Electrical Signal Analysis of Different Types of Oxygen Efficient Plants

2018 ◽  
pp. 63-73
Author(s):  
Anurag Kumar ◽  
Neetu Sood ◽  
Indu Saini
Keyword(s):  
Signal Analysis ◽  
Electrical Signal ◽  
Different Types

Arc stability indexes evaluation of ultrasonic wave-assisted underwater FCAW using electrical signal analysis

2019 ◽  
Vol 103 (5-8) ◽  
pp. 2593-2608 ◽  
Author(s):  
Jianfeng Wang ◽  
Qingjie Sun ◽  
Tao Zhang ◽  
Xiaoyang Tao ◽  
Peng Jin ◽  
...  
Keyword(s):  
Ultrasonic Wave ◽  
Signal Analysis ◽  
Electrical Signal ◽  
Arc Stability ◽  
Stability Indexes

CWT × DWT × DTWT × SDTWT: Clarifying terminologies and roles of different types of wavelet transforms

2020 ◽  
Vol 18 (06) ◽  
pp. 2030001 ◽  
Author(s):  
Rodrigo Capobianco Guido ◽  
Fernando Pedroso ◽  
André Furlan ◽  
Rodrigo Colnago Contreras ◽  
Luiz Gustavo Caobianco ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Discrete Time ◽  
Wavelet Transforms ◽  
Signal Analysis ◽  
Applied Mathematics ◽  
Discrete Wavelet ◽  
Frequency Signal ◽  
Time Frequency ◽  
Subsequent Investigation ◽  
Different Types

Wavelets have been placed at the forefront of scientific researches involving signal processing, applied mathematics, pattern recognition and related fields. Nevertheless, as we have observed, students and young researchers still make mistakes when referring to one of the most relevant tools for time–frequency signal analysis. Thus, this correspondence clarifies the terminologies and specific roles of four types of wavelet transforms: the continuous wavelet transform (CWT), the discrete wavelet transform (DWT), the discrete-time wavelet transform (DTWT) and the stationary discrete-time wavelet transform (SDTWT). We believe that, after reading this correspondence, readers will be able to correctly refer to, and identify, the most appropriate type of wavelet transform for a certain application, selecting relevant and accurate material for subsequent investigation.

Optic-Electrical Signal Analysis of Plasma Fluctuation Characteristics in Laser Deep Penetration Welding

2018 ◽  
Vol 45 (4) ◽  
pp. 0402001
Author(s):  
邱文聪 Qiu Wencong ◽  
杨立军 Yang Lijun ◽  
刘桐 Liu Tong ◽  
赵德金 Zhao Dejin
Keyword(s):  
Signal Analysis ◽  
Electrical Signal ◽  
Deep Penetration ◽  
Laser Deep Penetration Welding ◽  
Plasma Fluctuation ◽  
Deep Penetration Welding

Design, Deploy and Signal Analysis of Different Types of CR

2012 ◽  
Vol 239-240 ◽  
pp. 1238-1241
Author(s):  
Jing Hui Fan ◽  
Hong Li Zhao ◽  
Yi Wang
Keyword(s):  
Data Processing ◽  
Signal Analysis ◽  
Corner Reflector ◽  
Sar Image ◽  
Sar Images ◽  
Differential Processing ◽  
Imaging Parameters ◽  
Sar Imaging ◽  
Different Types ◽  
Reflection Intensity

In Shuping area, it is difficulty to find enough nature coherent targets to use InSAR technique to monitor the landslide. In order to solve the problem, CRs(Corner Reflectors) were used to improve the effectiveness of this technology. In this article Pyramidal Corner Reflector, Asymmetric Cubic Corner Reflector, Double Direction Asymmetric Cubic Corner Reflector are designed. In order to achieve maximum reflection intensity of CR, it's attitude must be carefully adjusted to match with the SAR imaging parameters exactly. After the CRs were deployed, the strong echo signals can be retrieved from SAR images. Analyzing the SAR image peak signals to locate the CR is the premise of differential processing. Through image location and phase analysis of ASAR and RADARSAT-2, the CRs’ signals can be recognition certainly. During the data processing of ASAR and PALSAR, the CRs can be regarded as ideal coherence target points.

scholarly journals Electrostatic force microscopy for the accurate characterization of interphases in nanocomposites

2018 ◽  
Vol 9 ◽  
pp. 2999-3012
Author(s):  
Diana El Khoury ◽  
Richard Arinero ◽  
Jean-Charles Laurentie ◽  
Mikhaël Bechelany ◽  
Michel Ramonda ◽  
...  
Keyword(s):  
Signal Analysis ◽  
Electrostatic Force ◽  
Interface State ◽  
Electrostatic Force Microscopy ◽  
Polystyrene Spheres ◽  
Promising Technique ◽  
Force Microscopy ◽  
Different Types ◽  
Experimental Protocols

