In this dissertation, dimensionality reduction for hyperspectral remote sensing imagery is investigated to alleviate practical application difficulties caused by high data dimension. Band selection and band clustering are applied for this purpose. Based on availability of object prior information, supervised, semi-supervised, and unsupervised techniques are proposed. To take advantage of modern computational architecture, parallel implementations on cluster and graphics processing units (GPU) are developed. The impact of dimensionality reduction on the following data analysis is also evaluated. Specific contributions are as below. 1. A similarity-based unsupervised band selection algorithm is developed to select distinctive and informative bands, which outperforms other existing unsupervised band selection approaches in the literature. 2. An efficient supervised band selection method based on minimum estimated abundance covariance is developed, which outperforms other frequently-used metrics. This new method does not need to conduct classification during band selection process or examine original bands/band combinations as do traditional approaches. 3. An efficient semi-supervised band clustering method is proposed, which uses class signatures to conduct band partition. Compared to traditional unsupervised clustering, computational complexity is significantly reduced. 4. Parallel GPU implementations with computational cost saving strategies for the developed algorithms are designed to facilitate onboard processing. 5. As an application example, band selection results are used for urban land cover classification. With a few selected bands, classification accuracy can be greatly improved, compared to the one using all the original bands or those from other frequently-used dimensionality reduction methods.

Processing hyperspectral image data can be computationally expensive and difficult to employ for real-time applications due to its extensive spatial and spectral information. Further, applications in which computational resources may be limited can be hindered by the volume of data that is common with airborne hyperspectral image data. This paper proposes utilizing band selection to down-select the number of spectral bands to consider for a given classification task such that classification can be done at the edge. Specifically, we consider the following state of the art band selection techniques: Fast Volume-Gradient-based Band Selection (VGBS), Improved Sparse Subspace Clustering (ISSC), Maximum-Variance Principal Component Analysis (MVPCA), and Normalized Cut Optimal Clustering MVPCA (NC-OC-MVPCA), to investigate their feasibility at identifying discriminative bands such that classification performance is not drastically hindered. This would greatly benefit applications where time-sensitive solutions are needed to ensure optimal outcomes. In this research, an NVIDIA AGX Xavier module is used as the edge device to run trained models on as a simulated deployed unmanned aerial system. Performance of the proposed approach is measured in terms of classification accuracy and run time.

Hyperspectral imagery is a highly dimensional type of data resulting in high computational costs during analysis. Band selection aims to reduce the original hyperspectral image to a smaller subset that reduces these costs while preserving the maximum amount of spectral information within the data. This thesis explores various types of band selection techniques used in hyperspectral image processing. Modifying Neural network-based techniques and observing the effects on the band selection process due to the change in network architecture or objective are of particular focus in this thesis. Herein, a generalized neural network-based band selection technique is developed and compared to state-of-the-art algorithms that are applied to a unique dataset and the Pavia City Center dataset where the subsequent selected bands are fed into a classifier to gather comparison results.

