by Dr. Spring of the principles of light detection and the formation of the video signal. Dr. Salmon will then describe video-enhanced contrast microscopy. This transmitted light imaging technique, widely utilized to visualize low contrast specimens exploits the wide dynamic range and high gain of video cameras for the capture of images that are virtually invisible through the eyepieces because of high background light.

A Method of Wide Dynamic Range Compression Based on Curve Compression Applying on Hearing Aids

2015 ◽  
Vol 719-720 ◽  
pp. 1074-1081
Author(s):  
Xiang Xiang Luo ◽  
Zhao Yang Guo ◽  
Zhi Liang Zhao ◽  
Feng Jie Xue ◽  
Xin An Wang
Keyword(s):  
Hearing Aids ◽  
Background Noise ◽  
Dynamic Range ◽  
Wide Dynamic Range ◽  
Compression Technique ◽  
High Frequency Signal ◽  
Theoretical Simulation ◽  
Dynamic Range Compression ◽  
Curve Compression ◽  
Linear Compression

Recently, there is a growing trend to investigate wide dynamic range compression technique with the development of hearing aids. However, existing methods used to enhance expected speech as well as the background noise and classical linear compression can’t achieve the requirements of hearing impaired individuals’ reaction to the loudness compensation flexibly. To cope with the problems, this paper proposes a new approach to suppress the enhanced background noise and to dismiss abnormal sound caused by severe jittering from high frequency signal. What’s more, a scheme of curve compression is introduced to improve the loudness amplifying flexibility instead of conventional linear compression. Both the theoretical simulation and testing on cellphone hearing aid APP based on android system prove the proposed method can improve the performance of speech enhancement and provide more choices to strength sound with the curve compression ratio.

scholarly journals A low-noise and wide dynamic range 15MHz CMOS receiver for pulsed time-of-flight laser ranging

2021 ◽  
pp. 1-1
Author(s):  
Sami Kurtti ◽  
Aram Baharmast ◽  
Jussi-Pekka Jansson ◽  
Juha Kostamovaara
Keyword(s):  
Dynamic Range ◽  
Time Of Flight ◽  
Low Noise ◽  
Laser Ranging ◽  
Wide Dynamic Range ◽  
Cmos Receiver

Convolution and Deconvolution For 3D Imaging Of Cell Physiology

1998 ◽  
Vol 4 (S2) ◽  
pp. 886-887
Author(s):  
Leslie M. Loew ◽  
Mark Sapia ◽  
James Schaff
Keyword(s):  
3D Imaging ◽  
Dynamic Range ◽  
Optical Axis ◽  
Physiological State ◽  
Three Dimensions ◽  
Cell Physiology ◽  
3D Image ◽  
Wide Dynamic Range ◽  
Quantitative Fluorescence

Fluorescence intensity in a spectrofluorometer is proportional to the concentration of analyte over a wide dynamic range and is, therefore, an excellent quantitative analytical tool. In this paper we use the techniques of deconvolution and convolution to extend the utility of quantitative fluorescence to the measurement of analyte concentrations inside cells and their organelles. This permits us to assess the physiological state of living cells in situ.To successfully turn a voxel within a 3D image into a microcuvette, 2 conditions must be met. First, to be sure the voxel is associated with only one fluorescent structure, the object that is being measured must be fully resolved from neighboring objects in all three dimensions. For structures separated by distances larger than about 300nm in the xy plane or 600nm in z (where z is the optical axis), this can be achieved with either digital deconvolution of 3D widefield images or by confocal microscopy.

A CAPD based time-of-flight ranging pixel with wide dynamic range

2008 ◽  
Author(s):  
Daniel Van Nieuwenhove ◽  
Ward Van der Tempel ◽  
Riemer Grootjans ◽  
Maarten Kuijk
Keyword(s):  
Dynamic Range ◽  
Time Of Flight ◽  
Wide Dynamic Range

scholarly journals Statistical Modelling of SPADs for Time-of-Flight LiDAR

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4481
Author(s):  
Alfonso Incoronato ◽  
Mauro Locatelli ◽  
Franco Zappa
Keyword(s):  
Single Photon ◽  
Time Of Flight ◽  
Statistical Modelling ◽  
Operating Conditions ◽  
Single Photons ◽  
Distance Measurements ◽  
Distance Information ◽  
High Background ◽  
Lidar Return ◽  
Storage Cells

Time-of-Flight (TOF) based Light Detection and Ranging (LiDAR) is a widespread technique for distance measurements in both single-spot depth ranging and 3D mapping. Single Photon Avalanche Diode (SPAD) detectors provide single-photon sensitivity and allow in-pixel integration of a Time-to-Digital Converter (TDC) to measure the TOF of single-photons. From the repetitive acquisition of photons returning from multiple laser shots, it is possible to accumulate a TOF histogram, so as to identify the laser pulse return from unwelcome ambient light and compute the desired distance information. In order to properly predict the TOF histogram distribution and design each component of the LiDAR system, from SPAD to TDC and histogram processing, we present a detailed statistical modelling of the acquisition chain and we show the perfect matching with Monte Carlo simulations in very different operating conditions and very high background levels. We take into consideration SPAD non-idealities such as hold-off time, afterpulsing, and crosstalk, and we show the heavy pile-up distortion in case of high background. Moreover, we also model non-idealities of timing electronics chain, namely, TDC dead-time, limited number of storage cells for TOF data, and TDC sharing. Eventually, we show how the exploit the modelling to reversely extract the original LiDAR return signal from the distorted measured TOF data in different operating conditions.

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