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Classification of Signals01:30

Classification of Signals

437
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...
437
Signal and System01:26

Signal and System

644
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...
644
Classification of Systems-I01:26

Classification of Systems-I

179
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:
179
Classification of Systems-II01:31

Classification of Systems-II

139
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,
139
Field Application of Global Positioning System01:28

Field Application of Global Positioning System

42
The Global Positioning System (GPS) has become an indispensable tool in fieldwork, offering unparalleled precision and efficiency for surveying, navigation, and infrastructure development. By harnessing signals from a constellation of satellites, GPS receivers determine the location of objects with remarkable speed and accuracy, often completing calculations within a second.Advantages of Modern GPS TechnologyContemporary GPS receivers are designed to meet the practical demands of field...
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Design Example: Identifying the Locations of Monuments in the Field Using Global Positioning System Device01:30

Design Example: Identifying the Locations of Monuments in the Field Using Global Positioning System Device

27
Surveyors use Global Positioning System (GPS) technology to measure the precise location and elevation of points on Earth. In a recent survey, GPS receivers were used to determine the coordinates and elevations of two park monuments. The process involved careful mission planning, data collection, and correction to ensure accuracy. The survey began with mission planning to identify optimal satellite visibility and minimize Position Dilution of Precision (PDOP). A geodetic control point...
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Related Experiment Video

Updated: Jun 23, 2025

A Lightweight, Headphones-based System for Manipulating Auditory Feedback in Songbirds
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5G AI-IoT System for Bird Species Monitoring and Song Classification.

Jaume Segura-Garcia1, Sean Sturley2, Miguel Arevalillo-Herraez1

  • 1Escola Tecnica Superior d'Enginyeria, Universitat de Valencia, 46100 Burjassot, Spain.

Sensors (Basel, Switzerland)
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Machine learning and deep learning advance birdsong identification. New deep convolutional neural networks (DCNNs) offer efficient bird identification for edge devices, balancing accuracy and size.

Keywords:
AI-IoTCNNaudiobirdsong classification

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Area of Science:

  • Ornithology
  • Ecology
  • Computer Science
  • Artificial Intelligence

Background:

  • Accurate animal species identification is crucial for biological and ecological studies, including bird protection and ecosystem health assessment.
  • Machine learning (ML) and deep learning (DL) have significantly improved birdsong identification capabilities.
  • AI-Internet of Things (AI-IoT) approaches leverage audio data for bird identification.

Purpose of the Study:

  • To develop and evaluate AI-IoT systems for bird identification using audio recordings.
  • To compare the performance of different deep convolutional neural networks (CNNs) for birdsong classification.
  • To design energy-efficient DCNN models suitable for deployment on small, resource-constrained devices like single-board computers (SBCs) and microcontrollers (MCUs).

Main Methods:

  • Utilized a 5G IoT system for collecting raw audio data.
  • Employed image feature comparison techniques using CNNs (EfficientNet, MobileNet) trained with ImageNet weights on bird song spectrograms.
  • Developed and tested two custom deep CNNs (DCNNs) for birdsong classification, focusing on parameter reduction and efficiency.

Main Results:

  • ImageNet-weighted CNNs achieved up to 75% accuracy in identifying most bird species.
  • These standard CNNs possess a large number of parameters, resulting in lower energy efficiency during inference.
  • The custom-designed DCNNs successfully reduced model size while maintaining a competitive level of accuracy.

Conclusions:

  • Deep learning models, particularly custom DCNNs, show significant promise for accurate and efficient birdsong identification.
  • The developed DCNNs are suitable for integration into edge computing devices (SBCs, MCUs) for real-time bird monitoring.
  • This research contributes to advancing AI applications in ornithology and ecological monitoring.