Les images hyperspectrales (HSI) fournissent des informations spectrales détaillées sur les objets analysés. Étant donné que différents matériaux ont des signatures spectrales distinctes, les objets ayant des couleurs et des formes similaires peuvent être distingués dans le domaine spectral. Toutefois, l’énorme quantité de données peut poser des problèmes en termes de stockage et de transmission des données. De plus, la haute dimensionnalité des images hyperspectrales peut entraîner un surajustement du classificateur en cas de données d'apprentissage insuffisantes. Une façon de résoudre de tels problèmes consiste à effectuer une sélection de bande (BS), car elle réduit la taille du jeu de données tout en conservant des informations utiles et originales. Dans cette thèse, nous proposons trois méthodes de sélection de bande différentes. La première est supervisée, conçu pour utiliser seulement 20% des données disponibles. Pour chaque classe du jeu de données, une classification binaire un contre tous utilisant un réseau de neurones est effectuée et les bandes liées aux poids le plus grand et le plus petit sont sélectionnées. Au cours de ce processus, les bandes les plus corrélées avec les bandes déjà sélectionnées sont rejetées. Par conséquent, la méthode proposée peut être considérée comme une approche de sélection de bande orientée par des classes. La deuxième méthode que nous proposons est une version non supervisée du premier framework. Au lieu d'utiliser les informations de classe, l'algorithme K-Means est utilisé pour effectuer une classification binaire successive de l'ensemble de données. Pour chaque paire de grappes, un réseau de neurones à une seule couche est utilisé pour rechercher l'hyperplan de séparation, puis la sélection des bandes est effectuée comme décrit précédemment. Pour la troisième méthode de BS proposée, nous tirons parti de la nature non supervisée des auto-encodeurs. Pendant la phase d'apprentissage, le vecteur d'entrée est soumis au bruit de masquage. Certaines positions de ce vecteur sont basculées de manière aléatoire sur zéro et l'erreur de reconstruction est calculée sur la base du vecteur d'entrée non corrompu. Plus l'erreur est importante, plus les fonctionnalités masquées sont importantes. Ainsi, à la fin, il est possible d'avoir un classement des bandes spectrales de l'ensemble de données. / Hyperspectral images (HSIs) are capable of providing a detailed spectral information about scenes or objects under analysis. It is possible thanks to both numerous and contiguous bands contained in such images. Given that di_erent materials have distinct spectral signatures, objects that have similar colors and shape can be distinguished in the spectral domain that goes beyond the visual range. However, in a pattern recognition system, the huge amount of data contained in HSIs may pose problems in terms of data storage and transmission. Also, the high dimensionality of hyperspectral images can cause the overfitting of the classifer in case of insufficient training data. One way to solve such problems is to perform band selection(BS) in HSIs, because it decreases the size of the dataset while keeping both useful and original information. In this thesis, we propose three different band selection frameworks. The first one is a supervised one, and it is designed to use only 20% of the available training data. For each class in the dataset, a binary one-versus-all classification using a single-layer neural network is performed, and the bands linked to the largest and smallest coefficients of the resulting hyperplane are selected. During this process, the most correlated bands with the bands already selected are automatically discarded, following a procedure also proposed in this thesis. Consequently, the proposed method may be seen as a classoriented band selection approach, allowing a BS criterion that meets the needs of each class. The second method we propose is an unsupervised version of the first framework. Instead of using the class information, the K-Means algorithm is used to perform successive binary clustering of the dataset. For each pair of clusters, a single-layer neural network is used to find the separating hyperplane, then the selection of bands is done as previously described. For the third proposed BS framework, we take advantage of the unsupervised nature of autoencoders. During the training phase, the input vector is subjected to masking Noise - some positions of this vector are randomly flipped to zero and the reconstruction error is calculated based on the uncorrupted input vector. The bigger the error, the more important the masked features are. Thus, at the end, it is possible to have a ranking of the spectral bands of the dataset.

This thesis presents the CMOS integer-N frequency synthesizer for 5 GHz WCDMA applications with 1.8V power supply. The frequency synthesizer is fabricated in a TSMC 0.18£gm CMOS 1P6M technology process. The frequency synthesizer consists of a phase-frequency detector, a charge pump, a low-pass loop filter, a voltage control oscillator, an auto-band selection, and a pulse-swallow divider. In pulse-swallow divider, this thesis use true single phase clock DFF proposed by Yuan and Svensson to work on high frequency region and to save the circuit area and power. This thesis also proposes an auto-band selection circuit to control the output frequency more precise and easier, and it can also reduce the frequency drift effect caused by technology process or temperature variation.

