Robust Precise Time Difference Estimation Based on Digital Zero-Crossing Detection Algorithm

This paper presents a method for identifying tonal signal parameters using zero crossing detection. The signal parameters: frequency, amplitude and phase can change slowly in time. The described method allows to obtain accurate detection using possibly small number of signal samples. The detection algorithm consists of the following steps: frequency filtering, zero crossing detection and parameter reading. Filtering of the input signal is aimed at obtaining a signal consisting of a single tonal component. Zero crossing detection allows the elimination of multiple random zero crossings, which do not occur in a pure sine wave signal. The frequency is based on the frequency of transitions through zero, the amplitude is the largest value of the signal in the analysed time interval, and the initial phase is derived from the moment at which the transition through zero occurs. The obtained parameters were used to synthesise a compensation signal in an active tonal component reduction algorithm. The results of the algorithm confirmed the high efficiency of the method.

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The Performance Correlation Hilbert Time-Delay Estimation for Passive Detection

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.602-605.1768 ◽

2014 ◽

Vol 602-605 ◽

pp. 1768-1771

Author(s):

Feng Wang ◽

Mei Quan Liu ◽

Jiang Wei Fan

Keyword(s):

Time Delay ◽

High Speed ◽

Time Lag ◽

Estimation Method ◽

Time Delay Estimation ◽

Time Difference ◽

Delay Estimation ◽

Main Peak ◽

Zero Crossing ◽

Passive Detection

Passive time difference detection method is distance, high speed and good concealment which has broad military application prospects. One of the key technologies for passive detection is to extract the time lag through effective signal processing. Relevant method is the most basic method to estimate the time difference and is the basic theory of all correlative time-delay estimation algorithms. The method is simple. But good results rely on the spectrum characteristics of signal and noise is ideal. Time delay estimation based on Hilbert transform is the expansion of the generalized correlation time-delay estimation method which changes the correlation function from accidentally symmetry into odd symmetry. Detecting correlation peak is converted into zero crossing detection. The method sharps the main peak value point and improved the precision of time delay estimation which gets better time-delay estimation performance in the narrowband signal.

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Synchronization system with Zero-Crossing Peak Detection algorithm for power system applications

The 2010 International Power Electronics Conference - ECCE ASIA - ◽

10.1109/ipec.2010.5543716 ◽

2010 ◽

Cited By ~ 8

Author(s):

Adrian Z. Amanci ◽

Francis P. Dawson

Keyword(s):

Power System ◽

Detection Algorithm ◽

Peak Detection ◽

Zero Crossing ◽

Synchronization System ◽

Peak Detection Algorithm

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Zero-Crossing Detection Algorithm Based on Narrowband Filtering

2020 IEEE 3rd Student Conference on Electrical Machines and Systems (SCEMS) ◽

10.1109/scems48876.2020.9352306 ◽

2020 ◽

Author(s):

Zunxian Wang ◽

Shouyuan Wu ◽

Mingxu Wang ◽

Yongxin Yang ◽

Xiaoming Luan ◽

...

Keyword(s):

Detection Algorithm ◽

Zero Crossing

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A Design of an Automatic Single Phase Power Factor Controller by Using Arduino Uno Rev-3

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.785.419 ◽

2015 ◽

Vol 785 ◽

pp. 419-423 ◽

Cited By ~ 1

Author(s):

Nurul Huda Ishak ◽

Muhammad Naim Zainodin ◽

Nur Ashida Salim ◽

Faizal Muhamad Twon Tawi ◽

Aini Hafiza Mohd Saod

Keyword(s):

Power Factor ◽

Single Phase ◽

Hardware Implementation ◽

Time Difference ◽

Signal Pulse ◽

Unity Power Factor ◽

Current Transformers ◽

Inductive Load ◽

Zero Crossing ◽

Arduino Uno

This paper presents a computationally accurate technique to design an automatic single phase power factor controller by using microcontroller. The hardware implementation was developed by using Arduino Uno Rev-3 main board which uses the ATmega328 as the microcontroller. The power factor value from the load was measured by using voltage and current transformers, zero crossing detectors, and ATmega328 microcontroller. Zero crossing detectors produced current and voltage signals which will be measured and calculated for the time difference between both signals by ATmega328 microcontroller using appropriate algorithms in order to obtain value of power factor. The ATmega328 is programmed to automatically switch on and off capacitor function in order to control the signal pulse send to relay to connect and disconnect capacitor parallel with the load when energized and de energized if the calculated power factor value is either below 0.9 or above 0.9, respectively. Simulation of the power factor controller model was performed by using Proteus software. Arduino IDE software was used as the compiler for the Arduino Uno. The main objective is to study and develop the technology of automatic single phase power factor controller using Arduino Uno Rev-3 by controlling the power factor of the inductive load near to unity power factor. The results of the power factor value were displayed on the LCD and the power factor is corrected if it falls below 0.9 in order to prevent penalty by the power supplier. The effectiveness of the proposed technique could assist the monitoring unit in order to maintain the power factor at the value set by the utility.

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Role of NMDAR plasticity in a computational model of synaptic memory

Scientific Reports ◽

10.1038/s41598-021-00516-y ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Ekaterina D. Gribkova ◽

Rhanor Gillette

Keyword(s):

Computational Model ◽

Single Neuron ◽

Network Models ◽

Time Difference ◽

Neural Network Models ◽

Silent Synapses ◽

Aspartate Receptor ◽

Artificial Neural Network Models ◽

Precise Time

AbstractA largely unexplored question in neuronal plasticity is whether synapses are capable of encoding and learning the timing of synaptic inputs. We address this question in a computational model of synaptic input time difference learning (SITDL), where N‐methyl‐d‐aspartate receptor (NMDAR) isoform expression in silent synapses is affected by time differences between glutamate and voltage signals. We suggest that differences between NMDARs’ glutamate and voltage gate conductances induce modifications of the synapse’s NMDAR isoform population, consequently changing the timing of synaptic response. NMDAR expression at individual synapses can encode the precise time difference between signals. Thus, SITDL enables the learning and reconstruction of signals across multiple synapses of a single neuron. In addition to plausibly predicting the roles of NMDARs in synaptic plasticity, SITDL can be usefully applied in artificial neural network models.

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