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Klyuchko, O. M., and P. V. Beloshitsky. "Biosensor concept and data input to biomedical infornation systems." Medical Informatics and Engineering, no. 3 (June 10, 2021): 51–69. http://dx.doi.org/10.11603/mie.1996-1960.2020.3.11698.
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Background. In present publication we generalized and analyzed deeply the experience of some biosensors studying in biophysical experiments with aim to incorporate them further to electronic information systems. Output biosensor electrical signals were input ones to electronic information system making their connection into joined bioinformation system. Materials and methods. Methods of comparative analysis of the characteristics of input and output electrical information signals of biosensor were applied; its physical and mathematical models were developed. For biosensor properties studies the methods of transmembrane electric currents recording in voltage-clamp mode as well as patch-clamp on hippocampal neuronal membranes were used. Results. Biosensor concept and their general characteristic were given, corresponding prototypes were observed. The physical model of biosensor was developed and some test results of this device were suggested. The biosensor was examined as abstraction in consistent unity of its functions: signal receiver — filter — analyzer — encoder/decoder. A brief mathematical description of biosensor functioning was given as well as corresponding algorithm. As a result of performed works the possibilities of this biosensor incorporation to bioinformation electronic systems were substantiated and the example of such system «EcoIS» was observed. Conclusion. In conclusion following results of the works were summarized. The detailed description of technical devices — biosensors as elements of biomedical information systems were done as well as analysis of electrical information signals at output of biosensor, its ability to encode information and detailed analysis of the possibility to incorporate this biotechnical device into electronic information systems due to biosensor output electricals signals.
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He, Fei, Jiabei Shen, Zhi Tang, Xiaomeiao Qi, and Haoran Li. "Influencing Factors of Rock Electrical Signal Analysis Based on Artificial Intelligence." Mobile Information Systems 2021 (October 21, 2021): 1–9. http://dx.doi.org/10.1155/2021/1165686.
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To get the generation mechanism and influence factors of the coal-rock electrical signal, it provides a reference for coal-rock electric signal prediction and dynamic disaster. Firstly, the law of charge energy relation is deduced by using the universal or functional relation of Newton’s law, and then coal and rock mass with different properties such as coal, granite, and sandstone are selected. Using the established test system, the influence of coal and rock electric signal is analyzed from the coal-rock temperature, coal and rock properties, coal-rock friction, coal-rock loading speed, and coal-rock load size. The chief results can be summarized as follows: in the process of coal-rock rising with temperature, the change rule of electrical signals can be divided into three stages according to the strength of electrical signals generated. The amplitudes of electrical signals generated by uniaxial compression of coal-rock mass with different properties are obviously different. Electrical signals can be generated during the friction of coal and rock. With the augment of loading speed, the electrical semaphore generated when the coal rock is about to fracture tends to be enhanced. The size of the electrical signal is not proportional to the size of the load, but the electrical signal is greatest when the rupture is imminent. The analysis of the influence factors of coal and rock electrical signals can provide a reference for the research on the generation mechanism of coal-rock electrical signals.
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Cai, Weiming, and Qingke Qi. "Study on Electrophysiological Signal Monitoring of Plant under Stress Based on Integrated Op-Amps and Patch Electrode." Journal of Electrical and Computer Engineering 2017 (2017): 1–7. http://dx.doi.org/10.1155/2017/4182546.
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Electrophysiological signal in plant is a weak electrical signal, which can fluctuate with the change of environment. An amplification detection system was designed for plant electrical signal acquisition by using integrated op-amps (CA3140, AD620, and INA118), patch electrode, data acquisition card (NI USB-6008), computer, and shielded box. Plant electrical signals were also studied under pressure and flooding stress. The amplification detection system can make nondestructive acquisition for Aquatic Scindapsus and Guaibcn with high precision, high sensitivity, low power consumption, high common mode rejection ratio, and working frequency bandwidth. Stress experiments were conducted through the system; results show that electrical signals were produced in the leaf of Aquatic Scindapsus under the stress of pressure. Electrical signals in the up-leaf surface of Aquatic Scindapsus were stronger than the down-leaf surface. Electrical signals produced in the leaf of Guaibcn were getting stronger when suffering flooding stress. The more the flooding stress was severe, the faster the electrical signal changed, the longer the time required for returning to a stable state was, and the greater the electrical signal got at the stable state was.
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Zhang, Hong, Xinxin Lu, and Xiuye Yin. "Reverse Synchronous Transmission of Electrical Signals Based on Parallel Injection and Series Pickup." Traitement du Signal 37, no. 4 (2020): 655–60. http://dx.doi.org/10.18280/ts.370415.
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To eliminate the interference with the transmission of electrical signals, this paper puts forward a reverse synchronous transmission (RST) control method based on parallel injection and series pickup. Firstly, the synchronous transmission mechanism of electrical signals was analyzed, followed by the design of the framework and workflow of signal transmission. Next, an RST channel model was established for electrical signals, and the transmission parameters were configured based on the transmission properties of these signals. Through alternative current (AC) impedance analysis, the Laplace transform was performed on the transmission loop to increase the voltage of the transmission channel, and to elevate the signal-to-noise ratio (SNR) of the voltage across the resistor. Finally, the voltage comparator was adopted to obtain the digital information of the baseband signal, and the power signal was transmitted to the RST channel, completing the RST control of electrical signals. The experimental results show that the transmission speed of the system was 0.7488, and the reverse transmission of electrical signals was only delayed by 5ms, when the intensity of electromagnetic radiation was 2.0μT. Through parallel injection and series pickup, the proposed system can effectively realize the RST of electrical signals, without changing the topology of the transmission system.
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Carneiro, Mirella, Victor Oliveira, Fernanda Oliveira, Marco Teixeira, and Milena Pinto. "Simulation Analysis of Signal Conditioning Circuits for Plants’ Electrical Signals." Technologies 10, no. 6 (2022): 121. http://dx.doi.org/10.3390/technologies10060121.
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Electrical signals are generated and transmitted through plants in response to stimuli caused by external environment factors, such as touching, luminosity, and leaf burning. By analyzing a specific plant’s electrical responses, it is possible to interpret the impact of external aspects in the plasma membrane potential and, thus, determine the cause of the electrical signal. Moreover, these signals permit the whole plant structure to be informed almost instantaneously. This work presents a brief discussion of plants electrophysiology theory and low-cost signal conditioning circuits, which are necessary for the acquisition of plants’ electrical signals. Two signal conditioning circuits, which must be chosen depending on the signal to be measured, are explained in detail and electrical simulation results, performed in OrCAD Capture Software are presented. Furthermore, Monte Carlo simulations were performed to evaluate the impact of components variations on the accuracy and efficiency of the signal conditioning circuits. Those simulations showed that, even after possible component variations, the filters’ cut-off frequencies had at most 4% variation from the mean.
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Saatci, Ertugrul, and Esra Saatci. "Multifractal Behaviour of Respiratory Signals." Electrica 20, no. 2 (2020): 182–88. http://dx.doi.org/10.5152/electrica.2020.20011.