The title of the thesis focuses the attention on hyperspectral image segmentation, that is, we want to detect salient regions in a hyperspectral image and isolate them as accurate as possible. This purpose presents two main problems: Firstly, the fact of using hyperspectral imaging not only give us a huge amount of information, but we also have to face the problem of selecting somehow the information avoiding redundancies.Secondly, the problem of segmentation strictly speaking is still a challenging question whatever the input image would be.This thesis is focused on solving the whole process by means of building an image processing method that analyses and optimises the information acquired by a multispectral device. After that, it detects the main regions that are present in the scene in an image segmentation procedure. Therefore, this work will be divided into two parts. In the first part, an approach for selecting the most relevant subset of input bands will be presented. In the second part, this reduced representation of the initial bands will be the input data of a segmentation method.Finally, the main contributions of this PhD work could be briefly summarised as follows. On the one hand, we have proposed a pre-processing stage with an unsupervised band selection approach based on information measures that reduces considerably the amount of data. This approach has been successfully compared with well-known algorithms of the literature, showing its good performance with regard to pixel image classification tasks. On the other hand, after the band selection stage, two unsupervised segmentation procedures for detecting the main parts in multispectral images have been also developed. Regarding to this segmentation part, we have mainly contributed with two measures of similarity among regions. An objective functional for selecting an optimal (or close to optimal) partition of the image is another relevant contribution too.

Stem rot of peanut (Arachis hypogaea L.), caused by a soilborne fungus Athelia rolfsii (Curzi) C. C. Tu and Kimbr. (anamorph: Sclerotium rolfsii Sacc.), is one of the most important diseases in peanut production worldwide. Though new varieties with increased partial resistance to this disease have been developed, there is still a need to utilize fungicides for disease control during the growing season. Fungicides with activity against A. rolfsii are available, and several new products have been recently registered for control of stem rot in peanut. However, fungicides are most effective when applied before or during the early stages of infection. Current scouting methods can detect disease once signs or symptoms are present, but to optimize the timing of fungicide applications and protect crop yield, a method for early detection of soilborne diseases is needed. Previous studies have utilized optical sensors combined with machine learning analysis for the early detection of plant diseases, but these studies mainly focused on foliar diseases. Few studies have applied these technologies for the early detection of soilborne diseases in field crops, including peanut. Thus, the overall goal of this research was to integrate sensor technologies, modern data analytic tools, and properties of standard and newly registered fungicides to develop improved management strategies for stem rot control in peanuts. The specific objectives of this work were to 1) characterize the spectral and thermal responses of peanut to infection with A. rolfsii under controlled conditions, 2) identify optimal wavelengths to detect stem rot of peanut using hyperspectral sensor and machine learning, and 3) evaluate the standard and newly registered peanut fungicides with different modes of action for stem rot control in peanuts using a laboratory bioassay. For Objective 1, spectral reflectance and leaf temperature of peanut plants were measured by spectral and thermal sensors in controlled greenhouse experiments. Differences in sensor-based responses between A. rolfsii-infected and non-infected plants were detected 0 to 1 day after observation of foliar disease symptoms. In addition, spectral responses of peanut to the infection of A. rolfsii were more pronounced and consistent than thermal changes as the disease progressed. Objective 2 aimed to identify specific signatures of stem rot from reflectance data collected in Objective 1 utilizing a machine learning approach. Wavelengths around 505, 690, and 884 nm were repeatedly selected by different methods. The top 10 wavelengths identified by the recursive feature selection methods performed as well as all bands for the classification of healthy peanut plants and plants at different stages of disease development. Whereas the first two objectives focused on disease detection, Objective 3 focused on disease control and compared the properties of different fungicides that are labeled for stem rot control in peanut using a laboratory bioassay of detached peanut tissues. All of the foliar-applied fungicides evaluated provided inhibition of A. rolfsii for up to two weeks on plant tissues that received a direct application. Succinate dehydrogenase inhibitors provided less basipetal protection of stem tissues than quinone outside inhibitor or demethylation inhibitor fungicides. Overall, results of this research provide a foundation for developing sensor/drone-based methods that use disease-specific spectral indices for scouting in the field and for making fungicide application recommendations to manage stem rot of peanut and other soilborne diseases. / Doctor of Philosophy / Plant diseases are a major constraint to crop production