Editorial for the Special Issue “Advanced Machine Learning for Time Series Remote Sensing Data Analysis”

This Special Issue intended to probe the impact of the adoption of advanced machine learning methods in remote sensing applications including those considering recent big data analysis, compression, multichannel, sensor and prediction techniques. In principal, this edition of the Special Issue is focused on time series data processing for remote sensing applications with special emphasis on advanced machine learning platforms. This issue is intended to provide a highly recognized international forum to present recent advances in time series remote sensing. After review, a total of eight papers have been accepted for publication in this issue

Toward Sensor-Based Context Aware Systems

This paper proposes a methodology for sensor data interpretation that can combine sensor outputs with contexts represented as sets of annotated business rules. Sensor readings are interpreted to generate events labeled with the appropriate type and level of uncertainty. Then, the appropriate context is selected. Reconciliation of different uncertainty types is achieved by a simple technique that moves uncertainty from events to business rules by generating combs of standard Boolean predicates. Finally, context rules are evaluated together with the events to take a decision. The feasibility of our idea is demonstrated via a case study where a context-reasoning engine has been connected to simulated heartbeat sensors using prerecorded experimental data. We use sensor outputs to identify the proper context of operation of a system and trigger decision-making based on context information

Proceedings of the Fifth Italian Conference on Computational Linguistics CLiC-it 2018

On behalf of the Program Committee, a very warm welcome to the Fifth Italian Conference on Computational Linguistics (CLiC-­‐it 2018). This edition of the conference is held in Torino. The conference is locally organised by the University of Torino and hosted into its prestigious main lecture hall “Cavallerizza Reale”. The CLiC-­‐it conference series is an initiative of the Italian Association for Computational Linguistics (AILC) which, after five years of activity, has clearly established itself as the premier national forum for research and development in the fields of Computational Linguistics and Natural Language Processing, where leading researchers and practitioners from academia and industry meet to share their research results, experiences, and challenges

Special issue on deep learning for emerging big multimedia super-resolution

The main goal of the super-resolution (SR) is to restore a visually pleasing high-resolution image using a low-resolution image of video sequence. The higher resolution image is composed of higher pixel density with fine and precise details as compared with the low-resolution images or video. The majority of the applications, such as video surveillance, ultra-high-definition TV, low-resolution face recognition and remote sensing imaging are based on super-resolutions. Thus benefiting from the broader spectrun of these applications, the super-resolution has attracted massive interest form both acemedia and industry. And currently, a most active research field in today’s era

Embedded systems for mobile sensors

In the era of IoT, embedded systems based on mobile sensor ideas turn into increasingly compound and need understanding in various training tools such as signal processing, artificial intelligence, and multimedia communication. After a long period of computational capacity centralization, we are facing a period where the computation capacity is migrating back to the periphery of the systems and then to smart objects and sensors. Currently IoT is constituted of computationally heterogeneous devices for which the ability of executing preelaborations or complete processing is fundamental as well as the ability of doing them, keeping low power consumptions and maintaining a certain level of security. The combination of signal processing techniques for embedded systems is a principal and timely research in mobile sensors and communications. The articles contained in the present issue include both reviews and basic scientific studies focused on mobile multimedia communication using fundamental or applied signal processing methods for embedded systems and applications of IoT. This issue comprises the description of streams processing (e.g., images, video, and audio) for embedded systems involved in IoT as mobile sensors/aggregators including preprocessing for signal quality enhancement (e.g., images, noise reduction), security concerns (e.g., privacy), and evolution of communication protocols and mobile sensor network architectures

Advanced Localization of Mobile Terminal in Cellular Network

The growing diffusion of pervasive collaboration environments and technical advancement of sensing technologies have fostered the development of a new wave of online services whose functionalities are based on users ’ physical position. Thanks to the widespread diffusion of mobile devices (e.g. cell phones), many services can be greatly enriched with data reporting where people are, how they are moving, or whether they are close by specific locations. Geolocation of mobile terminals relies on the cellular network infrastructure and protocols to provide a reliable and accurate estimate of mobile terminals ’ position, without the need of global positioning systems, such as GPS. In this paper, we present a novel lookup table correlation technique for geolocation, with multiple position estimations and optimal location techniques. Our approach provides high precise location and tracking of mobile terminals by exploiting advanced propagation models for mobile radio networks design, and by querying Geographical Information Systems (GIS), in conjunction with Kalman predictive filtering