A Context-Aware Adaptive Streaming Media Distribution System in a Heterogeneous Network with Multiple Terminals

We consider the problem of streaming media transmission in a heterogeneous network from a multisource server to home multiple terminals. In wired network, the transmission performance is limited by network state (e.g., the bandwidth variation, jitter, and packet loss). In wireless network, the multiple user terminals can cause bandwidth competition. Thus, the streaming media distribution in a heterogeneous network becomes a severe challenge which is critical for QoS guarantee. In this paper, we propose a context-aware adaptive streaming media distribution system (CAASS), which implements the context-aware module to perceive the environment parameters and use the strategy analysis (SA) module to deduce the most suitable service level. This approach is able to improve the video quality for guarantying streaming QoS. We formulate the optimization problem of QoS relationship with the environment parameters based on the QoS testing algorithm for IPTV in ITU-T G.1070. We evaluate the performance of the proposed CAASS through 12 types of experimental environments using a prototype system. Experimental results show that CAASS can dynamically adjust the service level according to the environment variation (e.g., network state and terminal performances) and outperforms the existing streaming approaches in adaptive streaming media distribution according to peak signal-to-noise ratio (PSNR)

Recent Advances in Image Restoration with Applications to Real World Problems

In the past few decades, imaging hardware has improved tremendously in terms of resolution, making widespread usage of images in many diverse applications on Earth and planetary missions. However, practical issues associated with image acquisition are still affecting image quality. Some of these issues such as blurring, measurement noise, mosaicing artifacts, low spatial or spectral resolution, etc. can seriously affect the accuracy of the aforementioned applications. This book intends to provide the reader with a glimpse of the latest developments and recent advances in image restoration, which includes image super-resolution, image fusion to enhance spatial, spectral resolution, and temporal resolutions, and the generation of synthetic images using deep learning techniques. Some practical applications are also included

Alpine Glaciology: An Historical Collaboration between Volunteers and Scientists and the Challenge Presented by an Integrated Approach

European Alpine glaciology has a long tradition of studies and activities, in which researchers have often relied on the field work of some specialized volunteer operators. Despite the remarkable results of this cooperation, some problems in field data harmonization and in covering the whole range of monitored glaciers are still present. Moreover, dynamics of reduction, fragmentation and decline, which in recent decades characterize Alpine glaciers, make more urgent the need to improve spatial and temporal monitoring, still maintaining adequate quality standards. Scientific field monitoring activities on Alpine glaciers run parallel to a number of initiatives by individuals and amateur associations, keepers of alternative, experiential and para-scientific knowledge of the glacial environment. Problems of harmonization, coordination, recruitment and updating can be addressed with the help of a collaborative approach—citizen science-like—in which the scientific coordination guarantees information quality and web 2.0 tools operate as mediators between expert glaciologists and non-expert contributors. This paper gives an overview of glaciological information currently produced in the European Alpine region, representing it in an organized structure, functional to the discussion. An empowering solution is then proposed, both methodological and technological, for the integration of multisource data. Its characteristics, potentials and problems are discussed

Fatigue Detection for Ship OOWs Based on Input Data Features, from The Perspective of Comparison with Vehicle Drivers: A Review

Ninety percent of the world’s cargo is transported by sea, and the fatigue of ship officers of the watch (OOWs) contributes significantly to maritime accidents. The fatigue detection of ship OOWs is more difficult than that of vehicles drivers owing to an increase in the automation degree. In this study, research progress pertaining to fatigue detection in OOWs is comprehensively analysed based on a comparison with that in vehicle drivers. Fatigue detection techniques for OOWs are organised based on input sources, which include the physiological/behavioural features of OOWs, vehicle/ship features, and their comprehensive features. Prerequisites for detecting fatigue in OOWs are summarised. Subsequently, various input features applicable and existing applications to the fatigue detection of OOWs are proposed, and their limitations are analysed. The results show that the reliability of the acquired feature data is insufficient for detecting fatigue in OOWs, as well as a non-negligible invasive effect on OOWs. Hence, low-invasive physiological information pertaining to the OOWs, behaviour videos, and multisource feature data of ship characteristics should be used as inputs in future studies to realise quantitative, accurate, and real-time fatigue detections in OOWs on actual ships

Remote Collaborative BIM-based Mixed Reality Approach for Supporting Facilities Management Field Tasks

Facilities Management (FM) day-to-day tasks require suitable methods to facilitate work orders and improve performance by better collaboration between the office and the field. Building Information Modeling (BIM) provides opportunities to support collaboration and to improve the efficiency of Computerized Maintenance Management Systems (CMMSs) by sharing building information between different applications/users throughout the lifecycle of the facility. However, manual retrieval of building element information can be challenging and time consuming for field workers during FM operations. Mixed Reality (MR) is a visualization technique that can be used to improve the visual perception of the facility by superimposing 3D virtual objects and textual information on top of the view of real-world building objects. The objectives of this research are: (1) investigating an automated method to capture and record task-related data (e.g., defects) with respect to a georeferenced BIM model and share them directly with the remote office based on the field worker point of view in mobile situations; (2) investigating the potential of using MR, BIM, and sensory data for FM tasks to provide improved visualization and perception that satisfy the needs of the facility manager at the office and the field workers with less visual and mental disturbance; and (3) developing an effective method for interactive visual collaboration to improve FM field tasks. This research discusses the development of a collaborative BIM-based MR approach to support facilities field tasks. The research framework integrates multisource facilities information, BIM models, and hybrid tracking in an MR-based setting to retrieve information based on time (e.g., inspection schedule) and the location of the field worker, visualize inspection and maintenance operations, and support remote collaboration and visual communication between the field worker and the manager at the office. The field worker uses an Augmented Reality (AR) application installed on his/her tablet. The manager at the office uses an Immersive Augmented Virtuality (IAV) application installed on a desktop computer. Based on the field worker location, as well as the inspection or maintenance schedule, the field worker is assigned work orders and instructions from the office. Other sensory data (e.g., infrared thermography) can provide additional layers of information by augmenting the actual view of the field worker and supporting him/her in making effective decisions about existing and potential problems while communicating with the office in an Interactive Virtual Collaboration (IVC) mode. The contributions of this research are (1) developing a MR framework for facilities management which has a field AR module and an office IAV module. These modules can be used independently or combined using remote IVC, (2) developing visualization methods for MR including the virtual hatch and multilayer views to enhance visual depth and context perception, (3) developing methods for AR and IAV modeling including BIM-based data integration and customization suitable for each MR method, and (4) enhancing indoor tracking for AR FM systems by developing a hybrid tracking method. To investigate the applicability of the research method, a prototype system called Collaborative BIM-based Markerless Mixed Reality Facility Management System (CBIM3R-FMS) is developed and tested in a case study. The usability testing and validation show that the proposed methods have high potential to improve FM field tasks

Adversarial Training in Affective Computing and Sentiment Analysis: Recent Advances and Perspectives

Over the past few years, adversarial training has become an extremely active research topic and has been successfully applied to various Artificial Intelligence (AI) domains. As a potentially crucial technique for the development of the next generation of emotional AI systems, we herein provide a comprehensive overview of the application of adversarial training to affective computing and sentiment analysis. Various representative adversarial training algorithms are explained and discussed accordingly, aimed at tackling diverse challenges associated with emotional AI systems. Further, we highlight a range of potential future research directions. We expect that this overview will help facilitate the development of adversarial training for affective computing and sentiment analysis in both the academic and industrial communities