Multimedia

The nowadays ubiquitous and effortless digital data capture and processing capabilities offered by the majority of devices, lead to an unprecedented penetration of multimedia content in our everyday life. To make the most of this phenomenon, the rapidly increasing volume and usage of digitised content requires constant re-evaluation and adaptation of multimedia methodologies, in order to meet the relentless change of requirements from both the user and system perspectives. Advances in Multimedia provides readers with an overview of the ever-growing field of multimedia by bringing together various research studies and surveys from different subfields that point out such important aspects. Some of the main topics that this book deals with include: multimedia management in peer-to-peer structures & wireless networks, security characteristics in multimedia, semantic gap bridging for multimedia content and novel multimedia applications

State–of–the–art report on nonlinear representation of sources and channels

This report consists of two complementary parts, related to the modeling of two important sources of nonlinearities in a communications system. In the first part, an overview of important past work related to the estimation, compression and processing of sparse data through the use of nonlinear models is provided. In the second part, the current state of the art on the representation of wireless channels in the presence of nonlinearities is summarized. In addition to the characteristics of the nonlinear wireless fading channel, some information is also provided on recent approaches to the sparse representation of such channels

An intelligent approach to quality of service for MPEG-4 video transmission in IEEE 802.15.1

Nowadays, wireless connectivity is becoming ubiquitous spreading to companies and in domestic areas. IEEE 802.15.1 commonly known as Bluetooth is high-quality, high-security, high-speed and low-cost radio signal technology. This wireless technology allows a maximum access range of 100 meters yet needs power as low as 1mW. Regrettably, IEEE 802.15.1 has a very limited bandwidth. This limitation can become a real problem If the user wishes to transmit a large amount of data in a very short time. The version 1.2 which is used in this project could only carry a maximum download rate of 724Kbps and an upload rate of 54Kbps In its asynchronous mode. But video needs a very large bandwidth to be transmitted with a sufficient level of quality. Video transmission over IEEE 802.15.1 networks would therefore be difficult to achieve, due to the limited bandwidth. Hence, a solution to transmit digital video with a sufficient quality of picture to arrive at the receiving end is required. A hybrid scheme has been developed in this thesis, comprises of a fuzzy logic set of rules and an artificial neural network algorithms. MPEG-4 video compression has been used in this work to optimise the transmission. This research further utilises an ‘added-buffer’ to prevent excessive data loss of MPEG-4 video over IEEE 802.15.1transmission and subsequently increase picture quality. The neural-fuzzy scheme regulates the output rate of the added-buffer to ensure that MPEG-4 video stream conforms to the traffic conditions of the IEEE 802.15.1 channel during the transmission period, that is to send more data when the bandwidth is not fully used and keep the data in the buffers if the bandwidth is overused. Computer simulation results confirm that intelligence techniques and added-buffer do improve quality of picture, reduce data loss and communication delay, as compared with conventional MPEG video transmission over IEEE 802.15.1

Signals of Opportunity for Positioning Purposes

O ver the last years, location-based services (LBS) have become popular due to the emergence of smartphones with capabilities of positioning their user’s location on Earth at unprecedented speed and convenience. Behind such feat are the technological advances in global navigation satellite systems (GNSS), such as Galileo, Globalnaya Navigazionnaya Sputnikovaya Sistema (GLONASS), Global Positioning Service (GPS) and Beidou. The easiness of smartphones and the improvement of positioning technology has driven LBS to be at the core of many business models. Some of these business models rely on the user’s location to pick him up on a car, relinquish a meal to him, offer insights on sports performance, locate items to be picked up on a warehouse, among many others.While LBS are driving the need to continuously locate the user at higher degrees of accuracy and across any environment, be it in a city park, an urban canyon or inside a corporate office, some of these environments pose a challenge for GNSS. Indoor environments are particularly challenging for GNSS due to the attenuation and strong multipath imposed by walls and building materials. Such challenges and difficulties in signal acquisition have led to the development of solutions and technologies to improve positioning in indoor environments.While there are several commercial systems available to fulfill the needs of most LBS in indoor environments, most of these are not feasible to deploy at a global scale due to their infrastructure costs. Hence, several solutions have sought to build upon existing infrastructure to provide positioning information.Building upon existing infrastructure is what leads to the main topic of this thesis, the concept of signals of opportunity (SoO). A SoO is any wireless signal that can be exploited for a positioning purpose despite its initial design seeking to fulfill a different purpose. A few examples of these signals are IEEE 802.11 signals, commonly known as WiFi, Bluetooth, digital video broadcasting - terrestrial (DVB-T) and many of the cellular signals, such as long-term evolution (LTE), universal mobile telecommunications system (UMTS) and global mobile system (GSM).The goal of this thesis is to address various challenges related to SoO for positioning. From the identification of SoO at the physical layer, how to merge them at the algorithmic level and how to put them in use for a cognitive positioning system (CPS)

