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butterworth</title> <meta name="description" content="Search results for: butterworth"> <meta name="keywords" content="butterworth"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block d-lg-none navbar-toggler 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id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="butterworth"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 11</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: butterworth</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">11</span> Transforming Butterworth Low Pass Filter into Microstrip Line Form at LC-Band Applications</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Liew%20Hui%20Fang">Liew Hui Fang</a>, <a href="https://publications.waset.org/abstracts/search?q=Syed%20Idris%20Syed%20Hassan"> Syed Idris Syed Hassan</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohd%20Fareq%20Abd.%20Malek"> Mohd Fareq Abd. Malek</a>, <a href="https://publications.waset.org/abstracts/search?q=Yufridin%20Wahab"> Yufridin Wahab</a>, <a href="https://publications.waset.org/abstracts/search?q=Norshafinash%20Saudin"> Norshafinash Saudin </a> </p> <p class="card-text"><strong>Abstract:</strong></p> The paper implementation new approach method applied into transforming lumped element circuit into microstrip line form for Butterworth low pass filter which is operating at LC band. The filter’s lumped element circuits and microstrip line form were first designed and simulated using Advanced Design Software (ADS) to obtain the best filter characteristic based on S-parameter and implemented on FR4 substrate for order N=3,4,5,6,7,8 and 9. The importance of a new approach of transforming method as a correction factor has been considered into designed microstrip line. From ADS simulation results proved that the response of microstrip line circuit of Butterworth low pass filter with fringing correction factor has an excellent agreement with its lumped circuit. This shows that the new approach of transforming lumped element circuit into microstrip line is able to solve the conventional design of complexity size of circuit of Butterworth low pass filter (LPF) into microstrip line. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Butterworth%20low%20pass%20filter" title="Butterworth low pass filter">Butterworth low pass filter</a>, <a href="https://publications.waset.org/abstracts/search?q=number%20of%20order" title=" number of order"> number of order</a>, <a href="https://publications.waset.org/abstracts/search?q=microstrip%20line" title=" microstrip line"> microstrip line</a>, <a href="https://publications.waset.org/abstracts/search?q=microwave%20filter" title=" microwave filter"> microwave filter</a>, <a href="https://publications.waset.org/abstracts/search?q=maximally%20flat" title=" maximally flat"> maximally flat</a> </p> <a href="https://publications.waset.org/abstracts/2002/transforming-butterworth-low-pass-filter-into-microstrip-line-form-at-lc-band-applications" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/2002.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">334</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">10</span> Digital Watermarking Based on Visual Cryptography and Histogram</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=R.%20Rama%20Kishore">R. Rama Kishore</a>, <a href="https://publications.waset.org/abstracts/search?q=Sunesh"> Sunesh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Nowadays, robust and secure watermarking algorithm and its optimization have been need of the hour. A watermarking algorithm is presented to achieve the copy right protection of the owner based on visual cryptography, histogram shape property and entropy. In this, both host image and watermark are preprocessed. Host image is preprocessed by using Butterworth filter, and watermark is with visual cryptography. Applying visual cryptography on water mark generates two shares. One share is used for embedding the watermark, and the other one is used for solving any dispute with the aid of trusted authority. Usage of histogram shape makes the process more robust against geometric and signal processing attacks. The combination of visual cryptography, Butterworth filter, histogram, and entropy can make the algorithm more robust, imperceptible, and copy right protection of the owner. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=digital%20watermarking" title="digital watermarking">digital watermarking</a>, <a href="https://publications.waset.org/abstracts/search?q=visual%20cryptography" title=" visual cryptography"> visual cryptography</a>, <a href="https://publications.waset.org/abstracts/search?q=histogram" title=" histogram"> histogram</a>, <a href="https://publications.waset.org/abstracts/search?q=butter%20worth%20filter" title=" butter worth filter"> butter worth filter</a> </p> <a href="https://publications.waset.org/abstracts/48320/digital-watermarking-based-on-visual-cryptography-and-histogram" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/48320.