A comparative evaluation for liver segmentation from spir images and a novel level set method using signed pressure force function

Thesis (Doctoral)--Izmir Institute of Technology, Electronics and Communication Engineering, Izmir, 2013Includes bibliographical references (leaves: 118-135)Text in English; Abstract: Turkish and Englishxv, 145 leavesDeveloping a robust method for liver segmentation from magnetic resonance images is a challenging task due to similar intensity values between adjacent organs, geometrically complex liver structure and injection of contrast media, which causes all tissues to have different gray level values. Several artifacts of pulsation and motion, and partial volume effects also increase difficulties for automatic liver segmentation from magnetic resonance images. In this thesis, we present an overview about liver segmentation methods in magnetic resonance images and show comparative results of seven different liver segmentation approaches chosen from deterministic (K-means based), probabilistic (Gaussian model based), supervised neural network (multilayer perceptron based) and deformable model based (level set) segmentation methods. The results of qualitative and quantitative analysis using sensitivity, specificity and accuracy metrics show that the multilayer perceptron based approach and a level set based approach which uses a distance regularization term and signed pressure force function are reasonable methods for liver segmentation from spectral pre-saturation inversion recovery images. However, the multilayer perceptron based segmentation method requires a higher computational cost. The distance regularization term based automatic level set method is very sensitive to chosen variance of Gaussian function. Our proposed level set based method that uses a novel signed pressure force function, which can control the direction and velocity of the evolving active contour, is faster and solves several problems of other applied methods such as sensitivity to initial contour or variance parameter of the Gaussian kernel in edge stopping functions without using any regularization term

Energy efficient enabling technologies for semantic video processing on mobile devices

Semantic object-based processing will play an increasingly important role in future multimedia systems due to the ubiquity of digital multimedia capture/playback technologies and increasing storage capacity. Although the object based paradigm has many undeniable benefits, numerous technical challenges remain before the applications becomes pervasive, particularly on computational constrained mobile devices. A fundamental issue is the ill-posed problem of semantic object segmentation. Furthermore, on battery powered mobile computing devices, the additional algorithmic complexity of semantic object based processing compared to conventional video processing is highly undesirable both from a real-time operation and battery life perspective. This thesis attempts to tackle these issues by firstly constraining the solution space and focusing on the human face as a primary semantic concept of use to users of mobile devices. A novel face detection algorithm is proposed, which from the outset was designed to be amenable to be offloaded from the host microprocessor to dedicated hardware, thereby providing real-time performance and reducing power consumption. The algorithm uses an Artificial Neural Network (ANN), whose topology and weights are evolved via a genetic algorithm (GA). The computational burden of the ANN evaluation is offloaded to a dedicated hardware accelerator, which is capable of processing any evolved network topology. Efficient arithmetic circuitry, which leverages modified Booth recoding, column compressors and carry save adders, is adopted throughout the design. To tackle the increased computational costs associated with object tracking or object based shape encoding, a novel energy efficient binary motion estimation architecture is proposed. Energy is reduced in the proposed motion estimation architecture by minimising the redundant operations inherent in the binary data. Both architectures are shown to compare favourable with the relevant prior art

Deep learning and feature engineering techniques applied to the myoelectric signal for accurate prediction of movements

Técnicas de reconhecimento de padrões no Sinal Mioelétrico (EMG) são empregadas no desenvolvimento de próteses robóticas, e para isso, adotam diversas abordagens de Inteligência Artificial (IA). Esta Tese se propõe a resolver o problema de reconhecimento de padrões EMG através da adoção de técnicas de aprendizado profundo de forma otimizada. Para isso, desenvolveu uma abordagem que realiza a extração da característica a priori, para alimentar os classificadores que supostamente não necessitam dessa etapa. O estudo integrou a plataforma BioPatRec (estudo e desenvolvimento avançado de próteses) a dois algoritmos de classificação (Convolutional Neural Network e Long Short-Term Memory) de forma híbrida, onde a entrada fornecida à rede já possui características que descrevem o movimento (nível de ativação muscular, magnitude, amplitude, potência e outros). Assim, o sinal é rastreado como uma série temporal ao invés de uma imagem, o que nos permite eliminar um conjunto de pontos irrelevantes para o classificador, tornando a informação expressivas. Na sequência, a metodologia desenvolveu um software que implementa o conceito introduzido utilizando uma Unidade de Processamento Gráfico (GPU) de modo paralelo, esse incremento permitiu que o modelo de classificação aliasse alta precisão com um tempo de treinamento inferior a 1 segundo. O modelo paralelizado foi chamado de BioPatRec-Py e empregou algumas técnicas de Engenharia de Features que conseguiram tornar a entrada da rede mais homogênea, reduzindo a variabilidade, o ruído e uniformizando a distribuição. A pesquisa obteve resultados satisfatórios e superou os demais algoritmos de classificação na maioria dos experimentos avaliados. O trabalho também realizou uma análise estatística dos resultados e fez o ajuste fino dos hiper-parâmetros de cada uma das redes. Em última instancia, o BioPatRec-Py forneceu um modelo genérico. A rede foi treinada globalmente entre os indivíduos, permitindo a criação de uma abordagem global, com uma precisão média de 97,83%.Pattern recognition techniques in the Myoelectric Signal (EMG) are employed in the development of robotic prostheses, and for that, they adopt several approaches of Artificial Intelligence (AI). This Thesis proposes to solve the problem of recognition of EMG standards through the adoption of profound learning techniques in an optimized way. The research developed an approach that extracts the characteristic a priori to feed the classifiers that supposedly do not need this step. The study integrated the BioPatRec platform (advanced prosthesis study and development) to two classification algorithms (Convolutional Neural Network and Long Short-Term Memory) in a hybrid way, where the input provided to the network already has characteristics that describe the movement (level of muscle activation, magnitude, amplitude, power, and others). Thus, the signal is tracked as a time series instead of an image, which allows us to eliminate a set of points irrelevant to the classifier, making the information expressive. In the sequence, the methodology developed software that implements the concept introduced using a Graphical Processing Unit (GPU) in parallel this increment allowed the classification model to combine high precision with a training time of less than 1 second. The parallel model was called BioPatRec-Py and employed some Engineering techniques of Features that managed to make the network entry more homogeneous, reducing variability, noise, and standardizing distribution. The research obtained satisfactory results and surpassed the other classification algorithms in most of the evaluated experiments. The work performed a statistical analysis of the outcomes and fine-tuned the hyperparameters of each of the networks. Ultimately, BioPatRec-Py provided a generic model. The network was trained globally between individuals, allowing the creation of a standardized approach, with an average accuracy of 97.83%

