Multimedia delivery in the future internet

The term “Networked Media” implies that all kinds of media including text, image, 3D graphics, audio and video are produced, distributed, shared, managed and consumed on-line through various networks, like the Internet, Fiber, WiFi, WiMAX, GPRS, 3G and so on, in a convergent manner [1]. This white paper is the contribution of the Media Delivery Platform (MDP) cluster and aims to cover the Networked challenges of the Networked Media in the transition to the Future of the Internet. Internet has evolved and changed the way we work and live. End users of the Internet have been confronted with a bewildering range of media, services and applications and of technological innovations concerning media formats, wireless networks, terminal types and capabilities. And there is little evidence that the pace of this innovation is slowing. Today, over one billion of users access the Internet on regular basis, more than 100 million users have downloaded at least one (multi)media file and over 47 millions of them do so regularly, searching in more than 160 Exabytes1 of content. In the near future these numbers are expected to exponentially rise. It is expected that the Internet content will be increased by at least a factor of 6, rising to more than 990 Exabytes before 2012, fuelled mainly by the users themselves. Moreover, it is envisaged that in a near- to mid-term future, the Internet will provide the means to share and distribute (new) multimedia content and services with superior quality and striking flexibility, in a trusted and personalized way, improving citizens’ quality of life, working conditions, edutainment and safety. In this evolving environment, new transport protocols, new multimedia encoding schemes, cross-layer inthe network adaptation, machine-to-machine communication (including RFIDs), rich 3D content as well as community networks and the use of peer-to-peer (P2P) overlays are expected to generate new models of interaction and cooperation, and be able to support enhanced perceived quality-of-experience (PQoE) and innovative applications “on the move”, like virtual collaboration environments, personalised services/ media, virtual sport groups, on-line gaming, edutainment. In this context, the interaction with content combined with interactive/multimedia search capabilities across distributed repositories, opportunistic P2P networks and the dynamic adaptation to the characteristics of diverse mobile terminals are expected to contribute towards such a vision. Based on work that has taken place in a number of EC co-funded projects, in Framework Program 6 (FP6) and Framework Program 7 (FP7), a group of experts and technology visionaries have voluntarily contributed in this white paper aiming to describe the status, the state-of-the art, the challenges and the way ahead in the area of Content Aware media delivery platforms

Optimized mobile thin clients through a MPEG-4 BiFS semantic remote display framework

According to the thin client computing principle, the user interface is physically separated from the application logic. In practice only a viewer component is executed on the client device, rendering the display updates received from the distant application server and capturing the user interaction. Existing remote display frameworks are not optimized to encode the complex scenes of modern applications, which are composed of objects with very diverse graphical characteristics. In order to tackle this challenge, we propose to transfer to the client, in addition to the binary encoded objects, semantic information about the characteristics of each object. Through this semantic knowledge, the client is enabled to react autonomously on user input and does not have to wait for the display update from the server. Resulting in a reduction of the interaction latency and a mitigation of the bursty remote display traffic pattern, the presented framework is of particular interest in a wireless context, where the bandwidth is limited and expensive. In this paper, we describe a generic architecture of a semantic remote display framework. Furthermore, we have developed a prototype using the MPEG-4 Binary Format for Scenes to convey the semantic information to the client. We experimentally compare the bandwidth consumption of MPEG-4 BiFS with existing, non-semantic, remote display frameworks. In a text editing scenario, we realize an average reduction of 23% of the data peaks that are observed in remote display protocol traffic

Spatial Characterization, Resolution, And Volumetric Change Of Coastal Dunes Using Airborne LIDAR: Cape Hatteras, North Carolina

The technological advancement in topographic mapping known as airborne Light Detection and Ranging (LIDAR) allows researchers to gather highly accurate and densely sampled coastal elevation data at a rapid rate. The problem is to determine the optimal resolutions at which to represent coastal dunes for volumetric change analysis. This study uses digital elevation models (DEM) generated from LIDAR data and spatial statistics to better understand dune characterization at a series of spatial resolutions. The LIDAR data were collected jointly by the National Aeronautics and Space Administration (NASA), the National Oceanic and Atmospheric Administration (NOAA), and the U.S. Geological Survey (USGS). DEMs of two study sites (100×200 m) located in Cape Hatteras National Seashore, North Carolina were generated using a raster-based geographic information system (GIS). Changes in the dune volume were calculated for a 1-year period of time (Fall 1996–1997) at grid cell resolutions ranging from 1×1 to 20×20 m. Directional statistics algorithms were used to calculate local variance and characterize topographic complexity. Data processing was described in detail in order to provide an introduction to working with LIDAR data in a GIS. Results from these study sites indicated that a 1–2 m resolution provided the most reliable representation of coastal dunes on Cape Hatteras and most accurate volumetric change measurements. Results may vary at other sites and at different spatial extents, but the methods developed here can be applied to other locations to determine the optimum resolutions at which to represent and characterize topography using common GIS and database software

