Relevant bibliographies by topics / Daubechies wavelet of type 4

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Academic literature on the topic 'Daubechies wavelet of type 4'

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Published: 24 April 2022

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Journal articles on the topic "Daubechies wavelet of type 4"

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Ardianti, Chrisentia Widya, Rukun Santoso, and Sudarno Sudarno. "ANALISIS ARIMA DAN WAVELET UNTUK PERAMALAN HARGA CABAI MERAH BESAR DI JAWA TENGAH." Jurnal Gaussian 9, no. 3 (August 30, 2020): 247–62. http://dx.doi.org/10.14710/j.gauss.v9i3.28906.

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Time series is a type of data collected according to the sequence of times in a certain time span. Time series data can be used as a predictor of future conditions. Analysis of time series data, one of the ARIMA units, is a parametric method that requires an assumption to get valid results. Data stationarity is one of the factors that must be fulfilled. Wavelet is a non-parametric method that is able to represent time and frequency information simultaneously, so that it can analyze non-stationary data. This research presents forecasting the price of red chili in Central Java using ARIMA and wavelet with the approach of the Multiscale Autoregressive (MAR) model. The best model is the one with the smallest MSE value. The results showed that the ARIMA(0,1,1) model was said to be the best model with MSE = 2252142. However, because the assumption of normality is not fulfilled, an alternative process is done with wavelet. Wavelet approach results show that the MAR model Haar filter level (j) = 4 with MSE = 2175906 is better than Daubechies 4 filter 4 level (j) = 1 with MSE = 3999669. Therefore, the Haar wavelet is considered better in the time series analysis. Keyword : ARIMA, wavelet, MAR, forecasting, MSE
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Kaneko, Kenichi, Hitoshi Makabe, Kazuyuki Mito, Kazuyoshi Sakamoto, Yoshiya Kawanori, and Kiyoshi Yonemoto. "Characteristics of Lower Limb Muscle Activity in Elderly Persons After Ergometric Exercise." Gerontology and Geriatric Medicine 6 (January 2020): 233372142097980. http://dx.doi.org/10.1177/2333721420979800.

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This study examined the characteristics of lower limb muscle activity in elderly persons after ergometric pedaling exercise for 1 month. To determine the effect of the exercise, surface electromyography (SEMG) of lower limb muscles was subjected to Daubechies-4 wavelet transformation, and mean wavelet coefficients were compared with the pre-exercise coefficients and the post-exercise coefficients in each wavelet level. The characteristics of muscle activity after pedaling exercise were also compared between the elderly subjects and young subjects. For the elderly subjects, the mean wavelet coefficients were significantly decreased in the tibialis anterior and the gastrocnemius medialis at wavelet levels of 3, 4, and 5 (125–62.5, 62.5–31.25, and 31.25–15.625 Hz, respectively), by pedaling exercise. However, the mean power of wavelet levels of 2 and 3 (250–125 and 125–62.5 Hz) within the rectus femoris and the biceps femoris were significantly increased in the young subjects. The effect of pedaling exercise is different from the effects of heavy-resistance training. It was suggested that the muscle coordination, motor unit (MU) firing frequency, and firing fiber type of lower limb muscles are changed with the different characteristics between elderly and young persons by pedaling exercise for 1 month.
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Habsah Asman, Saidatul, and Ahmad Farid Abidin. "Comparative Study of Extension Mode Method in Reducing Border Distortion Effect for Transient Voltage Disturbance." Indonesian Journal of Electrical Engineering and Computer Science 6, no. 3 (June 1, 2017): 628. http://dx.doi.org/10.11591/ijeecs.v6.i3.pp628-637.

