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.
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.
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.
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
We provide an overview of the most commonly used social network measures in animal research for static networks or time‐aggregated networks.
For each of these measures, we provide clear explanations ...as to what they measure, we describe their respective variants, we underline the necessity to consider these variants according to the research question addressed, and we indicate considerations that have not been taken so far.
We provide a guideline indicating how to use them depending on the data collection protocol, the social system studied and the research question addressed. Finally, we inform about the existent gaps and remaining challenges in the use of several variants and provide future research directions.
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
This work develops a novel approach toward performance guarantees for all links in arbitrarily large wireless networks. It introduces a spatial network calculus, consisting of spatial regulation ...properties for stationary point processes and the first steps of a calculus for this regulation, which can be seen as an extension to space of the classical network calculus. Specifically, two classes of regulations are defined: one includes ball regulation and shot-noise regulation, which are shown to be equivalent and upper constraint interference; the other one includes void regulation, which lower constraints the signal power. These regulations are defined both in the strong and weak sense: the former requires the regulations to hold everywhere in space, whereas the latter only requires the regulations to hold as observed by a jointly stationary point process. Using this approach, we derive performance guarantees in device-to-device, ad hoc, and cellular networks under proper regulations. We give universal bounds on the SINR for all links, which give link service guarantees based on information-theoretic achievability. They are combined with classical network calculus to provide end-to-end latency guarantees for all packets in wireless queuing networks. Such guarantees do not exist in networks that are not spatially regulated, e.g., Poisson networks.
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