Jove
Visualize
Contact Us

Related Concept Videos

Classification of Systems-I01:26

Classification of Systems-I

386
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:
386
Classification of Systems-II01:31

Classification of Systems-II

297
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,
297
Aggregates Classification01:29

Aggregates Classification

512
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...
512
Force Classification01:22

Force Classification

1.9K
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,...
1.9K
Classification of Signals01:30

Classification of Signals

1.1K
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...
1.1K
Methods of Classification and Identification01:28

Methods of Classification and Identification

566
Bacterial identification relies on a diverse array of techniques to classify and understand microorganisms, each tailored to uncover specific characteristics. Traditional morphological approaches, while still valuable, are limited for closely related or structurally simple organisms. Modern methods integrate biochemical, serological, genetic, and advanced molecular tools to achieve greater accuracy.Morphological and Biochemical TechniquesMorphological characteristics, such as cell shape and...
566

You might also read

Related Articles

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

Sort by
Same author

A CMOS Image Readout Circuit with On-Chip Defective Pixel Detection and Correction.

Sensors (Basel, Switzerland)·2023
Same author

Motion-Based Object Location on a Smart Image Sensor Using On-Pixel Memory.

Sensors (Basel, Switzerland)·2022
Same author

Survey of Cooperative Advanced Driver Assistance Systems: From a Holistic and Systemic Vision.

Sensors (Basel, Switzerland)·2022
Same author

Face Recognition on a Smart Image Sensor Using Local Gradients.

Sensors (Basel, Switzerland)·2021
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: Nov 7, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.8K

A Heterogeneous Hardware Accelerator for Image Classification in Embedded Systems.

Ignacio Pérez1, Miguel Figueroa1

  • 1Department of Electrical Engineering, Universidad de Concepción, Concepción 4070386, Chile.

Sensors (Basel, Switzerland)
|April 30, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a novel, low-power Field-Programmable Gate Array (FPGA) accelerator for MobileNet V2 Convolutional Neural Networks (CNNs). The design optimizes resource utilization for efficient real-time image classification on edge devices.

Keywords:
MobileNet V2convolutional neural networkfield-programmable gate arraypower consumption

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

1.4K
Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

4.7K

Related Experiment Videos

Last Updated: Nov 7, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.8K
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

1.4K
Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

4.7K

Area of Science:

  • Computer Engineering
  • Artificial Intelligence
  • Hardware Acceleration

Background:

  • Convolutional Neural Networks (CNNs) excel at image classification but require significant computational power for real-time inference.
  • Graphics Processing Units (GPUs) are often power-prohibitive for mobile devices, making Field-Programmable Gate Arrays (FPGAs) a viable alternative for high-speed inference.
  • Existing FPGA CNN implementations face challenges with high on-chip memory and arithmetic resource demands, limiting their use on resource-constrained edge devices.

Purpose of the Study:

  • To develop a scalable, low-power, and resource-efficient accelerator architecture for MobileNet V2 CNN inference.
  • To address the limitations of current FPGA-based CNN accelerators for edge computing applications.

Main Methods:

  • Designed a heterogeneous system integrating an embedded processor, external memory, and reconfigurable logic with scalable Processing Elements (PEs).
  • Implemented the accelerator architecture on a XCZU7EV FPGA operating at 200 MHz.
  • Utilized a scalable number of PEs for adaptable performance.

Main Results:

  • Achieved 87% top-5 accuracy for MobileNet V2 inference.
  • Processed a 224x224 pixel image in 220 ms.
  • Consumed only 7.35 W of power.
  • Required less than 30% of the logic and arithmetic resources compared to other MobileNet FPGA accelerators.

Conclusions:

  • The proposed FPGA accelerator architecture offers a significant improvement in power and resource efficiency for MobileNet V2 inference.
  • This design is well-suited for real-time image classification tasks on resource-constrained edge devices.
  • The scalability of the Processing Elements allows for flexible deployment across various edge computing scenarios.