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Li, Chang Cheng, Lai Wu Yin, Dong Chen, and Shu Jie Xu. "Lossless Compression of Weak Electrical Signal of Ginseng Molecule Based on Discrete Wavelet Transform and Siesta Program." Advanced Materials Research 986-987 (July 2014): 1950–53. http://dx.doi.org/10.4028/www.scientific.net/amr.986-987.1950.
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This paper proposed the electron density of time series by using the Siesta software to calculate the weak electrical signals of ginseng molecule, combining with the lifting scheme DWT to remove ginseng molecular spatial redundancy. For the acquisition and identification of weak electrical signals of ginseng molecule in physical environment , based on the analysis of collection and identification’s principles, the noise coefficient is removed to reconstruct the signal and retain the useful signal components through applying the multi-decomposition of DWT transform to divide weak electrical signals of ginseng molecule into wavelet coefficients of different scales. The experimental results show that the multi-resolution analysis of DWT transform is performed for the weak electrical signal of ginseng molecule with different rhythms and different frequency ranges, and the weak electrical signal size of ginseng molecule before and after compression, the percentage of high frequency coefficients set to zero, and the average energy percentage after compression are, respectively, increased to 77.73%, 46.88%, and 99.99%. This algorithm operates fast enough to ease hardware implementation, providing an effective method for lossless compression of the weak electrical signals of ginseng molecule.
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Wang, Hanbo. "Compressed Sensing: Theory and Applications." Journal of Physics: Conference Series 2419, no. 1 (2023): 012042. http://dx.doi.org/10.1088/1742-6596/2419/1/012042.
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Abstract Compressed sensing is a new technique for solving underdetermined linear systems. Because of its good performance, it has been widely used in academia. It is applied in electrical engineering to recover sparse signals, especially in signal processing. This technique exploits the signal’s sparse nature, allowing the original signals to recover from fewer samples. This paper discusses the fundamentals of compressed sensing theory, the research progress in compressed sensing signal processing, and the applications of compressed sensing theory in nuclear magnetic resonance imaging and seismic exploration acquisition. Compressed sensing allows for the digitization of analogue data with inexpensive sensors and lowers the associated costs of processing, storage, and transmission. Behind its sophisticated mathematical expression, compressed sensing theory contains a subtle idea. Compressed sensing is a novel theory that is an ideal complement and improvement to conventional signal processing. It is a theory with a high vitality level, and its research outcomes may substantially influence signal processing and other fields.
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Zhuang, Qiu Hui, Guo Jun Liu, Xiu Hua Fu, and San Qiang Wang. "Brain Electrical Signal Digital Processing System Design." Applied Mechanics and Materials 278-280 (January 2013): 958–61. http://dx.doi.org/10.4028/www.scientific.net/amm.278-280.958.
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Through the amplification system to extract the brain electrical signal, although already can be displayed, but is not clear; in addition, the analog signal into the computer to carry on the analysis, also must pass to convert analog signals to digital signals (A/D converter).Therefore the need for further use of digital processing, this paper adopts the digital way, on brain electrical analog signal digital filter, through the 40Hz low-pass filter and 50Hz filter, get clear, stable signal, to achieve the design objective.
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Shi, Qiong, JingCi Zhou, JianPing Xiang, and YangChun Shi. "Research on multi-fault diagnosis method and test platform of main transmission Machinery of wind Turbine based on electrical signal." Journal of Physics: Conference Series 2268, no. 1 (2022): 012011. http://dx.doi.org/10.1088/1742-6596/2268/1/012011.
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Abstract An optimised design was carried out based on the original semi-physical simulation system for the main drive chain of a wind turbine. The input of upper computer simulating wind condition, the safety control of electrical signal and vibration signal acquisition module, relay and AC contactor and dual motor control were added. Simultaneously, the simulation schemes of some common faults on the main drive of wind turbines were also designed in order to perform the experimental simulation of the single fault and multi fault. The primary purpose of simulation is to use practical approaches(such as FFT,EMD)to analyze the changes of the fault signals based on the collected electrical signals, and to determine whether there is a fault through comparison and theoretical analysis. In addition, it aimed for verifying the feasibility of electrical signals under multi-fault conditions, and discussing advantages of electrical signals over vibration signals.
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Tian, Yonghui, Huifu Xiao, Xiaosuo Wu, et al. "Experimental realization of an optical digital comparator using silicon microring resonators." Nanophotonics 7, no. 3 (2018): 669–75. http://dx.doi.org/10.1515/nanoph-2017-0073.
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AbstractWe propose and experimentally demonstrate a silicon photonic circuit that can perform the comparison operation of two-bit digital signals based on microring resonators (MRRs). Two binary electrical signals regarded as two operands of desired comparison digital signals are applied to three MRRs to modulate their resonances through the microheaters fabricated on the top of MRRs, respectively (here, one binary electrical signal is applied to two MRRs by a 1×2 electrical power splitter, which means that the two MRRs are modulated by the same binary electrical signal). The comparison results of two binary electrical signals can be obtained at two output ports in the form of light. The proposed device is fabricated on a silicon-on-insulator substrate using the complementary metal-oxide-semiconductor fabrication process, and the dynamic characterization of the device with the operation speed of 10 kbps is demonstrated successfully.
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Duan, Li, Jianxian Cai, Juan Liang, Danqi Chen, and Xiaoye Sun. "Identification and Analysis of Non-Stationary Time Series Signals Based on Data Preprocessing and Deep Learning." Traitement du Signal 39, no. 5 (2022): 1703–9. http://dx.doi.org/10.18280/ts.390528.
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Deep learning is not the most accurate way for recognizing time series signals, and it is unable to identify non-stationary time series signals with numerous chaotic classes. Moreover, the signal detection benefits from data preprocessing have gone unnoticed. Therefore, this paper investigates the detection and analysis of non-stationary time series signals using deep learning and data preprocessing. The fitting model of the historical stationarity index is built based on the Gaussian mixture model of single Gaussian models, and the change point of the non-stationary time series signal is detected. To further increase the signal's recognition rate, the non-stationary time series signal is preprocessed using the truncated migration algorithm. The main classification task and the auxiliary classification tasks are constructed to identify non-stationary time series signals characterized by huge chaotic classes through multi-task learning. The efficiency of the suggested method and model is validated by experimental data.
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Chatterjee, Shre Kumar, Saptarshi Das, Koushik Maharatna, et al. "Exploring strategies for classification of external stimuli using statistical features of the plant electrical response." Journal of The Royal Society Interface 12, no. 104 (2015): 20141225. http://dx.doi.org/10.1098/rsif.2014.1225.
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Plants sense their environment by producing electrical signals which in essence represent changes in underlying physiological processes. These electrical signals, when monitored, show both stochastic and deterministic dynamics. In this paper, we compute 11 statistical features from the raw non-stationary plant electrical signal time series to classify the stimulus applied (causing the electrical signal). By using different discriminant analysis-based classification techniques, we successfully establish that there is enough information in the raw electrical signal to classify the stimuli. In the process, we also propose two standard features which consistently give good classification results for three types of stimuli—sodium chloride (NaCl), sulfuric acid (H 2 SO 4 ) and ozone (O 3 ). This may facilitate reduction in the complexity involved in computing all the features for online classification of similar external stimuli in future.
