Relevant bibliographies by topics / Waveform Classification

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers
  5. Reports

Academic literature on the topic 'Waveform Classification'

Author: Grafiati
Published: 4 June 2021
Last updated: 21 February 2023

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Journal articles on the topic "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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Dissertations / Theses on the topic "Waveform Classification"

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Miao, Jianwei. "Component feature-based digital waveform analysis and classification." Diss., Georgia Institute of Technology, 1996. http://hdl.handle.net/1853/13742.

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Wang, Ke Nan. "Illumination Waveform Design for Non-Gaussian Multi-Hypothesis Target Classification in Cognitive Radar." Thesis, Monterey, California. Naval Postgraduate School, 2012. http://hdl.handle.net/10945/7427.

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A cognitive radar (CR) system is one that observes and learns from the environment, then uses a dynamic closed-loop feedback mechanism to adapt the illumination waveform so as to provide system performance improvements over traditional radar systems. A CR system that performs multiple hypothesis target classification and exploits the spectral sparsity of correlated narrowband target responses to achieve significant performance improvements over traditional radars that use wideband illumination pulses was recently developed. This CR system, which was designed for Gaussian target responses, is extended to non-Gaussian targets. In this thesis, the CR system is generalized to deal effectively with arbitrary non-Gaussian distributed target responses via two key contributions (1) an important statistical expected value operation that is usually evaluated in closed form is evaluated numerically using an ensemble averaging operation, and (2) a powerful new statistical sampling algorithm and a kernel density estimator are applied to draw complex target samples from target distributions specified by both a desired power spectral density and an arbitrary desired probability density function. Simulations using non-Gaussian targets demonstrate very effective algorithm performance. As expected, this performance gain is realized at the expense of increased computational complexity.
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Nieh, Jo-Yen. "Integrated range-Doppler map and extended target classification with adaptive waveform for cognitive radar." Thesis, Monterey, California: Naval Postgraduate School, 2014. http://hdl.handle.net/10945/44632.

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Approved for public release; distribution is unlimited
We set out to design an extended target classification scheme while determining the target’s range-and-Doppler location with the use of adaptive waveform for a closed-loop cognitive radar platform. To that end, this work is divided into three objectives: 1) in support of determining range-Doppler locations, we investigate the ambiguity function of the matched waveform called eigenwaveform, 2) in support of target classification, we look at an adaptive waveform technique called probability-weighted eigenwaveform (PWE) and introduce two new waveforms, and 3) we design an integrated range-Doppler map and extended target classification scheme. In this work, we show that the fundamental properties of ambiguity function for extended targets are different when compared to classical waveforms for point targets. We improve on the adaptive waveform called maximum a posteriori PWE and introduce two new waveforms called match-filtered PWE and two-stage PWE. We propose an integrated range-Doppler map and identification scheme for multiple moving extended targets. Performance comparisons in terms of joint probability of identification and determining targets’ range-Doppler locations with traditional wideband waveform and the three PWE-based waveforms are shown. It is shown that the three PWE-based waveforms perform better than the classical wideband waveform.
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Simões, Gaspar Ivan. "Waveform Advancements and Synchronization Techniques for Generalized Frequency Division Multiplexing." Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2016. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-201875.

