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Related Concept Videos

Convolution Properties II01:17

Convolution Properties II

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The important convolution properties include width, area, differentiation, and integration properties.
The width property indicates that if the durations of input signals are T1 and T2, then the width of the output response equals the sum of both durations, irrespective of the shapes of the two functions. For instance, convolving two rectangular pulses with durations of 2 seconds and 1 second results in a function with a width of 3 seconds.
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The large ribosomal subunit has several important structures essential to translation. These include the peptidyl transferase center (PTC) - which is the site where the peptide bond is formed - and a large, internal, water-filled tube through which the nascent polypeptide moves. This latter structure is called the Peptide Exit Tunnel, and it begins at the PTC and spans the body of the large ribosomal subunit. During translation, as the nascent polypeptide chain is synthesized, it passes through...
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Convolution Properties I01:20

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Convolution computations can be simplified by utilizing their inherent properties.
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Green algae, also referred to as chlorophytes, are different from red algae in having the chloroplasts containing chlorophylls a and b, which give them their distinct green hue. However, they lack phycobiliproteins, preventing them from developing the red or blue-green pigmentation seen in red algae. In terms of photosynthetic pigment composition, green algae closely resemble plants and share a close evolutionary relationship with them. Taxonomically Green algae belong to Phylum Chlorophyta in...
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Red algae, also known as rhodophytes, are primarily found in marine environments, though some species inhabit freshwater and terrestrial ecosystems. These organisms exist in both unicellular and multicellular forms, with some multicellular varieties reaching macroscopic sizes.As phototrophic organisms, red algae contain chlorophyll a; however, their chloroplasts lack chlorophyll b. Instead, they possess phycobiliproteins, which serve as major light-harvesting pigments, similar to those found in...
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Related Experiment Video

Updated: Feb 11, 2026

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Reconfigurable Dual-Terminal WSe2/h-BN p-n Photodetectors for In-Sensor Red-Green-Blue Convolution and Motion

Chao Dou1, Yan Wang1, Haoyue Lu1

  • 1State Key Laboratory of Precision Measuring Technology and Instruments, School of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin 300072, China.

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|February 9, 2026
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Summary

This study introduces a novel WSe2/h-BN heterostructure vision sensor that overcomes limitations in current AI hardware. The device offers energy-efficient, real-time image processing with nonvolatile, gate-free photoresponse modulation for advanced autonomous systems.

Keywords:
WSe2 homojunctionbiomimetic visionin-sensor computingreconfigurable optoelectronic deviceultraviolet light doping

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Area of Science:

  • Materials Science
  • Device Physics
  • Artificial Intelligence Hardware

Background:

  • Conventional von Neumann architectures suffer from high power consumption and latency due to separated sensing, memory, and computing units.
  • Existing in-sensor computing and neuromorphic systems face challenges including limited photoresponse tunability, volatile memory, and complex device structures.

Purpose of the Study:

  • To develop a novel dual-terminal vision sensor overcoming the limitations of existing architectures for energy-efficient, real-time processing.
  • To demonstrate nonvolatile, gate-free photoresponse modulation for enhanced performance and integration in autonomous systems and edge AI.

Main Methods:

  • Fabrication of a WSe2/h-BN heterostructure dual-terminal vision sensor.
  • Utilizing ultraviolet-induced doping and defect-mediated carrier trapping for nonvolatile reconfigurable p-n homojunctions.
  • Characterization of photoresponse states, rectification ratio, and photoresponsivity.

Main Results:

  • Achieved 81 nonvolatile, bidirectionally programmable photoresponse states (>6-bit) with zero static power consumption.
  • Demonstrated exceptional device performance: 1.2 × 10^5 rectification ratio and 0.32 A·W^-1 photoresponsivity.
  • Successfully implemented in-sensor neural network operations, including image denoising, RGB convolution (ResNet-18), and motion detection.

Conclusions:

  • The WSe2/h-BN heterostructure sensor provides a paradigm shift for vision hardware, addressing the von Neumann bottleneck, power constraints, and integration challenges.
  • This platform enables next-generation intelligent perception systems for autonomous applications, robotics, and edge AI.