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

Interference and Diffraction02:18

Interference and Diffraction

53.5K
Interference is a characteristic phenomenon exhibited by waves. When two electromagnetic waves interact with their peaks and troughs coinciding, a resulting wave with enhanced amplitude is produced. This is known as constructive interference. In this case, the two waves interacting are in phase with each other.
53.5K
Masking and Demasking Agents01:19

Masking and Demasking Agents

3.9K
EDTA titrations may necessitate masking and demasking agents to temporarily protect a particular metal ion in a mixture from the EDTA reaction. These agents facilitate the sequential analysis of the metal ions by forming stable complexes with some—but not all—metal ions during certain steps.
There are many masking agents, such as cyanide, fluoride, triethanolamine, thiourea, and 2,3-bis(sulfanyl)propan-1-ol (formerly 2,3-dimercapto-1-propanol), with the masking agent chosen based on...
3.9K
Reducing Line Loss01:18

Reducing Line Loss

430
In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss in...
430
Deconvolution01:20

Deconvolution

669
Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
669
Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

822
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...
822
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

457
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...
457

You might also read

Related Articles

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

Sort by
Same author

Investigation of Additive Friction Stir Deposition of Inconel 718: Mechanical Performance and Microstructural Evolution.

Materials (Basel, Switzerland)·2026
Same author

RISE-iEEG: Robust to Inter-Subject Electrodes Implantation Variability iEEG Classifier.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same author

Experimental investigation of the ambient light effects on V2V optical camera communication systems.

Optics express·2025
Same author

Effect of a vehicle's mobility on SNR and SINR in vehicular optical camera communication systems.

Optics express·2024
Same author

Developing a comprehensive model for underwater MIMO OCC system.

Optics express·2023
Same author

Demarcating Z-line and Gastric Folds Boundary Based on the Segmentation of the Lower Esophageal Sphincter Images.

Journal of medical signals and sensors·2023

Related Experiment Video

Updated: May 5, 2026

Lensless Fluorescent Microscopy on a Chip
11:23

Lensless Fluorescent Microscopy on a Chip

Published on: August 17, 2011

17.6K

Mitigating inter-pixel interference in MIMO-OCC systems with deep learning: addressing out-of-focus blur and very

Ehsan Bagheri, Asghar Gholami, Behzad Nazari

    Applied Optics
    |March 17, 2026
    PubMed
    Summary

    This study tackles out-of-focus blur and very low-resolution challenges in optical camera communication (OCC) systems. A deep learning Restormer network effectively improves signal quality and extends communication link length.

    More Related Videos

    Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform
    06:25

    Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

    Published on: February 13, 2014

    7.8K

    Related Experiment Videos

    Last Updated: May 5, 2026

    Lensless Fluorescent Microscopy on a Chip
    11:23

    Lensless Fluorescent Microscopy on a Chip

    Published on: August 17, 2011

    17.6K
    Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform
    06:25

    Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

    Published on: February 13, 2014

    7.8K

    Area of Science:

    • Optical Communications
    • Signal Processing
    • Machine Learning

    Background:

    • Optical Camera Communication (OCC) systems face performance degradation due to out-of-focus blur and very low-resolution (VLR) issues.
    • These challenges lead to inter-pixel interference (IPI), limiting system performance and communication range in Multiple-Input Multiple-Output (MIMO) configurations.

    Purpose of the Study:

    • To experimentally investigate the impact of out-of-focus blur and VLR on MIMO-OCC systems.
    • To propose and evaluate a deep learning-based mitigation strategy using a Restormer network.

    Main Methods:

    • Experimental setup for MIMO-OCC systems under blur and VLR conditions.
    • Implementation and comparison of a Restormer deep learning network against other deep learning techniques.
    • Performance evaluation based on signal-to-noise ratio (SNR) and maximum communication link length.

    Main Results:

    • The Restormer network significantly improves SNR by approximately 10 dB under out-of-focus blur.
    • Mitigation of VLR challenges extends the maximum achievable link length from 14 m to 32 m at a 12 dB SNR.
    • The proposed method demonstrates superior performance compared to other deep learning approaches.

    Conclusions:

    • Deep learning, specifically the Restormer network, offers an effective solution for mitigating blur and VLR in MIMO-OCC systems.
    • The proposed approach enhances system robustness, enabling longer and more reliable optical wireless communication links.