Relevant bibliographies by topics / Predictive Spectral Analysis

Academic literature on the topic 'Predictive Spectral Analysis'

Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Predictive Spectral Analysis"

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Robert, P., D. Bertrand, M. Crochon, and J. Sabino. "A New Mathematical Procedure for NIR Analysis: The Lattice Technique. Application to the Prediction of Sugar Content of Apples." Applied Spectroscopy 43, no. 6 (August 1989): 1045–49. http://dx.doi.org/10.1366/0003702894203723.

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Analytical applications of near-infrared spectroscopy require the determination of calibration equations linking chemical and spectral values. Such equations are difficult to update by including new calibration specimens. A new procedure for prediction which was not based on multiple linear regression has been investigated. This procedure could be included in a data base system. The proposed method consists of three steps: compression of the spectral data by applying principal component analysis, creation of a predictive lattice, and projection of the spectra of unknown specimens on to the predictive lattice. This enables the prediction of chemical data that are not perfectly linked to spectral data by a linear relationship. The procedure has been applied to the prediction of the refractive index of apples. A predictive lattice was designed with the use of 45 specimens of calibration. A prediction with 43 verification specimens gave a standard error of 0.8%, which appeared sufficient for grading apples in quality classes. Further studies are required in order to include the proposed method in spectral libraries specializing in analytical applications.
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Baishya, Nystha, Mohammad Mamouei, Karthik Budidha, Meha Qassem, Pankaj Vadgama, and Panayiotis A. Kyriacou. "Comparison of Dual Beam Dispersive and FTNIR Spectroscopy for Lactate Detection." Sensors 21, no. 5 (March 8, 2021): 1891. http://dx.doi.org/10.3390/s21051891.

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Near Infrared (800–2500 nm) spectroscopy has been extensively used in biomedical applications, as it offers rapid, in vivo, bed-side monitoring of important haemodynamic parameters, which is especially important in critical care settings. However, the choice of NIR spectrometer needs to be investigated for biomedical applications, as both the dual beam dispersive spectrophotomer and the FTNIR spectrometer have their own advantages and disadvantages. In this study, predictive analysis of lactate concentrations in whole blood were undertaken using multivariate techniques on spectra obtained from the two spectrometer types simultaneously and results were compared. Results showed significant improvement in predicting analyte concentration when analysis was performed on full range spectral data. This is in comparison to analysis of limited spectral regions or lactate signature peaks, which yielded poorer prediction models. Furthermore, for the same region, FTNIR showed 10% better predictive capability than the dual beam dispersive NIR spectrometer.
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Lilo, Taha, Camilo Morais, Kate Ashton, Ana Pardilho, Timothy Dawson, Nihal Gurusinghe, Charles Davis, and Frank Martin. "Predicting meningioma recurrence using spectrochemical analysis of tissues and subsequent predictive computational algorithms." Neuro-Oncology 21, Supplement_4 (October 2019): iv5. http://dx.doi.org/10.1093/neuonc/noz167.020.

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Abstract Introduction Meningioma recurrence remains a clinical dilemma. This has a significant clinical and huge financial implication. Hence, the search for predictors for meningioma recurrence has become an increasingly urgent research topic in recent years. Objective Using spectrochemical analytical methods such as attenuated total reflection Fourier-transform infrared (ATR-FTIR) and Raman spectroscopy, our primary objective is to compare the spectral fingerprint signature of WHO grade I meningioma vs. WHO grade I meningioma that recurred. Secondary objectives compare WHO grade I meningioma vs. WHO grade II meningioma and WHO grade II meningioma vs. WHO grade I meningioma recurrence. Materials and Methods Our selection criteria included convexity meningioma only restricted to Simpson grade I & II only and WHO grade I & grade II only with a minimum 5 years follow up. We obtained tissue from tumour blocks retrieved from the tissue bank. These were sectioned onto slides and de-waxed prior to ATR-FTIR or Raman spectrochemical analysis. Derived spectral datasets were then explored for discriminating features using computational algorithms in the IRootLab toolbox within MATLAB; this allowed for classification and feature extraction. Results After analysing the data using various classification algorithms with cross-validation to avoid over-fitting of the spectral data, we can readily and blindly segregate those meningioma samples that recurred from those that did not recur in the follow-up timeframe. The forward feature extraction classification algorithms generated results that exhibited excellent sensitivity and specificity, especially with spectra obtained following ATR-FTIR spectroscopy. Our secondary objectives remain to be fully developed.
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Collette, Timothy W., and Adam J. Szladow. "Use of Rough Sets and Spectral Data for Building Predictive Models of Reaction Rate Constants." Applied Spectroscopy 48, no. 11 (November 1994): 1379–86. http://dx.doi.org/10.1366/0003702944028047.

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A model for predicting the log of the rate constants for alkaline hydrolysis of organic esters has been developed with the use of gas-phase mid-infrared library spectra and a rule-building software system based on the mathematical theory of rough sets. A diverse set of 41 esters was used as training compounds. The model is an advance in the development of a generalized system for predicting environmentally important reactivity parameters based on spectroscopic data. By comparison to a previously developed model using the same training set with multiple linear regression (MLR), the rough-sets model provided better predictive power, was more widely applicable, and required less spectral data manipulation. [For the previous MLR model, a standard error of prediction (SEP) of 0.59 was calculated for 88% of the training set data under leave-one-out cross-validation. In the present study using rough sets, an SEP of 0.52 was calculated for 95% of the data set.] More importantly, analysis of the decision rules generated by rough-sets analysis can lead to a better understanding of both the reaction process under study and important trends in the spectral data, as well as underlying relationships between the two.
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Fischer, G., A. Neurauter, L. Wieser, H. U. Strohmenger, and C. N. Nowak. "Prediction of Countershock Success." Methods of Information in Medicine 48, no. 05 (2009): 486–92. http://dx.doi.org/10.3414/me0580.

