Journal articles: 'Waveform Classification'

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Moon, Sang-Keun, Jin-O. Kim, and Charles Kim. "Multi-Labeled Recognition of Distribution System Conditions by a Waveform Feature Learning Model." Energies 12, no. 6 (March 22, 2019): 1115. http://dx.doi.org/10.3390/en12061115.

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A waveform contains recognizable feature patterns. To extract the features of various equipment disturbance conditions from a waveform, this paper presents a practical model to estimate distribution line (DL) conditions by means of a multi-label extreme learning machine. The motivation for the waveform learning is to develop device-embedded models which are capable of detecting and classifying abnormal operations on the DLs. In waveform analysis, power quality waveform modeling criteria are adopted for pattern classification. Typical disturbance waveforms are applied as class models, and the formula-generated waveform features are compared with field-acquired waveforms for disturbance classification. In particular, filtered symmetrical components of the modified varying window scale are applied for feature extraction. The proposed model interacts suitably with the parameter update method in classifying the waveforms in real network situations. The classification result showed disturbance features on model with the real DL waveform data holds a potential for determining additional DL conditions and improving its classification performance through the update mechanism of the learning machine.

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Wang, Jiaquan, Qijun Huang, Qiming Ma, Sheng Chang, Jin He, Hao Wang, Xiao Zhou, Fang Xiao, and Chao Gao. "Classification of VLF/LF Lightning Signals Using Sensors and Deep Learning Methods." Sensors 20, no. 4 (February 14, 2020): 1030. http://dx.doi.org/10.3390/s20041030.

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Lightning waveform plays an important role in lightning observation, location, and lightning disaster investigation. Based on a large amount of lightning waveform data provided by existing real-time very low frequency/low frequency (VLF/LF) lightning waveform acquisition equipment, an automatic and accurate lightning waveform classification method becomes extremely important. With the widespread application of deep learning in image and speech recognition, it becomes possible to use deep learning to classify lightning waveforms. In this study, 50,000 lightning waveform samples were collected. The data was divided into the following categories: positive cloud ground flash, negative cloud ground flash, cloud ground flash with ionosphere reflection signal, positive narrow bipolar event, negative narrow bipolar event, positive pre-breakdown process, negative pre-breakdown process, continuous multi-pulse cloud flash, bipolar pulse, skywave. A multi-layer one-dimensional convolutional neural network (1D-CNN) was designed to automatically extract VLF/LF lightning waveform features and distinguish lightning waveforms. The model achieved an overall accuracy of 99.11% in the lightning dataset and overall accuracy of 97.55% in a thunderstorm process. Considering its excellent performance, this model could be used in lightning sensors to assist in lightning monitoring and positioning.

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Müller, Felix L., Stephan Paul, Stefan Hendricks, and Denise Dettmering. "Monitoring Arctic thin ice: a comparison between CryoSat-2 SAR altimetry data and MODIS thermal-infrared imagery." Cryosphere 17, no. 2 (February 17, 2023): 809–25. http://dx.doi.org/10.5194/tc-17-809-2023.

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Abstract. Areas of thin sea ice in the polar regions not only are experiencing the highest rate of sea-ice production but also are, therefore, important hot spots for ocean ventilation as well as heat and moisture exchange between the ocean and the atmosphere. Through co-location of (1) an unsupervised waveform classification (UWC) approach applied to CryoSat-2 radar waveforms with (2) Moderate Resolution Imaging Spectroradiometer-derived (MODIS) thin-ice-thickness estimates and (3) Sentinel-1A/B synthetic-aperture radar (SAR) reference data, thin-ice-based waveform shapes are identified, referenced, and discussed with regard to a manifold of waveform shape parameters. Here, strong linear dependencies are found between binned thin-ice thickness up to 25 cm from MODIS and the CryoSat-2 waveform shape parameters that show the possibility of either developing simple correction terms for altimeter ranges over thin ice or directing adjustments to current retracker algorithms specifically for very thin sea ice. This highlights the potential of CryoSat-2-based SAR altimetry to reliably discriminate between occurrences of thick sea ice, open-water leads, and thin ice within recently refrozen leads or areas of thin sea ice. Furthermore, a comparison to the ESA Climate Change Initiative's (CCI) CryoSat-2 surface type classification with classes sea ice, lead, and unknown reveals that the newly found thin-ice-related waveforms are divided up almost equally between unknown (46.3 %) and lead type (53.4 %) classifications. Overall, the UWC results in far fewer unknown classifications (1.4 % to 38.7 %). Thus, UWC provides more usable information for sea-ice freeboard and thickness retrieval and at the same time reduces range biases from thin-ice waveforms processed as regular sea ice in the CCI classification.

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Babadi, M., M. Sattari, and S. Iran Pour. "EXPLORING THE POTENTIAL OF FULL WAVEFORM AIRBORNE LIDAR FEATURES AND ITS FUSION WITH RGB IMAGE IN CLASSIFICATION OF A SPARSELY FORESTED AREA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W18 (October 18, 2019): 147–52. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w18-147-2019.

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Abstract. Precise measurements of forest trees is very important in environmental protection. Measuring trees parameters by use of ground- based inventories is time and cost consuming. Employing advanced remote sensing techniques to obtain forest parameters has recently made a great progress step in this research area. Among the information resources of the study field, full waveform LiDAR data have attracted the attention of researchers in the recent years. However, decomposing LiDAR waveforms is one of the challenges in the data processing. In fact, the procedure of waveform decomposition causes some of the useful information in waveforms to be lost. In this study, we aim to investigate the potential use of non-decomposed full waveform LiDAR features and its fusion with optical images in classification of a sparsely forested area. We consider three classes including i) ground, ii) Quercus wislizeni and iii) Quercus douglusii for the classification procedure. In order to compare the results, five different strategies, namely i) RGB image, ii) common LiDAR features, iii) fusion of RGB image and common LiDAR features, iv) LiDAR waveform structural features and v) fusion of RGB image and LiDAR waveform structural features have been utilized for classifying the study area. The results of our study show that classification via using fusion of LiDAR waveform features and RGB image leads to the highest classification accuracy.

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Guilcher, Antoine, Damien Laneelle, and Guillaume Mahé. "Use of a Pre-Trained Neural Network for Automatic Classification of Arterial Doppler Flow Waveforms: A Proof of Concept." Journal of Clinical Medicine 10, no. 19 (September 28, 2021): 4479. http://dx.doi.org/10.3390/jcm10194479.

