Investigation of Air Transportation Technology at Princeton University, 1989-1990

The Air Transportation Technology Program at Princeton University proceeded along six avenues during the past year: microburst hazards to aircraft; machine-intelligent, fault tolerant flight control; computer aided heuristics for piloted flight; stochastic robustness for flight control systems; neural networks for flight control; and computer aided control system design. These topics are briefly discussed, and an annotated bibliography of publications that appeared between January 1989 and June 1990 is given

ILASR: Privacy-Preserving Incremental Learning for Automatic Speech Recognition at Production Scale

Incremental learning is one paradigm to enable model building and updating at scale with streaming data. For end-to-end automatic speech recognition (ASR) tasks, the absence of human annotated labels along with the need for privacy preserving policies for model building makes it a daunting challenge. Motivated by these challenges, in this paper we use a cloud based framework for production systems to demonstrate insights from privacy preserving incremental learning for automatic speech recognition (ILASR). By privacy preserving, we mean, usage of ephemeral data which are not human annotated. This system is a step forward for production levelASR models for incremental/continual learning that offers near real-time test-bed for experimentation in the cloud for end-to-end ASR, while adhering to privacy-preserving policies. We show that the proposed system can improve the production models significantly(3%) over a new time period of six months even in the absence of human annotated labels with varying levels of weak supervision and large batch sizes in incremental learning. This improvement is 20% over test sets with new words and phrases in the new time period. We demonstrate the effectiveness of model building in a privacy-preserving incremental fashion for ASR while further exploring the utility of having an effective teacher model and use of large batch sizes.Comment: 9 page

Analyzing the Impact of Airborne Particulate Matter on Urban Contamination with the Help of Hybrid Neural Networks

In this study, particulate matter (PM), total suspended particulate (TSP), PM10, and PM2.5 fractions) concentrations were recorded in various cities from south of Romania to build the corresponding time series for various intervals. First, the time series of each pollutant were used as inputs in various configurations of feed-forward neural networks (FANN) to find the most suitable network architecture to the PM specificity. The outputs were evaluated using mean absolute error (MAE), mean absolute percentage error (MAPE), root mean square error (RMSE), and Pearson correlation coefficient (r) between observed series and output series. Second, each time series was decomposed using Daubechies wavelets of third order into its corresponding components. Each decomposed component of a PM time series was used as input in the optimal feed-forward neural networks (FANN) architecture established in the first step. The output of each component was re-included to form the modeled series of the original pollutant time series

Smart distance measurement module for football robot

Diplomová práce se zabývá vývojem dálkoměrného modulu určeného pro rozšíření senzorické výbavy fotbalového robotu kategorie MiroSot. Tento modul na vstupu přijímá data ze senzorické jednotky vyvinuté na Ústavu automatizace a měřicí techniky a z těchto dat extrahuje polohu míčku. Je srovnáno využití neuronové sítě a zjednodušené Houghovy transformace pro získání polohy těžiště míčku. V práci je popsána pomocná implementace funkcionality v prostředích MATLAB a C#.NET i hlavní implementace pro signálový mikrokontrolér Freescale MC56F8013. Výsledný modul splňuje nároky zadání a je plně funkční.The master's thesis concerns with the design of a distance measurement module destined for a MiroSot-category soccer robot. The module accepts data outputted by a sensor unit developed on Department of Control and Instrumentation and uses it to determine the ball position. Utilization of a neural network and a simplified Hough transform for ball finding is discussed. The thesis describes auxiliary implementations in MATLAB and C#.NET environments as well as the main implementation for digital signal controller Freescale MC56F8013. The resulting module meets requirements of the submission and is fully functional.

Novel Artificial Neural Network Application for Prediction of Inverse Kinematics of Robot Manipulator

The robot control problem can be divided into two main areas, kinematics control (the coordination of the links of kinematics chain to produce desire motion of the robot), and dynamic control (driving the actuator of the mechanism to follow the commanded position velocities). In general the control strategies used in robot involves position coordination in Cartesian space by direct or indirect kinematics method. Inverse kinematics comprises the computation need to find the join angles for a given Cartesian position and orientation of the end effectors. This computation is fundamental to control of robot arms but it is very difficult to calculate an inverse kinematics solution of robot manipulator. For this solution most industrial robot arms are designed by using a non-linear algebraic computation to finding the inverse kinematics solution. From the literature it is well described that there is no unique solution for the inverse kinematics. That is why it is significant to apply an artificial neural network models. Here structured artificial neural network (ANN) models an approach has been proposed to control the motion of robot manipulator. In these work two types of ANN models were used. The first kind ANN model is MLP (multi-layer perceptrons) which was famous as back propagation neural network model. In this network gradient descent type of learning rules are applied. The second kind of ANN model is PPN (polynomial poly-processor neural network) where polynomial equation was used. Here, work has been undertaken to find the best ANN configuration for the problem. It was found that between MLP and PPN, MLP gives better result as compared to PPN by considering average percentage error, as the performance index

An overview of decision table literature 1982-1995.

This report gives an overview of the literature on decision tables over the past 15 years. As much as possible, for each reference, an author supplied abstract, a number of keywords and a classification are provided. In some cases own comments are added. The purpose of these comments is to show where, how and why decision tables are used. The literature is classified according to application area, theoretical versus practical character, year of publication, country or origin (not necessarily country of publication) and the language of the document. After a description of the scope of the interview, classification results and the classification by topic are presented. The main body of the paper is the ordered list of publications with abstract, classification and comments.

Hidden Markov models and neural networks for speech recognition

The Hidden Markov Model (HMMs) is one of the most successful modeling approaches for acoustic events in speech recognition, and more recently it has proven useful for several problems in biological sequence analysis. Although the HMM is good at capturing the temporal nature of processes such as speech, it has a very limited capacity for recognizing complex patterns involving more than first order dependencies in the observed data sequences. This is due to the first order state process and the assumption of state conditional independence between observations. Artificial Neural Networks (NNs) are almost the opposite: they cannot model dynamic, temporally extended phenomena very well, but are good at static classification and regression tasks. Combining the two frameworks in a sensible way can therefore lead to a more powerful model with better classification abilities. The overall aim of this work has been to develop a probabilistic hybrid of hidden Markov models and neural networks and ..