Background: Temporary mechanical circulatory support device without sternotomy has been highly advocated for severe cardiogenic shock patient but little is known when coupled with amniotic stem cell therapy. Case presentation: This case reports the first dual therapy of temporary left ventricular extracorporeal device CentriMag with distal banding technique and human amniotic stem cell injection for treating a severe refractory cardiogenic shock of an 68-year-old female patient. A minimally-invasive off-pump LVAD was established by draining from the left ventricle and returning to the right axillary artery with distal arterial banding to prevent right upper extremity hyperperfusion. Amniotic stem cells were injected intramyocardially at the left ventricular apex, lateral wall, inferior wall, and right subclavian vein. Conclusion: The concomitant use of the temporary minimally-invasive off-pump CentriMag placement and stem cell therapy not only provided an alternative to cardiopulmonary bypass and full-median sternotomy procedures but may have also synergistically enhanced myocardial reperfusion and regeneration.

The purpose of the present study was to evaluate the reliability of parallel-forms of the Comprehensive Neuropsychological Screening Device (CNS). Forty-five subjects ranging in age from 16 to 69 were administered Form A and Form B of the CNS at two week intervals. Results indicated that the CNS has adequate test-retest reliability. The results suggest the applicability of using the CNS as a screening device for brain dysfunction.

Wireless communication systems are growing at an unprecedented pace, making the wireless spectrum at a premium, especially as billions of new Internet-of-Things (IoT) devices worldwide are demanding wireless connections. To accommodate the ever-growing spectrum demand, a promising solution is Non-Orthogonal Multiple Access (NOMA) that enables two users to communicate with the same spectrum resource at the same time, while decoding the two-user superimposed signal at the receiver. By doing this, the previously detrimental wireless interference caused by two concurrent transmitters becomes decodable at the receiver, potential for higher utilization of the wireless spectrum. Existing NOMA technologies, however, rely on strict power control to sequentially decode the two-user superimposed signal, which is infeasible for many IoT devices that are heterogeneous and often low-cost. In contrast, in this dissertation, we propose new NOMA schemes that are designed for wireless communication systems and can decode the two-user superimposed signals without power control. This dissertation makes four major contributions. First, it presents the first design to implement dynamic signal offsets tracking and reacting schemes to detect and decode two-user superimposed signals, robust against hardware imperfections and feasible for heterogeneous IoT devices. Second, by investigating the relationship between the channel condition and the bit-error-rate (BER) in decoding superimposed signals, we design a reliable NOMA scheme to combat dynamic channel conditions that are inevitable in many practical scenarios and may cause severe decoding errors. Third, considering the wireless communication systems in mobile scenarios, mobility is a vital feature of many applications but can cause severe signal variations and make the hardware offsets harder to predict, resulting in an unreliable decoding performance. To address this, we develop a diversity transmission and smart combining scheme to achieve high reliable decoding performance. Finally, we combine rotation coding to transmit and decode the superimposed signal to achieve both high spectrum efficiency and high reliability performance. To demonstrate our contributions, we derive the theoretical relationship of the BER under different practical settings, validate the performance with simulations, and conduct experiments using software-defined radio based platforms with static indoor, outdoor scenarios and mobile scenarios. The experimental results demonstrate that, compared with the state-of-the-art methods, our schemes can achieve higher reliability and spectrum efficiency in decoding the superimposed signal for wireless communication systems without power control. / Graduate

This thesis identifies and addresses two barriers to the adoption of Long Term Evolution (LTE) Device-to-Device (D2D) communication enabled smart grid applications in out of core network coverage regions. The first barrier is the lack of accessible simulation software for engineers to develop and test the feasibility of their D2D LTE enabled smart grid application designs. The second barrier is the lack of a distributed resource allocation algorithm for LTE D2D communications that has been tailored to the needs of smart grid applications. A solution was proposed to the first barrier in the form of a simulator constructed in Matlab/Simulink used to simulate power systems and the underlying communication system, i.e., D2D communication protocol stack of Long Term Evolution (LTE). The simulator is built using Matlab's LTE System Toolbox, SimEvents, and Simscape Power Systems in addition to an in-house developed interface software to facilitate D2D communications in smart grid applications. To test the simulator, a simple fault location, isolation, and restoration (FLISR) application was implemented using the simulator to show that the LTE message timing is consistent with the relay signaling in the power system. A solution was proposed to the second barrier in the form of a multi-agent Q-learning based resource allocation algorithm that allows Long Term Evolution (LTE) enabled device-to-device (D2D) communication agents to generate orthogonal transmission schedules outside of network coverage. This algorithm reduces packet drop rates (PDR) in distributed D2D communication networks to meet the quality of service requirements of microgrid communications. The PDR and latency performance of the proposed algorithm was compared to the existing random self-allocation mechanism introduced under the Third Generation Partnership Project's LTE Release 12. The proposed algorithm outperformed the LTE algorithm for all tested scenarios, demonstrating 20-40% absolute reductions in PDR and 10-20 ms reductions in latency for all microgrid applications.

