Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Multi-input and Multi-variable systems

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...

Classification of Systems-I

Classification of Systems-I

Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for k_a Estimation

This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...

Cross Product

Cross Product

The cross product is a fundamental concept in vector algebra that is a vector operation on two different vectors to obtain a third vector. Unlike the scalar product, the cross product results in a vector quantity perpendicular to both the original vectors.
The magnitude of the cross product is obtained by multiplying the magnitude of both the vectors and the sine of the angle between them. This means that a larger angle between the vectors will lead to a greater magnitude of the cross product.

Classification of Systems-II

Classification of Systems-II

Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,

You might also read

Related Articles

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

Sort by

Same author

Publisher Correction: In situ nanocrystal confinement for efficient blue perovskite LEDs.

Nature·2026

Same author

Bidirectional Associations Between Frailty and Preeclampsia: A Two-Sample Mendelian Randomization Study.

International journal of women's health·2026

Same author

Perceptual organization of Mandarin consonants in cochlear implant users.

The Journal of the Acoustical Society of America·2026

Same author

Thrombus-targeting nanosystems: Differential performance in arterial and venous circulation.

Journal of controlled release : official journal of the Controlled Release Society·2026

Same author

In situ nanocrystal confinement for efficient blue perovskite LEDs.

Nature·2026

Same author

Self-supervised T2WI-bridged framework for liver segmentation and PDFF prediction from US images.

IEEE transactions on medical imaging·2026

Same journal

AgonicDreamer: Enhancing Multi-View Consistency in Text-to-3D Generation via Rectified Score Distillation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026

Same journal

BiCM-Prompt: Bidirectional Cross-Modal Prompt Tuning for Class-Incremental Learning on Multisource Remote Sensing Images.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026

Same journal

GoP-based Quality Enhancement on Video Compression.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026

Same journal

Align then Tensorize: Multi-Level Consistent Anchor Graph Learning for Scalable Multi-View Clustering.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026

Same journal

Beyond Fidelity: Diverse Image Synthesis via Retrieval-Augmented Diffusion.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026

Same journal

Multi-Branch Tree-based Fusion Neural Architecture Search with Zero-Cost Screen for Multi-Modal Classification.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026

See all related articles

Search research articles

Related Experiment Video

Updated: May 10, 2026

Cross-Modal Multivariate Pattern Analysis

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

Cross-domain object recognition via input-output kernel analysis.

Zhenyu Guo¹, Z Jane Wang

¹Department of Electrical and Computer Engineering, University of British Columbia, Vancouver V6T 1Z4, Canada. zhenyug@ece.ubc.ca

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|June 8, 2013

Summary

This summary is machine-generated.

Domain adaptation for image object recognition is crucial. A new domain adaptive input-output kernel learning (DA-IOKL) algorithm effectively addresses feature distribution changes, outperforming existing methods.

More Related Videos

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

Related Experiment Videos

Last Updated: May 10, 2026

Cross-Modal Multivariate Pattern Analysis

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

Area of Science:

Computer Vision
Machine Learning
Artificial Intelligence

Background:

Image object recognition faces challenges due to varying data distributions across different domains.
Existing methods struggle with feature distribution shifts in cross-domain learning scenarios.

Purpose of the Study:

To investigate the domain adaptation problem in image object recognition.
To develop a novel algorithm that addresses feature distribution changes in cross-domain learning.

Main Methods:

Propose a domain adaptive input-output kernel learning (DA-IOKL) algorithm.
Simultaneously learn input and output kernels using a discriminative vector-valued decision function.
Extend the method to handle multiple source domains.

Main Results:

The DA-IOKL algorithm effectively reduces data mismatch and minimizes structural error.
The proposed method consistently outperforms state-of-the-art domain adaptation and multiple kernel learning techniques.
Evaluated on two cross-domain object recognition benchmark datasets.

Conclusions:

DA-IOKL offers a robust solution for domain adaptation in image object recognition.
The simultaneous learning of input and output kernels enhances cross-domain generalization.
The method shows significant improvements over existing approaches for diverse domain adaptation tasks.