Deep learning architectures for 2D and 3D scene perception

Scene understanding is a fundamental problem in computer vision tasks, that is being more intensively explored in recent years with the development of deep learning. In this dissertation, we proposed deep learning structures to address challenges in 2D and 3D scene perception. We developed several novel architectures for 3D point cloud understanding at city-scale point by effectively capturing both long-range and short-range information to handle the challenging problem of large variations in object size for city-scale point cloud segmentation. GLSNet++ is a two-branch network for multiscale point cloud segmentation that models this complex problem using both global and local processing streams to capture different levels of contextual and structural 3D point cloud information. We developed PointGrad, a new graph convolution gradient operator for capturing structural relationships, that encoded point-based directional gradients into a high-dimensional multiscale tensor space. Using the Point- Grad operator with graph convolution on scattered irregular point sets captures the salient structural information in the point cloud across spatial and feature scale space, enabling efficient learning. We integrated PointGrad with several deep network architectures for large-scale 3D point cloud semantic segmentation, including indoor scene and object part segmentation. In many real application areas including remote sensing and aerial imaging, the class imbalance is common and sufficient data for rare classes is hard to acquire or has high-cost associated with expert labeling. We developed MDXNet for few-shot and zero-shot learning, which emulates the human visual system by leveraging multi-domain knowledge from general visual primitives with transfer learning for more specialized learning tasks in various application domains. We extended deep learning methods in various domains, including the material domain for predicting carbon nanotube forest attributes and mechanical properties, biomedical domain for cell segmentation.Includes bibliographical references

Nano1D: An accurate Computer Vision model for segmentation and analysis of low-dimensional objects

Microscopy images are usually analyzed qualitatively or manually and there is a need for autonomous quantitative analysis of objects. In this paper, we present a physics-based computational model for accurate segmentation and geometrical analysis of one-dimensional irregular and deformable objects from microscopy images. This model, named Nano1D, has four steps of preprocessing, segmentation, separating overlapped objects and geometrical measurements. The model is tested on Ag nanowires, and successfully segments and analyzes their geometrical characteristics including length, width and distributions. The function of the algorithm is not undermined by the size, number, density, orientation and overlapping of objects in images. The main strength of the model is shown to be its ability to segment and analyze overlapping objects successfully with more than 99% accuracy, while current machine learning and computational models suffer from inaccuracy and inability to segment overlapping objects. Nano1D can analyze one-dimensional (1D) nanoparticles including nanowires, nanotubes, nanorods in addition to other 1D features of microstructures like microcracks, dislocations etc

A deep learning approach for determining the chiral indices of carbon nanotubes from high-resolution transmission electron microscopy images

Chiral indices determine important properties of carbon nanotubes (CNTs). Unfortunately, their determination from high-resolution transmission electron microscopy (HRTEM) images, the most accurate method for assigning chirality, is a tedious task. We develop a Convolutional Neural Network that automatizes this process. A large and realistic training data set of CNT images is obtained by means of atomistic computer simulations coupled with the multi-slice approach for image generation. In most cases, results of the automated assignment are in excellent agreement with manual classification, and the origin of failures is identified. The current approach, which combines HRTEM imaging and deep learning algorithms allows the analysis of a statistically significant number of HRTEM images of carbon nanotubes, paving the way for robust estimates of experimental chiral distributions.Comment: for use of the discussed computer code, please contact the corresponding autho

Design and Implementation of an Integrated Biosensor Platform for Lab-on-a-Chip Diabetic Care Systems