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Background: Arterial Doppler flow waveform analysis is a tool recommended for the management of lower extremity peripheral arterial disease (PAD). To standardize the waveform analysis, classifications have been proposed. Neural networks have shown a great ability to categorize data. The aim of the present study was to use an existing neural network to evaluate the potential for categorization of arterial Doppler flow waveforms according to a commonly used classification. Methods: The Pareto efficient ResNet-101 (ResNet-101) neural network was chosen to categorize 424 images of arterial Doppler flow waveforms according to the Simplified Saint-Bonnet classification. As a reference, the inter-operator variability between two trained vascular medicine physicians was also assessed. Accuracy was expressed in percentage, and agreement was assessed using Cohen’s Kappa coefficient. Results: After retraining, ResNet-101 was able to categorize waveforms with 83.7 ± 4.6% accuracy resulting in a kappa coefficient of 0.79 (0.75–0.83) (CI 95%), compared with a kappa coefficient of 0.83 (0.79–0.87) (CI 95%) between the two physicians. Conclusion: This study suggests that the use of transfer learning on a pre-trained neural network is feasible for the automatic classification of images of arterial Doppler flow waveforms.

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Ma, L., M. Zhou, and C. Li. "LAND COVERS CLASSIFICATION BASED ON RANDOM FOREST METHOD USING FEATURES FROM FULL-WAVEFORM LIDAR DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W7 (September 12, 2017): 263–68. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w7-263-2017.

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In this study, a Random Forest (RF) based land covers classification method is presented to predict the types of land covers in Miyun area. The returned full-waveforms which were acquired by a LiteMapper 5600 airborne LiDAR system were processed, including waveform filtering, waveform decomposition and features extraction. The commonly used features that were distance, intensity, Full Width at Half Maximum (FWHM), skewness and kurtosis were extracted. These waveform features were used as attributes of training data for generating the RF prediction model. The RF prediction model was applied to predict the types of land covers in Miyun area as trees, buildings, farmland and ground. The classification results of these four types of land covers were obtained according to the ground truth information acquired from CCD image data of the same region. The RF classification results were compared with that of SVM method and show better results. The RF classification accuracy reached 89.73% and the classification Kappa was 0.8631.

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Guilcher, Antoine, Damien Lanéelle, Clément Hoffmann, Jérôme Guillaumat, Joel Constans, Luc Bressollette, Claire Le Hello, et al. "Comparison of the Use of Arterial Doppler Waveform Classifications in Clinical Routine to Describe Lower Limb Flow." Journal of Clinical Medicine 10, no. 3 (January 26, 2021): 464. http://dx.doi.org/10.3390/jcm10030464.

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Background: Characterisation of arterial Doppler waveforms is a persistent problem and a source of confusion in clinical practice. Classifications have been proposed to address the problem but their efficacy in clinical practice is unknown. The aim of the present study was to compare the efficacy of the categorisation rate of Descotes and Cathignol, Spronk et al. and the simplified Saint-Bonnet classifications. Methods: This is a multicentre prospective study where 130 patients attending a vascular arterial ultrasound were enrolled and Doppler waveform acquisition was performed at the common femoral, the popliteal, and the distal arteries at both sides. Experienced vascular specialists categorized these waveforms according to the three classifications. Results: of 1033 Doppler waveforms, 793 (76.8%), 943 (91.3%) and 1014 (98.2%) waveforms could be categorized using Descotes and Cathignol, Spronk et al. and the simplified Saint-Bonnet classifications, respectively. Differences in categorisation between classifications were significant (Chi squared test, p < 0.0001). Of 19 waveforms uncategorized using the simplified Saint-Bonnet classification, 58% and 84% were not categorized using the Spronk et al. and Descotes and Cathignol classifications, respectively. Conclusions: The results of the present study suggest that the simplified Saint-Bonnet classification provides a superior categorisation rate when compared with Spronk et al. and Descotes and Cathignol classifications.

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Ouyoung, Te, Wan-Ling Weng, Ting-Yu Hu, Chia-Chien Lee, Li-Wei Wu, and Hsin Hsiu. "Machine-Learning Classification of Pulse Waveform Quality." Sensors 22, no. 22 (November 8, 2022): 8607. http://dx.doi.org/10.3390/s22228607.

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Pulse measurements made using wearable devices can aid the monitoring of human physiological condition. Accurate estimation of waveforms is often difficult for nonexperts; motion artifacts may occur during tonometry measurements when the skin–sensor contact pressure is insufficient. An alternative approach is to extract only high-quality pulses for use in index calculations. The present study aimed to determine the effectiveness of using machine-learning analysis in discriminating between high-quality and low-quality pulse waveforms induced by applying different contact pressures. Radial blood pressure waveform (BPW) signals were measured noninvasively in healthy young subjects using a strain-gauge transducer. One-minute-long trains of pulse data were measured when applying the appropriate contact pressure (67.80 ± 1.55 mmHg) and a higher contact pressure (151.80 ± 3.19 mmHg). Eight machine-learning algorithms were employed to evaluate the following 40 harmonic pulse indices: amplitude proportions and their coefficients of variation and phase angles and their standard deviations. Significant differences were noted in BPW indices between applying appropriate and higher skin–surface contact pressures. The present appropriate contact pressure could not only provide a suitable holding force for the wearable device but also helped to maintain the physiological stability of the underlying tissues. Machine-learning analysis provides an effective method for distinguishing between the high-quality and low-quality pulses with excellent discrimination performance (leave-one-subject-out test: random-forest AUC = 0.96). This approach will aid the development of an automatic screening method for waveform quality and thereby improve the noninvasive acquisition reliability. Other possible interfering factors in practical applications can also be systematically studied using a similar procedure.

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Kumari Chilukuri, Raja, Hari Kishore Kakarla, and K. Subba Rao. "Radar Signal Recognition Based on Multilayer Perceptron Neural Network." International journal of electrical and computer engineering systems 14, no. 1 (January 26, 2023): 29–36. http://dx.doi.org/10.32985/ijeces.14.1.4.

