Time-fluctuating signals are ubiquitous and diverse in many physical, chemical, and biological systems, among which random telegraph signals (RTSs) refer to a series of instantaneous switching events between two discrete levels from single-particle movements. A reliable RTS analysis is a crucial prerequisite to identify underlying mechanisms related to device performance and sensitivity. When numerous levels are involved, complex patterns of multilevel RTSs occur and make their quantitative analysis exponentially difficult, hereby systematic approaches are often elusive. In this work, we present a three-step analysis protocol via progressive knowledge-transfer, where the outputs of the early step are passed onto a subsequent step. Especially, to quantify complex RTSs, we resort to three deep neural network architectures whose trained models can process raw temporal data directly. We furthermore demonstrate the model accuracy extensively with a large dataset of different RTS types in terms of additional background noise types and amplitude size. Our protocol offers structured schemes to extract the parameter values of complex RTSs as imperative information with which researchers can draw meaningful and relevant interpretations and inferences of given devices and systems. 

/pdf/deep-neural-network-analysis-models-for-complex-random-25n5v2yx.pdf

Deep neural network analysis models for complex random telegraph signals

The Maximal Overlap Discrete Wavelet Transform-Autoregressive Integrated Moving Average (MODWT-ARIMA) is a forecasting method that uses the ARIMA model generated from MODWT data. The purpose of this study is to analyze an investigation into the MODWT-ARIMA model with regard to the total number of COVID-19 cases in DKI Jakarta. For this study, daily data on cases of Covid-19COVID-19 in DKI Jakarta were obtained. The model is trained with data from April 3, 2022, to June 11, 2022 (referred to as the "in-sample"), and the outcomes of the prediction are tested with data from June 12, 2022, to June 18, 2022 (referred to as the "out-sample"). These data exhibit trends and are organizedorganised into four data series using MODWT. The ARIMA modelling technique is applied to each of the produced sequences. When using the MODWT-ARIMA approach, the RMSE value obtained from the in-sample data is found to be lower than the RMSE value obtained from the out-sample data. In light of the findings, Iit t became clear that MODWT-ARIMA is better suited for estimation than prediction. The fact that the RMSE value for the data acquired from the in-sample is lower than the RMSE value for the data collected from the out-sample demonstrates that this is the case. 

The MODWT-ARIMA Model in Forecasting The COVID-19 Cases

To solve the problem of missing or distorted detail texture when manually adjusting image parameters, a weak illumination image enhancement algorithm based on cyclic generation game network is proposed. The image features are normalized by Gaussian distribution. Combined with homomorphic filtering theory and defogging operation, the image is generated and denoised according to the network brightness to enhance the weak illumination image. The experimental results show that after using this method to process the image, the image entropy increases by 6.8%, the contrast increases by 27.5%, and the noise content decreases by 24.1%. It has better contrast. It can not only meet the enhancement effect of weak light, but also ensure the details of the image, so that the image has richer details and good visual appearance.

Weak illumination image enhancement algorithm based on cyclic generation countermeasure network

A denoising autoencoder based on U-Net and bidirectional long short-term memory for multi-level random telegraph signal analysis

This article explores the phenomenon of dark current random telegraph signal (DC-RTS) noise in commercial off-the-shelf CMOS image sensors. Five sensors were irradiated with high-energy photons to a variety of doses and analyzed with a wavelet-based signal reconstruction algorithm. The algorithm is explained in detail and the radiation effects on individual pixels are discussed. Finally, the production rate of RTS pixels as a function of dose is explored, providing information on the underlying defect structure responsible for this noise source.

Wavelet Analysis of RTS Noise in CMOS Image Sensors Irradiated With High-Energy Photons

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Ben Hendrickson

Author Tools

Chat about Author

Papers

Wavelet Analysis of RTS Noise in CMOS Image Sensors Irradiated With High-Energy Photons