The challenges of developing neuromorphic vision systems inspired by the human eye come not only from how to recreate the flexibility, sophistication, and adaptability of animal systems, but also how to do so with computational efficiency and elegance. Similar to biological systems, these neuromorphic circuits integrate functions of image sensing, memory and processing into the device, and process continuous analog brightness signal in real-time. High-integration, flexibility and ultra-sensitivity are essential for practical artificial vision systems that attempt to emulate biological processing. Here, we present a flexible optoelectronic sensor array of 1024 pixels using a combination of carbon nanotubes and perovskite quantum dots as active materials for an efficient neuromorphic vision system. The device has an extraordinary sensitivity to light with a responsivity of 5.1 × 10 A/W and a specific detectivity of 2 × 10 Jones, and demonstrates neuromorphic reinforcement learning by training the sensor array with a weak light pulse of 1 μW/cm.
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http://dx.doi.org/10.1038/s41467-021-22047-w | DOI Listing |
Food Chem
January 2025
Ministry of Education Key Laboratory for the Synthesis and Application of Organic Functional Molecules, Hubei Province Key Laboratory of Biotechnology of Chinese Traditional Medicine, College of Health Science and Engineering, Hubei University, Wuhan 430062, China; School of Chemistry and Chemical Engineering, Huazhong University of Science and Technology, Wuhan 430074, China; HuaShan Technology Company Limited, Qianjiang 433136, China. Electronic address:
Highly sensitive and portable pesticide residues detection are indispensable for safeguarding food safety and environmental health. Herein, we introduce a one-step vacuum filtration strategy for the scalable production of cobalt-based conjugated coordination polymers (CoCCPs) electrode arrays, utilizing carboxylated single-walled carbon nanotubes (c-SWNTs) as bonding bridges (CoCCPs@c-SWNTs). Due to their abundant active sites and high conductivity, the CoCCPs@c-SWNTs arrays exhibit superior electrochemical performance (e.
View Article and Find Full Text PDFACS Nano
January 2025
Department of Mechanical Engineering, University of California at Riverside, Riverside, California 92521, United States.
Sensing light's polarization and wavefront direction enables surface curvature assessment, material identification, shadow differentiation, and improved image quality in turbid environments. Traditional polarization cameras utilize multiple sensor measurements per pixel and polarization-filtering optics, which result in reduced image resolution. We propose a nanophotonic pipeline that enables compressive sensing and reduces the sampling requirements with a low-refractive-index, self-assembled optical encoder.
View Article and Find Full Text PDFJASA Express Lett
January 2025
Scripps Institution of Oceanography, University of California San Diego, La Jolla, California 92093,
This work demonstrates the feasibility of performing through-the-sensor (TTS) sub-bottom imaging using low-frequency ([100 Hz-1kHz]) self-noise generated by the propulsion of an autonomous underwater vehicle (AUV) acting as a source of opportunity. The self-noise was recorded by a short towed horizontal line array (11.4 m aperture) by the same AUV while it operated ∼35 m above the seabed along a range-dependent section at the New England shelf break.
View Article and Find Full Text PDFAnal Methods
January 2025
Department of Chemistry, Capital Normal University, Beijing, 100048, China.
Catechins in tea, as promoters of human health, have attracted widespread attention. Herein, a dual-signal mode (colorimetric and fluorescence) sensor array for catechin species fingerprinting was built based on PtNi bunched nanoparticle (PtNi-BNP)--phenylenediamine (OPD)-HO system. PtNi-BNPs catalyze the reaction between OPD and HO to produce oxidized OPD (oxOPD) with both colorimetric (yellow) and fluorescent properties.
View Article and Find Full Text PDFJ Acoust Soc Am
January 2025
Department of Informatics, University of Oslo, 0316 Oslo, Norway.
In adaptive beamforming, the array signal processing adjusts its sensor delays and weights based on the incoming data. In conventional beamforming, these parameters are instead given from a predefined model. Adaptive beamformers can improve measurement precision by dynamically rejecting spatial interference.
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