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Related Concept Videos

Signal and System01:26

Signal and System

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A signal x(t) is a set of data or a time function representing a variable of interest. Signals typically convey information about a phenomenon, such as atmospheric temperature, humidity, human voice, television images, a dog's bark, or birdsongs. More generally, a signal can be a function of more than one independent variable. For instance, images depend on horizontal and vertical positions and can be regarded as two-dimensional signals. However, this text will focus on one-dimensional...
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Parallel Processing01:20

Parallel Processing

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The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...
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Sensory Perception: Organization of the Somatosensory System01:11

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The somatosensory system is the central and peripheral nervous system component that senses and processes touch, pressure, pain, temperature, and body position or proprioception. The process of sensation takes place at three levels:
The receptor level:
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What is a Sensory System?01:31

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Sensory systems detect stimuli—such as light and sound waves—and transduce them into neural signals that can be interpreted by the nervous system. In addition to external stimuli detected by the senses, some sensory systems detect internal stimuli—such as the proprioceptors in muscles and tendons that send feedback about limb position.
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The spinal cord is an integral hub for motor and sensory information that enables the brain to communicate with the peripheral nervous system (PNS). This communication consists of relaying sensory data and transmission of motor commands.
Sensory Information Processing
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Signal Sequences and Sorting Receptors01:41

Signal Sequences and Sorting Receptors

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Signal sequences are short amino acid sequences that guide newly synthesized proteins to their proper location within the cell. Classical signal sequences are fifteen to sixty amino acids long and present at the N-terminus of a polypeptide chain. Each signal sequence has a conserved segment of basic residues towards their N terminus, a hydrophobic core, and a C-terminus rich in polar residues. The C-terminus also contains a signal cleavage site and features a -3 -1 sequence motif. The -3-1...
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Sensor Signal and Information Processing II.

Wai Lok Woo1, Bin Gao2

  • 1Department of Computer and Information Sciences, Northumbria University, Newcastle upon Tyne NE1 8ST, UK.

Sensors (Basel, Switzerland)
|July 9, 2020
PubMed
Summary
This summary is machine-generated.

This Special Issue explores innovative sensor signal processing using computational intelligence. Algorithms learn from diverse data, including wireless, machinery, and internet signals, enabling self-discovery and adaptation to new information.

Keywords:
3D processingcompressive sensingdeep learningimage processingmachine learningphase arrayssensorssignal processingwireless communication

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Area of Science:

  • Engineering and Computer Science
  • Artificial Intelligence and Machine Learning

Background:

  • This Special Issue presents novel research on integrating sensor signals with advanced information processing techniques.
  • It covers a broad spectrum of sensor data, including wireless communication, machinery, ultrasound, imaging, and internet data.

Discussion:

  • The core theme is the application of computational intelligence within algorithms for sensor signal processing.
  • This includes methodologies like deep learning, machine learning, compressive sensing, and variational Bayesian inference.
  • These approaches enable algorithms to generalize and autonomously discover knowledge from data.

Key Insights:

  • Algorithms incorporating computational intelligence demonstrate enhanced problem-solving capabilities.
  • The ability to learn and adapt to unseen data is a critical feature of these advanced systems.
  • Innovative developments span diverse sensor types and processing techniques.

Outlook:

  • Future research will likely focus on further refining computational intelligence for real-time sensor data analysis.
  • Expanding the application of these techniques to new domains and complex data streams is anticipated.
  • The development of self-learning systems for sensor information processing holds significant potential for technological advancement.