Facilitating Cooperation for Wireless SystemsCooperative Communications: Hardware, Channel PHYfocuses on issues pertaining to the PHY layer of wireless communication networks, offering a rigorous ...taxonomy of this dispersed field, along with a range of application scenarios for cooperative and distributed schemes, demonstrating how these techniques can be employed. The authors discuss hardware, complexity and power consumption issues, which are vital for understanding what can be realized at the PHY layer, showing how wireless channel models differ from more traditional models, and highlighting the reliance of PHY algorithm performance on the underlying channel models. Numerous transparent and regenerative relaying protocols are described in detail for a variety of transparent and regenerative cooperative schemes.Key Features:Introduces background, concepts, applications, milestones and thorough taxonomy Identifies the potential in this emerging technology applied to e.g. LTE/WiMAX, WSN Discusses latest wireless channel models for transparent and regenerative protocols Addresses the fundamentals as well as latest emerging PHY protocols Introduces transparent distributed STBC, STTC, multiplexing and beamforming protocols Quantifies regenerative distributed space-time, channel and network coding protocols Explores system optimization, such as distributed power allocation and relay selection Introduces and compares analog and digital hardware architectures Quantifies complexity, memory and power consumption of 3G UMTS 4G LTE/WiMAX relay Highlights future research challenges within the cooperative communications field This book is an invaluable guide for professionals and researchers in communications fields. It will also be of interest to graduates of communications and electronic engineering courses. It forms part of an entire series dedicated to cooperative wireless systems.
With the recent advances in remote sensing technologies for Earth observation, many different remote sensors are collecting data with distinctive properties. The obtained data are so large and ...complex that analyzing them manually becomes impractical or even impossible. Therefore, understanding remote sensing images effectively, in connection with physics, has been the primary concern of the remote sensing research community in recent years. For this purpose, machine learning is thought to be a promising technique because it can make the system learn to improve itself. With this distinctive characteristic, the algorithms will be more adaptive, automatic, and intelligent. This book introduces some of the most challenging issues of machine learning in the field of remote sensing, and the latest advanced technologies developed for different applications. It integrates with multi-source/multi-temporal/multi-scale data, and mainly focuses on learning to understand remote sensing images. Particularly, it presents many more effective techniques based on the popular concepts of deep learning and big data to reach new heights of data understanding. Through reporting recent advances in the machine learning approaches towards analyzing and understanding remote sensing images, this book can help readers become more familiar with knowledge frontier and foster an increased interest in this field.
With the recent advances in remote sensing technologies for Earth observation, many different remote sensors are collecting data with distinctive properties. The obtained data are so large and ...complex that analyzing them manually becomes impractical or even impossible. Therefore, understanding remote sensing images effectively, in connection with physics, has been the primary concern of the remote sensing research community in recent years. For this purpose, machine learning is thought to be a promising technique because it can make the system learn to improve itself. With this distinctive characteristic, the algorithms will be more adaptive, automatic, and intelligent. This book introduces some of the most challenging issues of machine learning in the field of remote sensing, and the latest advanced technologies developed for different applications. It integrates with multi-source/multi-temporal/multi-scale data, and mainly focuses on learning to understand remote sensing images. Particularly, it presents many more effective techniques based on the popular concepts of deep learning and big data to reach new heights of data understanding. Through reporting recent advances in the machine learning approaches towards analyzing and understanding remote sensing images, this book can help readers become more familiar with knowledge frontier and foster an increased interest in this field.
For decades, the spatial approach to network analysis has principally focused on planar and technical networks from a classic graph theory perspective. Reference to models and methods developed by ...other disciplines on non-planar networks, such as sociology and physics, is recent, limited, and dispersed. Conversely, the physics literature that developed the popular scale-free and small-world models pays an increasing attention to the spatial dimension of networks. Reviewing how complex network research has been integrated into geography and regional science reveals a high heterogeneity among spatial scientists as well as key directions for increasing their role inside multidisciplinary researches on networks.
A convolutional neural network (CNN) is one of the most significant networks in the deep learning field. Since CNN made impressive achievements in many areas, including but not limited to computer ...vision and natural language processing, it attracted much attention from both industry and academia in the past few years. The existing reviews mainly focus on CNN's applications in different scenarios without considering CNN from a general perspective, and some novel ideas proposed recently are not covered. In this review, we aim to provide some novel ideas and prospects in this fast-growing field. Besides, not only 2-D convolution but also 1-D and multidimensional ones are involved. First, this review introduces the history of CNN. Second, we provide an overview of various convolutions. Third, some classic and advanced CNN models are introduced; especially those key points making them reach state-of-the-art results. Fourth, through experimental analysis, we draw some conclusions and provide several rules of thumb for functions and hyperparameter selection. Fifth, the applications of 1-D, 2-D, and multidimensional convolution are covered. Finally, some open issues and promising directions for CNN are discussed as guidelines for future work.
