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Related Concept Videos

Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...
Neural Circuits01:25

Neural Circuits

Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
Machines: Problem Solving II01:30

Machines: Problem Solving II

Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.
Machines: Problem Solving I01:22

Machines: Problem Solving I

A toggle clamp is a mechanical device commonly used for holding and clamping objects in various applications, such as woodworking, metalworking, and assembly operations. Consider a toggle clamp subjected to a force of 200 N at the handle. The vertical clamping force can be calculated, provided the dimensions of the toggle clamp are known.
The toggle clamp system is a machine structure consisting of movable, pin-connected multi-force members that form a stabilized system to transmit forces. The...
Electro-mechanical Systems01:19

Electro-mechanical Systems

Electromechanical systems are intricate configurations that effectively combine electrical and mechanical elements to achieve a desired outcome. Central to many of these systems is the DC motor, a device that converts electrical energy into mechanical motion, enabling various applications ranging from simple fans to complex robotic mechanisms.
A key component of the DC motor is the armature, a rotating circuit positioned within a magnetic field. As an electric current passes through the...
Neural Regulation01:37

Neural Regulation

Digestion begins with a cephalic phase that prepares the digestive system to receive food. When our brain processes visual or olfactory information about food, it triggers impulses in the cranial nerves innervating the salivary glands and stomach to prepare for food.

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Related Experiment Video

Updated: Jun 25, 2026

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

EdgeVolution: democratizing multi-objective neural architecture search and end-to-end deployment on microcontrollers.

René Groh1, Stefan Dendorfer2, Mateo Ávila Pava2

  • 1Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Bavaria, Germany. rene.groh@fau.de.

Communications Engineering
|June 23, 2026
PubMed
Summary
This summary is machine-generated.

EdgeVolution optimizes artificial neural networks for edge devices, enhancing performance and reproducibility. This platform simplifies deploying AI models on resource-constrained hardware, making edge AI more accessible.

Related Experiment Videos

Last Updated: Jun 25, 2026

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Embedded Systems

Background:

  • Edge AI enables running artificial neural networks on resource-constrained devices like microcontrollers.
  • Optimizing and deploying neural networks on edge hardware is challenging due to a lack of specialized tools, impacting performance and reproducibility.

Purpose of the Study:

  • To present EdgeVolution, an end-to-end hardware-in-the-loop platform for optimizing and deploying neural networks on edge devices.
  • To address challenges in hardware-specific adaptation, reproducibility, and performance for AI on edge.

Main Methods:

  • EdgeVolution provides a hardware-in-the-loop platform for multi-objective optimization and neural architecture selection.
  • The platform facilitates direct deployment onto target edge hardware.
  • A generic and adaptable pipeline is utilized for tailored model creation.

Main Results:

  • Demonstrated the versatility of EdgeVolution through four diverse application use cases.
  • Showcased wide-ranging applicability of the platform across different scenarios.
  • Validated the improvement in accessibility, performance, and reproducibility for AI on edge devices.

Conclusions:

  • EdgeVolution enhances the creation and deployment of neural network models for edge devices.
  • The platform successfully tailors AI models to specific datasets, tasks, and hardware constraints.
  • EdgeVolution improves the overall efficiency and reliability of AI applications on edge hardware.