Littérature scientifique sur le sujet « PD Signal »

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Articles de revues sur le sujet "PD Signal"

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Sun, Zhang, Shi, and Gou. "Extraction of Partial Discharge Pulses from the Complex Noisy Signals of Power Cables Based on CEEMDAN and Wavelet Packet." Energies 12, no. 17 (2019): 3242. http://dx.doi.org/10.3390/en12173242.

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While both periodic narrowband noise and white noise are significant sources of interference in the detection and localization of partial discharge (PD) signals in power cables, existing research has focused nearly exclusively on white noise suppression. This paper addresses this issue by proposing a new signal extraction method for effectively detecting random PD signals in power cables subject to complex noise environments involving both white noise and periodic narrowband noise. Firstly, the power cable signal was decomposed using complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), and the periodic narrowband noise and frequency aliasing in the obtained signal components were suppressed using singular value decomposition. Then, signal components contributing significantly to the PD signal were determined according to the cross-correlation coefficient between each component and the original PD signal, and the PD signal was reconstructed solely from the obtained significant components. Finally, the wavelet packet threshold method was used to filter out residual white noise in the reconstructed PD signal. The performance of the proposed algorithm was demonstrated by its application to synthesized PD signals with complex noise environments composed of both Gaussian white noise and periodic narrowband noise. In addition, the time-varying kurtosis method was demonstrated to accurately determine the PD signal arrival time when applied to PD signals extracted by the proposed method from synthesized signals in complex noise environments with signal-to-noise ratio (SNR) values as low as −6 dB. When the SNR was reduced to −23 dB, the arrival time error of the PD signal was only one sampling point.
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Zhou, Kai, Mingzhi Li, Yuan Li, Min Xie, and Yonglu Huang. "An Improved Denoising Method for Partial Discharge Signals Contaminated by White Noise Based on Adaptive Short-Time Singular Value Decomposition." Energies 12, no. 18 (2019): 3465. http://dx.doi.org/10.3390/en12183465.

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To extract partial discharge (PD) signals from white noise efficiently, this paper proposes a denoising method for PD signals, named adaptive short-time singular value decomposition (ASTSVD). First, a sliding window was moved along the time axis of a PD signal to cut a whole signal into segments with overlaps. The singular value decomposition (SVD) method was then applied to each segment to obtain its singular value sequence. The minimum description length (MDL) criterion was used to determine the number of effective singular values automatically. Then, the selected singular values of each signal segment were used to reconstruct the noise-free signal segment, from which the denoised PD signal was obtained. To evaluate ASTSVD, we applied ASTSVD and two other methods on simulated, laboratory-measured, and field-detected noisy PD signals, respectively. Compared to the other two methods, the denoised PD signals of ASTSVD contain less residual noise and exhibit smaller waveform distortion.
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Zhang, Anan, Cong He, Maoyi Sun, Qian Li, Hong Wei Li, and Lin Yang. "Partial discharge signal self-adaptive sparse decomposition noise abatement based on spectral kurtosis and S-transform." COMPEL - The international journal for computation and mathematics in electrical and electronic engineering 37, no. 1 (2018): 293–306. http://dx.doi.org/10.1108/compel-03-2017-0126.

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Purpose Noise abatement is one of the key techniques for Partial Discharge (PD) on-line measurement and monitoring. However, how to enhance the efficiency of PD signal noise suppression is a challenging work. Hence, this study aims to improve the efficiency of PD signal noise abatement. Design/methodology/approach In this approach, the time–frequency characteristics of PD signal had been obtained based on fast kurtogram and S-transform time–frequency spectrum, and these characteristics were used to optimize the parameters for the signal matching over-complete dictionary. Subsequently, a self-adaptive selection of matching atoms was realized when using Matching Pursuit (MP) to analyze PD signals, which leading to seldom noise signal element was represented in sparse decomposition. Findings The de-noising of PD signals was achieved efficiently. Simulation and experimental results show that the proposed method has good adaptability and significant noise abatement effect compared with Empirical Mode Decomposition, Wavelet Threshold and global signal sparse decomposition of MP. Originality/value A self-adaptive noise abatement method was proposed to improve the efficiency of PD signal noise suppression based on the signal sparse representation and its MP algorithm, which is significant to on-line PD measurement.
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Kui Fern, Chin, Chai Chang Yii, Asfarina Abu Bakar, et al. "Adaptive Wavelet De-noising Algorithm using Absolute Difference Optimization Technique for Partial Discharge Signal." Journal of Engineering and Science Research 7, no. 3 (2023): 26–31. http://dx.doi.org/10.26666/rmp.jesr.2023.3.4.