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Summary Objectives: Spectral analysis of the ventricular fibrillation (VF) ECG has been used for predicting countershock success, where the Fast Fourier Transformation (FFT) is the standard spectral estimator. Autoregressive (AR) spectral estimation should compute the spectrum with less computation time. This study compares the predictive power and computational performance of features obtained by the FFT and AR methods. Methods: In an animal model of VF cardiac arrest, 41 shocks were delivered in 25 swine. For feature parameter analysis, 2.5 s signal intervals directly before the shock and directly before the hands-off interval were used, respectively. Invasive recordings of the arterial pressure were used for assessing the outcome of each shock. For a proof of concept, a micro-controller program was implemented. Results: Calculating the area under the receiver operating characteristic (ROC) curve (AUC), the results of the AR-based features called spectral pole power (SPP) and spectral pole power with dominant frequency (DF) weighing (SPPDF) yield better outcome prediction results (85 %; 89 %) than common parameters based on FFT calculation method (centroid frequency (CF), amplitude spectrum area (AMSA)) (72%; 78%) during hands-off interval. Moreover, the predictive power of the feature parameters during ongoing CPR was not invalidated by closed-chest compressions. The calculation time of the AR-based parameters was nearly 2.5 times faster than the FFT-based features. Conclusion: Summing up, AR spectral estimators are an attractive option compared to FFT due to the reduced computational speed and the better outcome prediction. This might be of benefit when implementing AR prediction features on the microprocessor of a semi-automatic defibrillator.
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Vrazhnov, Denis, Anastasia Knyazkova, Maria Konnikova, Oleg Shevelev, Ivan Razumov, Evgeny Zavjalov, Yury Kistenev, Alexander Shkurinov, and Olga Cherkasova. "Analysis of Mouse Blood Serum in the Dynamics of U87 Glioblastoma by Terahertz Spectroscopy and Machine Learning." Applied Sciences 12, no. 20 (October 19, 2022): 10533. http://dx.doi.org/10.3390/app122010533.

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In this research, an experimental U87 glioblastoma small animal model was studied. The association between glioblastoma stages and the spectral patterns of mouse blood serum measured in the terahertz range was analyzed by terahertz time-domain spectroscopy (THz-TDS) and machine learning. The THz spectra preprocessing included (i) smoothing using the Savitsky–Golay filter, (ii) outlier removing using isolation forest (IF), and (iii) Z-score normalization. The sequential informative feature-selection approach was developed using a combination of principal component analysis (PCA) and a support vector machine (SVM) model. The predictive data model was created using SVM with a linear kernel. This model was tested using k-fold cross-validation. Achieved prediction accuracy, sensitivity, specificity were over 90%. Also, a relation was established between tumor size and the THz spectral profile of blood serum samples. Thereby, the possibility of detecting glioma stages using blood serum spectral patterns in the terahertz range was demonstrated.
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Tang, Siyu, Chong Du, and Tangzhe Nie. "Inversion Estimation of Soil Organic Matter in Songnen Plain Based on Multispectral Analysis." Land 11, no. 5 (April 21, 2022): 608. http://dx.doi.org/10.3390/land11050608.

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Sentinel-2A multi-spectral remote sensing image data underwent high-efficiency differential processing to extract spectral information, which was then matched to soil organic matter (SOM) laboratory test values from field samples. From this, multiple-linear stepwise regression (MLSR) and partial least square (PLSR) models were established based on a differential algorithm for surface SOM modeling. The original spectra were subjected to basic transformations with first- and second-derivative processing. MLSR and PLSR models were established based on these methods and the measured values, respectively. The results show that Sentinel-2A remote sensing imagery and SOM content correlated in some bands. The correlation between the spectral value and SOM content was significantly improved after mathematical transformation, especially square-root transformation. After differential processing, the multi-band model had better predictive ability (based on fitting accuracy) than single-band and unprocessed multi-band models. The MLSR and PLSR models of SOM had good prediction functionality. The reciprocal logarithm first-order differential MLSR regression model had the best prediction and inversion results (i.e., most consistent with the real-world data). The MLSR model is more stable and reliable for monitoring SOM content, and provides a feasible method and reference for SOM content-mapping of the study area.
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Dean, Roger T., Andrew J. Milne, and Freya Bailes. "Spectral Pitch Similarity is a Predictor of Perceived Change in Sound- as Well as Note-Based Music." Music & Science 2 (January 1, 2019): 205920431984735. http://dx.doi.org/10.1177/2059204319847351.

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Spectral pitch similarity (SPS) is a measure of the similarity between spectra of any pair of sounds. It has proved powerful in predicting perceived stability and fit of notes and chords in various tonal and microtonal instrumental contexts, that is, with discrete tones whose spectra are harmonic or close to harmonic. Here we assess the possible contribution of SPS to listeners’ continuous perceptions of change in music with fewer discrete events and with noisy or profoundly inharmonic sounds, such as electroacoustic music. Previous studies have shown that time series of perception of change in a range of music can be reasonably represented by time series models, whose predictors comprise autoregression together with series representing acoustic intensity and, usually, the timbral parameter spectral flatness. Here, we study possible roles for SPS in such models of continuous perceptions of change in a range of both instrumental (note-based) and sound-based music (generally containing more noise and fewer discrete events). In the first analysis, perceived change in three pieces of electroacoustic and one of piano music is modeled, to assess the possible contribution of (de-noised) SPS in cooperation with acoustic intensity and spectral flatness series. In the second analysis, a broad range of nine pieces is studied in relation to the wider range of distinctive spectral predictors useful in previous perceptual work, together with intensity and SPS. The second analysis uses cross-sectional (mixed-effects) time series analysis to take advantage of all the individual response series in the dataset, and to assess the possible generality of a predictive role for SPS. SPS proves to be a useful feature, making a predictive contribution distinct from other spectral parameters. Because SPS is a psychoacoustic “bottom up” feature, it may have wide applicability across both the familiar and the unfamiliar in the music to which we are exposed.
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Burke, Harry B. "Proteomics: Analysis of Spectral Data." Cancer Informatics 1 (January 2005): 117693510500100. http://dx.doi.org/10.1177/117693510500100102.

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The goal of disease-related proteogenomic research is a complete description of the unfolding of the disease process from its origin to its cure. With a properly selected patient cohort and correctly collected, processed, analyzed data, large scale proteomic spectra may be able to provide much of the information necessary for achieving this goal. Protein spectra, which are one way of representing protein expression, can be extremely useful clinically since they can be generated from blood rather than from diseased tissue. At the same time, the analysis of circulating proteins in blood presents unique challenges because of their heterogeneity, blood contains a large number of different abundance proteins generated by tissues throughout the body. Another challenge is that protein spectra are massively parallel information. One can choose to perform top-down analysis, where the entire spectra is examined and candidate peaks are selected for further assessment. Or one can choose a bottom-up analysis, where, via hypothesis testing, individual proteins are identified in the spectra and related to the disease process. Each approach has advantages and disadvantages that must be understood if protein spectral data are to be properly analyzed. With either approach, several levels of information must be in tegrated into a predictive model. This model will allow us to detect disease and it will allow us to discover therapeutic interventions that reduce the risk of disease in at-risk individuals and effectively treat newly diagnosed disease.
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Zhang, Xianglin, Jie Xue, Yi Xiao, Zhou Shi, and Songchao Chen. "Towards Optimal Variable Selection Methods for Soil Property Prediction Using a Regional Soil Vis-NIR Spectral Library." Remote Sensing 15, no. 2 (January 12, 2023): 465. http://dx.doi.org/10.3390/rs15020465.