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Marzaki, Abderrezak, V. Bidal, R. Laffont, et al. "On the Investigation of a Novel Dual-Control-Gate Floating Gate Transistor for VCO Applications." Bulletin of Electrical Engineering and Informatics 2, no. 3 (2013): 212–17. http://dx.doi.org/10.11591/eei.v2i3.206.
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A new MOS device called Dual-Control Gate Floating Gate Transistor (DCG-FGT) is used as a building block in analog design. This device offers new approaches in circuit design and allows developing new functionalities through two operating modes: Threshold Voltage Adjustable Mode, where the DCG-FGT behaves like a MOS transistor with an electrically adjustable threshold voltage. Mixer Signal Mode where the DCG-FGT can mix two independent signals on its floating gate. This device is developed to be fully compliant with CMOS Non Volatile Memory (NVM) process. An electrical model of the DCG-FGT has been implemented in an electrical simulator to be available for analog design. A DCG-FGT based ring oscillator is studied in this paper.
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Ma, Xiao, and Hoi Wai Choi. "Observation of ground loop signals in GaN monolithically integrated devices." Journal of Vacuum Science & Technology B 41, no. 1 (2023): 012207. http://dx.doi.org/10.1116/6.0002245.
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The observation of ground loop signals in nonelectrically isolated GaN monolithic systems has prompted an investigation on its origins. The study is carried out with devices comprising monolithic light-emitting diodes (LED) and photodetectors (PD) that are either electrically isolated by completely etching through the GaN epitaxial layers, or nonelectrically isolated devices where the etch terminates at the n-GaN layer, through TCAD simulations and experiments. While the devices behave identically to DC input signals, a signal can be observed across the PD of the nonelectrically isolated devices when an AC signal is fed to the LED, even at voltages below the LED’s turn-on voltage. The [Formula: see text] phase difference of the output PD potential with respect to the input LED potential indicates that the signal, regarded as a ground loop signal, couples through the junction capacitance of the LED and PD. The ground loop signal increases with increasing frequency due to the frequency-dependence of the junction impedance. The insertion of a grounded metal line between the LED and PD reduces the ground loop signal, but not to a sufficient extent not to affect the photovoltage. The findings illustrate the necessity of electrical isolation among devices for GaN monolithic systems, especially those operating at higher frequencies, such as photonic integrated systems.
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Song, Ning, Lian Ying Ji, and Yong Peng Xu. "Denoising of the Respiratory Signal of Electrical Bio-Impedance." Advanced Materials Research 718-720 (July 2013): 1024–28. http://dx.doi.org/10.4028/www.scientific.net/amr.718-720.1024.
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Human respiratory signal provides important information in modern medical care. In daily life, respiratory signal is usually captured under different motion states with the help of Electrical impedance pneumography (EIP). Consequently, the captured signal is easily corrupted by electronic/electromagnetic noise, internal mechanical vibration of the lung and motion artifacts. Because respiratory signal and interferences co-exist in an overlapping spectra manner, classical filtering method cannot work here. In this paper, we present a new signal processing method for eliminating the noise and interferences included in EIP signal, by separating the correlated motion artifacts from the raw EIP and 3-axis Acceleration (ACC) signals, restoring the rough respiration signal from the mixed signal, and further processing using wavelet analysis approach. Results are compared to traditional denosing algorithms by wiener filter, which indicates that the new signal processing method we presented is suitable for EIP signals under the motion states.
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Pollard, Brian J. "Electrical signals." Current Anaesthesia & Critical Care 9, no. 3 (1998): 105. http://dx.doi.org/10.1016/s0953-7112(98)80001-x.
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Bychkov, Anatoly V., Irina Yu Bychkova, Nadezhda N. Suslova, and Kurbangali K. Alimov. "NEURAL NET USING TO DETERMINE DEPTH AND FREQUENCY OF SIGNALS’ MODULATION FOR ELECTRICAL EQUIPMENT ULTRASONIC VIBROCONTROL." Vestnik Chuvashskogo universiteta, no. 3 (September 29, 2021): 21–30. http://dx.doi.org/10.47026/1810-1909-2021-3-21-30.
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The apparatus of artificial neural networks (ANN) is proposed to be used for signal processing in active ultrasonic (US) vibration control of electrical equipment. A feature of the applied neural network algorithm is that the required information about vibration parameters is embedded in the ultrasound signal’s phase change at its constant amplitude. Under these conditions, traditional spectral analysis of signals requires a high sampling rate and a significant recording duration. When using the direct propagation’s ANN with three hidden layers, it was shown that it is sufficient to use a sampling frequency of 5-6 points for the period of an ultrasonic wave and a recording duration of 4-5 periods to estimate the nonstationary frequency and amplitude of the vibration signal. Estimates of the error in determining the amplitude, frequency and phase of vibrations are obtained. The root-mean-square errors of the neural network algorithm do not exceed units of percent. The possibilities of using a trained neural network for signal processing in a «sliding window» are demonstrated. The accuracy characteristics of the proposed neural network algorithm of signal processing and the possibility of its optimization for electrical equipment’s vibration control are discussed.
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Le, Thi-Thu-Huong, and Howon Kim. "Non-Intrusive Load Monitoring Based on Novel Transient Signal in Household Appliances with Low Sampling Rate." Energies 11, no. 12 (2018): 3409. http://dx.doi.org/10.3390/en11123409.
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Nowadays climate change problems have been more and more concerns and urgent in the real world. Especially, the energy power consumption monitoring is a considerate trend having positive effects in decreasing affecting climate change. Non-Intrusive Load Monitoring (NILM) is the best economic solution to solve the electrical consumption monitoring issue. NILM captures the electrical signals from the aggregate energy consumption, feature extraction from these signals and then learning and predicting the switch ON/OFF of appliances used these feature extracted. This paper proposed a NILM framework including data acquisition, data feature extraction, and classification model. The main contribution is to develop a new transient signal in a different aspect. The proposed transient signal is extracted from the active power signal in the low-frequency sampling rate. This transient signal is used to detect the event of household appliances. In household appliances event detection, we applied to Decision Tree and Long Short-Time Memory (LSTM) models. The average accuracies of these models achieved 92.64% and 96.85%, respectively. The computational and result experiments present the solution effectiveness for the accurate transient signal extraction in the electrical input signals.
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Wang, Ling Ling, Hong Xia Luo, Jian Hua Cao, Kun Lu, Ji Hua Fang, and Shuai Chen. "The Test and Time-Frequency Domain Analysis on Plant Electrical Signal Based on Virtual Instrument." Applied Mechanics and Materials 738-739 (March 2015): 686–89. http://dx.doi.org/10.4028/www.scientific.net/amm.738-739.686.