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<p>Wavelet transform is an essential method for preprocessing and analyzing non-stationary signal of power quality disturbances. Recently, power quality disturbances cause various effect which reduce the accuracy of the signal such as border distortion. This paper is presenting the comparative study on extension mode scheme to reduce border distortion effect in Discrete Wavelet Transform. The three different method namely zero padding, smooth padding of order 1 and symmetrization mode have been carried to observe their capability on reducing border distortion effectively. The implementation of these modes has been carried out in Matlab Software version R2014a. The analysis is considering the decomposition coefficient at level 4 with mother wavelet type Daubechies. With the aid of soft- threshold function, the noise and unwanted signal is effectively removed to recover the original signal. The comparative study provides the best mode to reduce border distortion effect with the presence of transient voltage is smooth padding of order 1.</p>
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Too, Jingwei, A. R. Abdullah, Norhashimah Mohd Saad, N. Mohd Ali, and H. Musa. "A Detail Study of Wavelet Families for EMG Pattern Recognition." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 6 (December 1, 2018): 4221. http://dx.doi.org/10.11591/ijece.v8i6.pp4221-4229.

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<p>Wavelet transform (WT) has recently drawn the attention of the researchers due to its potential in electromyography (EMG) recognition system. However, the optimal mother wavelet selection remains a challenge to the application of WT in EMG signal processing. This paper presents a detail study for different mother wavelet function in discrete wavelet transform (DWT) and continuous wavelet transform (CWT). Additionally, the performance of different mother wavelet in DWT and CWT at different decomposition level and scale are also investigated. The mean absolute value (MAV) and wavelength (WL) features are extracted from each CWT and reconstructed DWT wavelet coefficient. A popular machine learning method, support vector machine (SVM) is employed to classify the different types of hand movements. The results showed that the most suitable mother wavelet in CWT are Mexican hat and Symlet 6 at scale 16 and 32, respectively. On the other hand, Symlet 4 and Daubechies 4 at the second decomposition level are found to be the optimal wavelet in DWT. From the analysis, we deduced that Symlet 4 at the second decomposition level in DWT is the most suitable mother wavelet for accurate classification of EMG signals of different hand movements. </p>
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Makandar, Aziz, and Anita Patrot. "Wavelet Statistical Feature based Malware Class Recognition and Classification using Supervised Learning Classifier." Oriental journal of computer science and technology 10, no. 2 (May 2, 2017): 400–406. http://dx.doi.org/10.13005/ojcst/10.02.20.

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Malware is a malicious instructions which may harm to the unauthorized private access through internet. The types of malware are incresing day to day life, it is a challenging task for the antivius vendors to predict and caught on access time. This paper aims to design an automated analysis system for malware classes based on the features extracted by Discrete Wavelet Transformation (DWT) and then by applying four level decomposition of malware. The proposed system works in three stages, pre-processing, feature extraction and classification. In preprocessing, input image is normalized in to 256x256 by applying wavelet we are denoising the image which helps to enhance the image. In feature extraction, DWT is used to decompose image into four level. For classification the support vector machine (SVM) classifiers are used to discriminate the malware classes with statistical features extracted from level 4 decomposition of DWT such as Daubechies (db4), Coiflet (coif5) and Bi-orthogonal (bior 2.8). Among these wavelet features the db4 features effectively classify the malware class type with high accuracy 91.05% and 92.53% respectively on both dataset. The analysis of proposed method conducted on two dataset and the results are promising.
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Ibrahim, Noraini, and Norhaiza Ahmad. "Comparative performance of support vector regressions for accurate streamflow predictions." Malaysian Journal of Fundamental and Applied Sciences 13, no. 4-1 (December 5, 2017): 325–30. http://dx.doi.org/10.11113/mjfas.v13n4-1.876.

