Results 61 to 70 of about 4,131 (208)

Multi‐LiDAR Observations of Coastal Atmospheric Duct Evolution in Hampton Roads

Journal of Geophysical Research: Atmospheres, Volume 131, Issue 12, 28 June 2026.
Abstract To better understand the influence of atmospheric ducts on radio frequency (RF) operations, continuous high‐resolution observations of temperature and water vapor are essential. Hampton University's (HU) 48‐inch Raman‐Mie LiDAR provides such measurements through rotational and vibrational Raman techniques, enabling detailed monitoring of ...
Jia Su, Melvin A. Felton Jr., Ruben Delgado, David A. Ligon, Deryk James   +4 more
wiley   +1 more source

WAVELET-BASED COMPUTATIONAL FRAMEWORK FOR THE SOLOW-SWAN ECONOMIC MODEL

Barekeng
In this paper, we introduce an innovative numerical technique for addressing the classical Solow-Swan economic growth model through the application of the Haar wavelet approach.
Jay Kishore Sahani, Pankaj Sharma, Nikhil Khanna, Ajay Kumar   +3 more
doaj   +1 more source

FMDNet: Spatial‐frequency feature routing for low‐dose CT denoising

Journal of Applied Clinical Medical Physics, Volume 27, Issue 6, June 2026.
Abstract Background Low‐dose computed tomography (LDCT) is widely used to reduce radiation exposure, but the reduced photon budget amplifies quantum noise and can introduce structured artifacts that obscure subtle boundaries and textures. Many deep learning denoisers process features in a single stream, which may encourage either over‐smoothing of weak
Yujie Yao, Yunsen Liang, Wen Xiao, Zibo Zhou, Yaoxue Xu, Yonghao Pan, Xiao Xia   +6 more
wiley   +1 more source

Enhanced Crack Detection in Composite Plates: Integrating Haar Wavelet Transform with Convolutional Neural Networks [PDF]

E3S Web of Conferences
In order to ensure structural integrity, detecting cracks, as a common structural flaw, is crucial. The current study presents a method for crack detection and prediction in plates under free vibration using the Convolutional Neural Network (CNN) and the
Mehrparvar Marmar, Majak Jüri, Karjust Kristo   +2 more
doaj   +1 more source

Advancing COVID-19 Understanding: Simulating Omicron Variant Spread Using Fractional-Order Models and Haar Wavelet Collocation

Mathematics, 2023
This study presents a novel approach for simulating the spread of the Omicron variant of the SARS-CoV-2 virus using fractional-order COVID-19 models and the Haar wavelet collocation method.
Zehba Raizah, Rahat Zarin
doaj   +1 more source

A Scalable Perovskite Platform With Multi‐State Photoresponsivity for In‐Sensor Saliency Detection

Advanced Materials, Volume 38, Issue 32, 8 June 2026.
A scalable in‐sensor computing platform (32 × 32 array) with ultra‐low variability is developed by incorporating ferroelectric copolymers into halide perovskite thin films. These devices achieve 1000 programmable photoresponsivity states and high thermal reliability.
Xuechao Xing, Anubhab Tripathi, Si En Ng, Natalia Yantara, Yue Gong, Qinjie Wu, Yeow Boon Tay, Wen Siang Lew, Yang Chai, Arindam Basu, Nripan Mathews   +10 more
wiley   +1 more source

Haar wavelet method for solving nonlinear age-structured population models

, 2017
In this study, Haar wavelet method is implemented for solving the nonlinear age-structured population model which is the nonclassic type of partial differential equation associated with boundary integral equation.
Mohammad Heydari, Zakieh Avazzadeh
core   +1 more source

Haar Wavelet Based Implementation Method of the Non–integer Order Differentiation and its Application to Signal Enhancement

Measurement Science Review, 2015
Non–integer order differentiation is changing application of traditional differentiation because it can achieve a continuous interpolation of the integer order differentiation. However, implementation of the non–integer order differentiation is much more
Li Yuanlu, Pan Chang, Meng Xiao, Ding Yaqing, Chen Haixiu   +4 more
doaj   +1 more source

Haar Wavelet Method for Vibration Analysis of Beams and Parameter Quantification

, 2021
https://www.ester.ee/record ...
openaire   +2 more sources

Optimized Deep Learning Framework for Diabetic Retinopathy Based on Systematic Multi‐Stage Transfer Learning and Predictive Risk Stratification

Applied AI Letters, Volume 7, Issue 2, June 2026.
A multi‐stage transfer learning framework systematically optimizes CNN architectures for diabetic retinopathy detection and risk stratification from fundus images. EfficientNetB7 achieves 96% accuracy while a survival‐inspired model generates continuous risk scores aligned with DR severity for clinically meaningful patient risk assessment.
Mujidat Jibrin, Abdsamad Benkrid
wiley   +1 more source