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Discrete Wavelet Transform (DWT) de-noising method is widely used for one-dimension partial discharge (PD) signals measured from medium voltage underground cable. However, DWT de-noising has several drawbacks that prevent the DWT de-noising from improving its de-noising effectiveness In DWT de-noising, the two most important parameters are decomposition level and mother wavelet. The aforementioned parameters must be varied according to the noise level in the measured PD signal in order to effectively suppress the noise of the measured PD signal. In this paper, an adaptive DWT de-noising algorithm based on the Absolute Difference Optimizing (ADO) technique is presented to effectively suppress the varying noise levels in measured PD signal. First, the measured PD signal will be de-noised using a Daubechies 3 (db3) mother wavelet and a DWT decomposition level ranging from 1 to 10. Second, the de-noised PD signal will be subjected to the ADO technique. The sum of the absolute difference of local maxima in the de-noised PD signal will be used as an indicator to select the best decomposition level for the de-noised PD signal. Finally, the best-selected de-noised PD signal by using the ADO technique will be used to estimate the PD location on the underground cable. The results of PD location error using the ADO technique and normal DWT de-noising will be compared. The findings show that the ADO-based adaptive DWT de-noising algorithm significantly improved the de-noising process of the measured PD signal.
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Chen, Baichao, Weiqiang Qi, Jiaxin Yuan, and Yihong You. "Recognition of High-Voltage Cable Partial Discharge Signal Based on Adaptive Fuzzy C-Means Clustering." International Journal of Pattern Recognition and Artificial Intelligence 31, no. 06 (2017): 1759009. http://dx.doi.org/10.1142/s0218001417590091.

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Partial discharge (PD) detection is an effective means to find high-voltage cable defects. However, various interference signals affect the PD signal in practical applications, resulting in wrong judgment. In order to improve the accuracy of PD detection of high voltage cables, an adaptive fuzzy C-means (FCM) clustering was proposed to identify PD signals. The adaptive threshold pulse extraction algorithm based on fixed interval width was employed. The threshold was changed adaptively to extract the effective PD pulse waveform according to the change of the background noise and the degree of PD. Then PD pulse features were analyzed in time and frequency domain by employing the equivalent time frequency analysis method. The adaptive fuzzy clustering algorithm was used to classify the signals. The phase distribution concentration of all kinds of pulse signal was calculated. The phase standard deviation of various types of pulse was taken as the index that measures the concentration of density to distinguish between PD and interference signals. Results show that the adaptive FCM clustering algorithm, compared with the traditional method, can not only identify the PD signal accurately, but also be conscious of the PD category. The PD recognition method proposed in this paper has strong applicability and high accuracy, which is particularly suitable for application in the field of engineering.
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Guo, Bowen, Songyuan Li, Qinghua Tang, Lin Li, and Pengfei Li. "A Fuzzy Interpolation Cross-Correlation Method for Time-Delay Estimation of Partial Discharge UHF Pulse Signals." Journal of Electrical and Computer Engineering 2021 (October 23, 2021): 1–10. http://dx.doi.org/10.1155/2021/8532714.

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Time-delay estimation of partial discharge (PD) ultrahigh-frequency (UHF) pulse signals is one of the effective means to diagnose the local defect of electrical equipment. In order to improve the time-delay estimation accuracy of a multielement PD UHF pulse signal sensor array and reduce the cost of the PD detection system, a fuzzy interpolation cross-correlation method for time-delay estimation of PD UHF pulse signals was proposed in this paper. Improving the signal sampling rate is an effective way to improve the time-delay estimation accuracy. In this work, the fuzzy interpolation inference method was applied to interpolate intermediate values into the feature area of the PD UHF signal to improve the sampling rate, and then the cross-correlation method was used to estimate the time delay. This method not only improves the system sampling rate by fuzzy interpolation inference, which can make up for the deficiency of system equipment sampling performance, but also reduces the estimation error caused by noninteger multiple sampling intervals of signal time delay. The comparative experiment results demonstrate the power of the proposed method in improving the accuracy of the time-delay estimation of PD UHF pulse signals.
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Muhammad Izwan Abdul Halim, Nur Zahirah MohdRazaly, Mohamad Nur Khairul Hafizi Rohani, et al. "Multiple Partial Discharge Signal Classification Using Artificial Neural Network Technique in XLPE Power Cable." Journal of Advanced Research in Applied Sciences and Engineering Technology 29, no. 3 (2023): 214–27. http://dx.doi.org/10.37934/araset.29.3.214227.