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Obtaining accurate streamflow predictions can be challenging due to the inherent variabilities and complex nonlinear nature in streamflow generation processes. Support vector regression model is an effective forecasting tool to forecast streamflow as it is able to capture the nonlinearity in the data and attain the global optimum parameters in the forecasted model. However, the efficiency of SVR might be hindered by noise that typically exists in any hydrological time series data through random influences and inaccuracies in recording. Thus, this condition could compromise the quality of input data into SVR. In this study, we investigate the effectiveness of forecasting monthly streamflow data using different settings of SVR in two ways. First, we use different variations of wavelet denoising technique using different selections of wavelet decomposition levels and mother wavelets in order to preserve information and reduce distortion of the original time series. For this purpose, we measured the impact of six different wavelets on SVR namely Daubechies of type db3, db4, db5, db6 and db7 with two different levels of decomposition which are level 3 and level 4. There is more information that may contribute to better performance of the model when the decomposition level is increase. Then, the data are applied using radial basis function (RBF) by performing K-fold cross-validation to obtain the optimal parameter for kernel function in forecasting streamflow. We illustrate the methods using the monthly streamflow data observed at Segamat River in the state of Johor. The results demonstrated that SVR based wavelet denoising for 1-month lead time streamflow forecasting of type db5 with level 3 give better results using Gaussian (RBF) kernel function based on K-fold cross-validation compared to regular SVR. This implies that reduced variance in the denoising procedure and obtain optimal parameter in kernel function may improve forecasting accuracy.
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Rashid, Rakan Saadallah, and Jafar Ramadhan Mohammed. "Securing speech signals by watermarking binary images in the wavelet domain." Indonesian Journal of Electrical Engineering and Computer Science 18, no. 2 (May 1, 2020): 1096. http://dx.doi.org/10.11591/ijeecs.v18.i2.pp1096-1103.

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<span>Digital watermarking is the process of embedding particular information into other signal data in such a way that the quality of the original data is maintained and secured. Watermarking can be performed on images, videos, texts, or audio to protect them from copyright violation. Among all of these types of watermarking, audio watermarking techniques are gaining more interest and becoming more challenging because the quality of such signals is highly affected by the watermarked code. This paper introduces some efficient approaches that have capability to maintain the signals’ quality and preserves the important features of the audio signals. Moreover, the proposed digital audio watermarking approaches are performed in the transform domain. These approaches are gaining more attention due to their robustness or resistance to the attackers. These transform domains include discrete cosine transform (DCT), short-term Fourier transform (STFT), and digital wavelet transform (DWT). Furthermore, the most digital wavelet transforms were found to be applicable for speech watermarking are the Haar and the Daubechies-4. </span>
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Narendiranath Babu, T., N. Senthilnathan, Shailesh Pancholi, S. P. Nikhil Kumar, D. Rama Prabha, Noor Mohammed, and Razia Sultana Wahab. "Fault analysis on continuous variable transmission using DB-06 wavelet decomposition and fault classification using ANN." Journal of Intelligent & Fuzzy Systems 41, no. 1 (August 11, 2021): 1297–307. http://dx.doi.org/10.3233/jifs-210199.

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This study aims at developing a novel method for condition monitoring technique for detection and classification of developing faults and increase the working life of continuous variable transmission (CVT) using Daubechies Wavelet 06 (DB-06). The vibration data is collected for 4 different types of faults and healthy condition. Using a magnetic accelerometer and signal analyser, vibration data is collected from the system in the time-domain which is then used as input for a MATLAB code producing the plot of the frequency-domain signal. Maximum frequency is determined to diagnose the faults which are induced over three different belts. Collected data for large scale automotive system (CVT) is used to train the network and then it is tested based on random data points. Faults were classified using ANN with a classification rate of 90.8 %.
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Hendrawan, Muhammad Afif, Pramana Yoga Saputra, and Cahya Rahmad. "Identification of optimum segment in single channel EEG biometric system." Indonesian Journal of Electrical Engineering and Computer Science 23, no. 3 (September 1, 2021): 1847. http://dx.doi.org/10.11591/ijeecs.v23.i3.pp1847-1854.

