Evidence map›Paper›PMID 34602968›Full record

ArticleFrontiers in neuroscience2021

A Quantized Convolutional Neural Network Implemented With Memristor for Image Denoising and Recognition.

Yuejun Zhang, Zhixin Wu, Shuzhi Liu, Zhecheng Guo, Qilai Chen, Pingqi Gao, Pengjun Wang, Gang Liu

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PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Yuejun ZhangFaculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo, China.
Zhixin WuFaculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo, China.
Shuzhi LiuDepartment of Micro/Nano Electronics, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China.
Zhecheng GuoFaculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo, China.
Qilai ChenDepartment of Micro/Nano Electronics, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China.
Pingqi GaoSchool of Materials, Sun Yat-sen University, Guangzhou, China.
Pengjun WangCollege of Mathematics, Physics, and Electronic Information Engineering, Wenzhou University, Wenzhou, China.
Gang LiuDepartment of Micro/Nano Electronics, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The interference of noise will cause the degradation of image quality, which can have a negative impact on the subsequent image processing and visual effect. Although the existing image denoising algorithms are relatively perfect, their computational efficiency is restricted by the performance of the computer, and the computational process consumes a lot of energy. In this paper, we propose a method for image denoising and recognition based on multi-conductance states of memristor devices. By regulating the evolution of Pt/ZnO/Pt memristor wires, 26 continuous conductance states were obtained. The image feature preservation and noise reduction are realized

Indexed as

conductance fine-tuningconvolutional neural networkimage denoisingmemristorsynaptic plasticity

Identifiers

PMID34602968
PMCPMC8481819

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.