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Low Probability of Intercept (LPI) radars are developed on an advanced architecture by making use of coded waveforms. Detection and classification of radar waveforms are important in many critical applications like electronic warfare, threat to radar and surveillance. Precise estimation of parameter and classification of the type of waveform will provide information about the threat to the radar and also helps to develop sophisticated intercept receiver. The present work is on classification of modulation waveforms of LPI radar using multilayer perceptron neural (MLPN) network. The classification approach is based on the following two steps. In the first step, the waveforms are analysed using cyclstationary technique which models the signal in bi-frequency (BF) plane. Using this algorithm, the BF images of the signals are obtained. In the second step, the BF images are fed to a feature extraction unit to get the salient features of the waveform and then to the multilayer perceptron neural (MLPN) network for classification. Nine types of noise free modulation waveforms (Frank, four polyphase codes and four poly time codes) are classified using the images obtained in the first step. The success rate achieved is 100 % for noise free signals. The experiment is repeated for various noise levels up to -12dB SNR. The noisy signals, before feeding to the MLPN network, are denoised using two types of denoising filters connected in cascade and the classification success rate achieved is 93.3% for signals up to -12dB SNR.

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Sinurat, Maya Eria, Bisman Nababan, Jonson Lumban Gaol, Henry Munandar Manik, and Nurul Hazrina Idris. "WAVEFORM CLASSIFICATION AND RETRACKING OF JASON-2 AND JASON-3 IN HALMAHERA SEA." Jurnal Teknologi 83, no. 3 (April 11, 2021): 107–17. http://dx.doi.org/10.11113/jurnalteknologi.v83.15125.

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The accuracy of sea surface heights (SSHs) estimation from satellite altimeters is strongly influenced by the microwave reflected signals (or waveforms). Waveforms in open oceans generally have ideal shapes following the Brown (1977) model. However, in coastal and shallow waters, the signals are disturbed by lands, thus resulting in complicated waveforms (non-Brown). Non-Brown waveforms produce inaccurate SSH estimations; therefore, specialized protocols such as waveform classification and retracking are crucial when attempting to produce accurate estimations. In this study, waveforms of Jason-2 and Jason-3 satellite altimeters in the Halmahera were classified and retracked using several algorithms, such as Offset Centre of Gravity (OCOG), Ice, Threshold, and Improved Threshold. The results showed that waveforms in the Halmahera Sea had ten generic classes with dominant class of the Browns. The validation results showed that all retrackers (except OCOG) had the value of correlations exceeding 0.75, and Root Mean Square Error (RMSE) smaller than 25 cm at a distance of 5-20 km from the land. The Threshold 10% was the most common retracker that appeared with the highest improvement percentage (IMP), meanwhile the Ice retracker consistently produced the best correlation (0.86) and the lowest RMSE (16cm). The retracking results showed that waveform retracking generally can improve SSH estimation accuracy from ocean (standard) retracker.

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Lewicki, Michael S. "Bayesian Modeling and Classification of Neural Signals." Neural Computation 6, no. 5 (September 1994): 1005–30. http://dx.doi.org/10.1162/neco.1994.6.5.1005.

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Identifying and classifying action potential shapes in extracellular neural waveforms have long been the subject of research, and although several algorithms for this purpose have been successfully applied, their use has been limited by some outstanding problems. The first is how to determine shapes of the action potentials in the waveform and, second, how to decide how many shapes are distinct. A harder problem is that action potentials frequently overlap making difficult both the determination of the shapes and the classification of the spikes. In this report, a solution to each of these problems is obtained by applying Bayesian probability theory. By defining a probabilistic model of the waveform, the probability of both the form and number of spike shapes can be quantified. In addition, this framework is used to obtain an efficient algorithm for the decomposition of arbitrarily complex overlap sequences. This algorithm can extract many times more information than previous methods and facilitates the extracellular investigation of neuronal classes and of interactions within neuronal circuits.

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Zhou, M., C. R. Li, L. Ma, and H. C. Guan. "LAND COVER CLASSIFICATION FROM FULL-WAVEFORM LIDAR DATA BASED ON SUPPORT VECTOR MACHINES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B3 (June 9, 2016): 447–52. http://dx.doi.org/10.5194/isprs-archives-xli-b3-447-2016.

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In this study, a land cover classification method based on multi-class Support Vector Machines (SVM) is presented to predict the types of land cover in Miyun area. The obtained backscattered full-waveforms were processed following a workflow of waveform pre-processing, waveform decomposition and feature extraction. The extracted features, which consist of distance, intensity, Full Width at Half Maximum (FWHM) and back scattering cross-section, were corrected and used as attributes for training data to generate the SVM prediction model. The SVM prediction model was applied to predict the types of land cover in Miyun area as ground, trees, buildings and farmland. The classification results of these four types of land covers were obtained based on the ground truth information according to the CCD image data of Miyun area. It showed that the proposed classification algorithm achieved an overall classification accuracy of 90.63%. In order to better explain the SVM classification results, the classification results of SVM method were compared with that of Artificial Neural Networks (ANNs) method and it showed that SVM method could achieve better classification results.

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Zhou, M., C. R. Li, L. Ma, and H. C. Guan. "LAND COVER CLASSIFICATION FROM FULL-WAVEFORM LIDAR DATA BASED ON SUPPORT VECTOR MACHINES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B3 (June 9, 2016): 447–52. http://dx.doi.org/10.5194/isprsarchives-xli-b3-447-2016.

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In this study, a land cover classification method based on multi-class Support Vector Machines (SVM) is presented to predict the types of land cover in Miyun area. The obtained backscattered full-waveforms were processed following a workflow of waveform pre-processing, waveform decomposition and feature extraction. The extracted features, which consist of distance, intensity, Full Width at Half Maximum (FWHM) and back scattering cross-section, were corrected and used as attributes for training data to generate the SVM prediction model. The SVM prediction model was applied to predict the types of land cover in Miyun area as ground, trees, buildings and farmland. The classification results of these four types of land covers were obtained based on the ground truth information according to the CCD image data of Miyun area. It showed that the proposed classification algorithm achieved an overall classification accuracy of 90.63%. In order to better explain the SVM classification results, the classification results of SVM method were compared with that of Artificial Neural Networks (ANNs) method and it showed that SVM method could achieve better classification results.

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Subbiah, Stalin, and Suresh Subramanian. "Biomedical Arrhythmia Heart Diseases Classification Based on Artificial Neural Network and Machine Learning Approach." International Journal of Engineering & Technology 7, no. 3.27 (August 15, 2018): 10. http://dx.doi.org/10.14419/ijet.v7i3.27.17642.

