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prediction</title> <meta name="description" content="Search results for: signal prediction"> <meta name="keywords" content="signal prediction"> <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 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action="https://publications.waset.org/abstracts/search"> <div 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="signal prediction"> <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> 3804</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: signal prediction</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3714</span> Statistical Analysis with Prediction Models of User Satisfaction in Software Project Factors </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Katawut%20Kaewbanjong">Katawut Kaewbanjong</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We analyzed a volume of data and found significant user satisfaction in software project factors. A statistical significance analysis (logistic regression) and collinearity analysis determined the significance factors from a group of 71 pre-defined factors from 191 software projects in ISBSG Release 12. The eight prediction models used for testing the prediction potential of these factors were Neural network, k-NN, Naïve Bayes, Random forest, Decision tree, Gradient boosted tree, linear regression and logistic regression prediction model. Fifteen pre-defined factors were truly significant in predicting user satisfaction, and they provided 82.71% prediction accuracy when used with a neural network prediction model. These factors were client-server, personnel changes, total defects delivered, project inactive time, industry sector, application type, development type, how methodology was acquired, development techniques, decision making process, intended market, size estimate approach, size estimate method, cost recording method, and effort estimate method. These findings may benefit software development managers considerably. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=prediction%20model" title="prediction model">prediction model</a>, <a href="https://publications.waset.org/abstracts/search?q=statistical%20analysis" title=" statistical analysis"> statistical analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=software%20project" title=" software project"> software project</a>, <a href="https://publications.waset.org/abstracts/search?q=user%20satisfaction%20factor" title=" user satisfaction factor"> user satisfaction factor</a> </p> <a href="https://publications.waset.org/abstracts/121683/statistical-analysis-with-prediction-models-of-user-satisfaction-in-software-project-factors" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/121683.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">124</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">3713</span> Recent Advancement in Fetal Electrocardiogram Extraction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Savita">Savita</a>, <a href="https://publications.waset.org/abstracts/search?q=Anurag%20Sharma"> Anurag Sharma</a>, <a href="https://publications.waset.org/abstracts/search?q=Harsukhpreet%20Singh"> Harsukhpreet Singh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Fetal Electrocardiogram (fECG) is a widely used technique to assess the fetal well-being and identify any changes that might be with problems during pregnancy and to evaluate the health and conditions of the fetus. Various techniques or methods have been employed to diagnose the fECG from abdominal signal. This paper describes the facile approach for the estimation of the fECG known as Adaptive Comb. Filter (ACF). The ACF can adjust according to the temporal variations in fundamental frequency by itself that used for the estimation of the quasi periodic signal of ECG signal. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=aECG" title="aECG">aECG</a>, <a href="https://publications.waset.org/abstracts/search?q=ACF" title=" ACF"> ACF</a>, <a href="https://publications.waset.org/abstracts/search?q=fECG" title=" fECG"> fECG</a>, <a href="https://publications.waset.org/abstracts/search?q=mECG" title=" mECG"> mECG</a> </p> <a href="https://publications.waset.org/abstracts/49031/recent-advancement-in-fetal-electrocardiogram-extraction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/49031.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">408</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">3712</span> Signal Processing Approach to Study Multifractality and Singularity of Solar Wind Speed Time Series</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tushnik%20Sarkar">Tushnik Sarkar</a>, <a href="https://publications.waset.org/abstracts/search?q=Mofazzal%20H.%20Khondekar"> Mofazzal H. Khondekar</a>, <a href="https://publications.waset.org/abstracts/search?q=Subrata%20Banerjee"> Subrata Banerjee</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper investigates the nature of the fluctuation of the daily average Solar wind speed time series collected over a period of 2492 days, from 1<sup>st </sup>January, 1997 to 28<sup>th</sup> October, 2003. The degree of self-similarity and scalability of the Solar Wind Speed signal has been explored to characterise the signal fluctuation. Multi-fractal Detrended Fluctuation Analysis (MFDFA) method has been implemented on the signal which is under investigation to perform this task. Furthermore, the singularity spectra of the signals have been also obtained to gauge the extent of the multifractality of the time series signal. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=detrended%20fluctuation%20analysis" title="detrended fluctuation analysis">detrended fluctuation analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=generalized%20hurst%20exponent" title=" generalized hurst exponent"> generalized hurst exponent</a>, <a href="https://publications.waset.org/abstracts/search?q=holder%20exponents" title=" holder exponents"> holder exponents</a>, <a href="https://publications.waset.org/abstracts/search?q=multifractal%20exponent" title=" multifractal exponent"> multifractal exponent</a>, <a href="https://publications.waset.org/abstracts/search?q=multifractal%20spectrum" title=" multifractal spectrum"> multifractal spectrum</a>, <a href="https://publications.waset.org/abstracts/search?q=singularity%20spectrum" title=" singularity spectrum"> singularity spectrum</a>, <a href="https://publications.waset.org/abstracts/search?q=time%20series%20analysis" title=" time series analysis"> time series analysis</a> </p> <a href="https://publications.waset.org/abstracts/62350/signal-processing-approach-to-study-multifractality-and-singularity-of-solar-wind-speed-time-series" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/62350.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">393</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">3711</span> Calibration Model of %Titratable Acidity (Citric Acid) for Intact Tomato by Transmittance SW-NIR Spectroscopy</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=K.%20Petcharaporn">K. Petcharaporn</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Kumchoo"> S. Kumchoo</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The acidity (citric acid) is one of the chemical contents that can refer to the internal quality and the maturity index of tomato. The titratable acidity (%TA) can be predicted by a non-destructive method prediction by using the transmittance short wavelength (SW-NIR). Spectroscopy in the wavelength range between 665-955 nm. The set of 167 tomato samples divided into groups of 117 tomatoes sample for training set and 50 tomatoes sample for test set were used to establish the calibration model to predict and measure %TA by partial least squares regression (PLSR) technique. The spectra were pretreated with MSC pretreatment and it gave the optimal result for calibration model as (R = 0.92, RMSEC = 0.03%) and this model obtained high accuracy result to use for %TA prediction in test set as (R = 0.81, RMSEP = 0.05%). From the result of prediction in test set shown that the transmittance SW-NIR spectroscopy technique can be used for a non-destructive method for %TA prediction of tomatoes. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=tomato" title="tomato">tomato</a>, <a href="https://publications.waset.org/abstracts/search?q=quality" title=" quality"> quality</a>, <a href="https://publications.waset.org/abstracts/search?q=prediction" title=" prediction"> prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=transmittance" title=" transmittance"> transmittance</a>, <a href="https://publications.waset.org/abstracts/search?q=titratable%20acidity" title=" titratable acidity"> titratable acidity</a>, <a href="https://publications.waset.org/abstracts/search?q=citric%20acid" title=" citric acid"> citric acid</a> </p> <a href="https://publications.waset.org/abstracts/11536/calibration-model-of-titratable-acidity-citric-acid-for-intact-tomato-by-transmittance-sw-nir-spectroscopy" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/11536.