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To enable a new level of connectivity among machines as well as between people and machines, future wireless applications will demand higher requirements on data rates, response time, and reliability from the communication system. This will lead to a different system design, comprising a wide range of deployment scenarios. One important aspect is the evolution of physical layer (PHY), specifically the waveform modulation. The novel generalized frequency division multiplexing (GFDM) technique is a prominent proposal for a flexible block filtered multicarrier modulation. This thesis introduces an advanced GFDM concept that enables the emulation of other prominent waveform candidates in scenarios where they perform best. Hence, a unique modulation framework is presented that is capable of addressing a wide range of scenarios and to upgrade the PHY for 5G networks. In particular, for a subset of system parameters of the modulation framework, the problem of symbol time offset (STO) and carrier frequency offset (CFO) estimation is investigated and synchronization approaches, which can operate in burst and continuous transmissions, are designed. The first part of this work presents the modulation principles of prominent 5G candidate waveforms and then focuses on the GFDM basic and advanced attributes. The GFDM concept is extended towards the use of OQAM, introducing the novel frequency-shift OQAM-GFDM, and a new low complexity model based on signal processing carried out in the time domain. A new prototype filter proposal highlights the benefits obtained in terms of a reduced out-of-band (OOB) radiation and more attractive hardware implementation cost. With proper parameterization of the advanced GFDM, the achieved gains are applicable to other filtered OFDM waveforms. In the second part, a search approach for estimating STO and CFO in GFDM is evaluated. A self-interference metric is proposed to quantify the effective SNR penalty caused by the residual time and frequency misalignment or intrinsic inter-symbol interference (ISI) and inter-carrier interference (ICI) for arbitrary pulse shape design in GFDM. In particular, the ICI can be used as a non-data aided approach for frequency estimation. Then, GFDM training sequences, defined either as an isolated preamble or embedded as a midamble or pseudo-circular pre/post-amble, are designed. Simulations show better OOB emission and good estimation results, either comparable or superior, to state-of-the-art OFDM system in wireless channels.
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Alexander, Cicimol. "Classification of full-waveform airborne laser scanning data and extraction of attributes of vegetation for topographic mapping." Thesis, University of Leicester, 2010. http://hdl.handle.net/2381/9950.

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There is an increasing demand for urban vegetation mapping, and airborne laser scanning (ALS) has the unique ability to provide geo-referenced three-dimensional data useful for mapping of surface features. This thesis examines the ability of full-waveform and discrete return ALS point data to distinguish urban surface features, and represent the three-dimensional attributes of vegetation at different scales in a vector-based GIS environment. Two full-waveform datasets, at a wavelength of 1550 nm, and a discrete return dataset, at 1064 nm, are used. Points extracted from the first full-waveform dataset are classified with k-means clustering and decision tree into vegetation, buildings and roads, based on the attributes of individual points and the relationships between neighbouring points. A decision tree is shown to perform significantly better (74.62%) than k-means clustering (51.59%) based on the overall accuracies. Grass and paved areas could be distinguished better using intensity from discrete return data than amplitude from full-waveform data, both values proportional to the strength of the return signal. The differences in the signatures of surfaces could be related to the wavelengths of the lasers, and need to be explored further. Calibration of intensity is currently possible only with full-waveform data. When the decision tree is applied on the second full-waveform dataset, the backscatter coefficient proves to be a more useful attribute than amplitude, pointing to the need for calibration if a classification method using intensity is to be applied on datasets with different scanning geometries. A vector-based approach for delineating tree crowns is developed and implemented at three scales. The first scale provides a good estimation of the tree crown area and structure, suitable for estimating biomass and canopy gaps. The third scale identifies the number of trees and their locations and can be used for modelling individual trees.
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Hokanson, William H. "Identifying Complex Fluvial Sandstone Reservoirs Using Core, Well Log, and 3D Seismic Data: Cretaceous Cedar Mountain and Dakota Formations, Southern Uinta Basin, Utah." BYU ScholarsArchive, 2011. https://scholarsarchive.byu.edu/etd/2597.

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The Cedar Mountain and Dakota Formations are significant gas producers in the southern Uinta Basin of Utah. To date, however, predicting the stratigraphic distribution and lateral extent of potential gas-bearing channel sandstone reservoirs in these fluvial units has proven difficult due to their complex architecture, and the limited spacing of wells in the region. A new strategy to correlate the Cedar Mountain and Dakota Formations has been developed using core, well-log, and 3D seismic data. The detailed stratigraphy and sedimentology of the interval were interpreted using descriptions of a near continuous core of the Dakota Formation from the study area. The gamma-ray and density-porosity log signatures of interpreted mud-dominated overbank, coal-bearing overbank, and channel sandstone intervals from the cored well were used to identify the same lithologies in nearby wells and correlate similar stratal packages across the study area. Data from three 3D seismic surveys covering approximately 140 mi2 (225 km2) of the study area were utilized to generate spectral decomposition, waveform classification, and percent less-than-threshold attributes of the Dakota-Cedar Mountain interval. These individual attributes were combined to create a composite attribute that was merged with interpreted lithological data from the well-log correlations. The overall process resulted in a high-resolution correlation of the Dakota-Cedar Mountain interval that permitted the identification and mapping of fluvial-channel reservoir fairways and channel belts throughout the study area. In the future, the strategy employed in this study may result in improved well-success rates in the southern Uinta Basin and assist in more detailed reconstructions of the Cedar Mountain and Dakota Formation depositional systems.
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Chen, Qinqin. "Cognitive Gateway to Promote Interoperability, Coverage and Throughput in Heterogeneous Communication Systems." Diss., Virginia Tech, 2009. http://hdl.handle.net/10919/30216.