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Soil visible and near-infrared (Vis-NIR, 350–2500 nm) spectroscopy has been proven as an alternative to conventional laboratory analysis due to its advantages being rapid, cost-effective, non-destructive and environmentally friendly. Different variable selection methods have been used to deal with the high redundancy, heavy computation, and model complexity of using full spectra in spectral modelling. However, most previous studies used a linear algorithm in the variable selection, and the application of a non-linear algorithm remains poorly explored. To address the current knowledge gap, based on a regional soil Vis-NIR spectral library (1430 soil samples), we evaluated seven variable selection algorithms together with three predictive algorithms in predicting seven soil properties. Our results showed that Cubist overperformed partial least squares regression (PLSR) and random forests (RF) in most soil properties (R2 > 0.75 for soil organic matter, total nitrogen and pH) when using the full spectra. Most of variable selection can greatly reduce the number of spectral bands and therefore simplified predictive models without losing accuracy. The results also showed that there was no silver bullet for the optimal variable selection algorithm among different predictive algorithms: (1) competitive adaptive reweighted sampling (CARS) always performed best for the PLSR algorithm, followed by forward recursive feature selection (FRFS); (2) recursive feature elimination (RFE) and genetic algorithm (GA) generally had better accuracy than others for the Cubist algorithm; and (3) FRFS had the best model performance for the RF algorithm. In addition, the performance was generally better when the algorithm used in the variable selection matched the predictive algorithm. The outcome of this study provides a valuable reference for predicting soil information using spectroscopic techniques together with variable selection algorithms.
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Dissertations / Theses on the topic "Predictive Spectral Analysis"

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Chhatwal, Harprit Singh. "Spectral modelling techniques for speech signals based on linear predictive analysis." Thesis, Imperial College London, 1988. http://hdl.handle.net/10044/1/46996.

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Losik, Len. "Adapting Fourier Analysis for Predicting Earth, Mars and Lunar Orbiting Satellite's Telemetry Behavior." International Foundation for Telemetering, 2011. http://hdl.handle.net/10150/595773.

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ITC/USA 2011 Conference Proceedings / The Forty-Seventh Annual International Telemetering Conference and Technical Exhibition / October 24-27, 2011 / Bally's Las Vegas, Las Vegas, Nevada
Prognostic technology uses a series of algorithms, combined forms a prognostic-based inference engine (PBIE) for the identification of deterministic behavior embedded in completely normal appearing telemetry from fully functional equipment. The algorithms used to define normal behavior in the PBIE from which deterministic behavior is identified can be adapted to quantify normal spacecraft telemetry behavior while in orbit about a moon or planet or during interplanetary travel. Time-series analog engineering data (telemetry) from orbiting satellites and interplanetary spacecraft are defined by harmonic and non-harmonic influences, which shape it behavior. Spectrum analysis can be used to understand and quantify the fundamental behavior of spacecraft analog telemetry and relate the behavior's frequency and phase to its time-series behavior through Fourier analysis.
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Losik, Len. "Adapting Fourier Analysis for Predicting Earth, Mars and Lunar Orbiting Satellite's Telemetry Behavior." International Foundation for Telemetering, 2010. http://hdl.handle.net/10150/604279.

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ITC/USA 2010 Conference Proceedings / The Forty-Sixth Annual International Telemetering Conference and Technical Exhibition / October 25-28, 2010 / Town and Country Resort & Convention Center, San Diego, California
Prognostic technology uses a series of algorithms, combined forms a prognostic-based inference engine (PBIE) for the identification of deterministic behavior embedded in completely normal appearing telemetry from fully functional equipment. The algorithms used to define normal behavior in the PBIE from which deterministic behavior is identified can be adapted to quantify normal spacecraft telemetry behavior while in orbit about a moon or planet or during interplanetary travel. Time-series analog engineering data (telemetry) from orbiting satellites and interplanetary spacecraft are defined by harmonic and non-harmonic influences, which shape it behavior. Spectrum analysis can be used to understand and quantify the fundamental behavior of spacecraft analog telemetry and relate the behavior's frequency and phase to its time-series behavior through Fourier analysis.
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Losik, Len. "Using Telemetry Science, An Adaptation of Prognostic Algorithms for Predicting Normal Space Vehicle Telemetry Behavior from Space for Earth and Lunar Satellites and Interplanetary Spacecraft." International Foundation for Telemetering, 2009. http://hdl.handle.net/10150/606150.

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ITC/USA 2009 Conference Proceedings / The Forty-Fifth Annual International Telemetering Conference and Technical Exhibition / October 26-29, 2009 / Riviera Hotel & Convention Center, Las Vegas, Nevada
Prognostic technology uses a series of algorithms, combined forms a prognostic-based inference engine (PBIE) for the identification of deterministic behavior embedded in completely normal appearing telemetry from fully functional equipment. The algorithms used to define normal behavior in the PBIE from which deterministic behavior is identified can be adapted to quantify normal spacecraft telemetry behavior while in orbit about a moon or planet or during interplanetary travel. Time-series analog engineering data (telemetry) from orbiting satellites and interplanetary spacecraft are defined by harmonic and non-harmonic influences which shape it behavior. Spectrum analysis can be used to understand and quantify the fundamental behavior of spacecraft analog telemetry and relate the behavior's frequency and phase to its time-series behavior through Fourier analysis.
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Guldemir, Hanifi. "Prediction of induction motor line current spectra from design data." Thesis, University of Nottingham, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.287180.

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Kwag, Jae-Hwan. "A comparative study of LP methods in MR spectral analysis /." free to MU campus, to others for purchase, 1999. http://wwwlib.umi.com/cr/mo/fullcit?p9962536.

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Wang, Nancy. "Spectral Portfolio Optimisation with LSTM Stock Price Prediction." Thesis, KTH, Matematisk statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-273611.

