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Related Concept Videos

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A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
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The Discrete-Time Fourier Series (DTFS) is a fundamental concept in signal processing, serving as the discrete-time counterpart to the continuous-time Fourier series. It allows for the representation and analysis of discrete-time periodic signals in terms of their frequency components. Unlike its continuous counterpart, which utilizes integrals, the calculation of DTFS expansion coefficients involves summations due to the discrete nature of the signal.
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Basic continuous-time signals include the unit step function, unit impulse function, and unit ramp function, collectively referred to as singularity functions. Singularity functions are characterized by discontinuities or discontinuous derivatives.
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System stability is a fundamental concept in signal processing, often assessed using convolution. For a system to be considered bounded-input bounded-output (BIBO) stable, any bounded input signal must produce a bounded output signal. A bounded input signal is one where the modulus does not exceed a certain constant at any point in time.
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The relative frequency depicts the proportion of data points that have each value. The frequency tells the number of data points that have each value. Like the histogram, a relative frequency histogram also has the same shape with a horizontal scale (the x-axis), but the vertical scale (the y-axis) is marked with relative frequencies (percentages of the whole) instead of actual frequencies. A relative frequency histogram is a graphical representation of a frequency distribution where the...
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Updated: Jul 27, 2025

Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons
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Yet Another Compact Time Series Data Representation Using CBOR Templates (YACTS).

Sebastian Molina Araque1, Ivan Martinez2, Georgios Z Papadopoulos1

  • 1IMT Atlantique Campus Rennes, SRCD, IRISA, 35510 Brest, France.

Sensors (Basel, Switzerland)
|June 10, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a new data format for Internet of Things (IoT) time series data using Concise Binary Object Representation (CBOR). The novel format significantly reduces data transmission size and extends IoT device battery life.

Keywords:
ASN.1CBORInternet of Things (IoT)JSONProtobufTime Series (TS)interoperability

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Data Science

Background:

  • The Internet of Things (IoT) is rapidly expanding, leading to massive deployment of devices.
  • Interoperability challenges persist for IoT information systems, particularly with Time Series (TS) data.
  • Existing TS data formats lack standardization and efficient handling for constrained IoT devices.

Purpose of the Study:

  • To introduce a new, standardized TS data format for IoT based on CBOR.
  • To address interoperability issues and enhance the efficiency of constrained IoT devices.
  • To reduce data transmission size and extend device battery life.

Main Methods:

  • Developed a new TS data format leveraging CBOR's compactness with delta values, tags, and templates.
  • Introduced refined, structured metadata for enhanced measurement information.
  • Utilized Concise Data Definition Language (CDDL) for validating CBOR structures.
  • Conducted a detailed performance evaluation comparing the new format with JSON, CBOR, ASN.1, and Protocol Buffers.

Main Results:

  • Achieved data size reduction of 88%-94% compared to JSON, 82%-91% vs. CBOR/ASN.1, and 60%-88% vs. Protocol Buffers.
  • Reduced Time-on-Air by 84%-94% on Low Power Wide Area Networks (LPWAN).
  • Extended battery life by up to 12-fold compared to CBOR and 9-16 fold vs. Protocol Buffers/ASN.1.
  • Proposed metadata added only 0.5% to overall data transmission.

Conclusions:

  • The proposed CBOR-based TS format offers a compact representation, significantly reducing data transmission.
  • The format effectively extends IoT device battery life and improves overall device lifetime.
  • The approach is adaptable to different data types and integrates seamlessly into existing IoT systems.