A5102803560- Wireless Signal Modulation Classification
- Bayesian Methods and Mixture Models
- Nanoplatforms for cancer theranostics
- Advanced Statistical Methods and Models
- SARS-CoV-2 and COVID-19 Research
- Digital Media Forensic Detection
- Fault Detection and Control Systems
- RNA Interference and Gene Delivery
- Colorectal Cancer Treatments and Studies
- Bioinformatics and Genomic Networks
- Nanoparticle-Based Drug Delivery
- Computational Drug Discovery Methods
- Cancer Research and Treatments
- MicroRNA in disease regulation
- COVID-19 diagnosis using AI
- Hate Speech and Cyberbullying Detection
- Biometric Identification and Security
- Integrated Circuits and Semiconductor Failure Analysis
- COVID-19 epidemiological studies
- Internet Traffic Analysis and Secure E-voting
- Statistical Methods and Inference
- Full-Duplex Wireless Communications
Chinese Academy of Medical Sciences & Peking Union Medical College
2025
Indiana University Bloomington
2023
Indiana University – Purdue University Indianapolis
2023
Indiana University
2023
Southeast University
2019-2021
ABSTRACT The insufficient infiltration and functional inhibition of CD8 + T cells due to tumor microenvironment (TME) are considered enormous obstacles anti‐tumor immunotherapy. Herein, a pH‐responsive core‐shell manganese phosphate nanomodulator co‐loading siPD‐L1 Mn 2+ into nanoparticles coated with hyaluronic acid was prepared, which aimed at the bidirectional reprogramming microenvironment: (1) “Brakes off,” restoring function by knockdowning PD‐L1 expression cells; (2) “Step on...
Radio-frequency fingerprinting (RFF) exploiting hardware characteristics has been employed for device recognition to enhance the overall security. However, performance unreliability in long-term experiments, channel fading interference, and unauthorized devices verification are three open problems that restrict development of RFF recognition. To address these issues, a robust extraction scheme based on corresponding algorithms is studied. For first problem, stacking repetitive symbols...
The diagnosis and treatment of non-small cell lung cancer in clinical settings face serious challenges, particularly due to the lack integration between two processes, which limit real-time adjustments plans based on patient's condition drive-up costs. Here, we present a multifunctional pH-sensitive core-shell nanoparticle containing quercetin (QCT), termed AHA@MnP/QCT NPs, designed for simultaneous cancer. Mechanistic studies indicated that QCT Mn2+ exhibited excellent peroxidase-like...
Global system for mobile communications (GSM) is one of the most widely used communication standards in world today, which still has a large number users, so it great security significance to identify devices operating GSM network. This paper proposes novel radio frequency fingerprinting (RFF) based device identifications method phones. A differential constellation trace figure (DCTF) physical layer RFF extraction and convolutional neural network (CNN) classification scheme designed...
Radio frequency (RF) fingerprint has gained wide attention as it takes advantages of inherent characteristics in hardware for identification and verification. However, performance unreliability with long-ago training data channel fading interference are two open problems that restrict the development RF identification. To address former issue, we propose a long-term stacking repetitive symbols algorithm to turn measurement noise toward standard Gaussian distribution, which contributes both...
Parameter estimation of mixture regression model using the expectation maximization (EM) algorithm is highly sensitive to outliers. Here we propose a fast and efficient robust algorithm, called Component-wise Adaptive Trimming (CAT) method. We consider simultaneous outlier detection parameter minimize effect contamination. Robust has many important applications including in human cancer genomics data, where population often displays strong heterogeneity added by unwanted technological...
Abstract As the SARS-CoV-2 virus rapidly evolves, predicting trajectory of viral variations has become a critical yet complex task. A deep understanding future mutation patterns, in particular mutations that will prevail near future, is vital steering diagnostics, therapeutics, and vaccine strategies coming months. In this study, we developed model to forecast surges real-time, using historical frequency data from USA. To improve upon accuracy traditional time-series models, transformed...