An Effective Way of J Wave Separation Based on Multilayer NMF

J wave is getting more and more important in the clinical diagnosis as a new index of the electrocardiogram (ECG) of ventricular bipolar, but its signal often mixed in normal ST segment, using the traditional electrocardiograph, and diagnosed by experience cannot meet the practical requirements. Therefore, a new method of multilayer nonnegative matrix factorization (NMF) in this paper is put forward, taking the hump shape J wave, for example, which can extract the original J wave signal from the ST segment and analyze the accuracy of extraction, showing the characteristics of hump shape J wave from the aspects of frequency domain, power spectrum, and spectral type, providing the basis for clinical diagnosis and increasing the reliability of the diagnosis of J wave

TasNet: time-domain audio separation network for real-time, single-channel speech separation

Robust speech processing in multi-talker environments requires effective speech separation. Recent deep learning systems have made significant progress toward solving this problem, yet it remains challenging particularly in real-time, short latency applications. Most methods attempt to construct a mask for each source in time-frequency representation of the mixture signal which is not necessarily an optimal representation for speech separation. In addition, time-frequency decomposition results in inherent problems such as phase/magnitude decoupling and long time window which is required to achieve sufficient frequency resolution. We propose Time-domain Audio Separation Network (TasNet) to overcome these limitations. We directly model the signal in the time-domain using an encoder-decoder framework and perform the source separation on nonnegative encoder outputs. This method removes the frequency decomposition step and reduces the separation problem to estimation of source masks on encoder outputs which is then synthesized by the decoder. Our system outperforms the current state-of-the-art causal and noncausal speech separation algorithms, reduces the computational cost of speech separation, and significantly reduces the minimum required latency of the output. This makes TasNet suitable for applications where low-power, real-time implementation is desirable such as in hearable and telecommunication devices.Comment: Camera ready version for ICASSP 2018, Calgary, Canad

Manipulation and exploitation of the dynamic processes of Skyrmions

Magnetic skyrmions are emergent, topological quasiparticles with vortex-like magnetisation profiles, stabilised in magnetic systems hosting the Dzyaloshinskii–Moriya (DM) interaction. Their high physical stability and ease of propagation by electrical currents, means that skyrmions have the potential to act as information carriers in future low-power data storage and transmission devices. However, for skyrmions to be used in such a manner, their stability under non-equilibrium conditions and in a range of magnetic systems must be investigated further. This thesis provides a contribution to such studies, through investigating the dynamics and physical stability of magnetic skyrmions in a range of material systems. Arrays of skyrmions may be stabilised into a hexagonal skyrmion crystal lattice (SkX), under favourable conditions of magnetic field and temperature. Image analysis was performed on high frame rate Fresnel LTEM video data, showing repeated, spontaneous skyrmion motion across a SkX domain boundary. It was observed that the motion involved the creation and annihilation of skyrmions through splitting and merging of deformed skyrmions. The energy landscape of a SkX domain boundary region was investigated using micromagnetic simulations, with SkX defects found to be dominated by the interplay between exchange and Zeeman energies. Informed by Fresnel LTEM imaging and micromagnetic simulations, a mechanism for skyrmion creation and annihilation involving anti-skyrmions is proposed. This work demonstrates that in regions of high energy density, skyrmions may exhibit such large deformations that they are able to spontaneously split or merge with neighbouring skyrmions. These observations highlight the limits of skyrmion stability when alternative energy pathways involving topological structures are available. Advanced image processing including total variation (TV) denoising and nonnegative matrix factorisation (NMF) was carried out on sub-millisecond Fresnel image frames showing skyrmion dynamics, providing the ability to automatically identify transient skyrmion states. These analysis techniques provide a means of improving signal-to-noise ratio (SNR) and configurational state identification using machine learning. Perturbation of the SkX is carried out through the application of sub-µs perpendicular magnetic field pulses within the TEM. The realisation of such in-situ magnetic field pulses was achieved through the design, creation and testing of a bespoke microcoil and current pulser. Realisation of extensive disorder, and the formation of many defect regions within the SkX is demonstrated, where long-range reorientation of the SkX is unable to occur due to the duration of the pulses. Application of precise magnetic field pulses will allow for greater control over skyrmion nucleation and may aid in the study of skyrmion metastability. Methods for enhancing skyrmion stability in the technologically relevant synthetic antiferromagnetic (SAF) systems are investigated through the utilisation of micromagnetic simulations. Variation in skyrmion stability within multilayer SAF systems is described, through studying the process of offsetting the destabilising dipole interactions with interlayer exchange coupling. SAF systems with ’spinterface’ regions are investigated, focusing on the skyrmion stabilising effect of including a C60–metal interface. Non-trivial skyrmion size behaviour is shown when the C60 is explicitly simulated as a dilute magnetic layer, due to coupling between three distinct magnetic subsystems. These micromagnetic studies highlight the ability to tune magnetic interactions, and thus skyrmion stability, through the engineering of multilayer condensed matter systems

