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

Classification of Systems-I01:26

Classification of Systems-I

161
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
161
Force Classification01:22

Force Classification

1.1K
Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
1.1K
Classification of Systems-II01:31

Classification of Systems-II

129
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
129
Aggregates Classification01:29

Aggregates Classification

292
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
292
Classification of Signals01:30

Classification of Signals

355
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
355
End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

194
A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
194

You might also read

Related Articles

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

Sort by
Same author

Technology Roadmap of Bioinspired Computing Hardware.

ACS nano·2026
Same author

Versatile and Robust Reservoir Computing with PWM-Driven Heterogenous R-C Circuits.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2025
Same author

Self-Induced Light Emission in Solid-State Memristors Replicates Neuronal Biophotons.

ACS nano·2024
Same author

Versatile Nanoscale Three-Terminal Memristive Switch Enabled by Gating.

ACS nano·2024
Same author

Atomic scale memristive photon source.

Light, science & applications·2022
Same author

Broadband, High-Temperature Stable Reflector for Aerospace Thermal Radiation Protection.

ACS applied materials & interfaces·2020
Same journal

Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Learning Topology-Aware Representations via Test-Time Adaptation for Anomaly Segmentation.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

TraGraph-GS: Trajectory Graph-based Gaussian Splatting for Arbitrary Large-Scale Scene Rendering.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

SWIFT: A Small-World Interaction Framework for Flow-Aware Trajectory Prediction in Autonomous Driving.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

HardFlow: Hard-Constrained Sampling for Flow-Matching Models Via Trajectory Optimization.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Industrial Brain: Self-Evolving Neuro-Symbolic Autonomy with Causal Resilience for Cyber-Physical Systems.

IEEE transactions on pattern analysis and machine intelligence·2026
See all related articles

Related Experiment Video

Updated: May 16, 2025

Tracking Rats in Operant Conditioning Chambers Using a Versatile Homemade Video Camera and DeepLabCut
08:32

Tracking Rats in Operant Conditioning Chambers Using a Versatile Homemade Video Camera and DeepLabCut

Published on: June 15, 2020

12.3K

Rethinking Efficient and Effective Point-Based Networks for Event Camera Classification and Regression.

Hongwei Ren, Yue Zhou, Jiadong Zhu

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |April 1, 2025
    PubMed
    Summary
    This summary is machine-generated.

    EventMamba offers a novel Point Cloud approach for event camera data, outperforming frame-based methods in action recognition and relocalization tasks while using minimal computational resources.

    More Related Videos

    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
    03:31

    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

    Published on: December 15, 2023

    438
    Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
    08:25

    Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

    Published on: May 7, 2019

    8.9K

    Related Experiment Videos

    Last Updated: May 16, 2025

    Tracking Rats in Operant Conditioning Chambers Using a Versatile Homemade Video Camera and DeepLabCut
    08:32

    Tracking Rats in Operant Conditioning Chambers Using a Versatile Homemade Video Camera and DeepLabCut

    Published on: June 15, 2020

    12.3K
    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
    03:31

    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

    Published on: December 15, 2023

    438
    Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
    08:25

    Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

    Published on: May 7, 2019

    8.9K

    Area of Science:

    • Computer Vision
    • Neuromorphic Engineering
    • Machine Learning

    Background:

    • Event cameras offer low latency and high dynamic range with minimal power consumption.
    • Current frame-based processing of event data is computationally intensive and loses temporal details.
    • Existing point-based methods struggle with spatio-temporal event streams.

    Purpose of the Study:

    • To develop an efficient and effective framework for processing event camera data using Point Cloud representation.
    • To address the limitations of frame-based and previous point-based methods.
    • To enhance the extraction of temporal information from event streams.

    Main Methods:

    • Proposed EventMamba, a framework utilizing Point Cloud representation for event camera data.
    • Implemented a hierarchical structure with staged modules for processing temporal features.
    • Redesigned the global extractor using temporal aggregation and State Space Model (SSM) based Mamba for enhanced temporal extraction.

    Main Results:

    • Achieved state-of-the-art (SOTA) point-based performance on six action recognition datasets.
    • Outperformed all frame-based methods on Camera Pose Relocalization (CPR) and eye-tracking regression tasks.
    • Demonstrated minimal computational resource consumption.

    Conclusions:

    • EventMamba effectively bridges the gap between event cloud and point cloud representations.
    • The proposed method excels in capturing spatio-temporal information for various tasks.
    • EventMamba represents a significant advancement in efficient and effective event camera data processing.