Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next sampling...
Linear time-invariant Systems01:23

Linear time-invariant Systems

A system is linear if it displays the characteristics of homogeneity and additivity, together termed the superposition property. This principle is fundamental in all linear systems. Linear time-invariant (LTI) systems include systems with linear elements and constant parameters.
The input-output behavior of an LTI system can be fully defined by its response to an impulsive excitation at its input. Once this impulse response is known, the system's reaction to any other input can be calculated...
Convolution: Math, Graphics, and Discrete Signals01:24

Convolution: Math, Graphics, and Discrete Signals

In any LTI (Linear Time-Invariant) system, the convolution of two signals is denoted using a convolution operator, assuming all initial conditions are zero. The convolution integral can be divided into two parts: the zero-input or natural response and the zero-state or forced response, with t0 indicating the initial time.
To simplify the convolution integral, it is assumed that both the input signal and impulse response are zero for negative time values. The graphical convolution process...
Convolution Properties I01:20

Convolution Properties I

Convolution computations can be simplified by utilizing their inherent properties.
The commutative property reveals that the input and the impulse response of an LTI (Linear Time-Invariant) system can be interchanged without affecting the output:
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length, the...
Effects of feedback01:24

Effects of feedback

Feedback in control systems plays a critical role in shaping various operational parameters, extending beyond simple error reduction to influence stability, bandwidth, gain, impedance, and sensitivity. Understanding these effects requires examining a basic feedback system characterized by defined input, output, error, and feedback signals.
Feedback significantly modifies the gain of a control system. The gain of a system without feedback is altered by a factor of one plus GH, where G represents...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Finding Druggable Sites in Proteins Using TACTICS.

Journal of chemical information and modeling·2021
Same author

Trainee-associated outcomes in laparoscopic colectomy for cancer: propensity score analysis accounting for operative time, procedure complexity and patient comorbidity.

Surgical endoscopy·2017
Same author

Parylene C topographic micropattern as a template for patterning PDMS and Polyacrylamide hydrogel.

Scientific reports·2017
Same author

Photokinetic analysis of the forces and torques exerted by optical tweezers carrying angular momentum.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences·2017
Same author

Post-operative morbidity, but not mortality, is worsened by operative delay in septic diverticulitis.

International journal of colorectal disease·2016
Same author

Comparative effectiveness of Roux-en-Y gastric bypass and sleeve gastrectomy in super obese patients.

Surgical endoscopy·2016

Related Experiment Video

Updated: Jun 26, 2026

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

Inverse computational feedback optimization imaging applied to time varying changes in a homogeneous structure.

Daniel J Evans1, Mark L Manwaring, Terence Soule

  • 1Computer Science, University of Idaho, Moscow, ID 83844, USA. photonthunder@gmail.com

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|January 24, 2009
PubMed
Summary
This summary is machine-generated.

This study introduces inverse computational feedback optimization imaging, a method that uses computing power instead of complex equipment for tissue imaging. It accurately locates objects using minimal voltage measurements and finite element method software.

More Related Videos

A Guide to Structured Illumination TIRF Microscopy at High Speed with Multiple Colors
11:15

A Guide to Structured Illumination TIRF Microscopy at High Speed with Multiple Colors

Published on: May 30, 2016

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform
06:25

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Published on: February 12, 2014

Related Experiment Videos

Last Updated: Jun 26, 2026

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

A Guide to Structured Illumination TIRF Microscopy at High Speed with Multiple Colors
11:15

A Guide to Structured Illumination TIRF Microscopy at High Speed with Multiple Colors

Published on: May 30, 2016

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform
06:25

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Published on: February 12, 2014

Area of Science:

  • Medical Imaging
  • Computational Modeling
  • Optimization Techniques

Background:

  • Conventional imaging systems like MRI and CT are complex and costly.
  • There is a need for advanced imaging techniques that reduce hardware dependency.
  • Computational methods offer a promising alternative for imaging applications.

Purpose of the Study:

  • To present an inverse computational feedback optimization imaging technique.
  • To demonstrate the feasibility of replacing complex imaging hardware with computational power.
  • To develop a method for imaging varying tissues using computational models.

Main Methods:

  • Utilizing a baseline scan with finite element method (FEM) computational software.
  • Iteratively running the computational model to match physically measurable parameters (e.g., voltage, temperature).
  • Implementing optimization routines and swarm optimization for accelerated data processing and object localization.

Main Results:

  • A computational model was developed to simulate the inverse imaging technique.
  • The model successfully demonstrated the ability to image a simple homogeneous sample with a circular structure.
  • Accurate object localization was achieved using only a few point measurements (voltage data).

Conclusions:

  • Inverse computational feedback optimization imaging offers a viable alternative to complex imaging systems.
  • This technique effectively trades imaging equipment complexity for computational power.
  • The presented model shows promise for efficient object detection and imaging in various applications.