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">273</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">3710</span> Ground Surface Temperature History Prediction Using Long-Short Term Memory Neural Network Architecture</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Venkat%20S.%20Somayajula">Venkat S. Somayajula</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Ground surface temperature history prediction model plays a vital role in determining standards for international nuclear waste management. International standards for borehole based nuclear waste disposal require paleoclimate cycle predictions on scale of a million forward years for the place of waste disposal. This research focuses on developing a paleoclimate cycle prediction model using Bayesian long-short term memory (LSTM) neural architecture operated on accumulated borehole temperature history data. Bayesian models have been previously used for paleoclimate cycle prediction based on Monte-Carlo weight method, but due to limitations pertaining model coupling with certain other prediction networks, Bayesian models in past couldn’t accommodate prediction cycle’s over 1000 years. LSTM has provided frontier to couple developed models with other prediction networks with ease. Paleoclimate cycle developed using this process will be trained on existing borehole data and then will be coupled to surface temperature history prediction networks which give endpoints for backpropagation of LSTM network and optimize the cycle of prediction for larger prediction time scales. Trained LSTM will be tested on past data for validation and then propagated for forward prediction of temperatures at borehole locations. This research will be beneficial for study pertaining to nuclear waste management, anthropological cycle predictions and geophysical features <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bayesian%20long-short%20term%20memory%20neural%20network" title="Bayesian long-short term memory neural network">Bayesian long-short term memory neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=borehole%20temperature" title=" borehole temperature"> borehole temperature</a>, <a href="https://publications.waset.org/abstracts/search?q=ground%20surface%20temperature%20history" title=" ground surface temperature history"> ground surface temperature history</a>, <a href="https://publications.waset.org/abstracts/search?q=paleoclimate%20cycle" title=" paleoclimate cycle"> paleoclimate cycle</a> </p> <a href="https://publications.waset.org/abstracts/124063/ground-surface-temperature-history-prediction-using-long-short-term-memory-neural-network-architecture" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/124063.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">128</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">3709</span> Hybrid Fuzzy Weighted K-Nearest Neighbor to Predict Hospital Readmission for Diabetic Patients </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Soha%20A.%20Bahanshal">Soha A. Bahanshal</a>, <a href="https://publications.waset.org/abstracts/search?q=Byung%20G.%20Kim"> Byung G. Kim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Identification of patients at high risk for hospital readmission is of crucial importance for quality health care and cost reduction. Predicting hospital readmissions among diabetic patients has been of great interest to many researchers and health decision makers. We build a prediction model to predict hospital readmission for diabetic patients within 30 days of discharge. The core of the prediction model is a modified k Nearest Neighbor called Hybrid Fuzzy Weighted k Nearest Neighbor algorithm. The prediction is performed on a patient dataset which consists of more than 70,000 patients with 50 attributes. We applied data preprocessing using different techniques in order to handle data imbalance and to fuzzify the data to suit the prediction algorithm. The model so far achieved classification accuracy of 80% compared to other models that only use k Nearest Neighbor. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title="machine learning">machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=prediction" title=" prediction"> prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=classification" title=" classification"> classification</a>, <a href="https://publications.waset.org/abstracts/search?q=hybrid%20fuzzy%20weighted%20k-nearest%20neighbor" title=" hybrid fuzzy weighted k-nearest neighbor"> hybrid fuzzy weighted k-nearest neighbor</a>, <a href="https://publications.waset.org/abstracts/search?q=diabetic%20hospital%20readmission" title=" diabetic hospital readmission"> diabetic hospital readmission</a> </p> <a href="https://publications.waset.org/abstracts/129397/hybrid-fuzzy-weighted-k-nearest-neighbor-to-predict-hospital-readmission-for-diabetic-patients" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/129397.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">186</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">3708</span> Using High Performance Computing for Online Flood Monitoring and Prediction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Stepan%20Kuchar">Stepan Kuchar</a>, <a href="https://publications.waset.org/abstracts/search?q=Martin%20Golasowski"> Martin Golasowski</a>, <a href="https://publications.waset.org/abstracts/search?q=Radim%20Vavrik"> Radim Vavrik</a>, <a href="https://publications.waset.org/abstracts/search?q=Michal%20Podhoranyi"> Michal Podhoranyi</a>, <a href="https://publications.waset.org/abstracts/search?q=Boris%20Sir"> Boris Sir</a>, <a href="https://publications.waset.org/abstracts/search?q=Jan%20Martinovic"> Jan Martinovic</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The main goal of this article is to describe the online flood monitoring and prediction system Floreon+ primarily developed for the Moravian-Silesian region in the Czech Republic and the basic process it uses for running automatic rainfall-runoff and hydrodynamic simulations along with their calibration and uncertainty modeling. It takes a long time to execute such process sequentially, which is not acceptable in the online scenario, so the use of high-performance computing environment is proposed for all parts of the process to shorten their duration. Finally, a case study on the Ostravice river catchment is presented that shows actual durations and their gain from the parallel implementation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=flood%20prediction%20process" title="flood prediction process">flood prediction process</a>, <a href="https://publications.waset.org/abstracts/search?q=high%20performance%20computing" title=" high performance computing"> high performance computing</a>, <a href="https://publications.waset.org/abstracts/search?q=online%20flood%20prediction%20system" title=" online flood prediction system"> online flood prediction system</a>, <a href="https://publications.waset.org/abstracts/search?q=parallelization" title=" parallelization"> parallelization</a> </p> <a href="https://publications.waset.org/abstracts/21155/using-high-performance-computing-for-online-flood-monitoring-and-prediction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/21155.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">492</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">3707</span> Prediction of PM₂.₅ Concentration in Ulaanbaatar with Deep Learning Models</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Suriya">Suriya</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Rapid socio-economic development and urbanization have led to an increasingly serious air pollution problem in Ulaanbaatar (UB), the capital of Mongolia. PM₂.₅ pollution has become the most pressing aspect of UB air pollution. Therefore, monitoring and predicting PM₂.₅ concentration in UB is of great significance for the health of the local people and environmental management. As of yet, very few studies have used models to predict PM₂.₅ concentrations in UB. Using data from 0:00 on June 1, 2018, to 23:00 on April 30, 2020, we proposed two deep learning models based on Bayesian-optimized LSTM (Bayes-LSTM) and CNN-LSTM. We utilized hourly observed data, including Himawari8 (H8) aerosol optical depth (AOD), meteorology, and PM₂.₅ concentration, as input for the prediction of PM₂.₅ concentrations. The correlation strengths between meteorology, AOD, and PM₂.