Recent Trends in Communication Networks

In recent years there has been many developments in communication technology. This has greatly enhanced the computing power of small handheld resource-constrained mobile devices. Different generations of communication technology have evolved. This had led to new research for communication of large volumes of data in different transmission media and the design of different communication protocols. Another direction of research concerns the secure and error-free communication between the sender and receiver despite the risk of the presence of an eavesdropper. For the communication requirement of a huge amount of multimedia streaming data, a lot of research has been carried out in the design of proper overlay networks. The book addresses new research techniques that have evolved to handle these challenges

Advanced Applications of Rapid Prototyping Technology in Modern Engineering

Rapid prototyping (RP) technology has been widely known and appreciated due to its flexible and customized manufacturing capabilities. The widely studied RP techniques include stereolithography apparatus (SLA), selective laser sintering (SLS), three-dimensional printing (3DP), fused deposition modeling (FDM), 3D plotting, solid ground curing (SGC), multiphase jet solidification (MJS), laminated object manufacturing (LOM). Different techniques are associated with different materials and/or processing principles and thus are devoted to specific applications. RP technology has no longer been only for prototype building rather has been extended for real industrial manufacturing solutions. Today, the RP technology has contributed to almost all engineering areas that include mechanical, materials, industrial, aerospace, electrical and most recently biomedical engineering. This book aims to present the advanced development of RP technologies in various engineering areas as the solutions to the real world engineering problems

Advancements and Breakthroughs in Ultrasound Imaging

Ultrasonic imaging is a powerful diagnostic tool available to medical practitioners, engineers and researchers today. Due to the relative safety, and the non-invasive nature, ultrasonic imaging has become one of the most rapidly advancing technologies. These rapid advances are directly related to the parallel advancements in electronics, computing, and transducer technology together with sophisticated signal processing techniques. This book focuses on state of the art developments in ultrasonic imaging applications and underlying technologies presented by leading practitioners and researchers from many parts of the world

A new user interface for musical timbre design

This thesis characterises and addresses problems and issues associated with the design of intuitive user interfaces for timbral control. The usability of a range of synthesis methods and representative implementations of these methods is assessed, and three interface architectures - fixed architecture, architecture specification and direct specification - are identified. The characteristics of each of these architectures, as well as problems of usability inherent to each of them are discussed; it is argued that none of them provide intuitive tools for the manipulation and control of timbre. The study examines the nature of timbre and the notion of timbre space; different kinds of timbre space are considered and criteria are proposed for the selection of suitable timbre spaces as vehicles for synthesis. A number of listening tests, designed to demonstrate the feasibility of subsequent work, were devised and carried out; the results of these tests provide evidence that, where Euclidean distances between sounds located in a given timbre space are reflected in perceptual distances, the ability of subjects to detect relative distances in different parts of the space varies with the perceptual granularity of the space. Three contrasting timbre spaces conforming to the proposed criteria for use in synthesis are constructed; the purpose of these spaces is to provide an environment for a novel user interaction approach for timbral design which incorporates a search strategy based on weighted centroid localization. Two prototypes which exemplify the proposed approach in alternative ways are designed, implemented and tested with potential users in order to validate the approach; a third contrasting prototype which represents a simple contrasting alternative is tested for purposes of comparison. The results of these tests are evaluated and discussed, and areas of further work identified

Marshall Space Flight Center Research and Technology Report 2019

Today, our calling to explore is greater than ever before, and here at Marshall Space Flight Centerwe make human deep space exploration possible. A key goal for Artemis is demonstrating and perfecting capabilities on the Moon for technologies needed for humans to get to Mars. This years report features 10 of the Agencys 16 Technology Areas, and I am proud of Marshalls role in creating solutions for so many of these daunting technical challenges. Many of these projects will lead to sustainable in-space architecture for human space exploration that will allow us to travel to the Moon, on to Mars, and beyond. Others are developing new scientific instruments capable of providing an unprecedented glimpse into our universe. NASA has led the charge in space exploration for more than six decades, and through the Artemis program we will help build on our work in low Earth orbit and pave the way to the Moon and Mars. At Marshall, we leverage the skills and interest of the international community to conduct scientific research, develop and demonstrate technology, and train international crews to operate further from Earth for longer periods of time than ever before first at the lunar surface, then on to our next giant leap, human exploration of Mars. While each project in this report seeks to advance new technology and challenge conventions, it is important to recognize the diversity of activities and people supporting our mission. This report not only showcases the Centers capabilities and our partnerships, it also highlights the progress our people have achieved in the past year. These scientists, researchers and innovators are why Marshall and NASA will continue to be a leader in innovation, exploration, and discovery for years to come