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

IR Frequency Region: Fingerprint Region01:03

IR Frequency Region: Fingerprint Region

1.9K
IR spectra are divided into two main regions: the diagnostic region and the fingerprint region. The diagnostic region of the spectrum lies above 1500 cm−1. The absorptions resulting from single-bond vibrations of the N–H, C–H, and O–H stretch at higher wavenumbers and appear on the left side of the spectrum. The stretching absorptions of the C≡C and C≡N occur between 2100–2300 cm−1. In contrast, those arising from stretching absorptions of the...
1.9K
Growth Models with Integration: Problem Solving01:27

Growth Models with Integration: Problem Solving

36
In population modeling, integration provides a systematic way to determine accumulated quantities from known rates of change. One such application arises in ecology, where the total weight of a fish population in a body of water is referred to as its biomass. When the rate of growth of this biomass is known as a function of time, calculus can be used to determine the total biomass at a future date.Growth Rate and Biomass FunctionLet the growth rate of the fish population be represented by a...
36
Model Approaches for Pharmacokinetic Data: Physiological Models01:15

Model Approaches for Pharmacokinetic Data: Physiological Models

250
Physiological models in pharmacokinetics are instrumental in understanding the distribution and elimination of drugs within the body. These models describe the drug concentration within target organs, influenced by factors such as drug uptake, tissue volume, and blood flow. Drug uptake is governed by the partition coefficient, which signifies the drug concentration ratio in tissue to that in the blood. The blood flow rate to a specific tissue is expressed as Qt, and the rate of change in tissue...
250
Model Approaches for Pharmacokinetic Data: Compartment Models01:14

Model Approaches for Pharmacokinetic Data: Compartment Models

536
Compartmental analysis is a widely adopted approach to characterizing drug pharmacokinetics. It uses compartment models that conceptualize the body as a collection of reversibly communicating compartments, each representing a group of tissues exhibiting similar drug distribution characteristics. The movement rate of the drug between these compartments is typically described by first-order kinetics.
Two primary types of compartment models are recognized: mammillary and catenary. The more...
536
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

244
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
244
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches

499
Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...
499

You might also read

Related Articles

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

Sort by
Same author

Reference values for metal elements in the plasma of adults in rural southern Xinjiang, China.

Clinica chimica acta; international journal of clinical chemistry·2026
Same author

CeO<sub>2</sub>-Stabilized Ni<sub>4</sub>Mo Alloys as Robust Alkaline Hydrogen-Evolving Electrocatalysts for Anion Exchange Membrane Water Electrolysis.

Nano letters·2026
Same author

The efficacy and safety of darolutamide combination therapy in advanced prostate cancer: a systematic review and meta-analysis of randomized controlled trials.

Frontiers in pharmacology·2026
Same author

Melatonin alleviates fluoride-induced developmental neurotoxicity by restoring SIRT3/HIF-1α axis-mediated mitochondrial dysfunction and reversing energy metabolism reprogramming.

Ecotoxicology and environmental safety·2026
Same author

Amino-functionalized chitosan/carboxymethyl cellulose aerogel for efficient Cr(VI) removal and its carbonized derivative as a photocatalyst for tetracycline degradation.

International journal of biological macromolecules·2026
Same author

Recent Advances and Future Directions in the Mechanisms and Therapeutic Strategies Targeting the Complement System in Diabetic Nephropathy.

American journal of nephrology·2026

Related Experiment Video

Updated: Jan 22, 2026

Application of DNA Fingerprinting using the D1S80 Locus in Lab Classes
08:35

Application of DNA Fingerprinting using the D1S80 Locus in Lab Classes

Published on: July 17, 2021

23.0K

A CSI-Based Indoor Fingerprinting Localization with Model Integration Approach.

Yuqing Yin1,2, Changze Song1,2, Ming Li1,2

  • 1School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221000, China.

Sensors (Basel, Switzerland)
|July 10, 2019
PubMed
Summary

This study introduces a device-free indoor localization method using Deep Neural Networks (DNN) and channel state information (CSI). The novel algorithm achieves over 97% accuracy by integrating multiple DNN models for precise positioning.

Keywords:
channel state informationindoor fingerprinting localizationmulti-model integration

More Related Videos

Assessment of Zebrafish Lens Nucleus Localization and Sutural Integrity
07:16

Assessment of Zebrafish Lens Nucleus Localization and Sutural Integrity

Published on: May 6, 2019

9.0K
HPLC Coupled with Chemical Fingerprinting for Multi-Pattern Recognition for Identifying the Authenticity of Clematidis Armandii Caulis
07:29

HPLC Coupled with Chemical Fingerprinting for Multi-Pattern Recognition for Identifying the Authenticity of Clematidis Armandii Caulis

Published on: November 11, 2022

2.4K

Related Experiment Videos

Last Updated: Jan 22, 2026

Application of DNA Fingerprinting using the D1S80 Locus in Lab Classes
08:35

Application of DNA Fingerprinting using the D1S80 Locus in Lab Classes

Published on: July 17, 2021

23.0K
Assessment of Zebrafish Lens Nucleus Localization and Sutural Integrity
07:16

Assessment of Zebrafish Lens Nucleus Localization and Sutural Integrity

Published on: May 6, 2019

9.0K
HPLC Coupled with Chemical Fingerprinting for Multi-Pattern Recognition for Identifying the Authenticity of Clematidis Armandii Caulis
07:29

HPLC Coupled with Chemical Fingerprinting for Multi-Pattern Recognition for Identifying the Authenticity of Clematidis Armandii Caulis

Published on: November 11, 2022

2.4K

Area of Science:

  • Computer Science
  • Electrical Engineering
  • Signal Processing

Background:

  • Device-free indoor localization is crucial for enhancing quality of life.
  • Wi-Fi Channel State Information (CSI) is affected by human presence and location.
  • Existing methods often require specific hardware or are less accurate.

Purpose of the Study:

  • To develop a novel, accurate, and device-free indoor localization algorithm.
  • To leverage Deep Neural Networks (DNN) and multi-model integration for improved positioning.
  • To utilize Wi-Fi CSI data for classifying user locations within indoor environments.

Main Methods:

  • Data preprocessing using Local Outlier Factor (LOF) for anomaly detection.
  • Training three distinct DNN models to classify location fingerprints from CSI data collected by three antennas.
  • Employing Group Method of Data Handling (GMDH) for fusing predictions from multiple DNN models.

Main Results:

  • The proposed algorithm demonstrated high accuracy in a test-bed experiment.
  • Final positioning accuracy reached a minimum of 97% in an empty corridor setting.
  • The multi-model integration strategy effectively enhanced localization precision.

Conclusions:

  • The DNN-based approach with multi-model integration offers a robust solution for device-free indoor localization.
  • Accurate indoor positioning is achievable using readily available Wi-Fi CSI signals.
  • This method holds significant potential for various location-aware applications.