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By learning the Gaussian mixture distribution of the received energy vector, we show that multiple transmitters' footprints can be learnt irrespective of their spatial overlap and potentially anisotropic shape. Learning the footprints also enables sampling the activity of each incumbent radio. By identifying radios that transmit as a response to the transmission of another, we learn the causal links between pairs of incumbent radios, i.

Thus, we can identify the potential receivers when a particular incumbent radio is transmitting. Hence, we can identify the communication links that can be established without causing interference to the incumbent receivers. Thus, our work is important for the coexistence of communicating networks with arbitrary geographical coverage such as LTE-Unlicensed and WiFi, radars and WiFi, etc. Our inferences can also be used to improve sensing schedules, access strategies, and routing in ad hoc networks. Danijela Cabric received the Dipl. She received her Ph. Her research interests include novel radio architecture, signal processing, and networking techniques for cognitive radio, 5G and massive MIMO systems.

Toward the Development of Intelligent Geosystems Time: Earthen dams are critical components in our world's water resource infrastructure, but many are at or near their intended design life.

Imagine an earthen dam that monitors itself daily, detects early onset of internal erosion, and takes action to avoid a catastrophic failure. The goal of the SmartGeo program at the Colorado School of Mines is to turn this type of system into reality. We are working to develop intelligent geosystems, in order to enable engineered and natural earth structures and environments to 1 sense their environment and 2 adapt to improve performance.

In this presentation, we will begin with the motivation for our SmartGeo program and the challenges that exist to reach our goal of intelligent geosystems. We will then delve into one specific challenge. Specifically, designing an efficient wireless sensor network capable of real-time, continuous e. We, therefore, have been investigating whether compressive sensing techniques are effective with our geophysical data and how we can implement in our resource constrained environment.

Our results are promising, and lead us to promote compressive sensing as a useful tool for other wireless sensor network applications. She is the Founder and Director of the Toilers http: Her current research interests include the credibility of ad hoc network simulation studies and the use of wireless sensor networks in geosystems. This funding has produced 12 software packages that have been requested from and shared with more than researchers in 86 countries as of October Camp has published over 80 refereed articles and 12 invited articles, and these articles have been cited almost 4, times per Microsoft Academic Search and over 7, times per Google Scholar as of December She has enjoyed being a Fulbright Scholar in New Zealand in , a Distinguished Visitor at the University of Bonn in Germany in , and a keynote presenter at several venues, e.

In December , Dr. She shares her life with Max born in , Emma born in , her husband Glen , and three pets two cats and a dog. The four humans are vegetarians who tremendously enjoy living in the foothills of the Rockies. Shared Networks and the Evolution towards 5G Time: The diverse and demanding requirements for the next generation of mobile networks necessitate a shift away from the rigid networks of previous generations, towards greater versatility and adaptability.

Essential enablers for this versatility include: In this talk, we will present our recent results on radio access network and spectrum sharing and how they affect planning for future network deployments. We will also discuss how our vision of broad resource sharing aligns with trends in 5G including softwarization, virtualization, and network slicing. His research focuses on distributed and adaptive resource management in wireless networks, and in particular radio resource sharing and the application of game theory to wireless networks. He is also a Fellow of the IEEE, for contributions to cognitive networks and to resource management in wireless networks.

Cyber Physical Social Systems: Research on Cyber Physical Systems CPS has led to quite a number of astonishing technical solutions that are becoming standard in many application domains affecting our everyday life. The technical innovations range from control theory concepts to real-time wireless communication to networked control. Some of the most challenging applications include co- operative autonomous driving and industry automation. Despite all these great findings, our research community frequently lost track on the impact of individual human beings that are an integral part of the systems — both as a user as well as a source of disruption.

Studying the impact of CPS on humans and vice versa, hybridization, i.

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This is also a basis for final public acceptance as a key to success of new technologies. We investigate these ideas based on the application domain of cooperative autonomous driving and identify core research challenges of such hybridized CPSS. Dressler received his M. His research objectives include adaptive wireless networking, self-organization techniques, and embedded system design with applications in ad hoc and sensor networks, vehicular networks, industrial wireless networks, and nano-networking.

