A5111875696- Particle physics theoretical and experimental studies
- Medical Image Segmentation Techniques
- Integrated Circuits and Semiconductor Failure Analysis
- Radiomics and Machine Learning in Medical Imaging
- Brain Tumor Detection and Classification
- Advanced MRI Techniques and Applications
- High-Energy Particle Collisions Research
- Dark Matter and Cosmic Phenomena
- Video Surveillance and Tracking Methods
- Cosmology and Gravitation Theories
- Advanced Vision and Imaging
- Image Retrieval and Classification Techniques
- Medical Imaging Techniques and Applications
- Physics of Superconductivity and Magnetism
- Robotics and Sensor-Based Localization
- Advancements in Semiconductor Devices and Circuit Design
- Computational Physics and Python Applications
- Gait Recognition and Analysis
- Semiconductor materials and devices
- Quantum Chromodynamics and Particle Interactions
- Human Pose and Action Recognition
Electronics for Imaging (United States)
2024
Fermi National Accelerator Laboratory
2023
University of Chicago
2023
Lund University
2020
Macronix International (Taiwan)
2006
Zhejiang University
2001
A bstract We show that simultaneously explaining dark matter and the observed value of muon’s magnetic dipole moment may lead to yet unexplored photon signals at LHC. consider Minimal Supersymmetric Standard Model with electroweakino masses in few-to-several hundred GeV range, opposite sign Bino mass parameter respect both Higgsino Wino parameters. In such region space, spin-independent elastic scattering cross section a Bino-like candidate direct detection experiment is suppressed by...
A novel trapping-nitride-based non-volatile memory cell by using a gated-diode structure is proposed. Fowler-Nordheim (FN) electron injection and band-to-band-tunneling induced hot-hole (BTBT HH) are utilized as the erase program methods, respectively. BTBT current modulated trapped charges sensing signal to distinguish cell's bit state. This overcomes channel-length related drawbacks in convention field-effect-transistor (FET)-based cells. Furthermore, its array architecture bias methods...
It is difficult to automatically segment and classify tomographic images of an actual patient's brain. Therefore, many interactive operations are performed. very time consuming its precision user-dependent. Here, the authors combined a brain atlas 3D fuzzy image segmentation into matching. can not only find out precise boundary anatomic structure, but also save in operation. At first, information mapped onto tomograph with matching method. Then, based on mapping result, structure mask...
A multimodality brain atlas is implemented based on the visible human dataset. Firstly, VHD's images of different modalities are interpolated and registered into a common cubic space. global rotation translation then used to transform dataset standard Talairach coordinate. The mapped onto with piecewise linear scaling. texture segmentation method developed label tissue types according knowledge provided by atlas. Finally, 3D rendering results provided.