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

Acceleration Vectors01:30

Acceleration Vectors

19.6K
In everyday conversation, accelerating means speeding up. Acceleration is a vector in the same direction as the change in velocity, Δv, therefore the greater the acceleration, the greater the change in velocity over a given time. Since velocity is a vector, it can change in magnitude, direction, or both. Thus acceleration is a change in speed or direction, or both. For example, if a runner traveling at 10 km/h due east slows to a stop, reverses direction, and continues their run at 10 km/h...
19.6K
Classification of Signals01:30

Classification of Signals

1.6K
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...
1.6K
Classification of Systems-I01:26

Classification of Systems-I

742
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:
742
Classification of Systems-II01:31

Classification of Systems-II

651
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,
651
Aggregates Classification01:29

Aggregates Classification

1.0K
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...
1.0K
Force Classification01:22

Force Classification

2.8K
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,...
2.8K

You might also read

Related Articles

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

Sort by
Same author

Inferring entire spiking activity from local field potentials.

Scientific reportsยท2021
Same author

Robust and accurate decoding of hand kinematics from entire spiking activity using deep learning.

Journal of neural engineeringยท2021
Same author

Impact of referencing scheme on decoding performance of LFP-based brain-machine interface.

Journal of neural engineeringยท2020
Same author

Estimation of neuronal firing rate using Bayesian Adaptive Kernel Smoother (BAKS).

PloS oneยท2018
Same author

Spike Rate Estimation Using Bayesian Adaptive Kernel Smoother (BAKS) and Its Application to Brain Machine Interfaces.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conferenceยท2018
Same author

fpgaConvNet: Mapping Regular and Irregular Convolutional Neural Networks on FPGAs.

IEEE transactions on neural networks and learning systemsยท2018

Related Experiment Video

Updated: Apr 30, 2026

Design and Evaluation of Smart Glasses for Food Intake and Physical Activity Classification
07:47

Design and Evaluation of Smart Glasses for Food Intake and Physical Activity Classification

Published on: February 14, 2018

11.0K

Novel cascade FPGA accelerator for support vector machines classification.

Markos Papadonikolakis, Christos-Savvas Bouganis

    IEEE Transactions on Neural Networks and Learning Systems
    |May 9, 2014
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a scalable Field Programmable Gate Array (FPGA) architecture to accelerate Support Vector Machine (SVM) classification. The novel design achieves significant speed-ups, outperforming existing methods for complex machine learning tasks.

    More Related Videos

    Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
    08:27

    Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

    Published on: January 5, 2024

    1.8K

    Related Experiment Videos

    Last Updated: Apr 30, 2026

    Design and Evaluation of Smart Glasses for Food Intake and Physical Activity Classification
    07:47

    Design and Evaluation of Smart Glasses for Food Intake and Physical Activity Classification

    Published on: February 14, 2018

    11.0K
    Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
    08:27

    Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

    Published on: January 5, 2024

    1.8K

    Area of Science:

    • Computer Engineering
    • Machine Learning
    • Hardware Acceleration

    Background:

    • Support Vector Machines (SVMs) offer high accuracy in classification but face computational complexity.
    • Linear dependencies on support vectors and dimensionality hinder SVM performance.

    Purpose of the Study:

    • To present a scalable Field Programmable Gate Array (FPGA) architecture for accelerating SVM classification.
    • To exploit device heterogeneity and dynamic range diversities for efficient computation.

    Main Methods:

    • Developed a fully scalable FPGA architecture for SVM classification.
    • Proposed an adaptive, fully-customized processing unit leveraging heterogeneous FPGA resources.
    • Introduced the first FPGA-oriented cascade SVM classifier scheme.

    Main Results:

    • Achieved a speed-up factor of 2-3 orders of magnitude compared to CPU implementations.
    • Outperformed other FPGA and Graphics Processing Unit (GPU) approaches by over seven times.
    • The cascade scheme further increased throughput without resource penalty.

    Conclusions:

    • The heterogeneous FPGA architecture provides significant acceleration for SVM classification.
    • The proposed cascade SVM classifier enhances throughput efficiency on FPGAs.
    • This work offers a highly efficient hardware solution for complex machine learning classification problems.