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

462
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:
462
Classification of Systems-II01:31

Classification of Systems-II

390
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,
390
Parallel Processing01:20

Parallel Processing

468
The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...
468
Aggregates Classification01:29

Aggregates Classification

584
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...
584
Force Classification01:22

Force Classification

2.1K
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,...
2.1K
Classification of Signals01:30

Classification of Signals

1.2K
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...
1.2K

You might also read

Related Articles

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

Sort by
Same author

High-Radix Taylor-Optimized Tone Mapping Processor for Adaptive 4K HDR Video at 30 FPS.

Sensors (Basel, Switzerland)·2025
Same author

Five-Direction Occlusion Filling with Five Layer Parallel Two-Stage Pipeline for Stereo Matching with Sub-Pixel Disparity Map Estimation.

Sensors (Basel, Switzerland)·2022
Same author

A Reconfigurable Visual-Inertial Odometry Accelerated Core with High Area and Energy Efficiency for Autonomous Mobile Robots.

Sensors (Basel, Switzerland)·2022
Same author

Lane Departure Assessment via Enhanced Single Lane-Marking.

Sensors (Basel, Switzerland)·2022
Same author

[ATM/H2AX and repair of sperm-DNA damage during cryopreservation].

Zhonghua nan ke xue = National journal of andrology·2011
Same author

Predicting accident frequency at their severity levels and its application in site ranking using a two-stage mixed multivariate model.

Accident; analysis and prevention·2011

Related Experiment Video

Updated: Dec 2, 2025

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

853

A Multi-Core Object Detection Coprocessor for Multi-Scale/Type Classification Applicable to IoT Devices.

Peng Xu1, Zhihua Xiao1, Xianglong Wang1

  • 1School of Microelectronics, Southern University of Science and Technology, Shenzhen 518055, China.

Sensors (Basel, Switzerland)
|November 4, 2020
PubMed
Summary

This study introduces a power-efficient, multi-core object-detection coprocessor for Internet of Things (IoT) devices. The compact design achieves low power consumption per core, enabling efficient multi-scale and multi-type classification.

Keywords:
block-level once sliding detection windowhistogram of oriented gradientmulti-shape detection-windowobject-detection coprocessorpower efficiencysupport vector machine

Related Experiment Videos

Last Updated: Dec 2, 2025

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

853

Area of Science:

  • Computer Engineering
  • Embedded Systems
  • Artificial Intelligence Hardware

Background:

  • Power efficiency is a critical challenge for Internet of Things (IoT) devices.
  • Object detection requires significant computational resources, impacting device power budgets.

Purpose of the Study:

  • To present a compact, multi-core object-detection coprocessor designed for enhanced power efficiency in IoT applications.
  • To enable multi-scale and multi-type object classification with scalable processing capabilities.

Main Methods:

  • Developed a multi-core coprocessor architecture supporting scalable block sizes for multi-shape detection.
  • Implemented a memory-reuse strategy utilizing a single dual-port SRAM to minimize memory footprint.
  • Integrated the coprocessor onto an Intel DE4 development board with a Stratix IV FPGA.

Main Results:

  • The coprocessor supports frame-image sizes up to 2048 × 2048 for multi-scale classification.
  • Achieved a low power consumption of only 80.98 mW per core on the FPGA.
  • Demonstrated a memory-efficient design through the implemented memory-reuse strategy.

Conclusions:

  • The proposed object-detection coprocessor offers a viable solution for power-constrained IoT devices.
  • The design facilitates efficient multi-scale and multi-type classification with reduced memory requirements.
  • The low power consumption per core highlights its suitability for embedded AI applications.