₅ were analyzed using the gray correlation analysis method; the comparison of the performance improvement of the model by using the AOD input value was tested, and the performance of these models was evaluated using mean absolute error (MAE) and root mean square error (RMSE). The prediction accuracies of Bayes-LSTM and CNN-LSTM deep learning models were both improved when AOD was included as an input parameter. Improvement of the prediction accuracy of the CNN-LSTM model was particularly enhanced in the non-heating season; in the heating season, the prediction accuracy of the Bayes-LSTM model slightly improved, while the prediction accuracy of the CNN-LSTM model slightly decreased. We propose two novel deep learning models for PM₂.₅ concentration prediction in UB, Bayes-LSTM, and CNN-LSTM deep learning models. Pioneering the use of AOD data from H8 and demonstrating the inclusion of AOD input data improves the performance of our two proposed deep learning models. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title="deep learning">deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=AOD" title=" AOD"> AOD</a>, <a href="https://publications.waset.org/abstracts/search?q=PM2.5" title=" PM2.5"> PM2.5</a>, <a href="https://publications.waset.org/abstracts/search?q=prediction" title=" prediction"> prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=Ulaanbaatar" title=" Ulaanbaatar"> Ulaanbaatar</a> </p> <a href="https://publications.waset.org/abstracts/185289/prediction-of-pm25-concentration-in-ulaanbaatar-with-deep-learning-models" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/185289.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">48</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">3706</span> Life Prediction of Condenser Tubes Applying Fuzzy Logic and Neural Network Algorithms</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=A.%20Majidian">A. Majidian</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The life prediction of thermal power plant components is necessary to prevent the unexpected outages, optimize maintenance tasks in periodic overhauls and plan inspection tasks with their schedules. One of the main critical components in a power plant is condenser because its failure can affect many other components which are positioned in downstream of condenser. This paper deals with factors affecting life of condenser. Failure rates dependency vs. these factors has been investigated using Artificial Neural Network (ANN) and fuzzy logic algorithms. These algorithms have shown their capabilities as dynamic tools to evaluate life prediction of power plant equipments. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=life%20prediction" title="life prediction">life prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=condenser%20tube" title=" condenser tube"> condenser tube</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20network" title=" neural network"> neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20logic" title=" fuzzy logic"> fuzzy logic</a> </p> <a href="https://publications.waset.org/abstracts/12186/life-prediction-of-condenser-tubes-applying-fuzzy-logic-and-neural-network-algorithms" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/12186.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">351</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">3705</span> Empirical Mode Decomposition Based Denoising by Customized Thresholding</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Wahiba%20Mohguen">Wahiba Mohguen</a>, <a href="https://publications.waset.org/abstracts/search?q=Ra%C3%AFs%20El%E2%80%99hadi%20Bekka"> Raïs El’hadi Bekka</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a denoising method called EMD-Custom that was based on Empirical Mode Decomposition (EMD) and the modified Customized Thresholding Function (Custom) algorithms. EMD was applied to decompose adaptively a noisy signal into intrinsic mode functions (IMFs). Then, all the noisy IMFs got threshold by applying the presented thresholding function to suppress noise and to improve the signal to noise ratio (SNR). The method was tested on simulated data and real ECG signal, and the results were compared to the EMD-Based signal denoising methods using the soft and hard thresholding. The results showed the superior performance of the proposed EMD-Custom denoising over the traditional approach. The performances were evaluated in terms of SNR in dB, and Mean Square Error (MSE). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=customized%20thresholding" title="customized thresholding">customized thresholding</a>, <a href="https://publications.waset.org/abstracts/search?q=ECG%20signal" title=" ECG signal"> ECG signal</a>, <a href="https://publications.waset.org/abstracts/search?q=EMD" title=" EMD"> EMD</a>, <a href="https://publications.waset.org/abstracts/search?q=hard%20thresholding" title=" hard thresholding"> hard thresholding</a>, <a href="https://publications.waset.org/abstracts/search?q=soft-thresholding" title=" soft-thresholding"> soft-thresholding</a> </p> <a href="https://publications.waset.org/abstracts/67421/empirical-mode-decomposition-based-denoising-by-customized-thresholding" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/67421.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">302</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">3704</span> Wind Speed Prediction Using Passive Aggregation Artificial Intelligence Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tarek%20Aboueldahab">Tarek Aboueldahab</a>, <a href="https://publications.waset.org/abstracts/search?q=Amin%20Mohamed%20Nassar"> Amin Mohamed Nassar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Wind energy is a fluctuating energy source unlike conventional power plants, thus, it is necessary to accurately predict short term wind speed to integrate wind energy in the electricity supply structure. To do so, we present a hybrid artificial intelligence model of short term wind speed prediction based on passive aggregation of the particle swarm optimization and neural networks. As a result, improvement of the prediction accuracy is obviously obtained compared to the standard artificial intelligence method. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=artificial%20intelligence" title="artificial intelligence">artificial intelligence</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20networks" title=" neural networks"> neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=particle%20swarm%20optimization" title=" particle swarm optimization"> particle swarm optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=passive%20aggregation" title=" passive aggregation"> passive aggregation</a>, <a href="https://publications.waset.org/abstracts/search?q=wind%20speed%20prediction" title=" wind speed prediction"> wind speed prediction</a> </p> <a href="https://publications.waset.org/abstracts/45705/wind-speed-prediction-using-passive-aggregation-artificial-intelligence-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/45705.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">450</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">3703</span> Analysis of the Impact of Refractivity on Ultra High Frequency Signal Strength over Gusau, North West, Nigeria</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=B.%20G.%20Ayantunji">B. G. Ayantunji</a>, <a href="https://publications.waset.org/abstracts/search?q=B.%20Musa"> B. Musa</a>, <a href="https://publications.waset.org/abstracts/search?q=H.%20Mai-Unguwa"> H. Mai-Unguwa</a>, <a href="https://publications.waset.org/abstracts/search?q=L.%20A.%20Sunmonu"> L. A. Sunmonu</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20S.%20Adewumi"> A. S. Adewumi</a>, <a href="https://publications.waset.org/abstracts/search?q=L.%20Sa%27ad"> L. Sa'ad</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Kado"> A. Kado</a> </p> <p class="card-text"><strong>Abstract:</strong></p> For achieving reliable and efficient communication system, both terrestrial and satellite communication, surface refractivity is critical in planning and design of radio links. This study analyzed the impact of atmospheric parameters on Ultra High Frequency (UHF) signal strength over Gusau, North West, Nigeria. The analysis exploited meteorological data measured simultaneously with UHF signal strength for the month of June 2017 using a Davis Vantage Pro2 automatic weather station and UHF signal strength measuring devices respectively. The instruments were situated at the premise of Federal University, Gusau (6° 78' N, 12° 13' E). The refractivity values were computed using ITU-R model. The result shows that the refractivity value attained the highest value of 366.28 at 2200hr and a minimum value of 350.66 at 2100hr local time. The correlation between signal strength and refractivity is 0.350; Humidity is 0.532 and a negative correlation of -0.515 for temperature. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=refractivity" title="refractivity">refractivity</a>, <a href="https://publications.waset.org/abstracts/search?q=UHF%20%28ultra%20high%20frequency%29%20signal%20strength" title=" UHF (ultra high frequency) signal strength"> UHF (ultra high frequency) signal strength</a>, <a href="https://publications.waset.org/abstracts/search?q=free%20space" title=" free space"> free space</a>, <a href="https://publications.waset.org/abstracts/search?q=automatic%20weather%20station" title=" automatic weather station"> automatic weather station</a> </p> <a href="https://publications.waset.org/abstracts/83775/analysis-of-the-impact-of-refractivity-on-ultra-high-frequency-signal-strength-over-gusau-north-west-nigeria" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/83775.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">197</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">3702</span> Wavelet Based Signal Processing for Fault Location in Airplane Cable </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Reza%20Rezaeipour%20Honarmandzad">Reza Rezaeipour Honarmandzad </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Wavelet analysis is an exciting method for solving difficult problems in mathematics, physics, and engineering, with modern applications as diverse as wave propagation, data compression, signal processing, image processing, pattern recognition, etc. Wavelets allow complex information such as signals, images and patterns to be decomposed into elementary forms at different positions and scales and subsequently reconstructed with high precision. In this paper a wavelet-based signal processing algorithm for airplane cable fault location is proposed. An orthogonal discrete wavelet decomposition and reconstruction algorithm is used to eliminate the noise in the aircraft cable fault signal. The experiment result has shown that the character of emission pulse and reflect pulse used to test the aircraft cable fault point are reserved and the high-frequency noise are eliminated by means of the proposed algorithm in this paper. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=wavelet%20analysis" title="wavelet analysis">wavelet analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=signal%20processing" title=" signal processing"> signal processing</a>, <a href="https://publications.waset.org/abstracts/search?q=orthogonal%20discrete%20wavelet" title=" orthogonal discrete wavelet"> orthogonal discrete wavelet</a>, <a href="https://publications.waset.org/abstracts/search?q=noise" title=" noise"> noise</a>, <a href="https://publications.waset.org/abstracts/search?q=aircraft%20cable%20fault%20signal" title=" aircraft cable fault signal"> aircraft cable fault signal</a> </p> <a href="https://publications.waset.org/abstracts/29799/wavelet-based-signal-processing-for-fault-location-in-airplane-cable" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/29799.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">524</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">3701</span> A Combined Feature Extraction and Thresholding Technique for Silence Removal in Percussive Sounds </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=B.%20Kishore%20Kumar">B. Kishore Kumar</a>, <a href="https://publications.waset.org/abstracts/search?q=Pogula%20Rakesh"> Pogula Rakesh</a>, <a href="https://publications.waset.org/abstracts/search?q=T.%20Kishore%20Kumar"> T. Kishore Kumar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The music analysis is a part of the audio content analysis used to analyze the music by using the different features of audio signal. In music analysis, the first step is to divide the music signal to different sections based on the feature profiles of the music signal. In this paper, we present a music segmentation technique that will effectively segmentize the signal and thresholding technique to remove silence from the percussive sounds produced by percussive instruments, which uses two features of music, namely signal energy and spectral centroid. The proposed method impose thresholds on both the features which will vary depends on the music signal. Depends on the threshold, silence part is removed and the segmentation is done. The effectiveness of the proposed method is analyzed using MATLAB. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=percussive%20sounds" title="percussive sounds">percussive sounds</a>, <a href="https://publications.waset.org/abstracts/search?q=spectral%20centroid" title=" spectral centroid"> spectral centroid</a>, <a href="https://publications.waset.org/abstracts/search?q=spectral%20energy" title=" spectral energy"> spectral energy</a>, <a href="https://publications.waset.org/abstracts/search?q=silence%20removal" title=" silence removal"> silence removal</a>, <a href="https://publications.waset.org/abstracts/search?q=feature%20extraction" title=" feature extraction"> feature extraction</a> </p> <a href="https://publications.waset.org/abstracts/25510/a-combined-feature-extraction-and-thresholding-technique-for-silence-removal-in-percussive-sounds" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/25510.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">593</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">3700</span> Evaluation of Spatial Distribution Prediction for Site-Scale Soil Contaminants Based on Partition Interpolation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Pengwei%20Qiao">Pengwei Qiao</a>, <a href="https://publications.waset.org/abstracts/search?q=Sucai%20Yang"> Sucai Yang</a>, <a href="https://publications.waset.org/abstracts/search?q=Wenxia%20Wei"> Wenxia Wei</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Soil pollution has become an important issue in China. Accurate spatial distribution prediction of pollutants with interpolation methods is the basis for soil remediation in the site. However, a relatively strong variability of pollutants would decrease the prediction accuracy. Theoretically, partition interpolation can result in accurate prediction results. In order to verify the applicability of partition interpolation for a site, benzo (b) fluoranthene (BbF) in four soil layers was adopted as the research object in this paper. IDW (inverse distance weighting)-, RBF (radial basis function)-and OK (ordinary kriging)-based partition interpolation accuracies were evaluated, and their influential factors were analyzed; then, the uncertainty and applicability of partition interpolation were determined. Three conclusions were drawn. (1) The prediction error of partitioned interpolation decreased by 70% compared to unpartitioned interpolation. (2) Partition interpolation reduced the impact of high CV (coefficient of variation) and high concentration value on the prediction accuracy. (3) The prediction accuracy of IDW-based partition interpolation was higher than that of RBF- and OK-based partition interpolation, and it was suitable for the identification of highly polluted areas at a contaminated site. These results provide a useful method to obtain relatively accurate spatial distribution information of pollutants and to identify highly polluted areas, which is important for soil pollution remediation in the site. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=accuracy" title="accuracy">accuracy</a>, <a href="https://publications.waset.org/abstracts/search?q=applicability" title=" applicability"> applicability</a>, <a href="https://publications.waset.org/abstracts/search?q=partition%20interpolation" title=" partition interpolation"> partition interpolation</a>, <a href="https://publications.waset.org/abstracts/search?q=site" title=" site"> site</a>, <a href="https://publications.waset.org/abstracts/search?q=soil%20pollution" title=" soil pollution"> soil pollution</a>, <a href="https://publications.waset.org/abstracts/search?q=uncertainty" title=" uncertainty"> uncertainty</a> </p> <a href="https://publications.waset.org/abstracts/110125/evaluation-of-spatial-distribution-prediction-for-site-scale-soil-contaminants-based-on-partition-interpolation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/110125.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">144</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">3699</span> EEG Signal Processing Methods to Differentiate Mental States</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sun%20H.%20Hwang">Sun H. Hwang</a>, <a href="https://publications.waset.org/abstracts/search?q=Young%20E.