Radio frequency non-destructive testing and evaluation of defects under insulation

PhD ThesisThe use of insulation such as paint coatings has grown rapidly over the past decades. However, defects and corrosion under insulation (CUI) still present challenges for current non-destructive testing and evaluation (NDT&E) techniques. One of such challenges is the large lift-off introduced by thick insulation layer. Inaccessibility due to insulation leads corrosion and defects to be undetected, which can lead to catastrophic failure. Furthermore, lift-off effects due to the insulation layers reduce the sensitivities. The limitations of existing NDT&E techniques heighten the need for novel approaches to the characterisation of corrosion and defects under insulation. This research project is conducted in collaboration with International Paint®, and a radio frequency non-destructive evaluation for monitoring structural condition is proposed. High frequency (HF) passive RFID in conjunction with microwave NDT is proposed for monitoring and imaging under insulation. The small-size, battery-free and cost-efficient nature of RFID makes it attractive for long-term condition monitoring. To overcome the limitations of RFID-based sensing system such as effective monitoring area and lift-off tolerance, microwave NDT is proposed for the imaging of larger areas under thick insulation layers. Experimental studies are carried out in conjunction with specially designed mild steel sample sets to demonstrate the detection capabilities of the proposed systems. The contributions of this research can be summarised as follows. Corrosion detection using HF passive RFID-based sensing and microwave NDT is demonstrated in experimental feasibility studies considering variance in surface roughness, conductivity and permeability. The lift-off effects introduced by insulation layers are reduced by applying feature extraction with principal component analysis and non-negative matrix factorisation. The problem of thick insulation layers is overcome by employing a linear sweep frequency with PCA to improve the sensitivity and resolution of microwave NDT-based imaging. Finally, the merits of microwave NDT are identified for imaging defects under thick insulation in a realistic test scenario. In conclusion, HF passive RFID can be adapted for long term corrosion monitoring of steel under insulation, but sensing area and lift-off tolerance are limited. In contrast, the microwave NDT&E has shown greater potential and capability for monitoring corrosion and defects under insulation

Towards More Efficient DNN-Based Speech Enhancement Using Quantized Correlation Mask

Many studies on deep learning-based speech enhancement (SE) utilizing the computational auditory scene analysis method typically employs the ideal binary mask or the ideal ratio mask to reconstruct the enhanced speech signal. However, many SE applications in real scenarios demand a desirable balance between denoising capability and computational cost. In this study, first, an improvement over the ideal ratio mask to attain more superior SE performance is proposed through introducing an efficient adaptive correlation-based factor for adjusting the ratio mask. The proposed method exploits the correlation coefficients among the noisy speech, noise and clean speech to effectively re-distribute the power ratio of the speech and noise during the ratio mask construction phase. Second, to make the supervised SE system more computationally-efficient, quantization techniques are considered to reduce the number of bits needed to represent floating numbers, leading to a more compact SE model. The proposed quantized correlation mask is utilized in conjunction with a 4-layer deep neural network (DNN-QCM) comprising dropout regulation, pre-training and noise-aware training to derive a robust and high-order mapping in enhancement, and to improve generalization capability in unseen conditions. Results show that the quantized correlation mask outperforms the conventional ratio mask representation and the other SE algorithms used for comparison. When compared to a DNN with ideal ratio mask as its learning targets, the DNN-QCM provided an improvement of approximately 6.5% in the short-time objective intelligibility score and 11.0% in the perceptual evaluation of speech quality score. The introduction of the quantization method can reduce the neural network weights to a 5-bit representation from a 32-bit, while effectively suppressing stationary and non-stationary noise. Timing analyses also show that with the techniques incorporated in the proposed DNN-QCM system to increase its compac..

