APD optimal bias voltage compensation method based on machine learning.

08:00 EDT 6th August 2019 | BioPortfolio

Summary of "APD optimal bias voltage compensation method based on machine learning."

The signal-to-noise ratio (SNR) of avalanche photodiode (APD) in optical detection system is greatly influenced by background radiation and operating temperature, so an APD optimal bias voltage compensation method based on machine learning is designed to accurately judge the current laser emission state and APD working state so that dichotomy compensation can be carried out to make APD work in optimal state. By means of cross-verification, the accuracy of judging laser emission state and APD working state is as high as 100% and 99.3% separately, then the number of input variables in the model is reduced appropriately by experiment and the prediction speed of the algorithm is further improved. Finally, road detection application is taken as the experimental background and comparison between the proposed method and the most widely used signal amplitude feedback compensation method is carried out. The results of this study suggest that the proposed APD optimal bias voltage compensation method based on machine learning offers a new and promising approach.


Journal Details

This article was published in the following journal.

Name: ISA transactions
ISSN: 1879-2022


DeepDyve research library

PubMed Articles [27946 Associated PubMed Articles listed on BioPortfolio]

Air-coupled ultrasonic ferroelectret receiver with additional bias voltage.

High sensitivity is an important requirement for air-coupled ultrasonic sensors applied to materials testing. With a lower acoustic impedance than any piezoelectric material, charged cellular polyprop...

Predicting Outpatient Appointment Demand Using Machine Learning and Traditional Methods.

Traditional methods have long been used for clinical demand forecasting. Machine learning methods represent the next evolution in forecasting, but model choice and optimization remain challenging for ...

Performance of a machine learning-based decision model to help clinicians decide the extent of lymphadenectomy (D1 vs. D2) in gastric cancer before surgical resection.

Controversy still exists on the optimal surgical resection for potentially curable gastric cancer (GC). Use of radiologic evaluation and machine learning algorithms might predict extent of lymphadenec...

Effect of Tube Voltage on Diagnostic Performance of Fractional Flow Reserve Derived From Coronary CT Angiography With Machine Learning: Results From the MACHINE Registry.

Coronary CT angiography (CCTA)-based methods allow noninvasive estimation of fractional flow reserve (cFFR), recently through use of a machine learning (ML) algorithm (cFFR). However, attenuation valu...

Voxel-Based Morphometry: Improving the Diagnosis of Alzheimer's disease based on an Extreme Learning Machine method from the ADNI cohort.

Computer-aided diagnosis has become a widely-used auxiliary tool for the diagnosis of Alzheimer's disease (AD). In this study, we developed an extreme learning machine (ELM) model to discriminate betw...

Clinical Trials [9482 Associated Clinical Trials listed on BioPortfolio]

Machine Learning From Fetal Flow Waveforms to Predict Adverse Perinatal Outcomes

The aim of this study is to get a proof of concept for using a computational model of fetal haemodynamics, combined with machine learning based on Doppler patterns of the fetal cardiovascu...

Machine Learning-Based Risk Profile Classification of Patients Undergoing Elective Heart Valve Surgery

Machine learning methods potentially provide a highly accurate and detailed assessment of expected individual patient risk before elective cardiac surgery. Correct anticipation of this ris...

Subpopulation-Specific Sepsis Identification Using Machine Learning

The investigators propose to develop and evaluate a hospital department-specific machine learning based clinical decision support (CDS) system for early sepsis prediction, focused on impro...

Prospective Use of Awake Endoscopy for Inspire Activation

The aim of this study is to examine a new method of device configuration for the Inspire upper airway stimulator. First, the investigators will attempt to determine optimal configuration b...

Bipolar Disorder and Oxidative Stress Injury Mechanism - Clinical Big Data Analysis Based on Machine Learning

This study is a single-center, retrospective, cross-sectional study. We plan to work with our network information center to analysis the related indicators of oxidative stress injury in pa...

Medical and Biotech [MESH] Definitions

A MACHINE LEARNING paradigm used to make predictions about future instances based on a given set of unlabeled paired input-output training (sample) data.

A MACHINE LEARNING paradigm used to make predictions about future instances based on a given set of labeled paired input-output training (sample) data.

SUPERVISED MACHINE LEARNING algorithm which learns to assign labels to objects from a set of training examples. Examples are learning to recognize fraudulent credit card activity by examining hundreds or thousands of fraudulent and non-fraudulent credit card activity, or learning to make disease diagnosis or prognosis based on automatic classification of microarray gene expression profiles drawn from hundreds or thousands of samples.

Experience-based techniques for problem-solving, learning, and discovery that find a solution which is not guaranteed to be optimal, but sufficient for a given set of goals.

A type of ARTIFICIAL INTELLIGENCE that enable COMPUTERS to independently initiate and execute LEARNING when exposed to new data.

Quick Search

DeepDyve research library

Searches Linking to this Article