Submitted by JOSIANE SANTOS DE OLIVEIRA (josianeso) on 2017-09-28T14:18:05Z No. of bitstreams: 1 Clarissa Gonçalves Pereira_.pdf: 8670836 bytes, checksum: 533233f8fd5ced1f57703f2449cef5ee (MD5) / Made available in DSpace on 2017-09-28T14:18:05Z (GMT). No. of bitstreams: 1 Clarissa Gonçalves Pereira_.pdf: 8670836 bytes, checksum: 533233f8fd5ced1f57703f2449cef5ee (MD5) Previous issue date: 2017-03-21 / Nenhuma / A indústria audiovisual faz parte do conjunto de setores que compõem a economia criativa. Sua atuação baseia-se principalmente no desenvolvimento de projetos e na busca de conciliar o raciocínio objetivo da indústria com o pensamento subjetivo da criação. A complexidade de encontrar o equilíbrio entre estas esferas em um ambiente dinâmico e caracterizado por incertezas configura-se como um desafio para o setor e transparece no seu processo decisório. O presente estudo parte do pressuposto que a indústria audiovisual poderia aprimorar seus processos de projeto se as tomadas de decisões estratégicas fossem apoiadas por instrumentos capazes de conjugar estas visões distintas. O design é um campo que está familiarizado em articular o conhecimento e a criatividade com o propósito de gerar aprendizagem, realizar escolhas e resolver problemas. Dentre os recursos estratégicos que o design utiliza para trabalhar com aspectos divergentes, a construção de cenários apresenta-se como um exercício capaz lidar com múltiplas perspectivas, auxiliar a tomada de decisão e consequentemente aprimorar o processo de projeto. Nesse sentido, o objetivo geral desta pesquisa é compreender como a construção de cenários contribui para a tomada de decisão estratégica no âmbito audiovisual. Para tanto, realizou-se entrevistas com experts do setor audiovisual e foi utilizada a análise de protocolo para explorar a construção dos cenários com a técnica think aloud. Como principais resultados, percebeu-se que os cenários de design atuam como uma ponte estratégica capaz de propiciar a compreensão de conceitos diferentes, esclarecer ideias e negociar critérios. A conversa estimulada pelo exercício promove além da geração de novos conhecimentos a aproximação entre os envolvidos, a qual produz uma sensação de segurança e confiança que são aspectos valorizados na tomada de decisão no audiovisual. Para que sejam alcançados os resultados considerados característicos dos cenários como a amplitude do modelo mental e o deslocamento de antigas visões, a pesquisa indicou que é fundamental que as propriedades da prática sejam bem exploradas e que, em especial, o fator “tempo” seja considerado como uma condição chave para a realização do exercício. Além disso, estudo propõe um modelo conceitual que sintetiza de que maneira a construção de cenários de design pode contribuir com a tomada de decisão estratégica do audiovisual e, eventualmente, contribuir com outras áreas do saber, enriquecendo os conhecimentos sobre este tema. / The audiovisual industry is part of the set of sectors that makes up the creative economy. Its performance is based mainly on the development of projects and steadfast search to reconcile the industry objective reasoning with creation subjective thought. The complexity of finding the balance between these spheres in a dynamic environment characterized by uncertainties is a challenge for the industry and is evident in its strategic decision-making process. This study assumes that the audiovisual industry could improve its project procedures if strategic decision-making were supported by instruments capable of combining these different visions. Design is used to articulate knowledge and creativity (taken in this research as objective and subjective thinking), with the purpose of learning process, making choices and solving problems. Among the strategic resources that design uses to deal with divergent aspects, the construction of scenarios is a practice able to handle with multiple perspectives, helping decision making and eventually improving the project process. Thus, this research general aim is to understand how building different scenarios up may help to the strategic decision making in the audiovisual industry. For this purpose, experts from the audiovisual sector were interviewed and protocol analysis was evaluated to explore the construction of scenarios using the think aloud technique. As main results, it was noticed that designing scenarios have effect on providing knowledge of different concepts as well as clarifying ideas and negotiating criteria. Practice stimulated changing impressions and promotes not only the generation of new knowledge, but also the approach between the involved, which produces security and confidence that are important aspects in the decision making process in the audiovisual. In order to achieve the results considered characteristic of building the scenarios up, for instance the mental model amplitude and old visions displacement, the research demonstrated that it is fundamental that the properties of the practice should be well explored and, especially, time must be considered as a key condition for performing the exercise. In addition, the study proposes a conceptual model that synthesizes how the construction of design scenarios can contribute to the strategic decision making of the audiovisual sector and, possibly, contribute with other areas of knowledge, enriching the knowledge on this theme.