worldwide. Developing effective and economical management strategies for these diseases, including selection of proper fungicide chemistries and making timely fungicide application, is dependent on the ability to accurately detect and diagnose their signs and/or symptoms prior to widespread development in a crop. Optical sensors combined with machine learning analysis are promising tools for automated crop disease detection, but research is still needed to optimize and validate methods for the detection of specific plant diseases. The overarching goal of this research was to use the peanut-stem rot plant disease system to identify and evaluate sensor-based technologies and different fungicide chemistries that can be utilized for the management of soilborne plant diseases. The specific objectives of this work were to 1) characterize the temporal progress of spectral and thermal responses of peanut to infection and colonization with Athelia rolfsii, the causal agent of peanut stem rot 2) identify optimal wavelengths to detect stem rot of peanut using hyperspectral sensor and machine learning, and 3) evaluate standard and newly registered peanut fungicides with different modes of action for stem rot control in peanuts using a laboratory bioassay. Results of this work demonstrate that spectral reflectance measurements are able to distinguish between diseased and healthy plants more consistently than thermal measurements. Several wavelengths were identified using machine learning approaches that can accurately differentiate between peanut plants with symptoms of stem rot and non-symptomatic plants. In addition, a new method was developed to select the top-ranked, non-redundant wavelengths with a custom distance. These selected wavelengths performed better than using all wavelengths, providing a basis for designing low-cost optical filters to specifically detect this disease. In the laboratory bioassay evaluation of fungicides, all of the foliar-applied fungicides provided inhibition of A. rolfsii for up to two weeks on leaf tissues that received a direct application. Percent inhibition of A. rolfsii decreased over time, and the activity of all fungicides decreased at a similar rate. Overall, the findings of this research provide a foundation for developing sensor-based methods for disease scouting and making fungicide application recommendations to manage stem rot of peanut and other soilborne diseases.

L’imagerie hyperspectrale, qui consiste à acquérir l'image d'une scène dans un grand nombre de bandes spectrales, permet de détecter des cibles là où l'imagerie couleur classique ne permettrait pas de conclure. Les imageurs hyperspectraux à acquisition séquentielle sont inadaptés aux applications de détection en temps réel. Dans cette thèse, nous proposons d’utiliser un imageur multispectral snapshot, capable d’acquérir simultanément un nombre réduit de bandes spectrales sur un unique détecteur matriciel. Le capteur offrant un nombre de pixels limité, il est nécessaire de réaliser un compromis en choisissant soigneusement le nombre et les profils spectraux des filtres de l'imageur afin d’optimiser la performance de détection. Dans cet objectif, nous avons développé une méthode de sélection de bandes qui peut être utilisée dans la conception d’imageurs multispectraux basés sur une matrice de filtres fixes ou accordables. Nous montrons, à partir d'images hyperspectrales issues de différentes campagnes de mesure, que la sélection des bandes spectrales à acquérir peut conduire à des imageurs multispectraux capables de détecter des cibles ou des anomalies avec une efficacité de détection proche de celle obtenue avec une résolution hyperspectrale. Nous développons conjointement un démonstrateur constitué d'une matrice de 4 filtres de Fabry-Perot accordables électroniquement en vue de son implantation sur un imageur multispectral snapshot agile. Ces filtres sont développés en technologie MOEMS (microsystèmes opto-électro-mécaniques) en partenariat avec l'Institut d'Electronique Fondamentale. Nous présentons le dimensionnement optique du dispositif ainsi qu'une étude de tolérancement qui a permis de valider sa faisabilité. / Hyperspectral imaging, which consists in acquiring the image of a scene in a large number of spectral bands, can be used to detect targets that are not visible using conventional color imaging. Hyperspectral imagers based on sequential acquisition are unsuitable for real-time detection applications. In this thesis, we propose to use a snapshot multispectral imager able to acquire simultaneously a small number of spectral bands on a single image sensor. As the sensor offers a limited number of pixels, it is necessary to achieve a trade-off by carefully choosing the number and the spectral profiles of the imager’s filters in order to optimize the detection performance. For this purpose, we developed a band selection method that can be used to design multispectral imagers based on arrays of fixed or tunable filters. We use real hyperspectral images to show that the selection of spectral bands can lead to multispectral imagers able to compete against hyperspectral imagers for target detection and anomaly detection applications while allowing snapshot acquisition and real-time detection. We jointly develop an adaptive snapshot multispectral imager based on an array of 4 electronically tunable Fabry-Perot filters. The filters are developed in MOEMS technology (Micro-Opto-Electro-Mechanical Systems) in partnership with the Institut d'Electronique Fondamentale. We present the optical design of the device and a study of tolerancing which has validated its feasibility.

