Mismatch in the Classification of Linear Subspaces: Sufficient Conditions for Reliable Classification

This paper considers the classification of linear subspaces with mismatched classifiers. In particular, we assume a model where one observes signals in the presence of isotropic Gaussian noise and the distribution of the signals conditioned on a given class is Gaussian with a zero mean and a low-rank covariance matrix. We also assume that the classifier knows only a mismatched version of the parameters of input distribution in lieu of the true parameters. By constructing an asymptotic low-noise expansion of an upper bound to the error probability of such a mismatched classifier, we provide sufficient conditions for reliable classification in the low-noise regime that are able to sharply predict the absence of a classification error floor. Such conditions are a function of the geometry of the true signal distribution, the geometry of the mismatched signal distributions as well as the interplay between such geometries, namely, the principal angles and the overlap between the true and the mismatched signal subspaces. Numerical results demonstrate that our conditions for reliable classification can sharply predict the behavior of a mismatched classifier both with synthetic data and in a motion segmentation and a hand-written digit classification applications.Comment: 17 pages, 7 figures, submitted to IEEE Transactions on Signal Processin

Sparse Modeling for Image and Vision Processing

In recent years, a large amount of multi-disciplinary research has been conducted on sparse models and their applications. In statistics and machine learning, the sparsity principle is used to perform model selection---that is, automatically selecting a simple model among a large collection of them. In signal processing, sparse coding consists of representing data with linear combinations of a few dictionary elements. Subsequently, the corresponding tools have been widely adopted by several scientific communities such as neuroscience, bioinformatics, or computer vision. The goal of this monograph is to offer a self-contained view of sparse modeling for visual recognition and image processing. More specifically, we focus on applications where the dictionary is learned and adapted to data, yielding a compact representation that has been successful in various contexts.Comment: 205 pages, to appear in Foundations and Trends in Computer Graphics and Visio

Enhancing the Resolution of the Spectrogram of Non-Stationary Mobile Radio Channels by Using Massive MIMO Techniques

This paper is concerned with the enhancement of the resolution of the spectrogram of non-stationary mobile radio channels using massive multiple-input multiple-output (MIMO) techniques. By starting from a new generic geometrical model for a non-stationary MIMO channel, we derive the complex MIMO channel gains under the assumption that the mobile station (MS) moves with time-variant speed. Closed-form solutions are derived for the spectrogram of the complex MIMO channel gains by using a Gaussian window. It is shown that the window spread can be optimized subject to the MS's speed change. Furthermore, it is shown that the spectrogram can be split into an auto-term and a cross-term. The auto-term contains the useful time-variant spectral information, while the cross-term can be identified as a sum of spectral interference components, which restrict considerably the time-frequency resolution of the spectrogram. Moreover, it is shown that the effect of the cross-term can be drastically reduced by using massive MIMO techniques. The proposed method is not only important for estimating timevariant Doppler power spectra with high resolution, but it also pioneers the development of new passive acceleration/deceleration estimation methods and the development of new non-wearable fall detection systems.acceptedVersionnivå

AFM-based characterization method of capacitive MEMS pressure sensors for cardiological applications

Current CMOS-micro-electro-mechanical systems (MEMS) fabrication technologies permit cardiological implantable devices with sensing capabilities, such as the iStents, to be developed in such a way that MEMS sensors can be monolithically integrated together with a powering/transmitting CMOS circuitry. This system on chip fabrication allows the devices to meet the crucial requirements of accuracy, reliability, low-power, and reduced size that any life-sustaining medical application imposes. In this regard, the characterization of stand-alone prototype sensors in an efficient but affordable way to verify sensor performance and to better recognize further areas of improvement is highly advisable. This work proposes a novel characterization method based on an atomic force microscope (AFM) in contact mode that permits to calculate the maximum deflection of the flexible top plate of a capacitive MEMS pressure sensor without coating, under a concentrated load applied to its center. The experimental measurements obtained with this method have allowed to verify the bending behavior of the sensor as predicted by simulation of analytical and finite element (FE) models. This validation process has been carried out on two sensor prototypes with circular and square geometries that were designed using a computer-aided design tool specially-developed for capacitive MEMS pressure sensors.This research was funded by the Spanish Government’s “Ministerio de Economía, Industria y Competitividad” under the joint projects TEC2013-46242-C3-2-P and TEC2013-46242-C3, co-financed with FEDER

Estimation of Avalanche Development and Frontal Velocities Based on the Spectrogram of the Seismic Signals Generated at the Vallée de la Sionne Test Site.

The changes in the seismic signals generated by avalanches recorded at three sites along a path at the Vallée de la Sionne (VdlS) experimental site are presented. We discuss and correlate the differences in the duration, signal amplitudes, and frequency content of the sections (Signal ONset (ON), Signal Body (SBO), and Signal TAil and Signal ENd STA-SEN) of the spectrograms with the evolution of the powder, transitional and wet snow avalanches along a path. The development of the avalanche front was quantified using the exponential function in time F (t) = K' exp (β t) fitted to the shape of the signal ONset (SON section of the spectrogram. The speed of the avalanche front is contained in β. To this end, a new method was developed. The three seismic components were converted into one seismic component (FS), when expressing the vector in polar coordinates. We linked the theoretical function of the shape of the FS-SON section of the spectrogram to the numerical coefficients of its shape after considering the spectrogram as an image. This allowed us to obtain the coefficients K' and β. For this purpose, the Hough Transform (HT) was applied to the image. The values of the resulting coefficients K' and β are included in different ranges in accordance with the three types of avalanche. Curves created with these coefficients enable us to estimate the development of the different avalanche types along the path. Our results show the feasibility of classifying the type of avalanche through these coefficients. Average speeds of the avalanches approaching the recording sites were estimated. The speed values of wet and transitional avalanches are consistent with those derived from GEODAR (GEOphysical Doppler radAR) measurements, when available. The absence of agreement in the speed values obtained from seismic signals and GEODAR measurements for powder snow avalanches indicates, for this type of avalanche, a different source of the measured signal. Hence, the use of the two measuring systems proves to be complementary