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In present day, several types of developments are carried toward the medical application. There has been increased improvement in the processing of ECG signals. The accurate detection of ECG signals with the help of detection of P, Q, R and S waveform. However these waveforms are suffered from some disturbances like noise. Initially denoising the ECG signal using filters and detect the PQRS waveforms. Four filters are carried out to remove the ECG noises that are Median, Gaussian, FIR and Butterworth filter. ECG signal is analyzed or classify using Extreme Learning Machine (ELM) and it compared with Support Vector Machine (SVM) and Back Propagation Neural Network (BPNN). The paper classifies the ECG signal into two classes, Normal and Abnormal. ECG waveform is detected and analyzed using the 48 records of the MIT-BIH arrhythmia database. Denoising results are evaluated using MSE, RMSE, PSNR, NAE and NCC. The classifier performance is measured in terms of Sensitivity (Se), Positive Predictivity (PP) and Specificity (SP).

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Jin, Ji, Xingguang Geng, Yitao Zhang, Haiying Zhang, and Tianchun Ye. "Pulse Wave Analysis Method of Cardiovascular Parameters Extraction for Health Monitoring." International Journal of Environmental Research and Public Health 20, no. 3 (January 31, 2023): 2597. http://dx.doi.org/10.3390/ijerph20032597.

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Objective: A pulse waveform is regarded as an information carrier of the cardiovascular system, which contains multiple interactive cardiovascular parameters reflecting physio-pathological states of bodies. Hence, multiple parameter analysis is increasingly meaningful to date but still cannot be easily achieved one by one due to the complex mapping between waveforms. This paper describes a new analysis method based on waveform recognition aimed for extracting multiple cardiovascular parameters to monitor public health. The objective of this new method is to deduce multiple cardiovascular parameters for a target pulse waveform based on waveform recognition to a most similar reference waveform in a given database or pattern library. Methods: The first part of the methodology includes building the sub-pattern libraries and training classifier. This provides a trained classifier and the sub-pattern library with reference pulse waveforms and known parameters. The second part is waveform analysis. The target waveform will be classified and output a state category being used to select the corresponding sub-pattern library with the same state. This will reduce subsequent recognition scope and computation costs. The mainstay of this new analysis method is improved dynamic time warping (DTW). This improved DTW and K-Nearest Neighbors (KNN) were applied to recognize the most similar waveform in the pattern library. Hence, cardiovascular parameters can be assigned accordingly from the most similar waveform in the pattern library. Results: Four hundred and thirty eight (438) randomly selected pulse waveforms were tested to verify the effectiveness of this method. The results show that the classification accuracy is 96.35%. Using statistical analysis to compare the target sample waveforms and the recognized reference ones from within the pattern library, most correlation coefficients are beyond 0.99. Each set of cardiovascular parameters was assessed using the Bland-Altman plot. The extracted cardiovascular parameters are in strong agreement with the original verifying the effectiveness of this new approach. Conclusion: This new method using waveform recognition shows promising results that can directly extract multiple cardiovascular parameters from waveforms with high accuracy. This new approach is efficient and effective and is very promising for future continuous monitoring of cardiovascular health.

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Kim, Hyoung-soo, Nathan A. Goodman, Junhyeong Bae, and Chankil Lee. "Classification waveform optimization for MIMO radar." IEICE Communications Express 6, no. 8 (2017): 501–6. http://dx.doi.org/10.1587/comex.2017xbl0080.

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Tora, Hakan, Gursel Karacor, and Baran Uslu. "Vowel Classification Based on Waveform Shapes." Advances in Science, Technology and Engineering Systems Journal 4, no. 3 (2019): 16–24. http://dx.doi.org/10.25046/aj040303.

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Sowelam, S. M., and A. H. Tewfik. "Waveform selection in radar target classification." IEEE Transactions on Information Theory 46, no. 3 (May 2000): 1014–29. http://dx.doi.org/10.1109/18.841178.

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Lee, An-Chen, and Ji1Ng-Shyang Chou. "Computer classification of the PCG waveform." International Journal of Systems Science 21, no. 3 (March 1, 1990): 593–609. http://dx.doi.org/10.1080/00207729008910391.

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Hong, Zhong, Kunhong Li, Mingjun Su, and Guangmin Hu. "Improved spectral clustering approach — A new tool for unsupervised seismic facies analysis of variable window length." Interpretation 9, no. 2 (April 7, 2021): T407—T420. http://dx.doi.org/10.1190/int-2020-0059.1.

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The traditional constant time window-based waveform classification method is a robust tool for seismic facies analysis. However, when the interval thickness is seismically variable, the fixed time window is not able to contain the complete geologic information of interest. Therefore, the constant time window-based waveform classification method is inapplicable to conduct seismic facies analysis. To expand the application scope of seismic waveform classification in the strata with varying thickness, we have proposed a novel scheme for unsupervised seismic facies analysis of variable window length. The input of the top and bottom horizons can guarantee the comprehensive geologic information of the target interval. Throughout the whole workflow, we use the dynamic time warping (DTW) distance to measure the similarities between seismic waveforms of different lengths. First, we improve the traditional spectral clustering algorithm by replacing the Euclidean distance with the DTW distance. Therefore, it can be applicable in the interval of variable thickness. Second, to solve the problem of large computation when applying the improved spectral clustering approach, we adopt the method of seismic data thinning based on the technology of the superpixel. We combine these two algorithms and perform the integrated workflow of improved spectral clustering. The experiments on synthetic data show that the proposed workflow outperforms the traditional fixed time window-based clustering algorithm in recognizing the boundaries of different lithologies and lithologic associations with varying thickness. The practical application shows great promise for reservoir characterization of interval with varying thickness. The plane map of waveform classification provides convincing reference to delineate reservoir distribution of the data set.

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Wang, Zhen, Yan Kun Wang, Man Luo, Kong Hong Ling, and Ya Ping Lin. "Carbonate Depositional Facies Analysis and Reservoir Prediction for Central Block in Pre-Caspian Basin." Advanced Materials Research 734-737 (August 2013): 305–10. http://dx.doi.org/10.4028/www.scientific.net/amr.734-737.305.

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In waveform classification for which abundant seismic data are fully used, neural network algorithm is applied to compare and classify the actual seismic waveforms by traces for one specific formation, so as to delineate the lateral variation of seismic signal in details and thus acquire the seismic facies maps corresponding to geologic characteristics. Moreover, through analysis of drilling data, logging data and depositional facies, the depositional facies belts are further divided for formation and lithologic reservoir prediction. Carbonate reservoir in the Central Block in the east margin of Pre-Caspian Basin is discussed as an example to introduce the application of waveform classification and depositional facies demarcation in the Carboniferous Carbonate reservoir. Favorable reservoir beds are also predicted, contributing to a big breakthrough for risk exploration in this area.