L'imagerie hyperspectrale permet d'acquérir des informations spectrales riches d'une scène dans plusieurs centaines, voire milliers de bandes spectrales étroites et contiguës. Cependant, avec le nombre élevé de bandes spectrales, la forte corrélation inter-bandes spectrales et la redondance de l'information spectro-spatiale, l'interprétation de ces données hyperspectrales massives est l'un des défis majeurs pour la communauté scientifique de la télédétection. Dans ce contexte, le grand défi posé est la réduction du nombre de bandes spectrales inutiles, c'est-à-dire de réduire la redondance et la forte corrélation de bandes spectrales tout en préservant l'information pertinente. Par conséquent, des approches de projection visent à transformer les données hyperspectrales dans un sous-espace réduit en combinant toutes les bandes spectrales originales. En outre, des approches de sélection de bandes tentent à chercher un sous-ensemble de bandes spectrales pertinentes. Dans cette thèse, nous nous intéressons d'abord à la classification d'imagerie hyperspectrale en essayant d'intégrer l'information spectro-spatiale dans la réduction de dimensions pour améliorer la performance de la classification et s'affranchir de la perte de l'information spatiale dans les approches de projection. De ce fait, nous proposons un modèle hybride permettant de préserver l'information spectro-spatiale en exploitant les tenseurs dans l'approche de projection préservant la localité (TLPP) et d'utiliser l'approche de sélection non supervisée de bandes spectrales discriminantes à base de contraintes (CBS). Pour modéliser l'incertitude et l'imperfection entachant ces approches de réduction et les classifieurs, nous proposons une approche évidentielle basée sur la théorie de Dempster-Shafer (DST). Dans un second temps, nous essayons d'étendre le modèle hybride en exploitant des connaissances sémantiques extraites à travers les caractéristiques obtenues par l'approche proposée auparavant TLPP pour enrichir la sélection non supervisée CBS. En effet, l'approche proposée permet de sélectionner des bandes spectrales pertinentes qui sont à la fois informatives, discriminantes, distinctives et peu redondantes. En outre, cette approche sélectionne les bandes discriminantes et distinctives en utilisant la technique de CBS en injectant la sémantique extraite par les techniques d'extraction de connaissances afin de sélectionner d'une manière automatique et adaptative le sous-ensemble optimal de bandes spectrales pertinentes. La performance de notre approche est évaluée en utilisant plusieurs jeux des données hyperspectrales réelles. / Hyperspectral imagery allows to acquire a rich spectral information of a scene in several hundred or even thousands of narrow and contiguous spectral bands. However, with the high number of spectral bands, the strong inter-bands spectral correlation and the redundancy of spectro-spatial information, the interpretation of these massive hyperspectral data is one of the major challenges for the remote sensing scientific community. In this context, the major challenge is to reduce the number of unnecessary spectral bands, that is, to reduce the redundancy and high correlation of spectral bands while preserving the relevant information. Therefore, projection approaches aim to transform the hyperspectral data into a reduced subspace by combining all original spectral bands. In addition, band selection approaches attempt to find a subset of relevant spectral bands. In this thesis, firstly we focus on hyperspectral images classification attempting to integrate the spectro-spatial information into dimension reduction in order to improve the classification performance and to overcome the loss of spatial information in projection approaches.Therefore, we propose a hybrid model to preserve the spectro-spatial information exploiting the tensor