Advances in Raman and Surface-Enhanced Raman Spectroscopy: Instrumentation, Plasmonic Engineering and Biomolecular Sensing

Raman spectroscopy is a powerful technique for label-free molecular sensing and imaging in various fields. High molecular specificity, non-invasive sampling approach and the need for little or no sample preparation make Raman spectroscopy uniquely advantageous compared to other analytical techniques. However, Raman spectroscopy suffers from the intrinsic limitation of weak signal intensity. Therefore, time-sensitive studies such as diagnosis and clinical applications require improving the throughput of Raman instrumentation. Alternatively, surface-enhanced Raman scattering (SERS) improves the sensitivity by 10^6 to 10^14 times, making the weak Raman intensity no longer a limitation. Nevertheless, it is still a big challenge to engineer plasmonic substrates with high SERS enhancement, good uniformity and reproducibility. This thesis presents advances in: (1) Raman instrumentation towards high-throughput, environmental, biological and biomedical analysis; (2) SERS substrates with high enhancement factor (EF), uniformity and reproducibility; (3) biosensing applications including imaging of cell population and detection of biomolecules towards high time efficiency and sensitivity. In Raman instrumentation, we have built a high-throughput line-scan Raman microscope system and a novel parallel Raman microscope based on multiple-point active-illumination and wide-field hyperspectral data collection. Using the line-scan Raman microscope, we have performed chemical imaging of intact biological cells at the cell population level. We have also demonstrated more flexibility and throughput from the active-illumination Raman microscope in rapid chemical identification and screening of micro and nanoparticles and bacterial spores. Both Raman microscopes have been used to evaluate the large-area SERS uniformity of DC-sputtered gold nanoislands, a low-cost means to fabricate plasmonic substrates. In plasmonic engineering, we have introduced patterned nanoporous gold nanoparticles that feature 3-dimensional mesoporous network with pore size on the order of 10 nm throughput the sub-wavelength nanoparticles. We showed that the plasmonic resonance can be tuned by geometrical engineering of either the external nanoparticle size and shape or the nanoporous network. As an example, we have developed disk-shaped entities, also known as nanoporous gold disks (NPGD) with highly uniform and reproducible SERS EF exceeding 10^8. Label-free, multiplexed molecular sensing and imaging has been demonstrated on NPGD substrates. Using the line-scan Raman microscope and the NPGD substrates, we have successfully developed a label-free DNA hybridization sensor at the single-molecule level in microfluidics. We have observed discrete, individual DNA hybridization events by in situ monitoring the hybridization process using SERS. The advances and promising results presented in this thesis demonstrate potential impact in Raman/SERS imaging and sensing in environmental, biological and biomedical applications.Electrical and Computer Engineering, Department o

Hyperspectral imaging solutions for brain tissue metabolic and hemodynamic monitoring: past, current and future developments

Hyperspectral imaging (HSI) technologies have been used extensively in medical research, targeting various biological phenomena and multiple tissue types. Their high spectral resolution over a wide range of wavelengths enables acquisition of spatial information corresponding to different light-interacting biological compounds. This review focuses on the application of HSI to monitor brain tissue metabolism and hemodynamics in life sciences. Different approaches involving HSI have been investigated to assess and quantify cerebral activity, mainly focusing on: (1) mapping tissue oxygen delivery through measurement of changes in oxygenated (HbO₂) and deoxygenated (HHb) hemoglobin; and (2) the assessment of the cerebral metabolic rate of oxygen (CMRO₂) to estimate oxygen consumption by brain tissue. Finally, we introduce future perspectives of HSI of brain metabolism, including its potential use for imaging optical signals from molecules directly involved in cellular energy production. HSI solutions can provide remarkable insight in understanding cerebral tissue metabolism and oxygenation, aiding investigation on brain tissue physiological processes

Making sense of light: the use of optical spectroscopy techniques in plant sciences and agriculture

As a result of the development of non-invasive optical spectroscopy, the number of prospective technologies of plant monitoring is growing. Being implemented in devices with different functions and hardware, these technologies are increasingly using the most advanced data processing algorithms, including machine learning and more available computing power each time. Optical spectroscopy is widely used to evaluate plant tissues, diagnose crops, and study the response of plants to biotic and abiotic stress. Spectral methods can also assist in remote and non-invasive assessment of the physiology of photosynthetic biofilms and the impact of plant species on biodiversity and ecosystem stability. The emergence of high-throughput technologies for plant phenotyping and the accompanying need for methods for rapid and non-contact assessment of plant productivity has generated renewed interest in the application of optical spectroscopy in fundamental plant sciences and agriculture. In this perspective paper, starting with a brief overview of the scientific and technological backgrounds of optical spectroscopy and current mainstream techniques and applications, we foresee the future development of this family of optical spectroscopic methodologies.info:eu-repo/semantics/publishedVersio