A Model of an Oscillatory Neural Network with Multilevel Neurons for Pattern Recognition and Computing

The current study uses a novel method of multilevel neurons and high order synchronization effects described by a family of special metrics, for pattern recognition in an oscillatory neural network (ONN). The output oscillator (neuron) of the network has multilevel variations in its synchronization value with the reference oscillator, and allows classification of an input pattern into a set of classes. The ONN model is implemented on thermally-coupled vanadium dioxide oscillators. The ONN is trained by the simulated annealing algorithm for selection of the network parameters. The results demonstrate that ONN is capable of classifying 512 visual patterns (as a cell array 3 * 3, distributed by symmetry into 102 classes) into a set of classes with a maximum number of elements up to fourteen. The classification capability of the network depends on the interior noise level and synchronization effectiveness parameter. The model allows for designing multilevel output cascades of neural networks with high net data throughput. The presented method can be applied in ONNs with various coupling mechanisms and oscillator topology.Comment: 26 pages, 24 figure

Implementation of a Synchronized Oscillator Circuit for Fast Sensing and Labeling of Image Objects

We present an application-specific integrated circuit (ASIC) CMOS chip that implements a synchronized oscillator cellular neural network with a matrix size of 32 × 32 for object sensing and labeling in binary images. Networks of synchronized oscillators are a recently developed tool for image segmentation and analysis. Its parallel network operation is based on a “temporary correlation” theory that attempts to describe scene recognition as if performed by the human brain. The synchronized oscillations of neuron groups attract a person’s attention if he or she is focused on a coherent stimulus (image object). For more than one perceived stimulus, these synchronized patterns switch in time between different neuron groups, thus forming temporal maps that code several features of the analyzed scene. In this paper, a new oscillator circuit based on a mathematical model is proposed, and the network architecture and chip functional blocks are presented and discussed. The proposed chip is implemented in AMIS 0.35 μm C035M-D 5M/1P technology. An application of the proposed network chip for the segmentation of insulin-producing pancreatic islets in magnetic resonance liver images is presented

A Method for Evaluating Chimeric Synchronization of Coupled Oscillators and Its Application for Creating a Neural Network Information Converter

This paper presents a new method for evaluating the synchronization of quasi-periodic oscillations of two oscillators, termed "chimeric synchronization". The family of metrics is proposed to create a neural network information converter based on a network of pulsed oscillators. In addition to transforming input information from digital to analogue, the converter can perform information processing after training the network by selecting control parameters. In the proposed neural network scheme, the data arrives at the input layer in the form of current levels of the oscillators and is converted into a set of non-repeating states of the chimeric synchronization of the output oscillator. By modelling a thermally coupled VO2-oscillator circuit, the network setup is demonstrated through the selection of coupling strength, power supply levels, and the synchronization efficiency parameter. The distribution of solutions depending on the operating mode of the oscillators, sub-threshold mode, or generation mode are revealed. Technological approaches for the implementation of a neural network information converter are proposed, and examples of its application for image filtering are demonstrated. The proposed method helps to significantly expand the capabilities of neuromorphic and logical devices based on synchronization effects.Comment: 25 pages, 20 figure

