Colorectal Cancer Tissue Classification Based on Machine Learning

For digital pathology, automatic recognition of different tissue types in histological images is important for diagnostic assistance and healthcare. Since histological images generally contain more than one tissue type, multi-class texture analysis plays a critical role to solve this problem. This study examines the important statistical features including Gray Level Co-occurrence Matrix (GLCM), Discrete Wavelet Transform (DWT), Spatial filters, Wiener filter, Gabor filters, Haralick features, fractal filters, and local binary pattern (LBP) for colorectal cancer tissue identification by using support vector machine (SVM) and decision fusion of feature selection. The average experimental results achieve high identification rate which is significantly superior to the existing known methods. In summary, the proposed method based on machine learning outperforms the techniques described in the literatures and achieve high classification accuracy rate at 93.17% for eight classes and 96.02% for ten classes which demonstrate promising applications for cancer tissue classification of histological image

Improve Tracking Speed of Beamformer With Simplified Zero Placement Algorithm

This paper presents a new structure and algorithm to improve the tracking speed of a Generalized Sidelobe Canceler (GSC) based adaptive beamformer. Iterative methods like Conjugate Gradient algorithm to calculate the beamformer weight vector eliminates the complexity of Matrix reversing. But the reduced complexity comes with time cost which requires iterations of calculation before converging to the desired direction. To combat the problem, a Simplified Zero Placement algorithm is proposed to set the initial weight vector to make the starting value near the optimum location of weight vector. Numerical simulation and analysis confirms the effectiveness of the proposed solution

UWB for medical applications

The aim of this project is to be familiarized with UWB radar technology for medical applications. The extremely high-resolution UWB signals together with the low transmit power are good candidates for non-invasive patient monitoring. For instance, breathe rate monitoring. The project will investigate the UWB radar signal detection for breathe monitoring, supported with real experiments. HW equipment consist of TIME DOMAIN® PulsON® 400 Series for Ranging and Communications Application System model and companion Matlab software will support the analysis. ProgrThe respiratory frequency monitoring is an important indicator to the medical field. Also, the need of sensor system solutions for home monitoring is growing as the life expectancy of the world population is increasing. For those reasons, this thesis considers the use of an impulse-radio (IR) UWB radar system to track respiratory frequency and respiratory patterns, as apnoea episodes, in a non-invasive and real-time way. We start our analysis with well-known spectral estimators, like the Periodogram or Bartlett estimator to obtain the first results and insights over the estimation of steady frequencies in an offline regime. Later, we consider the use of adaptive algorithms like the LMS together with AR modelling to monitor the breathing rate transitions and variations. Simulations have been performed to validate and adjust the parameters of the algorithms, balancing between its trade-offs to suit our solution to the problem. Finally, the results of the experiments in different environments are presented meeting the expected requirements and performance of the system.La monitorización de la frecuencia respiratoria es un importante indicador en el campo de la medicina. De la misma manera, la necesidad de soluciones basadas en sistemas de sensores para monitorizar pacientes no hospitalizados en sus hogares crece al mismo ritmo que la esperanza de vida de la población mundial crece. Por esas razones, esta tesis considera el uso de un sistema de radar basado en impulse-radio (IR) UWB para controlar la frecuencia respiratoria, y a la vez, patrones respiratorios, como episodios de apnea, de una manera no invasiva y a tiempo real. Empezamos nuestro análisis con estimadores espectrales como el Periodograma o Estimador Bartlett para obtener los primeros resultados en la estimación de frecuencias estables en una configuración no en tiempo real. Más tarde, consideramos el uso de algoritmos adaptativos como LMS junto a modelado AR para monitorizar las transiciones y variaciones en la frecuencia respiratoria. Se han llevado a cabo simulaciones para validar y ajustar los parámetros de los algoritmos, intentando compensar sus diferentes características para ajustarlos a nuestra problemática. Finalmente, los resultados de experimentos en diferentes escenarios son presentados cumpliendo con los requerimientos y rendimientos esperados del sistema. La monitorització de la freqüència respiratòria es un important indicador en el camp de la medicina. De la mateixa manera, la necessitat de solucions basades en sistemes de sensors per a monitoritzar pacients no hospitalitzats a les seves llars creix a mesura que la esperança de vida de la població mundial creix. Per aquestes raons, aquesta tesi considera l’ús d’un sistema de radar basat en impulse-radio (IR) UWB per a controlar la freqüència respiratòria, i al mateix temps, patrons de respiració, com episodis d’apnea, d’una manera no invasiva i a temps real. Comencem el nostre anàlisi amb estimadors espectrals com el Periodograma o l’Estimador Bartlett per a obtenir els primers resultats en l’estimació de freqüències estables en una configuració no en temps real , per continuar amb, l’ús d’algoritmes adaptatius com LMS junt a modelat AR per a monitoritzar les transicions y variacions en la freqüència respiratòria. Hem dut a terme simulacions per a validar i ajustar els paràmetres dels algoritmes, intentant compensar les seves diferents característiques per a ajustar-los a la nostra problemàtica. Finalment, els resultats de experiments en diferent escenaris son presentats acomplint amb els requisits i rendiments esperats del sistema

