Forestry timber typing. Tanana demonstration project, Alaska ASVT

The feasibility of using LANDSAT digital data in conjunction with topographic data to delineate commercial forests by stand size and crown closure in the Tanana River basin of Alaska was tested. A modified clustering approach using two LANDSAT dates to generate an initial forest type classification was then refined with topographic data. To further demonstrate the ability of remotely sensed data in a fire protection planning framework, the timber type data were subsequently integrated with terrain information to generate a fire hazard map of the study area. This map provides valuable assistance in initial attack planning, determining equipment accessibility, and fire growth modeling. The resulting data sets were incorporated into the Alaska Department of Natural Resources geographic information system for subsequent utilization

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

Clustering in Multi-Channel Cognitive Radio Ad Hoc and Sensor Networks

© 1979-2012 IEEE. CR enables dynamic spectrum access to utilize licensed spectrum when it is idle. CR technology is applied to wireless ad hoc and sensor networks to form CRAHNs and CRSNs, respectively. Clustering is an efficient topology management technique to regulate communication and allocate spectrum resources by CR capabilities of nodes in CRAHNs and CRSNs. In this article, we thoroughly investigate the benefits and functionalities of clustering such as topology, spectrum, and energy management in these networks. We also overview motivations for and challenges of clustering in CRAHNs and CRSNs. Existing clustering schemes are reviewed and compared. We conclude by revealing key considerations and possible solutions for spectrum-aware clustering in multi-channel CRAHNs and CRSNs

Superallocation and Cluster‐Based Cooperative Spectrum Sensing in 5G Cognitive Radio Network

Consequently, the research and development for the 5G systems have already been started. This chapter presents an overview of potential system network architecture and highlights a superallocation technique that could be employed in the 5G cognitive radio network (CRN). A superallocation scheme is proposed to enhance the sensing detection performance by rescheduling the sensing and reporting time slots in the 5G cognitive radio network with a cluster‐based cooperative spectrum sensing (CCSS). In the 4G CCSS scheme, first, all secondary users (SUs) detect the primary user (PU) signal during a rigid sensing time slot to check the availability of the spectrum band. Second, during the SU reporting time slot, the sensing results from the SUs are reported to the corresponding cluster heads (CHs). Finally, during CH reporting time slots, the CHs forward their hard decision to a fusion center (FC) through the common control channels for the global decision. However, the reporting time slots for the SUs and CHs do not contribute to the detection performance. In this chapter, a superallocation scheme that merges the reporting time slots of SUs and CHs by rescheduling the reporting time slots as a nonfixed sensing time slot for SUs to detect the PU signal promptly and more accurately is proposed. In this regard, SUs in each cluster can obtain a nonfixed sensing time slot depending on their reporting time slot order. The effectiveness of the proposed chapter that can achieve better detection performance under –28 to –10 dB environments and thus reduce reporting overhead is shown through simulations

An Architecture for Coexistence with Multiple Users in Frequency Hopping Cognitive Radio Networks

The radio frequency (RF) spectrum is a limited resource. Spectrum allotment disputes stem from this scarcity as many radio devices are con confined to a fixed frequency or frequency sequence. One alternative is to incorporate cognition within a configurable radio platform, therefore enabling the radio to adapt to dynamic RF spectrum environments. In this way, the radio is able to actively observe the RF spectrum, orient itself to the current RF environment, decide on a mode of operation, and act accordingly, thereby sharing the spectrum and operating in more flexible manner. This research presents a novel framework for incorporating several techniques for the purpose of adapting radio operation to the current RF spectrum environment. Specifically, this research makes six contributions to the field of cognitive radio: (1) the framework for a new hybrid hardware/software middleware architecture, (2) a framework for testing and evaluating clustering algorithms in the context of cognitive radio networks, (3) a new RF spectrum map representation technique, (4) a new RF spectrum map merging technique, (5) a new method for generating a random key-based adaptive frequency-hopping waveform, and (6) initial integration testing toward implementing the proposed system on a field-programmable gate array (FPGA)

