Articles tagged with Wavelets
Researchers developed a wavelet-based approach to detect shortwave rail defects, intensifying vibrations between wheels and bridges. The method achieves over 95% detection accuracy, identifying small defects as small as 0.5 mm in amplitude.
The researchers propose a novel defense algorithm, Wavelet-Based Adversarial Training (WBAD), to protect medical digital twins. The two-stage defense mechanism achieves 98% accuracy in breast cancer prediction, even under adversarial attacks, providing a comprehensive and effective defense against cyberattacks.
A study led by Prof. Yong Wei analyzed epidemic records from AD 0 to 1840 in ancient China, revealing a correlation between solar activity and epidemics. The research applied wavelet analysis and ensemble empirical mode decomposition, showing similar periodic changes between the epidemic index and sunspot number.
A University of Texas at Arlington bioengineering professor and her team will integrate a portable brain imaging system with an advanced signal-processing technique for newborns. This project aims to provide real-time analysis needed to treat encephalopathy or brain swelling more quickly, saving the lives of sick babies.
Researchers at UT Southwestern Medical Center have adapted a weather-forecast tool to assess the effectiveness of therapies for reducing brain injury in newborns who suffered oxygen deprivation during birth. The non-invasive method produces real-time heat maps of the infant's brain, helping doctors determine whether therapies are working.
A novel wavelet variational model is proposed to segment ultrasound videos efficiently, tackling low contrast, shadow effects, and complex noise statistics. The model achieves accurate ROI tracking with robustness and flexibility, making it suitable for real-time clinical applications.
Research published in the International Journal of Biomedical Engineering and Technology reveals a wavelet transform approach that improves medical imaging. The technique boosts the signal-to-noise ratio and reduces artifacts, allowing for earlier disease detection and diagnosis.
Theodore Kim's invention of wavelet turbulence enabled faster and more easily art-directable simulation of large-scale fluid smoke effects, used in dozens of major motion pictures. The algorithm was first presented publicly in 2008 and quickly caught fire in the industry.
Professor Ingrid Daubechies of Duke University received the John von Neumann Lecture Prize for her fundamental work in developing the foundations and applications of wavelets. Her research focuses on time-frequency analysis, with contributions to mathematical sciences and engineering fields.
A model developed by Zhengdong Cheng simulates biochemical reactions within the human body, identifying wave patterns that can signal diseased or dead cells. This innovation may lead to better understanding of organ structure and extent of damage, enabling timely diagnosis and treatment.
Researchers at the University of Warwick have developed an automated technique using wavelets to analyze brain tumors, providing a preliminary diagnosis in seconds. The method improves upon existing methods by analyzing hundreds of slides with millions of pixels, increasing accuracy and reducing analysis time.
Researchers develop a new method that embeds data in ultra-short pulses of laser light, shaped via fractal modulation as wavelets, to improve communication reliability. The system achieves higher average bit rates than conventional optical wireless links and ensures on-the-fly operation without significant electronic processing.
A new statistical technique called wavelet bootstrapping enables researchers to extract meaningful information from a single data run by applying re-sampling methods in the wavelet domain. This technique has applications in geophysical sciences, bioinformatics, and medical imaging, among others.
Researchers have developed a new technique using wavelet analysis to speed up radiation dose calculations, allowing for more accurate control over radiation doses and reduced damage to adjacent healthy tissues. This breakthrough uses wavelets to give good fast approximations to smooth data fields with 'rough' noise.
A Lehigh University professor has developed a system that combines images from optical and millimeter-wave cameras to detect concealed weapons, which could save security guards seconds in the search process. The technology uses wireless communication links and has potential applications for law enforcement and military use.
A new system is being developed to aid plastic surgeons in reconstructive surgery by analyzing faces using wavelet analysis, recommending procedures and assessing outcomes. This technology has potential applications outside of plastic surgery, including facial recognition and security, as well as quantifying beauty.