Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page

Jinseob Jeong

dblp:63/6431 · DBLP profile ↗
← Back
2ranked-venue papers
2as first author
1since 2021 · last 2023
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Systems, architecture and hardware · 1 · 1 first-authorSecurity and privacy · 1 · 1 first-author · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Network and information security
1 paper
Cyber-physical and IoT security · 100%
Computer architecture, parallel and distributed computing, and storage systems
1 paper
Embedded and real-time systems · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Cyber-physical and IoT security › sensor security
acoustic injection attacks
0.712023
Un-Rocking Drones: Foundations of Acoustic Injection Attacks and Recovery Thereof · NDSS 2023
Cyber-physical and IoT security
UAV security
0.712023
Un-Rocking Drones: Foundations of Acoustic Injection Attacks and Recovery Thereof · NDSS 2023
Embedded and real-time systems
cyber-physical system platforms
0.212023
Un-Rocking Drones: Foundations of Acoustic Injection Attacks and Recovery Thereof · NDSS 2023

Methods — techniques the papers use, named apart from their topics

signal processing · 1.3
YearPublicationVenuePosition
2023 Un-Rocking Drones: Foundations of Acoustic Injection Attacks and Recovery Thereof
Jinseob Jeong, Dongkwan Kim 0001, Joon-Ha Jang, Juhwan Noh, Changhun Song, Yongdae Kim
NDSS1
2008 Statistical mixed Vt allocation of body-biased circuits for reduced leakage variation
abstract
Leakage current is susceptible to variation of transistor parameters and environment such as temperature, which results in wide spread in leakage distribution. The spread can be reduced by employing body biasing: reverse body bias for too leaky dies and forward body bias for too slow dies. We investigate body biasing of mixed Vtcircuits. It is shown that the conventional body biasing has limitation in reducing leakage variation of mixed Vtcircuits. This is because low- and high-Vtdevices do not track each other and their body biasing sensitivities are different. We present alternative body biasing scheme that targets compensating die-to-die variation of low Vt. Under this body biasing scheme, within-die profiles of low- and high-Vt, which we need for statistical allocation of mixed Vt, get wider thus become different from the original ones. We present an analytical procedure to derive new within-die profiles. Experiments with 45-nm predictive model show that the spread in leakage distribution (ratio of maximum and minimum leakage) can be reduced to 4.5 as opposed to 9.4 from conventional body biasing on mixed Vtcircuits.
Jinseob Jeong, Seungwhun Paik, Youngsoo Shin
ASP-DAC1