DistriX : an implementation of UNIX on transputers

Bibliography: pages 104-110.Two technologies, distributed operating systems and UNIX are very relevant in computing today. Many distributed systems have been produced and many are under development. To a large extent, distributed systems are considered to be the only way to solve the computing needs of the future. UNIX, on the other hand, is becoming widely recognized as the industry standard for operating systems. The transputer, unlike. UNIX and distributed systems is a relatively new innovation. The transputer is a concurrent processing machine based on mathematical principles. Increasingly, the transputer is being used to solve a wide range of problems of a parallel nature. This thesis combines these three aspects in creating a distributed implementation of UNIX on a network of transputers. The design is based on the satellite model. In this model a central controlling processor is surrounded by worker processors, called satellites, in a master/ slave relationship

Comparing CNN and Human Crafted Features for Human Activity Recognition

Deep learning techniques such as Convolutional Neural Networks (CNNs) have shown good results in activity recognition. One of the advantages of using these methods resides in their ability to generate features automatically. This ability greatly simplifies the task of feature extraction that usually requires domain specific knowledge, especially when using big data where data driven approaches can lead to anti-patterns. Despite the advantage of this approach, very little work has been undertaken on analyzing the quality of extracted features, and more specifically on how model architecture and parameters affect the ability of those features to separate activity classes in the final feature space. This work focuses on identifying the optimal parameters for recognition of simple activities applying this approach on both signals from inertial and audio sensors. The paper provides the following contributions: (i) a comparison of automatically extracted CNN features with gold standard Human Crafted Features (HCF) is given, (ii) a comprehensive analysis on how architecture and model parameters affect separation of target classes in the feature space. Results are evaluated using publicly available datasets. In particular, we achieved a 93.38% F-Score on the UCI-HAR dataset, using 1D CNNs with 3 convolutional layers and 32 kernel size, and a 90.5% F-Score on the DCASE 2017 development dataset, simplified for three classes (indoor, outdoor and vehicle), using 2D CNNs with 2 convolutional layers and a 2x2 kernel size