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With the reality that diverse air interfaces and dissimilar access networks coexist, accompanied by the trend that dynamic spectrum access (DSA) is allowed and will be gradually employed, cognition and cooperation form a promising framework to achieve the ideality of seamless ubiquitous connectivity in future communication networks. In this dissertation, the cognitive gateway (CG), conceived as a special cognitive radio (CR) node, is proposed and designed to facilitate universal interoperability among incompatible waveforms. A proof-of-concept prototype is built and tested. Located in places where various communication nodes and diverse access networks coexist, the CG can be easily set up and works like a network server with differentiated service (Diffserv) architecture to provide automatic traffic relaying and link establishment. The author extracts a scalable '“source-CG-destination“ snapshot from the entire network and investigates the key enabling technologies for such a snapshot. The CG features provide universal interoperability, which is enabled by a generic waveform representation format and the reconfigurable software defined radio platform. According to the trend of an all IP-based solution for future communication systems, the term “waveform“ in this dissertation has been defined as a protocol stack specification suite. The author gives a generic waveform representation format based on the five-layer TCP/IP protocol stack architecture. This format can represent the waveforms used by Ethernet, WiFi, cellular system, P25, cognitive radios etc. A significant advantage of CG over other interoperability solutions lies in its autonomy, which is supported by appropriate signaling processes and automatic waveform identification. The service process in a CG is usually initiated by the users who send requests via their own waveforms. These requests are transmitted during the signaling procedures. The complete operating procedure of a CG is depicted as a waveform-oriented cognition loop, which is primarily executed by the waveform identifier, scenario analyzer, central controller, and waveform converter together. The author details the service process initialized by a primary user (e.g. legacy public safety radio) and that initialized by a secondary user (e.g. CR), and describes the signaling procedures between CG and clients for the accomplishment of CG discovery, user registration and un-registration, link establishment, communication resumption, service termination, route discovery, etc. From the waveforms conveyed during the signaling procedures, the waveform identifier extracts the parameters that can be used for a CG to identify the source waveform and the destination waveform. These parameters are called “waveform indicators.“ The author analyzes the four types of waveforms of interest and outlines the waveform indicators for different types of communication initiators. In particular, a multi-layer waveform identifier is designed for a CG to extract the waveform indicators from the signaling messages. For the physical layer signal recognition, a Universal Classification Synchronization (UCS) system has been invented. UCS is conceived as a self-contained system which can detect, classify, synchronize with a received signal and provide all parameters needed for physical layer demodulation without prior information from the transmitter. Currently, it can accommodate the modulations including AM, FM, FSK, MPSK, QAM and OFDM. The design and implementation details of a UCS have been presented. The designed system has been verified by over-the-air (OTA) experiments and its performance has been evaluated by theoretical analysis and software simulation. UCS can be ported to different platforms and can be applied for various scenarios. An underlying assumption for UCS is that the target signal is transmitted continually. However, it is not the case for a CG since the detection objects of a CG are signaling messages. In order to ensure higher recognition accuracy, signaling efficiency, and lower signaling overhead, the author addresses the key issues for signaling scheme design and their dependence on waveform identification strategy. In a CG, waveform transformation (WT) is the last step of the link establishment process. The resources required for transformation of waveform pairs, together with the application priority, constitute the major factors that determine the link control and scheduling scheme in a CG. The author sorts different WT into five categories and describes the details of implementing the four typical types of WT (including physical layer analog – analog gateway, up to link layer digital – digital gateway, up-to-network-layer digital gateway, and Voice over IP (VoIP) – an up to transport layer gateway) in a practical CG prototype. The issues that include resource management and link scheduling have also been addressed. This dissertation presents a CG prototype implemented on the basis of GNU Radio plus multiple USRPs. In particular, the service process of a CG is modeled as a two-stage tandem queue, where the waveform identifier queues at the first stage can be described as M/D/1/1 models and the waveform converter queue at the second stage can be described as G/M/K/K model. Based on these models, the author derives the theoretical block probability and throughput of a CG. Although the “source-CG-destination” snapshot considers only neighboring nodes which are one-hop away from the CG, it is scalable to form larger networks. CG can work in either ad-hoc or infrastructure mode. Utilizing its capabilities, CG nodes can be placed in different network architectures/topologies to provide auxiliary connectivity. Multi-hop cooperative relaying via CGs will be an interesting research topic deserving further investigation.
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Buchenroth, Anthony. "Ambiguity-Based Classification of Phase Modulated Waveforms." Wright State University / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=wright1453302765.

