Hierarchical Associative Memory Based on Oscillatory Neural Network

In this thesis we explore algorithms and develop architectures based on emerging nano-device technologies for cognitive computing tasks such as recognition, classification, and vision. In particular we focus on pattern matching in high dimensional vector spaces to address the nearest neighbor search problem. Recent progress in nanotechnology provides us novel nano-devices with special nonlinear response characteristics that fit cognitive tasks better than general purpose computing. We build an associative memory (AM) by weakly coupling nano-oscillators as an oscillatory neural network and design a hierarchical tree structure to organize groups of AM units. For hierarchical recognition, we first examine an architecture where image patterns are partitioned into different receptive fields and processed by individual AM units in lower levels, and then abstracted using sparse coding techniques for recognition at higher levels. A second tree structure model is developed as a more scalable AM architecture for large data sets. In this model, patterns are classified by hierarchical k-means clustering and organized in hierarchical clusters. Then the recognition process is done by comparison between the input patterns and centroids identified in the clustering process. The tree is explored in a "depth-only" manner until the closest image pattern is output. We also extend this search technique to incorporate a branch-and-bound algorithm. The models and corresponding algorithms are tested on two standard face recognition data-sets. We show that the depth-only hierarchical model is very data-set dependent and performs with 97% or 67% recognition when compared to a single large associative memory, while the branch and bound search increases time by only a factor of two compared to the depth-only search

Recent Advances in Social Data and Artificial Intelligence 2019

The importance and usefulness of subjects and topics involving social data and artificial intelligence are becoming widely recognized. This book contains invited review, expository, and original research articles dealing with, and presenting state-of-the-art accounts pf, the recent advances in the subjects of social data and artificial intelligence, and potentially their links to Cyberspace

Large Area Land Cover Mapping Using Deep Neural Networks and Landsat Time-Series Observations

This dissertation focuses on analysis and implementation of deep learning methodologies in the field of remote sensing to enhance land cover classification accuracy, which has important applications in many areas of environmental planning and natural resources management. The first manuscript conducted a land cover analysis on 26 Landsat scenes in the United States by considering six classifier variants. An extensive grid search was conducted to optimize classifier parameters using only the spectral components of each pixel. Results showed no gain in using deep networks by using only spectral components over conventional classifiers, possibly due to the small reference sample size and richness of features. The effect of changing training data size, class distribution, or scene heterogeneity were also studied and we found all of them having significant effect on classifier accuracy. The second manuscript reviewed 103 research papers on the application of deep learning methodologies in remote sensing, with emphasis on per-pixel classification of mono-temporal data and utilizing spectral and spatial data dimensions. A meta-analysis quantified deep network architecture improvement over selected convolutional classifiers. The effect of network size, learning methodology, input data dimensionality and training data size were also studied, with deep models providing enhanced performance over conventional one using spectral and spatial data. The analysis found that input dataset was a major limitation and available datasets have already been utilized to their maximum capacity. The third manuscript described the steps to build the full environment for dataset generation based on Landsat time-series data using spectral, spatial, and temporal information available for each pixel. A large dataset containing one sample block from each of 84 ecoregions in the conterminous United States (CONUS) was created and then processed by a hybrid convolutional+recurrent deep network, and the network structure was optimized with thousands of simulations. The developed model achieved an overall accuracy of 98% on the test dataset. Also, the model was evaluated for its overall and per-class performance under different conditions, including individual blocks, individual or combined Landsat sensors, and different sequence lengths. The analysis found that although the deep model performance per each block is superior to other candidates, the per block performance still varies considerably from block to block. This suggests extending the work by model fine-tuning for local areas. The analysis also found that including more time stamps or combining different Landsat sensor observations in the model input significantly enhances the model performance

Neuroimage

The brain can be considered as an information processing network, where complex behavior manifests as a result of communication between large-scale functional systems such as visual and default mode networks. As the communication between brain regions occurs through underlying anatomical pathways, it is important to define a "traffic pattern" that properly describes how the regions exchange information. Empirically, the choice of the traffic pattern can be made based on how well the functional connectivity between regions matches the structural pathways equipped with that traffic pattern. In this paper, we present a multimodal connectomics paradigm utilizing graph matching to measure similarity between structural and functional connectomes (derived from dMRI and fMRI data) at node, system, and connectome level. Through an investigation of the brain's structure-function relationship over a large cohort of 641 healthy developmental participants aged 8-22 years, we demonstrate that communicability as the traffic pattern describes the functional connectivity of the brain best, with large-scale systems having significant agreement between their structural and functional connectivity patterns. Notably, matching between structural and functional connectivity for the functionally specialized modular systems such as visual and motor networks are higher as compared to other more integrated systems. Additionally, we show that the negative functional connectivity between the default mode network (DMN) and motor, frontoparietal, attention, and visual networks is significantly associated with its underlying structural connectivity, highlighting the counterbalance between functional activation patterns of DMN and other systems. Finally, we investigated sex difference and developmental changes in brain and observed that similarity between structure and function changes with development.S10 OD023495/CD/ODCDC CDC HHS/United StatesR01 HD089390/HD/NICHD NIH HHS/United StatesR01 NS096606/NS/NINDS NIH HHS/United StatesS10 OD023495/OD/NIH HHS/United StatesRC2 MH089983/MH/NIMH NIH HHS/United States2020-10-01T00:00:00Z31141738PMC66889608401vault:3371

Visual Tracking and Motion Estimation for an On-orbit Servicing of a Satellite

This thesis addresses visual tracking of a non-cooperative as well as a partially cooperative satellite, to enable close-range rendezvous between a servicer and a target satellite. Visual tracking and estimation of relative motion between a servicer and a target satellite are critical abilities for rendezvous and proximity operation such as repairing and deorbiting. For this purpose, Lidar has been widely employed in cooperative rendezvous and docking missions. Despite its robustness to harsh space illumination, Lidar has high weight and rotating parts and consumes more power, thus undermines the stringent requirements of a satellite design. On the other hand, inexpensive on-board cameras can provide an effective solution, working at a wide range of distances. However, conditions of space lighting are particularly challenging for image based tracking algorithms, because of the direct sunlight exposure, and due to the glossy surface of the satellite that creates strong reflection and image saturation, which leads to difficulties in tracking procedures. In order to address these difficulties, the relevant literature is examined in the fields of computer vision, and satellite rendezvous and docking. Two classes of problems are identified and relevant solutions, implemented on a standard computer are provided. Firstly, in the absence of a geometric model of the satellite, the thesis presents a robust feature-based method with prediction capability in case of insufficient features, relying on a point-wise motion model. Secondly, we employ a robust model-based hierarchical position localization method to handle change of image features along a range of distances, and localize an attitude-controlled (partially cooperative) satellite. Moreover, the thesis presents a pose tracking method addressing ambiguities in edge-matching, and a pose detection algorithm based on appearance model learning. For the validation of the methods, real camera images and ground truth data, generated with a laboratory tet bed similar to space conditions are used. The experimental results indicate that camera based methods provide robust and accurate tracking for the approach of malfunctioning satellites in spite of the difficulties associated with specularities and direct sunlight. Also exceptional lighting conditions associated to the sun angle are discussed, aimed at achieving fully reliable localization system in a certain mission