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Nobel Prize-winning modern portfolio theory (MPT) has been considered to be one of the most important and influential economic theories within finance and investment management. MPT assumes investors to be riskaverse and uses the variance of asset returns as a proxy of risk to maximise the performance of a portfolio. Successful portfolio management reply, thus on accurate risk estimate and asset return prediction. Risk estimates are commonly obtained through traditional asset pricing factor models, which allow the systematic risk to vary over time domain but not in the frequency space. This approach can impose limitations in, for instance, risk estimation. To tackle this shortcoming, interest in applications of spectral analysis to financial time series has increased lately. Among others, the novel spectral portfolio theory and the spectral factor model which demonstrate enhancement in portfolio performance through spectral risk estimation [1][11]. Moreover, stock price prediction has always been a challenging task due to its non-linearity and non-stationarity. Meanwhile, Machine learning has been successfully implemented in a wide range of applications where it is infeasible to accomplish the needed tasks traditionally. Recent research has demonstrated significant results in single stock price prediction by artificial LSTM neural network [6][34]. This study aims to evaluate the combined effect of these two advancements in a portfolio optimisation problem and optimise a spectral portfolio with stock prices predicted by LSTM neural networks. To do so, we began with mathematical derivation and theoretical presentation and then evaluated the portfolio performance generated by the spectral risk estimates and the LSTM stock price predictions, as well as the combination of the two. The result demonstrates that the LSTM predictions alone performed better than the combination, which in term performed better than the spectral risk alone.
Den nobelprisvinnande moderna portföjlteorin (MPT) är utan tvekan en av de mest framgångsrika investeringsmodellerna inom finansvärlden och investeringsstrategier. MPT antar att investerarna är mindre benägna till risktagande och approximerar riskexponering med variansen av tillgångarnasränteavkastningar. Nyckeln till en lyckad portföljförvaltning är därmed goda riskestimat och goda förutsägelser av tillgångspris. Riskestimering görs vanligtvis genom traditionella prissättningsmodellerna som tillåter risken att variera i tiden, dock inte i frekvensrummet. Denna begränsning utgör bland annat ett större fel i riskestimering. För att tackla med detta har intresset för tillämpningar av spektraanalys på finansiella tidsserier ökat de senast åren. Bland annat är ett nytt tillvägagångssätt för att behandla detta den nyintroducerade spektralportföljteorin och spektralfak- tormodellen som påvisade ökad portföljenprestanda genom spektralriskskattning [1][11]. Samtidigt har prediktering av aktierpriser länge varit en stor utmaning på grund av dess icke-linjära och icke-stationära egenskaper medan maskininlärning har kunnat använts för att lösa annars omöjliga uppgifter. Färska studier har påvisat signifikant resultat i aktieprisprediktering med hjälp av artificiella LSTM neurala nätverk [6][34]. Detta arbete undersöker kombinerade effekten av dessa två framsteg i ett portföljoptimeringsproblem genom att optimera en spektral portfölj med framtida avkastningar predikterade av ett LSTM neuralt nätverk. Arbetet börjar med matematisk härledningar och teoretisk introduktion och sedan studera portföljprestation som genereras av spektra risk, LSTM aktieprispredikteringen samt en kombination av dessa två. Resultaten visar på att LSTM-predikteringen ensam presterade bättre än kombinationen, vilket i sin tur presterade bättre än enbart spektralriskskattningen.
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Bahrampouri, Mahdi. "Ground Motion Prediction Equations for Non-Spectral Parameters using the KiK-net Database." Thesis, Virginia Tech, 2017. http://hdl.handle.net/10919/87704.

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The KiK-net ground motion database is used to develop ground motion prediction equations for Arias Intensity (Ia), 5-95% Significant Duration (Ds5-95), and 5-75% Significant Duration (Ds5-75). Relationships are developed both for shallow crustal earthquakes and subduction zone earthquakes (hypocentral depth less than 45 km). The models developed consider site amplification using VS30 and the depth to a layer with VS=800 m/s (h800). We observe that the site effect for is magnitude dependent. For Ds5-95 and Ds5-75, we also observe strong magnitude dependency in distance attenuation. We compare the results with previous GMPEs for Japanese earthquakes and observe that the relationships are similar. The results of this study also allow a comparison between earthquakes in shallow-crustal regions, and subduction regions. This comparison shows that Arias Intensity has similar magnitude and distance scaling between both regions and generally Arias Intensity of shallow crustal motions are higher than subduction motions. On the other hand, the duration of shallow crustal motions are longer than subduction earthquakes except for records with large distance and small magnitude causative earthquakes. Because small shallow crustal events saturate with distance, ground motions with large distances and small magnitudes have shorter duration for shallow crustal events than subduction earthquakes.
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Winn, Olivia, and Sivaram Kiran Thekkemadathil. "Near-Infrared Spectral Measurements and Multivariate Analysis for Predicting Glass Contamination of Boiler Fuel." Thesis, Mälardalens högskola, Akademin för ekonomi, samhälle och teknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-36058.

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This degree project investigates how glass contamination in refuse-derived fuel for a fluidised bed boiler can be detected using near-infrared spectroscopy. It is motivated by the potential to reduce greenhouse gas emissions by replacing fossil fuels with refuse-derived fuel. The intent was to develop a multivariate predictive model of near-infrared spectral data to detect the presence of glass cullet against a background material that represents refuse-derived fuel. Existing literature was reviewed to confirm the usage of near-infrared spectroscopy as a sensing technology and determine the necessity of glass detection. Four unique background materials were chosen to represent the main components in municipal solid waste: wood shavings, shredded coconut, dry rice and whey powder. Samples of glass mixed with the background material were imaged using near-infrared spectroscopy, the resulting data was pre-processed and analysed using partial least squares regression. It was shown that a predictive model for quantifying coloured glass cullet content in one of several background materials were reasonably accurate with a validation coefficient of determination of 0.81 between the predicted and reference data. Models that used data from a single type of background material, wood shavings, were more accurate. Models for quantifying clear glass cullet content were significantly less accurate. These types of models could be applied to predict coloured glass content in different kinds of background materials. However, the presence of clear glass in municipal solid waste, and thus refuse-derived fuel, limit the opportunities to apply these methods to the detection of glass contamination in fuel.
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Badenhorst, Dirk Jakobus Pretorius. "Improving the accuracy of prediction using singular spectrum analysis by incorporating internet activity." Thesis, Stellenbosch : Stellenbosch University, 2013. http://hdl.handle.net/10019.1/80056.