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Ogino, Akihiro, and Kiyoshi Onishi. "Vascular Waveform Analysis of Flap-Feeding Vessels Using Color Doppler Ultrasonography." Plastic Surgery International 2014 (April 7, 2014): 1–8. http://dx.doi.org/10.1155/2014/249670.

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We performed vascular waveform analysis of flap-feeding vessels using color Doppler ultrasonography and evaluated the blood flow in the flaps prior to surgery. Vascular waveform analysis was performed in 19 patients. The analyzed parameters included the vascular diameter, flow volume, flow velocity, resistance index, pulsatility index, and acceleration time. The arterial waveform was classified into 5 types based on the partially modified blood flow waveform classification reported by Hirai et al.; in particular, D-1a, D-1b, and D-2 were considered as normal waveforms. They were 4 patients which observed abnormal vascular waveform among 19 patients (D-4 : 1, D-3 : 1, and Poor detect : 2). The case which presented D-4 waveform changed the surgical procedure, and a favorable outcome was achieved. Muscle flap of the case which presented D-3 waveform was partially necrosed. The case which detected blood flow poorly was judged to be the vascular obstruction of the internal thoracic artery. In the evaluation of blood flow in flaps using color Doppler ultrasonography, determination of not only basic blood flow information, such as the vascular distribution and diameter and flow velocity, but also the flow volume, vascular resistance, and arterial waveform is essential to elucidate the hemodynamics of the flap.

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Lukovenkova, Olga, Yury Senkevich, Alexandra Solodchuk, and Albert Shcherbina. "Overview of processing and analysis methods for pulse geophysical signals." E3S Web of Conferences 196 (2020): 02023. http://dx.doi.org/10.1051/e3sconf/202019602023.

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The paper discusses the processing and analysis methods for the geoacoustic and electromagnetic emission pulse signals recorded for more than 20 years at the IKIR FEB RAS geodynamic proving ground (Kamchatka Peninsula). The methods for pulse detection, waveform reconstruction, pulse time-frequency analysis using adaptive sparse approximation, structural description of pulse waveforms and pulse classification are proposed. To detect pulses, the adaptive threshold scheme is used. It adjusts to the noise level of a processed signal. To analyze time-frequency structure of the pulses, the adaptive matching pursuit algorithm is used. To identify pulse waveform, the structural description method is proposed. It encodes pulses with special image matrices. The method of the identified pulses classification is considered. Since the methods for pulse structure analysis are sensitive to noise and distortions, the authors propose the method for pulse waveform reconstruction based on wavelet filtering. The geophysical signal information features determined during the analysis can be used to search for anomalies in the data, and then establish a relationship between these anomalies and deformation process dynamics, in particular, with earthquake development processes.

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Zygmuntowska, M., K. Khvorostovsky, V. Helm, and S. Sandven. "Waveform classification of airborne synthetic aperture radar altimeter over Arctic sea ice." Cryosphere 7, no. 4 (August 19, 2013): 1315–24. http://dx.doi.org/10.5194/tc-7-1315-2013.

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Abstract. Sea ice thickness is one of the most sensitive variables in the Arctic climate system. In order to quantify changes in sea ice thickness, CryoSat-2 was launched in 2010 carrying a Ku-band radar altimeter (SIRAL) designed to measure sea ice freeboard with a few centimeters accuracy. The instrument uses the synthetic aperture radar technique providing signals with a resolution of about 300 m along track. In this study, airborne Ku-band radar altimeter data over different sea ice types have been analyzed. A set of parameters has been defined to characterize the differences in strength and width of the returned power waveforms. With a Bayesian-based method, it is possible to classify about 80% of the waveforms from three parameters: maximum of the returned power waveform, the trailing edge width and pulse peakiness. Furthermore, the maximum of the power waveform can be used to reduce the number of false detections of leads, compared to the widely used pulse peakiness parameter. For the pulse peakiness the false classification rate is 12.6% while for the power maximum it is reduced to 6.5%. The ability to distinguish between different ice types and leads allows us to improve the freeboard retrieval and the conversion from freeboard into sea ice thickness, where surface type dependent values for the sea ice density and snow load can be used.

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Dutta, Surajit, Suvajit Ghosh, and Praveen Kumar Thakur. "A brief demonstration of a tool for SARAL/AltiKa waveform clustering." Journal of Geography and Cartography 2, no. 1 (June 1, 2019): 17. http://dx.doi.org/10.24294/jgc.v2i1.751.

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This article describes a classification tool to cluster SARAL/AltiKa waveforms. The tool was made using Python scripts. Radar altimetry systems (e.g., SARAL/AltiKa) measures the distance from the satellite centre to a target surface by calculating the satellite-to-surface round-trip time of a radar pulse. An altimeter waveform represents the energy reflected by the earth’s surface to the satellite antenna with respect to time. The tool clusters the altimetric waveforms data into desired groups. For the clustering, we used evolutionary minimize indexing function (EMIF) with k-means cluster mechanism. The idea was to develop a simple interface which takes the altimetry waveforms data from a folder as inputs and provides single value (using EMIF algorithm) for each waveform. These values are further used for clustering. This is a simple light weighted tool and user can easily interact with it.

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Issar, Deepa, Ryan C. Williamson, Sanjeev B. Khanna, and Matthew A. Smith. "A neural network for online spike classification that improves decoding accuracy." Journal of Neurophysiology 123, no. 4 (April 1, 2020): 1472–85. http://dx.doi.org/10.1152/jn.00641.2019.

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Separating neural signals from noise can improve brain-computer interface performance and stability. However, most algorithms for separating neural action potentials from noise are not suitable for use in real time and have shown mixed effects on decoding performance. With the goal of removing noise that impedes online decoding, we sought to automate the intuition of human spike-sorters to operate in real time with an easily tunable parameter governing the stringency with which spike waveforms are classified. We trained an artificial neural network with one hidden layer on neural waveforms that were hand-labeled as either spikes or noise. The network output was a likelihood metric for each waveform it classified, and we tuned the network’s stringency by varying the minimum likelihood value for a waveform to be considered a spike. Using the network’s labels to exclude noise waveforms, we decoded remembered target location during a memory-guided saccade task from electrode arrays implanted in prefrontal cortex of rhesus macaque monkeys. The network classified waveforms in real time, and its classifications were qualitatively similar to those of a human spike-sorter. Compared with decoding with threshold crossings, in most sessions we improved decoding performance by removing waveforms with low spike likelihood values. Furthermore, decoding with our network’s classifications became more beneficial as time since array implantation increased. Our classifier serves as a feasible preprocessing step, with little risk of harm, that could be applied to both off-line neural data analyses and online decoding. NEW & NOTEWORTHY Although there are many spike-sorting methods that isolate well-defined single units, these methods typically involve human intervention and have inconsistent effects on decoding. We used human classified neural waveforms as training data to create an artificial neural network that could be tuned to separate spikes from noise that impaired decoding. We found that this network operated in real time and was suitable for both off-line data processing and online decoding.

