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

System of Memory01:23

System of Memory

6.4K
Memory is categorized into three major systems: sensory memory, short-term memory (STM), and long-term memory (LTM). These systems differ in their capacity and the duration for which they can hold information. Sensory memory captures raw sensory input from the environment, holding it for just a few seconds or less. For example, on hearing a brief, loud sound, like a car horn honking, the sound seems to linger in the mind for a moment even after it stops. This is an instance of sensory memory...
6.4K
Understanding Memory01:19

Understanding Memory

611
Memory is the retention of information or experiences over time, facilitated through three main processes: encoding, storage, and retrieval. Encoding is the process of inputting information into the memory system. For instance, when listening to a lecture, watching a play, reading a book, or having a conversation, the brain is actively encoding information. This initial stage involves transforming sensory input into a form that can be processed and stored by the brain. Various factors, such as...
611
Long-Term Memory01:18

Long-Term Memory

246
Long-term memory is a relatively permanent type of memory, capable of storing vast amounts of information over extended periods. Its storage capacity is generally considered unlimited.
Long-term memory can be categorized into two primary types: explicit and implicit memory. Explicit memory, also known as declarative memory, involves the conscious recollection of information that we deliberately try to remember, recall, and articulate. This type of memory encompasses specific facts, events, and...
246
Associative Learning01:27

Associative Learning

548
Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
548
Eyewitness Memory01:22

Eyewitness Memory

154
Eyewitness memory refers to the recollection of events by someone who has directly witnessed them, often serving as critical evidence in legal settings. This type of memory is commonly used in criminal cases where a witness describes details like a suspect's appearance, clothing, or behavior during a crime. However, despite its perceived reliability, eyewitness memory is prone to significant errors.
One such error is memory distortion, which occurs because human memory does not function...
154
False Memories01:18

False Memories

141
False memories represent a cognitive distortion in which individuals recall events that did not happen, or remember them in an altered form. This phenomenon highlights the brain's constructive nature in processing and recalling memories, emphasizing that memory is not a perfect representation of past events but rather a dynamic reconstruction influenced by various factors.
One primary source of false memories is misattribution, where individuals incorrectly associate external information...
141

You might also read

Related Articles

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

Sort by
Same author

Lightweight authentication for IoT devices (LAID) in sustainable smart cities.

Scientific reports·2025
Same author

BAuth-ZKP-A Blockchain-Based Multi-Factor Authentication Mechanism for Securing Smart Cities.

Sensors (Basel, Switzerland)·2023
Same author

A Deterministic Model for Determining Degree of Friendship Based on Mutual Likings and Recommendations on OTT Platforms.

Computational intelligence and neuroscience·2022
Same author

Cyber-Physical Systems and Smart Cities in India: Opportunities, Issues, and Challenges.

Sensors (Basel, Switzerland)·2021
Same author

Efficient Data Communication Using Distributed Ledger Technology and IOTA-Enabled Internet of Things for a Future Machine-to-Machine Economy.

Sensors (Basel, Switzerland)·2021
Same author

Distributed ledger technology based robust access control and real-time synchronization for consumer electronics.

PeerJ. Computer science·2021

Related Experiment Video

Updated: Sep 3, 2025

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
05:41

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

9.5K

A Novel Memory and Time-Efficient ALPR System Based on YOLOv5.

Piyush Batra1, Imran Hussain1, Mohd Abdul Ahad1

  • 1Department of Computer Science and Engineering, Jamia Hamdard, New Delhi 110062, India.

Sensors (Basel, Switzerland)
|July 27, 2022
PubMed
Summary

This study introduces an efficient automatic license plate recognition (ALPR) system using YOLOv5, ideal for IoT devices. The novel approach achieves high accuracy and speed, making advanced ALPR accessible on resource-constrained hardware.

Keywords:
ALPRIoTOCRYOLOv5urban mobilityvehicle license plate detection and recognition

More Related Videos

Novel Object Recognition Test for the Investigation of Learning and Memory in Mice
08:52

Novel Object Recognition Test for the Investigation of Learning and Memory in Mice

Published on: August 30, 2017

74.2K
Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

1.6K

Related Experiment Videos

Last Updated: Sep 3, 2025

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
05:41

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

9.5K
Novel Object Recognition Test for the Investigation of Learning and Memory in Mice
08:52

Novel Object Recognition Test for the Investigation of Learning and Memory in Mice

Published on: August 30, 2017

74.2K
Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

1.6K

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Deep learning advancements have spurred innovative license plate recognition systems.
  • Current systems often demand significant computational resources, limiting their use on IoT devices.
  • Automatic License Plate Recognition (ALPR) has diverse applications in traffic management and security.

Purpose of the Study:

  • To propose a novel, memory and time-efficient ALPR system suitable for IoT devices.
  • To develop an ALPR system using YOLOv5 that balances performance and resource consumption.
  • To compare the performance of YOLOv5 against YOLOv4 for ALPR tasks.

Main Methods:

  • Developed a two-stage ALPR system: custom transfer learned YOLOv5 for license plate detection and an LSTM-based OCR engine for recognition.
  • Utilized a combined dataset including Google Open Images and Indian License Plates for training.
  • Trained and compared YOLOv5 and YOLOv4 models on the same dataset.

Main Results:

  • The proposed YOLOv5-based ALPR system achieved a mean average precision of 87.2% with a testing time of 4.8 ms per image (Nvidia T4 GPU).
  • The complete system (detection and recognition) operates within 85 milliseconds.
  • The resulting YOLOv5 model is compact, weighing only 14 megabytes.

Conclusions:

  • The developed ALPR system offers a memory and time-efficient solution, suitable for deployment on resource-limited IoT devices.
  • YOLOv5 demonstrates strong performance and efficiency for ALPR tasks compared to YOLOv4.
  • This research contributes to making advanced ALPR technology more accessible and practical for various applications.