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

Manipulation and Analysis

Manipulation and Analysis

GIS manipulation and analysis functions are vital for decision-making and planning. These activities range from data retrieval tasks, such as selecting information based on specific criteria, to advanced analytical techniques that address complex spatial problems.One critical GIS analysis method is overlaying, which combines multiple data layers to examine impacts. For example, overlaying a river-dammed lake boundary with road networks can identify affected infrastructure. Another common...

Levels of Use of a GIS

Levels of Use of a GIS

Geographic Information Systems (GIS) operate across three levels of application, each representing an increasing degree of complexity: data management, analysis, and prediction. These levels reflect the expanding functionality and versatility of GIS technology in handling spatial data for diverse purposes.Data ManagementAt its foundational level, GIS serves as a tool for data management, enabling the input, storage, retrieval, and organization of spatial data. This level is often employed in...

Steps in Outbreak Investigation

Steps in Outbreak Investigation

In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...

You might also read

Related Articles

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

Sort by

Same author

Performance Analysis of NB-IoT Uplink in Low Earth Orbit Non-Terrestrial Networks.

Sensors (Basel, Switzerland)·2022

Same author

Deep Q-Learning-Based Transmission Power Control of a High Altitude Platform Station with Spectrum Sharing.

Sensors (Basel, Switzerland)·2022

Same author

RRH Clustering Using Affinity Propagation Algorithm with Adaptive Thresholding and Greedy Merging in Cloud Radio Access Network.

Sensors (Basel, Switzerland)·2021

Same author

Development of software sensors for determining total phosphorus and total nitrogen in waters.

International journal of environmental research and public health·2013

Search research articles

Related Experiment Video

Updated: Dec 25, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

Path Loss Prediction based on Machine Learning Techniques: Principal Component Analysis, Artificial Neural Network

Han-Shin Jo¹, Chanshin Park², Eunhyoung Lee³

¹Department of Electronics and Control Engineering, Hanbat National University, Dajeon 34158, Korea.

Sensors (Basel, Switzerland)

|April 3, 2020

Summary

This summary is machine-generated.

This study introduces a machine learning framework for wireless sensor network path loss modeling. The advanced model combines artificial neural networks (ANN) and Gaussian processes for more accurate and flexible path loss prediction in complex environments.

Keywords:

Gaussian process artificial neural network (ANN)feature selection machine learning multi-dimensional regression path loss principle component analysis (PCA)shadowing wireless sensor network

More Related Videos

Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon

Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon

Published on: October 16, 2018

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Related Experiment Videos

Last Updated: Dec 25, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon

Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon

Published on: October 16, 2018

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Area of Science:

Wireless Communication
Signal Propagation
Machine Learning Applications

Background:

Existing linear log-distance path loss models lack accuracy and flexibility in complex environments.
Advanced modeling is crucial for optimizing wireless sensor network performance.
Accurate path loss prediction is essential for reliable wireless communication systems.

Purpose of the Study:

To propose a novel machine learning framework for enhanced path loss modeling in wireless sensor networks.
To improve the accuracy and flexibility of path loss prediction compared to conventional models.
To integrate artificial neural networks, Gaussian processes, and principal component analysis for comprehensive modeling.

Main Methods:

Utilized Principle Component Analysis (PCA) for feature selection and dimensionality reduction of path loss data.
Employed Artificial Neural Networks (ANN) for multi-dimensional regression to learn path loss structures.
Applied Gaussian processes for variance analysis to model the shadowing effect in wireless propagation.

Main Results:

The proposed machine learning framework demonstrated superior accuracy and flexibility in path loss modeling.
The combined model outperformed conventional linear path loss plus log-normal shadowing models.
Empirical validation was conducted using path loss data measured in a suburban area in Korea.

Conclusions:

The integrated machine learning approach offers a significant advancement in wireless path loss modeling.
The framework provides a more robust and adaptable solution for complex radio environments.
This research contributes to the development of more efficient and reliable wireless sensor networks.