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Thesis (MComm)--Stellenbosch University, 2013.
ENGLISH ABSTRACT: Researchers and investors have been attempting to predict stock market activity for years. The possible financial gain that accurate predictions would offer lit a flame of greed and drive that would inspire all kinds of researchers. However, after many of these researchers have failed, they started to hypothesize that a goal such as this is not only improbable, but impossible. Previous predictions were based on historical data of the stock market activity itself and would often incorporate different types of auxiliary data. This auxiliary data ranged as far as imagination allowed in an attempt to find some correlation and some insight into the future, that could in turn lead to the figurative pot of gold. More often than not, the auxiliary data would not prove helpful. However, with the birth of the internet, endless amounts of new sources of auxiliary data presented itself. In this thesis I propose that the near in finite amount of data available on the internet could provide us with information that would improve stock market predictions. With this goal in mind, the different sources of information available on the internet are considered. Previous studies on similar topics presented possible ways in which we can measure internet activity, which might relate to stock market activity. These studies also gave some insights on the advantages and disadvantages of using some of these sources. These considerations are investigated in this thesis. Since a lot of this work is therefore based on the prediction of a time series, it was necessary to choose a prediction algorithm. Previously used linear methods seemed too simple for prediction of stock market activity and a new non-linear method, called Singular Spectrum Analysis, is therefore considered. A detailed study of this algorithm is done to ensure that it is an appropriate prediction methodology to use. Furthermore, since we will be including auxiliary information, multivariate extensions of this algorithm are considered as well. Some of the inaccuracies and inadequacies of these current multivariate extensions are studied and an alternative multivariate technique is proposed and tested. This alternative approach addresses the inadequacies of existing methods. With the appropriate methodology chosen and the appropriate sources of auxiliary information chosen, a concluding chapter is done on whether predictions that includes auxiliary information (obtained from the internet) improve on baseline predictions that are simply based on historical stock market data.
AFRIKAANSE OPSOMMING: Navorsers en beleggers is vir jare al opsoek na maniere om aandeelpryse meer akkuraat te voorspel. Die moontlike finansiële implikasies wat akkurate vooruitskattings kan inhou het 'n vlam van geldgierigheid en dryf wakker gemaak binne navorsers regoor die wêreld. Nadat baie van hierdie navorsers onsuksesvol was, het hulle begin vermoed dat so 'n doel nie net onwaarskynlik is nie, maar onmoontlik. Vorige vooruitskattings was bloot gebaseer op historiese aandeelprys data en sou soms verskillende tipes bykomende data inkorporeer. Die tipes data wat gebruik was het gestrek so ver soos wat die verbeelding toegelaat het, in 'n poging om korrelasie en inligting oor die toekoms te kry wat na die guurlike pot goud sou lei. Navorsers het gereeld gevind dat hierdie verskillende tipes bykomende inligting nie van veel hulp was nie, maar met die geboorte van die internet het 'n oneindige hoeveelheid nuwe bronne van bykomende inligting bekombaar geraak. In hierdie tesis stel ek dus voor dat die data beskikbaar op die internet dalk vir ons kan inligting gee wat verwant is aan toekomstige aandeelpryse. Met hierdie doel in die oog, is die verskillende bronne van inligting op die internet gebestudeer. Vorige studies op verwante werk het sekere spesifieke maniere voorgestel waarop ons internet aktiwiteit kan meet. Hierdie studies het ook insig gegee oor die voordele en die nadele wat sommige bronne inhou. Hierdie oorwegings word ook in hierdie tesis bespreek. Aangesien 'n groot gedeelte van hierdie tesis dus gebasseer word op die vooruitskatting van 'n tydreeks, is dit nodig om 'n toepaslike vooruitskattings algoritme te kies. Baie navorsers het verkies om eenvoudige lineêre metodes te gebruik. Hierdie metodes het egter te eenvoudig voorgekom en 'n relatiewe nuwe nie-lineêre metode (met die naam "Singular Spectrum Analysis") is oorweeg. 'n Deeglike studie van hierdie algoritme is gedoen om te verseker dat die metode van toepassing is op aandeelprys data. Verder, aangesien ons gebruik wou maak van bykomende inligting, is daar ook 'n studie gedoen op huidige multivariaat uitbreidings van hierdie algoritme en die probleme wat dit inhou. 'n Alternatiewe multivariaat metode is toe voorgestel en getoets wat hierdie probleme aanspreek. Met 'n gekose vooruitskattingsmetode en gekose bronne van bykomende data is 'n gevolgtrekkende hoofstuk geskryf oor of vooruitskattings, wat die bykomende internet data inkorporeer, werklik in staat is om te verbeter op die eenvoudige vooruitskattings, wat slegs gebaseer is op die historiese aandeelprys data.
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Books on the topic "Predictive Spectral Analysis"

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P, Shepherd Kevin, and Langley Research Center, eds. Wind turbine acoustics. Washington, D.C: National Aeronautics and Space Administration, Office of Management, Scientific and Technical Information Division, 1990.

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Book chapters on the topic "Predictive Spectral Analysis"

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Elsner, James B., and Anastasios A. Tsonis. "Prediction." In Singular Spectrum Analysis, 133–41. Boston, MA: Springer US, 1996. http://dx.doi.org/10.1007/978-1-4757-2514-8_9.

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Khandekar, M. L. "Wave Prediction: Spectral Models." In Operational Analysis and Prediction of Ocean Wind Waves, 68–103. New York, NY: Springer New York, 1989. http://dx.doi.org/10.1007/978-1-4613-8952-1_5.

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Hermansky, Hynek. "Modulation Spectrum in Speech Processing." In Signal Analysis and Prediction, 395–406. Boston, MA: Birkhäuser Boston, 1998. http://dx.doi.org/10.1007/978-1-4612-1768-8_27.

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Weber, Rodolphe, and Christian Faye. "Coarsely Quantized Spectral Estimation of Radio Astronomic Sources in Highly Corruptive Environments." In Signal Analysis and Prediction, 103–12. Boston, MA: Birkhäuser Boston, 1998. http://dx.doi.org/10.1007/978-1-4612-1768-8_7.

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Micsonai, András, Éva Bulyáki, and József Kardos. "BeStSel: From Secondary Structure Analysis to Protein Fold Prediction by Circular Dichroism Spectroscopy." In Methods in Molecular Biology, 175–89. New York, NY: Springer US, 2020. http://dx.doi.org/10.1007/978-1-0716-0892-0_11.

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Abstract Far-UV circular dichroism (CD) spectroscopy is a classical method for the study of the secondary structure of polypeptides in solution. It has been the general view that the α-helix content can be estimated accurately from the CD spectra. However, the technique was less reliable to estimate the β-sheet contents as a consequence of the structural variety of the β-sheets, which is reflected in a large spectral diversity of the CD spectra of proteins containing this secondary structure component. By taking into account the parallel or antiparallel orientation and the twist of the β-sheets, the Beta Structure Selection (BeStSel) method provides an improved β-structure determination and its performance is more accurate for any of the secondary structure types compared to previous CD spectrum analysis algorithms. Moreover, BeStSel provides extra information on the orientation and twist of the β-sheets which is sufficient for the prediction of the protein fold. The advantage of CD spectroscopy is that it is a fast and inexpensive technique with easy data processing which can be used in a wide protein concentration range and under various buffer conditions. It is especially useful when the atomic resolution structure is not available, such as the case of protein aggregates, membrane proteins or natively disordered chains, for studying conformational transitions, testing the effect of the environmental conditions on the protein structure, for verifying the correct fold of recombinant proteins in every scientific fields working on proteins from basic protein science to biotechnology and pharmaceutical industry. Here, we provide a brief step-by-step guide to record the CD spectra of proteins and their analysis with the BeStSel method.
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Bidwai, Sandeep, Shilpa Mayannavar, and Uday V. Wali. "Performance Comparison of Markov Chain and LSTM Models for Spectrum Prediction in GSM Bands." In Machine Learning for Predictive Analysis, 289–98. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-7106-0_29.