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We tried to obtain numbers of the plant electrical signal with the help of experiments. We made comprehensive treatment and analysis by using the time domain analysis method and frequency domain analysis method to the large numbers of data of the plant electrical signal. The experiment finds that that the plant electrical signal is a kind of low frequency,tiny and fairly stable signal. The features of plant electrical signals remarkably responsive to external stimuli. The measuring precision is limited in the test, so the conclusion only can be mined and used for for future research.
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Uysal, Can, and Tansu Filik. "MUSIC Algorithm for Respiratory Rate Estimation Using RF Signals." Electrica 18, no. 2 (2018): 300–309. http://dx.doi.org/10.26650/electrica.2018.03405.
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Xu, Liuzhu, Di Peng, Yuwen Qin, et al. "Image-Rejected Multi-Band Frequency Down-Conversion Based on Photonic Sampling." Photonics 10, no. 1 (2022): 35. http://dx.doi.org/10.3390/photonics10010035.
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An image-rejected multi-band frequency down-conversion scheme is proposed and experimentally demonstrated based on photonic sampling. The multi-band radio-frequency (RF) signals to be processed are copied into two replicas in quadrature, which are then sampled by an ultra-short optical pulse train via a polarization-multiplexed modulator. After polarization demultiplexing and detection using a pair of low-speed photodetectors, the multi-band RF signals are simultaneously down-converted to the intermediate frequency (IF) band. The image components can be suppressed by quadrature coupling the two generated IF signals via an electrical 90° hybrid coupler (HC). In the experiment, multi-band RF signals in the frequency range of 6 GHz to 39 GHz are down-converted to the IF band below 4 GHz using a local oscillator (LO) signal at 8 GHz to generate the ultra-short optical pulse train. Image rejection is achieved in the digital domain using digital signal processing to compensate for the amplitude and phase mismatch between the two IF signals and to implement quadrature coupling. In addition, through using an electrical phase shifter, an electrical attenuator, and an electrical 90° HC to achieve quadrature coupling of the two IF signals, image-rejected multi-band frequency down-conversion is also verified in the analog domain.
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Liang, Chunhui, Chao Ding, Jinfa Li, and Xiaoyang Zuo. "Research on State Detection Method of Electrical Equipment Based on Wireless Sensor Network Signal Processing." Traitement du Signal 39, no. 6 (2022): 2237–45. http://dx.doi.org/10.18280/ts.390640.
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False alarms and omissions of key measurement signals may even affect the reliability of decision-making in power IoT system, lead to safety accidents and bring huge economic losses. In order to improve the reliability of system operation and the information management level of electrical equipment, it is necessary to identify and extract suddenly changed signals from massive measurement signals collected by wireless sensor networks, and to detect the working state of electrical equipment by judging the source of signals. Therefore, this article studies the state detection method of electrical equipment based on wireless sensor network signal processing. In the second chapter, a cluster splitting and merging method is designed to solve the problem that the existing detection methods tend to ignore the imbalance of cluster size. In the third chapter, according to the data characteristics of measurement signals collected by wireless sensor networks, a similarity measurement criterion for composite time series of measurement signals is proposed, and the corresponding distance matrix is generated based on this criterion. In the fourth chapter, wavelet decomposition is used to decompose the initial measurement signals collected by wireless sensor networks, and then the signals are compressed twice based on compressed sensing. Then the abnormal signal data information is imported into support vector machine for training to realize the real-time detection of abnormal signals of electrical equipment state. Experimental results verify the effectiveness of the proposed algorithm.
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Balazs, Peter, Christian Kasess, Wolfgang Kreuzer, Thomas Maly, Zdeněk Průša, and Florent Jaillet. "Anwendung von Rahmen-Multiplikatoren für die Extraktion von Kurvenquietschen von Zugsaufnahmen." e & i Elektrotechnik und Informationstechnik 138, no. 3 (2021): 206–11. http://dx.doi.org/10.1007/s00502-021-00880-7.
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ZusammenfassungFür viele Anwendungen in der Akustik ist es notwendig, Signale und Funktionen mithilfe von zeitvarianten Filtern zu bearbeiten, z. B. um Komponenten aus einem Signal zu entfernen, deren Frequenzverlauf sich über die Zeit ändert. Es wird eine Methode vorgestellt, die auf einer Darstellung des Signals durch Rahmen (engl. Frames) basiert, und mit deren Hilfe Filter auf der Zeit-Frequenz-Ebene definiert werden können. Nach einer kurzen Beschreibung des theoretischen Hintergrunds von Rahmen wird ihre Anwendung anhand eines Beispiels aus der Lärmforschung erläutert. Mithilfe einer einfachen grafischen Oberfläche wird aus einer Aufnahme einer Kurvenfahrt eines Zugs eine durch den Dopplereffekt zeitvariante Komponente (Kurvenquietschen) herausgeschnitten und in ein zweites Signal eingefügt. Auf diese Art und Weise lassen sich kontrollierte Signale generieren, die dann zur Lärmbewertung eingesetzt werden können.
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KOROVKIN, Nikolay V., and St S. GRITSUTENKO. "Introduction of the Low-Entropy Signal Concept." Elektrichestvo 10, no. 10 (2020): 33–43. http://dx.doi.org/10.24160/0013-5380-2020-10-33-43.
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The article introduces the concept of a low-entropy signal as a time dependence that has a small variability coefficient. A high-entropy radio signal and a low-entropy electrical signal are compared. It is determined as a result of modeling that the variability coefficient of the electrical signal is 100 or more times smaller than the radio signal variability coefficient; therefore, this indicator is selected as a criterion for discrimination of electrical and radio signals. A conclusion is drawn about the need to improve the mathematical techniques for digital processing of signals for analyzing the currents and voltages in electric power systems. It is also shown that low entropy of an electrical signal can be used for improving the accuracy of instrumentation and control equipment. The effect of quantization noise degeneration into a set of harmonic components in modeling the analog-to-digital conversion of currents and voltages in measurement devices is considered. A procedure for compensating this effect by adding white Gaussian noise prior to carry out the quantization operation is proposed. A procedure that allows better accuracy of instrumentation and control equipment to be obtained by using the low entropy of an electric signal is presented. The analog-to-digital converter circuit diagram is given, and the gain in the conversion accuracy is estimated.
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Ding, Jin Li, and Lan Zhou Wang. "Forecast and Processing of Weak Electrical Signals in Clivia miniata by RBF Neural Networks." Advanced Materials Research 216 (March 2011): 388–92. http://dx.doi.org/10.4028/www.scientific.net/amr.216.388.
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Original weak electrical signals in Clivia miniata were tested by a touching test system of self-made double shields with platinum sensors. Tested data of electrical signals denoised by the wavelet soft threshold and using Gaussian radial base function (RBF) as the time series at a delayed input window chosen at 50. An intelligent RBF forecasting system was set up to forecast the signal in plants. Testing result shows that it is feasible to forecast the plant electrical signal for a short period. The forecast data can be used as an important preferences for the intelligent automatic control system based on the adaptive characteristic of plants to achieve the energy saving on agricultural production both the greenhouse and /or the plastic lookum.