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Kogut, T., M. Weistock, and K. Bakuła. "CLASSIFICATION OF DATA FROM AIRBORNE LIDAR BATHYMETRY WITH RANDOM FOREST ALGORITHM BASED ON DIFFERENT FEATURE VECTORS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W16 (September 17, 2019): 143–48. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w16-143-2019.

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<p><strong>Abstract.</strong> Modern full-waveform laser bathymetric scanners offer the possibility of a practical application of airborne laser bathymetry (ALB) data algorithms as a valuable source of information in the study of the aquatic environment. The reliability of the obtained results and the efficiency of the classification depend on the applied features. The input data for the classifier should consist of variables that have the ability to discriminate within the data set, for the detection and classification of objects on the seabed. The automatic detection of underwater objects is based on machine learning solutions. In this paper, the ALB data were used to present a classification process based on the random forest algorithm. The classification was carried out using two independent approaches with two feature vectors. The quality of classifications based on the full-waveform features vector and the geometric features vector was compared. The efficiency of each classification was verified using a confusion matrix. The obtained efficiency of the point classification in both cases was about 100% for the water surface, 99.9% for the seabed and about 60% for underwater objects. Better results for the classification of objects were obtained for the features vector based on features obtained directly from full-waveform data than for the vector obtained from geometric relationships in the point cloud.</p>

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Tigges, Timo, Zenit Music, Alexandru Pielmus, Michael Klum, Aarne Feldheiser, Oliver Hunsicker, and Reinhold Orglmeister. "Classification of morphologic changes in photoplethysmographic waveforms." Current Directions in Biomedical Engineering 2, no. 1 (September 1, 2016): 203–7. http://dx.doi.org/10.1515/cdbme-2016-0046.

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AbstractAn ever increasing number of research is examining the question to what extent physiological information beyond the blood oxygen saturation could be drawn from the photoplethysmogram. One important approach to elicit that information from the photoplethysmogram is the analysis of its waveform. One prominent example for the value of photoplethysmographic waveform analysis in cardiovascular monitoring that has emerged is hemodynamic compensation assessment in the peri-operative setting or trauma situations, as digital pulse waveform dynamically changes with alterations in vascular tone or pulse wave velocity. In this work, we present an algorithm based on modern machine learning techniques that automatically finds individual digital volume pulses in photoplethysmographic signals and sorts them into one of the pulse classes defined by Dawber et al. We evaluate our approach based on two major datasets – a measurement study that we conducted ourselves as well as data from the PhysioNet MIMIC II database. As the results are satisfying we could demonstrate the capabilities of classification algorithms in the automated assessment of the digital volume pulse waveform measured by photoplethysmographic devices.

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Jia, Danbing, Dongyu Zhang, and Naimin Li. "Pulse Waveform Classification Using Support Vector Machine with Gaussian Time Warp Edit Distance Kernel." Computational and Mathematical Methods in Medicine 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/947254.

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Advances in signal processing techniques have provided effective tools for quantitative research in traditional Chinese pulse diagnosis. However, because of the inevitable intraclass variations of pulse patterns, the automatic classification of pulse waveforms has remained a difficult problem. Utilizing the new elastic metric, that is, time wrap edit distance (TWED), this paper proposes to address the problem under the support vector machines (SVM) framework by using the Gaussian TWED kernel function. The proposed method, SVM with GTWED kernel (GTWED-SVM), is evaluated on a dataset including 2470 pulse waveforms of five distinct patterns. The experimental results show that the proposed method achieves a lower average error rate than current pulse waveform classification methods.

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Zhang, Yu, Bei Wang, Jin Jing, Jian Zhang, Junzhong Zou, and Masatoshi Nakamura. "A Comparison Study on Multidomain EEG Features for Sleep Stage Classification." Computational Intelligence and Neuroscience 2017 (2017): 1–8. http://dx.doi.org/10.1155/2017/4574079.

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Feature extraction from physiological signals of EEG (electroencephalogram) is an essential part for sleep staging. In this study, multidomain feature extraction was investigated based on time domain analysis, nonlinear analysis, and frequency domain analysis. Unlike the traditional feature calculation in time domain, a sequence merging method was developed as a preprocessing procedure. The objective is to eliminate the clutter waveform and highlight the characteristic waveform for further analysis. The numbers of the characteristic activities were extracted as the features from time domain. The contributions of features from different domains to the sleep stages were compared. The effectiveness was further analyzed by automatic sleep stage classification and compared with the visual inspection. The overnight clinical sleep EEG recordings of 3 patients after the treatment of Continuous Positive Airway Pressure (CPAP) were tested. The obtained results showed that the developed method can highlight the characteristic activity which is useful for both automatic sleep staging and visual inspection. Furthermore, it can be a training tool for better understanding the appearance of characteristic waveforms from raw sleep EEG which is mixed and complex in time domain.

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Xin, Chong-wei, Fu-xing Jiang, and Guo-dong Jin. "Microseismic Signal Classification Based on Artificial Neural Networks." Shock and Vibration 2021 (July 28, 2021): 1–14. http://dx.doi.org/10.1155/2021/6697948.

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The classification of multichannel microseismic waveform is essential for real-time monitoring and hazard prediction. The accuracy and efficiency could not be guaranteed by manual identification. Thus, based on 37310 waveform data of Junde Coal Mine, eight features of statistics, spectrum, and waveform were extracted to generate a complete data set. An automatic classification algorithm based on artificial neural networks (ANNs) has been proposed. The model presented an excellent performance in identifying three preclassified signals in the test set. Operated with two hidden layers and the Logistic activation function, the multiclass area under the receiver operating characteristic curve (AUC) reached 98.6%.

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A. Khalil, Mohamed. "Groundwater Classification by Using Fourier Analysis." Global Journal of Earth Science and Engineering 9 (August 22, 2022): 65–73. http://dx.doi.org/10.15377/2409-5710.2022.09.5.