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According to partial discharge (PD) damage in the electrodes that are not entirely bridging, the presence of PD in the high voltage (HV) power cable might lead to insulation failure. PD defects can damage cross-linked polyethylene (XLPE) cables directly, which is one of the most critical electrical issues in the industry. Poor workmanship during cable jointing, aging, or exposure to the surrounding environment is the most common cause of PD in HV cable systems. As a result, the location of the PD signals that occur cannot be classified without identifying the multiple PD signals present in the cable system. In this study, the artificial neural network (ANN) based feedforward back propagation classification technique is used as a diagnostic tool thru MATLAB software in which the PD signal was approached to determine the accuracy of the location PD signal. In addition, statistical feature extraction was added to compare the accuracy of classification with the standard method. The three-point technique is also an approach used to locate PD signals in a single line 11 kV XLPE underground power cable. The results show that the statistical feature extraction had been successful classify the PD signal location with the accuracy of 80% compared to without statistical feature extraction. The distance between PD signals and the PD source affected the result of the three-point technique which proved that a lower error means a near distance between them.
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Wang, Yanwen, Peng Chen, Yongmei Zhao, and Yanying Sun. "A Denoising Method for Mining Cable PD Signal Based on Genetic Algorithm Optimization of VMD and Wavelet Threshold." Sensors 22, no. 23 (2022): 9386. http://dx.doi.org/10.3390/s22239386.

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When the pulse current method is used for partial discharge (PD) monitoring of mining cables, the detected PD signals are seriously disturbed by the field noise, which are easily submerged in the noise and cannot be extracted. In order to realize the effective separation of the PD signal and the interference signal of the mining cable and improve the signal-to-noise ratio of the PD signal, a denoising method for the PD signal of the mining cable based on genetic algorithm optimization of variational mode decomposition (VMD) and wavelet threshold is proposed in this paper. Firstly, the genetic algorithm is used to optimize the VMD, and the optimal value of the number of modal components K and the quadratic penalty factor α is determined; secondly, the PD signal is decomposed by the VMD algorithm to obtain K intrinsic mode functions (IMF). Then, wavelet threshold denoising is applied to each IMF, and the denoised IMFs are reconstructed. Finally, the feasibility of the denoising method proposed in this paper is verified by simulation and experiment.
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Alhaidar, Abdul Rahman, Mohamed Yacin Sikkandar, and Abdulaziz A. Alkathiry. "Reconstruction of dual tasking gait pattern in Parkinson’s disease subjects using radial basis function based artificial intelligence." Journal of Intelligent & Fuzzy Systems 39, no. 4 (2020): 5437–48. http://dx.doi.org/10.3233/jifs-189027.

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Vertical Ground Reaction Force (VGRF) is a force obtained during gait cycle beneath the feet and is used to screen the severity of Parkinson’s disease (PD) patient’s in clinical environment. This article investigates the VGRF signals (left and right) semblance nature among PD patients and control subjects as a function of time and possibility of reconstructing dual tasking VGRF signal from normal walking VGRF signals using radial basis function (RBF) based artificial intelligence (AI). There are many traditional methods for gait analysis and these methods are purely subjective and none made semblance analysis of same subjects gait pattern in different tasking. In order to overcome the difficulties faced by PD patients, RBF based AI is proposed in this research to reconstruct the dual tasking VGRF signal from normal walking VGRF signal. 93 PD patients with mean age: 66.3 years (63% men), and 73 healthy controls with mean age: 66.3 years (55% men) datasets are used in this work. Proposed RBF network is trained on VGRF signals obtained in normal walking and dual tasking conditions from control. The network was trained with 60% of VGRF data and tested on remaining 40% data. Semblance analysis results are encouraging, and it shows that semblance is high in PD patients than control subjects during dual tasking (P < 0.05). In order to test the findings of semblance analysis, we explicitly reconstruct VGRF signal of clinically significant dual tasking from VGRF signal of normal walking by the proposed RBF method. Findings proved that the proposed RBF network can reconstruct dual tasking VGRF signal of PD patients from their normal walking VGRF signal with high cross correlation (P < 0.0001). These findings pave way for a new adjunct tool to diagnose the gait dynamics of PD patients using the proposed reconstruction method.
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Bohari, Z. H., M. Isa, A. Z. Abdullah, P. J. Soh, and M. F. Sulaima. "A smart partial discharge classification SOM with optimized statistical transformation feature." Bulletin of Electrical Engineering and Informatics 10, no. 2 (2021): 1054–62. http://dx.doi.org/10.11591/eei.v10i2.2751.

