Scheduling algorithm is the algorithm to allocate system resources among processes and data flows. Joint channel-assignment and workload-based (CAWS) is a recently developed algorithm for scheduling in the downlink of multi-channel wireless systems, such as OFDM. Compared to well known algorithms, CAWS algorithm has been proved to throughput optimal with flow-level dynamics. In this master thesis project, we design a system that accounts for power control and for the characteristics of common radio channels. We evaluate the efficiency of the algorithm under a diverse set of conditions. We also do analysis of CAWS algorithm under different traffic density.

Disasters, ranging from manmade events to natural occurrences, can happen anywhere on the planet, and their consequences can range from economic loss to catastrophic loss of life. Determining how the transportation system fares in the face of these disasters is important so that proper planning can take place before, rather than after, an event has happened. Modeling the transportation system gives operators the ability to discover bottlenecks, to determine the possible benefit of using lane reversals, and to find out the influence of evacuation speed on system efficiency. Models have already been created that are able to model some of these types of disasters with some level of accuracy. These models range from microscopic simulation to regional, macroscopic models. This research examines how an off-the-shelf regional modeling software package, TransCAD, can be used to model emergency evacuations. More specifically, this thesis presents four case studies involving three different types of disasters in Western Massachusetts. Because this research documents a first-hand experience using TransCAD in emergency evacuation planning, the results give regional modelers the ability to modify their models to fit their specific region. These case studies demonstrate how the modified inputs and existing portions of the four-step transportation planning model can be used in place of the usual data demands of the software. Dynamic traffic assignment is used in three of the case studies while the fourth case study uses static traffic assignment. An evaluation of the software package along with lessons learned is provided to measure the performance of the software.

The planning of snow removal routes is complicated by the fact that the amount it snows, and thus the amount of resources, that is, vehicles, needed to clear it, varies from year to year. This variation has created a demand for a way to quickly generate efficient snow removal plans that take the resources that are available into account. In this report we describe the development of an ad hoc heuristic algorithm that improves already existing feasible solutions to the snow removal problem. It accomplishes this by reassigning tasks from the vehicles with the longest tours to those with the shortest tours, followed by reordering their tasks to ensure that the solution remains feasible. This algorithm is meant to be implemented in a larger piece of software and it is tested on a set of pre-generated solutions for a given network and number of vehicles, including the best known ones. Over half of the previously best known solutions were improved by this algorithm.

Future cellular networks, the so-called 5G, are expected to provide explosive data volumes and data rates. To meet such a demand, the research communities are investigating new wireless transmission technologies. One of the most promising candidates is in-band full-duplex communications. These communications are characterized by that a wireless device can simultaneously transmit and receive on the same frequency channel. In-band full-duplex communications have the potential to double the spectral efficiencywhen compared to current half duplex systems. The traditional drawback of full-duplex was the interference that leaks from the own transmitter to its own receiver, the so- called self-interference, which renders the receiving signal unsuitable for communication.However, recent advances in self-interference suppression techniques have provided high cancellation and reduced the self-interference to noise floor levels, which shows full-duplex is becoming a realistic technology component of advanced wireless systems. Although in-band full-duplex promises to double the data rate of existing wireless technologies, its deployment in cellular networks is challenging due to the large number of legacy devices working in half-duplex. A viable introduction in cellular networks is offered by three-node full-duplex deployments, in which only the base stations are full-duplex, whereas the user- or end-devices remain half-duplex. However, in addition to the inherent self-interference, now the interference between users, the user-to-user interference, may become the performance bottleneck, especially as the capability to suppress self-interference improves. Due to this new interference situation, user pairing and frequency channel assignment become of paramount importance, because both mechanisms can help to mitigate the user-to-user interference. It is essential to understand the trade-offs in the performance of full-duplex cellular networks, specially three-node full-duplex, in the design of spectral and energy efficient as well as fair mechanisms. This thesis investigates the design of spectral efficient and fair mechanisms to improve the performance of full-duplex in cellular networks. The novel analysis proposed in this thesis suggests centralized and distributed user pairing, frequency channel assignment and power allocation solutions to maximize the spectral efficiency and fairness in future full-duplex cellular networks. The investigations are based on distributed optimization theory with mixed integer-real variables and novel extensions of Fast-Lipschitz optimization. The analysis sheds lights on two fundamental problems of standard cellular networks, namely the spectral efficiency and fairness maximization, but in the new context of full-duplex communications. The results in this thesis provide important understanding in the role of user pairing, frequency assignment and power allocation, and reveal the special behaviourbetween the legacy self-interference and the new user-to-user interference. This thesis can provide input to the standardization process of full-duplex communications, and have the potential to be used in the implementation of future full-duplex in cellular networks. / <p>QC 20170403</p>

