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

iChip01:24

iChip

The cultivation of environmental microorganisms has long been hindered by the inability to replicate complex native conditions in vitro. The isolation chip (iChip) addresses this limitation by facilitating the growth of previously uncultivable microorganisms through in situ incubation. Designed for high-throughput microbial cultivation, the iChip comprises hundreds of microchambers, each capable of housing a single microbial cell. These microchambers are loaded with a mixture of molten agar and...
Stereotype Content Model02:16

Stereotype Content Model

The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence categorization, a person will feel...

You might also read

Related Articles

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

Sort by
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

Related Experiment Video

Updated: Jun 27, 2026

Setup of Consumer Wearable Devices for Exposure and Health Monitoring in Population Studies
15:00

Setup of Consumer Wearable Devices for Exposure and Health Monitoring in Population Studies

Published on: February 3, 2023

AI-Assisted ISP and Chip-Off Forensic Framework for Damaged Android Devices.

Leila Rzayeva1, Aigerim Alibek1, Altynbay Abdykassym2

  • 1Research and Innovation Center "CyberTech", Astana IT University, Astana 010000, Kazakhstan.

Sensors (Basel, Switzerland)
|June 26, 2026
PubMed
Summary
This summary is machine-generated.

This study presents a new forensic workflow for damaged smartphones, combining hardware extraction with AI to recover data. It significantly reduces data loss and expert review time for mobile forensics.

Keywords:
Chip-Off extractionIn-System Programming (ISP)UFS memoryandroid forensicsdamaged mobile devicesdigital forensic extractioneMMC memoryembedded memory analysismobile forensics

More Related Videos

Fluorescent Paper Strips for the Detection of Diesel Adulteration with Smartphone Read-out
07:10

Fluorescent Paper Strips for the Detection of Diesel Adulteration with Smartphone Read-out

Published on: November 9, 2018

Related Experiment Videos

Last Updated: Jun 27, 2026

Setup of Consumer Wearable Devices for Exposure and Health Monitoring in Population Studies
15:00

Setup of Consumer Wearable Devices for Exposure and Health Monitoring in Population Studies

Published on: February 3, 2023

Fluorescent Paper Strips for the Detection of Diesel Adulteration with Smartphone Read-out
07:10

Fluorescent Paper Strips for the Detection of Diesel Adulteration with Smartphone Read-out

Published on: November 9, 2018

Area of Science:

  • Digital Forensics
  • Computer Science
  • Artificial Intelligence

Background:

  • Physical damage to smartphones is a major obstacle in mobile forensics, often leading to data loss.
  • Conventional logical acquisition methods fail when devices are physically compromised.

Purpose of the Study:

  • To develop and evaluate an integrated forensic workflow for data extraction from physically damaged smartphones.
  • To address the limitations of conventional methods by combining hardware-level intervention with AI-assisted analysis.

Main Methods:

  • The study combined In-System Programming (ISP) and Chip-Off memory extraction techniques.
  • An AI-assisted artifact localization and prioritization layer using a 1D-CNN classifier was implemented.
  • The workflow was tested on 18 physically damaged Android smartphones.

Main Results:

  • Hardware extraction successfully produced verified memory images from all 18 damaged devices.
  • The AI classifier achieved an F1-score of 0.88 and ROC-AUC of 0.94 for artifact localization.
  • Manual review volume decreased by 78%, expert review time by 63%, and time to first artifact by 65%.

Conclusions:

  • The integrated workflow effectively recovers data from physically damaged smartphones, overcoming limitations of standard forensic practices.
  • The AI component significantly enhances the efficiency of artifact analysis, reducing expert workload and time.
  • The study provides a decision model for ISP vs. Chip-Off selection and validated thermal extraction profiles.