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Condition-based monitoring (CBM) has been a vital engineering method to assess high voltage (HV) equipment and power cables conditions or health levels. One of the effective CBM methods is partial discharge (PD) measurement or detection. PD event is the phenomenon that always associated with insulation healthiness. PD has been measured and evaluated in this paper to discriminate PD signals from a good signal. A mixed-signal being fed at an AI technique with statistical modified input data to do fast classification (less than five seconds) with nearly zero error. In this paper, an unsupervised neural network is applied for PD classification. The methods combine the self-organizing maps (SOMs) and feature statistical transformation. By the combination of these methods, the ‘range’ normalization method produced the best classification outcomes. This development decided that PD information was effectively correlated and grouped by means of MATLAB’s SOM Toolbox and transformation device to discriminate the normal signal from the PD signal.
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Thèses sur le sujet "PD Signal"

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Tsai, Shu-Jen Steven. "Power Transformer Partial Discharge (PD) Acoustic Signal Detection using Fiber Sensors and Wavelet Analysis, Modeling, and Simulation." Thesis, Virginia Tech, 2002. http://hdl.handle.net/10919/35983.

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In this work, we first analyze the behavior of the acoustic wave from the theoretical point of view using a simplified 1-dimensional model. The model was developed based on the conservation of mass, the conservation of momentum, and the state equation; in addition, the fluid medium obeys Stokes assumption and it is homogeneous, adiabatic and isentropic. Experiment and simulation results show consistency to theoretical calculation. The second part of this thesis focuses on the PD signal analysis from an on-site PD measurement of the in-house design fiber optic sensors (by Virginia Tech, Center for Photonics Technology). Several commercial piezoelectric transducers (PZTs) were also used to compare the measurement results. The signal analysis employs the application of wavelet-based denoising technique to remove the noises, which mainly came from vibration, EMI, and light sources, embedded in the PD signal. The denoising technique includes the discrete wavelet transform (DWT) decomposition, thresh-holding of wavelet coefficients, and signal recovery by inverse discrete wavelet transform. Several approaches were compared to determine the optimal mother wavelet. The threshold limits are selected to remove the maximum Gaussian noises for each level of wavelet coefficients. The results indicate that this method could extract the PD spike from the noisy measurement effectively. The frequency of the PD pulse is also analyzed; it is shown that the frequencies lie in the range of 70 kHz to 250 kHz. In addition, with the assumed acoustic wave propagation delay between PD source and sensors, it was found that all PD activities occur in the first and third quadrant in reference to the applied sinusoidal transformer voltage.<br>Master of Science
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Song, Lijun. "Detection and Position Location of Partial Discharges in Transformers Using Fiber Optic Sensors." Thesis, Virginia Tech, 2004. http://hdl.handle.net/10919/35928.

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Power transformers are one of the most important components in the electrical energy network. Extending transformer life is very economically valuable due to power outage. Therefore the development of instruments to monitor the transformer condition is of great interest. Detection of partial discharges (PDs) in power transformers is an effective diagnostic because it may reveal and quantify an important aging factor and provide information on the condition of the transformer. However, partial discharge diagnostics are still not effectively used for online monitoring of transformers because of the complexity of PD measurements and difficulties of discriminating of PDs and other noise sources. This thesis presents a further study of detection and location of partial discharges in power transformers based on previous work conducted at the Center for Photonics Technology (CPT) at Virginia Tech. The detection and positioning system consists of multiple extrinsic Fabry-Parot interferometric (EFPI) fiber acoustic sensors which can survive the harsh environment of oil-filled transformers. This thesis work is focused on optimal arrangement of multiple sensors to monitor and locate PD activities in a power transformer. This includes the following aspects. First, the sensor design requirements are discussed in order to successfully detect and accurately position the PD sources. In the following sections, Finite Element Method (FEM) is used to model the EFPI sensor fabricated at CPT. Experiments were conducted to measure the angular dependence of the frequency response of the sensor. It is shown that within the range of ±45º incident angles, the sensitivity varies by 3-5dB. Finally, the thesis demonstrates a PD positioning experiment in a 500 gallon water tank (R à H = 74" à 30" cylinder) using a hyperbolic positioning algorithm and time difference of arrival (TDOA). Finally we demonstrated that 100% of the positioning data is bounded by a 22.7à 4.1à 5.3 mm3 cube, with a sensing range of 810 mm using the leading edge method with FIR filtering.<br>Master of Science
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Ortler, Sonja. "Die Bedeutung koinhibitorischer Signale in der ZNS Immunregulation: die Rolle des B7-Homologs B7-H1 (PD-L1)." kostenfrei, 2009. http://www.opus-bayern.de/uni-wuerzburg/volltexte/2009/3478/.

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Carballo-Carbajal, Iria [Verfasser]. "Signal transduction pathways modulated by the PD-causative gene LRRK2 / Iria Carballo-Carbajal." 2009. http://d-nb.info/1001793382/34.

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Shetty, Pradeep Kumar. "Recognition And Retrieval Of Partial Discharges In Power Equipments : A Statistical Signal Modeling And Feature Extraction Approach." Thesis, 2004. https://etd.iisc.ac.in/handle/2005/1182.

