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Functional Classification of Joints01:09

Functional Classification of Joints

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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
An...
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Classification of Signals01:30

Classification of Signals

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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.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
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Sensory Modalities01:15

Sensory Modalities

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Sensation typically is the process by which the sensory receptors and sense organs detect stimuli from the internal and external environment and transmit this information to the central nervous system for processing.
General senses refer to the broad category of sensory information detected by receptors in the body and can be further grouped into somatic and visceral senses. Somatic sensations include touch, pressure, temperature, and pain and are essential for navigating our environment and...
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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.
A fibrous joint is where the adjacent bones are united by fibrous connective...
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Classification of Systems-I01:26

Classification of Systems-I

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Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
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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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Related Experiment Video

Updated: Aug 29, 2025

Cross-Modal Multivariate Pattern Analysis
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A Joint Automatic Modulation Classification Scheme in Spatial Cognitive Communication.

Mengtao Wang1, Youchen Fan1, Shengliang Fang1

  • 1School of Space Information, Space Engineering University, Beijing 101416, China.

Sensors (Basel, Switzerland)
|September 9, 2022
PubMed
Summary

This study introduces a joint deep learning (DL) and expert feature model for automatic modulation discrimination (AMC). The novel approach enhances the distinction between similar Quadrature Amplitude Modulation (QAM) signals, improving classification accuracy.

Keywords:
automatic modulation classificationconvolutional neural networksdeep learningexpert feature methodsspatial cognitive communication

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Area of Science:

  • Electrical Engineering
  • Signal Processing
  • Artificial Intelligence

Background:

  • Automatic modulation discrimination (AMC) is crucial for intelligent receivers in cognitive communication systems.
  • Deep learning (DL) models face challenges in distinguishing between similar modulation schemes like 16QAM and 64QAM due to intra-class diversity.

Purpose of the Study:

  • To propose a novel joint AMC model that combines DL with expert features.
  • To overcome the limitations of existing DL-based AMC methods in differentiating similar QAM signals.

Main Methods:

  • A DL network was developed to extract time-series and phase features from in-phase and quadrature (IQ) samples.
  • Expert features were constructed to enhance the accurate classification of QAM signals.
  • The proposed joint model integrates both DL-extracted and expert-defined features.

Main Results:

  • The joint AMC model demonstrated superior performance compared to benchmark networks.
  • Classification accuracy increased by 11.5% at a 10 dB signal-to-noise ratio (SNR).
  • Improved discrimination capabilities for similar QAM signals were achieved.

Conclusions:

  • The proposed joint AMC model effectively addresses the intra-class diversity challenge in QAM signal discrimination.
  • Combining DL and expert features offers a promising approach for high-performance AMC in intelligent receivers.