The unusual properties of nanocomposites are commonly explained by the structure of their interphase. Therefore, these nanoscale interphase regions need to be precisely characterized; however, the existing high resolution experimental methods have not been reliably adapted to this purpose. Electrostatic force microscopy (EFM) represents a promising technique to fulfill this objective, although no complete and accurate interphase study has been published to date and EFM signal interpretation is not straightforward. The aim of this work was to establish accurate EFM signal analysis methods to investigate interphases in nanodielectrics using three experimental protocols. Samples with well-known, controllable properties were designed and synthesized to electrostatically model nanodielectrics with the aim of “calibrating” the EFM technique for future interphase studies. EFM was demonstrated to be able to discriminate between alumina and silicon dioxide interphase layers of 50 and 100 nm thickness deposited over polystyrene spheres and different types of matrix materials. Consistent permittivity values were also deduced by comparison of experimental data and numerical simulations, as well as the interface state of silicone dioxide layers.

scholarly journals Different types of calcium channels

1986 ◽  
Vol 124 (1) ◽  
pp. 177-190 ◽  
Author(s):  
E. W. McCleskey ◽  
A. P. Fox ◽  
D. Feldman ◽  
R. W. Tsien
Keyword(s):  
Membrane Potential ◽  
Cell Membrane ◽  
Electrical Signal ◽  
Multiple Channel ◽  
Contraction Coupling ◽  
The Family ◽  
Different Types ◽  
Chemical Messenger ◽  
Sensing Membrane ◽  
Ca2 Channels

Ca2+ channels allow passage of Ca2+ ions into the cytoplasm through a selective pore which is opened in response to depolarization of the cell membrane (for reviews see Hagiwara & Byerly, 1981, 1983; Tsien, 1983; Reuter, 1983). The Ca2+ flux creates a net inward, depolarizing current and the resulting accumulation of Ca2+ in the cytoplasm can act as a chemical trigger for secretion of hormones and neurotransmitters, contraction of muscle and a variety of other Ca2+-sensitive events. Thus, upon sensing membrane potential changes, Ca2+ channels simultaneously generate an electrical signal while directly creating an intracellular chemical messenger. This dual ability is unique among the family of ion channels and allows the Ca2+ channel to play a variety of roles in excitation-secretion and excitation-contraction coupling. It has now become clear that versatility of function is reflected by diversity of the types of Ca2+ channels on the membrane of individual cells. This article describes the nature of data which have demonstrated multiple channel types, reviews the literature suggesting that many cells have several kinds of Ca2+ channels, and discusses newer data regarding a neurotoxin that distinguishes among different Ca2+ channels.

scholarly journals Evaluation of Seven Time-Frequency Representation Algorithms Applied to Broadband Echolocation Signals

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Josefin Starkhammar ◽  
Maria Hansson-Sandsten
Keyword(s):  
Signal Analysis ◽  
Time Difference ◽  
Frequency Difference ◽  
Transient Signals ◽  
Signal Component ◽  
Time Frequency ◽  
Exact Location ◽  
Frequency Representation ◽  
Different Types ◽  
Echolocation Signal

Time-frequency representation algorithms such as spectrograms have proven to be useful tools in marine biosonar signal analysis. Although there are several different time-frequency representation algorithms designed for different types of signals with various characteristics, it is unclear which algorithms that are best suited for transient signals, like the echolocation signals of echolocating whales. This paper describes a comparison of seven different time-frequency representation algorithms with respect to their usefulness when it comes to marine biosonar signals. It also provides the answer to how close in time and frequency two transients can be while remaining distinguishable as two separate signals in time-frequency representations. This is, for instance, relevant in studies where echolocation signal component azimuths are compared in the search for the exact location of their acoustic sources. The smallest time difference was found to be 20 µs and the smallest frequency difference 49 kHz of signals with a −3 dB bandwidth of 40 kHz. Among the tested methods, the Reassigned Smoothed Pseudo Wigner-Ville distribution technique was found to be the most capable of localizing closely spaced signal components.

Improved ECG Signal Analysis Using Wavelet and Feature Extraction

2007 ◽  
Vol 46 (02) ◽  
pp. 227-230 ◽  
Author(s):  
M. Jonkman ◽  
F. de Boer ◽  
A. Matsuyama
Keyword(s):  
Feature Extraction ◽  
Signal Analysis ◽  
Significant Variation ◽  
Wavelet Decomposition ◽  
Ecg Signal ◽  
Ecg Signals ◽  
Feature Vectors ◽  
Different Types ◽  
Before And After

Summary Objectives : Automatic detection of arrhythmias is important for diagnosis of heart problems. However, in ECG signals, there is significant variation of waveforms in both normal and abnormal beats. It is this phenomenon, which makes it difficult to analyse ECG signals. The aim of developing methodology is to distinguish between normal beats and abnormal beats in an ECG signal. Methods : ECG signals were first decomposed using wavelet transform. The feature vectors were then extracted from these decomposed signals as normalised energy and entropy. To improve the classification of the feature vectors of normal and abnormal beats, the normal beats which occur before and after the abnormal beats were eliminated from the group of normal beats. Results : With our proposed methods, the normal beats and abnormal beats formed different clusters of vector points. By eliminating normal beats which occur before and after the abnormal beats, the clusters of different types of beats showed more apparent separation. Conclusions : The combination of wavelet decomposition and the classification using feature vectors of the beats in ECG signals separate abnormal beats from normal beats. The elimination of the normal beats which occur before and after the abnormal beats succeeded in minimising the size of normal beats cluster.

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