Quantitative methods for electron energy loss spectroscopy

[spa] Este trabajo explora las posibilidades analíticas que ofrece la técnica de espectroscopia electrónica de bajas pérdidas (low-loss EELS), capaces de revelar la configuración estructural de los más avanzados dispositivos semiconductores. El uso de modernos microscopios electrónicos de transmisión-barrido (STEM) nos permite obtener información espectroscópica a partir de volúmenes reducidos, hasta llegar a resolución atómica. Por ello, EELS es cada vez mas popular para la observación de los dispositivos semiconductores, a medida que los tamaños característicos de sus estructuras constituyentes se miniaturiza. Los espectros de pérdida de energía contienen mucha información: dado que el haz de electrones sufre unos bien conocidos procesos de dispersión inelástica, podemos trazar relaciones entre estos espectros y excitaciones elementales en la configuración atómica de los elementos y compuestos constituyentes de cada material. Se describe un marco teórico para el estudio del low-loss EELS: el modelo dieléctrico de dispersión inelástica, que toma en consideración las propiedades electrodinámicas del haz de electrones y la descripción mecano-cuántica de los materiales. Adicionalmente, se describen en detalle las herramientas utilizadas en el análisis de datos experimentales o la simulación teórica de espectros. Monitorizando las energías de band gap y plasmon en los datos experimentales de low-loss EELS se obtiene información directa sobre propiedades electrónicas de los materiales. Además, usando análisis Kramers-Kronig en los espectros se obtiene información dieléctrica que puede ser comparada con las simulaciones o con otras técnicas (ópticas). Se demuestra el uso de estas herramientas con una serie de estudios sobre estructuras basadas en nitruros del grupo-III. Por otro lado, el uso de algoritmos para el análisis multivariante permite separar las contribuciones individuales que se miden mezcladas en espectros de estructuras complicadas. Hemos utilizado estas avanzadas herramientas para el análisis de estructuras basadas en silicio que contienen nano-cristales embebidos en matrices dieléctricas.[eng] This thesis explores the analytical capabilities of low-loss electron energy loss spectroscopy (EELS), applied to disentangle the intimate configuration of advanced semiconductor heterostructures. Modern aberration corrected scanning transmission electron microscopy (STEM) allows extracting spectroscopic information from extremely constrained areas, down to atomic resolution. Because of this, EELS is becoming increasingly popular for the examination of novel semiconductor devices, as the characteristic size of their constituent structures shrinks. Energy-loss spectra contain a high amount of information, and since the electron beam undergoes well-known inelastic scattering processes, we can trace the features in these spectra down to elementary excitations in the atomic electronic configuration. In Chapter 1, the general theoretical framework for low-loss EELS is described. This formulation, the dielectric model of inelastic scattering, takes into account the electrodynamic properties of the fast electron beam and the quantum mechanical description of the materials. Low-loss EELS features are originated both from collective mode (plasmons) and single electron excitations (e.g. band gap), that contain relevant chemical and structural information. The nature of these excitations and the inelastic processes involved has to be taken into account in order to analyze experimental data or to perform simulations. The computational tools required to perform these tasks are presented in Chapter 2. Among them, calibration, deconvolution and Kramers-Kronig analysis (KKA) of the spectrum constitute the most relevant procedures, that ultimately help obtain the dielectric information in the form of a complex dielectric function (CDF). This information may be then compared to the one obtained by optical techniques or with the results from simulations. Additional techniques are explained, focusing first on multivariate analysis (MVA) algorithms that exploit the hyperspectral acquisition of EELS, i.e. spectrum imaging (SI) modes. Finally, an introduction to the density functional theory (DFT) simulations of the energy-loss spectrum is given. In Chapter 3, DFT simulations concerning (Al, Ga, In)N binary and ternary compounds are introduced. The prediction of properties observed in low-loss EELS of these semiconductor materials, such as the band gap energy, is improved in these calculations. Moreover, a super-cell approach allows to obtain the composition dependence of both band gap and plasmon energies from the theoretical dielectric response coefficients of ternary alloys. These results are exploited in the two following chapters, in which we experimentally probe structures based on group-III nitride binary and ternary compounds. In Chapter 4, two distributed Bragg reflector structures are examined (based upon AlN/GaN and InAlN/GaN multilayers, respectively) through different strategies for the characterization of composition from plasmon energy shift. Moreover; HAADF image simulation is used to corroborate he obtained results; plasmon width, band gap energy and other features are measured; and, KKA is performed to obtain the CDF of GaN. In Chapter 5, a multiple InGaN quantum well (QW) structure is examined. In these QWs (indium rich layers of a few nanometers in width), we carry out an analysis of the energy-loss spectrum taking into account delocalization and quantum confinement effects. We propose useful alternatives complementary to the study of plasmon energy, using KKA of the spectrum. Chapters 6 and 7 deal with the analysis of structures that present pure silicon-nanocrystals (Si-NCs) embedded in silicon-based dielectric matrices. Our aim is to study the properties of these nanoparticles individually, but the measured low-loss spectrum always contains mixed signatures from the embedding matrix as well. In this scenario, Chapter 6 proposes the most straightforward solution; using a model-based fit that contains two peaks. Using this strategy, the Si-NCs embedded in an Er-doped SiO2 layer are characterized. Another strategy, presented in Chapter 7, uses computer-vision tools and MVA algorithms in low-loss EELS-SIs to separate the signature spectra of the Si-NCs. The advantages and drawbacks of this technique are revealed through its application to three different matrices (SiO2, Si3N4 and SiC). Moreover, the application of KKA to the MVA results is demonstrated, which allows to extract CDFs for the Si-NCs and surrounding matrices

Hyperspectral image analysis techniques for the detection and classification of the early onset of plant disease and stress

This review explores how imaging techniques are being developed with a focus on deployment for crop monitoring methods. Imaging applications are discussed in relation to both field and glasshouse-based plants, and techniques are sectioned into ‘healthy and diseased plant classification’ with an emphasis on classification accuracy, early detection of stress, and disease severity. A central focus of the review is the use of hyperspectral imaging and how this is being utilised to find additional information about plant health, and the ability to predict onset of disease. A summary of techniques used to detect biotic and abiotic stress in plants is presented, including the level of accuracy associated with each method