Distributed scene reconstruction from multiple mobile platforms

Recent research on mobile robotics has produced new designs that provide house-hold robots with omnidirectional motion. The image sensor embedded in these devices motivates the application of 3D vision techniques on them for navigation and mapping purposes. In addition to this, distributed cheapsensing systems acting as unitary entity have recently been discovered as an efficient alternative to expensive mobile equipment. In this work we present an implementation of a visual reconstruction method, structure from motion (SfM), on a low-budget, omnidirectional mobile platform, and extend this method to distributed 3D scene reconstruction with several instances of such a platform. Our approach overcomes the challenges yielded by the plaform. The unprecedented levels of noise produced by the image compression typical of the platform is processed by our feature filtering methods, which ensure suitable feature matching populations for epipolar geometry estimation by means of a strict quality-based feature selection. The robust pose estimation algorithms implemented, along with a novel feature tracking system, enable our incremental SfM approach to novelly deal with ill-conditioned inter-image configurations provoked by the omnidirectional motion. The feature tracking system developed efficiently manages the feature scarcity produced by noise and outputs quality feature tracks, which allow robust 3D mapping of a given scene even if - due to noise - their length is shorter than what it is usually assumed for performing stable 3D reconstructions. The distributed reconstruction from multiple instances of SfM is attained by applying loop-closing techniques. Our multiple reconstruction system merges individual 3D structures and resolves the global scale problem with minimal overlaps, whereas in the literature 3D mapping is obtained by overlapping stretches of sequences. The performance of this system is demonstrated in the 2-session case. The management of noise, the stability against ill-configurations and the robustness of our SfM system is validated on a number of experiments and compared with state-of-the-art approaches. Possible future research areas are also discussed

A Review of Predictive Quality of Experience Management in Video Streaming Services

Satisfying the requirements of devices and users of online video streaming services is a challenging task. It requires not only managing the network quality of service but also to exert real-time control, addressing the user's quality of experience (QoE) expectations. QoE management is an end-to-end process that, due to the ever-increasing variety of video services, has become too complex for conventional “reactive” techniques. Herein, we review the most significant “predictive” QoE management methods for video streaming services, showing how different machine learning approaches may be used to perform proactive control. We pinpoint a selection of the best suited machine learning methods, highlighting advantages and limitations in specific service conditions. The review leads to lessons learned and guidelines to better address QoE requirements in complex video services

Design and Evaluation of Compression, Classification and Localization Schemes for Various IoT Applications

Nowadays we are surrounded by a huge number of objects able to communicate, read information such as temperature, light or humidity, and infer new information through ex- changing data. These kinds of objects are not limited to high-tech devices, such as desktop PC, laptop, new generation mobile phone, i.e. smart phone, and others with high capabilities, but also include commonly used object, such as ID cards, driver license, clocks, etc. that can made smart by allowing them to communicate. Thus, the analog world of just a few years ago is becoming the a digital world of the Inter- net of Things (IoT), where the information from a single object can be retrieved from the Internet. The IoT paradigm opens several architectural challenges, including self-organization, self-managing, self-deployment of the smart objects, as well as the problem of how to minimize the usage of the limited resources of each device. The concept of IoT covers a lot of communication paradigms such as WiFi, Radio Frequency Identification (RFID), and Wireless Sensor Network (WSN). Each paradigm can be thought of as an IoT island where each device can communicate directly with other devices. The thesis is divided in sections in order to cover each problem mentioned above. The first step is to understand the possibility to infer new knowledge from the deployed device in a scenario. For this reason, the research is focused on the web semantic, web 3.0, to assign a semantic meaning to each thing inside the architecture. The sole semantic concept is unusable to infer new information from the data gathered; in fact, it is necessary to organize the data through a hierarchical form defined by an Ontology. Through the exploitation of the Ontology, it is possible to apply semantic engine reasoners to infer new knowledge about the network. The second step of the dissertation deals with the minimization of the usage of every node in a WSN. The main purpose of each node is to collect environmental data and to exchange hem with other nodes. To minimize battery consumption, it is necessary to limit the radio usage. Therefore, we implemented Razor, a new lightweight algorithm which is expected to improve data compression and classification by leveraging on the advantages offered by data mining methods for optimizing communications and by enhancing information transmission to simplify data classification. Data compression is performed studying the well-know Vector Quantization (VQ) theory in order to create the codebooks necessary for signal compression. At the same time, it is requested to give a semantic meaning to un- known signals. In this way, the codebook feature is able not only to compress the signals, but also to classify unknown signals. Razor is compared with both state-of-the-art compression and signal classification techniques for WSN . The third part of the thesis covers the concept of smart object applied to Robotic research. A critical issue is how a robot can localize and retrieve smart objects in a real scenario without any prior knowledge. In order to achieve the objectives, it is possible to exploit the smart object concept and localize them through RSSI measurements. After the localization phase, the robot can exploit its own camera to retrieve the objects. Several filtering algorithms are developed in order to mitigate the multi–path issue due to the wireless communication channel and to achieve a better distance estimation through the RSSI measurement. The last part of the dissertation deals with the design and the development of a Cognitive Network (CN) testbed using off the shelf devices. The device type is chosen considering the cost, usability, configurability, mobility and possibility to modify the Operating System (OS) source code. Thus, the best choice is to select some devices based on Linux kernel as Android OS. The feature to modify the Operating System is required to extract the TCP/IP protocol stack parameters for the CN paradigm. It is necessary to monitor the network status in real-time and to modify the critical parameters in order to improve some performance, such as bandwidth consumption, number of hops to exchange the data, and throughput