Graph-based Data Modeling and Analysis for Data Fusion in Remote Sensing

Hyperspectral imaging provides the capability of increased sensitivity and discrimination over traditional imaging methods by combining standard digital imaging with spectroscopic methods. For each individual pixel in a hyperspectral image (HSI), a continuous spectrum is sampled as the spectral reflectance/radiance signature to facilitate identification of ground cover and surface material. The abundant spectrum knowledge allows all available information from the data to be mined. The superior qualities within hyperspectral imaging allow wide applications such as mineral exploration, agriculture monitoring, and ecological surveillance, etc. The processing of massive high-dimensional HSI datasets is a challenge since many data processing techniques have a computational complexity that grows exponentially with the dimension. Besides, a HSI dataset may contain a limited number of degrees of freedom due to the high correlations between data points and among the spectra. On the other hand, merely taking advantage of the sampled spectrum of individual HSI data point may produce inaccurate results due to the mixed nature of raw HSI data, such as mixed pixels, optical interferences and etc. Fusion strategies are widely adopted in data processing to achieve better performance, especially in the field of classification and clustering. There are mainly three types of fusion strategies, namely low-level data fusion, intermediate-level feature fusion, and high-level decision fusion. Low-level data fusion combines multi-source data that is expected to be complementary or cooperative. Intermediate-level feature fusion aims at selection and combination of features to remove redundant information. Decision level fusion exploits a set of classifiers to provide more accurate results. The fusion strategies have wide applications including HSI data processing. With the fast development of multiple remote sensing modalities, e.g. Very High Resolution (VHR) optical sensors, LiDAR, etc., fusion of multi-source data can in principal produce more detailed information than each single source. On the other hand, besides the abundant spectral information contained in HSI data, features such as texture and shape may be employed to represent data points from a spatial perspective. Furthermore, feature fusion also includes the strategy of removing redundant and noisy features in the dataset. One of the major problems in machine learning and pattern recognition is to develop appropriate representations for complex nonlinear data. In HSI processing, a particular data point is usually described as a vector with coordinates corresponding to the intensities measured in the spectral bands. This vector representation permits the application of linear and nonlinear transformations with linear algebra to find an alternative representation of the data. More generally, HSI is multi-dimensional in nature and the vector representation may lose the contextual correlations. Tensor representation provides a more sophisticated modeling technique and a higher-order generalization to linear subspace analysis. In graph theory, data points can be generalized as nodes with connectivities measured from the proximity of a local neighborhood. The graph-based framework efficiently characterizes the relationships among the data and allows for convenient mathematical manipulation in many applications, such as data clustering, feature extraction, feature selection and data alignment. In this thesis, graph-based approaches applied in the field of multi-source feature and data fusion in remote sensing area are explored. We will mainly investigate the fusion of spatial, spectral and LiDAR information with linear and multilinear algebra under graph-based framework for data clustering and classification problems

