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

Research on communication emitter identification based on semi-supervised dimensionality reduction in complex electromagnetic environment

The individual identification of communication emitters is a process of identifying different emitters based on the radio frequency fingerprint features extracted from the received signals. Due to the inherent non-linearity of the emitter power amplifier, the fingerprints provide distinguishing features for emitter identification. In this study, approximate entropy is introduced into variational mode decomposition, whose features performed in each mode which is decomposed from the reconstructed signal are extracted while the local minimum removal method is used to filter out the noise mode to improve SNR. We proposed a semi-supervised dimensionality reduction method named exponential semi-supervised discriminant analysis in order to reduce the high-dimensional feature vectors of the signals, and LightGBM is applied to build a classifier for communication emitter identification. The experimental results show that the method performs better than the state-of-the-art individual communication emitter identification technology for the steady signal data set of radio stations with the same plant, batch and model

Multifractal techniques for analysis and classification of emphysema images

This thesis proposes, develops and evaluates different multifractal methods for detection, segmentation and classification of medical images. This is achieved by studying the structures of the image and extracting the statistical self-similarity measures characterized by the Holder exponent, and using them to develop texture features for segmentation and classification. The theoretical framework for fulfilling these goals is based on the efficient computation of fractal dimension, which has been explored and extended in this work. This thesis investigates different ways of computing the fractal dimension of digital images and validates the accuracy of each method with fractal images with predefined fractal dimension. The box counting and the Higuchi methods are used for the estimation of fractal dimensions. A prototype system of the Higuchi fractal dimension of the computed tomography (CT) image is used to identify and detect some of the regions of the image with the presence of emphysema. The box counting method is also used for the development of the multifractal spectrum and applied to detect and identify the emphysema patterns. We propose a multifractal based approach for the classification of emphysema patterns by calculating the local singularity coefficients of an image using four multifractal intensity measures. One of the primary statistical measures of self-similarity used in the processing of tissue images is the Holder exponent (α-value) that represents the power law, which the intensity distribution satisfies in the local pixel neighbourhoods. The fractal dimension corresponding to each α-value gives a multifractal spectrum f(α) that was used as a feature descriptor for classification. A feature selection technique is introduced and implemented to extract some of the important features that could increase the discriminating capability of the descriptors and generate the maximum classification accuracy of the emphysema patterns. We propose to further improve the classification accuracy of emphysema CT patterns by combining the features extracted from the alpha-histograms and the multifractal descriptors to generate a new descriptor. The performances of the classifiers are measured by using the error matrix and the area under the receiver operating characteristic curve (AUC). The results at this stage demonstrated the proposed cascaded approach significantly improves the classification accuracy. Another multifractal based approach using a direct determination approach is investigated to demonstrate how multifractal characteristic parameters could be used for the identification of emphysema patterns in HRCT images. This further analysis reveals the multi-scale structures and characteristic properties of the emphysema images through the generalized dimensions. The results obtained confirm that this approach can also be effectively used for detecting and identifying emphysema patterns in CT images. Two new descriptors are proposed for accurate classification of emphysema patterns by hybrid concatenation of the local features extracted from the local binary patterns (LBP) and the global features obtained from the multifractal images. The proposed combined feature descriptors of the LBP and f(α) produced a very good performance with an overall classification accuracy of 98%. These performances outperform other state-of-the-art methods for emphysema pattern classification and demonstrate the discriminating power and robustness of the combined features for accurate classification of emphysema CT images. Overall, experimental results have shown that the multifractal could be effectively used for the classifications and detections of emphysema patterns in HRCT images

Condensed-Matter-Principia Based Information & Statistical Measures

This book summarizes the efforts of ten papers collected by the Special Issue "Condensed-Matter-Principia Based Information & Statistical Measures: From Classical to Quantum". It calls for papers which deal with condensed-matter systems, or their interdisciplinary analogs, for which well-defined classical–statistical vs. quantum information measures can be inferred while based on the entropy concept. The contents have mainly been rested upon objectives addressed by an international colloquium held on October 2019, in UTP Bydgoszcz, Poland (see http://zmpf.imif.utp.edu.pl/rci-jcs/rci-jcs-4/), with an emphasis placed on the achievements of Professor Gerard Czajkowski, who commenced his research activity with open diffusion–reaction systems under the supervision of Roman S. Ingarden (Toruń), a father of Polish synergetics, and original thermodynamic approaches to self-organization. The active cooperation of Professor Czajkowski, mainly with German physicists (Friedrich Schloegl, Aachen; Werner Ebeling, Berlin), ought to be highlighted. In light of this, a development of his research, as it has moved from statistical thermodynamics to solid state theory, pursued in terms of nonlinear solid-state optics (Franco Bassani, Pisa), and culminated very recently with large quasiparticles termed Rydberg excitons, and their coherent interactions with light, is worth delineating

Radio frequency fingerprint collaborative intelligent blind identification for green radios