Recent advances in semiconductor processing and microfabrication techniques allow the implementation of complex microstructures in a single platform or lab on chip. These devices require fewer samples, allow lightweight implementation, and offer high sensitivities. However, the use of these microstructures place stringent performance constraints on sensor readout architecture. In glucose sensing for diabetic patients, portable handheld devices are common, and have demonstrated significant performance improvement over the last decade. Fluctuations in glucose levels with patient physiological conditions are highly unpredictable and glucose monitors often require complex control algorithms along with dynamic physiological data. Recent research has focused on long term implantation of the sensor system. Glucose sensors combined with sensor readout, insulin bolus control algorithm, and insulin infusion devices can function as an artificial pancreas. However, challenges remain in integrated glucose sensing which include degradation of electrode sensitivity at the microscale, integration of the electrodes with low power low noise readout electronics, and correlation of fluctuations in glucose levels with other physiological data. This work develops 1) a low power and compact glucose monitoring system and 2) a low power single chip solution for real time physiological feedback in an artificial pancreas system. First, glucose sensor sensitivity and robustness is improved using robust vertically aligned carbon nanofiber (VACNF) microelectrodes. Electrode architectures have been optimized, modeled and verified with physiologically relevant glucose levels. Second, novel potentiostat topologies based on a difference-differential common gate input pair transimpedance amplifier and low-power voltage controlled oscillators have been proposed, mathematically modeled and implemented in a 0.18μm [micrometer] complementary metal oxide semiconductor (CMOS) process. Potentiostat circuits are widely used as the readout electronics in enzymatic electrochemical sensors. The integrated potentiostat with VACNF microelectrodes achieves competitive performance at low power and requires reduced chip space. Third, a low power instrumentation solution consisting of a programmable charge amplifier, an analog feature extractor and a control algorithm has been proposed and implemented to enable continuous physiological data extraction of bowel sounds using a single chip. Abdominal sounds can aid correlation of meal events to glucose levels. The developed integrated sensing systems represent a significant advancement in artificial pancreas systems

Automatic RADAR Target Recognition System at THz Frequency Band. A Review

The development of technology for communication in the THz frequency band has seen rapid progress recently. Due to the wider bandwidth a THz frequency RADAR provides the possibility of higher precision imaging compared to conventional RADARs. A high resolution RADAR operating at THz frequency can be used for automatically detecting and segmenting concealed objects. Recent advancements in THz circuit integration have opened up a wide range of possibilities for on chip applications, like of security and surveillance. The development of various sources and detectors for generation and detection of THz frequency has been driven by other techniques such as spectroscopy, imaging and impulse ranging. One of the central vision of this type of security system aims at ambient intelligence: the computation and communication carried out intelligently. The need for higher mobility with limited size and power consumption has led to development of nanotechnology based THz generators. In addition to this some of the soft computing tools are used for detection of radar target automatically based on some algorithms named as ANN, RNN, Neuro-Fuzzy and Genetic algorithms. This review article includes UWB radar for THz signal, its characteristics and application, Nanotechnology for THz generation and issues related to ATR

Deep Learning, Feature Learning, and Clustering Analysis for SEM Image Classification

In this paper, we report upon our recent work aimed at improving and adapting machine learning algorithms to automatically classify nanoscience images acquired by the Scanning Electron Microscope (SEM). This is done by coupling supervised and unsupervised learning approaches. We first investigate supervised learning on a ten-category data set of images and compare the performance of the different models in terms of training accuracy. Then, we reduce the dimensionality of the features through autoencoders to perform unsupervised learning on a subset of images in a selected range of scales (from 1 $\mu$m to 2 $\mu$m). Finally, we compare different clustering methods to uncover intrinsic structures in the images

Parallel computing for brain simulation

[Abstract] Background: The human brain is the most complex system in the known universe, it is therefore one of the greatest mysteries. It provides human beings with extraordinary abilities. However, until now it has not been understood yet how and why most of these abilities are produced. Aims: For decades, researchers have been trying to make computers reproduce these abilities, focusing on both understanding the nervous system and, on processing data in a more efficient way than before. Their aim is to make computers process information similarly to the brain. Important technological developments and vast multidisciplinary projects have allowed creating the first simulation with a number of neurons similar to that of a human brain. Conclusion: This paper presents an up-to-date review about the main research projects that are trying to simulate and/or emulate the human brain. They employ different types of computational models using parallel computing: digital models, analog models and hybrid models. This review includes the current applications of these works, as well as future trends. It is focused on various works that look for advanced progress in Neuroscience and still others which seek new discoveries in Computer Science (neuromorphic hardware, machine learning techniques). Their most outstanding characteristics are summarized and the latest advances and future plans are presented. In addition, this review points out the importance of considering not only neurons: Computational models of the brain should also include glial cells, given the proven importance of astrocytes in information processing.Galicia. Consellería de Cultura, Educación e Ordenación Universitaria; GRC2014/049Galicia. Consellería de Cultura, Educación e Ordenación Universitaria; R2014/039Instituto de Salud Carlos III; PI13/0028