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Nowadays, biometric modalities have gained popularity in security systems. Nevertheless, the conventional commercial-grade biometric system addresses some issues. The biggest problem is that they can be imposed by artificial biometrics. The electroencephalogram (EEG) is a possible solution. It is nearly impossible to replicate because it is dependent on human mental activity. Several studies have already demonstrated a high level of accuracy. However, it requires a large number of sensors and time to collect the signal. This study proposed a biometric system using single-channel EEG recorded during resting eyes open (EO) conditions. A total of 45 EEG signals from 9 subjects were collected. The EEG signal was segmented into 5 second lengths. The alpha band was used in this study. Discrete wavelet transform (DWT) with Daubechies type 4 (db4) was employed to extract the alpha band. Power spectral density (PSD) was extracted from each segment as the main feature. Linear discriminant analysis (LDA) and support vector machine (SVM) were used to classify the EEG signal. The proposed method achieved 86% accuracy using LDA only from the third segment. Therefore, this study showed that it is possible to utilize single-channel EEG during a resting EO state in a biometric system.
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Kapoor, Gaurav. "Fifteen phase transmission line protection using daubechies-4 wavelet transform." International Journal of Engineering, Science and Technology 12, no. 1 (April 30, 2020): 1–14. http://dx.doi.org/10.4314/ijest.v12i1.1.

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In this work, the daubechies-4 wavelet transform (Db4WT) is utilized for fault detection and faulted phase recognition in a fifteen phase transmission line (FPTL). The daubechies-4 wavelet transform has been widely investigated using the MATLAB model of a 765 kV, 200 km long fifteen phase transmission line. The fifteen phase fault currents measured by the current measurements blocks connected at the bus-1 of the fifteen phase transmission line are entered to the wavelet transform algorithm. The first level detail coefficients of the fifteen phase currents are then calculated. The fault detection is carried out with great correctness. Keywords: fault detection, faulty phase recognition, fifteen phase transmission line protection, wavelet transform.
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Dissertations / Theses on the topic "Daubechies wavelet of type 4"

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MADISHETTY, SHIVA KUMAR. "VLSI Architectures for the 4-Tap and 6-Tap 2-D Daubechies Wavelet Filters Using Algebraic Integers." University of Akron / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=akron1376921751.

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Chiu, Cheui-Lu, and 邱垂祿. "VLSI Architectures for 2-D Forward and Inverse Discrete Wavelet Transform Using 4-tap Daubechies Filters." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/87912371214070891187.

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碩士
中華大學
電機工程學系碩士班
94
In the field of digital image processing, the JPEG-2000 standard uses the discrete wavelet transform for image compression; hence, the two-dimensional (2-D) forward discrete wavelet transform (FDWT)/ inverse DWT (IDWT) has recently been used as a powerful tool for image coding/decoding systems. 2-D FDWT/IDWT demands massive computations, hence, it requires a parallel and pipelined architecture to implement high-efficiency application-specific integrated circuits (ASIC) or field programmable gate array (FPGA). At the heart of the analysis stage of the system is the FDWT. In the synthesis stage, the IDWT recovers the original image from the coefficients of FDWT. This paper proposes two architectures with computing time for 2-D FDWT and IDWT. The first high-efficiency architecture comprises a transform module, an address generator, and a RAM module. The transform module has uniform and regular structure, simple control flow. The second architecture features parallel and pipelined computation and high throughput, both of the proposed architectures are 100% hardware-utilization and suitable for 2-D digital image processing, such as JPEG-2000. The proposed VLSI architectures are realized with Verilog HDL, synthesized by the Synopsys Design Compiler. Finally, the layouts for those designs are generated with the Synopsys Astro Tools in a 0.18 1P6M CMOS technology.
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Book chapters on the topic "Daubechies wavelet of type 4"

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Rafi Lone, Mohd, and Najeeb-ud-Din Hakim. "Multiplier-Less Architecture for 4-Tap Daubechies Wavelet Filters Using Algebraic Integers." In International Conference on Intelligent Computing and Smart Communication 2019, 1251–58. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-0633-8_122.

