The scanning multichannel microwave radiometer results for the Gulf of Alaska Seasat Experiment Workshop are quite encouraging, especially in view of the immaturity of the data-processing algorithms. For open ocean, rain-free cells of highest-quality surface truth wind determinations exhibit standard deviations of 3 meters per second about a bias of 1.5 meters per second. The sea-surface temperature shows a standard deviation of approximately 1.5 degrees C about a bias of 3 degrees to 5 degrees C under a variety of changing meteorological conditions.
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http://dx.doi.org/10.1126/science.204.4400.1415 | DOI Listing |
Neurophotonics
January 2025
California Institute of Technology, Department of Electrical Engineering, Pasadena, California, United States.
Significance: Cerebral blood flow (CBF) and cerebral blood volume (CBV) are key metrics for regional cerebrovascular monitoring. Simultaneous, non-invasive measurement of CBF and CBV at different brain locations would advance cerebrovascular monitoring and pave the way for brain injury detection as current brain injury diagnostic methods are often constrained by high costs, limited sensitivity, and reliance on subjective symptom reporting.
Aim: We aim to develop a multi-channel non-invasive optical system for measuring CBF and CBV at different regions of the brain simultaneously with a cost-effective, reliable, and scalable system capable of detecting potential differences in CBF and CBV across different regions of the brain.
Ann N Y Acad Sci
January 2025
Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China.
Deep learning has revolutionized electroencephalograph (EEG) decoding, with convolutional neural networks (CNNs) being a predominant tool. However, CNNs struggle with long-term dependencies in sequential EEG data. Models like long short-term memory and transformers improve performance but still face challenges of computational efficiency and long sequences.
View Article and Find Full Text PDFAnalyst
January 2025
Guangdong Provincial Key Laboratory of Electronic Functional Materials and Devices, Huizhou University, Huizhou, 516007, China.
Disordered polymerization of polymers widens the polymerization degree distribution, which leads to uncontrollable thickness and significantly weakens their sensing performance. Herein, poly(sodium -styrenesulfonate)-functionalized reduced graphene oxide (PSS-rGO) with multichannel chain structures coated with thin polyaniline layer (PSS-rGO/PANI) nanocomposites was synthesized a facile interfacial polymerization route. The morphology and microstructure of the PSS-rGO/PANI nanocomposites were characterized using Fourier transform infrared (FTIR) spectroscopy, Raman spectroscopy, X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), and scanning electron microscopy (SEM).
View Article and Find Full Text PDFComput Methods Programs Biomed
January 2025
Key Laboratory of Computer Network and Information Integration (Southeast University), Ministry of Education, Nanjing, China; School of Computer Science and Engineering, Southeast University, Nanjing, China.
Purpose: Dual-energy computed tomography (DECT) enables the differentiation of different materials. Additionally, DECT images consist of multiple scans of the same sample, revealing information similarity within the energy domain. To leverage this information similarity and address safety concerns related to excessive radiation exposure in DECT imaging, sparse view DECT imaging is proposed as a solution.
View Article and Find Full Text PDFJ Thorac Imaging
September 2024
School of Computer Science and Engineering, The Hebrew University of Jerusalem.
Purpose: Radiological follow-up of oncology patients requires the detection of metastatic lung lesions and the quantitative analysis of their changes in longitudinal imaging studies. Our aim was to evaluate SimU-Net, a novel deep learning method for the automatic analysis of metastatic lung lesions and their temporal changes in pairs of chest CT scans.
Materials And Methods: SimU-Net is a simultaneous multichannel 3D U-Net model trained on pairs of registered prior and current scans of a patient.
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