With the rise of the Internet of Things (IoT), applications have become smarter and connected devices give rise to their exploitation in all aspects of a modern city. As the volume of the collected data increases, Machine Learning (ML) techniques are applied to further enhance the intelligence and the capabilities of an application. The field of smart transportation has attracted many researchers and it has been approached with both ML and IoT techniques. In this review, smart transportation is considered to be an umbrella term that covers route optimization, parking, street lights, accident prevention/detection, road anomalies, and infrastructure applications. The purpose of this paper is to make a self-contained review of ML techniques and IoT applications in Intelligent Transportation Systems (ITS) and obtain a clear view of the trends in the aforementioned fields and spot possible coverage needs. From the reviewed articles it becomes profound that there is a possible lack of ML coverage for the Smart Lighting Systems and Smart Parking applications. Additionally, route optimization, parking, and accident/detection tend to be the most popular ITS applications among researchers.

This study describes a wide range of salient radio-over-fibre system issues. Impulse radio and multi-band ultra-wideband signal distribution over both single-mode fibre and multi-mode fibre (MMF) implementations are considered. Carrier frequencies ranging from 3.1 to 10.6 GHz, up to 60 GHz, are featured, and the use of microring laser transmitters is discussed. A cost–performance comparative analysis of competing distributed antenna system topologies is presented, and a theoretical approach to understanding the factors underlying radio-over-MMF performance for within-building applications is discussed. Finally, techniques to minimise thermal impacts on performance are described and novel energy-efficient schemes are introduced. Overall, this study provides a snap-shot of research being undertaken by European institutes involved in the Building the future Optical Network in Europe (BONE) project.

Currently, unit-selection text-to-speech technology is the common approach for near-natural speech synthesis systems. Such systems provide an important aid for blind or partially-sighted people, when combined with screen reading software. However, although the overall quality of the synthetic speech achieved by such systems can be quite high, this fact alone does not guarantee a high level of user satisfaction. Many issues have to be coped with in order to fulfill users' expectations when integrating such systems with screen reading tools aiming to assist blind users. This work describes the design and the implementation approaches for the efficient integration of this technology into screen reading environments. In particular, the issues of natural language processing, speed optimization, multilingual design and overall quality optimization are mainly addressed in this paper. In order to evaluate the resulting system, we carried out subjective assessment tests where expert users provided feedback about performance, quality and overall experience.

Detecting emotional traits in call centre interactions can be beneficial to the quality management of the services provided, since this reveals the positioning of both speakers, i.e. satisfaction or frustration and anger on the customers' side, and stress detection, disappointment mitigation or failure to provide the requested service on the operators' side. This paper describes a machine learning approach to classify emotional speech units occurring in a call centre dataset by employing emotion-related labels, automatically extracted acoustic features as well as additional context-related features.

Nowadays, unit selection based text-to-speech technology is the mainstream approach for near natural speech synthesis systems. However, this is achieved at the expense of raised requirements in terms of computational resources. This work describes design and implementation approaches for the efficient integration of this technology in computational environments with limited resources, such as mobile devices, with no considerable speech quality degradation. In particular, the issues of database reduction, acoustic inventory compression and runtime computational load minimization are mainly addressed in this paper. Both objective and subjective assessments confirm the effectiveness of these approaches in terms of constructing a general purpose embedded unit selection TTS system and reducing the computational requirements while maintaining high speech quality.

Over the last decade, blockchain technology has evolved from being a peer-to-peer implementation of digital currency into a framework for numerous applications. That is, blockchain is widely recognized as the technology behind cryptocurrencies, such as bitcoin, but recent research indicates that it can be exploited way more than that. Among the most notable characteristics of the blockchain technology are system decentralization, data immutability, transaction transparency, and user privacy. Thus, it becomes apparent that it can significantly benefit many consumer applications, where trust and security are crucial aspects of the implementation. In this paper, we review and present, in a tutorial manner, blockchain's fundamental postulates and characteristics, and we introduce a novel point of view, where we visualize a layered representation of the blockchain infrastructure, so as to analyze it as a framework. Thinking of blockchain technology as a framework may assist consumers to better understand its capabilities and limitations. In addition, we review emerging blockchain-based applications that span across many different scientific fields. We also discuss the relative merits and restrictions. Namely, we have identified applications in finance, smart contracts, Internet of Things, e-voting systems, storage and data protection, reputation systems, healthcare, and transportation, indicating that the blockchain technology is becoming a framework for endless applications.