Modulation recognition of low-SNR UAV radar signals based on bispectral slices and GA-BP neural network

In this paper, we address the challenge of low recognition rates in existing methods for radar signals from unmanned aerial vehicles (UAV) with low signal-to-noise ratios (SNRs). To overcome this challenge, we propose the utilization of the bispectral slice approach for accurate recognition of complex UAV radar signals. Our approach involves extracting the bispectral diagonal slice and the maximum bispectral amplitude horizontal slice from the bispectrum amplitude spectrum of the received UAV radar signal. These slices serve as the basis for subsequent identification by calculating characteristic parameters such as convexity, box dimension, and sparseness. To accomplish the recognition task, we employ a GA-BP neural network. The significant variations observed in the bispectral slices of different signals, along with their robustness against Gaussian noise, contribute to the high separability and stability of the extracted bispectral convexity, bispectral box dimension, and bispectral sparseness. Through simulations involving five radar signals, our proposed method demonstrates superior performance. Remarkably, even under challenging conditions with an SNR as low as −3 dB, the recognition accuracy for the five different radar signals exceeds 90%. Our research aims to enhance the understanding and application of modulation recognition techniques for UAV radar signals, particularly in scenarios with low SNRs

Hidden Markov Models

Hidden Markov Models (HMMs), although known for decades, have made a big career nowadays and are still in state of development. This book presents theoretical issues and a variety of HMMs applications in speech recognition and synthesis, medicine, neurosciences, computational biology, bioinformatics, seismology, environment protection and engineering. I hope that the reader will find this book useful and helpful for their own research

Physics of the solar system

Solar system, solar physics, planetary atmospheres and structure, and origin of planets and moon - conferenc

Abstracts on Radio Direction Finding (1899 - 1995)

The files on this record represent the various databases that originally composed the CD-ROM issue of "Abstracts on Radio Direction Finding" database, which is now part of the Dudley Knox Library's Abstracts and Selected Full Text Documents on Radio Direction Finding (1899 - 1995) Collection. (See Calhoun record https://calhoun.nps.edu/handle/10945/57364 for further information on this collection and the bibliography). Due to issues of technological obsolescence preventing current and future audiences from accessing the bibliography, DKL exported and converted into the three files on this record the various databases contained in the CD-ROM. The contents of these files are: 1) RDFA_CompleteBibliography_xls.zip [RDFA_CompleteBibliography.xls: Metadata for the complete bibliography, in Excel 97-2003 Workbook format; RDFA_Glossary.xls: Glossary of terms, in Excel 97-2003 Workbookformat; RDFA_Biographies.xls: Biographies of leading figures, in Excel 97-2003 Workbook format]; 2) RDFA_CompleteBibliography_csv.zip [RDFA_CompleteBibliography.TXT: Metadata for the complete bibliography, in CSV format; RDFA_Glossary.TXT: Glossary of terms, in CSV format; RDFA_Biographies.TXT: Biographies of leading figures, in CSV format]; 3) RDFA_CompleteBibliography.pdf: A human readable display of the bibliographic data, as a means of double-checking any possible deviations due to conversion

Non-destructive quality control of carbon anodes using modal analysis, acousto-ultrasonic and latent variable methods