In this talk I will explore one of the most relevant challenges for a decade to come: I will present a fresh look at this problem, and examine how to integrate people, software services, and things with their data, into one novel resilient ecosystem, which can be modeled, programmed, and deployed on a large scale in an elastic way.

He is the Editor-in-Chief of Computing Springer. Learning the topology of graphs as well as processes evolving over graphs are tasks emerging in application domains as diverse as gene-regulatory, brain, power, and social networks, to name a few. Scalable approaches to deal with such high-dimensional settings aim to address the unique modeling and computational challenges associated with data-driven science in the modern era of big data analytics.

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Albeit simple and tractable, linear time-invariant models are limited as they are incapable of modeling changing topologies, as well as nonlinear and dynamic dependencies between nodal processes. To this end, novel approaches are presented to leverage nonlinear counterparts of partial correlation and partial Granger causality, as well as nonlinear structural equations and vector auto-regressions, along with attributes such as low rank, sparsity, and smoothness to capture even directional dependencies with abrupt change points, as well as dynamic processes over possibly time-evolving topologies.

The unifying framework inherits the versatility and generality of kernel-based methods, and lends itself to batch and computationally affordable online learning algorithms, which include novel Kalman filters and smoothers over graphs. Real data experiments highlight the impact of the nonlinear and dynamic models on gene-regulatory and functional connectivity of brain networks, where connectivity patterns revealed exhibit discernible differences relative to existing approaches. From to he was with the Univ. He was with the University of Virginia from to , and since he has been a professor with the Univ.

His general interests span the areas of communications, networking and statistical signal processing — subjects on which he has published more than journal papers, conference papers, 25 book chapters, two edited books and two research monographs h-index Current research focuses on big data analytics, wireless cognitive radios, network science with applications to social, brain, and power networks with renewables.

In the Intelligent Vehicle Grid, the car is becoming a formidable sensor platform, absorbing information from the environment, from other cars and from the driver and feeding it to other cars and infrastructure to assist in safe navigation, pollution control and traffic management.

Wireless Information Networks, 2nd Edition

Like other important IOT examples e. It will also use V2V communications between peers to complement on board sensor inputs and provide safe and efficient navigation. In this paper, we first describe several vehicular applications that leverage V2V and V2I. Communications with infrastructure and with other vehicles, however, can create new problems — privacy and security violations.

In the second part of the paper we address these issues and more specifically focus on the need to guarantee location privacy to mobile users. His team is developing a Campus Vehicular Testbed. Parallel research activities are wireless medical monitoring using smart phones and cognitive radios in urban environments. The advent of big data offers unprecedented opportunities for data-driven discovery and decision-making in virtually every area of human endeavor. We further zoom in to study two specific cases.

First, supported by local utility companies through electric power analytics consortium, we analyze real smart meter big data for load profiling and smart pricing. We employ techniques such as Bayesian nonparametric learning, sublinear algorithm, and deep learning. Finally, other research activities of our group will also be briefly described.


From to , he was a Research Associate at the University of Maryland. From to , he was an assistant professor at Boise State University, Idaho. His research interests include wireless resource allocation and management, wireless communications and networking, game theory, big data analysis, security, and smart grid. Big data has become an important topic worldwide over the past several years. Among many aspects of the big data research and development, imbalanced learning has become a critical component as many data sets in real-world applications are imbalanced, ranging from surveillance, security, Internet, finance, social network, to medical and healthy related data analysis.

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In general, the imbalanced learning problem is concerned with the performance of learning algorithms in the presence of underrepresented data and severe class distribution skews. Due to the inherent complex characteristics of imbalanced data sets, learning from such data requires new understandings, principles, algorithms, and tools to transform vast amounts of raw data efficiently and effectively into information and knowledge representation to support decision-making process.

In this talk, I will start with an overview of the nature and foundation of the imbalanced learning problem, and then focus on the state-of-the-art methods and technologies in dealing with the imbalanced data, followed by a systematic discussion on the assessment metrics to evaluate learning performance under the imbalanced learning scenario. I will also present the latest research development in our group that we have developed and tested on various imbalanced data sets.

Furthermore, as a relatively new challenge to the community, I will highlight the major opportunities and challenges, as well as potential important research directions for learning from imbalanced data facing the big data era. More information can be found at: What is the minimum energy required to transport one bit of information over a wireless network? In this talk we will explore this question from a fundamental information theory point of view. We will discuss how network topology, interference, and cooperation affects energy, and how the information theory insights can be used in designing networks.