Beyond imaging with coherent anti-Stokes Raman scattering microscopy

La microscopie optique permet de visualiser des échantillons biologiques avec une bonne sensibilité et une résolution spatiale élevée tout en interférant peu avec les échantillons. La microscopie par diffusion Raman cohérente (CARS) est une technique de microscopie non linéaire basée sur l’effet Raman qui a comme avantage de fournir un mécanisme de contraste endogène sensible aux vibrations moléculaires. La microscopie CARS est maintenant une modalité d’imagerie reconnue, en particulier pour les expériences in vivo, car elle élimine la nécessité d’utiliser des agents de contraste exogènes, et donc les problèmes liés à leur distribution, spécificité et caractère invasif. Cependant, il existe encore plusieurs obstacles à l’adoption à grande échelle de la microscopie CARS en biologie et en médecine : le coût et la complexité des systèmes actuels, les difficultés d’utilisation et d’entretient, la rigidité du mécanisme de contraste, la vitesse de syntonisation limitée et le faible nombre de méthodes d’analyse d’image adaptées. Cette thèse de doctorat vise à aller au-delà de certaines des limites actuelles de l’imagerie CARS dans l’espoir que cela encourage son adoption par un public plus large. Tout d’abord, nous avons introduit un nouveau système d’imagerie spectrale CARS ayant une vitesse de syntonisation de longueur d’onde beaucoup plus rapide que les autres techniques similaires. Ce système est basé sur un laser à fibre picoseconde synchronisé qui est à la fois robuste et portable. Il peut accéder à des lignes de vibration Raman sur une plage importante (2700–2950 cm-1) à des taux allant jusqu’à 10 000 points spectrales par seconde. Il est parfaitement adapté pour l’acquisition d’images spectrales dans les tissus épais. En second lieu, nous avons proposé une nouvelle méthode d’analyse d’images pour l’évaluation de la structure de la myéline dans des images de sections longitudinales de moelle épinière. Nous avons introduit un indicateur quantitatif sensible à l’organisation de la myéline et démontré comment il pourrait être utilisé pour étudier certaines pathologies. Enfin, nous avons développé une méthode automatisé pour la segmentation d’axones myélinisés dans des images CARS de coupes transversales de tissu nerveux. Cette méthode a été utilisée pour extraire des informations morphologique des fibres nerveuses dans des images CARS de grande échelle.Optical-based microscopy techniques can sample biological specimens using many contrast mechanisms providing good sensitivity and high spatial resolution while minimally interfering with the samples. Coherent anti-Stokes Raman scattering (CARS) microscopy is a nonlinear microscopy technique based on the Raman effect. It shares common characteristics of other optical microscopy modalities with the added benefit of providing an endogenous contrast mechanism sensitive to molecular vibrations. CARS is now recognized as a great imaging modality, especially for in vivo experiments since it eliminates the need for exogenous contrast agents, and hence problems related to the delivery, specificity, and invasiveness of those markers. However, there are still several obstacles preventing the wide-scale adoption of CARS in biology and medicine: cost and complexity of current systems as well as difficulty to operate and maintain them, lack of flexibility of the contrast mechanism, low tuning speed and finally, poor accessibility to adapted image analysis methods. This doctoral thesis strives to move beyond some of the current limitations of CARS imaging in the hope that it might encourage a wider adoption of CARS as a microscopy technique. First, we introduced a new CARS spectral imaging system with vibrational tuning speed many orders of magnitude faster than other narrowband techniques. The system presented in this original contribution is based on a synchronized picosecond fibre laser that is both robust and portable. It can access Raman lines over a significant portion of the highwavenumber region (2700–2950 cm-1) at rates of up to 10,000 spectral points per second and is perfectly suitable for the acquisition of CARS spectral images in thick tissue. Secondly, we proposed a new image analysis method for the assessment of myelin health in images of longitudinal sections of spinal cord. We introduced a metric sensitive to the organization/disorganization of the myelin structure and showed how it could be used to study pathologies such as multiple sclerosis. Finally, we have developped a fully automated segmentation method specifically designed for CARS images of transverse cross sections of nerve tissue.We used our method to extract nerve fibre morphology information from large scale CARS images

The Future of Humanoid Robots

This book provides state of the art scientific and engineering research findings and developments in the field of humanoid robotics and its applications. It is expected that humanoids will change the way we interact with machines, and will have the ability to blend perfectly into an environment already designed for humans. The book contains chapters that aim to discover the future abilities of humanoid robots by presenting a variety of integrated research in various scientific and engineering fields, such as locomotion, perception, adaptive behavior, human-robot interaction, neuroscience and machine learning. The book is designed to be accessible and practical, with an emphasis on useful information to those working in the fields of robotics, cognitive science, artificial intelligence, computational methods and other fields of science directly or indirectly related to the development and usage of future humanoid robots. The editor of the book has extensive R&D experience, patents, and publications in the area of humanoid robotics, and his experience is reflected in editing the content of the book

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

A Survey on Reservoir Computing and its Interdisciplinary Applications Beyond Traditional Machine Learning

Reservoir computing (RC), first applied to temporal signal processing, is a recurrent neural network in which neurons are randomly connected. Once initialized, the connection strengths remain unchanged. Such a simple structure turns RC into a non-linear dynamical system that maps low-dimensional inputs into a high-dimensional space. The model's rich dynamics, linear separability, and memory capacity then enable a simple linear readout to generate adequate responses for various applications. RC spans areas far beyond machine learning, since it has been shown that the complex dynamics can be realized in various physical hardware implementations and biological devices. This yields greater flexibility and shorter computation time. Moreover, the neuronal responses triggered by the model's dynamics shed light on understanding brain mechanisms that also exploit similar dynamical processes. While the literature on RC is vast and fragmented, here we conduct a unified review of RC's recent developments from machine learning to physics, biology, and neuroscience. We first review the early RC models, and then survey the state-of-the-art models and their applications. We further introduce studies on modeling the brain's mechanisms by RC. Finally, we offer new perspectives on RC development, including reservoir design, coding frameworks unification, physical RC implementations, and interaction between RC, cognitive neuroscience and evolution.Comment: 51 pages, 19 figures, IEEE Acces

Doppler Radar Techniques for Distinct Respiratory Pattern Recognition and Subject Identification.

Ph.D. Thesis. University of Hawaiʻi at Mānoa 2017