Radio frequency fingerprint identification (RFFI) technology identifies the emitter by extracting one or more unintentional features of the signal from the emitter. To solve the problem that the traditional deep learning network is not highly adaptable for the contour features extracted from the signal, this paper proposes a novel RFFI method based on a deformable convolutional network. This network makes the convolution operation more biased towards the useful information content in the feature map with higher energy, and ignores part of the background noise information. Moreover, a distributed federated learning system is used to solve the problem of insufficient number of local training samples for a multi-party joint training model without exchanging the original data of the samples. The federated learning center receives the network parameters uploaded by all local models for aggregation, and feeds the aggregated parameters back to each local model for a global update. The proposed blind identification method requires less information and no training sequences and pilots. Thus, it achieves energy-efficiency and spectrum-efficiency. Simulation verifies that the proposed method can achieve better recognition performance and is beneficial for green radio

Master of Science

thesisMeningiomas are the most common primary brain tumors, accounting for 36.6% of all tumors with ∼20,000 cases annually in the U.S. Although 65-80% of cases are benign (World Health Organization [WHO] Grade I), recurrence over a long period can be seen, especially for subtotal resections and higher-grade tumors (II and III). Radiotherapy is a common primary or adjuvant therapy, but its mechanisms of action in the setting of distinct subtypes of meningioma remain unknown. Hypoxia-inducible factor 1 (HIF1) plays a key role in cellular response to oxygen tension, modulates multiple downstream genes, controls tissue vascularization, and may serve as a resistance-promoting mechanism in tumors. The aim of this study was to evaluate the clinical impact of the HIF1-signaling pathway in meningioma characterization as well as the impact of radiotherapy on meningiomas in the setting of HIF1 knockout. Clinical samples from patients with meningiomas, primary derived cell lines (GAR, JEN, SAM, MCT, BSH, IOMM-LEE), and HIF1 generated knockouts (GAR-1589) were utilized. Multiple immunohistochemical markers and a fractal-based microvascularity quantification showed that Grade I meningiomas ≥3 cm showed greater staining for MIB and von Willebrand Factor as well as an average 19-month shorter survival. In addition, a MIB index ≥3 showed high specificity (82.5%) but not sensitivity (36%) for predicting progression-free survival. Cell proliferation and apoptosis in response to radiation doses depended on cell density, HIF1A mutational status, and oxygen tension. Higher plated densities of cells showed resistance to radiation for various primary meningioma cell lines. GAR cells demonstrated greater response to high-dose radiation than GAR-1589 cells in 2D and 3D cultures, while neither cell line responded to fractionated radiotherapy. Hypoxic environments reduced the efficacy of radiation, in fact showing increased cell proliferation with low doses of radiation. GAR-1589 cell, however, showed greater increases in cell apoptosis during radiotherapy in normoxic environments than GAR cells. Multimodal imaging using tumor bioluminescence, positron emission tomography tracers, and MRI showed potential for evaluating various characteristics of primary brain tumors noninvasively using an orthotopic rodent model. These results offer some correlation clinically and experimentally regarding the importance of HIF1 and tumor resistance

A radial basis classifier for the automatic detection of aspiration in children with dysphagia

BACKGROUND: Silent aspiration or the inhalation of foodstuffs without overt physiological signs presents a serious health issue for children with dysphagia. To date, there are no reliable means of detecting aspiration in the home or community. An assistive technology that performs in these environments could inform caregivers of adverse events and potentially reduce the morbidity and anxiety of the feeding experience for the child and caregiver, respectively. This paper proposes a classifier for automatic classification of aspiration and swallow vibration signals non-invasively recorded on the neck of children with dysphagia. METHODS: Vibration signals associated with safe swallows and aspirations, both identified via videofluoroscopy, were collected from over 100 children with neurologically-based dysphagia using a single-axis accelerometer. Five potentially discriminatory mathematical features were extracted from the accelerometry signals. All possible combinations of the five features were investigated in the design of radial basis function classifiers. Performance of different classifiers was compared and the best feature sets were identified. RESULTS: Optimal feature combinations for two, three and four features resulted in statistically comparable adjusted accuracies with a radial basis classifier. In particular, the feature pairing of dispersion ratio and normality achieved an adjusted accuracy of 79.8 ± 7.3%, a sensitivity of 79.4 ± 11.7% and specificity of 80.3 ± 12.8% for aspiration detection. Addition of a third feature, namely energy, increased adjusted accuracy to 81.3 ± 8.5% but the change was not statistically significant. A closer look at normality and dispersion ratio features suggest leptokurticity and the frequency and magnitude of atypical values as distinguishing characteristics between swallows and aspirations. The achieved accuracies are 30% higher than those reported for bedside cervical auscultation. CONCLUSION: The proposed aspiration classification algorithm provides promising accuracy for aspiration detection in children. The classifier is conducive to hardware implementation as a non-invasive, portable "aspirometer". Future research should focus on further enhancement of accuracy rates by considering other signal features, classifier methods, or an augmented variety of training samples. The present study is an important first step towards the eventual development of wearable intelligent intervention systems for the diagnosis and management of aspiration

Aspects of the biology and behaviour of Lernaeocera branchialis (Linnaeus, 1767)(Copepoda : Pennellidae)