La performance des cuves d’électrolyse utilisées dans la production d’aluminium primaire par le procédé Hall-Héroult est fortement influencée par la qualité des anodes de carbone. Celles-ci sont de plus en plus variables en raison de la qualité décroissante des matières premières (coke et braie) et des changements de fournisseurs qui deviennent de plus en plus fréquents afin de réduire le coût d’achat et de rencontrer les spécifications des usines. En effet, les défauts des anodes, tels les fissures, les pores et les hétérogénéités, causés par cette variabilité, doivent être détectés le plus tôt possible afin d’éviter d’utiliser des anodes défectueuses dans les cuves et/ou d’apporter des ajustements au niveau du procédé de fabrication des anodes. Cependant, les fabricants d’anodes ne sont pas préparés pour réagir à cette situation afin de maintenir une qualité d'anode stable. Par conséquent, il devient prioritaire de développer des techniques permettant d’inspecter le volume complet de chaque anode individuelle afin d’améliorer le contrôle de la qualité des anodes et de compenser la variabilité provenant des matières premières. Un système d’inspection basé sur les techniques d’analyse modale et d’acousto-ultrasonique est proposé pour contrôler la qualité des anodes de manière rapide et non destructive. Les données massives (modes de vibration et signaux acoustiques) ont été analysées à l'aide de méthodes statistiques à variables latentes, telles que l'Analyse en Composantes Principales (ACP) et la Projection sur les Structures Latentes (PSL), afin de regrouper les anodes testées en fonction de leurs signatures vibratoires et acousto-ultrasoniques. Le système d'inspection a été premièrement investigué sur des tranches d'anodes industrielles et ensuite testé sur plusieurs anodes pleine grandeur produites sous différentes conditions à l’usine de Alcoa Deschambault au Québec (ADQ). La méthode proposée a permis de distinguer les anodes saines de celles contenant des défauts ainsi que d’identifier le type et la sévérité des défauts, et de les localiser. La méthode acousto-ultrasonique a été validée qualitativement par la tomographie à rayon-X, pour les analyses des tranches d’anodes. Pour les tests réalisés sur les blocs d’anode, la validation a été réalisée au moyen de photos recueillies après avoir coupé certaines anodes parmi celles testées.The performance of the Hall-Héroult electrolysis reduction process used for the industrial aluminium smelting is strongly influenced by the quality of carbon anodes, particularly by the presence of defects in their internal structure, such as cracks, pores and heterogeneities. This is partly due to the decreasing quality and increasing variability of the raw materials available on the market as well as the frequent suppliers changes made in order to meet the smelter’s specifications and to reduce purchasing costs. However, the anode producers are not prepared to cope with these variations and in order to maintain consistent anode quality. Consequently, it becomes a priority to develop alternative methods for inspecting each anode block to improve quality control and maintain consistent anode quality in spite of the variability of incoming raw materials.A rapid and non-destructive inspection system for anode quality control is proposed based on modal analysis and acousto-ultrasonic techniques. The large set of vibration and acousto-ultrasonic data collected from baked anode materials was analyzed using multivariate latent variable methods, such as Principal Component Analysis (PCA) and Partial Least Squares (PLS), in order to cluster the tested anodes based on vibration and their acousto-ultrasonic signatures. The inspection system was investigated first using slices collected from industrial anodes and then on several full size anodes produced under different conditions at the Alcoa Deschambault in Québec (ADQ). It is shown that the proposed method allows discriminating defect-free anodes from those containing various types of defects. In addition, the acousto-ultrasonic features obtained in different frequency ranges were found to be sensitive to the defects severities and were able to locate them in anode blocks. The acousto-ultrasonic method was validated qualitatively using X-ray computed tomography, when studying the anode slices. The results obtained on the full size anode blocks were validated by means of images collected after cutting some tested anodes

Numerical modelling of mesoscale atmospheric dispersion

Fall 1992.Includes bibliographical references

Speech Recognition

Chapters in the first part of the book cover all the essential speech processing techniques for building robust, automatic speech recognition systems: the representation for speech signals and the methods for speech-features extraction, acoustic and language modeling, efficient algorithms for searching the hypothesis space, and multimodal approaches to speech recognition. The last part of the book is devoted to other speech processing applications that can use the information from automatic speech recognition for speaker identification and tracking, for prosody modeling in emotion-detection systems and in other speech processing applications that are able to operate in real-world environments, like mobile communication services and smart homes

Machine Learning for Structural Monitoring and Anomaly Detection

Autonomous structural health monitoring (SHM) of a large number of structures became a topic of paramount importance for maintenance purposes and safety reasons in the last few decades. Civil infrastructures are the backbone of modern society, and the assessment of their conditions is of renowned importance. This aspect is even more exacerbated because of the existing system that are fast approaching their service life. Since the replacement of those structures is functionally and economically demanding, maintenance and retrofitting operations must be planned wisely. Moreover, the increasing amount and variety of data generated by users and sensors interconnected to the future 6G network requires new strategies to manage several types of data with highly different characteristics and also requires solutions to power the wireless network with renewable energies. In this scenario, the adoption of artificial intelligence and in particular machine learning (ML) strategies represents a flexible and potentially powerful solution that must be investigated. To manage the big and widespread amount of data generated by the extensive usage of multiple types of sensors, several ML techniques can be investigated, with the aim to perform data fusion and reduce the amount of data. Furthermore, the usage of anomaly detection techniques to identify potentially critical situations in infrastructures and buildings represents a topic of particular interest that still needs a significant investigation effort. In this research activity, we provide the fundamental guidelines to perform automatic damage detection, which combines SHM strategies and ML algorithms capable of performing anomaly detection on a wide set of structures. In particular, several algorithms and strategies capable of extracting relevant features from large amounts of data generated by different types of sensors are investigated. Finally, to effectively manage such an amount of data in communication constraints, we obtained some design rules for the acquisition system for bridge monitoring