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Durka, P. J., J. Z. ygierewicz, and K. J. Blinowska. "Time-Frequency Analysis of Brain Electrical Activity – Adaptive Approximations." Methods of Information in Medicine 43, no. 01 (2004): 70–73. http://dx.doi.org/10.1055/s-0038-1633838.
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Summary Objectives: We present an approach to time-frequency analysis of bioelectrical signals. Methods: The method relays on the decomposition of the signal into a set of waveforms that have good localization both in time and in frequency. The waveforms belong to a highly redundant set of functions – allowing for a very accurate description of signal components. Results: Properties of the method are illustrated by simulations and applications to EEG. Conclusion: The presented method delivers a common formalism suitable for describing both gross statistical properties of structures present in bioelectrical signals, as well as microstructure of chosen phenomena.
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Pan, Qing-Xin, Yang Li, Nan Wang, Peng-Fei Zhao, Lan Huang, and Zhong-Yi Wang. "Variational Mode Decomposition-based Synchronous Multi-Frequency Electrical Impedance Tomography." Information Technology and Control 51, no. 3 (2022): 446–66. http://dx.doi.org/10.5755/j01.itc.51.3.30014.
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Electrical Impedance Tomography (EIT) can perform non-invasive, low-cost, safe, fast, and simple system structure and functional imaging to map the distribution and changes of root zone. Multi frequency EIT solves the problem that single-frequency EIT can only carry more impedance information than a given single excitation frequency. It still remains challenges to simultaneously obtain multi-frequency electrical impedance tomography. To address the problem, a mixed signal superimposed by multiple frequencies is injected to the object. Essentially, separating the measured mixed voltage signals, which can be used to obtain electrical impedance information at different frequencies at the same time quickly. Since the measurement signal is a multi-frequency signal, the effect of decomposing the multi-frequency signal directly affects the accuracy of imaging. In order to obtain more accurate data, this article used the variational mode decomposition (VMD) method to decompose the measured multi-frequency signal. Accurate amplitude and phase information could be obtained simultaneously at the same time in multi-frequency excitation, and these data could be used to reconstruct electrical impedance distribution The results showed that the proposed method can achieve the expected imaging effect. It was concluded that using the variational modal decomposition method to process the data of multi-frequency signals is more accurate and the imaging effect is better, and it can be applied to multi-frequency electrical impedance imaging in practice.
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Rajani, A., and V. Sandeep. "A Novel method of QRS Detection Using Adaptive Multilevel Thresholding with Statistical False Peak Elimination." International Journal for Research in Applied Science and Engineering Technology 10, no. 9 (2022): 1406–13. http://dx.doi.org/10.22214/ijraset.2022.46848.
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Abstract: Heart is the vital organ of a Human Body, because of its involvement in various works and processes in the entire body such as blood pumping etc., so recording a heart function is also a great thing, it is done through ECG signals. ECG signal records the electrical signals and activity of a Human Heart based on the electrical signals released by the Heart. ECG signal consists of PQRST waves, which are the reference points on an ECG signal. But, recording them is much easy than extracting and analyzing them, so, as to extract them, we are applying an advanced adaptive multi-level thresholding (AAMT) along with a selective statistical false peak elimination for the detection of QRS peaks of an ECG signal. Initially, median and moving average filters are applied for removing noise as well as terms. After AAMT is implemented on the complete dataset of ECG signals. Then selective statistical false peak elimination (SSFPE) is implemented for removing noise terms that might be missed out during filtering. At last, a search back stage will be implemented to search for low amplitude useful peaks.
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Huang, Zhongwei, Lifen Cheng, and Yang Liu. "Key Feature Extraction Method of Electroencephalogram Signal by Independent Component Analysis for Athlete Selection and Training." Computational Intelligence and Neuroscience 2022 (April 15, 2022): 1–9. http://dx.doi.org/10.1155/2022/6752067.
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Emotion is an important expression generated by human beings to external stimuli in the process of interaction with the external environment. It affects all aspects of our lives all the time. Accurate identification of human emotional states and further application in artificial intelligence can better improve and assist human life. Therefore, the research on emotion recognition has attracted the attention of many scholars in the field of artificial intelligence in recent years. Brain electrical signal conversion becomes critical, and it needs a brain electrical signal processing method to extract the effective signal to realize the human-computer interaction However, nonstationary nonlinear characteristics of EEG signals bring great challenge in characteristic signal extraction. At present, although there are many feature extraction methods, none of them can reflect the global feature of the signal. The following solutions are used to solve the above problems: (1) this paper proposed an ICA and sample entropy algorithm-based framework for feature extraction of EEG signals, which has not been applied for EEG and (2) simulation signals were used to verify the feasibility of this method, and experiments were carried out on two real-world data sets, to show the advantages of the new algorithm in feature extraction of EEG signals.
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Huang, Baoshan, Guojin Feng, Xiaoli Tang, et al. "A Performance Evaluation of Two Bispectrum Analysis Methods Applied to Electrical Current Signals for Monitoring Induction Motor-Driven Systems." Energies 12, no. 8 (2019): 1438. http://dx.doi.org/10.3390/en12081438.
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This paper investigates the performance of the conventional bispectrum (CB) method and its new variant, the modulation signal bispectrum (MSB) method, in analysing the electrical current signals of induction machines for the condition monitoring of rotor systems driven by electrical motors. Current signal models which include the phases of the various electrical and magnetic quantities are explained first to show the theoretical relationships of spectral sidebands and their associated phases due to rotor faults. It then discusses the inefficiency of CB and the proficiency of MSB in characterising the sidebands based on simulated signals. Finally, these two methods are applied to analyse current signals measured from different rotor faults, including broken rotor bar (BRB), downstream gearbox wear progressions and various compressor faults, and the diagnostic results show that the MSB outperforms the CB method significantly in that it provides more accurate and sparse diagnostics, thanks to its unique capability of nonlinear modulation detection and random noise suppression.
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Dobrzycki, Arkadiusz, Stanisław Mikulski, and Władysław Opydo. "Using ANN and SVM for the Detection of Acoustic Emission Signals Accompanying Epoxy Resin Electrical Treeing." Applied Sciences 9, no. 8 (2019): 1523. http://dx.doi.org/10.3390/app9081523.
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Electrical treeing is one of the effects of partial discharges in the solid insulation of high-voltage electrical insulating systems. The process involves the formation of conductive channels inside the dielectric. Acoustic emission (AE) is a method of partial discharge detection and measurement, which belongs to the group of non-destructive methods. If electrical treeing is detected, the measurement, recording, and analysis of signals, which accompany the phenomenon, become difficult due to the low signal-to-noise ratio and possible multiple signal reflections from the boundaries of the object. That is why only selected signal parameters are used for the detection and analysis of the phenomenon. A detailed analysis of various acoustic emission signals is a complex and time-consuming process. It has inspired the search for new methods of identifying the symptoms related to partial discharge in the recorded signal. Bearing in mind that a similar signal is searched, denoting a signal with similar characteristics, the use of artificial neural networks seems pertinent. The paper presents an effort to automate the process of insulation material condition identification based on neural classifiers. An attempt was made to develop a neural classifier that enables the detection of the symptoms in the recorded acoustic emission signals, which are evidence of treeing. The performed studies assessed the efficiency with which different artificial neural networks (ANN) are able to detect treeing-related signals and the appropriate selection of such input parameters as statistical indicators or analysis windows. The feedforward network revealed the highest classification efficiency among all analyzed networks. Moreover, the use of primary component analysis helps to reduce the teaching data to one variable at a classification efficiency of up to 1%.
