Remote Sensing Data Compression

A huge amount of data is acquired nowadays by different remote sensing systems installed on satellites, aircrafts, and UAV. The acquired data then have to be transferred to image processing centres, stored and/or delivered to customers. In restricted scenarios, data compression is strongly desired or necessary. A wide diversity of coding methods can be used, depending on the requirements and their priority. In addition, the types and properties of images differ a lot, thus, practical implementation aspects have to be taken into account. The Special Issue paper collection taken as basis of this book touches on all of the aforementioned items to some degree, giving the reader an opportunity to learn about recent developments and research directions in the field of image compression. In particular, lossless and near-lossless compression of multi- and hyperspectral images still remains current, since such images constitute data arrays that are of extremely large size with rich information that can be retrieved from them for various applications. Another important aspect is the impact of lossless compression on image classification and segmentation, where a reasonable compromise between the characteristics of compression and the final tasks of data processing has to be achieved. The problems of data transition from UAV-based acquisition platforms, as well as the use of FPGA and neural networks, have become very important. Finally, attempts to apply compressive sensing approaches in remote sensing image processing with positive outcomes are observed. We hope that readers will find our book useful and interestin

Design of Wireless Communication Networks for Cyber-Physical Systems with Application to Smart Grid

Cyber-Physical Systems (CPS) are the next generation of engineered systems in which computing, communication, and control technologies are tightly integrated. On one hand, CPS are generally large with components spatially distributed in physical world that has lots of dynamics; on the other hand, CPS are connected, and must be robust and responsive. Smart electric grid, smart transportation system are examples of emerging CPS that have significant and far-reaching impact on our daily life. In this dissertation, we design wireless communication system for CPS. To make CPS robust and responsive, it is critical to have a communication subsystem that is reliable, adaptive, and scalable. Our design uses a layered structure, which includes physical layer, multiple access layer, network layer, and application layer. Emphases are placed on multiple access and network layer. At multiple access layer, we have designed three approaches, namely compressed multiple access, sample-contention multiple access, and prioritized multiple access, for reliable and selective multiple access. At network layer, we focus on the problem of creating reliable route, with service interruption anticipated. We propose two methods: the first method is a centralized one that creates backup path around zones posing high interruption risk; the other method is a distributed one that utilizes Ant Colony Optimization (ACO) and positive feedback, and is able to update multipath dynamically. Applications are treated as subscribers to the data service provided by the communication system. Their data quality requirements and Quality of Service (QoS) feedback are incorporated into cross-layer optimization in our design. We have evaluated our design through both simulation and testbed. Our design demonstrates desired reliability, scalability and timeliness in data transmission. Performance gain is observed over conventional approaches as such random access