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">357</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9</span> Reconfigurable Efficient IIR Filter Design Using MAC Algorithm </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rajesh%20Mehra">Rajesh Mehra</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper an IIR filter has been designed and simulated on an FPGA. The implementation is based on MAC algorithm which uses multiply-and-accumulate operations IIR filter design implementation. Parallel Pipelined structure is used to implement the proposed IIR Filter taking optimal advantage of the look up table of the FPGA device. The designed filter has been synthesized on DSP slice based FPGA to perform multiplier function of MAC unit. The DSP slices are useful to enhance the speed performance. The developed IIR filter is designed and simulated with MATLAB and synthesized with Xilinx Synthesis Tool (XST), and implemented on Virtex 5 and Spartan 3 ADSP FPGA devices. The IIR filter implemented on Virtex 5 FPGA can operate at an estimated frequency of 81.5 MHz as compared to 40.5 MHz in case of Spartan 3 ADSP FPGA. The Virtex 5 based implementation also consumes less slices and slice flip flops of target FPGA in comparison to Spartan 3 ADSP based implementation to provide cost effective solution for signal processing applications. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=butterworth" title="butterworth">butterworth</a>, <a href="https://publications.waset.org/abstracts/search?q=DSP" title=" DSP"> DSP</a>, <a href="https://publications.waset.org/abstracts/search?q=IIR" title=" IIR"> IIR</a>, <a href="https://publications.waset.org/abstracts/search?q=MAC" title=" MAC"> MAC</a>, <a href="https://publications.waset.org/abstracts/search?q=FPGA" title=" FPGA "> FPGA </a> </p> <a href="https://publications.waset.org/abstracts/13274/reconfigurable-efficient-iir-filter-design-using-mac-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/13274.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">357</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8</span> FPGA Based IIR Filter Design Using MAC Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rajesh%20Mehra">Rajesh Mehra</a>, <a href="https://publications.waset.org/abstracts/search?q=Bharti%20Thakur"> Bharti Thakur</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, an IIR filter has been designed and simulated on an FPGA. The implementation is based on MAC algorithm which uses multiply-and-accumulate operations IIR filter design implementation. Parallel Pipelined structure is used to implement the proposed IIR Filter taking optimal advantage of the look up table of the FPGA device. The designed filter has been synthesized on DSP slice based FPGA to perform multiplier function of MAC unit. The DSP slices are useful to enhance the speed performance. The developed IIR filter is designed and simulated with Matlab and synthesized with Xilinx Synthesis Tool (XST), and implemented on Virtex 5 and Spartan 3 ADSP FPGA devices. The IIR filter implemented on Virtex 5 FPGA can operate at an estimated frequency of 81.5 MHz as compared to 40.5 MHz in case of Spartan 3 ADSP FPGA. The Virtex 5 based implementation also consumes less slices and slice flip flops of target FPGA in comparison to Spartan 3 ADSP based implementation to provide cost effective solution for signal processing applications. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Butterworth%20filter" title="Butterworth filter">Butterworth filter</a>, <a href="https://publications.waset.org/abstracts/search?q=DSP" title=" DSP"> DSP</a>, <a href="https://publications.waset.org/abstracts/search?q=IIR" title=" IIR"> IIR</a>, <a href="https://publications.waset.org/abstracts/search?q=MAC" title=" MAC"> MAC</a>, <a href="https://publications.waset.org/abstracts/search?q=FPGA" title=" FPGA"> FPGA</a> </p> <a href="https://publications.waset.org/abstracts/41409/fpga-based-iir-filter-design-using-mac-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/41409.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">388</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7</span> An Intelligent Nondestructive Testing System of Ultrasonic Infrared Thermal Imaging Based on Embedded Linux</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hao%20Mi">Hao Mi</a>, <a href="https://publications.waset.org/abstracts/search?q=Ming%20Yang"> Ming Yang</a>, <a href="https://publications.waset.org/abstracts/search?q=Tian-yue%20Yang"> Tian-yue Yang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Ultrasonic infrared nondestructive testing is a kind of testing method with high speed, accuracy and localization. However, there are still some problems, such as the detection requires manual real-time field judgment, the methods of result storage and viewing are still primitive. An intelligent non-destructive detection system based on embedded linux is put forward in this paper. The hardware part of the detection system is based on the ARM (Advanced Reduced Instruction Set Computer Machine) core and an embedded linux system is built to realize image processing and defect detection of thermal images. The CLAHE algorithm and the Butterworth filter are used to process the thermal image, and then the boa server and CGI (Common Gateway Interface) technology are used to transmit the test results to the display terminal through the network for real-time monitoring and remote monitoring. The system also liberates labor and eliminates the obstacle of manual judgment. According to the experiment result, the system provides a convenient and quick solution for industrial non-destructive testing. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=remote%20monitoring" title="remote monitoring">remote monitoring</a>, <a href="https://publications.waset.org/abstracts/search?q=non-destructive%20testing" title=" non-destructive testing"> non-destructive testing</a>, <a href="https://publications.waset.org/abstracts/search?q=embedded%20Linux%20system" title=" embedded Linux system"> embedded Linux system</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20processing" title=" image processing"> image processing</a> </p> <a href="https://publications.waset.org/abstracts/101979/an-intelligent-nondestructive-testing-system-of-ultrasonic-infrared-thermal-imaging-based-on-embedded-linux" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/101979.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">223</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6</span> Denoising of Motor Unit Action Potential Based on Tunable Band-Pass Filter</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Khalida%20S.%20Rijab">Khalida S. Rijab</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohammed%20E.%20Safi"> Mohammed E. Safi</a>, <a href="https://publications.waset.org/abstracts/search?q=Ayad%20A.%20%20Ibrahim"> Ayad A. Ibrahim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> When electrical electrodes are mounted on the skin surface of the muscle, a signal is detected when a skeletal muscle undergoes contraction; the signal is known as surface electromyographic signal (EMG). This signal has a noise-like interference pattern resulting from the temporal and spatial summation of action potentials (AP) of all active motor units (MU) near electrode detection. By appropriate processing (Decomposition), the surface EMG signal may be used to give an estimate of motor unit action potential. In this work, a denoising technique is applied to the MUAP signals extracted from the spatial filter (IB2). A set of signals from a non-invasive two-dimensional grid of 16 electrodes from different types of subjects, muscles, and sex are recorded. These signals will acquire noise during recording and detection. A digital fourth order band- pass Butterworth filter is used for denoising, with a tuned band-pass frequency of suitable choice of cutoff frequencies is investigated, with the aim of obtaining a suitable band pass frequency. Results show an improvement of (1-3 dB) in the signal to noise ratio (SNR) have been achieved, relative to the raw spatial filter output signals for all cases that were under investigation. Furthermore, the research’s goal included also estimation and reconstruction of the mean shape of the MUAP. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=EMG" title="EMG">EMG</a>, <a href="https://publications.waset.org/abstracts/search?q=Motor%20Unit" title=" Motor Unit"> Motor Unit</a>, <a href="https://publications.waset.org/abstracts/search?q=Digital%20Filter" title=" Digital Filter"> Digital Filter</a>, <a href="https://publications.waset.org/abstracts/search?q=Denoising" title=" Denoising"> Denoising</a> </p> <a href="https://publications.waset.org/abstracts/63012/denoising-of-motor-unit-action-potential-based-on-tunable-band-pass-filter" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/63012.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">401</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5</span> Why is the Recurrence Rate of Residual or Recurrent Disease Following Endoscopic Mucosal Resection (EMR) of the Oesophageal Dysplasia’s and T1 Tumours Higher in the Greater Midlands Cancer Network?</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Harshadkumar%20Rajgor">Harshadkumar Rajgor</a>, <a href="https://publications.waset.org/abstracts/search?q=Jeff%20Butterworth"> Jeff Butterworth</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Background: Barretts oesophagus increases the risk of developing oesophageal adenocarcinoma. Over the last 40 years, there has been a 6 fold increase in the incidence of oesophageal adenocarcinoma in the western world and the incidence rates are increasing at a greater rate than cancers of the colon, breast and lung. Endoscopic mucosal resection (EMR) is a relatively new technique being used by 2 centres in the greater midlands cancer network. EMR can be used for curative or staging purposes, for high-grade dysplasia’s and T1 tumours of the oesophagus. EMR is also suitable for those who are deemed high risk for oesophagectomy. EMR has a recurrence rate of 21% according to the Wiesbaden data. Method: A retrospective study of prospectively collected data was carried out involving 24 patients who had EMR for curative or staging purposes. Complications of residual or recurrent disease following EMR that required further treatment were investigated. Results: In 54% of cases residual or recurrent disease was suspected. 