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In automotive engineering, car suspension systems often employ Proportional Derivative (PD) controllers to enhance performance. PD controllers are utilized to adjust the damping force in response to road conditions. A controller, acting as an amplifier with a constant gain, demonstrates proportional control, with output directly mirroring input.
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The human nervous system handles vast amounts of information by translating sensory stimuli into neural impulses, which the brain processes, creating thoughts expressed through language or stored as memories. The brain also synthesizes information from emotions and memories, which significantly influence thoughts and behaviors. This intricate process creates a comprehensive mental picture.
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Proportional Integral (PI) controllers are a fundamental component in modern control systems, widely used to enhance performance and mitigate steady-state errors. They are particularly effective in applications such as automatic brightness adjustment on smartphones, where they excel at mitigating steady-state errors for step-function inputs. Unlike PD controllers, which require time-varying errors to function optimally, PI controllers leverage their integral component to address residual...
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Related Experiment Video

Updated: Sep 22, 2025

Generation of a Human iPSC-Based Blood-Brain Barrier Chip
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Concept development of an on-chip PET system.

Christoph Clement1, Gabriele Birindelli2, Marco Pizzichemi3,4

  • 1Department of Nuclear Medicine, Inselspital Bern, University of Bern, Bern, Switzerland. christoph.clement@students.unibe.ch.

EJNMMI Physics
|May 19, 2022
PubMed
Summary
This summary is machine-generated.

We developed an On-Chip Positron Emission Tomography (PET) system for imaging Organs-on-Chips (OOCs). This novel system achieves a spatial resolution of 0.55 mm, crucial for advancing OOC technology in drug discovery.

Keywords:
CNNDeep learningGATEMonte-Carlo simulationOrgans-on-chipsPETReconstructionSART

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Area of Science:

  • Biomedical Engineering
  • Medical Imaging
  • Microfluidics

Background:

  • Organs-on-Chips (OOCs) are increasingly used for disease modeling and drug discovery.
  • Effective imaging is vital for monitoring physiological processes within OOCs.
  • Current Positron Emission Tomography (PET) systems lack the spatial resolution required for OOC imaging.

Purpose of the Study:

  • To propose and optimize an On-Chip PET system for high-resolution imaging of OOCs.
  • To overcome the limitations of existing PET technology for microdevice applications.

Main Methods:

  • Designed an On-Chip PET system with four detectors, each comprising LYSO crystals and Silicon photomultipliers (SiPMs).
  • Utilized Monte Carlo Simulation (MCS) to optimize the system design.
  • Employed a Convolutional Neural Network (CNN) trained via MCS to predict gamma-ray interaction points from SiPM light patterns.

Main Results:

  • Achieved a mean average prediction error of 0.80 mm for gamma-ray interaction positions.
  • Demonstrated a system sensitivity of 34.81% with 13 mm thick crystals.
  • Reconstructed images with a mean spatial resolution of 0.55 mm for a grid of point sources.

Conclusions:

  • Successfully demonstrated a PET system with sub-millimeter spatial resolution for OOC imaging.
  • Identified optimal detector configurations, noting thinner crystals and specific SiPM surfaces yield better performance.
  • Confirmed the feasibility of using CNNs with SiPM light patterns for precise scintillation localization in PET.