Made available in DSpace on 2016-04-25T16:44:51Z (GMT). No. of bitstreams: 1 ADM - Ivone Maria Bruno.pdf: 604104 bytes, checksum: 00e4e79a95f6b11d2f9aaecf9062dbc7 (MD5) Previous issue date: 2006-10-16 / The organizations world transits through constant transformations making that the abilities of the responsible by the decisions taken speed up the competitiveness, and the administrators have to adopt strategies, structures, technologies, products and services that constantly need to be adapted to contexts generally full of conflicts what makes to need of professional advising. The secretaryship professional formation and its development and continuous accompaniment of the technological, managemental and cultural changes in the world of the work and remaining it the front of the pertinent requirements, made possible to this professional gives the necessary support the administrators in the organizations. The objective of this research is to analyze which are the factors favorable to the secretaryship professional that make possible its advising with effectiveness to the administrator and consequently influencing the decision process. Considering that their position is strategical in the organizations, therefore they have access to the information in "first hand" and a systemically vision of the organizational process and its culture, it will propitiate, directly, its power of data and information articulation objectifying facilitates to influence in the decision power / O mundo das organizações transita por transformações constantes fazendo com que as competências dos responsáveis pelas decisões tomadas acelerem a competitividade, tendo os administradores que adotar estratégias, estruturas, tecnologias, produtos e serviços que necessitam ser constantemente adaptados a contextos geralmente repletos de conflitos o que faz necessitar do assessoramento profissional. A formação profissional do secretariado e o seu desenvolvimento e acompanhamento contínuo das alterações tecnológicas, gerenciais e culturais no mundo do trabalho e mantendo-se à frente das exigências pertinentes possibilitou ao mesmo dar o suporte necessário aos administradores nas organizações. O objetivo desta pesquisa é analisar quais são os fatores favoráveis ao profissional de secretariado que possibilitam o seu assessoramento com eficácia ao administrador e conseqüentemente influenciando o processo decisório. Considerando que sua posição é estratégica nas organizações, pois têm acesso às informações em primeira mão e uma visão sistêmica do processo organizacional e da sua cultura, isso irá propiciar, diretamente, poder de articulação dos dados e informações, objetivando facilitar a influência no processo decisório

Les politiques publiques environnementales cherchent à impacter des comportements de consommation. Néanmoins, la relation causale entre l’action publique mise en œuvre et le changement de comportement se caractérise par des discontinuités. Elle doit donc être approfondie en combinant l’angle d’analyse des politiques publiques et du processus de décision du consommateur. En effet, ce dernier dépend également d’autres déterminants psychosociaux et d’autres facteurs contextuels. L’impact spécifique des instruments des politiques publiques doit cependant pouvoir y être distingué. Notre étude sur la politique publique environnementale française visant à l’acquisition de voitures à faibles émissions de carbone permet de comprendre l’impact des instruments des politiques publiques sur le processus de décision d’achat du consommateur. En effet, l’attitude envers les instruments de l’action publique produit des effets sur le processus de décision du consommateur. Cet impact n’est pas direct, mais il modère les relations causales entre les principaux déterminants du comportement. Ces effets modérateurs dépendent de la nature psychologique ou structurelle des instruments des politiques publiques qui impactent des relations spécifiques du processus de décision du consommateur / Environmental public policy tools aim to impact consumer behavior. Nevertheless, the causal relationship system between the implementation of a public policy and behavior is full of disconnections. Thus, it should be deepen with the combined analysis of public policies and consumer decision process. Indeed, this latter also depends on others psychosocial determinants towards behavior and other contextual forces. The impact of public policy tools need to be distinguished among them.Our study on the French environmental public policy aimed at acquiring low-carbon emission cars focuses on understanding the impact of public policy tools on consumer buying decision process. Indeed, the attitude towards public policy tools affects consumer decision process. It results that the impact is not so direct but it moderates the relationship between the main determinants of behavior. These moderation effects depend on the psychological or structural nature of the public policy tools which impacts specific relationships of the consumer decision process