96% of patients were given clear and concise information regarding their diagnosis of high-grade dysplasia or T1 tumours. All 24 patients consulted the same specialist healthcare team. Conclusion: EMR is a safe and effective treatment for patients who have high-grade dysplasia and T1NO tumours. In 54% of cases residual or recurrent disease was suspected. Initially, only single resections were undertaken. Multiple resections are now being carried out to reduce the risk of recurrence. Complications from EMR remain low in this series and consisted of a single episode of post procedural bleeding. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=endoscopic%20mucosal%20resection" title="endoscopic mucosal resection">endoscopic mucosal resection</a>, <a href="https://publications.waset.org/abstracts/search?q=oesophageal%20dysplasia" title=" oesophageal dysplasia"> oesophageal dysplasia</a>, <a href="https://publications.waset.org/abstracts/search?q=T1%20tumours" title=" T1 tumours"> T1 tumours</a>, <a href="https://publications.waset.org/abstracts/search?q=cancer%20network" title=" cancer network"> cancer network</a> </p> <a href="https://publications.waset.org/abstracts/22352/why-is-the-recurrence-rate-of-residual-or-recurrent-disease-following-endoscopic-mucosal-resection-emr-of-the-oesophageal-dysplasias-and-t1-tumours-higher-in-the-greater-midlands-cancer-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/22352.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">316</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4</span> Human Identification Using Local Roughness Patterns in Heartbeat Signal </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Md.%20Khayrul%20Bashar">Md. Khayrul Bashar</a>, <a href="https://publications.waset.org/abstracts/search?q=Md.%20Saiful%20Islam"> Md. Saiful Islam</a>, <a href="https://publications.waset.org/abstracts/search?q=Kimiko%20Yamashita"> Kimiko Yamashita</a>, <a href="https://publications.waset.org/abstracts/search?q=Yano%20Midori"> Yano Midori</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Despite having some progress in human authentication, conventional biometrics (e.g., facial features, fingerprints, retinal scans, gait, voice patterns) are not robust against falsification because they are neither confidential nor secret to an individual. As a non-invasive tool, electrocardiogram (ECG) has recently shown a great potential in human recognition due to its unique rhythms characterizing the variability of human heart structures (chest geometry, sizes, and positions). Moreover, ECG has a real-time vitality characteristic that signifies the live signs, which ensure legitimate individual to be identified. However, the detection accuracy of the current ECG-based methods is not sufficient due to a high variability of the individual’s heartbeats at a different instance of time. These variations may occur due to muscle flexure, the change of mental or emotional states, and the change of sensor positions or long-term baseline shift during the recording of ECG signal. In this study, a new method is proposed for human identification, which is based on the extraction of the local roughness of ECG heartbeat signals. First ECG signal is preprocessed using a second order band-pass Butterworth filter having cut-off frequencies of 0.00025 and 0.04. A number of local binary patterns are then extracted by applying a moving neighborhood window along the ECG signal. At each instant of the ECG signal, the pattern is formed by comparing the ECG intensities at neighboring time points with the central intensity in the moving window. Then, binary weights are multiplied with the pattern to come up with the local roughness description of the signal. Finally, histograms are constructed that describe the heartbeat signals of individual subjects in the database. One advantage of the proposed feature is that it does not depend on the accuracy of detecting QRS complex, unlike the conventional methods. Supervised recognition methods are then designed using minimum distance to mean and Bayesian classifiers to identify authentic human subjects. An experiment with sixty (60) ECG signals from sixty adult subjects from National Metrology Institute of Germany (NMIG) - PTB database, showed that the proposed new method is promising compared to a conventional interval and amplitude feature-based method. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=human%20identification" title="human identification">human identification</a>, <a href="https://publications.waset.org/abstracts/search?q=ECG%20biometrics" title=" ECG biometrics"> ECG biometrics</a>, <a href="https://publications.waset.org/abstracts/search?q=local%20roughness%20patterns" title=" local roughness patterns"> local roughness patterns</a>, <a href="https://publications.waset.org/abstracts/search?q=supervised%20classification" title=" supervised classification"> supervised classification</a> </p> <a href="https://publications.waset.org/abstracts/29806/human-identification-using-local-roughness-patterns-in-heartbeat-signal" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/29806.