%20Lee"> Young E. Lee</a>, <a href="https://publications.waset.org/abstracts/search?q=Yunhan%20Ga"> Yunhan Ga</a>, <a href="https://publications.waset.org/abstracts/search?q=Gilwon%20Yoon"> Gilwon Yoon</a> </p> <p class="card-text"><strong>Abstract:</strong></p> EEG is a very complex signal with noises and other bio-potential interferences. EOG is the most distinct interfering signal when EEG signals are measured and analyzed. It is very important how to process raw EEG signals in order to obtain useful information. In this study, the EEG signal processing techniques such as EOG filtering and outlier removal were examined to minimize unwanted EOG signals and other noises. The two different mental states of resting and focusing were examined through EEG analysis. A focused state was induced by letting subjects to watch a red dot on the white screen. EEG data for 32 healthy subjects were measured. EEG data after 60-Hz notch filtering were processed by a commercially available EOG filtering and our presented algorithm based on the removal of outliers. The ratio of beta wave to theta wave was used as a parameter for determining the degree of focusing. The results show that our algorithm was more appropriate than the existing EOG filtering. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=EEG" title="EEG">EEG</a>, <a href="https://publications.waset.org/abstracts/search?q=focus" title=" focus"> focus</a>, <a href="https://publications.waset.org/abstracts/search?q=mental%20state" title=" mental state"> mental state</a>, <a href="https://publications.waset.org/abstracts/search?q=outlier" title=" outlier"> outlier</a>, <a href="https://publications.waset.org/abstracts/search?q=signal%20processing" title=" signal processing"> signal processing</a> </p> <a href="https://publications.waset.org/abstracts/62057/eeg-signal-processing-methods-to-differentiate-mental-states" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/62057.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">283</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">3698</span> External Validation of Risk Prediction Score for Candidemia in Critically Ill Patients: A Retrospective Observational Study</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nurul%20Mazni%20Abdullah">Nurul Mazni Abdullah</a>, <a href="https://publications.waset.org/abstracts/search?q=Saw%20Kian%20Cheah"> Saw Kian Cheah</a>, <a href="https://publications.waset.org/abstracts/search?q=Raha%20Abdul%20Rahman"> Raha Abdul Rahman</a>, <a href="https://publications.waset.org/abstracts/search?q=Qurratu%20%27Aini%20Musthafa"> Qurratu 'Aini Musthafa</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Purpose: Candidemia was associated with high mortality in the critically ill patients. Early candidemia prediction is imperative for preemptive antifungal treatment. This study aimed to externally validate the candidemia risk prediction scores by Jameran et al. (2021) by identifying risk factors of acute kidney injury, renal replacement therapy, parenteral nutrition, and multifocal candida colonization. Methods: This single-center, retrospective observational study included all critically ill patients admitted to the intensive care unit (ICU) in a tertiary referral center from January 2018 to December 2023. The study evaluated the candidemia risk prediction score performance by analysing the occurrence of candidemia within the study period. Patients’ demographic characteristics, comorbidities, SOFA scores, and ICU outcomes were analyzed. Patients who were diagnosed with candidemia prior to ICU admission were excluded. Results: A total of 500 patients were analyzed with 2 dropouts due to incomplete data. Validation analysis showed that the candidemia risk prediction score has a sensitivity of 75.00% (95% CI: 59.66-86.81), specificity of 65.35% (95% CI: 60.78-69.72), positive predictive value of 17.28, and negative predictive value of 96.44. The incidence of candidemia was 8.86%, with no significant differences in demographics or comorbidities except for higher SOFA scoring in the candidemia group. The candidemia group showed significantly longer ICU, hospital LOS, and higher ICU in-hospital mortality. Conclusion: This study concluded the candidemia risk prediction score by Jameran et al. (2021) had good sensitivity and a high negative prediction value. Thus, the risk prediction score was validated for candidemia prediction in critically ill patients. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Candidemia" title="Candidemia">Candidemia</a>, <a href="https://publications.waset.org/abstracts/search?q=intensive%20care" title=" intensive care"> intensive care</a>, <a href="https://publications.waset.org/abstracts/search?q=acute%20kidney%20injury" title=" acute kidney injury"> acute kidney injury</a>, <a href="https://publications.waset.org/abstracts/search?q=clinical%20prediction%20rule" title=" clinical prediction rule"> clinical prediction rule</a>, <a href="https://publications.waset.org/abstracts/search?q=incidence" title=" incidence"> incidence</a> </p> <a href="https://publications.waset.org/abstracts/194782/external-validation-of-risk-prediction-score-for-candidemia-in-critically-ill-patients-a-retrospective-observational-study" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/194782.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">7</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">3697</span> Study on the Model Predicting Post-Construction Settlement of Soft Ground</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Pingshan%20Chen">Pingshan Chen</a>, <a href="https://publications.waset.org/abstracts/search?q=Zhiliang%20Dong"> Zhiliang Dong</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In order to estimate the post-construction settlement more objectively, the power-polynomial model is proposed, which can reflect the trend of settlement development based on the observed settlement data. It was demonstrated by an actual case history of an embankment, and during the prediction. Compared with the other three prediction models, the power-polynomial model can estimate the post-construction settlement more accurately with more simple calculation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=prediction" title="prediction">prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=model" title=" model"> model</a>, <a href="https://publications.waset.org/abstracts/search?q=post-construction%20settlement" title=" post-construction settlement"> post-construction settlement</a>, <a href="https://publications.waset.org/abstracts/search?q=soft%20ground" title=" soft ground"> soft ground</a> </p> <a href="https://publications.waset.org/abstracts/2187/study-on-the-model-predicting-post-construction-settlement-of-soft-ground" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/2187.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">425</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">3696</span> An Auxiliary Technique for Coronary Heart Disease Prediction by Analyzing Electrocardiogram Based on ResNet and Bi-Long Short-Term Memory</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yang%20Zhang">Yang Zhang</a>, <a href="https://publications.waset.org/abstracts/search?q=Jian%20He"> Jian He</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Heart disease is one of the leading causes of death in the world, and coronary heart disease (CHD) is one of the major heart diseases. Electrocardiogram (ECG) is widely used in the detection of heart diseases, but the traditional manual method for CHD prediction by analyzing ECG requires lots of professional knowledge for doctors. This paper introduces sliding window and continuous wavelet transform (CWT) to transform ECG signals into images, and then ResNet and Bi-LSTM are introduced to build the ECG feature extraction network (namely ECGNet). At last, an auxiliary system for coronary heart disease prediction was developed based on modified ResNet18 and Bi-LSTM, and the public ECG dataset of CHD from MIMIC-3 was used to train and test the system. The experimental results show that the accuracy of the method is 83%, and the F1-score is 83%. Compared with the available methods for CHD prediction based on ECG, such as kNN, decision tree, VGGNet, etc., this method not only improves the prediction accuracy but also could avoid the degradation phenomenon of the deep learning network. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bi-LSTM" title="Bi-LSTM">Bi-LSTM</a>, <a href="https://publications.waset.org/abstracts/search?q=CHD" title=" CHD"> CHD</a>, <a href="https://publications.waset.org/abstracts/search?q=ECG" title=" ECG"> ECG</a>, <a href="https://publications.waset.org/abstracts/search?q=ResNet" title=" ResNet"> ResNet</a>, <a href="https://publications.waset.org/abstracts/search?q=sliding%C2%A0window" title=" sliding window"> sliding window</a> </p> <a href="https://publications.waset.org/abstracts/165165/an-auxiliary-technique-for-coronary-heart-disease-prediction-by-analyzing-electrocardiogram-based-on-resnet-and-bi-long-short-term-memory" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/165165.