A great deal of attention has been given to deep learning over the past several years, and new deep learning techniques are emerging with improved functionality. Many computer and network ...applications actively utilize such deep learning algorithms and report enhanced performance through them. In this study, we present an overview of deep learning methodologies, including restricted Bolzmann machine-based deep belief network, deep neural network, and recurrent neural network, as well as the machine learning techniques relevant to network anomaly detection. In addition, this article introduces the latest work that employed deep learning techniques with the focus on network anomaly detection through the extensive literature survey. We also discuss our local experiments showing the feasibility of the deep learning approach to network traffic analysis.
Full text
Available for:
EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OBVAL, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Complex networks are one of the most challenging research focuses of disciplines, including physics, mathematics, biology, medicine, engineering, and computer science, among others. The interest in ...complex networks is increasingly growing, due to their ability to model several daily life systems, such as technology networks, the Internet, and communication, chemical, neural, social, political and financial networks. The Special Issue “Computation in Complex Networks" of Entropy offers a multidisciplinary view on how some complex systems behave, providing a collection of original and high-quality papers within the research fields of: • Community detection • Complex network modelling • Complex network analysis • Node classification • Information spreading and control • Network robustness • Social networks • Network medicine
In the Age of Enlightenment the German philosopher Immanuel Kant encouraged people to use their own mind as the basis for reasoning instead of following dogmatic religious rules - Sapere aude! (dare ...to know!) - Today wireless communication networks are increasingly becoming aware of the conditions of its their component parts and surrounding environment. Cognition, a continuous process involving sensing, reasoning, understanding and reacting, can be applied to wireless networks in order to adapt the system to the highly dynamic wireless ecosystem. The ultimate goals are to enhance the efficiency in the use of radio resources as well as to improve both link and network performance. This book presents a detailed overview of a rapidly emerging topic in modern communications: cognitive wireless networks. The key aspects of cognitive and cooperative principles in wireless networks are discussed in this book. Furthermore, Cognitive Wireless Networks advocates the concept of breaking up the cellular communication architecture by introducing cooperative strategies among wireless devices. Cognitive wireless networking is the key to success in handling the upcoming dynamic network configurations and exploiting this cross-over to the fullest extent. As wireless networks become pervasive, highly populated and increasingly complex, the essential preconditions for exploiting rich interactions among mobile devices are better fulfilled. Today, these trends are giving rise to new communications paradigms making use of cooperation and cognition as the main underlying principles. Cognition, together with its complementary principle cooperation, confer to the wireless networks some degree of consciousness or understanding about their own existence, such as internal structure, capabilities, relationships to the outside world, limitations, current use of radio resources and many more. Current wireless networks are aware of their surrounding environment to a very limited extent, but in future wireless networks this capability will be highly developed, as a consequence of exploiting jointly cooperative and cognitive principles. Especially for cooperative wireless networks, where the communication scenario is highly dynamic, the mobile devices need to adapt their capabilities in a flexible manner, taking advantage of cognitive principles.
Full text
Available for:
FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NUK, OBVAL, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Development in short-range wireless LAN (WLAN) and long-range wireless WAN (WWAN) technologies have motivated recent efforts to integrate the two. This creates new application scenarios that were not ...possible before. Vehicles with only WLAN radios can use other vehicles that have both WLAN and WWAN radios as mobile gateways and connect to the Internet while on the road. The most difficult challenge in the scenario is to deal with frequent route breakages due to dynamic mobility of vehicles on the road. Existing routing protocols that are widely used for mobile ad hoc networks are reactive in nature and wait until existing routes break before constructing new routes. The frequent route failures result in a significant amount of time needed for repairing existing routes or reconstructing new routes. In spite of the dynamic mobility, the motion of vehicles on highways is quite predictable compared to other mobility patterns for wireless ad hoc networks, with location and velocity information readily available. This can be exploited to predict how long a route will last between a vehicle requiring Internet connectivity and the gateway that provides a route to the Internet. Successful prediction of route lifetimes can significantly reduce the number of route failures. In this paper, we introduce a prediction-based routing (PBR) protocol that is specifically tailored to the mobile gateway scenario and takes advantage of the predictable mobility pattern of vehicles on highways. The protocol uses predicted route lifetimes to preemptively create new routes before existing ones fail. We study the performance of this protocol through simulation and demonstrate significant reductions in route failures compared to protocols that do not use preemptive routing. Moreover, we find that the overhead of preemptive routing is kept in check due to the ability of PBR to predict route lifetime
PDF