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Milluzzo, Vincenzo. "Seismic chacterization of Vulcano island and Aeolian area by tectonic and seismo-volcanic events." Doctoral thesis, Università di Catania, 2013. http://hdl.handle.net/10761/1330.

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We investigated the relationship between seismo-volcanic events, recorded at La Fossa crater of Vulcano (Aeolian Islands, Italy) during 2004-2009, and the dynamics of the hydrothermal system. During the period of study, six episodes of increasing numbers of seismo-volcanic events took place at the same time as geothermal and geochemical anomalies were observed. These geothermal and geochemical anomalies have been interpreted as resulting from an increasing deep magmatic component of the hydrothermal fluids. Four classes of seismic events (long period, high frequency, monochromatic and tornillos events), characterised by different spectral content and various similarity of the waveforms, have been recognised. These events, clustered mainly below La Fossa crater area at depths of 0.5 1.1 km b.s.l., were space-distributed according to the classes. Based on their features, we can infer that such events at Vulcano are related to two different source mechanisms: (1) fracturing processes of rocks and (2) resonance of cracks (or conduits) filled with hydrothermal fluid. In the light of these source mechanisms, the increase in the number of events, at the same time as geochemical and geothermal anomalies were observed, was interpreted as the result of an increasing magmatic component of the hydrothermal fluids, implying an increase of their flux. Indeed, such variation caused an increase of both the pore pressure within the rocks of the volcanic system and the amount of ascending fluids. Increased pore pressures gave rise to fracturing processes, while the increased fluid flux favoured resonance and vibration processes in cracks and conduits. Finally, a gradual temporal variation of the waveform of the hybrid events (one of the subclasses of long period events) was observed, likely caused by heating and drying of the hydrothermal system. After careful analysis of the seismo-volcanic events of the Aeolian Islands area, the attention was paid to the tectonic events, in order to find possible relationships with the volcanic activity in the area. The aim of this part of the thesis was to identify spatial clusters of earthquakes, locate active seismogenic zone and their relationships with the volcanic activity in the Aeolian Islands. High precision locations were performed in the present thesis, by applying the concept of the velocity model-hypocentres joint inversion and earthquake relocations, along with an analysis of the fault plane solutions. In order to improve our knowledge on the active seismo-tectonics areas we exploited a dataset encompassing 351 events recorded during a 17 year period (1993-2010). Overall, our results show that part of the seismicity is clustered along active seismogenic structures that concur with the main regional tectonic trends whose activity furnishes new elements to better understand the dynamics of the area. A cluster of 24 events in the northern part of Vulcano, NE-SW oriented, marks the presence of a structure that seems to play a key role in magma uprising at Vulcano. These earthquakes suggest the existence of a seismogenic structure (passing just below Vulcanello), which could be interpreted as a discontinuity linking the two magma accumulation zones, thereby representing a possible preferential pathway along which magma may intrude as well as being responsible for fluid migration toward the surface. The results presented in this thesis suggest that the comparison of seismic, ground deformation and temperature data can be useful for better understanding the dynamics of a complex volcano-hydrothermal system, including a better definition of the origin of a volcano unrest, and hence for improving the estimation of the level of the local volcanic hazard.
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De, Voir Christopher S. "Wavelet Based Feature Extraction and Dimension Reduction for the Classification of Human Cardiac Electrogram Depolarization Waveforms." PDXScholar, 2005. https://pdxscholar.library.pdx.edu/open_access_etds/1740.