Dans cette thèse, nous avons étudié les problèmes de décisions séquentielles, avec comme application la gestion de stocks d'énergie. Traditionnellement, ces problèmes sont résolus par programmation dynamique stochastique. Mais la grande dimension, et la non convexité du problème, amènent à faire des simplifications sur le modèle pour pouvoir faire fonctionner ces méthodes.Nous avons donc étudié une méthode alternative, qui ne requiert pas de simplifications du modèle: Monte Carlo Tree Search (MCTS). Nous avons commencé par étendre le MCTS classique (qui s’applique aux domaines finis et déterministes) aux domaines continus et stochastiques. Pour cela, nous avons utilisé la méthode de Double Progressive Widening (DPW), qui permet de gérer le ratio entre largeur et profondeur de l’arbre, à l’aide de deux méta paramètres. Nous avons aussi proposé une heuristique nommée Blind Value (BV) pour améliorer la recherche de nouvelles actions, en utilisant l’information donnée par les simulations passées. D’autre part, nous avons étendu l’heuristique RAVE aux domaines continus. Enfin, nous avons proposé deux nouvelles méthodes pour faire remonter l’information dans l’arbre, qui ont beaucoup amélioré la vitesse de convergence sur deux cas tests.Une part importante de notre travail a été de proposer une façon de mêler MCTS avec des heuristiques rapides pré-existantes. C’est une idée particulièrement intéressante dans le cas de la gestion d’énergie, car ces problèmes sont pour le moment résolus de manière approchée. Nous avons montré comment utiliser Direct Policy Search (DPS) pour rechercher une politique par défaut efficace, qui est ensuite utilisée à l’intérieur de MCTS. Les résultats expérimentaux sont très encourageants.Nous avons aussi appliqué MCTS à des processus markoviens partiellement observables (POMDP), avec comme exemple le jeu de démineur. Dans ce cas, les algorithmes actuels ne sont pas optimaux, et notre approche l’est, en transformant le POMDP en MDP, par un changement de vecteur d’état.Enfin, nous avons utilisé MCTS dans un cadre de méta-bandit, pour résoudre des problèmes d’investissement. Le choix d’investissement est fait par des algorithmes de bandits à bras multiples, tandis que l’évaluation de chaque bras est faite par MCTS.Une des conclusions importantes de ces travaux est que MCTS en continu a besoin de très peu d’hypothèses (uniquement un modèle génératif du problème), converge vers l’optimum, et peut facilement améliorer des méthodes suboptimales existantes. / In this thesis, we study sequential decision making problems, with a focus on the unit commitment problem. Traditionally solved by dynamic programming methods, this problem is still a challenge, due to its high dimension and to the sacrifices made on the accuracy of the model to apply state of the art methods. We investigate on the applicability of Monte Carlo Tree Search methods for this problem, and other problems that are single player, stochastic and continuous sequential decision making problems. We started by extending the traditional finite state MCTS to continuous domains, with a method called Double Progressive Widening (DPW). This method relies on two hyper parameters, and determines the ratio between width and depth in the nodes of the tree. We developed a heuristic called Blind Value (BV) to improve the exploration of new actions, using the information from past simulations. We also extended the RAVE heuristic to continuous domain. Finally, we proposed two new ways of backing up information through the tree, that improved the convergence speed considerably on two test cases.An important part of our work was to propose a way to mix MCTS with existing powerful heuristics, with the application to energy management in mind. We did so by proposing a framework that allows to learn a good default policy by Direct Policy Search (DPS), and to include it in MCTS. The experimental results are very positive.To extend the reach of MCTS, we showed how it could be used to solve Partially Observable Markovian Decision Processes, with an application to game of Mine Sweeper, for which no consistent method had been proposed before.Finally, we used MCTS in a meta-bandit framework to solve energy investment problems: the investment decision was handled by classical bandit algorithms, while the evaluation of each investment was done by MCTS.The most important take away is that continuous MCTS has almost no assumption (besides the need for a generative model), is consistent, and can easily improve existing suboptimal solvers by using a method similar to what we proposed with DPS.