model in the locality preserving projection approach (TLPP) and to use the constraint band selection (CBS) as unsupervised approach to select the discriminant spectral bands. To model the uncertainty and imperfection of these reduction approaches and classifiers, we propose an evidential approach based on the Dempster-Shafer Theory (DST). In the second step, we try to extend the hybrid model by exploiting the semantic knowledge extracted through the features obtained by the previously proposed approach TLPP to enrich the CBS technique. Indeed, the proposed approach makes it possible to select a relevant spectral bands which are at the same time informative, discriminant, distinctive and not very redundant. In fact, this approach selects the discriminant and distinctive spectral bands using the CBS technique injecting the extracted rules obtained with knowledge extraction techniques to automatically and adaptively select the optimal subset of relevant spectral bands. The performance of our approach is evaluated using several real hyperspectral data.

CoordenaÃÃo de AperfeiÃoamento de Pessoal de NÃvel Superior / Over the last years, the wireless broadband access has achieved a tremendous success. With that, the telecommunications industry has faced very important changes in terms of technology, heterogeneity, kind of applications, and massive usage (virtual data tsunami) derived from the introduction of smartphones and tablets; or even in terms of market structure and its main players/actors. Nonetheless, it is well-known that the electromagnetic spectrum is a scarce resource, being already fully occupied (or at least reserved for certain applications). Tra- ditional spectrum markets (where big monopolies dominate) and static spectrum management originated a paradoxal situation: the spectrum is occupied without actually being used! In one hand, with the global transition from analog to digital Television (TV), part of the spectrum previously licensed for TV is freed and geographically interleaved, originating the consequent Television White Spaces (TVWS); on the other hand, the direct communications between devices, commonly referred as Device-to-Device (D2D) communications, are attracting crescent attention by the scientific community and industry in order to overcome the scarcity problem and satisfy the increasing demand for extra capacity. As such, this thesis is divided in two main parts: (a) Spectrum market for TVWS: where a SWOT analysis for the use of TVWS is performed giving some highlights in the directions/actions that shall be followed so that its adoption becomes effective; and a tecno-economic evaluation study is done considering as a use-case a typical European city, showing the potential money savings that operators may reach if they adopt by the use of TVWS in a flexible market manner; (b) D2D communications: where a neighbor discovery technique for D2D communications is proposed in the single-cell scenario and further extended for the multi-cell case; and an interference mitigation algorithm based on the intelligent selection of Downlink (DL) or Uplink (UL) band for D2D communications underlaying cellular networks. A summary of the principal conclusions is as follows: (a) The TVWS defenders shall focus on the promotion of a real-time secondary spectrum market, where through the correct implementation of policies for protection ratios in the spectrum broker and geo-location database, incumbents are protected against interference; (b) It became evident that an operator would recover its investment around one year earlier if it chooses to deploy the network following a flexible spectrum market approach with an additional TVWS carrier, instead of the traditional market; (c) With the proposed neighbor discovery technique the time to detect all neighbors per Mobile Station (MS) is significantly reduced, letting