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Viswanath, K., and R. Gunasundari. "Modified Distance Regularized Level Set Segmentation Based Analysis for Kidney Stone Detection." In Medical Imaging, 693–710. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-0571-6.ch027.

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The abnormalities of the kidney can be identified by ultrasound imaging. The kidney may have structural abnormalities like kidney swelling, change in its position and appearance. Kidney abnormality may also arise due to the formation of stones, cysts, cancerous cells, congenital anomalies, blockage of urine etc. For surgical operations it is very important to identify the exact and accurate location of stone in the kidney. The ultrasound images are of low contrast and contain speckle noise. This makes the detection of kidney abnormalities rather challenging task. Thus preprocessing of ultrasound images is carried out to remove speckle noise. In preprocessing, first image restoration is done to reduce speckle noise then it is applied to Gabor filter for smoothening. Next the resultant image is enhanced using histogram equalization. The preprocessed ultrasound image is segmented using distance regularized level set segmentation (DR-LSS), since it yields better results. It uses a two-step splitting methods to iteratively solve the DR-LSS equation, first step is iterating LSS equation, and then solving the Sign distance equation. The second step is to regularize the level set function which is the obtained from first step for better stability. The DR is included for LSS for eliminating of anti-leakages on image boundary. The DR-LSS does not require any expensive re-initialization and it is very high speed of operation. The RD-LSS results are compared with distance regularized level set evolution DRLSE1, DRLSE2 and DRLSE3. Extracted region of the kidney after segmentation is applied to Symlets (Sym12), Biorthogonal (bio3.7, bio3.9 & bio4.4) and Daubechies (Db12) lifting scheme wavelet subbands to extract energy levels. These energy level gives an indication about presence of stone in that particular location which significantly vary from that of normal energy level. These energy levels are trained by Multilayer Perceptron (MLP) and Back Propagation (BP) ANN to identify the type of stone with an accuracy of 98.6%.
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Li, Peilin, Sang-Heon Lee, and Hung-Yao Hsu. "Use of Bi-Camera and Fusion of Pairwise Real Time Citrus Fruit Image for Classification Application." In Computer Vision and Image Processing in Intelligent Systems and Multimedia Technologies, 54–81. IGI Global, 2014. http://dx.doi.org/10.4018/978-1-4666-6030-4.ch004.

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In this chapter, the use of two images, the near infrared image and the color image, from a bi-camera machine vision system is investigated to improve the detection of the citrus fruits in the image. The application has covered the design of the bi-camera vision system to align two CCD cameras, the online acquisition of the citrus fruit tree image, and the fusion of two aligned images. In the system, two cameras have been registered with alignment to ensure the fusion of two images. A fusion method has been developed based on the Multiscale Decomposition Analysis (MSD) with a Discrete Wavelet Transform (DWT) application for the two dimensional signal. In the fusion process, two image quality issues have been addressed. One is the detail noise from the background, which is bounded with the envelope spectra and with similar spectra to orange citrus fruit and spatial variance property. The second is the enhancement of the fundamental envelope spectra using two source images. With level of MSD estimated, the noise is reduced by zeroing the high pass coefficients in DWT while the fundamental envelope spectra from the color image are enhanced by an arithmetic pixel level fusion rule. To evaluate the significant improvement of the image quality, some major classification methods are applied to compare the classified results from the fused image with the results from the types of color image. The misclassification error is measured by the empirical type errors using the manual segmentation reference image.
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Scafetta, N., and P. Grigolin. "Nonextensive Diffusion Entropy Analysis and Teen Birth Phenomena." In Nonextensive Entropy. Oxford University Press, 2004. http://dx.doi.org/10.1093/oso/9780195159769.003.0021.

