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

Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear.
Vector Algebra: Method of Components01:08

Vector Algebra: Method of Components

It is cumbersome to find the magnitudes of vectors using the parallelogram rule or using the graphical method to perform mathematical operations like addition, subtraction, and multiplication. There are two ways to circumvent this algebraic complexity. One way is to draw the vectors to scale, as in navigation, and read approximate vector lengths and angles (directions) from the graphs. The other way is to use the method of components.
In many applications, the magnitudes and directions of...
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...
Extraction: Partition and Distribution Coefficients01:14

Extraction: Partition and Distribution Coefficients

The distribution law or Nernst's distribution law is the law that governs the distribution of a solute between two immiscible solvents. This law, also known as the partition law, states that if a solute is added to the mixture of two immiscible solvents at a constant temperature, the solute is distributed between the two solvents in such a way that the ratio of solute concentrations in the solvents remains constant at equilibrium.
For extracting a solute from an aqueous phase into an organic...
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...
Direction Cosines of a Vector01:29

Direction Cosines of a Vector

Direction cosines, which help describe the orientation of a vector with respect to the coordinate axes, are an essential concept in the field of vector calculus. Consider vector A that is expressed in terms of the Cartesian vector form using i, j, and k unit vectors. The magnitude of vector A is defined as the square root of the sum of the squares of its components. The direction of this vector with respect to the x, y, and z axes is defined by the coordinate direction angles α, β, and γ,...

You might also read

Related Articles

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

Sort by
Same author

Research Progress of Composite Films in Postharvest Preservation of Fruits and Vegetables.

Molecules (Basel, Switzerland)·2026
Same author

Toward Sustainable Paclitaxel Bioproduction: Plant Biology, Biosynthesis and Platform Engineering.

Plants (Basel, Switzerland)·2026
Same author

A Low-SNR DOA Estimation Model Based on Sequential and Convolutional Feature Fusion.

Sensors (Basel, Switzerland)·2026
Same author

A Bayesian Off-Grid DOA Estimation Framework for Close-Angle Scenarios.

Sensors (Basel, Switzerland)·2026
Same author

A Load-Balancing-Aware Learning Framework for Collaborative UAV-MEC Computation Offloading.

Sensors (Basel, Switzerland)·2026
Same author

Clustering depression subgroups based on dorsolateral prefrontal-subgenual anterior cingulate cortex peak functional connectivity reveals different symptom profiles and TMS treatment outcomes.

BMC medicine·2026
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

Related Experiment Video

Updated: May 28, 2026

Measurement of the Directional Information Flow in fNIRS-Hyperscanning Data using the Partial Wavelet Transform Coherence Method
08:42

Measurement of the Directional Information Flow in fNIRS-Hyperscanning Data using the Partial Wavelet Transform Coherence Method

Published on: September 3, 2021

A Two-Stage Transformer Framework for Sparse-Array Direction-of-Arrival Estimation via Correlation Vector Recovery.

Wenchao He1,2, Yiran Shi2, Hongxi Zhao2

  • 1School of Mechanical and Electrical Engineering, Changchun Humanities and Sciences College, Changchun 130117, China.

Sensors (Basel, Switzerland)
|May 27, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a two-stage Transformer framework for accurate direction-of-arrival (DOA) estimation using sparse arrays. The method reconstructs sensor data, improving performance in low signal-to-noise ratio and limited observation scenarios.

Keywords:
covariance completiondirection-of-arrival estimationsparse arraytransformer

More Related Videos

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles
11:54

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

Published on: March 13, 2017

Related Experiment Videos

Last Updated: May 28, 2026

Measurement of the Directional Information Flow in fNIRS-Hyperscanning Data using the Partial Wavelet Transform Coherence Method
08:42

Measurement of the Directional Information Flow in fNIRS-Hyperscanning Data using the Partial Wavelet Transform Coherence Method

Published on: September 3, 2021

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles
11:54

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

Published on: March 13, 2017

Area of Science:

  • Array Signal Processing
  • Machine Learning for Signal Processing

Background:

  • Accurate direction-of-arrival (DOA) estimation is crucial for array sensing.
  • Sparse arrays and limited observations degrade conventional DOA estimator performance.
  • Incomplete second-order statistics cause significant performance degradation.

Purpose of the Study:

  • Propose a novel two-stage Transformer framework for robust DOA estimation.
  • Address challenges posed by sparse arrays and snapshot-limited data.
  • Enhance accuracy and stability in low signal-to-noise ratio (SNR) regimes.

Main Methods:

  • A two-stage Transformer framework separates correlation recovery and angle inference.
  • Stage one reconstructs complete correlation vectors using masking-aware tokenization and global-context modeling.
  • Stage two employs a Transformer regressor for direct DOA prediction from recovered features.

Main Results:

  • The proposed method demonstrates robust accuracy and improved stability in low-SNR and snapshot-limited conditions.
  • Achieves competitive performance at higher SNRs.
  • Recovery-based covariance outperforms conventional difference-coarray processing, especially under noise.

Conclusions:

  • The Transformer framework effectively reconstructs incomplete sensor data for DOA estimation.
  • The approach offers significant advantages in challenging sparse-array scenarios.
  • Provides a reliable alternative to traditional methods for DOA estimation.