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The article illustrates a statistical technique for the visual representation of geochemical data. Quaternary and Pre-Quaternary groundwater samples from Northern Sinai Peninsula, Egypt, were interpreted statistically using Andrews plots, which use Fourier analysis to transform and represent a set of multivariate data by a waveform pattern. The resulting waveform patterns were classified into low, middle, and high amplitudes, following up the increase in the total dissolved solids of the samples. Comparison with the traditional hydrochemical polygonal Stiff diagrams resulted in a complete matching. The proposed mixing between the Quaternary and Pre-Quaternary aquifers has been proved via the similarity of waveform patterns of the mixed water. The application of Andrews plots is investigated by comparison with the Stiff conventional diagrams. The correlation between different amplitudes and the TDS value of every sample indicates that the amplitude increases with the increase in the salinity.

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Saito, Naoki, and Ronald R. Coifman. "Extraction of geological information from acoustic well‐logging waveforms using time‐frequency wavelets." GEOPHYSICS 62, no. 6 (November 1997): 1921–30. http://dx.doi.org/10.1190/1.1444292.

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Recently developed classification and regression methods are applied to extract geological information from acoustic well‐logging waveforms. First, acoustic waveforms are classified into the ones propagated through sandstones and the ones propagated through shale using the local discriminant basis (LDB) method. Next, the volume fractions of minerals are estimated (e.g., quartz and gas) at each depth using the local regression basis (LRB) method. These methods first analyze the waveforms by decomposing them into a redundant set of time‐frequency wavelets, i.e., the orthogonal wiggle traces localized in both time and frequency. Then, they automatically extract the local waveform features useful for such classification and estimation or regression. Finally, these features are fed into conventional classifiers or predictors. Because these extracted features are localized in time and frequency, they allow intuitive interpretation. Using the field data set, we found that it was possible to classify the waveforms without error into sandstone and shale classes using the LDB method. It was more difficult, however, to estimate the volume fractions, in particular, that of gas, from the extracted waveform features. We also compared the performance of the LRB method with the prediction based on the commonly used ratio of compressional and shear‐wave velocities, [Formula: see text], and found that our method performed better than the [Formula: see text] method.

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Ghanbari, Yasser, Panos E. Papamichalis, and Larry Spence. "Graph-Laplacian Features for Neural Waveform Classification." IEEE Transactions on Biomedical Engineering 58, no. 5 (May 2011): 1365–72. http://dx.doi.org/10.1109/tbme.2010.2090349.

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Kim, H. ‐S, N. A. Goodman, C. K. Lee, and S. ‐I Yang. "Improved waveform design for radar target classification." Electronics Letters 53, no. 13 (June 2017): 879–81. http://dx.doi.org/10.1049/el.2017.0536.

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Song, Chengyun, Zhining Liu, Yaojun Wang, Xingming Li, and Guangmin Hu. "Multi-waveform classification for seismic facies analysis." Computers & Geosciences 101 (April 2017): 1–9. http://dx.doi.org/10.1016/j.cageo.2016.12.014.

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Chen, Wei, Min Bai, and Hui Song. "Seismic noise attenuation based on waveform classification." Journal of Applied Geophysics 167 (August 2019): 118–27. http://dx.doi.org/10.1016/j.jappgeo.2019.05.014.

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Walenczykowska, Marta, and Adam Kawalec. "Application of Continuous Wavelet Transform and Artificial Naural Network for Automatic Radar Signal Recognition." Sensors 22, no. 19 (September 30, 2022): 7434. http://dx.doi.org/10.3390/s22197434.

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This article aims to propose an algorithm for the automatic recognition of selected radar signals. The algorithm can find application in areas such as Electronic Warfare (EW), where automatic recognition of the type of intra-pulse modulation or the type of emitter operation mode can aid the decision-making process. The simulations carried out included the analysis of the classification possibilities of linear frequency modulated pulsed waveform (LFMPW), stepped frequency modulated pulsed waveform (SFMPW), phase coded pulsed waveform (PCPW), rectangular pulsed waveforms (RPW), frequency modulated continuous wave (FMCW), continuous wave (CW), Stepped Frequency Continuous Wave SFCW) and Phase Coded Continuous Waveform (PCCW). The algorithm proposed in this paper is based on the use of continuous wavelet transform (CWT) coefficients and higher-order statistics (HOS) in the feature determination of selected signals. The Principal Component Analysis (PCA) method was used for dimensionality reduction. An artificial neural network was then used as a classifier. Simulation studies took into account the presence of noise interference with signal-to-noise ratio (SNR) in the range from −5 to 10 dB. Finally, the obtained classification efficiency is presented in the form of a confusion matrix. The simulation results show a high recognition test accuracy, above 99% with a signal-to-noise ratio greater than 0 dB. The article also deals with the selection of the type and parameters of the wavelet. The authors also point to the problems encountered during the research and examples of how to solve them.

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Pashaei, Mohammad, Michael J. Starek, Craig L. Glennie, and Jacob Berryhill. "Terrestrial Lidar Data Classification Based on Raw Waveform Samples Versus Online Waveform Attributes." IEEE Transactions on Geoscience and Remote Sensing 60 (2022): 1–19. http://dx.doi.org/10.1109/tgrs.2021.3132356.

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Yang, Le, Mingsen Lin, Qinhuo Liu, and Delu Pan. "A coastal altimetry retracking strategy based on waveform classification and sub-waveform extraction." International Journal of Remote Sensing 33, no. 24 (July 9, 2012): 7806–19. http://dx.doi.org/10.1080/01431161.2012.701350.

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Yeh, Yun Chi, Tsung Fu Chien, Cheng Yuan Chang, and Tsui Shiun Chu. "A Mahalanobis Distance Measurement Method to Analyze Current Waveform for Determining the Motor’s Quality Types." Applied Mechanics and Materials 870 (September 2017): 317–22. http://dx.doi.org/10.4028/www.scientific.net/amm.870.317.

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This study proposes a Mahalanobis Distance Measurement (MDM) method to analyze current waveform for determining the motor’s quality types. The MDM method consists of three major stages: (i) the preprocessing stage which is for enlarging motor current waveforms’ amplitude and eliminating noises, and includes signal amplitude amplifier, filter circuit (eliminating noises), and analog-to-digital converter (ADC) parts, (ii) the qualitative features stage which is for qualitative feature selection on motor current waveforms, and (iii) the classification stage which is for determining motor quality types using the MDM method. It can recognize defective motors and their defective types in less than 0.5 second. In the experiment, the total classification accuracy (TCA) was approximately 99.03% in average. The proposed method has the advantages of good detection results, no complex mathematic computations, hi-speed, and hi-reliability.

