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

A fully hardware-based memristive multilayer neural network

Memristive crossbar arrays promise substantial improvements in computing throughput and power efficiency through in-memory analog computing. Previous machine learning demonstrations with memristive arrays, however, relied on software or digital processors to implement some critical functionalities, leading to frequent analog/digital conversions and more complicated hardware that compromises the energy efficiency and computing parallelism. Here, we show that, by implementing the activation function of a neural network in analog hardware, analog signals can be transmitted to the next layer without unnecessary digital conversion, communication, and processing. We have designed and built compact rectified linear units, with which we constructed a two-layer perceptron using memristive crossbar arrays, and demonstrated a recognition accuracy of 93.63% for the Modified National Institute of Standard and Technology (MNIST) handwritten digits dataset. The fully hardware-based neural network reduces both the data shuttling and conversion, capable of delivering much higher computing throughput and power efficiency

Identification of chemical species using artificial intelligence to interpret optical emission spectra

The nonlinear modeling capabilities of artificial neural networks (ANN’s) are renowned in the field of artificial intelligence (Al) for capturing knowledge that can be very difficult to understand otherwise. Their ability to be trained on representative data within a particular problem domain and generalise over a set of data make them efficient predictive models. One problem domain that contains complex data that would benefit from the predictive capabilities of ANN’s is that of optical emission spectra (OES). OES is an important diagnostic for monitoring plasma species within plasma processing. Normally, OES spectral interpretation requires significant prior expertise from a spectroscopist. One way of alleviating this intensive demand in order to quickly interpret OES spectra is to interpret the data using an intelligent pattern recognition technique like ANN’s. This thesis investigates and presents MLP ANN models that can successfully classify chemical species within OES spectral patterns. The primary contribution of the thesis is the creation of deployable ANN species models that can predict OES spectral line sizes directly from six controllable input process parameters; and the implementation of a novel rule extraction procedure to relate the real multi-output values of the spectral line sizes to individual input process parameters. Not only are the trained species models excellent in their predictive capability, but they also provide the foundation for extracting comprehensible rules. A secondary contribution made by this thesis is to present an adapted fuzzy rule extraction system that attaches a quantitative measure of confidence to individual rules. The most significant contribution to the field of Al that is generated from the work presented in the thesis is the fact that the rule extraction procedure utilises predictive ANN species models that employ real continuously valued multi-output data. This is an improvement on rule extraction from trained networks that normally focus on discrete binary output

Pyroelectric detector signal measurement and processing

Práce se zabývá fyzikálními vlastnostmi pyroelektrických senzorů a jejich praktickým využitím. Součástí práce je návrh a realizace měřící aparatury, jež bude využita k měření fyzikálních vlastností senzorů. Pro měření signálů pyroelektrického senzoru bude navržen nízkošumový zesilovač. Součástí práce je také návrh a realizace algoritmu pro lokalizaci infračerveného zdroje záření (plamene) v prostoru, na základě vyhodnoceného analogového signálu.The thesis analyzes the physical properties of the pyroelectric sensors and its practical use. Essential part of the work is the design and realization of the measuring set-up, which is used for the measurements of the sensors physical properties. With this workbench, main parameters of the pyroelectric sensors have been obtained. The second part of the work deals with a low noise preamplifier designing. This device was designed for the pyroelectric sensor signal measurements. The amplifier is designed to be used for a low noise, wide band measuring. During the process of amplifier designing, all the noise components have been investigated separately, using operational amplifiers models. The objective of the last part of this work is to develop the system, which would be able to localize an infrared (IR) emitting source located somewhere in the space between the installed pyroelectric sensors. For this purpose, classical localization methods could be used as well as the artificial neural networks (ANN), which are becoming still more popular these days. The system is able to detect the exact placement of the IR radiation source.

An Intelligent Neural Network Controlled Atmospheric Pressure Spatial Atomic Layer Deposition System for Tunable Metal Oxide Thin Films

Atmospheric pressure spatial atomic layer deposition (AP-SALD) is a novel thin film fabrication technique to create conformal and pin-hole free films. The growth rate of AP-SALD is hundreds of times greater than conventional vacuum based methods which makes AP-SALD particularly attractive for large-scale roll-to-roll manufacturing. In this work, an AP-SALD system was built from scratch, in situ characterization tools were developed to monitor film properties during the growth of the film, an artificial neural network (ANN) was trained to model the AP-SALD system and an inverse neural network algorithm was implemented to generate recipes to produce films with defined properties. A commercial controller was used to interface with all the sensors and actuators in the AP-SALD system. A time-optimal 5th order trajectory with a drift algorithm was implemented to oscillate the substrate stage back and forth, and a feedback control with feedforward and an anti-windup algorithm was used to maintain the temperature of the substrate during deposition. The position error of the stage was visually imperceptible after 1000 oscillations and the temperature settling time of the stage was around 10 minutes which was significantly less than the time required to set up the AP-SALD system. The AP-SALD system was able to repeatably deposit zinc oxide (ZnO) films with diethylzinc and water as precursors. In situ electrical and optical tools were developed to measure the resistivity, film thickness and bandgap during the deposition. A printed circuit board (PCB) was designed to act as the electrodes and the substrate. A source/measure unit along with custom electronics measured the resistance of the films. The ZnO film grew on the PCB in island formations before coalescing into a continuous film. The bulk resistivity of ZnO was found to be approximately 15 Ω·cm. Reflectance spectroscopy was used to measure the bandgap and thickness of the film during the deposition by fitting the spectrum to the Tauc-Lorentz model. A bandgap of 3.18 eV was found for ZnO films. The lowest growth rate achieved was 0.27 nm per ALD cycle indicating there is some mixing of the precursors. A shallow feedforward ANN was trained to model the AP-SALD. The mean squared error (MSE) of the training set and test set was 0.9792 and 1.3287, respectively, indicating a good fit that can generalize well to new data. An inverse neural network algorithm was implemented to find the deposition parameters given the desired film properties. Since there may be infinite solutions, the algorithm returns the first optimal solution. The MSE of the estimated parameters were 2.5851E-14 indicating the algorithm was able to accurately inverse the AP-SALD model

Research reports: 1990 NASA/ASEE Summer Faculty Fellowship Program

Reports on the research projects performed under the NASA/ASEE Summer Faculty Fellowship Program are presented. The program was conducted by The University of Alabama and MSFC during the period from June 4, 1990 through August 10, 1990. Some of the topics covered include: (1) Space Shuttles; (2) Space Station Freedom; (3) information systems; (4) materials and processes; (4) Space Shuttle main engine; (5) aerospace sciences; (6) mathematical models; (7) mission operations; (8) systems analysis and integration; (9) systems control; (10) structures and dynamics; (11) aerospace safety; and (12) remote sensin