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Yang, Sung Min, María Eugenia Vilarchao, Lorena Rela, and Lidia Szczupak. "Wide propagation of graded signals in nonspiking neurons." Journal of Neurophysiology 109, no. 3 (2013): 711–20. http://dx.doi.org/10.1152/jn.00934.2012.
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Signal processing in neuritic trees is ruled by the concerted action of passive and active membrane properties that, together, determine the degree of electrical compartmentalization of these trees. We analyzed how active properties modulate spatial propagation of graded signals in a pair of nonspiking (NS) neurons of the leech. NS neurons present a very extensive neuritic tree that mediates the interaction with all the excitatory motoneurons in leech ganglia. NS cells express voltage-activated Ca2+ conductances (VACCs) that, under certain experimental conditions, evoke low-threshold spikes. We studied the distribution of calcium transients in NS neurons loaded with fluorescent calcium probes in response to low-threshold spikes, electrical depolarizing pulses, and synaptic inputs. The three types of stimuli evoked calcium transients of similar characteristics in the four main branches of the neuron. The magnitude of the calcium transients evoked by electrical pulses was a graded function of the change in NS membrane potential and depended on the baseline potential level. The underlying VACCs were partially inactivated at rest and strongly inactivated at −20 mV. Stimulation of mechanosensory pressure cells evoked calcium transients in NS neurons whose amplitude was a linear function of the amplitude of the postsynaptic response. The results evidenced that VACCs aid an efficient propagation of graded signals, turning the vast neuritic tree of NS cells into an electrically compact structure.
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Yuan, Yuan, Meng He, Yuan-Wen Zou, Zhong-Bing Huang, Jin-Chuan Li, and Xue-Jin Huang. "An Adjustable Electrical Stimulator for Cell Culture." Journal of Circuits, Systems and Computers 25, no. 11 (2016): 1650146. http://dx.doi.org/10.1142/s0218126616501462.
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Electrical stimulations can promote cell growth, but most electrical stimulators could only output either voltage signals or current signals and do not output arbitrary waveforms according to need. In this paper, a wireless stimulator with adjustable stimulation signals is developed for cell culture. The original waveforms are produced by signal generating circuits. Then under the adjustment of amplification circuits, the original waveforms are converted into current stimulation signals or voltage stimulation signals. Finally, stimulation signals apply onto cells under the monitor of current measuring circuits. The stimulator can provide signals with the following characteristics: (a) required arbitrary waveforms at frequencies ranging from 0 Hz to 100[Formula: see text]kHz; (b) voltage signals at an amplitude ranging from [Formula: see text]15[Formula: see text]V to 15[Formula: see text]V with a resolution of 1[Formula: see text]mV; and (c) current signals at an amplitude ranging from [Formula: see text]1[Formula: see text]mA to 1[Formula: see text]mA with a resolution of 1[Formula: see text][Formula: see text]A when load resistance is less than 50.0[Formula: see text]k[Formula: see text]. Results of these experiments confirm that the developed instrument can provide adjustable stimulation signals for cell growth.
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Yudina, Lyubov, Ekaterina Gromova, Marina Grinberg, Alyona Popova, Ekaterina Sukhova, and Vladimir Sukhov. "Influence of Burning-Induced Electrical Signals on Photosynthesis in Pea Can Be Modified by Soil Water Shortage." Plants 11, no. 4 (2022): 534. http://dx.doi.org/10.3390/plants11040534.
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Local damage to plants can induce fast systemic physiological changes through generation and propagation of electrical signals. It is known that electrical signals influence numerous physiological processes including photosynthesis; an increased plant tolerance to actions of stressors is a result of these changes. It is probable that parameters of electrical signals and fast physiological changes induced by these signals can be modified by the long-term actions of stressors; however, this question has been little investigated. Our work was devoted to the investigation of the parameters of burning-induced electrical signals and their influence on photosynthesis under soil water shortage in pea seedlings. We showed that soil water shortage decreased the amplitudes of the burning-induced depolarization signals (variation potential) and the magnitudes of photosynthetic inactivation (decreasing photosynthetic CO2 assimilation and linear electron flow and increasing non-photochemical quenching of the chlorophyll fluorescence and cyclic electron flow around photosystem I) caused by these signals. Moreover, burning-induced hyperpolarization signals (maybe, system potentials) and increased photosynthetic CO2 assimilation could be observed under strong water shortage. It was shown that the electrical signal-induced increase of the leaf stomatal conductance was a potential mechanism for the burning-induced activation of photosynthetic CO2 assimilation under strong water shortage; this mechanism was not crucial for photosynthetic response under control conditions or weak water shortage. Thus, our results show that soil water shortage can strongly modify damage-induced electrical signals and fast physiological responses induced by these signals.
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Ming, Yan Wei, Tai Hong Cheng, Ke Huang, and Bi Li. "PZT Based Ultrasonic Wave Force Detecting Sensor." Applied Mechanics and Materials 511-512 (February 2014): 142–45. http://dx.doi.org/10.4028/www.scientific.net/amm.511-512.142.
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In the sensor system, the wave in the optical fibers conductivity will change when the surrounding medium is changed. Thus, minor changes according to the mechanical wave, optical fiber can be used to monitor changes in the surrounding environment. The working principle of the sensor system is the signal generator output signal transmitted to a piezoelectric ceramic. Because of the inverse piezoelectric effect of piezoelectric ceramics, conduction of the electrical signals can be converted to mechanical signals. The resulting mechanical wave transmitted to the other end of the piezoelectric ceramic by the optical fiber, and converted to an electrical signal. The proposed system consists of signal generator, a piezoelectric ceramic, fiber optics, oscilloscope. For investigation the performance of the sensor system the external point loading was applied. Results show that the sensor system can be successfully applied to the sensing the force value.
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Kemper, Guillermo, Angel Oshita, Ricardo Parra, and Carlos Herrera. "An algorithm for obtaining the frequency and the times of respiratory phases from nasal and oral acoustic signals." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 1 (2023): 358. http://dx.doi.org/10.11591/ijece.v13i1.pp358-373.