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Shetty, Pradeep Kumar. "Recognition And Retrieval Of Partial Discharges In Power Equipments : A Statistical Signal Modeling And Feature Extraction Approach." Thesis, 2004. http://etd.iisc.ernet.in/handle/2005/1182.

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Wu, Ping-Sheng, and 吳炳昇. "Small-Signal Analysis and Compensator Design of Flyback Converters with Variable-Frequency Peak-Current Control for USB-PD Application." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/vea47a.

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碩士<br>國立臺灣大學<br>電機工程學研究所<br>106<br>Flyback converters have been widely adopted in low-power adapters for many portable devices, mainly because of its simplicity and electrical isolation characteristic. In order to achieve high conversion efficiency over the entire load range, the variable-frequency peak-current mode (VFPCM) control scheme is often used in this topology. With this control scheme, the converter switching frequency can be automatically adjusted according to output load level, and therefore, maintains high efficiency under both the heavy-load and the light-load conditions. In this thesis, a flyback converter with VFPCM control used for the up-and-coming universal serial bus power delivery (USB-PD) application is the focus. For such an application, the converter has to supply various output voltage levels for powering different loads. Compared with traditional adapter application, the operation range of this specification is much wider and the stability issue of VFPCM controlled flyback converter becomes more severe. In this thesis, a detailed description of the circuit control behaviors throughout a complete wide operation range is given. Depending on the output load condition, there are four control modes for this converter. The small-signal models for each of four control modes are reviewed. Since only one compensator is used for keeping the converter stable, a worst case analysis of uncompensated loop gain transfer functions is indispensable. Based on the analysis, a design strategy of compensation network for USB-PD application is proposed. Simulations are conducted and a hardware experimental circuit is built to verify the validity of the proposed strategy.
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Ortler, Sonja [Verfasser]. "Die Bedeutung koinhibitorischer Signale in der ZNS-Immunregulation : die Rolle des B7-Homologs B7-H1 (PD-L1) / vorgelegt von Sonja Ortler." 2009. http://d-nb.info/993436269/34.

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Livres sur le sujet "PD Signal"

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Thien Lim, Thien, and Hubert H. Fernandez. Parkinson Disease. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780199937837.003.0003.

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Levodopa is the most efficacious medication to reduce motor impairment in Parkinson disease (PD). The effect of levodopa can wear off after time, which is treated by increasing the dose or shortening the inter-dose interval. Dyskinesias can be treated by a change in levodopa dosing or route of administration, such as by constant administration of levodopa as a gel through a jejunostomy tube or a change to dopamine agonists or amantadine. Non-motor signs including depression can be treated with several antidepressants. Surgical treatments including pallidotomy, thalamotomy, and deep brain stimulation (DBS) have emerged as effective therapies in selected patients with PD refractory to drug treatment.
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Chapitres de livres sur le sujet "PD Signal"

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Shetty, Pradeep Kumar. "A Combined fBm and PPCA Based Signal Model for On-Line Recognition of PD Signal." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11590316_31.

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Shetty, Pradeep Kumar. "A Long Memory Process Based Parametric Modeling and Recognition of PD Signal." In Neural Information Processing. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30499-9_121.

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Guozhi, Zhang, Tian Hanlv, Lu Changyue, and Zhang Xiaoxing. "Feasibility Simulation Study of Simultaneous PD Radiation Ultrasound and UHF Signal Sensing Using a Single Sensor." In The proceedings of the 10th Frontier Academic Forum of Electrical Engineering (FAFEE2022). Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-3404-1_34.

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Guozhi, Zhang, Tian Hanlv, Lu Changyue, and Zhang Xiaoxing. "Feasibility Simulation Study of Simultaneous PD Radiation Ultrasound and UHF Signal Sensing Using a Single Sensor." In The proceedings of the 10th Frontier Academic Forum of Electrical Engineering (FAFEE2022). Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-3404-1_34.

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Kryukov, Ya V., D. A. Pokamestov, and E. V. Rogozhnikov. "PD-NOMA Power Coefficients Calculation While Using QAM Signals." In Communications in Computer and Information Science. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-36625-4_13.

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Li, Jiefu, Jung-Youn Lee, and Li Liao. "Detecting De Novo Plasmodesmata Targeting Signals and Identifying PD Targeting Proteins." In Computational Advances in Bio and Medical Sciences. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-46165-2_1.

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Agba, Basile L., Fabien Sacuto, Minh Au, Fabrice Labeau, and François Gagnon. "Analysis and Modeling of Wideband RF Signals Induced by PD Using Second-Order Statistics." In Wireless Networks. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-91328-5_5.

