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

Microbial Biosensors01:17

Microbial Biosensors

Microbial biosensors are analytical devices that utilize living microbes to detect specific substances through measurable signals. These devices consist of two main components: biosensing organisms and signal-transducing elements. Biosensing organisms, such as Escherichia coli or Saccharomyces cerevisiae, are typically housed in multiwell plates connected to transducers, enabling rapid, real-time detection of target analytes.Signal Generation MechanismWhen a target analyte—such as...

You might also read

Related Articles

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

Sort by
Same author

Evaluation of supervised and unsupervised 3D star visualisation algorithms.

International journal of data mining and bioinformatics·2014
Same author

A Geographic Information System framework for the management of sensor deployments.

Sensors (Basel, Switzerland)·2012
Same author

Ontological problem-solving framework for dynamically configuring sensor systems and algorithms.

Sensors (Basel, Switzerland)·2011
Same author

Ontological problem-solving framework for assigning sensor systems and algorithms to high-level missions.

Sensors (Basel, Switzerland)·2011
Same author

A Prolog-based centroid algorithm for isovolume extraction from finite element torso simulations.

Computer methods and programs in biomedicine·2002
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

Related Experiment Video

Updated: May 13, 2026

Using a Real-Time Locating System to Measure Walking Activity Associated with Wandering Behaviors Among Institutionalized Older Adults
04:13

Using a Real-Time Locating System to Measure Walking Activity Associated with Wandering Behaviors Among Institutionalized Older Adults

Published on: February 8, 2019

Advancing profiling sensors with a wireless approach.

Alex Galvis1, David J Russomanno

  • 1Department of Electrical and Computer Engineering, Purdue School of Engineering and Technology, Indiana University-Purdue University Indianapolis, Indianapolis, IN 46202, USA. agalvis@iupui.edu

Sensors (Basel, Switzerland)
|February 28, 2013
PubMed
Summary
This summary is machine-generated.

A new wireless Near-Infrared (N-IR) profiling sensor uses sensor nodes and a neural network to classify humans, animals, or vehicles with 94% accuracy. This technology enhances deployment options for applications like intelligent fences.

More Related Videos

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data
11:21

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

Published on: July 27, 2018

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles
11:54

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

Published on: March 13, 2017

Related Experiment Videos

Last Updated: May 13, 2026

Using a Real-Time Locating System to Measure Walking Activity Associated with Wandering Behaviors Among Institutionalized Older Adults
04:13

Using a Real-Time Locating System to Measure Walking Activity Associated with Wandering Behaviors Among Institutionalized Older Adults

Published on: February 8, 2019

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data
11:21

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

Published on: July 27, 2018

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles
11:54

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

Published on: March 13, 2017

Area of Science:

  • Sensor Technology
  • Machine Learning
  • Optoelectronics

Background:

  • Early profiling sensors utilized wired Near-Infrared (N-IR) retro-reflective prototypes with vertical detector columns.
  • Limitations in deployment options and application scenarios existed with the wired approach.

Purpose of the Study:

  • To develop a wireless version of the N-IR profiling sensor.
  • To enhance object classification capabilities and improve deployment flexibility.

Main Methods:

  • Implementation of a wireless sensor network with distributed data collection and aggregation.
  • Development of a base station for data pre-processing and re-alignment.
  • Application of a back-propagation neural network for object classification.

Main Results:

  • The wireless N-IR profiling sensor achieved approximately 94% accuracy in classifying objects as human, animal, or vehicle.
  • The wireless architecture offers improved deployment options compared to wired systems.

Conclusions:

  • The wireless N-IR profiling sensor represents a significant advancement over its wired predecessor.
  • Enhanced classification and flexible deployment broaden potential applications, including intelligent fence systems.