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<span lang="EN-US">This work proposes a computational algorithm which extracts the frequency, timings and signal segments corresponding to respiratory phases, through buccal and nasal acoustic signal processing. The proposal offers a computational solution for medical applications which require on-site or remote patient monitoring and evaluation of pulmonary pathologies, such as coronavirus disease 19 (COVID-19). The state of the art presents a few respiratory evaluation proposals through buccal and nasal acoustic signals. Most proposals focus on respiratory signals acquired by a medical professional, using stethoscopes and electrodes located on the thorax. In this case the signal acquisition process is carried out through the use of a low cost and easy to use mask, which is equipped with strategically positioned and connected electret microphones, to maximize the proposed algorithm’s performance. The algorithm employs signal processing techniques such as signal envelope detection, decimation, fast Fourier transform (FFT) and detection of peaks and time intervals via estimation of local maxima and minima in a signal’s envelope. For the validation process a database of 32 signals of different respiratory modes and frequencies was used. Results show a maximum average error of 2.23% for breathing rate, 2.81% for expiration time and 3.47% for inspiration time.</span>
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Kazushchyk, A. L., E. S. Petrova, A. I. Savitsky, and D. B. Kulikovich. "Evaluation of the parameters of pulse signals applied in electrical stimulation using a new experimental device in the course of Medical and Biological Physics." Health and Ecology Issues, no. 3 (October 1, 2021): 80–85. http://dx.doi.org/10.51523/2708-6011.2021-18-3-10.
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Objective. To develop an experimental device, an algorithm for visual and analytical evaluation of the parameters of pulse signals used in electrical stimulation for further application in the course of Medical and Biological Physics.Materials and methods. The data obtained with the help of the experimental device were used as research materials. The analysis of the obtained results (characteristics of the pulse signal) was carried out with the analytical and numerical methods.Results. The proposed experimental device for receiving and analyzing pulse signals applied in electrical stimulation in education allows mastering the methods of the determination of the parameters of pulse signals, studying the methods to alter the characteristics of pulse signals used in electrical stimulation.Conclusion. The application of the experimental device makes it possible to acquire skills to evaluate the parameters and characteristics of difference pulse signals through their visualization and analytical evaluation.
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Nicolae, Ileana-Diana V. D., Dusan Kostic, Petre-Marian T. Nicolae, and Paul P. Popescu. "Comparison of commercial and original methods for denoising electrical waveforms with constant or linearly variable magnitudes." ITM Web of Conferences 49 (2022): 01005. http://dx.doi.org/10.1051/itmconf/20224901005.
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Acquired electrical waveforms can be affected by white noise. The 1-st part of the paper analysis deals with the denoising of multi-period steady signals by using 3 methods: mean signal method, an original method relying on wavelet packet trees and the method implemented by the wavelet-based Matlab function wden. The signal length influence over the mean signal method’s accuracy is studied. The results yielded by the other 2 methods are also analyzed considering signals with 7 periods. Afterward the wavelet-based methods are used to denoise segments of 7 periods with linearly variable magnitudes (ascending or descending) for 3 different slopes. Artificial test signals, with rich harmonic content, were used. They were polluted by sets of 10 white noises with different powers. Maximum absolute deviations and mean square root deviations were computed considering the original signals, before pollution, versus the corresponding denoised signal. The metrics were computed relative to the maximum absolute value of the noise and allowed to determine the most accurate method.
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Markovinović, Ivan, Miroslav Vrankić, and Saša Vlahinić. "Removal of eye-blink artifacts from EEG signal." Engineering review 40, no. 2 (2020): 101–11. http://dx.doi.org/10.30765/er.40.2.11.
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Electroencephalography (EEG) is well known method of recording electrical brain activity with electrodes placed along the scalp. One of the challenging tasks in this field is the removal of electrical signals that are not related to brain activity.In this paper, an algorithm for the removal of the EEG signals corresponding to the eye blink artifacts is presented. The presented algorithm is based on ADJUST artifact removing tool, which uses independent component analysis (ICA) for signal decomposition. For every signal component returned by the ICA algorithm, temporal-spatial features are calculated, upon which every independent component is classified as artifact or non-artifact, and removed accordingly.
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Suharinto, Catur, Anwar Budianto, and Nugroho Tri Sanyoto. "Design of Electrocardiograph Signal Simulator." Indonesian Journal of electronics, electromedical engineering, and medical informatics 2, no. 1 (2020): 43–47. http://dx.doi.org/10.35882/ijeeemi.v2i1.9.
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Medical equipment functional test and calibration is a routine activity that must be carried on periodically. Electrocardiograph (ECG) requires an ECG phantom to calibrate the function. This calibrator is commonly called ECG signal simulator. The purpose of this study is to design a simple ECG signal simulator with ten leads of signals that can be used to test ECG recorders with standard recording procedures. With the ECG signal simulator that was designed and made, the development of signal patterns can be made as needed. The normal human cardiac signal displayed on the ECGSIM software. The potential value that displayed on ECGSIM software can be extracted manually and assembled as a flash program of microcontroller, so this microcontroller will generate some digital code by each parallel port. This digital code then converted as an analog signal by DAC. The electrocardiograph signal simulator output is an analog signal that identical with each lead according to the recording method of bipolar, unipolar and precordial of ECG. This analog signal was tested using a standard ECG recorder. It is proved that the simulator is able to generate an electrical signal in accordance with the characteristics of the human cardiac signal displayed on ECGSIM software. The results of human electrocardiograph signal simulator design are a device that generates electrical signals with output specifications that correspond to the bioelectric signals of the human heart. The statistical test showed that the p-value is more than 0.05. It is mean that there is no significant difference between the design and standard. The signals pattern has met the specification of ECGSIM signal
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I. S. Amiri, I. S. Amiri, and J. Ali J. Ali. "Data signal processing via manchester coding-decoding method using chaotic signals generated by PANDA ring resonator." Chinese Optics Letters 11, no. 4 (2013): 041901–41904. http://dx.doi.org/10.3788/col201311.041901.
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Sun, Hanqing, Xiaohui Zhang, Zhou Yu, and Gang Xi. "Feature Recognition of Crop Growth Information in Precision Farming." Complexity 2018 (October 15, 2018): 1–10. http://dx.doi.org/10.1155/2018/9250832.
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To identify plant electrical signals effectively, a new feature extraction method based on multiwavelet entropy and principal component analysis is proposed. The wavelet energy entropy, wavelet singular entropy, and the wavelet variance entropy of plants’ electrical signals are extracted by a wavelet transformation to construct the combined features. Principal component analysis (PCA) is applied to treat the constructed features and eliminate redundant information among those features and extract features which can reflect signal type. Finally, the classification method of BP neural network is used to classify the obtained feature vectors. The experimental results show that this method can acquire comparatively high recognition rate, which proposed a new efficient solution for the identification of plant electrical signals.
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Kawamoto, Mitsuru, A. K. Barros, A. Mansour, Kiyotoshi Matsuoka, and Noboru Ohnishi. "Blind signal separation for convolved nonstationary signals." Electronics and Communications in Japan (Part III: Fundamental Electronic Science) 84, no. 2 (2000): 21–29. http://dx.doi.org/10.1002/1520-6440(200102)84:2<21::aid-ecjc3>3.0.co;2-p.