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">89</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">3695</span> Understanding Health-Related Properties of Grapes by Pharmacokinetic Modelling of Intestinal Absorption</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sophie%20N.%20Selby-Pham">Sophie N. Selby-Pham</a>, <a href="https://publications.waset.org/abstracts/search?q=Yudie%20Wang"> Yudie Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=Louise%20Bennett"> Louise Bennett</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Consumption of grapes promotes health and reduces the risk of chronic diseases due to the action of grape phytochemicals in regulation of Oxidative Stress and Inflammation (OSI). The bioefficacy of phytochemicals depends on their absorption in the human body. The time required for phytochemicals to achieve maximal plasma concentration (Tₘₐₓ) after oral intake reflects the time window of maximal bioefficacy of phytochemicals, with Tₘₐₓ dependent on physicochemical properties of phytochemicals. This research collated physicochemical properties of grape phytochemicals from white and red grapes to predict their Tₘₐₓ using pharmacokinetic modelling. The predicted values of Tₘₐₓ were then compared to the measured Tₘₐₓ collected from clinical studies to determine the accuracy of prediction. In both liquid and solid intake forms, white grapes exhibit a shorter Tₘₐₓ range (0.5-2.5 h) versus red grapes (1.5-5h). The prediction accuracy of Tₘₐₓ for grape phytochemicals was 33.3% total error of prediction compared to the mean, indicating high prediction accuracy. Pharmacokinetic modelling allows prediction of Tₘₐₓ without costly clinical trials, informing dosing frequency for sustained presence of phytochemicals in the body to optimize the health benefits of phytochemicals. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=absorption%20kinetics" title="absorption kinetics">absorption kinetics</a>, <a href="https://publications.waset.org/abstracts/search?q=phytochemical" title=" phytochemical"> phytochemical</a>, <a href="https://publications.waset.org/abstracts/search?q=phytochemical%20absorption%20prediction%20model" title=" phytochemical absorption prediction model"> phytochemical absorption prediction model</a>, <a href="https://publications.waset.org/abstracts/search?q=Vitis%20vinifera" title=" Vitis vinifera"> Vitis vinifera</a> </p> <a href="https://publications.waset.org/abstracts/95074/understanding-health-related-properties-of-grapes-by-pharmacokinetic-modelling-of-intestinal-absorption" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/95074.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">148</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">3694</span> Artificial Neural Network in FIRST Robotics Team-Based Prediction System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Cedric%20Leong">Cedric Leong</a>, <a href="https://publications.waset.org/abstracts/search?q=Parth%20Desai"> Parth Desai</a>, <a href="https://publications.waset.org/abstracts/search?q=Parth%20Patel"> Parth Patel</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The purpose of this project was to develop a neural network based on qualitative team data to predict alliance scores to determine winners of matches in the FIRST Robotics Competition (FRC). The game for the competition changes every year with different objectives and game objects, however the idea was to create a prediction system which can be reused year by year using some of the statistics that are constant through different games, making our system adaptable to future games as well. Aerial Assist is the FRC game for 2014, and is played in alliances of 3 teams going against one another, namely the Red and Blue alliances. This application takes any 6 teams paired into 2 alliances of 3 teams and generates the prediction for the final score between them. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=artifical%20neural%20network" title="artifical neural network">artifical neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=prediction%20system" title=" prediction system"> prediction system</a>, <a href="https://publications.waset.org/abstracts/search?q=qualitative%20team%20data" title=" qualitative team data"> qualitative team data</a>, <a href="https://publications.waset.org/abstracts/search?q=FIRST%20Robotics%20Competition%20%28FRC%29" title=" FIRST Robotics Competition (FRC)"> FIRST Robotics Competition (FRC)</a> </p> <a href="https://publications.waset.org/abstracts/10302/artificial-neural-network-in-first-robotics-team-based-prediction-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/10302.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">513</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">3693</span> Performance Degradation for the GLR Test-Statistics for Spatial Signal Detection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Olesya%20Bolkhovskaya">Olesya Bolkhovskaya</a>, <a href="https://publications.waset.org/abstracts/search?q=Alexander%20Maltsev"> Alexander Maltsev</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Antenna arrays are widely used in modern radio systems in sonar and communications. The solving of the detection problems of a useful signal on the background of noise is based on the GLRT method. There is a large number of problem which depends on the known a priori information. In this work, in contrast to the majority of already solved problems, it is used only difference spatial properties of the signal and noise for detection. We are analyzing the influence of the degree of non-coherence of signal and noise unhomogeneity on the performance characteristics of different GLRT statistics. The description of the signal and noise is carried out by means of the spatial covariance matrices C in the cases of different number of known information. The partially coherent signal is simulated as a plane wave with a random angle of incidence of the wave concerning a normal. Background noise is simulated as random process with uniform distribution function in each element. The results of investigation of degradation of performance characteristics for different cases are represented in this work. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=GLRT" title="GLRT">GLRT</a>, <a href="https://publications.waset.org/abstracts/search?q=Neumann-Pearson%E2%80%99s%20criterion" title=" Neumann-Pearson’s criterion"> Neumann-Pearson’s criterion</a>, <a href="https://publications.waset.org/abstracts/search?q=Test-statistics" title=" Test-statistics"> Test-statistics</a>, <a href="https://publications.waset.org/abstracts/search?q=degradation" title=" degradation"> degradation</a>, <a href="https://publications.waset.org/abstracts/search?q=spatial%20processing" title=" spatial processing"> spatial processing</a>, <a href="https://publications.waset.org/abstracts/search?q=multielement%20antenna%20array" title=" multielement antenna array"> multielement antenna array</a> </p> <a href="https://publications.waset.org/abstracts/1985/performance-degradation-for-the-glr-test-statistics-for-spatial-signal-detection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/1985.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">385</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">3692</span> A Hybrid Feature Selection Algorithm with Neural Network for Software Fault Prediction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Khalaf%20Khatatneh">Khalaf Khatatneh</a>, <a href="https://publications.waset.org/abstracts/search?q=Nabeel%20Al-Milli"> Nabeel Al-Milli</a>, <a href="https://publications.waset.org/abstracts/search?q=Amjad%20Hudaib"> Amjad Hudaib</a>, <a href="https://publications.waset.org/abstracts/search?q=Monther%20Ali%20Tarawneh"> Monther Ali Tarawneh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Software fault prediction identify potential faults in software modules during the development process. In this paper, we present a novel approach for software fault prediction by combining a feedforward neural network with particle swarm optimization (PSO). The PSO algorithm is employed as a feature selection technique to identify the most relevant metrics as inputs to the neural network. Which enhances the quality of feature selection and subsequently improves the performance of the neural network model. Through comprehensive experiments on software fault prediction datasets, the proposed hybrid approach achieves better results, outperforming traditional classification methods. The integration of PSO-based feature selection with the neural network enables the identification of critical metrics that provide more accurate fault prediction. Results shows the effectiveness of the proposed approach and its potential for reducing development costs and effort by detecting faults early in the software development lifecycle. Further research and validation on diverse datasets will help solidify the practical applicability of the new approach in real-world software engineering scenarios. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=feature%20selection" title="feature selection">feature selection</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20network" title=" neural network"> neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=particle%20swarm%20optimization" title=" particle swarm optimization"> particle swarm optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=software%20fault%20prediction" title=" software fault prediction"> software fault prediction</a> </p> <a href="https://publications.waset.org/abstracts/167733/a-hybrid-feature-selection-algorithm-with-neural-network-for-software-fault-prediction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/167733.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">94</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">3691</span> Soccer Match Result Prediction System (SMRPS) Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ajayi%20Olusola%20Olajide">Ajayi Olusola Olajide</a>, <a href="https://publications.waset.org/abstracts/search?q=Alonge%20Olaide%20Moses"> Alonge Olaide Moses </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Predicting the outcome of soccer matches poses an interesting challenge for which it is realistically impossible to successfully do so for every match. Despite this, there are lots of resources that are being expended on the correct prediction of soccer matches weekly, and all over the world. Soccer Match Result Prediction System Model (SMRPSM) is a system that is proposed whereby the results of matches between two soccer teams are auto-generated, with the added excitement of giving users a chance to test their predictive abilities. Soccer teams from different league football are loaded by the application, with each team’s corresponding manager and other information like team location, team logo and nickname. The user is also allowed to interact with the system by selecting the match to be predicted and viewing of the results of completed matches after registering/logging in. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=predicting" title="predicting">predicting</a>, <a href="https://publications.waset.org/abstracts/search?q=soccer%20match" title=" soccer match"> soccer match</a>, <a href="https://publications.waset.org/abstracts/search?q=outcome" title=" outcome"> outcome</a>, <a href="https://publications.waset.org/abstracts/search?q=soccer" title=" soccer"> soccer</a>, <a href="https://publications.waset.org/abstracts/search?q=matches" title=" matches"> matches</a>, <a href="https://publications.waset.org/abstracts/search?q=result%20prediction" title=" result prediction"> result prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=system" title=" system"> system</a>, <a href="https://publications.waset.org/abstracts/search?q=model" title=" model"> model</a> </p> <a href="https://publications.waset.org/abstracts/15730/soccer-match-result-prediction-system-smrps-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/15730.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">491</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">3690</span> Grey Wolf Optimization Technique for Predictive Analysis of Products in E-Commerce: An Adaptive Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shital%20Suresh%20Borse">Shital Suresh Borse</a>, <a href="https://publications.waset.org/abstracts/search?q=Vijayalaxmi%20Kadroli"> Vijayalaxmi Kadroli</a> </p> <p class="card-text"><strong>Abstract:</strong></p> E-commerce industries nowadays implement the latest AI, ML Techniques to improve their own performance and prediction accuracy. This helps to gain a huge profit from the online market. Ant Colony Optimization, Genetic algorithm, Particle Swarm Optimization, Neural Network & GWO help many e-commerce industries for up-gradation of their predictive performance. These algorithms are providing optimum results in various applications, such as stock price prediction, prediction of drug-target interaction & user ratings of similar products in e-commerce sites, etc. In this study, customer reviews will play an important role in prediction analysis. People showing much interest in buying a lot of services& products suggested by other customers. This ultimately increases net profit. In this work, a convolution neural network (CNN) is proposed which further is useful to optimize the prediction accuracy of an e-commerce website. This method shows that CNN is used to optimize hyperparameters of GWO algorithm using an appropriate coding scheme. Accurate model results are verified by comparing them to PSO results whose hyperparameters have been optimized by CNN in Amazon's customer review dataset. Here, experimental outcome proves that this proposed system using the GWO algorithm achieves superior execution in terms of accuracy, precision, recovery, etc. in prediction analysis compared to the existing systems. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=prediction%20analysis" title="prediction analysis">prediction analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=e-commerce" title=" e-commerce"> e-commerce</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=grey%20wolf%20optimization" title=" grey wolf optimization"> grey wolf optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=particle%20swarm%20optimization" title=" particle swarm optimization"> particle swarm optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=CNN" title=" CNN"> CNN</a> </p> <a href="https://publications.waset.org/abstracts/148039/grey-wolf-optimization-technique-for-predictive-analysis-of-products-in-e-commerce-an-adaptive-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/148039.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">113</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">3689</span> Hybrid Approach for Software Defect Prediction Using Machine Learning with Optimization Technique </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=C.%20Manjula">C. Manjula</a>, <a href="https://publications.waset.org/abstracts/search?q=Lilly%20Florence"> Lilly Florence</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Software technology is developing rapidly which leads to the growth of various industries. Now-a-days, software-based applications have been adopted widely for business purposes. For any software industry, development of reliable software is becoming a challenging task because a faulty software module may be harmful for the growth of industry and business. Hence there is a need to develop techniques which can be used for early prediction of software defects. Due to complexities in manual prediction, automated software defect prediction techniques have been introduced. These techniques are based on the pattern learning from the previous software versions and finding the defects in the current version. These techniques have attracted researchers due to their significant impact on industrial growth by identifying the bugs in software. Based on this, several researches have been carried out but achieving desirable defect prediction performance is still a challenging task. To address this issue, here we present a machine learning based hybrid technique for software defect prediction. First of all, Genetic Algorithm (GA) is presented where an improved fitness function is used for better optimization of features in data sets. Later, these features are processed through Decision Tree (DT) classification model. Finally, an experimental study is presented where results from the proposed GA-DT based hybrid approach is compared with those from the DT classification technique. The results show that the proposed hybrid approach achieves better classification accuracy. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=decision%20tree" title="decision tree">decision tree</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20algorithm" title=" genetic algorithm"> genetic algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=software%20defect%20prediction" title=" software defect prediction"> software defect prediction</a> </p> <a href="https://publications.waset.org/abstracts/85690/hybrid-approach-for-software-defect-prediction-using-machine-learning-with-optimization-technique" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/85690.