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Furferi, Rocco, Lapo Governi, and Yary Volpe. "Methods for Predicting Spectral Response of Fibers Blends." In New Trends in Image Analysis and Processing -- ICIAP 2015 Workshops, 79–86. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23222-5_10.

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Edelkamp, Stefan. "Prediction of Regular Search Tree Growth by Spectral Analysis." In KI 2001: Advances in Artificial Intelligence, 154–68. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45422-5_12.

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Hammond, J. K., R. F. Harrison, Y. H. Tsao, and J. S. Lee. "The prediction of time—frequency spectra using covariance-equivalent models." In Developments in Time Series Analysis, 355–73. Boston, MA: Springer US, 1993. http://dx.doi.org/10.1007/978-1-4899-4515-0_25.

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Drábik, Peter, Petr Šaloun, Ivan Zelinka, and Marie Vraná. "Better and Faster Spectra Analysis Using Analytical Programming on CUDA." In Nostradamus 2014: Prediction, Modeling and Analysis of Complex Systems, 153–60. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-07401-6_15.

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Conference papers on the topic "Predictive Spectral Analysis"

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Sheahan, Noirin F., Davis Coakley, and James F. Malone. "Comparison of conventional and Linear Predictive spectral analysis techniques." In 1992 14th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 1992. http://dx.doi.org/10.1109/iembs.1992.5761605.

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Sheahan, Coakley, and Malone. "Comparison Of Conventional And Linear Predictive Spectral Analysis Techniques." In Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 1992. http://dx.doi.org/10.1109/iembs.1992.592904.

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Lucchini, Andrea, Paolo Franchin, and Fabrizio Mollaioli. "A Spectrum-to-Spectrum Method for Calculating Uniform Hazard Floor Response Spectra." In ASME 2017 Pressure Vessels and Piping Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/pvp2017-65293.

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In codes’ provisions and design procedures for acceleration-sensitive nonstructural components, seismic demand is commonly defined by means of floor response spectra expressed in terms of pseudo-acceleration. Depending on the considered analysis method, floor response spectra may be derived from floors’ acceleration histories, based on structural response-history analysis, or calculated using a predictive equation from a given input ground motion spectrum. Methods for estimating floor response spectra that are based on the second alternative are commonly called spectrum-to-spectrum methods. The objective of this paper is to briefly review these methods, and to discuss the main assumptions they are based on. Both predictive equations from selected seismic codes and proposals from the literature are included in the review. A new probability-based method, recently developed by the Authors for generating uniform hazard floor response spectra, namely, floor response spectra whose ordinates are characterized by a given target value of the mean annual frequency of being exceeded, is also described. By using this method floor spectra are determined through closed-form equations, given the mean annual frequency of interest, the damping ratio of the spectra, the modal properties of the structure, and three uniform hazard ground spectra. The method is built on a proposal for a probabilistic seismic demand model that relates the ground spectral acceleration with the floor spectral acceleration, and is able to explicitly account for the ground motion variability of the nonstructural response. Results for a case study consisting of a service frame of a visbreaking unit in an oil refinery are presented to show the good predictive accuracy of the method with respect to exact uniform hazard floor response spectra obtained through a standard probabilistic analysis.
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Tchrakian, T. T., and B. Basu. "Real time traffic flow forecasting and predictive ramp-metering using spectral analysis." In IET Irish Signals and Systems Conference (ISSC 2009). IET, 2009. http://dx.doi.org/10.1049/cp.2009.1689.

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Anteliz Jaimes, Antonio Alexi. "Maintenance predictive in electric transformers, applying spectral analysis to the flow of magnetic dispersion." In 2012 XVII Symposium of Image, Signal Processing, and Artificial Vision (STSIVA). IEEE, 2012. http://dx.doi.org/10.1109/stsiva.2012.6340599.

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De Giorgi, Maria Grazia, Aldebara Sciolti, Elisa Pescini, and Antonio Ficarella. "Frequency Analysis and Predictive Identification of Flame Stability by Image Processing." In ASME 2014 8th International Conference on Energy Sustainability collocated with the ASME 2014 12th International Conference on Fuel Cell Science, Engineering and Technology. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/es2014-6599.

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Monitoring and characterization of combustion flames by digital image processing is an active research topic. This study experimentally investigates the feasibility of high speed visualization techniques for combustion instability monitoring in a swirl liquid-fueled lean combustor for different air/fuel ratios. Instability, in fact, is an unpleasant aspect in the combustive system that negatively impacts on combustion efficiency. This work investigates methods for extracting significant parameters using the geometrical and luminous data of the flame images; some flame features are related to the combustion regimes. The stability of the flame is identified using spectral and wavelet-based analysis of the pixel intensities of the flame images. In particular the most flame unstable regions were identified by analyzing the two dimensional maps of different physical quantities. The impact of the fuel/air ratio on the stability of the flame is investigated also by a Monochromator/Photomultiplier system (PMT). The results support the potential of the methods described for flame monitoring.
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Yoon, Heonjun, Byeng D. Youn, and Chulmin Cho. "Piezoelectric Energy Harvesting Analysis Under Non-Stationary Random Vibrations." In ASME 2013 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/detc2013-13547.

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Energy harvesting (EH), which scavenges electric power from ambient, otherwise wasted, energy sources, has received considerable attention for the purpose of powering wireless sensor networks and low-power electronics. Among ambient energy sources, widely available vibration energy can be converted into electrical energy using piezoelectric materials that generate an electrical potential in response to applied mechanical stress. As a basis for designing a piezoelectric energy harvester, an analytical model should be developed to estimate electric power under a given vibration condition. Many analytical models under the assumption of the deterministic excitation cannot deal with random nature in vibration signals, although the randomness considerably affects variation in harvestable electrical energy. Thus, predictive capability of the analytical models is normally poor under random vibration signals. Such a poor power prediction is mainly caused by the variation of the dominant frequencies and their peak acceleration levels. This paper thus proposes the three-step framework of the stochastic piezoelectric energy harvesting analysis under non-stationary random vibrations. As a first step, the statistical time-frequency analysis using the Wigner-Ville spectrum was used to estimate a time-varying power spectral density (PSD) of an input random excitation. The second step is to employ an existing electromechanical model as a linear operator for calculating the output voltage response. The final step is to estimate a time-varying PSD of the output voltage response from the linear relationship. Then, the expected electric power was estimated from the autocorrelation function that is inverse Fourier transform of the time-varying PSD of the output voltage response. Therefore, the proposed framework can be used to predict the expected electric power under non-stationary random vibrations in a stochastic manner.
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Zhang, Shengli, Jiong Tang, and Yu Ding. "Modeling and Analysis of Time-Periodic Gearbox Vibration." In ASME Turbo Expo 2014: Turbine Technical Conference and Exposition. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/gt2014-27230.