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An essential task for a pacemaker or implantable defibrillator is the accurate identification of rhythm categories so that the correct electrotherapy can be administered. Because some rhythms cause a rapid dangerous drop in cardiac output, it is necessary to categorize depolarization waveforms on a beat-to-beat basis to accomplish rhythm classification as rapidly as possible. In this thesis, a depolarization waveform classifier based on the Lifting Line Wavelet Transform is described. It overcomes problems in existing rate-based event classifiers; namely, (1) they are insensitive to the conduction path of the heart rhythm and (2) they are not robust to pseudo-events. The performance of the Lifting Line Wavelet Transform based classifier is illustrated with representative examples. Although rate based methods of event categorization have served well in implanted devices, these methods suffer in sensitivity and specificity when atrial, and ventricular rates are similar. Human experts differentiate rhythms by morphological features of strip chart electrocardiograms. The wavelet transform is a simple approximation of this human expert analysis function because it correlates distinct morphological features at multiple scales. The accuracy of implanted rhythm determination can then be improved by using human-appreciable time domain features enhanced by time scale decomposition of depolarization waveforms. The purpose of the present work was to determine the feasibility of implementing such a system on a limited-resolution platform. 78 patient recordings were split into equal segments of reference, confirmation, and evaluation sets. Each recording had a sampling rate of 512Hz, and a significant change in rhythm in the recording. The wavelet feature generator implemented in Matlab performs anti-alias pre-filtering, quantization, and threshold-based event detection, to produce indications of events to submit to wavelet transformation. The receiver operating characteristic curve was used to rank the discriminating power of the feature accomplishing dimension reduction. Accuracy was used to confirm the feature choice. Evaluation accuracy was greater than or equal to 95% over the IEGM recordings.
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Book chapters on the topic "Waveform Classification"

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Tanaka, Gouhei, Ryosho Nakane, Toshiyuki Yamane, Seiji Takeda, Daiju Nakano, Shigeru Nakagawa, and Akira Hirose. "Waveform Classification by Memristive Reservoir Computing." In Neural Information Processing, 457–65. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70093-9_48.

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Lee, Chang-Shing, and Mei-Hui Wang. "An Ontology-Based Intelligent Agent for Respiratory Waveform Classification." In Advances in Applied Artificial Intelligence, 1240–48. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11779568_131.

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Patil, Hemprasad Y., Priyanka B. Patil, Seema R. Baji, and Rohini S. Darade. "EEG Waveform Classification Using Transform Domain Features and SVM." In Advances in Intelligent Systems and Computing, 791–98. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1513-8_80.

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Zhang, Dongyu, Wangmeng Zuo, Yanlai Li, and Naimin Li. "Pulse Waveform Classification Using ERP-Based Difference-Weighted KNN Classifier." In Lecture Notes in Computer Science, 191–200. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13923-9_20.

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Niemeyer, Joachim, Jan Dirk Wegner, Clément Mallet, Franz Rottensteiner, and Uwe Soergel. "Conditional Random Fields for Urban Scene Classification with Full Waveform LiDAR Data." In Photogrammetric Image Analysis, 233–44. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24393-6_20.

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Zahraa, Afiqah, and Deva Ghosh. "Seismic Waveform Classification of Reservoir Properties Using Geological Facies Through Neural Network." In ICIPEG 2016, 525–35. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-3650-7_46.

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Maset, Eleonora, Roberto Carniel, and Fabio Crosilla. "Unsupervised Classification of Raw Full-Waveform Airborne Lidar Data by Self Organizing Maps." In Image Analysis and Processing — ICIAP 2015, 62–72. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23231-7_6.

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Zhou, Bo, Jing Zhao, Huanyao Dai, and Bin Jiao. "A Method for Optimization Design of Cognitive Radar Waveform of Non-Gauss Target Classification." In Wireless Communications, Networking and Applications, 821–27. New Delhi: Springer India, 2015. http://dx.doi.org/10.1007/978-81-322-2580-5_74.