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">404</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3</span> Comprehensive Analysis of Electrohysterography Signal Features in Term and Preterm Labor</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zhihui%20Liu">Zhihui Liu</a>, <a href="https://publications.waset.org/abstracts/search?q=Dongmei%20Hao"> Dongmei Hao</a>, <a href="https://publications.waset.org/abstracts/search?q=Qian%20Qiu"> Qian Qiu</a>, <a href="https://publications.waset.org/abstracts/search?q=Yang%20An"> Yang An</a>, <a href="https://publications.waset.org/abstracts/search?q=Lin%20Yang"> Lin Yang</a>, <a href="https://publications.waset.org/abstracts/search?q=Song%20Zhang"> Song Zhang</a>, <a href="https://publications.waset.org/abstracts/search?q=Yimin%20Yang"> Yimin Yang</a>, <a href="https://publications.waset.org/abstracts/search?q=Xuwen%20Li"> Xuwen Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Dingchang%20Zheng"> Dingchang Zheng</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Premature birth, defined as birth before 37 completed weeks of gestation is a leading cause of neonatal morbidity and mortality and has long-term adverse consequences for health. It has recently been reported that the worldwide preterm birth rate is around 10%. The existing measurement techniques for diagnosing preterm delivery include tocodynamometer, ultrasound and fetal fibronectin. However, they are subjective, or suffer from high measurement variability and inaccurate diagnosis and prediction of preterm labor. Electrohysterography (EHG) method based on recording of uterine electrical activity by electrodes attached to maternal abdomen, is a promising method to assess uterine activity and diagnose preterm labor. The purpose of this study is to analyze the difference of EHG signal features between term labor and preterm labor. Free access database was used with 300 signals acquired in two groups of pregnant women who delivered at term (262 cases) and preterm (38 cases). Among them, EHG signals from 38 term labor and 38 preterm labor were preprocessed with band-pass Butterworth filters of 0.08–4Hz. Then, EHG signal features were extracted, which comprised classical time domain description including root mean square and zero-crossing number, spectral parameters including peak frequency, mean frequency and median frequency, wavelet packet coefficients, autoregression (AR) model coefficients, and nonlinear measures including maximal Lyapunov exponent, sample entropy and correlation dimension. Their statistical significance for recognition of two groups of recordings was provided. The results showed that mean frequency of preterm labor was significantly smaller than term labor (p < 0.05). 5 coefficients of AR model showed significant difference between term labor and preterm labor. The maximal Lyapunov exponent of early preterm (time of recording < the 26th week of gestation) was significantly smaller than early term. The sample entropy of late preterm (time of recording > the 26th week of gestation) was significantly smaller than late term. There was no significant difference for other features between the term labor and preterm labor groups. Any future work regarding classification should therefore focus on using multiple techniques, with the mean frequency, AR coefficients, maximal Lyapunov exponent and the sample entropy being among the prime candidates. Even if these methods are not yet useful for clinical practice, they do bring the most promising indicators for the preterm labor. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=electrohysterogram" title="electrohysterogram">electrohysterogram</a>, <a href="https://publications.waset.org/abstracts/search?q=feature" title=" feature"> feature</a>, <a href="https://publications.waset.org/abstracts/search?q=preterm%20labor" title=" preterm labor"> preterm labor</a>, <a href="https://publications.waset.org/abstracts/search?q=term%20labor" title=" term labor "> term labor </a> </p> <a href="https://publications.waset.org/abstracts/68367/comprehensive-analysis-of-electrohysterography-signal-features-in-term-and-preterm-labor" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/68367.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">571</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2</span> The Direct Deconvolutional Model in the Large-Eddy Simulation of Turbulence</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ning%20Chang">Ning Chang</a>, <a href="https://publications.waset.org/abstracts/search?q=Zelong%20Yuan"> Zelong Yuan</a>, <a href="https://publications.waset.org/abstracts/search?q=Yunpeng%20Wang"> Yunpeng Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=Jianchun%20Wang"> Jianchun Wang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The utilization of Large Eddy Simulation (LES) has been extensive in turbulence research. LES concentrates on resolving the significant grid-scale motions while representing smaller scales through subfilter-scale (SFS) models. The deconvolution model, among the available SFS models, has proven successful in LES of engineering and geophysical flows. Nevertheless, the thorough investigation of how sub-filter scale dynamics and filter anisotropy affect SFS modeling accuracy remains lacking. The outcomes of LES are significantly influenced by filter selection and grid anisotropy, factors that have not been adequately addressed in earlier studies. This study examines two crucial aspects of LES: Firstly, the accuracy of direct deconvolution models (DDM) is evaluated concerning sub-filter