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Belkić, Dževad. "The product-difference (PD) recursive algorithm." In Quantum-Mechanical Signal Processing and Spectral Analysis. CRC Press, 2019. http://dx.doi.org/10.1201/9780429146534-68.

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"Narrowband Probability of Detection (PD) and false Alarm Rates (FAR)." In Signal Processing for Intelligent Sensor Systems. CRC Press, 2000. http://dx.doi.org/10.1201/9780203909959.ch11.

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Cruz, Gloria, Shengdong Nie, and Juan Ramírez. "Diffusion Magnetic Resonance Imaging (MRI)-Biomarkers for Diagnosis of Parkinson’s Disease." In Parkinson’s Disease - Animal Models, Current Therapies and Clinical Trials [Working Title]. IntechOpen, 2023. http://dx.doi.org/10.5772/intechopen.109807.

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Parkinson’s disease (PD) is a degenerative neurological disorder, the origin of which remains unclear. The efficacy of treatments is limited due to the small number of remaining neurons. Diffusion magnetic resonance imaging (MRI) has revolutionized clinical neuroimaging. This noninvasive and quantitative method gathers in vivo microstructural information to characterize pathological processes that modify nervous tissue integrity. The changes in signal intensity result from the motion of the water molecules; they can be quantified by diffusivity measures. Diffusion MRI has revealed “biomarkers” in several brain regions that could be useful for PD diagnosis. These regions include the olfactory tracts, putamen, white matter, superior cerebellar peduncles, middle cerebellar peduncle, pons, cerebellum, and substantia nigra. There are encouraging preliminary data that differentiate PD from atypical parkinsonian diseases based on these microstructural changes.
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Actes de conférences sur le sujet "PD Signal"

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Zanini, Rafael Anicet, and Esther Luna Colombini. "Parkinson sEMG signal prediction and generation with Neural Networks." In Concurso de Teses e Dissertações da SBC. Sociedade Brasileira de Computação, 2021. http://dx.doi.org/10.5753/ctd.2021.15759.

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Parkinson’s Disease (PD) is a neurodegenerative disorder characterized by symptoms like resting and action tremors, which cause severe impairments to the patient’s life. Recently, many assistance techniques have been proposed to minimize the disease’s impact on patients’ life. However, most of these methods depend on data from PD’s surface electromyography (sEMG), which is scarce. In this work, we propose the first methods, based on Neural Networks, for predicting, generating, and transferring the style of patient-specific PD sEMG tremor signals. This dissertation contributes to the area by i) comparing different NN models for predicting PD sEMG signals to anticipate resting tremor patterns ii) proposing the first approach based on Deep Convolutional Generative Adversarial Networks (DCGANs) to generate PD’s sEMG tremor signals; iii) applying Style Transfer (ST) for augmenting PD’s sEMG signals with publicly available datasets of non-PD subjects; iv) proposing metrics for evaluating the PD’s signal characterization in sEMG signals. These new data created by our methods could validate treatment approaches on different movement scenarios, contributing to the development of new techniques for tremor suppression in patients.
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Xi Li, Xiaohua Wang, Mingzhe Rong, et al. "Relationship between time-frequency representation of PD-induced UHF signal and PD current pulse." In 2016 International Conference on Condition Monitoring and Diagnosis (CMD). IEEE, 2016. http://dx.doi.org/10.1109/cmd.2016.7757803.

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Peram, Madhurima, Sandhyarani Mishra, Madhuri Vemulapaty, Bharat Verma, and Prabin K. Padhy. "Optimal PI-PD and I-PD Controller Design Using Cuckoo Search Algorithm." In 2018 5th International Conference on Signal Processing and Integrated Networks (SPIN). IEEE, 2018. http://dx.doi.org/10.1109/spin.2018.8474214.

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Thelen, Matthew, Fardeen Mazumder, Linda Zhu, Charlotte Tang, and Nathaniel S. Miller. "Reliability Test of Mobile Embedded Accelerometers in Measuring Postural Stability for People With Parkinson’s Disease." In ASME 2022 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/imece2022-94806.

