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Related Concept Videos

Structural Classification of Joints01:20

Structural Classification of Joints

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Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
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Functional Classification of Joints01:09

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Functional Classification of Joints
The functional classification of joints is determined by the amount of mobility between the adjacent bones. Joints are functionally classified as a synarthrosis or immobile joint, an amphiarthrosis or slightly moveable joint, or as a diarthrosis, a freely moveable joint. Fibrous and cartilaginous joints can be functionally classified as either synarthroses  or amphiarthroses, whereas all synovial joints are classified as diarthroses.
Synarthrosis
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Attenuated Total Reflectance (ATR) Infrared Spectroscopy: Overview01:13

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Attenuated total reflectance (ATR) infrared spectroscopy is a powerful analytical technique used to study the composition of materials. It is widely employed in chemistry, materials science, forensic science, and other fields where sample characterization is required. ATR has several advantages over traditional transmission IR spectroscopy, including the requirement of little to no sample preparation and the ability to analyze a wide range of samples.
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Classification of Signals01:30

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In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
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Identical bonds within a polyatomic group can stretch symmetrically (in-phase) or asymmetrically (out-of-phase). Similar to hydrogen bonding, these vibrations also influence the shape of the IR peak. Generally, asymmetric stretching frequencies are higher than symmetric stretching frequencies. For example, primary amines exhibit two distinct IR peaks between 3300–3500 cm−1 corresponding to the symmetric and asymmetric N-H stretching, while secondary amines exhibit a single...
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Classification of Systems-II01:31

Classification of Systems-II

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Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
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Hyperspectral Imaging as a Tool to Study Optical Anisotropy in Lanthanide-Based Molecular Single Crystals
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Spatial-Spectral Heterogeneity-Aware Network for Hyperspectral and LiDAR Joint Classification.

Shenfu Zhang, Qiang Liu, Zhenhua Zhang

    IEEE Transactions on Neural Networks and Learning Systems
    |June 27, 2025
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    Summary

    This study introduces a novel network (S2HANet) for land cover classification using hyperspectral (HS) and light detection and ranging (LiDAR) data. S2HANet effectively addresses spectral and spatial heterogeneities, improving classification accuracy.

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    Area of Science:

    • Remote Sensing
    • Geospatial Analysis
    • Machine Learning

    Background:

    • Hyperspectral (HS) and light detection and ranging (LiDAR) data integration is crucial for land cover classification.
    • Existing methods struggle with spectral-spatial heterogeneities and underutilize elevation data.

    Purpose of the Study:

    • To develop a novel network (S2HANet) for joint HS and LiDAR data classification.
    • To address limitations in handling spectral-spatial variations and elevation data representation.

    Main Methods:

    • Introduced a spatial-spectral heterogeneity-aware network (S2HANet).
    • Employed a shared spectral correction module (SSCM) and contrastive learning for spectral feature enhancement.
    • Utilized multichannel signed distance discrimination module (MCSDDM) for spatial boundary refinement.
    • Incorporated elevation boost (EBM) and elevation injection (EIM) modules for improved elevation data utilization.

    Main Results:

    • S2HANet demonstrated exceptional classification performance.
    • The network effectively handled spectral and spatial heterogeneities.
    • Improved utilization of elevation height information was achieved.

    Conclusions:

    • S2HANet offers a significant advancement in land cover classification using combined HS and LiDAR data.
    • The proposed modules effectively address limitations of previous approaches.
    • The method shows strong generalizability across benchmark datasets.