more time for the actual data transmission; and the power of MS consumed during the discovery process is also reduced because the main processing is done at the Base Station (BS), while the MS needs to ensure that D2D communication is possible just before the session establishment; (d) Despite being a simple concept, band selection improves the gains of cellular communications and limits the gains of D2D communications, regardless the position within the cell where D2D communications happen, providing a trade-off between system performance and interference mitigation. / Nos Ãltimos anos, o acesso de banda larga atingiu um grande sucesso. Com isso, a indÃstria das telecomunicaÃÃes passou por importantes transformaÃÃes em termos de tecnologia, heterogeneidade, tipo de aplicaÃÃes e uso massivo (tsunami virtual de dados) em consequÃncia da introduÃÃo dos smartphones e tablets; ou atà mesmo na estrutura de mercado e os seus principais jogadores/atores. PorÃm, à sabido que o espectro electromagnÃtico à um recurso limitado, estando jà ocupado (ou pelo menos reservado para alguma aplicaÃÃo). O mercado tradicional de espectro (onde os grandes monopÃlios dominam) e o seu gerenciamento estÃtico contribuÃram para essa situaÃÃo paradoxal: o espectro està ocupado mas nÃo està sendo usado! Por um lado, com a transiÃÃo mundial da TelevisÃo (TV) analÃgica para a digital, parte do espectro anteriormente licenciado para a TV à libertado e geograficamente multiplexado para evitar a interferÃncia entre sinais de torres vizinhas, dando origem a ÂespaÃos em branco na frequÃncia da TV ou Television White Spaces (TVWS); por outro lado, as comunicaÃÃes diretas entre usuÃrios, designadas por comunicaÃÃes diretas Dispositivo-a-Dispositivo (D2D), està gerando um crescente interesse da comunidade cientÃfica e indÃstria, com vista a ultrapassar o problema da escassez de espectro e satisfazer a crescente demanda por capacidade extra. Assim, a tese està dividida em duas partes principais: (a) Mercado de espectro eletromagnÃtico para TVWS: onde à feita uma anÃlise SWOT para o uso dos TVWS, dando direÃÃes/aÃÃes a serem seguidas para que o seu uso se torne efetivo; e um estudo tecno-econÃmico considerando como cenÃrio uma tÃpica cidade Europeia, onde se mostram as possÃveis poupanÃas monetÃrias que os operadores conseguem obter ao optarem pelo uso dos TVWS num mercado flexÃvel; (b) ComunicaÃÃes D2D: onde uma tÃcnica de descoberta de vizinhos para comunicaÃÃes D2D à proposta, primeiro para uma Ãnica cÃlula e mais tarde estendida para o cenÃrio multi-celular; e um algoritmo de mitigaÃÃo de interferÃncia baseado na seleÃÃo inteligente da banda Ascendente (DL) ou Descendente (UL) a ser reusada pelas comunicaÃÃes D2D que acontecem na rede celular. Um sumÃrio das principais conclusÃes à o seguinte: (a) Os defensores dos TVWS devem-se focar na promoÃÃo do mercado secundÃrio de espectro electromagnÃtico, onde atravÃs da correta implementaÃÃo de politicas de proteÃÃo contra a interferÃncia no broker de espectro e na base de dados, os usuÃrios primÃrio sÃo protegidos contra a interferÃncia; (b) Um operador consegue recuperar o seu investimento aproximadamente um ano antes se ele optar pelo desenvolvimento da rede seguindo um mercado secundÃrio de espectro com a banda adicional de TVWS, em vez do mercado tradicional; (c) Com a tÃcnica proposta de descoberta de vizinhos, o tempo de descoberta por usuÃrio à significativamente reduzido; e a potÃncia consumida nesse processo à tambÃm ela reduzida porque o maior processamento à feito na EstaÃÃo RÃdio Base (BS), enquanto que o usuÃrio sà precisa de se certificar que a comunicaÃÃo direta à possÃvel; (d) A seleÃÃo de banda, embora seja um conceito simples, melhora os ganhos das comunicaÃÃes celulares e limita os das comunicaÃÃes D2D, providenciando um compromisso entre a performance do sistema e a mitigaÃÃo de interferÃncia.