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A complex process is often a balance between nonscaling and scaling components. We show how the nonextensive Tsallis g-entropy indicator may be interpreted as a measure of the nonscaling condition in time series. This is done by applying the nonextensive entropy formalism to the diffusion entropy analysis (DEA). We apply the analysis to the study of the teen birth phenomenon. We find that the number of unmarried teen births is strongly influenced by social processes that induce an anomalous memory in the data. This memory is related to the strength of the nonscaling component of the signal and is more intense than that in the married teen birth time series. By using a wavelet multiresolution analysis, we attempt to provide a social interpretation of this effect…. One of the most exciting and rapidly developing areas of modern research is the quantitative study of "complexity." Complexity has special interdisciplinary impacts in the fields of physics, mathematics, information science, biology, sociology, and medicine. No definition of a complex system has been universally embraced, so here we adopt the working definition, "an arrangement of parts so intricate as to be hard to understand or deal with." Therefore, the main goal of the science of complexity is to develop mathematical methods in order to discriminate among the fundamental microscopic and macroscopic constituents of a complex system and to describe their interrelations in a concise way. Experiments usually yield results in the form of time series for physical observables. Typically, these time series contain both a slow regular variation, usually called a "signal," and a rapid erratic fluctuation, usually called "noise." Historically, the techniques applied to processing such time series have been based on equilibrium statistical mechanics and, therefore, they are not applicable to phenomena far from equilibrium. Among the fluctuating phenomena, a particularly important place is occupied by those phenomena characterized by some type of self-similar or scaling-fractal structures [4]. In this chapter we show that the nonextensive Tsallis g-entropy indicator may be interpreted as a measure of the strength of the nonscaling component of a time series.
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Conference papers on the topic "Daubechies wavelet of type 4"

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Padilla-Vivanco, Alfonso, Irwing Tellez-Arriaga, Carina Toxqui-Quitl, and C. Santiago-Tepantlan. "Multifocus microscope color image fusion based on Daub(2) and Daub(4) kernels of the Daubechies wavelet family." In SPIE Optical Engineering + Applications, edited by Andrew G. Tescher. SPIE, 2009. http://dx.doi.org/10.1117/12.824763.

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Sangeetha P., Karthik M., and KalavathiDevi T. "VLSI architectures for the 4-tap and 6-tap 2-D Daubechies wavelet filters using pipelined direct mapping method." In 2015 International Conference on Innovations in Information,Embedded and Communication Systems (ICIIECS). IEEE, 2015. http://dx.doi.org/10.1109/iciiecs.2015.7193010.

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Xin, Wu, Chu Jinkui, and Cao Weiqing. "A New Method of Dimensional Synthesis of Function Generator for Planar 4-BAR Linkage Based on Wavelet Characteristic Parameters." In ASME 1998 Design Engineering Technical Conferences. American Society of Mechanical Engineers, 1998. http://dx.doi.org/10.1115/detc98/mech-5926.

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Abstract The wavelet description of output function of planar 4-bar linkage is presented in the light of wavelet transform theory. By the aid of Daubechies orthogonal wavelet to transform and analyze the linkage’s output function, a new conception of Wavelet Characteristic Parameters (in short WCP) about output function is originated. Based on this conception, a resolution to function synthesis of planar linkage is provided according to WCP with fuzzy identification method employed. It helps to synthesize function generator that will approximately realize “infinite points” of the output function specially for output requirement with given range. Two examples will illustrate the efficiency and advantages of this method.
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Nasif, Hesham R., and Atsushi Neyama. "Integrated Multi Path Model to Calculate Radionuclide Release From a Repository Using Wavelet Galerkin Method." In 10th International Conference on Nuclear Engineering. ASMEDC, 2002. http://dx.doi.org/10.1115/icone10-22721.