We will first outline results on the limits of energy per bit in networks when there are no constraints on bandwidth or delay.

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We will next show how these results change when there is a bandwidth constraint, and show that the effect of interference on energy consumption can be drastically reduced through interference alignment. We will finally show that a delay constraint quite dramatically increases the energy required per bit. Lastly, we will moot commercialization challenges of this disruptive technology.

His main research interests are in optical wireless communications, hybrid optical wireless and RF communications, spatial modulation, and interference coordination in wireless networks. He first introduced and coined spatial modulation and LiFi. Haas was an invited speaker at TED Global , and his talk: This has been viewed online more than 2.

Professor Haas holds 43 patents and has more than 30 pending patent applications. He has published conference and journal papers including a paper in Science. His Google Scholar h-index is 68, and his worked has been cited more than 21, times. Haas is recipient of the Tam Dalyell Prize awarded by the University of Edinburgh for excellence in engaging the public with science. In , he received the outstanding achievement award from the International Solid-State Lighting Alliance.

He was elected a Fellow of the Royal Society of Edinburgh in This talk presents a method for increasing the spectral efficiency of the massive IP packet transmission over single channel. As has been known, an IP packet can be divided into the header and data unit DU. The former contains the sufficient information, such as information of the address, series number and CODEC, to the receiver and the latter carries the information bits of the communications.

When dealing with the large number of the DUs, we define series of time slots TS , each of which represents a number of bits. By doing this, the bits taken for channel realization is saved. Consequently, the spectral efficiency is increased in terms of data rate and power saving. The simulation results confirm this approach. His research interests are in the field of wireless communications and mobile healthcare. So far, he has published more than 70 journal papers. With the expected deployment of billions of IoT devices and applications the need for environment friendly network infrastructure is increasing.

At the same time the emerging 5G devices need to be times more energy efficient than 4G devices. Hence, the green IoT network architecture will be an important components of the future ICT platforms that will enable deployment of large number of IoT field devices in a cost effective manner. Main design goals of a green IoT network are: This talk will concentrate of the development low power wide area energy harvested networks for IoT applications.

Various energy harvesting techniques and practical harvester designs for outdoor IoT networks will be discussed. The design techniques will concentrate on harvesting energy from the operating environments. The presentation will then focus on energy harvest aware MAC and routing protocols for sensor and wide area sensor networks. Test and simulation results will be presented in the presentation.

His current research interests in areas of M2M and IoT networks, Energy harvesting networks, Vehicular networks and Network resource allocation techniques. He has published more than international research papers and an edited book on Wireless Body Area Networks. The Future of Wireless Networks: This talk provides a high-level discussion on scaling capacity, and analyses how to make it physically possible to scale the cellular capacity by orders of magnitude.

The three dimensions of densification, bandwidth, and spectral efficiency are discussed in detail. The one user per cell concept is introduced as the sweet spot for densification.

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The importance of idle modes, both for reducing interference and improving the energy efficiency in small cell networks is discussed. The impact of increased bandwidth on the power consumption is evaluated, and improving spectral efficiency with beamforming techniques is explored.

Finally, the technology mix that can enable an average capacity of 1 Gbps per UE is derived. Prior to this, David received the B. David has authored the book Small Cell Networks: He also holds over 41 patents applications. David received the Ph. Studies of clustering in Wireless Sensor Networks WSNs usually tackle the problems of designing new algorithms and compare them based on a set of properties e.

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Our approach tackles this lack of understanding by applying techniques developed by complex systems scientists. Functional topology graphs, which describe the interactions between system parts, are used to represent different implementations of clustering in WSNs. Wireless Information Networks, 2nd Edition.

Added to Your Shopping Cart. Description Towards location aware mobile ad hoc sensors A Systems Engineering Approach to Wireless Information Networks The Second Edition of this internationally respected textbook brings readers fully up to date with the myriad of developments in wireless communications. When first published in , wireless communications was synonymous with cellular telephones. Now wireless information networks are the most important technology in all branches of telecommunications. Readers can learn about the latest applications in such areas as ad hoc sensor networks, home networking, and wireless positioning.