FastPillars: A Deployment-friendly Pillar-based 3D Detector

The deployment of 3D detectors strikes one of the major challenges in real-world self-driving scenarios. Existing BEV-based (i.e., Bird Eye View) detectors favor sparse convolutions (known as SPConv) to speed up training and inference, which puts a hard barrier for deployment, especially for on-device applications. In this paper, to tackle the challenge of efficient 3D object detection from an industry perspective, we devise a deployment-friendly pillar-based 3D detector, termed FastPillars. First, we introduce a novel lightweight Max-and-Attention Pillar Encoding (MAPE) module specially for enhancing small 3D objects. Second, we propose a simple yet effective principle for designing a backbone in pillar-based 3D detection. We construct FastPillars based on these designs, achieving high performance and low latency without SPConv. Extensive experiments on two large-scale datasets demonstrate the effectiveness and efficiency of FastPillars for on-device 3D detection regarding both performance and speed. Specifically, FastPillars delivers state-of-the-art accuracy on Waymo Open Dataset with 1.8X speed up and 3.8 mAPH/L2 improvement over CenterPoint (SPConv-based). Our code is publicly available at: https://github.com/StiphyJay/FastPillars.Comment: Submitted to AAAI202

Study on quality in 3D digitisation of tangible cultural heritage: mapping parameters, formats, standards, benchmarks, methodologies and guidelines: final study report.

This study was commissioned by the Commission to help advance 3D digitisation across Europe and thereby to support the objectives of the Recommendation on a common European data space for cultural heritage (C(2021) 7953 final), adopted on 10 November 2021. The Recommendation encourages Member States to set up digital strategies for cultural heritage, which sets clear digitisation and digital preservation goals aiming at higher quality through the use of advanced technologies, notably 3D. The aim of the study is to map the parameters, formats, standards, benchmarks, methodologies and guidelines relating to 3D digitisation of tangible cultural heritage. The overall objective is to further the quality of 3D digitisation projects by enabling cultural heritage professionals, institutions, content-developers, stakeholders and academics to define and produce high-quality digitisation standards for tangible cultural heritage. This unique study identifies key parameters of the digitisation process, estimates the relative complexity and how it is linked to technology, its impact on quality and its various factors. It also identifies standards and formats used for 3D digitisation, including data types, data formats and metadata schemas for 3D structures. Finally, the study forecasts the potential impacts of future technological advances on 3D digitisation

Medical data processing and analysis for remote health and activities monitoring

Recent developments in sensor technology, wearable computing, Internet of Things (IoT), and wireless communication have given rise to research in ubiquitous healthcare and remote monitoring of human\u2019s health and activities. Health monitoring systems involve processing and analysis of data retrieved from smartphones, smart watches, smart bracelets, as well as various sensors and wearable devices. Such systems enable continuous monitoring of patients psychological and health conditions by sensing and transmitting measurements such as heart rate, electrocardiogram, body temperature, respiratory rate, chest sounds, or blood pressure. Pervasive healthcare, as a relevant application domain in this context, aims at revolutionizing the delivery of medical services through a medical assistive environment and facilitates the independent living of patients. In this chapter, we discuss (1) data collection, fusion, ownership and privacy issues; (2) models, technologies and solutions for medical data processing and analysis; (3) big medical data analytics for remote health monitoring; (4) research challenges and opportunities in medical data analytics; (5) examples of case studies and practical solutions

3D objects and scenes classification, recognition, segmentation, and reconstruction using 3D point cloud data: A review

Three-dimensional (3D) point cloud analysis has become one of the attractive subjects in realistic imaging and machine visions due to its simplicity, flexibility and powerful capacity of visualization. Actually, the representation of scenes and buildings using 3D shapes and formats leveraged many applications among which automatic driving, scenes and objects reconstruction, etc. Nevertheless, working with this emerging type of data has been a challenging task for objects representation, scenes recognition, segmentation, and reconstruction. In this regard, a significant effort has recently been devoted to developing novel strategies, using different techniques such as deep learning models. To that end, we present in this paper a comprehensive review of existing tasks on 3D point cloud: a well-defined taxonomy of existing techniques is performed based on the nature of the adopted algorithms, application scenarios, and main objectives. Various tasks performed on 3D point could data are investigated, including objects and scenes detection, recognition, segmentation and reconstruction. In addition, we introduce a list of used datasets, we discuss respective evaluation metrics and we compare the performance of existing solutions to better inform the state-of-the-art and identify their limitations and strengths. Lastly, we elaborate on current challenges facing the subject of technology and future trends attracting considerable interest, which could be a starting point for upcoming research studie