Performance Modeling and Analysis of Wireless Local Area Networks with Bursty Traffic

The explosive increase in the use of mobile digital devices has posed great challenges in the design and implementation of Wireless Local Area Networks (WLANs). Ever-increasing demands for high-speed and ubiquitous digital communication have made WLANs an essential feature of everyday life. With audio and video forming the highest percentage of traffic generated by multimedia applications, a huge demand is placed for high speed WLANs that provide high Quality-of-Service (QoS) and can satisfy end user’s needs at a relatively low cost. Providing video and audio contents to end users at a satisfactory level with various channel quality and current battery capacities requires thorough studies on the properties of such traffic. In this regard, Medium Access Control (MAC) protocol of the 802.11 standard plays a vital role in the management and coordination of shared channel access and data transmission. Therefore, this research focuses on developing new efficient analytical models that evaluate the performance of WLANs and the MAC protocol in the presence of bursty, correlated and heterogeneous multimedia traffic using Batch Markovian Arrival Process (BMAP). BMAP can model the correlation between different packet size distributions and traffic rates while accurately modelling aggregated traffic which often possesses negative statistical properties. The research starts with developing an accurate traffic generator using BMAP to capture the existing correlations in multimedia traffics. For validation, the developed traffic generator is used as an arrival process to a queueing model and is analyzed based on average queue length and mean waiting time. The performance of BMAP/M/1 queue is studied under various number of states and maximum batch sizes of BMAP. The results clearly indicate that any increase in the number of states of the underlying Markov Chain of BMAP or maximum batch size, lead to higher burstiness and correlation of the arrival process, prompting the speed of the queue towards saturation. The developed traffic generator is then used to model traffic sources in IEEE 802.11 WLANs, measuring important QoS metrics of throughput, end-to-end delay, frame loss probability and energy consumption. Performance comparisons are conducted on WLANs under the influence of multimedia traffics modelled as BMAP, Markov Modulated Poisson Process and Poisson Process. The results clearly indicate that bursty traffics generated by BMAP demote network performance faster than other traffic sources under moderate to high loads. The model is also used to study WLANs with unsaturated, heterogeneous and bursty traffic sources. The effects of traffic load and network size on the performance of WLANs are investigated to demonstrate the importance of burstiness and heterogeneity of traffic on accurate evaluation of MAC protocol in wireless multimedia networks. The results of the thesis highlight the importance of taking into account the true characteristics of multimedia traffics for accurate evaluation of the MAC protocol in the design and analysis of wireless multimedia networks and technologies

A survey on wireless indoor localization from the device perspective

With the marvelous development of wireless techniques and ubiquitous deployment of wireless systems indoors, myriad indoor location-based services (ILBSs) have permeated into numerous aspects of modern life. The most fundamental functionality is to pinpoint the location of the target via wireless devices. According to how wireless devices interact with the target, wireless indoor localization schemes roughly fall into two categories: device based and device free. In device-based localization, a wireless device (e.g., a smartphone) is attached to the target and computes its location through cooperation with other deployed wireless devices. In device-free localization, the target carries no wireless devices, while the wireless infrastructure deployed in the environment determines the target’s location by analyzing its impact on wireless signals. This article is intended to offer a comprehensive state-of-the-art survey on wireless indoor localization from the device perspective. In this survey, we review the recent advances in both modes by elaborating on the underlying wireless modalities, basic localization principles, and data fusion techniques, with special emphasis on emerging trends in (1) leveraging smartphones to integrate wireless and sensor capabilities and extend to the social context for device-based localization, and (2) extracting specific wireless features to trigger novel human-centric device-free localization. We comprehensively compare each scheme in terms of accuracy, cost, scalability, and energy efficiency. Furthermore, we take a first look at intrinsic technical challenges in both categories and identify several open research issues associated with these new challenges.</jats:p