Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Difference from Background: Limit of Detection

Difference from Background: Limit of Detection

The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...

You might also read

Related Articles

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

Sort by

Same author

Noise Reduction with Recursive Filtering for More Accurate Parameter Identification of Electrochemical Sources and Interfaces.

Sensors (Basel, Switzerland)·2025

Same author

On the Design of an Energy Efficient Digital IIR A-Weighting Filter Using Approximate Multiplication.

Sensors (Basel, Switzerland)·2021

Same author

<i>In vivo</i> continuous three-dimensional magnetic resonance microscopy: a study of metamorphosis in Carniolan worker honey bees (<i>Apis mellifera carnica</i>).

The Journal of experimental biology·2020

Same author

Data Transmission Efficiency in Bluetooth Low Energy Versions.

Sensors (Basel, Switzerland)·2019

Same author

Accurate Indoor Sound Level Measurement on a Low-Power and Low-Cost Wireless Sensor Node.

Sensors (Basel, Switzerland)·2018

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

Search research articles

Related Experiment Video

Updated: Oct 30, 2025

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

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

Published on: December 15, 2023

An Approximate GEMM Unit for Energy-Efficient Object Detection.

Ratko Pilipović¹, Vladimir Risojević², Janko Božič³

¹Faculty of Computer and Information Science, University of Ljubljana, 1000 Ljubljana, Slovenia.

Sensors (Basel, Switzerland)

|July 2, 2021

Summary

This summary is machine-generated.

This study introduces an approximate general matrix multiplication (AGEMM) unit for energy-efficient edge AI. The AGEMM unit significantly reduces area and power consumption in convolutional neural networks without performance loss.

Keywords:

GEMM YOLOv4-tiny approximate computing approximate general matrix multiplication approximate multipliers convolutional neural networks energy-efficient processing honeybee detection matrix core object detection tensor core

More Related Videos

Oligomerization Dynamics of Cell Surface Receptors in Living Cells by Total Internal Reflection Fluorescence Microscopy Combined with Number and Brightness Analysis

Oligomerization Dynamics of Cell Surface Receptors in Living Cells by Total Internal Reflection Fluorescence Microscopy Combined with Number and Brightness Analysis

Published on: November 6, 2019

Related Experiment Videos

Last Updated: Oct 30, 2025

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

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

Published on: December 15, 2023

Oligomerization Dynamics of Cell Surface Receptors in Living Cells by Total Internal Reflection Fluorescence Microscopy Combined with Number and Brightness Analysis

Oligomerization Dynamics of Cell Surface Receptors in Living Cells by Total Internal Reflection Fluorescence Microscopy Combined with Number and Brightness Analysis

Published on: November 6, 2019

Area of Science:

Electrical Engineering
Computer Science
Artificial Intelligence

Background:

Edge computing necessitates energy-efficient AI processing near data sources.
Approximate computing offers a trade-off between efficiency and accuracy for circuit design.
General Matrix Multiplication (GEMM) is a core operation in AI algorithms.

Purpose of the Study:

To propose and evaluate an Approximate General Matrix Multiplication (AGEMM) unit.
To assess the AGEMM unit's performance in terms of area and energy efficiency.
To validate the AGEMM unit's applicability in Convolutional Neural Networks (CNNs) for edge devices.

Main Methods:

Developed an AGEMM unit using approximate multipliers for 4x4 matrices in 16-bit fixed-point format.
Synthesized the AGEMM unit using the Nangate Open Cell Library (45 nm technology node).
Evaluated the AGEMM unit's performance in a YOLOv4-tiny CNN for honeybee detection.

Main Results:

The AGEMM unit achieved up to 64% reduction in area and 75% reduction in energy consumption compared to exact GEMM.
Deployment in YOLOv4-tiny for honeybee detection showed no noticeable performance degradation.
The AGEMM unit demonstrated suitability for CNNs that tolerate computational errors.

Conclusions:

The proposed AGEMM unit enables significant area and energy savings for edge AI applications.
AGEMM units can be integrated into CNNs, enhancing their efficiency for edge deployment.
This approach can extend the operational autonomy of sensors and edge nodes through reduced power demands.