Microstimulation and multicellular analysis: A neural interfacing system for spatiotemporal stimulation

Willfully controlling the focus of an extracellular stimulus remains a significant challenge in the development of neural prosthetics and therapeutic devices. In part, this challenge is due to the vast set of complex interactions between the electric fields induced by the microelectrodes and the complex morphologies and dynamics of the neural tissue. Overcoming such issues to produce methodologies for targeted neural stimulation requires a system that is capable of (1) delivering precise, localized stimuli a function of the stimulating electrodes and (2) recording the locations and magnitudes of the resulting evoked responses a function of the cell geometry and membrane dynamics. In order to improve stimulus delivery, we developed microfabrication technologies that could specify the electrode geometry and electrical properties. Specifically, we developed a closed-loop electroplating strategy to monitor and control the morphology of surface coatings during deposition, and we implemented pulse-plating techniques as a means to produce robust, resilient microelectrodes that could withstand rigorous handling and harsh environments. In order to evaluate the responses evoked by these stimulating electrodes, we developed microscopy techniques and signal processing algorithms that could automatically identify and evaluate the electrical response of each individual neuron. Finally, by applying this simultaneous stimulation and optical recording system to the study of dissociated cortical cultures in multielectode arrays, we could evaluate the efficacy of excitatory and inhibitory waveforms. Although we found that the proximity of the electrode is a poor predictor of individual neural excitation thresholds, we have shown that it is possible to use inhibitory waveforms to globally reduce excitability in the vicinity of the electrode. Thus, the developed system was able to provide very high resolution insight into the complex set of interactions between the stimulating electrodes and populations of individual neurons.Ph.D.Committee Chair: Stephen P. DeWeerth; Committee Member: Bruce Wheeler; Committee Member: Michelle LaPlaca; Committee Member: Robert Lee; Committee Member: Steve Potte

Object Classification Using Substance Based Neural Network

Object recognition has shown tremendous increase in the field of image analysis. The required set of image objects is identified and retrieved on the basis of object recognition. In this paper, we propose a novel classification technique called substance based image classification (SIC) using a wavelet neural network. The foremost task of SIC is to remove the surrounding regions from an image to reduce the misclassified portion and to effectively reflect the shape of an object. At first, the image to be extracted is performed with SIC system through the segmentation of the image. Next, in order to attain more accurate information, with the extracted set of regions, the wavelet transform is applied for extracting the configured set of features. Finally, using the neural network classifier model, misclassification over the given natural images and further background images are removed from the given natural image using the LSEG segmentation. Moreover, to increase the accuracy of object classification, SIC system involves the removal of the regions in the surrounding image. Performance evaluation reveals that the proposed SIC system reduces the occurrence of misclassification and reflects the exact shape of an object to approximately 10–15%

Substance Based Image Classification using Wavelet Neural Network

Background: Substance based Image Classification (SIC) using a wavelet neural network can use for efficient recognition. The foremost task of SIC is to remove the surrounding regions from an image to reduce the misclassified portion and to effectively reflect a shape of an object. At first, the image to be extracted is performed with SIC system through the segmentation of the image. Next, in order to attain more accurate information, with the extracted set of regions the wavelet transform is applied for extracting the configured set of features. Finally, using the neural network classifier model, misclassification over the given natural images and further background images are removed from the given natural image using the LSEG segmentation. Moreover, to increase the accuracy of object classification, SIC system involves the removal of the regions in the surrounding image.Objective: The main objective is provide better object recognition system for object classification with more accuracy with less error rate using substance based image classification. Results: Experimental results with natural image dataset substance based image classification are complementary to existing region boundary representation model. Performance evaluation reveals that the proposed SIC system reduces the occurrence of misclassification and reflects the exact shape of an object to approximately 10-15%. Conclusion: To reduce the misclassification over the given image data, the background regions are removed from the given image data based by adapting the LSEG segmentation. To obtain the more accurate information from the image object data, the wavelet transform is applied to obtain the configured quality features. Based on the feature set, the information about the image objects data from the region boundary images are obtained. Besides, the object classifier is implemented for classification of image to obtain the exact shape of the object. Experimental evaluation is conducted with the natural image dataset to check the performance of the proposed SIC system. Evaluation results revealed that the proposed SIC system achieved a higher classification rate by removing the surrounding regions of the image. Moreover, the feature extraction process provides the highest classification rate which enhances the performance of substance based image classification system. At the same time, the proposed SIC system revealed that the occurrence of misclassification of image data is less and the acquiring the image object data in the rate of 13% compared to the existing work

