Evidence map›Paper›PMID 36862008›Full record

ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2023

A Multimodal Sensing CMOS Imager Based on Dual-Focus Imaging.

Hao Dong, Xubin Zheng, Chen Cheng, Libin Qian, Yaoxuan Cui, Weiwei Wu, Qingjun Liu, Xing Chen, Yanli Lu, Qing Yang and 2 more

Abstract read
In one paragraph

Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. A Multimodal Sensing CMOS Imager Based on Dual-Focus Imaging.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2023
    Article
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

12 authors.

Hao DongIntelligent Perception Research Institute, Zhejiang Lab, Hangzhou, 311100, China.ORCID https://orcid.org/0000-0001-8521-4937
Xubin ZhengIntelligent Perception Research Institute, Zhejiang Lab, Hangzhou, 311100, China.
Chen ChengIntelligent Perception Research Institute, Zhejiang Lab, Hangzhou, 311100, China.
Libin QianIntelligent Perception Research Institute, Zhejiang Lab, Hangzhou, 311100, China.
Yaoxuan CuiIntelligent Perception Research Institute, Zhejiang Lab, Hangzhou, 311100, China.
Weiwei WuSchool of Advanced Materials and Nanotechnology, Interdisciplinary Research Center of Smart Sensors, Xidian University, Shaanxi, 710126, China.
Qingjun LiuBiosensor National Special Laboratory, Key Laboratory for Biomedical Engineering of Education Ministry, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, 310027, China.
Xing ChenBiosensor National Special Laboratory, Key Laboratory for Biomedical Engineering of Education Ministry, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, 310027, China.
Yanli LuIntelligent Perception Research Institute, Zhejiang Lab, Hangzhou, 311100, China.
Qing YangIntelligent Perception Research Institute, Zhejiang Lab, Hangzhou, 311100, China.
Fenni ZhangBiosensor National Special Laboratory, Key Laboratory for Biomedical Engineering of Education Ministry, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, 310027, China.
Di WangIntelligent Perception Research Institute, Zhejiang Lab, Hangzhou, 311100, China.ORCID https://orcid.org/0000-0003-1581-4982

Funding

China Postdoctoral Science Foundation 2020M681952Key Research Project of Zhejiang Lab 2020MC0AD01National Key R&D Program of China 2022YFF0610200National Natural Science Foundation of China 82102180National Natural Science Foundation of China 82172064Open Project of Zhejiang Lab 2021MC0AB02Zhejiang Provincial Natural Science Foundation of China LQ21F010003
6 · The paper itself

Abstract

Advanced machine intelligence is empowered not only by the ever-increasing computational capability for information processing but also by sensors for collecting multimodal information from complex environments. However, simply assembling different sensors can result in bulky systems and complex data processing. Herein, it is shown that a complementary metal-oxide-semiconductor (CMOS) imager can be transformed into a compact multimodal sensing platform through dual-focus imaging. By combining lens-based and lensless imaging, visual information, chemicals, temperature, and humidity can be detected with the same chip and output as a single image. As a proof of concept, the sensor is equipped on a micro-vehicle, and multimodal environmental sensing and mapping is demonstrated. A multimodal endoscope is also developed, and simultaneous imaging and chemical profiling along a porcine digestive tract is achieved. The multimodal CMOS imager is compact, versatile, and extensible and can be widely applied in microrobots, in vivo medical apparatuses, and other microdevices.

Indexed as

CMOS imagerscolorimetric arrayslab on chiplensless imagingmultimodal sensors

Identifiers

PMID36862008
PMCPMC10190568

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.