Nous analysons un type W de système de l’Assemble-à-commande avec des délais de livraison aléatoires, l'arrivée aléatoire de la demande et des ventes perdues, en temps continu. Nous formulons le problème en tant que processus de décision Markov à l'horizon infini. Nous nous éloignons de l'approche standard en caractérisant une région de l'espace d'état où toutes les propriétés de la fonction de coût tiennent. Nous caractérisons la politique optimale dans cette région. En particulier, nous montrons que, dans l'intérieur de la région récurrente, les composants sont toujours produits. Nous caractérisons également la politique d'allocation de composants optimale qui spécifie si une demande de produit arrivant devrait être remplie. Notre analyse révèle que la politique d'allocation optimale est contre-intuitive. Par exemple, même lorsqu'un produit domine l'autre, en termes de coût/taux de vente perdue, sa demande peut ne pas avoir une priorité absolue par rapport à la demande de l'autre produit. Une telle caractéristique n'a pas été observée dans de nombreux paramètres intégrés de production/inventaire où l'allocation d'inventaire suit une priorité fixe pour satisfaire les exigences. Nous montrons également que la structure de la politique optimale reste la même pour les systèmes à production par lots, les temps de production répartis par Erlang et la demande de produits non unitaire. Enfin, nous proposons des heuristiques efficaces qui peuvent être utilisées comme substitut à la politique optimale ou peuvent être utilisées comme une politique de départ pour les algorithmes communs utilisés pour obtenir une politique optimale dans le but de réduire leur temps de calcul. / We analyze a W-configuration assemble-to-order system with random lead times, random arrival of demand, and lost sales, in continuous time. We formulate the problem as an infinite-horizon Markov decision process. We deviate from the standard approach by first characterizing a region (the recurrent region) of the state space where all properties of the cost function hold. We then characterize the optimal policy within this region. In particular, we show that within the interior of the recurrent region components are always produced. We also characterize the optimal component allocation policy which specifies whether an arriving product demand should be fulfilled. Our analysis reveals that the optimal allocation policy is counter-intuitive. For instance, even when one product dominates the other, in terms of lost sale cost and lost sale cost rate (i.e., demand rate times the lost sale cost), its demand may not have absolute priority over the other product’s demand. Such a feature has not been observed in many integrated production/inventory settings where inventory allocation follows a fixed priority in satisfying demands. We also show that the structure of the optimal policy remains the same for systems with batch production, Erlang distributed production times, and non-unitary product demand. Finally, we propose efficient heuristics that can be either used as a substitute for the optimal policy or can be used as a starting policy for the common algorithms that are used to obtain the optimal policy in an effort to reduce their computational time

La régulation adaptative des feux de signalisation est un problème très important. Beaucoup de chercheurs travaillent continuellement afin de résoudre les problémes liés à l’embouteillage dans les intersections urbaines. Il devient par conséquent très utile d’employer des algorithmes intelligents afin d’améliorer les performances de régulation et la qualité du service. Dans cette thèse, nous essayons d'étudier ce problème d’une part à travers une modèlisation microscopique et dynamique en temps discret, et d’autre part en explorant plusieurs approches de résoltion pour une intersection isolée ainsi que pour un réseau distribué d'intersections.La première partie se concentre sur la modélisation dynamique des problèmes des feux de signalisation ainsi que de la charge du réseau d’intersections. Le mode de la “séquence de phase adaptative” (APS) dans un plan de feux est d'abord considéré. Quant à la modélisation du contrôle des feux aux intersections, elle est formulée grâce à un processus décisionnel de markov (MDP). En particulier, la notion de “l'état du système accordable” est alors proposée pour la coordination du réseau de trafic. En outre, un nouveau modèle de “véhicule-suiveur” est proposé pour l'environnement de trafic. En se basant sur la modélisation proposée, les méthodes de contrôle des feux dans cette thèse comportent des algorithmes optimaux et quasi-optimaux. Deux algorithmes exacts de résolution basées sur la programmation dynamique (DP) sont alors étudiés et les résultats montrent certaines limites de cette solution DP surtout dans quelques cas complexes où l'espace d'états est assez important. En raison de l’importance du temps d’execution de l'algorithme DP et du manque d'information du modèle (notamment l’information exacte relative à l’arrivée des véhicules à l’intersection), nous avons opté pour un algorithme de programmation dynamique approximative (ADP). Enfin, un algorithme quasi-optimal utilisant l'ADP combinée à la méthode d’amélioration RLS-TD (λ) est choisi. Dans les simulations, en particulier avec l'intégration du mode de phase APS, l'algorithme proposé montre de bons résultats notamment en terme de performance et d'efficacité de calcul. / Adaptive traffic signal control is a decision making optimization problem. People address this crucial problem constantly in order to solve the traffic congestion at urban intersections. It is very popular to use intelligent algorithms to improve control performances, such as traffic delay. In the thesis, we try to study this problem comprehensively with a microscopic and dynamic model in discrete-time, and investigate the related algorithms both for isolated intersection and distributed network control. At first, we focus on dynamic modeling for adaptive traffic signal control and network loading problems. The proposed adaptive phase sequence (APS) mode is highlighted as one of the signal phase control mechanisms. As for the modeling of signal control at intersections, problems are fundamentally formulated by Markov decision process (MDP), especially the concept of tunable system state is proposed for the traffic network coordination. Moreover, a new vehicle-following model supports for the network loading environment.Based on the model, signal control methods in the thesis are studied by optimal and near-optimal algorithms in turn. Two exact DP algorithms are investigated and results show some limitations of DP solution when large state space appears in complex cases. Because of the computational burden and unknown model information in dynamic programming (DP), it is suggested to use an approximate dynamic programming (ADP). Finally, the online near-optimal algorithm using ADP with RLS-TD(λ) is confirmed. In simulation experiments, especially with the integration of APS, the proposed algorithm indicates a great advantage in performance measures and computation efficiency.