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In a modern wind turbine, the gearbox is an expensive and fault-prone subsystem. Currently, condition monitoring, based on comparison of data of healthy baseline measurement and online measurement, followed by feature analysis and decision making, is the main approach of diagnosis and prognosis. Although having been employed in many practical implementations, such methods have limitations. For example, a huge database is needed when operating conditions change and normal variations are significant. Traditionally, first-principle-based modeling of gearboxes is considered very challenging, primarily due to their dynamic characteristics that exhibit time-periodicity and encompass a very wide frequency range. In this research, aiming at achieving the predictive modeling capability, a lumped-parameter model of a two-stage laboratory gearbox testbed is constructed based on the assumed mode method. This model can characterize the gearbox dynamic effects including the time-varying mesh stiffness and backlash. The Floquet theory and harmonic balance method are then applied to analytically investigate the system dynamics, where the eigenvalues of the time-periodic gearbox are extracted and correlated to the spectral analysis results of the time-domain response prediction. This modeling approach and the associated analysis lay down a foundation for establishing hybrid dynamic model of complex gearbox systems which will further be utilized in model-based diagnosis and prognosis.
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Sanchez, Rodolfo Bolan˜os, Agusti´n Sa´nchez Arcilla, Jesu´s Gomez, and Abdel Sairouni. "Spectral Evolution and Wave Age Analysis of an Exceptional Storm Off the Mediterranean Spanish Coast." In ASME 2003 22nd International Conference on Offshore Mechanics and Arctic Engineering. ASMEDC, 2003. http://dx.doi.org/10.1115/omae2003-37063.

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A severe storm arose in the North-western Mediterranean during November 2001, causing severe damage to the coast (including loss of beaches, structural damage to harbours and also loss of human life). This paper focuses on the storm description and the corresponding spectral analysis of buoy data recorded during the storm and wind data collected from meteorological stations along the Spanish coast. Additionally it shows wave hindcast for the event and the implications of the spectral characteristics for wind wave prediction in the Mediterranean. The buoys recorded wave heights (Hs) of up to 6 m and periods (Tz) of 10 s. The direction (θp) was recorded as mainly easterly during the storm peaks and north-westerly during calm conditions. The changes of wave-age during the development of the storm have not shown correlation with wave Hs prediction errors. The recorded directional spectra present bimodal features in frequency and direction related to wind variability. The directional spectrum predicted did not reproduced such bimodal features. It is suggested to consider the bimodal characteristics and/or spectral width of recorded data for a better wave-age description in the Catalan coast.
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Feng, Qin, and Richard Large. "Prediction of Fatigue Life of Shallow Water Offshore Platforms Using Spectral Fatigue Analysis Method." In ASME 2010 29th International Conference on Ocean, Offshore and Arctic Engineering. ASMEDC, 2010. http://dx.doi.org/10.1115/omae2010-20796.

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Spectral fatigue analysis approach is highly recommended for fixed offshore platform design and reassessment by API. This method is a computationally efficient method, being able to handle the random nature of environmental ocean wave conditions during calculating wave loads on the offshore platforms and subsequent structural responses. However, its fundamental theory is based on the assumption of linearity of both structural system and wave loading mechanism. Although this method is critically appropriate to be applied in offshore platform design and fatigue assessment for deep water scenarios where wave and force nonlinearities are not very severe, it has still been widely utilized for the design and assessment of shallow water platforms in offshore industry without carefully considering possible errors caused by strong nonlinear factors between ocean waves and forces. The source giving rise to the errors is because of the difficulties in choosing suitably correct wave heights for a series of wave periods required for producing transfer functions between sea state spectra and structural response spectra. Therefore, the studies to justify the possible errors of the spectral fatigue analysis method for shallow water platforms have been provoked. This paper presents the results of the studies of investigating the errors from currently existing spectral fatigue analysis method. A new technical approach that can reduce the errors in the spectral fatigue analysis of shallow water platforms is introduced. The proposed technical approach is mainly focused on producing realistic transfer functions between sea state spectra and structural response spectra, which can reasonably reflect the individually local sea state data by using wave height-period joint probability density function. Hence the fatigue damage and life at the tubular joints of offshore platforms can be more precisely predicted. The spectral fatigue analysis of a practical shallow water jacket platform in the recent platform design project has been performed using the proposed approach and the results are discussed.
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Reports on the topic "Predictive Spectral Analysis"

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Gamwo, I. K., A. Miller, and D. Gidaspow. Spectral analysis of CFB data: Predictive models of Circulating Fluidized Bed combustors. Office of Scientific and Technical Information (OSTI), April 1992. http://dx.doi.org/10.2172/5098191.

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Gamwo, I. K., A. Miller, and D. Gidaspow. Spectral analysis of CFB data: Predictive models of Circulating Fluidized Bed combustors. 11th technical progress report. Office of Scientific and Technical Information (OSTI), April 1992. http://dx.doi.org/10.2172/10156489.

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Alchanatis, Victor, Stephen W. Searcy, Moshe Meron, W. Lee, G. Y. Li, and A. Ben Porath. Prediction of Nitrogen Stress Using Reflectance Techniques. United States Department of Agriculture, November 2001. http://dx.doi.org/10.32747/2001.7580664.bard.

