scholarly journals Reduced Complexity Detection of Narrowband Secondary Synchronization Signal for NB-IoT Communication Systems

Symmetry ◽  
2020 ◽  
Vol 12 (8) ◽  
pp. 1342
Author(s):  
Young-Hwan You
Keyword(s):  
Internet Of Things ◽  
Signal Detection ◽  
Communication Systems ◽  
Detection Method ◽  
Signal Sequence ◽  
Search Space ◽  
Complementary Sequence ◽  
Synchronization Signal ◽  
Reduced Complexity ◽  
Simulation Results

Narrowband Internet of Things is one of the most promising technologies to support low cost, massive connection, deep coverage, and low power consumption. In this paper, a computationally efficient narrowband secondary synchronization signal detection method is proposed in the narrowband Internet of Things system. By decoupling the detection of complementary sequence and Zadoff–Chu sequence that make up the synchronization signal sequence, the search space of narrowband secondary synchronization signal hypotheses is reduced. Such a design strategy along with the use of the symmetric property of synchronization signals allows reduced-complexity synchronization signal detection in the narrowband Internet of Things system. Both theoretical and simulation results are provided to verify the usefulness of the proposed detector. It is shown via simulation results that the complexity of the proposed detection method is significantly reduced while producing some performance degradation, compared to the conventional detection method.

Fault Signal Detection Model for Large-Scale Circuit Communication System

2014 ◽  
Vol 651-653 ◽  
pp. 432-435
Author(s):  
Bao Sheng Chen
Keyword(s):  
Signal Detection ◽  
Communication System ◽  
Communication Systems ◽  
Large Scale ◽  
Detection Method ◽  
Wave Group ◽  
Detection Model ◽  
Group Detection ◽  
The Time Domain ◽  
Process Detection

In the process of fault signal detection for large-scale circuit communication systems, with traditional methods to process detection, the fault detection method is more conservative. A fault signal detection for large-scale circuit communication system based on QRS wave group detection method is proposed. The signal to be measured is transformed appropriately in the time domain or frequency domain to strengthen or separate the QRS component, in order to suppress interference from various noise to signals, and the fault point of circuit communication system fault signal is identified, the filter is utilized as representative to process multiscale decomposition for fault signals of circuit communication systems. Experiments show that QRS wave group detection method can determine the occurrence time of the circuit system fault signal, and further estimate the nature of the fault signal, thus, the fault point of communication system fault signal is found to improve the efficiency of detection.

Fault Tree Analysis for a Modern Communication System

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Kamal Hamid ◽  
Nadim Chahine
Keyword(s):  
Communication System ◽  
Communication Systems ◽  
Fault Tree ◽  
Fault Tree Analysis ◽  
Theory And Practice ◽  
Tree Analysis ◽  
Satisfactory Performance ◽  
System Components ◽  
Link Budget ◽  
Simulation Results

Wireless communications became one of the most widespread means for transferring information. Speed and reliability in transferring the piece of information are considered one of the most important requirements in communication systems in general. Moreover, Quality and reliability in any system are considered the most important criterion of the efficiency of this system in doing the task it is designed to do and its ability for satisfactory performance for a certain period of time, Therefore, we need fault tree analysis in these systems in order to determine how to detect an error or defect when happening in communication system and what are the possibilities that make this error happens. This research deals with studying TETRA system components, studying the physical layer in theory and practice, as well as studying fault tree analysis in this system, and later benefit from this study in proposing improvements to the structure of the system, which led to improve gain in Link Budget. A simulation and test have been done using MATLAB, where simulation results have shown that the built fault tree is able to detect the system’s work by 82.4%.

scholarly journals Rhesus monkey (Macaca mulatta) cool sensitivity measured by a signal detection method

1976 ◽  
Vol 19 (3) ◽  
pp. 246-251 ◽  
Author(s):  
Helen H. Molinari ◽  
Andrew J. Rózsa ◽  
Dan R. Kenshalo
Keyword(s):  
Rhesus Monkey ◽  
Signal Detection ◽  
Macaca Mulatta ◽  
Detection Method

scholarly journals Spectral Convolution Feature-Based SPD Matrix Representation for Signal Detection Using a Deep Neural Network

Entropy ◽  
2020 ◽  
Vol 22 (9) ◽  
pp. 949
Author(s):  
Jiangyi Wang ◽  
Min Liu ◽  
Xinwu Zeng ◽  
Xiaoqiang Hua
Keyword(s):  
Neural Network ◽  
Signal Detection ◽  
Convolutional Neural Network ◽  
Deep Neural Network ◽  
Detection Method ◽  
Learning Algorithm ◽  
Simulated Data ◽  
Data Sets ◽  
Feature Maps ◽  
Simulated Data Sets