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Gao, Chao, Jiaquan Wang, Xiao Zhou, Fang Xiao, and Qiming Ma. "Classification of Lightning Electric Field Waveform Based on Deep Residual One-Dimensional Convolutional Network." In Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery, 1648–59. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-70665-4_179.

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Wang, Kuanquan, Lu Wang, Dianhui Wang, and Lisheng Xu. "SVM Classification for Discriminating Cardiovascular Disease Patients from Non-cardiovascular Disease Controls Using Pulse Waveform Variability Analysis." In Lecture Notes in Computer Science, 109–19. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30549-1_10.

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Conference papers on the topic "Waveform Classification"

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Priezzhev, Ivan, and Surender Manral. "3D Seismic waveform classification." In Istanbul 2012 - International Geophysical Conference and Oil & Gas Exhibition. Society of Exploration Geophysicists and The Chamber of Geophysical Engineers of Turkey, 2012. http://dx.doi.org/10.1190/ist092012-001.92.

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Vespe, M., C. J. Baker, and H. D. Griffiths. "Node location for netted radar target classification." In 2006 International Waveform Diversity & Design Conference. IEEE, 2006. http://dx.doi.org/10.1109/wdd.2006.8321475.

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Baker, C. J., M. Vespe, and G. J. Jones. "Target classification by echo locating animals." In 2007 International Waveform Diversity and Design Conference. IEEE, 2007. http://dx.doi.org/10.1109/wddc.2007.4339441.

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Chakravarthy, Vasu, Rajgopal Kannan, and Shuangqing Wei. "Classification of wireless adhoc networks through misbehavior analysis." In 2006 International Waveform Diversity & Design Conference. IEEE, 2006. http://dx.doi.org/10.1109/wdd.2006.8321438.

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Okawa, Masaki, Takuya Saito, Naoki Sawada, and Hiromitsu Nishizaki. "Audio Classification of Bit-Representation Waveform." In Interspeech 2019. ISCA: ISCA, 2019. http://dx.doi.org/10.21437/interspeech.2019-1855.

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Shen, Shi’an, Xiaokai Wang, Yanhui Zhou, Zhensheng Shi, Wenchao Chen, Xi’an Jiaotong, and Cheng Wang. "Correntropy-based SOM for waveform classification." In First International Meeting for Applied Geoscience & Energy. Society of Exploration Geophysicists, 2021. http://dx.doi.org/10.1190/segam2021-3594220.1.

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Pashaei, Mohammad, Michael J. Starek, and Jacob Berryhill. "Full-Waveform Terrestrial Lidar Data Classification Using Raw Digitized Waveform Signals." In IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2022. http://dx.doi.org/10.1109/igarss46834.2022.9883782.

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Balleri, Alessio, Karl Woodbridge, Chris J. Baker, and Marc W. Holderied. "Classification of flowers by bats: comparison with the radar case." In 2009 International Waveform Diversity and Design Conference. IEEE, 2009. http://dx.doi.org/10.1109/wddc.2009.4800302.

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Pashaei, Mohammad, Michael J. Starek, Philippe Tissot, and Jacob Berryhill. "Full-Waveform Terrestrial Lidar Data Classification Using Raw Samples of Digitized Waveform." In IGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2021. http://dx.doi.org/10.1109/igarss47720.2021.9553327.

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Mingjun, Su, and Yuan Cheng. "Multiattribute variable-window waveform classification and application." In SEG Technical Program Expanded Abstracts 2018. Society of Exploration Geophysicists, 2018. http://dx.doi.org/10.1190/segam2018-2997400.1.

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Reports on the topic "Waveform Classification"

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Sticht, Chris. Power System Waveform Classification Using Time-Frequency and CNN. Office of Scientific and Technical Information (OSTI), January 2022. http://dx.doi.org/10.2172/1841478.

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De Voir, Christopher. Wavelet Based Feature Extraction and Dimension Reduction for the Classification of Human Cardiac Electrogram Depolarization Waveforms. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.1739.

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