Higher dimensional time-energy entanglement

Judging by the compelling number of innovations based on taming quantum mechanical effects, such as the development of transistors and lasers, further research in this field promises to tackle further technological challenges in the years to come. This statement gains even more importance in the information processing scenario. Here, the growing data generation and the correspondingly higher need for more efficient computational resources and secure high bandwidth networks are central problems which need to be tackled. In this sense, the required CPU minituarization makes the design of structures at atomic levels inevitable, as foreseen by Moore's law. From these perspectives, it is necessary to concentrate further research efforts into controlling and manipulating quantum mechanical systems. This enables for example to encode quantum superposition states to tackle problems which are computationally NP hard and which therefore cannot be solved efficiently by classical computers. The only limitation affecting these solutions is the low scalability of existing quantum systems. Similarly, quantum communication schemes are devised to certify the secure transmission of quantum information, but are still limited by a low transmission bandwidth. This thesis follows the guideline defined by these research projects and aims to further increase the scalability of the quantum mechanical systems required to perform these tasks. The method used here is to encode quantum states into photons generated by spontaneous parametric down-conversion (SPDC). An intrinsic limitation of photons is that the scalability of quantum information schemes employing them is limited by the low detection efficiency of commercial single photon detectors. This is addressed by encoding higher dimensional quantum states into two photons, increasing the scalability of the scheme in comparison to multi-photon states. Further on, the encoding of quantum information into the emission-time degree of freedom improves its applicability to long distance quantum communication schemes. By doing that, the intrinsic limitations of other schemes based on the encoding into the momentum and polarization degree of freedom are overcome. This work presents results on a scalable experimental implementation of time-energy encoded higher dimensional states, demonstrating the feasibility of the scheme. Further tools are defined and used to characterize the properties of the prepared quantum states, such as their entanglement, their dimension and their preparation fidelity. Finally, the method of quantum state tomography is used to fully determine the underlying quantum states at the cost of an increased measurement effort and thus operation time. It is at this point that results obtained from the research field of compressed sensing help to decrease the necessary number of measurements. This scheme is compared with an adaptive tomography scheme designed to offer an additional reconstruction speedup. These results display the scalability of the scheme to bipartite dimensions higher than 2x8, equivalent to the encoding of quantum information into more than 6 qubits.Es ist in den letzten Jahren immer deutlicher geworden, dass weitere Forschung zur Untersuchung von quantenmechanischen Systemen durchgeführt werden muss um die wachsenden Probleme in der heutigen Informationstechnologie zu adressieren. Insbesondere sticht hier die exponentiell wachsende Nachfrage nach Computerressourcen und nach sicheren Kommunikationsprotokollen mit hoher Bandbreite hervor, um der weiter wachsenden Datengenerationsrate standzuhalten. Dies stösst auf fundamentale Grenzen, wie die erforderliche Miniaturisierung von Prozessorstrukturen (CPUs) auf atomare Dimensionen demonstriert. Von dieser Perspektive her ist es erforderlich weitere Forschung zur Kontrolle und Manipulation von Quantenzuständen durchzuführen, wie sie zum Beispiel im Feld der Quanteninformation erfolgt ist. Diese Strategie ermöglicht es von weiteren Eigenschaften der Quantenmechanik, wie zum Beispiel der Präparation von Superpositionszuständen, Gebrauch zu machen. Dies ist insbesondere relevant, da es ermöglicht NP harte Probleme zu lösen, die durch klassische Computer nicht effizient gelöst werden können. Allerdings sind bisher experimentell realisierte quantenmechanische Systeme noch nicht skalierbar genug um den Anforderungen der klassischen Technologie gerecht zu werden. Ähnlichen Argumenten folgend sind Quantenkommunikationssysteme, die die Sicherheit von Kommunikationsprotokolle zertifizieren können, noch nicht in der Lage angemessene Bandbreiten zu gewährleisten. Diese Doktorarbeit gliedert sich diesen Forschungsprojekten an, mit dem Ziel die Skalierbarkeit von quantenmechanischen Systemen zu vergrössern und entsprechend den genannten Anforderungen gerecht zu machen. Die Strategie die hier verfolgt wird basiert auf die Kodierung von Quantenzuständen in Photonenpaare, die durch den Prozess der Spontanen Parametrischen Down-conversion (SPDC) erzeugt werden. Dieses Verfahren bringt allerdings eine limitierte Skalierbarkeit der Quantensysteme mit sich, da die Detektionseffizienz von kommerziell erhältlichen Einzelphotonendetektoren limitiert ist. Dieses Problem wird in dieser Arbeit umgangen indem die Quantenzustände in höher dimensionale Hilberträume eines Zweiphotonenzustands kodiert werden, was einen deutlichen Vorteil gegenüber der Kodierung in einen Mehrphotonenzustand darstellt. Darüber hinaus ermöglicht die Kodierung der Quantenzustände in den Emissionszeit Freiheitsgrad der Photonen intrinsische Vorteile bei ihrer Anwendung auf die Quantenkommunikation. Hier ist insbesondere der Vorteil gegenüber der Kodierung in den Impuls- und Polarisationsfreiheitsgrad gemeint, die durch deutliche Einschränkungen bei der Transmission über lange Strecken gekennzeichnet sind. Mit einem Augenmerk auf diese Ziele wird in dieser Arbeit die experimentelle Umsetzbarkeit des beschriebenen Schemas gezeigt. Dies wurde durch die Anwendung von geeigneten Maßen wie die Verschränkung, Dimension und Präparationsfidelity auf die generierten Zustände quantifiziert. Insbesondere bei der Abschätzung der Fidelity wurde von Forschungsergebnissen rund um Compressed Sensing Gebrauch gemacht und weiter mit einem adaptiven Messschema kombiniert, um die effektive Betriebszeit dieser Systeme zu verringern. Dies ist für die weitere skalierbare Anwendung zur Quanteninformationsverarbeitung von Vorteil. Die Ergebnisse verdeutlichen, dass eine Skalierbarkeit der Dimension des Systems auf grösser als 2x8 Dimensionen, äquivalent zur Dimension eines 6-Qubit Zustands, in der Reichweite einer experimentellen Umsetzung liegt

Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age

Simultaneous Localization and Mapping (SLAM)consists in the concurrent construction of a model of the environment (the map), and the estimation of the state of the robot moving within it. The SLAM community has made astonishing progress over the last 30 years, enabling large-scale real-world applications, and witnessing a steady transition of this technology to industry. We survey the current state of SLAM. We start by presenting what is now the de-facto standard formulation for SLAM. We then review related work, covering a broad set of topics including robustness and scalability in long-term mapping, metric and semantic representations for mapping, theoretical performance guarantees, active SLAM and exploration, and other new frontiers. This paper simultaneously serves as a position paper and tutorial to those who are users of SLAM. By looking at the published research with a critical eye, we delineate open challenges and new research issues, that still deserve careful scientific investigation. The paper also contains the authors' take on two questions that often animate discussions during robotics conferences: Do robots need SLAM? and Is SLAM solved

On Random Sampling for Compliance Monitoring in Opportunistic Spectrum Access Networks

In the expanding spectrum marketplace, there has been a long term evolution towards more market€“oriented mechanisms, such as Opportunistic Spectrum Access (OSA), enabled through Cognitive Radio (CR) technology. However, the potential of CR technologies to revolutionize wireless communications, also introduces challenges based upon the potentially non€“deterministic CR behaviour in the Electrospace. While establishing and enforcing compliance to spectrum etiquette rules are essential to realization of successful OSA networks in the future, there has only been recent increased research activity into enforcement. This dissertation presents novel work on the spectrum monitoring aspect, which is crucial to effective enforcement of OSA. An overview of the challenges faced by current compliance monitoring methods is first presented. A framework is then proposed for the use of random spectral sampling techniques to reduce data collection complexity in wideband sensing scenarios. This approach is recommended as an alternative to Compressed Sensing (CS) techniques for wideband spectral occupancy estimation, which may be difficult to utilize in many practical congested scenarios where compliance monitoring is required. Next, a low€“cost computational approach to online randomized temporal sensing deployment is presented for characterization of temporal spectrum occupancy in cognitive radio scenarios. The random sensing approach is demonstrated and its performance is compared to CS€“based approach for occupancy estimation. A novel frame€“based sampling inversion technique is then presented for cases when it is necessary to track the temporal behaviour of individual CRs or CR networks. Parameters from randomly sampled Physical Layer Convergence Protocol (PLCP) data frames are used to reconstruct occupancy statistics, taking account of missed frames due to sampling design, sensor limitations and frame errors. Finally, investigations into the use of distributed and mobile spectrum sensing to collect spatial diversity to improve the above techniques are presented, for several common monitoring tasks in spectrum enforcement. Specifically, focus is upon techniques for achieving consensus in dynamic topologies such as in mobile sensing scenarios