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Zhang, Zhou Sheng, Yi Xun Xu, and Ai Qing Ma. "Measurements and Classifications of PD Pulse on HDPE Specimens during Degradation." Advanced Materials Research 354-355 (October 2011): 1235–38. http://dx.doi.org/10.4028/www.scientific.net/amr.354-355.1235.

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Discharge may take place in high voltage apparatuses in the form of void discharge, corona discharge and surface discharge. Understanding of the discharge is very important to know the condition of the insulation. Partial discharge (PD) current pulse waveforms contain essentially all the available information concerning the PD generation and thus the available information concerning the physical mechanism of degradation and breakdown of insulation. In this paper, three types of discharge model for polyethelene insulation material are constructed and the PD current pulse waveforms are extracted from experiments. In order to obtain pulse waveform characteristics, the extracted pulse waveforms are pre-selected and normalized, and then a Sampling Counting Ratio (SCR) classification technique is investigated to reveal the multi-peak and oscillation information about the PD current pulse waveforms. Results show that the classification would enable us to estimate the progress and degree of the degradation.

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Saiz-Pérez, Ainara, Alejandro Torres-Forné, and José A. Font. "Classification of core-collapse supernova explosions with learned dictionaries." Monthly Notices of the Royal Astronomical Society 512, no. 3 (March 16, 2022): 3815–27. http://dx.doi.org/10.1093/mnras/stac698.

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ABSTRACT Core-collapse supernovae (CCSNs) are a prime source of gravitational waves. Estimations of their typical frequencies make them perfect targets for the current network of advanced, ground-based detectors. A successful detection could potentially reveal the underlying explosion mechanism through the analysis of the waveform. This has been illustrated using the Supernova Model Evidence Extractor (SMEE), an algorithm based on principal component analysis and Bayesian model selection. Here, we present a complementary approach to SMEE based on (supervised) dictionary-learning and show that it is able to reconstruct and classify CCSN signals according to their morphology. Our waveform signals are obtained from (a) two publicly available catalogues built from numerical simulations of neutrino-driven (Mur) and magneto-rotational (Dim) CCSN explosions and (b) from a third ‘mock’ catalogue of simulated sine-Gaussian (SG) waveforms. All of these signals are injected into coloured Gaussian noise to simulate the background noise of Advanced LIGO in its broad-band configuration and scaled to a freely specifiable signal-to-noise ratio (SNR). We show that our approach correctly classifies signals from all three dictionaries. In particular, for SNR = 15–20, we obtain perfect matches for both Dim and SG signals and about 85 per cent true classifications for Mur signals. These results are comparable to those reported by SMEE for the same CCSN signals when those are injected in only one LIGO detector. We discuss the main limitations of our approach as well as possible improvements.

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Fawcett, Timothy J., Chad S. Cooper, Ryan J. Longenecker, and Joseph P. Walton. "Machine learning, waveform preprocessing and feature extraction methods for classification of acoustic startle waveforms." MethodsX 8 (2021): 101166. http://dx.doi.org/10.1016/j.mex.2020.101166.

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Wang, Mingwei, Ziyin Wu, Fanlin Yang, Yue Ma, Xiao Wang, and Dineng Zhao. "Multifeature Extraction and Seafloor Classification Combining LiDAR and MBES Data around Yuanzhi Island in the South China Sea." Sensors 18, no. 11 (November 8, 2018): 3828. http://dx.doi.org/10.3390/s18113828.

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Airborne light detection and ranging (LiDAR) full waveforms and multibeam echo sounding (MBES) backscatter data contain rich information about seafloor features and are important data sources representing seafloor topography and geomorphology. Currently, to classify seafloor types using MBES, curve features are extracted from backscatter angle responses or grayscale, and texture features are extracted from backscatter images based on gray level co-occurrence matrix (GLCM). To classify seafloor types using LiDAR, waveform features are extracted from bottom returns. This paper comprehensively considers the features of both LiDAR waveforms and MBES backscatter images that include the eight feature factors of the LiDAR full waveforms (amplitude, peak location, full width half maximum (FWHM), skewness, kurtosis, area, distance, and cross-section) and the eight feature factors of MBES backscatter images (mean, standard deviation (STD), entropy, homogeneity, contrast, angular second moment (ASM), correlation, and dissimilarity). Based on a support vector machine (SVM) algorithm with different kernel functions and penalty factors, a new seafloor classification method that merges multiple features is proposed for a beneficial exploration of acousto-optic fusion. The experimental results of the seafloor classification around Yuanzhi Island in the South China Sea indicate that, when LiDAR waveform features are merged (using an Optech Aquarius system) with MBES backscatter image features (using a Sonic 2024) to classify three types of sands, reefs, and rocks, the overall accuracy is improved to 96.71%, and the kappa reaches 0.94. After merging multiple features, the classification accuracies of the SVM, genetic algorithm SVM (GA-SVM) and particle swarm optimization SVM (PSO-SVM) increase by an average of 9.06%, 3.60%, and 2.75%, respectively.

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Allen, J., and A. Murray. "Comparison of three arterial pulse waveform classification techniques." Journal of Medical Engineering & Technology 20, no. 3 (January 1996): 109–14. http://dx.doi.org/10.3109/03091909609008388.

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Lodwick, Bonnie, and Lawrence Grant-Woolley. "Waveform classification as a pseudo for reservoir thickness." ASEG Extended Abstracts 2016, no. 1 (December 2016): 1–4. http://dx.doi.org/10.1071/aseg2016ab303.

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Song, Chengyun, Zhining Liu, Yaojun Wang, Feng Xu, Xingming Li, and Guangmin Hu. "Adaptive phase k-means algorithm for waveform classification." Exploration Geophysics 49, no. 2 (April 2018): 213–19. http://dx.doi.org/10.1071/eg16111.

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Chen, Yang, Dong-Jie Zhao, Zi-Yang Wang, Zhong-Yi Wang, Guiliang Tang, and Lan Huang. "Plant Electrical Signal Classification Based on Waveform Similarity." Algorithms 9, no. 4 (October 15, 2016): 70. http://dx.doi.org/10.3390/a9040070.

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Azadbakht, M., C. S. Fraser, and K. Khoshelham. "Improved Urban Scene Classification Using Full-Waveform Lidar." Photogrammetric Engineering & Remote Sensing 82, no. 12 (December 1, 2016): 973–80. http://dx.doi.org/10.14358/pers.82.12.973.

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Journal articles on the topic 'Waveform Classification'

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