VISUAL OBJECT DETECTION BY COLOR, SHAPE AND DIMENSION (DETECCIÓN VISUAL DE OBJETOS POR COLOR, FORMA Y DIMENSIÓN)

Abstract 3D neural object detection by color, shape and dimension (3DOD-CSD) is a novel and powerful tool that can be used to build object detection systems in environments with uncontrolled illumination. Inspired by the global structure of the human visual system, it uses a neural network in the classification stage and determines the physical dimension of the object’s fea-tures using commercial digital cameras calibrated in stereo configuration. This permits the analysis of images of objects with the same form and color but different dimensions, such as scaled replicas or photographs of a photograph of a 3D object. With this method, a fixed distance from the camera to the object to be analyzed is not necessary - essential for a dynamic recognition system in changing conditions. The results show strong discrimination between desired and undesired objects. This system has many possible applications, including face identification and object selection in varying environments, utility pole detection, coin detection, and more. Keywords: Color, dimensional features, invariant features, neural network, stereo vision, 3D object recognition. Resumen La detección de objetos 3D por color, forma y dimensión (3DOD-CSD) es una herramienta para construir sistemas de detección de objetos en entornos con iluminación incontrolada. Inspirado en la estructura global del sistema visual humano, utiliza una red neuronal en la etapa de clasificación y determina la dimensión física de las características del objeto utilizando cámaras digitales comerciales calibradas en configuración estéreo. Esto permite el análisis de imágenes de objetos con la misma forma y color, pero de diferentes dimensiones, como réplicas a escala o fotografías de una fotografía de un objeto 3D. Con este método, no es necesaria una distancia fija entre la cámara y el objeto a analizar, algo esencial para un sistema de reconocimiento dinámico en condiciones cambiantes. Los resultados muestran una fuerte discriminación entre objetos deseados y no deseados. Este sistema tiene muchas aplicaciones posibles, incluida la identificación de rostros y la selección de objetos en entornos variantes, detección de postes, detección de monedas, entre otros. Palabras Clave: Color, características dimensionales, características invariantes, reconocimiento de objetos 3D, red neuronal, visión estéreo

Segmentation of images by color features: a survey

En este articulo se hace la revisión del estado del arte sobre la segmentación de imagenes de colorImage segmentation is an important stage for object recognition. Many methods have been proposed in the last few years for grayscale and color images. In this paper, we present a deep review of the state of the art on color image segmentation methods; through this paper, we explain the techniques based on edge detection, thresholding, histogram-thresholding, region, feature clustering and neural networks. Because color spaces play a key role in the methods reviewed, we also explain in detail the most commonly color spaces to represent and process colors. In addition, we present some important applications that use the methods of image segmentation reviewed. Finally, a set of metrics frequently used to evaluate quantitatively the segmented images is shown

Neuromorphic Character Recognition System With Two PCMO Memristors as a Synapse

Using memristor devices as synaptic connections has been suggested with different neural architectures in the literature. Most of the published works focus on simulating some plasticity mechanism for changing memristor conductance. This paper presents a neural architecture of a character recognition neural system using Al/Pr0.7Ca0.3MnO3 (PCMO) memristors. The PCMO memristor has an inhomogeneous barrier at the aluminum and PCMO interface which gives rise to an asymmetrical behavior when moving from high resistance to low resistance and vice versa. This paper details the design and simulations for solving this asymmetrical memristor behavior. Also, a general memory read/write framework is used to describe the running and plasticity of neural systems. The proposed neural system can be produced in hardware using a small 1 K crossbar memristor grid and CMOS neural nodes as presented in the simulation results.X111917Ysciescopu

Remote Sensing

This dual conception of remote sensing brought us to the idea of preparing two different books; in addition to the first book which displays recent advances in remote sensing applications, this book is devoted to new techniques for data processing, sensors and platforms. We do not intend this book to cover all aspects of remote sensing techniques and platforms, since it would be an impossible task for a single volume. Instead, we have collected a number of high-quality, original and representative contributions in those areas