Jove
Visualize
Contact Us

Related Concept Videos

Cluster Sampling Method01:20

Cluster Sampling Method

11.9K
Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
11.9K
Expected Frequencies in Goodness-of-Fit Tests01:19

Expected Frequencies in Goodness-of-Fit Tests

2.5K
A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n)  to the number of categories (k).
2.5K
Response Surface Methodology01:16

Response Surface Methodology

127
Response Surface Methodology (RSM) is a collection of statistical and mathematical techniques used to develop, improve, and optimize processes. It is particularly valuable when many input variables or factors potentially influence a response variable.
The process of RSM involves several key steps:
127
Random Sampling Method01:09

Random Sampling Method

11.1K
Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest. Among the various sampling methods used by...
11.1K
Sampling Plans01:23

Sampling Plans

181
Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
181
Sample Size Calculation01:19

Sample Size Calculation

3.3K
Knowledge of the sample size is the first requirement to conduct random sampling or an experiment. The sample size is the total number of units, observations, or groups (in some cases) used to get the data to estimate a population parameter. As the name suggests, the sample size is that of the sample drawn from the population and differs from the population size.
The sample size for the given experiment or sampling effort is fundamental to any study design. Sample size decides the number of...
3.3K

You might also read

Related Articles

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

Sort by
Same author

A Method Integrating the Matching Field Algorithm for the Three-Dimensional Positioning and Search of Underwater Wrecked Targets.

Sensors (Basel, Switzerland)·2025
Same author

Fuzzy Clustering-Based Deep Learning for Short-Term Load Forecasting in Power Grid Systems Using Time-Varying and Time-Invariant Features.

Sensors (Basel, Switzerland)·2024
Same author

On the Sparse Beamformer Design.

Sensors (Basel, Switzerland)·2018
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
See all related articles
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 Experiment Video

Updated: Jun 27, 2025

Easy and Accurate Mechano-profiling on Micropost Arrays
10:25

Easy and Accurate Mechano-profiling on Micropost Arrays

Published on: November 17, 2015

11.2K

Optimal Microphone Array Placement Design Using the Bayesian Optimization Method.

Yuhan Zhang1,2, Zhibao Li2, Ka Fai Cedric Yiu1

  • 1Department of Applied Mathematics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China.

Sensors (Basel, Switzerland)
|April 27, 2024
PubMed
Summary
This summary is machine-generated.

Optimizing microphone array placement is key for beamformer performance. Bayesian optimization efficiently finds optimal configurations, significantly reducing computation time compared to heuristic methods.

Keywords:
Bayesian optimizationGaussian process regressionacquisition functionbeamformer designmicrophone placement

More Related Videos

Design and Optimization Strategies of a High-Performance Vented Box
14:23

Design and Optimization Strategies of a High-Performance Vented Box

Published on: June 9, 2023

1.1K
Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception
05:48

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception

Published on: August 9, 2024

1.5K

Related Experiment Videos

Last Updated: Jun 27, 2025

Easy and Accurate Mechano-profiling on Micropost Arrays
10:25

Easy and Accurate Mechano-profiling on Micropost Arrays

Published on: November 17, 2015

11.2K
Design and Optimization Strategies of a High-Performance Vented Box
14:23

Design and Optimization Strategies of a High-Performance Vented Box

Published on: June 9, 2023

1.1K
Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception
05:48

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception

Published on: August 9, 2024

1.5K

Area of Science:

  • Signal Processing
  • Acoustics
  • Optimization Algorithms

Background:

  • Microphone array placement is critical for beamformer performance and speech quality.
  • Optimizing microphone array configuration is a non-convex, non-linear problem.
  • Existing heuristic algorithms are time-consuming and may not find the global optimum.

Purpose of the Study:

  • To extend Bayesian optimization for solving the microphone array configuration design problem.
  • To develop a gradient-free optimization method for microphone array placement.
  • To improve the efficiency and effectiveness of microphone array design.

Main Methods:

  • Utilizing Bayesian optimization, which employs Gaussian process regression and acquisition functions.
  • Developing a probabilistic model for the objective function, integrating out uncertainty.
  • Employing acquisition functions to guide the selection of the next placement point.

Main Results:

  • The Bayesian optimization method successfully identified optimal or near-optimal microphone array placements.
  • The proposed method achieved significant reductions in computational time.
  • Numerical experiments showed the Bayesian optimization method to be at least four times faster than the hybrid descent method.

Conclusions:

  • Bayesian optimization offers an efficient and effective approach to microphone array configuration design.
  • This method overcomes limitations of traditional heuristic algorithms.
  • The proposed technique enhances speech quality through optimized microphone placement with reduced computational cost.