Improving the translation environment for professional translators

When using computer-aided translation systems in a typical, professional translation workflow, there are several stages at which there is room for improvement. The SCATE (Smart Computer-Aided Translation Environment) project investigated several of these aspects, both from a human-computer interaction point of view, as well as from a purely technological side. This paper describes the SCATE research with respect to improved fuzzy matching, parallel treebanks, the integration of translation memories with machine translation, quality estimation, terminology extraction from comparable texts, the use of speech recognition in the translation process, and human computer interaction and interface design for the professional translation environment. For each of these topics, we describe the experiments we performed and the conclusions drawn, providing an overview of the highlights of the entire SCATE project

Wideband Autonomous Cognitive Radios: Spectrum Awareness and PHY/MAC Decision Making

The cognitive radios (CRs) have opened up new ways of better utilizing the scarce wireless spectrum resources. The CRs have been made feasible by recent advances in software-defined radios (SDRs), smart antennas, reconfigurable radio frequency (RF) front-ends, and full-duplex RF front-end architectures, to name a few. Generally, a CR is considered as a dynamically reconfigurable radio capable of adapting its operating parameters to the surrounding environment. Recent developments in spectrum policy and regulatory domains also allow more flexible and efficient utilization of wider RF spectrum range in the future. In line with the future directions of CRs, a new vision of a future autonomous CR device, called Radiobots, was previously proposed. The goals of the proposed Radiobot surpass the dynamic spectrum access (DSA) to achieve wideband operability and the main features of cognition. In order to ensure the practicality and robust operation of the Radiobot structure, the research focus of this dissertation includes the following aspects: 1) robust spectrum sensing and operability in a centralized CR network setup; 2) robust multivariate non-parametric quickest detection for dynamic spectrum usage tracking in an alien RF environment; 3) joint physical layer and medium access control layer (PHY/MAC) decision-making for wideband bandwidth aggregation (simultaneous operation over multiple modes/networks); and 4) autonomous spectrum sensing scheduling solutions in an alien ultra wideband RF environment. The major contribution of this dissertation is to investigate the feasibility of the autonomous CR operation in heterogeneous RF environments, and to provide novel solutions to the fundamental and crucial problems/challenges, including spectrum sensing, spectrum awareness, wideband operability, and autonomous PHY/MAC protocols, thus bringing the autonomous Radiobot one step closer to reality

Machine Learning-Aided Operations and Communications of Unmanned Aerial Vehicles: A Contemporary Survey

The ongoing amalgamation of UAV and ML techniques is creating a significant synergy and empowering UAVs with unprecedented intelligence and autonomy. This survey aims to provide a timely and comprehensive overview of ML techniques used in UAV operations and communications and identify the potential growth areas and research gaps. We emphasise the four key components of UAV operations and communications to which ML can significantly contribute, namely, perception and feature extraction, feature interpretation and regeneration, trajectory and mission planning, and aerodynamic control and operation. We classify the latest popular ML tools based on their applications to the four components and conduct gap analyses. This survey also takes a step forward by pointing out significant challenges in the upcoming realm of ML-aided automated UAV operations and communications. It is revealed that different ML techniques dominate the applications to the four key modules of UAV operations and communications. While there is an increasing trend of cross-module designs, little effort has been devoted to an end-to-end ML framework, from perception and feature extraction to aerodynamic control and operation. It is also unveiled that the reliability and trust of ML in UAV operations and applications require significant attention before full automation of UAVs and potential cooperation between UAVs and humans come to fruition.Comment: 36 pages, 304 references, 19 Figure

A Very Brief Introduction to Machine Learning With Applications to Communication Systems

Given the unprecedented availability of data and computing resources, there is widespread renewed interest in applying data-driven machine learning methods to problems for which the development of conventional engineering solutions is challenged by modelling or algorithmic deficiencies. This tutorial-style paper starts by addressing the questions of why and when such techniques can be useful. It then provides a high-level introduction to the basics of supervised and unsupervised learning. For both supervised and unsupervised learning, exemplifying applications to communication networks are discussed by distinguishing tasks carried out at the edge and at the cloud segments of the network at different layers of the protocol stack