Adaptive beamforming and switching in smart antenna systems

The ever increasing requirement for providing large bandwidth and seamless data access to commuters has prompted new challenges to wireless solution providers. The communication channel characteristics between mobile clients and base station change rapidly with the increasing traveling speed of vehicles. Smart antenna systems with adaptive beamforming and switching technology is the key component to tackle the challenges. As a spatial filter, beamformer has long been widely used in wireless communication, radar, acoustics, medical imaging systems to enhance the received signal from a particular looking direction while suppressing noise and interference from other directions. The adaptive beamforming algorithm provides the capability to track the varying nature of the communication channel characteristics. However, the conventional adaptive beamformer assumes that the Direction of Arrival (DOA) of the signal of interest changes slowly, although the interference direction could be changed dynamically. The proliferation of High Speed Rail (HSR) and seamless wireless communication between infrastructure ( roadside, trackside equipment) and the vehicles (train, car, boat etc.) brings a unique challenge for adaptive beamforming due to its rapid change of DOA. For a HSR train with 250km/h, the DOA change speed can be up to 4⁰ per millisecond. To address these unique challenges, faster algorithms to calculate the beamforming weight based on the rapid-changing DOA are needed. In this dissertation, two strategies are adopted to address the challenges. The first one is to improve the weight calculation speed. The second strategy is to improve the speed of DOA estimation for the impinging signal by leveraging on the predefined constrained route for the transportation market. Based on these concepts, various algorithms in beampattern generation and adaptive weight control are evaluated and investigated in this thesis. The well known Generalized Sidelobe Cancellation (GSC) architecture is adopted in this dissertation. But it faces serious signal cancellation problem when the estimated DOA deviates from the actual DOA which is severe in high mobility scenarios as in the transportation market. Algorithms to improve various parts of the GSC are proposed in this dissertation. Firstly, a Cyclic Variable Step Size (CVSS) algorithm for adjusting the Least Mean Square (LMS) step size with simplicity for implementation is proposed and evaluated. Secondly, a Kalman filter based solution to fuse different sensor information for a faster estimation and tracking of the DOA is investigated and proposed. Thirdly, to address the DOA mismatch issue caused by the rapid DOA change, a fast blocking matrix generation algorithm named Simplifized Zero Placement Algorithm (SZPA) is proposed to mitigate the signal cancellation in GSC. Fourthly, to make the beam pattern robust against DOA mismatch, a fast algorithm for the generation of at beam pattern named Zero Placement Flat Top (ZPFT) for the fixed beamforming path in GSC is proposed. Finally, to evaluate the effectiveness and performance of the beamforming algorithms, wireless channel simulation is needed. One of the challenging aspects for wireless simulation is the coupling between Probability Density Function (PDF) and Power Spectral Density (PSD) for a random variable. In this regard, a simplified solution to simulate Non Gaussian wireless channel is proposed, proved and evaluated for the effectiveness of the algorithm. With the above optimizations, the controlled simulation shows that the at top beampattern can be generated 380 times faster than iterative optimization method and blocking matrix can be generated 9 times faster than normal SVD method while the same overall optimum state performance can be achieved

A Rapid Introduction to Adaptive Filtering

In this book, the authors provide insights into the basics of adaptive filtering, which are particularly useful for students taking their first steps into this field. They start by studying the problem of minimum mean-square-error filtering, i.e., Wiener filtering. Then, they analyze iterative methods for solving the optimization problem, e.g., the Method of Steepest Descent. By proposing stochastic approximations, several basic adaptive algorithms are derived, including Least Mean Squares (LMS), Normalized Least Mean Squares (NLMS) and Sign-error algorithms. The authors provide a general framework to study the stability and steady-state performance of these algorithms. The affine Projection Algorithm (APA) which provides faster convergence at the expense of computational complexity (although fast implementations can be used) is also presented. In addition, the Least Squares (LS) method and its recursive version (RLS), including fast implementations are discussed. The book closes with the discussion of several topics of interest in the adaptive filtering field