Related Experiment Video
Updated: Oct 13, 2025
11:54
Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface
Published on: May 8, 2021
4.7K
Time Latency-Centric Signal Processing: A Perspective of Smart Manufacturing
Sharifu Ura1, Angkush Kumar Ghosh2
1Division of Mechanical and Electrical Engineering, Kitami Institute of Technology, 165 Koen-cho, Kitami 090-8507, Japan.
Sensors (Basel, Switzerland)
|November 13, 2021
Summary
Smart manufacturing embedded systems face signal delays. The delay domain offers a more informative and reliable signal processing method than time or frequency domains for these systems.
Area of Science:
- Engineering
- Computer Science
- Signal Processing
Background:
- Smart manufacturing relies on embedded systems like CNC machines and cyber-physical systems.
- Signal exchange within these systems introduces time latency, altering signal integrity.
- Conventional time and frequency domain analyses are insufficient for delay-centric issues.
Purpose of the Study:
- To investigate the efficacy of the delay domain for signal processing in smart manufacturing.
- To compare the delay domain against time and frequency domains for signal analysis.
- To demonstrate the delay domain's potential as a signature for machining situations.
Main Methods:
- Processing arbitrary signals in time, frequency, and delay domains.
- Analyzing real-life machining signals using frequency and delay domains.
- Evaluating signal informativeness and reliability across different domains.
Main Results:
- The delay domain provides more informative and reliable signal analysis compared to time and frequency domains.
- Delay domain analysis effectively addresses issues caused by unavoidable signal delays.
- The delay domain can uniquely characterize specific machining situations.
Conclusions:
- The delay domain is crucial for robust signal processing in smart manufacturing.
- Implementing delay domain-based signal processing is essential for optimizing embedded systems.
- Delay domain analysis enhances the functional capabilities of smart manufacturing technologies.
Related Concept Videos
01:21Time and frequency -Domain Interpretation of Phase-lag Control
161
Phase-lag controllers are widely used in control systems to improve stability and reduce steady-state errors. A dimmer switch controlling the brightness of a light bulb serves as a practical example of phase-lag control, gradually adjusting the bulb's brightness. Mathematically, phase-lag control or low-pass filtering is represented when the factor 'a' is less than 1.
Phase-lag controllers do not place a pole at zero, but instead influence the steady-state error by amplifying any...
Phase-lag controllers do not place a pole at zero, but instead influence the steady-state error by amplifying any...
161
01:23Linear time-invariant Systems
520
A system is linear if it displays the characteristics of homogeneity and additivity, together termed the superposition property. This principle is fundamental in all linear systems. Linear time-invariant (LTI) systems include systems with linear elements and constant parameters.
The input-output behavior of an LTI system can be fully defined by its response to an impulsive excitation at its input. Once this impulse response is known, the system's reaction to any other input can be...
The input-output behavior of an LTI system can be fully defined by its response to an impulsive excitation at its input. Once this impulse response is known, the system's reaction to any other input can be...
520
01:11Sampling Continuous Time Signal
410
In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
In the...
In the...
410
01:30Classification of Signals
1.0K
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...
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...
1.0K
01:23Design Example
398
The innovation of touch-tone telephony revolutionized the telecommunications industry by replacing the traditional rotary dial with a dual-tone multi-frequency (DTMF) signaling system. This system uses a matrix-style keypad with buttons arranged in four rows and three columns, creating 12 distinct signals each assigned to a pair of frequencies. Each button press results in a simultaneous generation of two sinusoidal tones – one from a low-frequency group (697 to 941 Hz) and one from a...
398
01:10Reconstruction of Signal using Interpolation
398
Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next...
398