Having students create Learning Objects (LOs) through renewable assignments has proved more helpful to the student creators compared to disposable assignments in some aspects. Additionally, some researchers have presented evidence where the student-created LOs could benefit the users of the LOs more than traditional learning resources. In this study, I examined a collection of LOs created by college students in the subject of mathematics to understand the nature of the value that a student-created LO could bring to other learners. By extending a framework for non-disposable assignments (Seraphin et al., 2019), I evaluated 3 components that affect the value of the LOs: design, accuracy, and visibility. The results show that some intended users of the student-created LOs could benefit from using the LOs. However, the intended users of the LOs may not come across those LOs even if they searched specifically for the LOs. Subsequently, it is concluded that LOs created to be used in future iterations of the same class may be more practical in bringing value to the broader community.

Urban transportation networks are vulnerable to various incidents. In order to combat the negative effects due to incident-related congestion, various mitigation strategies have been proposed and implemented. The effectiveness of these congestion mitigation strategies for incident conditions largely depends on the accuracy of information regarding network conditions. Therefore, an efficient and accurate procedure to determine the link states, reflected by flows and density over time, is essential to incident management. This thesis presents a user equilibrium Dynamic Traffic Assignment (DTA) model that incorporates the Cell Transmission Model (CTM) to evaluate the temporal variation of flow and density over links, which reflect the link states of a transportation network. Encapsulation of the CTM equips the model with the capability of accepting inputs of incidents like duration and capacity reduction. Moreover, the proposed model is capable of handling multiple origin-destination (OD) pairs. By using this model, the temporal variation of flows over links can be readily evaluated. The visualized prediction of link density variations is used to investigate the link state relationships. By isolating the effects of an incident, the parallel routes of a specific OD pair display the relationship of substituting for each other, which is consistent with the general expectation regarding such parallel routes. A closer examination of the density variations confirms the existence of a substitution relationship between the unshared links of the two parallel routes. This information regarding link state relationship can be used as general guidance for incident management purposes. / Master of Science