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Abstract Parkinson’s Disease (PD) is the second most common neurodegenerative disease in the United States, affecting at least one million people. The cardinal symptoms of PD are tremor, rigidity, slowed movement, and impaired balance. While some symptoms of PD are responsive to anti-PD medications, other symptoms, are less medication responsive, especially walking and balance. Moreover, daily activities, such as writing, using tools, and walking, affect the quality of life (QoL) of people with PD (PwPD). Monitoring PD symptoms is essential for clinical evaluations and adjusting medication to help maintain QoL for PwPD. we are developing a mobile app to conduct at-home PD symptom monitoring with the goal of providing more timely and frequent measurements of PD symptoms for both patients and clinicians. While the tremor and finger tapping results collected in the mobile app have been discussed in previous publications, this paper focuses on the design and testing of postural stability (balance) tests in the app and the validation of the reliability of the mobile embedded accelerometers. During the test, a dual-purpose shaker was employed to provide vibration in amplitude and frequency range similar to human postural stability signals. A head expander was attached to the shaker and the smartphone holder is screwed to it. The tilt and yaw angles of the smartphone holder are adjustable, therefore the smartphone could be tested in an angled position relative to the shaker. Various types of input signals were tested, including sweep and multiple real postural stability data previously collected from a volunteer. Two models of smartphones were used to measure the signal through multiple trials and the results were compared to the input benchmark signal to verify the accuracy of the smartphone measurements. Besides the evaluation of the time domain raw data, we have also employed several signal processing algorithms to extract postural stability factors, such as the root mean square (RMS) value, the derivative of acceleration, frequency factors, etc., with the goal of identifying the patterns of motion signals which could be used as a summary measures of balance for PD. These signal processing algorithms were used to process raw measurement data from multiple trials, on different input signals, and on different devices. The results were compared, and the consistency of these factors through multiple trials with different smartphone models is tested and summarized. These results help us to find the most reliable measure to be used in the smartphone application. Both the results in raw acceleration signals and calculated factors will be discussed to further the current understanding of the reliability of smartphone measurements with embedded accelerometers.
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Zhang, Guozhi, Changyue Lu, Xingyu Yu, Hanlv Tian, and Xiaoxing Zhang. "Research on Integrated Technology of PD Ultrasonic Signal and UHF Signal Detection." In 2022 IEEE International Conference on High Voltage Engineering and Applications (ICHVE). IEEE, 2022. http://dx.doi.org/10.1109/ichve53725.2022.9961712.

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Liu, Qi, and Qiang Gao. "Application of Mathematical Morphology in GIS PD Signal Analysis." In 2016 International Conference on Energy, Power and Electrical Engineering. Atlantis Press, 2016. http://dx.doi.org/10.2991/epee-16.2016.52.

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Kodama, Satoshi, Toshihide Yoshimatsu, and Hiroshi Ito. "Monolithic PD-EAM Optical Gates for Ultrafast Signal Processing." In 2003 International Conference on Solid State Devices and Materials. The Japan Society of Applied Physics, 2003. http://dx.doi.org/10.7567/ssdm.2003.a-5-1.

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Su, Yi, Lei Zhang, Shaoming Pan, Xiajin Rao, and Wei Huang. "Signal distortion characteristics of capacitive casing PD electromagnetic waves." In 2023 IEEE 6th International Electrical and Energy Conference (CIEEC). IEEE, 2023. http://dx.doi.org/10.1109/cieec58067.2023.10166279.

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Zhu, Na, and Nathaniel S. Miller. "Assessment of Parkinson’s Disease Tremor and Correlation Analysis With Applied Signal Processing." In ASME 2019 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/imece2019-10622.

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Abstract Accurate measurement and assessment of Parkinson’s disease (PD) tremor is important for patients, clinicians, and researchers to track changes in disease progression and the effectiveness of therapeutic interventions. This study measured resting, postural, and kinetic tremor from patient’s most-affected hand with accelerometers and gyrometers, thus the linear and rotational motions in the x, y, z directions were obtained. Data were collected when patients were both ON and OFF their anti-PD medications. A bandpass filter was applied to extract raw tremor information and several signal processing algorithms were used to analyze the data in both time and frequency domains, including the correlations between motions at different directions. The results of medication effectiveness on PD tremor and the correlational analyses will be discussed.
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Alam, Md Nafiul, Tamanna T. K. Munia, Ajay K. Verma, et al. "A Quantitative Assessment of Bradykinesia Using Inertial Measurement Unit." In 2017 Design of Medical Devices Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/dmd2017-3543.