資產配置乃是將資金分散投資到主要的資產類別中，諸如股票、債券、現金等。傳統的均數/變異數方法在資產配置上早已被廣泛的運用。但是，現今的金融情勢多變，多期配置的需求提高，傳統均數/變異數方法只處理單一期間的資產配置，且反應未來的能力不佳，顯然已經不適用。 本論文提供一種多期動態的資產配置，可以改良過去單點估計值的缺點，同時能夠將未來情境納入考量，使多期資產配置更富策略性。並實證在兩期的情況下，期中調整資產組合與不調整的差異性。從而瞭解持續的動態規劃，方能提升資產配置的效率性。 / Asset allocation is the process of dividing an investment fund among major asset classes such as equities, bonds, cash, etc. Traditional mean-variance portfolio selection is widely used for asset allocation. However, as time goes by, the financial condition changes rapidly. The method of mean-variance analysis has some limitations. It not only can’t deal with multiperiod asset allocation, but also cannot reflect future economic circumstances, especially for long-term investments. This research tries to use the method of multi-stage dynamic programming for asset allocation. This method can improve the pits of single estimate in using mean-variance analysis, and take future scenarios into account so that the model will become more useful in practice. The two-period empirical results have shown that using continuous dynamic programming to build strategic asset allocation decision can improve the efficiency of asset allocation.

Many real-world systems experience deterioration with usage and age, which often leads to low product quality, high production cost, and low system availability. Most previous maintenance and reliability models in the literature do not incorporate condition monitoring information for decision making, which often results in poor failure prediction for partially observable deteriorating systems. For that reason, the development of fault prediction and control scheme using condition-based maintenance techniques has received considerable attention in recent years. This research presents a new framework for predicting failures of a partially observable deteriorating system using Bayesian control techniques. A time series model is fitted to a vector observation process representing partial information about the system state. Residuals are then calculated using the fitted model, which are indicative of system deterioration. The deterioration process is modeled as a 3-state continuous-time homogeneous Markov process. States 0 and 1 are not observable, representing healthy (good) and unhealthy (warning) system operational conditions, respectively. Only the failure state 2 is assumed to be observable. Preventive maintenance can be carried out at any sampling epoch, and corrective maintenance is carried out upon system failure. The form of the optimal control policy that maximizes the long-run expected average availability per unit time has been investigated. It has been proved that a control limit policy is optimal for decision making. The model parameters have been estimated using the Expectation Maximization (EM) algorithm. The optimal Bayesian fault prediction and control scheme, considering long-run average availability maximization along with a practical statistical constraint, has been proposed and compared with the age-based replacement policy. The optimal control limit and sampling interval are calculated in the semi-Markov decision process (SMDP) framework. Another Bayesian fault prediction and control scheme has been developed based on the average run length (ARL) criterion. Comparisons with traditional control charts are provided. Formulae for the mean residual life and the distribution function of system residual life have been derived in explicit forms as functions of a posterior probability statistic. The advantage of the Bayesian model over the well-known 2-parameter Weibull model in system residual life prediction is shown. The methodologies are illustrated using simulated data, real data obtained from the spectrometric analysis of oil samples collected from transmission units of heavy hauler trucks in the mining industry, and vibration data from a planetary gearbox machinery application.