Intelligent Radio Spectrum Monitoring

[EN] Spectrum monitoring is an important part of the radio spectrum management process, providing feedback on the workflow that allows for our current wirelessly interconnected lifestyle. The constantly increasing number of users and uses of wireless technologies is pushing the limits and capabilities of the existing infrastructure, demanding new alternatives to manage and analyse the extremely large volume of data produced by existing spectrum monitoring networks. This study addresses this problem by proposing an information management system architecture able to increase the analytical level of a spectrum monitoring measurement network. This proposal includes an alternative to manage the data produced by such network, methods to analyse the spectrum data and to automate the data gathering process. The study was conducted employing system requirements from the Brazilian National Telecommunications Agency and related functional concepts were aggregated from the reviewed scientific literature and publications from the International Telecommunication Union. The proposed solution employs microservice architecture to manage the data, including tasks such as format conversion, analysis, optimization and automation. To enable efficient data exchange between services, we proposed the use of a hierarchical structure created using the HDF5 format. The suggested architecture was partially implemented as a pilot project, which allowed to demonstrate the viability of presented ideas and perform an initial refinement of the proposed data format and analytical algorithms. The results pointed to the potential of the solution to solve some of the limitations of the existing spectrum monitoring workflow. The proposed system may play a crucial role in the integration of the spectrum monitoring activities into open data initiatives, promoting transparency and data reusability for this important public service.[ES] El control y análisis de uso del espectro electromagnético, un servicio conocido como comprobación técnica del espectro, es una parte importante del proceso de gestión del espectro de radiofrecuencias, ya que proporciona la información necesaria al flujo de trabajo que permite nuestro estilo de vida actual, interconectado e inalámbrico. El número cada vez más grande de usuarios y el creciente uso de las tecnologías inalámbricas amplían las demandas sobre la infraestructura existente, exigiendo nuevas alternativas para administrar y analizar el gran volumen de datos producidos por las estaciones de medición del espectro. Este estudio aborda este problema al proponer una arquitectura de sistema para la gestión de información capaz de aumentar la capacidad de análisis de una red de equipos de medición dedicados a la comprobación técnica del espectro. Esta propuesta incluye una alternativa para administrar los datos producidos por dicha red, métodos para analizar los datos recolectados, así como una propuesta para automatizar el proceso de recopilación. El estudio se realizó teniendo como referencia los requisitos de la Agencia Nacional de Telecomunicaciones de Brasil, siendo considerados adicionalmente requisitos funcionales relacionados descritos en la literatura científica y en las publicaciones de la Unión Internacional de Telecomunicaciones. La solución propuesta emplea una arquitectura de microservicios para la administración de datos, incluyendo tareas como la conversión de formatos, análisis, optimización y automatización. Para permitir el intercambio eficiente de datos entre servicios, sugerimos el uso de una estructura jerárquica creada usando el formato HDF5. Esta arquitectura se implementó parcialmente dentro de un proyecto piloto, que permitió demostrar la viabilidad de las ideas presentadas, realizar mejoras en el formato de datos propuesto y en los algoritmos analíticos. Los resultados señalaron el potencial de la solución para resolver algunas de las limitaciones del tradicional flujo de trabajo de comprobación técnica del espectro. La utilización del sistema propuesto puede mejorar la integración de las actividades e impulsar iniciativas de datos abiertos, promoviendo la transparencia y la reutilización de datos generados por este importante servicio público[CA] El control i anàlisi d'ús de l'espectre electromagnètic, un servei conegut com a comprovació tècnica de l'espectre, és una part important del procés de gestió de l'espectre de radiofreqüències, ja que proporciona la informació necessària al flux de treball que permet el nostre estil de vida actual, interconnectat i sense fils. El número cada vegada més gran d'usuaris i el creixent ús de les tecnologies sense fils amplien la demanda sobre la infraestructura existent, exigint noves alternatives per a administrar i analitzar el gran volum de dades produïdes per les xarxes d'estacions de mesurament. Aquest estudi aborda aquest problema en proposar una arquitectura de sistema per a la gestió d'informació capaç d’augmentar la capacitat d’anàlisi d'una xarxa d'equips de mesurament dedicats a la comprovació tècnica de l'espectre. Aquesta proposta inclou una alternativa per a administrar les dades produïdes per aquesta xarxa, mètodes per a analitzar les dades recol·lectades, així com una proposta per a automatitzar el procés de recopilació. L'estudi es va realitzar tenint com a referència els requisits de l'Agència Nacional de Telecomunicacions del Brasil, sent considerats addicionalment requisits funcionals relacionats descrits en la literatura científica i en les publicacions de la Unió Internacional de Telecomunicacions. La solució proposada empra una arquitectura de microserveis per a l'administració de dades, incloent tasques com la conversió de formats, anàlisi, optimització i automatització. Per a permetre l'intercanvi eficient de dades entre serveis, suggerim l'ús d'una estructura jeràrquica creada usant el format HDF5. Aquesta arquitectura es va implementar parcialment dins d'un projecte pilot, que va permetre demostrar la viabilitat de les idees presentades, realitzar millores en el format de dades proposat i en els algorismes analítics. Els resultats van assenyalar el potencial de la solució per a resoldre algunes de les limitacions del tradicional flux de treball de comprovació tècnica de l'espectre. La utilització del sistema proposat pot millorar la integració de les activitats i impulsar iniciatives de dades obertes, promovent la transparència i la reutilització de dades generades per aquest important servei públicSantos Lobão, F. (2019). Intelligent Radio Spectrum Monitoring. http://hdl.handle.net/10251/128850TFG