scale (SFS) dynamics across varying filter-to-grid ratios (FGR) in isotropic turbulence. Various invertible filters are employed, including Gaussian, Helmholtz I and II, Butterworth, Chebyshev I and II, Cauchy, Pao, and rapidly decaying filters. The importance of FGR becomes evident as it plays a critical role in controlling errors for precise SFS stress prediction. When FGR is set to 1, the DDM models struggle to faithfully reconstruct SFS stress due to inadequate resolution of SFS dynamics. Notably, prediction accuracy improves when FGR is set to 2, leading to accurate reconstruction of SFS stress, except for cases involving Helmholtz I and II filters. Remarkably high precision, nearly 100%, is achieved at an FGR of 4 for all DDM models. Furthermore, the study extends to filter anisotropy and its impact on SFS dynamics and LES accuracy. By utilizing the dynamic Smagorinsky model (DSM), dynamic mixed model (DMM), and direct deconvolution model (DDM) with anisotropic filters, aspect ratios (AR) ranging from 1 to 16 are examined in LES filters. The results emphasize the DDM’s proficiency in accurately predicting SFS stresses under highly anisotropic filtering conditions. Notably high correlation coefficients exceeding 90% are observed in the a priori study for the DDM’s reconstructed SFS stresses, surpassing those of the DSM and DMM models. However, these correlations tend to decrease as filter anisotropy increases. In the a posteriori analysis, the DDM model consistently outperforms the DSM and DMM models across various turbulence statistics, including velocity spectra, probability density functions related to vorticity, SFS energy flux, velocity increments, strainrate tensors, and SFS stress. It is evident that as filter anisotropy intensifies, the results of DSM and DMM deteriorate, while the DDM consistently delivers satisfactory outcomes across all filter-anisotropy scenarios. These findings underscore the potential of the DDM framework as a valuable tool for advancing the development of sophisticated SFS models for LES in turbulence research. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=deconvolution%20model" title="deconvolution model">deconvolution model</a>, <a href="https://publications.waset.org/abstracts/search?q=large%20eddy%20simulation" title=" large eddy simulation"> large eddy simulation</a>, <a href="https://publications.waset.org/abstracts/search?q=subfilter%20scale%20modeling" title=" subfilter scale modeling"> subfilter scale modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=turbulence" title=" turbulence"> turbulence</a> </p> <a href="https://publications.waset.org/abstracts/171876/the-direct-deconvolutional-model-in-the-large-eddy-simulation-of-turbulence" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/171876.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">75</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1</span> The Direct Deconvolution Model for the Large Eddy Simulation of Turbulence</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ning%20Chang">Ning Chang</a>, <a href="https://publications.waset.org/abstracts/search?q=Zelong%20Yuan"> Zelong Yuan</a>, <a href="https://publications.waset.org/abstracts/search?q=Yunpeng%20Wang"> Yunpeng Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=Jianchun%20Wang"> Jianchun Wang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Large eddy simulation (LES) has been extensively used in the investigation of turbulence. LES calculates the grid-resolved large-scale motions and leaves small scales modeled by sublfilterscale (SFS) models. Among the existing SFS models, the deconvolution model has been used successfully in the LES of the engineering flows and geophysical flows. Despite the wide application of deconvolution models, the effects of subfilter scale dynamics and filter anisotropy on the accuracy of SFS modeling have not been investigated in depth. The results of LES are highly sensitive to the selection of filters and the anisotropy of the grid, which has been overlooked in previous research. In the current study, two critical aspects of LES are investigated. Firstly, we analyze the influence of sub-filter scale (SFS) dynamics on the accuracy of direct deconvolution models (DDM) at varying filter-to-grid ratios (FGR) in isotropic turbulence. An array of invertible filters are employed, encompassing Gaussian, Helmholtz I and II, Butterworth, Chebyshev I and II, Cauchy, Pao, and rapidly decaying filters. The significance of FGR becomes evident, as it acts as a pivotal factor in error control for precise SFS stress prediction. When FGR is set to 1, the DDM models cannot accurately reconstruct the SFS stress due to the insufficient resolution of SFS dynamics. Notably, prediction capabilities are enhanced at an FGR of 2, resulting in accurate SFS stress reconstruction, except for cases involving Helmholtz I and II filters. A remarkable precision close to 100% is achieved at an FGR of 4 for all DDM models. Additionally, the further exploration extends to the filter anisotropy to address its impact on the SFS dynamics and LES accuracy. By employing dynamic Smagorinsky model (DSM), dynamic mixed model (DMM), and direct deconvolution model (DDM) with the anisotropic filter, aspect ratios (AR) ranging from 1 to 16 in LES filters are evaluated. The findings highlight the DDM's proficiency in accurately predicting SFS stresses under highly anisotropic filtering conditions. High correlation coefficients exceeding 90% are observed in the a priori study for the DDM's reconstructed SFS stresses, surpassing those of the DSM and DMM models. However, these correlations tend to decrease as lter anisotropy increases. In the a posteriori studies, the DDM model consistently outperforms the DSM and DMM models across various turbulence statistics, encompassing velocity spectra, probability density functions related to vorticity, SFS energy flux, velocity increments, strain-rate tensors, and SFS stress. It is observed that as filter anisotropy intensify, the results of DSM and DMM become worse, while the DDM continues to deliver satisfactory results across all filter-anisotropy scenarios. The findings emphasize the DDM framework's potential as a valuable tool for advancing the development of sophisticated SFS models for LES of turbulence. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=deconvolution%20model" title="deconvolution model">deconvolution model</a>, <a href="https://publications.waset.org/abstracts/search?q=large%20eddy%20simulation" title=" large eddy simulation"> large eddy simulation</a>, <a href="https://publications.waset.org/abstracts/search?q=subfilter%20scale%20modeling" title=" subfilter scale modeling"> subfilter scale modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=turbulence" title=" turbulence"> turbulence</a> </p> <a href="https://publications.waset.org/abstracts/171846/the-direct-deconvolution-model-for-the-large-eddy-simulation-of-turbulence" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/171846.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">75</span> </span> </div> </div> </div> </main> <footer> <div id="infolinks" class="pt-3 pb-2"> <div class="container"> <div style="background-color:#f5f5f5;" class="p-3"> <div class="row"> <div class="col-md-2"> <ul class="list-unstyled"> About <li><a href="https://waset.org/page/support">About Us</a></li> <li><a href="https://waset.org/page/support#legal-information">Legal</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/WASET-16th-foundational-anniversary.pdf">WASET celebrates its 16th foundational anniversary</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Account <li><a href="https://waset.org/profile">My Account</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Explore <li><a href="https://waset.org/disciplines">Disciplines</a></li> <li><a href="https://waset.org/conferences">Conferences</a></li> <li><a href="https://waset.org/conference-programs">Conference Program</a></li> <li><a href="https://waset.org/committees">Committees</a></li> <li><a href="https://publications.waset.org">Publications</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Research <li><a href="https://publications.waset.org/abstracts">Abstracts</a></li> <li><a href="https://publications.waset.org">Periodicals</a></li> <li><a href="https://publications.waset.org/archive">Archive</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Open Science <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Open-Science-Philosophy.pdf">Open Science Philosophy</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Open-Science-Award.pdf">Open Science Award</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Open-Society-Open-Science-and-Open-Innovation.pdf">Open Innovation</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Postdoctoral-Fellowship-Award.pdf">Postdoctoral Fellowship Award</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Scholarly-Research-Review.pdf">Scholarly Research Review</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Support <li><a href="https://waset.org/page/support">Support</a></li> <li><a href="https://waset.org/profile/messages/create">Contact Us</a></li> <li><a href="https://waset.org/profile/messages/create">Report Abuse</a></li> </ul> </div> </div> </div> </div> </div> <div class="container text-center"> <hr style="margin-top:0;margin-bottom:.3rem;"> <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank" class="text-muted small">Creative Commons Attribution 4.0 International License</a> <div id="copy" class="mt-2">© 2024 World Academy of Science, Engineering and Technology</div> </div> </footer> <a href="javascript:" id="return-to-top"><i class="fas fa-arrow-up"></i></a> <div class="modal" id="modal-template"> <div class="modal-dialog"> <div class="modal-content"> <div class="row m-0 mt-1"> <div class="col-md-12"> <button type="button" class="close" data-dismiss="modal" aria-label="Close"><span aria-hidden="true">×</span></button> </div> </div> <div class="modal-body"></div> </div> </div> </div> <script src="https://cdn.waset.org/static/plugins/jquery-3.3.1.min.js"></script> <script src="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/js/bootstrap.bundle.min.js"></script> <script src="https://cdn.waset.org/static/js/site.js?v=150220211556"></script> <script> jQuery(document).ready(function() { /*jQuery.get("https://publications.waset.org/xhr/user-menu", function (response) { jQuery('#mainNavMenu').append(response); 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