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Parkinson’s disease (PD) is a neurodegenerative disorder known to affect movement. Approximately seven million people around the world suffer from PD [1]. Tremor in one hand characterizes the onset of PD. Population suffering with PD shows symptoms of slowed movement. Consequently, PD patients struggle to complete a simple task like picking up a book. This slowness of movement is called bradykinesia. Bradykinesia measurement is vital for monitoring the progression of PD. The current method of assessing bradykinesia requires patients to perform certain motor tasks in clinical settings. A Unified Parkinson Disease Rating Scale (UPDRS) score is assigned to each task based on the observation by a physician. However, PD patients do not always show natural symptoms during a clinical visit. Also, subjective bias occurs during such assessment of bradykinesia [2]. To overcome these limitations, several attempts have been made to quantify bradykinesia using wearable sensors [3]. Accelerometer, gyroscope or a combination of both have been employed for acquisition of movement data to evaluate bradykinesia [3]. Time domain parameters derived from sensor signals for characterizing bradykinesia which includes speed, amplitude, hesitations, and halt have been evaluated in previous studies. However, the effect of frequency domain parameters and non-linear features extracted from sensor signals for evaluating the severity of bradykinesia is unknown. Whether or not it leads to an improvement in the assessment of bradykinesia needs to be investigated. It is known that the patients suffering from severe bradykinesia have their movement signal distorted due to unpredictable movement or hesitation. Nonlinear features can characterize the degree of complexity and provide further relevant insights regarding the severity of bradykinesia. In this study, we investigated the efficacy of various frequency-domain and nonlinear parameters to quantify bradykinesia. The objective was to develop a predictive model based on a combination of sophisticated linear (frequency) and non-linear features derived from the sensor signal which has not been previously explored in the literature.
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Rapports d'organisations sur le sujet "PD Signal"

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Epel, Bernard L., Roger N. Beachy, A. Katz, et al. Isolation and Characterization of Plasmodesmata Components by Association with Tobacco Mosaic Virus Movement Proteins Fused with the Green Fluorescent Protein from Aequorea victoria. United States Department of Agriculture, 1999. http://dx.doi.org/10.32747/1999.7573996.bard.

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The coordination and regulation of growth and development in multicellular organisms is dependent, in part, on the controlled short and long-distance transport of signaling molecule: In plants, symplastic communication is provided by trans-wall co-axial membranous tunnels termed plasmodesmata (Pd). Plant viruses spread cell-to-cell by altering Pd. This movement scenario necessitates a targeting mechanism that delivers the virus to a Pd and a transport mechanism to move the virion or viral nucleic acid through the Pd channel. The identity of host proteins with which MP interacts, the mechanism of the targeting of the MP to the Pd and biochemical information on how Pd are alter are questions which have been dealt with during this BARD project. The research objectives of the two labs were to continue their biochemical, cellular and molecular studies of Pd composition and function by employing infectious modified clones of TMV in which MP is fused with GFP. We examined Pd composition, and studied the intra- and intercellular targeting mechanism of MP during the infection cycle. Most of the goals we set for ourselves were met. The Israeli PI and collaborators (Oparka et al., 1999) demonstrated that Pd permeability is under developmental control, that Pd in sink tissues indiscriminately traffic proteins of sizes of up to 50 kDa and that during the sink to source transition there is a substantial decrease in Pd permeability. It was shown that companion cells in source phloem tissue export proteins which traffic in phloem and which unload in sink tissue and move cell to cell. The TAU group employing MP:GFP as a fluorescence probe for optimized the procedure for Pd isolation. At least two proteins kinases found to be associated with Pd isolated from source leaves of N. benthamiana, one being a calcium dependent protein kinase. A number of proteins were microsequenced and identified. Polyclonal antibodies were generated against proteins in a purified Pd fraction. A T-7 phage display library was created and used to "biopan" for Pd genes using these antibodies. Selected isolates are being sequenced. The TAU group also examined whether the subcellular targeting of MP:GFP was dependent on processes that occurred only in the presence of the virus or whether targeting was a property indigenous to MP. Mutant non-functional movement proteins were also employed to study partial reactions. Subcellular targeting and movement were shown to be properties indigenous to MP and that these processes do not require other viral elements. The data also suggest post-translational modification of MP is required before the MP can move cell to cell. The USA group monitored the development of the infection and local movement of TMV in N. benthamiana, using viral constructs expressing GFP either fused to the MP of TMV or expressing GFP as a free protein. The fusion protein and/or the free GFP were expressed from either the movement protein subgenomic promoter or from the subgenomic promoter of the coat protein. Observations supported the hypothesis that expression from the cp sgp is regulated differently than expression from the mp sgp (Szecsi et al., 1999). Using immunocytochemistry and electron microscopy, it was determined that paired wall-appressed bodies behind the leading edge of the fluorescent ring induced by TMV-(mp)-MP:GFP contain MP:GFP and the viral replicase. These data suggest that viral spread may be a consequence of the replication process. Observation point out that expression of proteins from the mp sgp is temporary regulated, and degradation of the proteins occurs rapidly or more slowly, depending on protein stability. It is suggested that the MP contains an external degradation signal that contributes to rapid degradation of the protein even if expressed from the constitutive cp sgp. Experiments conducted to determine whether the degradation of GFP and MP:GFP was regulated at the protein or RNA level, indicated that regulation was at the protein level. RNA accumulation in infected protoplast was not always in correlation with protein accumulation, indicating that other mechanisms together with RNA production determine the final intensity and stability of the fluorescent proteins.
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