Lernaeocera branchialis (L., 1767) is a parasitic copepod that parasitises a range of gadoids by anchoring in the proximity of the branchial chamber of its host, deriving nutrition from the blood of its host and causing serious pathogenic effects. This study investigates the taxonomy of the juvenile free-swimming stages and host location behaviour in the pre-metamorphosed adult female. The large size and distinctive appearance of the metamorphosed adult female stage, coupled with the wide exploitation and commercial importance of one of its principle final gadoid hosts, the cod (Gadus morhua L.), means that this species has long been recognised in the scientific literature, and here the extensive literature concerning this potentially important and damaging pathogen is re-examined to provide an up to date overview, which includes both aquaculture and wild fisheries perspectives. Due to disagreements between several descriptions of the L. branchialis juvenile stages, and because the majority of the descriptions are over 60 years old, the juvenile free-swimming stages are re-described, using current terminology and a combination of both light and confocal microscopy. The time of hatching and moults in these stages is also examined. Techniques for the automated creation of taxonomic drawings from confocal images using computer software are investigated and the possibilities and implications of this technique are discussed. The method of host location in L. branchialis is unknown but is likely to involve a variety of mechanisms, possibly including chemo-reception, mechano-reception and the use of physical phenomena in the water column, such as haloclines and thermoclines, to search for fish hosts. In this study the role of host-associated chemical cues in host location by adult female L. branchialis is investigated by analysing the parasites behavioural responses to a range of host-derived cues, in both a choice chamber and a 3D tracking arena. To analyse the data from the experiments, specialised computer software (“Paratrack”) was developed to digitise the paths of the parasites’ movements, and calculate a variety of behavioural parameters, allowing behaviour patterns to be identified and compared. The results show that L. branchialis responds to host-associated chemical cues in a similar way to many copepods in the presence of chemical cues. Of the different cues tested, gadoid conditioned water appears to be most attractive to the parasites, although the wide variation in behavioural responses may indicate that other mechanisms are also required for host location. The different behavioural responses of parasites to whiting (Merlangius merlangus L.) and cod (Gadus morhua) conditioned water, which are both definitive hosts, provide some evidence for sub-speciation in L. branchialis. The role of chemical cues in host location of L. branchialis, and the relative importance of chemical and physical cues in host location are discussed. As well as demonstrating several techniques, which show potential for further development, this work has improved our knowledge of the biology and life-cycle of L. branchialis. Further study of this, and other areas of L. branchialis biology and its host-parasite interactions, should assist the development of contingency plans for the effective management and control of this widespread and potentially devastating pathogen

Digital Image Processing

Newspapers and the popular scientific press today publish many examples of highly impressive images. These images range, for example, from those showing regions of star birth in the distant Universe to the extent of the stratospheric ozone depletion over Antarctica in springtime, and to those regions of the human brain affected by Alzheimer’s disease. Processed digitally to generate spectacular images, often in false colour, they all make an immediate and deep impact on the viewer’s imagination and understanding. Professor Jonathan Blackledge’s erudite but very useful new treatise Digital Image Processing: Mathematical and Computational Methods explains both the underlying theory and the techniques used to produce such images in considerable detail. It also provides many valuable example problems - and their solutions - so that the reader can test his/her grasp of the physical, mathematical and numerical aspects of the particular topics and methods discussed. As such, this magnum opus complements the author’s earlier work Digital Signal Processing. Both books are a wonderful resource for students who wish to make their careers in this fascinating and rapidly developing field which has an ever increasing number of areas of application. The strengths of this large book lie in: • excellent explanatory introduction to the subject; • thorough treatment of the theoretical foundations, dealing with both electromagnetic and acoustic wave scattering and allied techniques; • comprehensive discussion of all the basic principles, the mathematical transforms (e.g. the Fourier and Radon transforms), their interrelationships and, in particular, Born scattering theory and its application to imaging systems modelling; discussion in detail - including the assumptions and limitations - of optical imaging, seismic imaging, medical imaging (using ultrasound), X-ray computer aided tomography, tomography when the wavelength of the probing radiation is of the same order as the dimensions of the scatterer, Synthetic Aperture Radar (airborne or spaceborne), digital watermarking and holography; detail devoted to the methods of implementation of the analytical schemes in various case studies and also as numerical packages (especially in C/C++); • coverage of deconvolution, de-blurring (or sharpening) an image, maximum entropy techniques, Bayesian estimators, techniques for enhancing the dynamic range of an image, methods of filtering images and techniques for noise reduction; • discussion of thresholding, techniques for detecting edges in an image and for contrast stretching, stochastic scattering (random walk models) and models for characterizing an image statistically; • investigation of fractal images, fractal dimension segmentation, image texture, the coding and storing of large quantities of data, and image compression such as JPEG; • valuable summary of the important results obtained in each Chapter given at its end; • suggestions for further reading at the end of each Chapter. I warmly commend this text to all readers, and trust that they will find it to be invaluable. Professor Michael J Rycroft Visiting Professor at the International Space University, Strasbourg, France, and at Cranfield University, England