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Commercial agriculture has come under increasing pressure to reduce nitrogen fertilizer inputs in order to minimize potential nonpoint source pollution of ground and surface waters. This has resulted in increased interest in site specific fertilizer management. One way to solve pollution problems would be to determine crop nutrient needs in real time, using remote detection, and regulating fertilizer dispensed by an applicator. By detecting actual plant needs, only the additional nitrogen necessary to optimize production would be supplied. This research aimed to develop techniques for real time assessment of nitrogen status of corn using a mobile sensor with the potential to regulate nitrogen application based on data from that sensor. Specifically, the research first attempted to determine the system parameters necessary to optimize reflectance spectra of corn plants as a function of growth stage, chlorophyll and nitrogen status. In addition to that, an adaptable, multispectral sensor and the signal processing algorithm to provide real time, in-field assessment of corn nitrogen status was developed. Spectral characteristics of corn leaves reflectance were investigated in order to estimate the nitrogen status of the plants, using a commercial laboratory spectrometer. Statistical models relating leaf N and reflectance spectra were developed for both greenhouse and field plots. A basis was established for assessing nitrogen status using spectral reflectance from plant canopies. The combined effect of variety and N treatment was studied by measuring the reflectance of three varieties of different leaf characteristic color and five different N treatments. The variety effect on the reflectance at 552 nm was not significant (a = 0.01), while canonical discriminant analysis showed promising results for distinguishing different variety and N treatment, using spectral reflectance. Ambient illumination was found inappropriate for reliable, one-beam spectral reflectance measurement of the plants canopy due to the strong spectral lines of sunlight. Therefore, artificial light was consequently used. For in-field N status measurement, a dark chamber was constructed, to include the sensor, along with artificial illumination. Two different approaches were tested (i) use of spatially scattered artificial light, and (ii) use of collimated artificial light beam. It was found that the collimated beam along with a proper design of the sensor-beam geometry yielded the best results in terms of reducing the noise due to variable background, and maintaining the same distance from the sensor to the sample point of the canopy. A multispectral sensor assembly, based on a linear variable filter was designed, constructed and tested. The sensor assembly combined two sensors to cover the range of 400 to 1100 nm, a mounting frame, and a field data acquisition system. Using the mobile dark chamber and the developed sensor, as well as an off-the-shelf sensor, in- field nitrogen status of the plants canopy was measured. Statistical analysis of the acquired in-field data showed that the nitrogen status of the com leaves can be predicted with a SEP (Standard Error of Prediction) of 0.27%. The stage of maturity of the crop affected the relationship between the reflectance spectrum and the nitrogen status of the leaves. Specifically, the best prediction results were obtained when a separate model was used for each maturity stage. In-field assessment of the nitrogen status of corn leaves was successfully carried out by non contact measurement of the reflectance spectrum. This technology is now mature to be incorporated in field implements for on-line control of fertilizer application.
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Соловйов, В. М., and В. В. Соловйова. Моделювання мультиплексних мереж. Видавець Ткачук О.В., 2016. http://dx.doi.org/10.31812/0564/1253.

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From the standpoint of interdisciplinary self-organization theories and synergetics analyzes current approaches to modeling socio-economic systems. It is shown that the complex network paradigm is the foundation on which to build predictive models of complex systems. We consider two algorithms to transform time series or a set of time series to the network: recurrent and graph visibility. For the received network designed dynamic spectral, topological and multiplex measures of complexity. For example, the daily values the stock indices show that most of the complexity measures behaving in a characteristic way in time periods that characterize the different phases of the behavior and state of the stock market. This fact encouraged to use monitoring and prediction of critical and crisis states in socio-economic systems.
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Rao, M. M. Spectral Analysis, Estimation, and Prediction of Multiple Harmonizable Time Series. Fort Belvoir, VA: Defense Technical Information Center, August 1990. http://dx.doi.org/10.21236/ada266758.

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Kowalski, Nina, Didier A. Depireux, and Shihab A. Shamma. Analysis of Dynamic Spectra in Ferret Primary Auditory Cortex. 2. Prediction of Unit Responses to Arbitrary Dynamic Spectra. Fort Belvoir, VA: Defense Technical Information Center, January 1995. http://dx.doi.org/10.21236/ada445591.

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Shamma, Shihab A., and Huib Versnel. Ripple Analysis in Ferret Primary Auditory Cortex. 3. Prediction of Unit Responses to Arbitrary Spectral Profiles. Fort Belvoir, VA: Defense Technical Information Center, January 1995. http://dx.doi.org/10.21236/ada455589.

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Feng, Zhichao, Zhimin Yan, and Qianyun Liu. MRI Signs for Prenatal Prediction of Placenta Accreta Spectrum Disorders and Invasiveness in High-risk Pregnant Women: A Systematic Review and Meta-Analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, November 2022. http://dx.doi.org/10.37766/inplasy2022.11.0003.

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Review question / Objective: This meta-analysis aimed to identify the significant MRI signs for placenta accreta spectrum in high-risk pregnant women and to determine their diagnostic value. Condition being studied: Placenta accreta spectrum (PAS) is a dangerous complication in pregnancies with increasing incidence worldwide, in which the villous tissue adheres or invades the uterine wall. Eligibility criteria: Articles assessing the diagnostic performance of MRI signs for PAS and/or placenta percreta in high-risk pregnant women underwent full-text review. Included studies required confirmation of diagnosis based on intraoperative and/or pathologic findings.
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Zhou, Yiwu. Early prediction models for Extended-spectrum β-lactamase-producing Escherichia coli infection in emergency department: A protocol for systematic review and meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, March 2021. http://dx.doi.org/10.37766/inplasy2021.3.0049.

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Derbentsev, V., A. Ganchuk, and Володимир Миколайович Соловйов. Cross correlations and multifractal properties of Ukraine stock market. Politecnico di Torino, 2006. http://dx.doi.org/10.31812/0564/1117.

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Recently the statistical characterizations of financial markets based on physics concepts and methods attract considerable attentions. The correlation matrix formalism and concept of multifractality are used to study temporal aspects of the Ukraine Stock Market evolution. Random matrix theory (RMT) is carried out using daily returns of 431 stocks extracted from database time series of prices the First Stock Trade System index (www.kinto.com) for the ten-year period 1997-2006. We find that a majority of the eigenvalues of C fall within the RMT bounds for the eigenvalues of random correlation matrices. We test the eigenvalues of C within the RMT bound for universal properties of random matrices and find good agreement with the results for the Gaussian orthogonal ensemble of random matrices—implying a large degree of randomness in the measured cross-correlation coefficients. Further, we find that the distribution of eigenvector components for the eigenvectors corresponding to the eigenvalues outside the RMT bound display systematic deviations from the RMT prediction. We analyze the components of the deviating eigenvectors and find that the largest eigenvalue corresponds to an influence common to all stocks. Our analysis of the remaining deviating eigenvectors shows distinct groups, whose identities correspond to conventionally identified business sectors. Comparison with the Mantegna minimum spanning trees method gives a satisfactory consent. The found out the pseudoeffects related to the artificial unchanging areas of price series come into question We used two possible procedures of analyzing multifractal properties of a time series. The first one uses the continuous wavelet transform and extracts scaling exponents from the wavelet transform amplitudes over all scales. The second method is the multifractal version of the detrended fluctuation analysis method (MF-DFA). The multifractality of a time series we analysed by means of the difference of values singularity stregth (or Holder exponent) ®max and ®min as a suitable way to characterise multifractality. Singularity spectrum calculated from daily returns using a sliding 250 day time window in discrete steps of 1. . . 10 days. We discovered that changes in the multifractal spectrum display distinctive pattern around significant “drawdowns”. Finally, we discuss applications to the construction of crushes precursors at the financial markets.
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