Many real-world systems experience deterioration with usage and age, which often leads to low product quality, high production cost, and low system availability. Most previous maintenance and reliability models in the literature do not incorporate condition monitoring information for decision making, which often results in poor failure prediction for partially observable deteriorating systems. For that reason, the development of fault prediction and control scheme using condition-based maintenance techniques has received considerable attention in recent years. This research presents a new framework for predicting failures of a partially observable deteriorating system using Bayesian control techniques. A time series model is fitted to a vector observation process representing partial information about the system state. Residuals are then calculated using the fitted model, which are indicative of system deterioration. The deterioration process is modeled as a 3-state continuous-time homogeneous Markov process. States 0 and 1 are not observable, representing healthy (good) and unhealthy (warning) system operational conditions, respectively. Only the failure state 2 is assumed to be observable. Preventive maintenance can be carried out at any sampling epoch, and corrective maintenance is carried out upon system failure. The form of the optimal control policy that maximizes the long-run expected average availability per unit time has been investigated. It has been proved that a control limit policy is optimal for decision making. The model parameters have been estimated using the Expectation Maximization (EM) algorithm. The optimal Bayesian fault prediction and control scheme, considering long-run average availability maximization along with a practical statistical constraint, has been proposed and compared with the age-based replacement policy. The optimal control limit and sampling interval are calculated in the semi-Markov decision process (SMDP) framework. Another Bayesian fault prediction and control scheme has been developed based on the average run length (ARL) criterion. Comparisons with traditional control charts are provided. Formulae for the mean residual life and the distribution function of system residual life have been derived in explicit forms as functions of a posterior probability statistic. The advantage of the Bayesian model over the well-known 2-parameter Weibull model in system residual life prediction is shown. The methodologies are illustrated using simulated data, real data obtained from the spectrometric analysis of oil samples collected from transmission units of heavy hauler trucks in the mining industry, and vibration data from a planetary gearbox machinery application.

As more and more wireless applications/services emerge in the market, the already heavily crowded radio spectrum becomes much scarcer. Meanwhile, however,as it is reported in the recent literature, there is a large amount of radio spectrum that is under-utilized. This motivates the concept of cognitive radio wireless networks that allow the unlicensed secondary-users (SUs) to dynamically use the vacant radio spectrum which is not being used by the licensed primary-users (PUs). In this dissertation, we investigate protocol design for both the synchronous and asynchronous cognitive radio networks with emphasis on the medium access control (MAC) layer. We propose various spectrum sharing schemes, opportunistic packet scheduling schemes, and spectrum sensing schemes in the MAC and physical (PHY) layers for different types of cognitive radio networks, allowing the SUs to opportunistically utilize the licensed spectrum while confining the level of interference to the range the PUs can tolerate. First, we propose the cross-layer based multi-channel MAC protocol, which integrates the cooperative spectrum sensing at PHY layer and the interweave-based spectrum access at MAC layer, for the synchronous cognitive radio networks. Second, we propose the channel-hopping based single-transceiver MAC protocol for the hardware-constrained synchronous cognitive radio networks, under which the SUs can identify and exploit the vacant channels by dynamically switching across the licensed channels with their distinct channel-hopping sequences. Third, we propose the opportunistic multi-channel MAC protocol with the two-threshold sequential spectrum sensing algorithm for asynchronous cognitive radio networks. Fourth, by combining the interweave and underlay spectrum sharing modes, we propose the adaptive spectrum sharing scheme for code division multiple access (CDMA) based cognitive MAC in the uplink communications over the asynchronous cognitive radio networks, where the PUs may have different types of channel usage patterns. Finally, we develop a packet scheduling scheme for the PU MAC protocol in the context of time division multiple access (TDMA)-based cognitive radio wireless networks, which is designed to operate friendly towards the SUs in terms of the vacant-channel probability. We also develop various analytical models, including the Markov chain models, M=GY =1 queuing models, cross-layer optimization models, etc., to rigorously analyze the performance of our proposed MAC protocols in terms of aggregate throughput, access delay, and packet drop rate for both the saturation network case and non-saturation network case. In addition, we conducted extensive simulations to validate our analytical models and evaluate our proposed MAC protocols/schemes. Both the numerical and simulation results show that our proposed MAC protocols/schemes can significantly improve the spectrum utilization efficiency of wireless networks.