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Slavutskii, Leonid A., and Elena V. Slavutskaya. "NEURAL NETWORK SIGNAL PROCESSING: THE TASKS WITHOUT “DEEP LEARNING”." Vestnik Chuvashskogo universiteta, no. 2 (June 30, 2023): 151–60. http://dx.doi.org/10.47026/1810-1909-2023-2-151-160.
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The purpose of the study is to evaluate the possibility of using artificial neural networks for electrical signals processing.
 
 Methods. The application of a multilayer perceptron for these purposes as the simplest neural feed forward network is considered. Its difference as the basis of neural network algorithms is that when analyzing dynamic processes, signal processing should be carried out in a “sliding time window”.
 
 Results. It is shown that neural network processing makes it possible to approximate the shape of the signal with high accuracy and determine its parameters in real time. Using the example of periodic signals and transients in electrical circuits, accuracy assessments are made and the features of neural network processing are analyzed. The necessary sizes of the training sample of signals and the level of errors that occur when testing a neural network are discussed. Estimates of the required signal sampling frequency, the “sliding window” duration and the variation range of signal parameters when creating a training sample are given.
 
 Conclusions. It is shown that the proposed approach does not require “deep learning” of neural networks with complex architecture. It enables to create a signals training sample based on simple analytical formulas and to control the neural network algorithm quality at intermediate stages of calculations.
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Meng, Qingqing, Zihang Zhu, Guodong Wang, He Li, Lingrui Xie, and Shanghong Zhao. "Photonics-Based Simultaneous DFS and AOA Measurement System without Direction Ambiguity." Micromachines 14, no. 2 (2023): 457. http://dx.doi.org/10.3390/mi14020457.
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A novel scheme that can simultaneously measure the Doppler frequency shift (DFS) and angle of arrival (AOA) of microwave signals based on a single photonic system is proposed. At the signal receiving unit (SRU), two echo signals and the reference signal are modulated by a Sagnac loop structure and sent to the central station (CS) for processing. At the CS, two low-frequency electrical signals are generated after polarization control and photoelectric conversion. The DFS without direction ambiguity and wide AOA measurement can be real-time acquired by monitoring the frequency and power of the two low-frequency electrical signals. In the simulation, an unambiguous DFS measurement with errors of ±3 × 10−3 Hz and a −90° to 90° AOA measurement range with errors of less than ±0.5° are successfully realized simultaneously. It is compact and cost-effective, as well as has enhanced system stability and improved robustness for modern electronic warfare systems.
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Bykov, Sergey V., Igor V. Isakov, and Bogdan S. Shwenk. "Remote control of the PROTEK 3201N spectrum analyzer to solve radiomonitoring tasks." Digital technology security, no. 3 (September 29, 2022): 9–25. http://dx.doi.org/10.17212/2782-2230-2022-3-9-25.
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Electrical measuring instruments related to spectrum analyzers allow you to quickly obtain information about the distribution of energy of electrical and electromagnetic signals in a certain frequency band. The range of signal frequencies that can be measured by spectrum analyzers depends on the specific instrument model. Extending the signal frequency range measured by the spectrum analyzer increases its cost. But depending on the signal processing algorithm, spectrum analyzers with the same frequency range can also vary significantly in cost. The PROTEK3201N spectrum analyzer considered in this article costs around 50,000 rubles, while the similar GW Instek GSP-7730 spectrum analyzer costs about 150,000 rubles. Given the range of prices, the PROTEK3201N spectrum analyzer can be considered as the best option for implementing a system for measuring the parameters of high-frequency electrical and electromagnetic signals. One of the areas of application of this equipment is radio monitoring within the controlled area. Radio monitoring is understood as a set of measures to determine the frequency and level of electromagnetic signals, and their identification. The essence of identification is to determine whether the detected electromagnetic signals belong to a regular radio transmitter or a device that performs unauthorized removal of information from a con-trolled area. One of the possible identification methods is a detailed analysis of the electromagnetic signal spectrum shape. This can be achieved by changing the scanning step of the frequency range by the radio receiver. It is spectrum analyzers that have the most developed functionality for changing the scanning step without changing the frequency range in which radiomonitoring is performed.
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Grayver, Alexander V., Neesha R. Schnepf, Alexey V. Kuvshinov, Terence J. Sabaka, Chandrasekharan Manoj, and Nils Olsen. "Satellite tidal magnetic signals constrain oceanic lithosphere-asthenosphere boundary." Science Advances 2, no. 9 (2016): e1600798. http://dx.doi.org/10.1126/sciadv.1600798.
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The tidal flow of electrically conductive oceans through the geomagnetic field results in the generation of secondary magnetic signals, which provide information on the subsurface structure. Data from the new generation of satellites were shown to contain magnetic signals due to tidal flow; however, there are no reports that these signals have been used to infer subsurface structure. We use satellite-detected tidal magnetic fields to image the global electrical structure of the oceanic lithosphere and upper mantle down to a depth of about 250 km. The model derived from more than 12 years of satellite data reveals a ≈72-km-thick upper resistive layer followed by a sharp increase in electrical conductivity likely associated with the lithosphere-asthenosphere boundary, which separates colder rigid oceanic plates from the ductile and hotter asthenosphere.
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Du, Shi Bin, Guan Yu Tian, Shu Zhong Bai, and Lan Tian. "An ICA-Based Audio Feature Fault Detection Method for Transformer Equipments." Advanced Materials Research 805-806 (September 2013): 706–11. http://dx.doi.org/10.4028/www.scientific.net/amr.805-806.706.
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Experienced engineers in transformer substation can judge the equipment condition via just listening to the working sounds of electrical equipments. Use audio signal processing applied in engines and other mechanical equipments for reference. A scheme to monitor the working condition of electrical equipments is proposed. Firstly, the basic principles and system structure of this scheme is outlined. It introduces the method of colleting electrical equipments working sounds by Microphone array, because Microphone array form a beam to target the source sound, which can reduce the noise and reverberation. When substation is working, the environmental background interference sounds exist and are independent from electrical working sound. So we can use FastICA algorithm that is based on the largest negentropy to separate the collected sound to several independent source signals. It has the advantage of fast convergence and robust. The simulation result shows this algorithm can effectively separate the multiple independent source signals. The separation accuracy is above 95% for typical sample mixed sounds and the reliability of electrical equipment fault detection system based on audio signal processing is ensured.
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Weng, Hai Yong, Da Peng Ye, Jin Gui Zheng, Hai Yun Li, Xiao Liang, and Shu Hui Chen. "Research on Characteristics of Plant Electrical Signal under High Voltage Pulse Stimulation." Applied Mechanics and Materials 513-517 (February 2014): 3122–25. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.3122.
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In order to further understand the Agave americana var. marginata electrical signals in High Voltage Pulse Stimulation conditions, a new method is provided in this paper by using wavelet soft threshold noise reduction and the power spectrum analysis. The results show that the level `of its electrical distribution in uV. The plant electrical signal amplitude arithmetic mean increases with the increase of pulse stimulation signal but for a little effect on the natural frequency of the plant, its frequency is mainly distributed in the 0.07813 Hz ~0.1953 Hz.