Convolutional neural networks have powerful performances in many visual tasks because of their hierarchical structures and powerful feature extraction capabilities. SPD (symmetric positive definition) matrix is paid attention to in visual classification, because it has excellent ability to learn proper statistical representation and distinguish samples with different information. In this paper, a deep neural network signal detection method based on spectral convolution features is proposed. In this method, local features extracted from convolutional neural network are used to construct the SPD matrix, and a deep learning algorithm for the SPD matrix is used to detect target signals. Feature maps extracted by two kinds of convolutional neural network models are applied in this study. Based on this method, signal detection has become a binary classification problem of signals in samples. In order to prove the availability and superiority of this method, simulated and semi-physical simulated data sets are used. The results show that, under low SCR (signal-to-clutter ratio), compared with the spectral signal detection method based on the deep neural network, this method can obtain a gain of 0.5–2 dB on simulated data sets and semi-physical simulated data sets.

scholarly journals Vocal plasticity in a reptile

2017 ◽  
Vol 284 (1855) ◽  
pp. 20170451 ◽  
Author(s):  
Henrik Brumm ◽  
Sue Anne Zollinger
Keyword(s):  
Signal Detection ◽  
Communication Systems ◽  
Vocal Communication ◽  
Animal Communication ◽  
High Amplitude ◽  
Vocal Plasticity ◽  
Lombard Effect ◽  
Gekko Gecko ◽  
High Degree ◽  
Signalling Systems

Sophisticated vocal communication systems of birds and mammals, including human speech, are characterized by a high degree of plasticity in which signals are individually adjusted in response to changes in the environment. Here, we present, to our knowledge, the first evidence for vocal plasticity in a reptile. Like birds and mammals, tokay geckos ( Gekko gecko ) increased the duration of brief call notes in the presence of broadcast noise compared to quiet conditions, a behaviour that facilitates signal detection by receivers. By contrast, they did not adjust the amplitudes of their call syllables in noise (the Lombard effect), which is in line with the hypothesis that the Lombard effect has evolved independently in birds and mammals. However, the geckos used a different strategy to increase signal-to-noise ratios: instead of increasing the amplitude of a given call type when exposed to noise, the subjects produced more high-amplitude syllable types from their repertoire. Our findings demonstrate that reptile vocalizations are much more flexible than previously thought, including elaborate vocal plasticity that is also important for the complex signalling systems of birds and mammals. We suggest that signal detection constraints are one of the major forces driving the evolution of animal communication systems across different taxa.

scholarly journals Over/Undervoltage and Undervoltage Shift of Hybrid Islanding Detection Method of Distributed Generation

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Manop Yingram ◽  
Suttichai Premrudeepreechacharn
Keyword(s):  
Control System ◽  
Distributed Generation ◽  
Hybrid Method ◽  
Detection Method ◽  
System Simulation ◽  
Detection Methods ◽  
Islanding Detection ◽  
Active Method ◽  
Passive Method ◽  
Simulation Results

The mainly used local islanding detection methods may be classified as active and passive methods. Passive methods do not perturb the system but they have larger nondetection zones, whereas active methods have smaller nondetection zones but they perturb the system. In this paper, a new hybrid method is proposed to solve this problem. An over/undervoltage (passive method) has been used to initiate an undervoltage shift (active method), which changes the undervoltage shift of inverter, when the passive method cannot have a clear discrimination between islanding and other events in the system. Simulation results on MATLAB/SIMULINK show that over/undervoltage and undervoltage shifts of hybrid islanding detection method are very effective because they can determine anti-islanding condition very fast.ΔP/P>38.41% could determine anti-islanding condition within 0.04 s;ΔP/P<-24.39% could determine anti-islanding condition within 0.04 s;-24.39%≤ΔP/P≤ 38.41% could determine anti-islanding condition within 0.08 s. This method perturbed the system, only in the case of-24.39% ≤ΔP/P ≤38.41% at which the control system of inverter injected a signal of undervoltage shift as necessary to check if the occurrence condition was an islanding condition or not.

LFM Signal Detection Method Based on Fractional Fourier Transform

2014 ◽  
Vol 989-994 ◽  
pp. 4001-4004 ◽  
Author(s):  
Yan Jun Wu ◽  
Gang Fu ◽  
Yu Ming Zhu
Keyword(s):  
Fourier Transform ◽  
Signal Detection ◽  
Frequency Analysis ◽  
Experimental Analysis ◽  
Fractional Fourier Transform ◽  
Detection Method ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Frequency Information ◽  
Strong Impulse

As a generalization of Fourier transform, the fractional Fourier Transform (FRFT) contains simultaneity the time-frequency information of the signal, and it is considered a new tool for time-frequency analysis. This paper discusses some steps of FRFT in signal detection based on the decomposition of FRFT. With the help of the property that a LFM signal can produce a strong impulse in the FRFT domain, the signal can be detected conveniently. Experimental analysis shows that the proposed method is effective in detecting LFM signals.

Sign in / Sign up

Export Citation Format

Share Document