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">329</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">3688</span> Machine Learning Techniques to Develop Traffic Accident Frequency Prediction Models</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rodrigo%20Aguiar">Rodrigo Aguiar</a>, <a href="https://publications.waset.org/abstracts/search?q=Adelino%20Ferreira"> Adelino Ferreira</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Road traffic accidents are the leading cause of unnatural death and injuries worldwide, representing a significant problem of road safety. In this context, the use of artificial intelligence with advanced machine learning techniques has gained prominence as a promising approach to predict traffic accidents. This article investigates the application of machine learning algorithms to develop traffic accident frequency prediction models. Models are evaluated based on performance metrics, making it possible to do a comparative analysis with traditional prediction approaches. The results suggest that machine learning can provide a powerful tool for accident prediction, which will contribute to making more informed decisions regarding road safety. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title="machine learning">machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=artificial%20intelligence" title=" artificial intelligence"> artificial intelligence</a>, <a href="https://publications.waset.org/abstracts/search?q=frequency%20of%20accidents" title=" frequency of accidents"> frequency of accidents</a>, <a href="https://publications.waset.org/abstracts/search?q=road%20safety" title=" road safety"> road safety</a> </p> <a href="https://publications.waset.org/abstracts/178875/machine-learning-techniques-to-develop-traffic-accident-frequency-prediction-models" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/178875.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">89</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">3687</span> An Approach to Noise Variance Estimation in Very Low Signal-to-Noise Ratio Stochastic Signals</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Miljan%20B.%20Petrovi%C4%87">Miljan B. Petrović</a>, <a href="https://publications.waset.org/abstracts/search?q=Du%C5%A1an%20B.%20Petrovi%C4%87"> Dušan B. Petrović</a>, <a href="https://publications.waset.org/abstracts/search?q=Goran%20S.%20Nikoli%C4%87"> Goran S. Nikolić</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper describes a method for AWGN (Additive White Gaussian Noise) variance estimation in noisy stochastic signals, referred to as Multiplicative-Noising Variance Estimation (MNVE). The aim was to develop an estimation algorithm with minimal number of assumptions on the original signal structure. The provided MATLAB simulation and results analysis of the method applied on speech signals showed more accuracy than standardized AR (autoregressive) modeling noise estimation technique. In addition, great performance was observed on very low signal-to-noise ratios, which in general represents the worst case scenario for signal denoising methods. High execution time appears to be the only disadvantage of MNVE. After close examination of all the observed features of the proposed algorithm, it was concluded it is worth of exploring and that with some further adjustments and improvements can be enviably powerful. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=noise" title="noise">noise</a>, <a href="https://publications.waset.org/abstracts/search?q=signal-to-noise%20ratio" title=" signal-to-noise ratio"> signal-to-noise ratio</a>, <a href="https://publications.waset.org/abstracts/search?q=stochastic%20signals" title=" stochastic signals"> stochastic signals</a>, <a href="https://publications.waset.org/abstracts/search?q=variance%20estimation" title=" variance estimation"> variance estimation</a> </p> <a href="https://publications.waset.org/abstracts/39515/an-approach-to-noise-variance-estimation-in-very-low-signal-to-noise-ratio-stochastic-signals" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/39515.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">386</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">3686</span> The Principle Probabilities of Space-Distance Resolution for a Monostatic Radar and Realization in Cylindrical Array</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Anatoly%20D.%20Pluzhnikov">Anatoly D. Pluzhnikov</a>, <a href="https://publications.waset.org/abstracts/search?q=Elena%20N.%20Pribludova"> Elena N. Pribludova</a>, <a href="https://publications.waset.org/abstracts/search?q=Alexander%20G.%20Ryndyk"> Alexander G. Ryndyk</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In conjunction with the problem of the target selection on a clutter background, the analysis of the scanning rate influence on the spatial-temporal signal structure, the generalized multivariate correlation function and the quality of the resolution with the increase pulse repetition frequency is made. The possibility of the object space-distance resolution, which is conditioned by the range-to-angle conversion with an increased scanning rate, is substantiated. The calculations for the real cylindrical array at high scanning rate are presented. The high scanning rate let to get the signal to noise improvement of the order of 10 dB for the space-time signal processing. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=antenna%20pattern" title="antenna pattern">antenna pattern</a>, <a href="https://publications.waset.org/abstracts/search?q=array" title=" array"> array</a>, <a href="https://publications.waset.org/abstracts/search?q=signal%20processing" title=" signal processing"> signal processing</a>, <a href="https://publications.waset.org/abstracts/search?q=spatial%20resolution" title=" spatial resolution"> spatial resolution</a> </p> <a href="https://publications.waset.org/abstracts/98259/the-principle-probabilities-of-space-distance-resolution-for-a-monostatic-radar-and-realization-in-cylindrical-array" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/98259.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">180</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">3685</span> Lab Bench for Synthetic Aperture Radar Imaging System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Karthiyayini%20Nagarajan">Karthiyayini Nagarajan</a>, <a href="https://publications.waset.org/abstracts/search?q=P.%20V.%20Ramakrishna"> P. V. Ramakrishna </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Radar Imaging techniques provides extensive applications in the field of remote sensing, majorly Synthetic Aperture Radar (SAR) that provide high resolution target images. This paper work puts forward the effective and realizable signal generation and processing for SAR images. The major units in the system include camera, signal generation unit, signal processing unit and display screen. The real radio channel is replaced by its mathematical model based on optical image to calculate a reflected signal model in real time. Signal generation realizes the algorithm and forms the radar reflection model. Signal processing unit provides range and azimuth resolution through matched filtering and spectrum analysis procedure to form radar image on the display screen. The restored image has the same quality as that of the optical image. This SAR imaging system has been designed and implemented using MATLAB and Quartus II tools on Stratix III device as a System (Lab Bench) that works in real time to study/investigate on radar imaging rudiments and signal processing scheme for educational and research purposes. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=synthetic%20aperture%20radar" title="synthetic aperture radar">synthetic aperture radar</a>, <a href="https://publications.waset.org/abstracts/search?q=radio%20reflection%20model" title=" radio reflection model"> radio reflection model</a>, <a href="https://publications.waset.org/abstracts/search?q=lab%20bench" title=" lab bench"> lab bench</a>, <a href="https://publications.waset.org/abstracts/search?q=imaging%20engineering" title=" imaging engineering"> imaging engineering</a> </p> <a href="https://publications.waset.org/abstracts/29485/lab-bench-for-synthetic-aperture-radar-imaging-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/29485.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">497</span> </span> </div> </div> <ul class="pagination"> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=signal%20prediction&page=3" rel="prev">&lsaquo;</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=signal%20prediction&page=1">1</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=signal%20prediction&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=signal%20prediction&page=3">3</a></li> <li class="page-item active"><span class="page-link">4</span></li> <li class="page-item"><a class="page-link" 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