Robots are increasingly utilized in industry for their capability to perform repetitive,complex tasks in environments unsuitable for humans. This surge in robotic applicationshas spurred research into Multi-Robot Systems (MRS), which aim to tackle complex tasksrequiring collaboration among multiple robots, thereby boosting overall efficiency. However,MRS introduces multifaceted challenges that span various domains, including robot perception,localization, task assignment, communication, and control. This dissertation delves into theintricate aspects of task assignment and path planning within MRS.The first area of focus is on multi-robot navigation, specifically addressing the limitationsinherent in current Multi-Agent Path Finding (MAPF) models. Traditional MAPF solutionstend to oversimplify, treating robots as holonomic units on grid maps. While this approachis impractical in real-world settings where robots have distinct geometries and kinematicrestrictions, it is important to note that even in its simplified form, MAPF is categorized as anNP-hard problem. The complexity inherent in MAPF becomes even more pronounced whenextending these models to non-holonomic robots, underscoring the significant computationalchallenges involved. To address these challenges, this thesis introduces a novel MAPF solverdesigned for non-holonomic, heterogeneous robots. This solver integrates the hybrid A*algorithm, accommodating kinematic constraints, with a conflict-based search (CBS) for efficientconflict resolution. A depth-first search approach in the conflict tree is utilized to accelerate theidentification of viable solutions.The second research direction explores synergizing task assignment with path-finding inMRS. While there is substantial research in both decentralized and centralized task assignmentstrategies, integrating these with path-finding remains underexplored. This dissertation evaluatesdecoupled methods for sequentially resolving task assignment and MAPF challenges. Oneproposed method combines the Hungarian algorithm and a Traveling Salesman Problem (TSP)solver for swift, albeit suboptimal, task allocation. Subsequently, robot paths are generatedindependently, under the assumption of collision-free navigation. During actual navigation, aNonlinear Model Predictive Controller (NMPC) is deployed for dynamic collision avoidance. Analternative approach seeks optimal solutions by conceptualizing task assignment as a MultipleTraveling Salesman Problem (MTSP), solved using a simulated annealing algorithm. In tandem,CBS is iteratively applied to minimize the cumulative path costs of the robots.

京都大学 / 新制・課程博士 / 博士(工学) / 甲第24217号 / 工博第5045号 / 新制||工||1788(附属図書館) / 京都大学大学院工学研究科都市社会工学専攻 / (主査)教授 山田 忠史, 教授 藤井 聡, 准教授 SCHMOECKER Jan-Dirk / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

U.S. politics, media, and citizens are highly polarized, stipulating that society is divided between Democrats and Republicans (Hameleers, 2019). The U.S. has seen an increased political polarization over the past 25 years (Heltzel & Laurin, 2020; Westfall, Van Boven, Chambers, & Judd, 2015). Technological development in the campaign environment has fueled this political polarization (Hong & Kim, 2016). In such a polarized technological society, partisan news media cover political issues and events from their ideological perspective (Arceneaux, Johnson, & Murphy, 2012), which may affect the polarized citizens.The Republican Party is conservative, while the Democratic Party is liberal (Westfall, Van Boven, Chambers, & Judd, 2015). Each party has issue agendas that they prioritize during the campaign. When political campaigns post a message on social media, they not only post just one issue but several related issues. These interlinked issues have a networked effect on the partisan news media and the polarized citizens (McCombs, Shaw, & Weaver, 2014). How political campaigns interlinked different issue agendas during campaigns in a polarized environment has not been investigated. This study intends to see the similarities and dissimilarities between the Democratic and Republican Party issue networks using a network agenda setting theory during the 2020 U.S. presidential campaign and how they build and set networked issue agendas in the partisan news media and the polarized public on Facebook. The study uses a hybrid content analysis and network analysis of issue agendas presented by the Biden and Trump campaigns, partisan media (CNN and Fox News), and the Democratic Party and the Republican Party supporters on Facebook. Facebook posts are collected using Facebook’s CrowdTangle Search option from January 1, 2021, to November 3, 2020. This study uses a hybrid content analysis method which engages with both human coders and computational means to analyze big data sets (Guo et al., 2016). The data analysis involves measuring core-periphery block model, clique analysis, network visualization, and Quadratic Assignment Procedures (QAP). A social networking analysis software, UCINET, is used for measuring core-periphery block model, clique analysis, and QAP correlations(Borgatti, Everett, & Johnson, 2018). The scholarship of political campaign communication needs to reconnect to the ideological positions of political campaigns, partisan news media, and party supporters. This holistic study is significant in terms of better understanding the mechanism of networked agenda-setting activities of presidential campaigns in a polarized environment on Facebook. Methodologically, this study offers new techniques for investigating networked issue agendas of campaigns, news media, and citizens. It uses core-periphery block model and clique analysis as indicators of network agenda building and network agenda-setting influences. Social media practitioners like campaign managers can consider the political polarization, fragmented nature of social media, and polarized audience during political campaigning.

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