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This work represents a WIRS code developed using wavelet Galerkin method to solve radionuclide transport model in near field and far field of a repository for high-level radioactive waste. After overpack failure, radionuclides diffuse through the bentonite buffer material to the water bearing fracture around the repository transport horizontally through this geosphere then transport vertically through the major water conducting fault (MWCF) reach the biosphere. The radionuclides transport barriers considered in this model are engineered barrier system (EBS), geosphere, and MWCF. Hydraulic conductivity of the bentonite is more than three orders of magnitude smaller than that of the surrounding host rock, so the only transport mechanism through EBS is diffusion. In the host rock, the problem is of advection-diffusion type with highly varying parameters from one medium to other due to the variability in length, transmissivity and other transport-relevant properties of the transport paths. Daubechies’ wavelet is used as a basis function to solve the nonlinear partial differential equations arising from the model formulation of the radionuclides transport. Since the scaling functions are compactly supported, only a finite number of the connection coefficients are nonzero. The resultant matrix has a block diagonal structure, which can be inverted easily. We tested our WGM algorithm with several problems to verify the model. The solutions are very accurate with a proper selection of Daubechies’ order and dilation order. The solution is very accurate at the interfaces where the radionuclide concentration exhibits very steep gradients.
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Zhang, Guangjian, Ilyass Khlifa, and Olivier Coutier-Delgosha. "Experimental Investigation of Turbulent Cavitating Flows in a Small Venturi Nozzle." In ASME-JSME-KSME 2019 8th Joint Fluids Engineering Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/ajkfluids2019-4781.

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Abstract The cavitating flows created in a small Venturi tube with throat cross section 4 × 15.34 mm2 are investigated based on ultra-fast x-ray imaging. The instantaneous velocities of the liquid and vapor are measured simultaneously by tracking seeding particles and vapor structures respectively while the vapor volume fraction is derived from the different x-ray attenuation. Wavelet decomposition with appropriate thresholds is used to separate seeding particles from vapor structures, so that image cross-correlations could be applied on the two phases separately. This study presents data on mean velocity and void ratio field, statistical turbulent quantities in three different cavitation levels with the same reference velocity. A type of cavitation associated with a weak but persistent re-entrant jet is described. The comparison between the cavitation and the noncavitating flow shows that the averaged flow field is significantly altered by the presence of cavitation and the vapor formation near the throat area is observed to suppress velocity fluctuations.
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Dellacasagrande, M., D. Lengani, D. Simoni, M. Ubaldi, and P. Zunino. "Evaluation of Turbulent Spot Production Rate in Boundary Layers Under Variable Pressure Gradients for Gas Turbine Applications." In ASME Turbo Expo 2019: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/gt2019-91229.

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Abstract The paper presents an experimental data base on transitional boundary layers developing on a flat plate installed within a variable area opening endwall channel. Measurements have been carried out by means of time-resolved PIV. The overall test matrix spans 3 Reynolds numbers, 4 free-stream turbulence intensity levels and 4 different flow adverse pressure gradients. For each condition, 16000 instantaneous flow fields have been acquired in order to obtain high statistical accuracy. The flow parameters have been varied in order to provide a gradual shift of the mode of transition from a bypass process occurring with mild adverse pressure gradients at high free-stream turbulence, to separated flow transition, occurring with low Reynolds number, low free-stream turbulence intensity and elevated adverse pressure gradient. In order to quantify the influence of the flow parameter variation on the boundary layer transition process, the transition onset and end positions, and the turbulent spot production rate have been evaluated with a wavelet based intermittency detection technique. This post-processing technique is in fact able to identify the vortical structures developing within the boundary layer, the intermittency function is then automatically evaluated for each tested condition counting the number of such structures and defining the cumulative probability function. The by-pass transition mode has the longest transition length that decreases with increasing the Reynolds number. The transition length of the separated flow case is smaller than the by-pass one, and the variation of the flow parameters has a similar impact. Similarly, the dimensionless turbulent spot production rate reduces when the Reynolds number is increasing. The variation of the inlet turbulence intensity has a small influence on this parameter except for the condition at the highest turbulence intensity, that always shows the lowest turbulent spot production rate because a by-pass type transition occurs. This large amount of data has been used to develop new correlations used to predict the spot production rate and the transition length in attached and separated flows.
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