Improving spectral-utilization is a core focus to cater the ever-increasing demand in data rate and system capacity required for the development of 5G. This dissertation focuses on three spectrum-reuse technologies that are envisioned to play an important role in 5G networks: device-to-device (D2D), full-duplex (FD), and nonorthogonal multiple access (NOMA). D2D allows proximal user-equipments (UEs) to bypass the cellular base-station and communicate with their intended receiver directly. In underlay D2D, the D2D UEs utilize the same spectral resources as the cellular UEs. FD communication allows a transmit-receive pair to transmit simultaneously on the same frequency channel. Due to the overwhelming self-interference encountered, FD was not possible until very recently courtesy of advances in transceiver design. NOMA allows multiple receivers (transmitters) to communicate with one transmitter (receiver) in one time-frequency resource-block by multiplexing in the power domain. Successive-interference cancellation is used for NOMA decoding. Each of these techniques significantly improves spectral efficiency and consequently data rate and throughput; however, the price paid is increased interference. Since each of these technologies allow multiple transmissions within a cell on a time-frequency resource-block, they result in interference within the cell (i.e., intracell interference). Additionally, due to the increased communication, they increase network interference from outside the cell under consideration as well (i.e., increased intercell interference). Real networks are becoming very dense; as a result, the impact of intercell interference coming from the entire network is significant. As such, using models that consider a single-cell/few-cell scenarios result in misleading conclusions. Hence, accurate modeling requires considering a large network. In this context, stochastic geometry is a powerful tool for analyzing random patterns of points such as those found in wireless networks. In this dissertation, stochastic geometry is used to model and analyze the different technologies that are to be deployed in 5G networks. This gives us insight into the network performance, showing us the impacts of deploying a certain technology into real 5G networks. Additionally, it allows us to propose schemes for integrating such technologies, mode-selection, parameter-selection, and resource-allocation that enhance the parameters of interest in the network such as data rate, coverage, and secure communication.

This thesis describes how to build a solution for transmitting data from an           Electroencephalography (EEG) device to a server in real-time while guiding the user through a number of predefined exercises. This solution will be used by Spinal Cord Injury (SCI) patients suffering from neuropathic pain, in order to understand if it is possible to predict such pain from EEG. The collected data will help clinicians analyze the brain activity data from patients who can submit the data from their home. To accomplish this development task, an application was built that connects to a portable EEG device, gather brain activity data from patients, guides patients through a set of imaginary tasks and sends the data to a server. This project made use of a Software Development Kit (SDK) for the Python programming language and a web sockets server written in JavaScript. The application was tested both in terms of usability and end-to-end latency, showing high usability and low latency. The proposed solution will support a clinical trial in Spain with 40 SCI patients.

It is foreseeable that device-to-device (D2D) communication will become a standard feature in the future, for the reason that it offloads the data traffic from network infrastructures to user devices. Recent researches prove that delivering delay-tolerant contents through content delivery network (CDN) by D2D helps network operators increase spectral and energy efficiency. However, protecting the private information of mobile users in D2D assistant CDN is the primary concern, which directly affects the willingness of mobile users to share their resources with others. In this thesis, we proposed a privacy concerned top layer system for selecting the sub-optimal set of mobile nodes as initial mobile content provider (MCP) for content delivery in any general D2D communications, which implies that our proposed system does not rely on private user information such as location, affinity, and personal preferences. We model the initial content carrier set problem as an incentive maximization problem to optimize the rewards for network operators and content providers. Then, we utilized the Markov random field (MRF) theory to build a probabilistic graphical model to make an inference on the observation of delivered contents. Furthermore, we proposed a greedy algorithm to solve the non-linear binary integer programming (NLBIP) problem for selecting the optimal initial content carrier set. The evaluations of the proposed system are based on both a simulated dataset and a real-world collected dataset corresponding to the off-line and on-line scenarios.

Le trafic mobile global atteindra 24,3 exa-octets en 2019. Accueillir cette croissance dans les réseaux d’accès radio devient un véritable casse-tête. Nous porterons donc toute notre attention sur l'une des solutions à ce problème : le délestage (offloading) grâce à des communications de dispositif à dispositif (D2D). Notre première contribution est DROiD, une stratégie qui exploite la disponibilité de l'infrastructure cellulaire comme un canal de retour afin de suivre l'évolution de la diffusion d’un contenu. DROiD s’adapte au rythme de la diffusion, permettant d'économiser une quantité élevée de données cellulaires, même dans le cas de contraintes de réception très serrées. Ensuite, nous mettons l'accent sur les gains que les communications D2D pourraient apporter si elles étaient couplées avec les transmissions multicast. Par l’utilisation équilibrée d'un mix de multicast, et de communications D2D, nous pouvons améliorer, à la fois, l'efficacité spectrale ainsi que la charge du réseau. Afin de permettre l’adaptation aux conditions réelles, nous élaborons une stratégie d'apprentissage basée sur l'algorithme dit ‘’bandit manchot’’ pour identifier la meilleure combinaison de communications multicast et D2D. Enfin, nous mettrons en avant des modèles de coûts pour les opérateurs, désireux de récompenser les utilisateurs qui coopèrent dans le délestage D2D. Nous proposons, pour cela, de séparer la notion de seeders (utilisateurs qui transportent contenu, mais ne le distribuent pas) et de forwarders (utilisateurs qui sont chargés de distribuer le contenu). Avec l'aide d’un outil analytique basée sur le principe maximal de Pontryagin, nous développons une stratégie optimale de délestage. / Mobile data traffic is expected to reach 24.3 exabytes by 2019. Accommodating this growth in a traditional way would require major investments in the radio access network. In this thesis, we turn our attention to an unconventional solution: mobile data offloading through device-to-device (D2D) communications. Our first contribution is DROiD, an offloading strategy that exploits the availability of the cellular infrastructure as a feedback channel. DROiD adapts the injection strategy to the pace of the dissemination, resulting at the same time reactive and relatively simple, allowing to save a relevant amount of data traffic even in the case of tight delivery delay constraints.Then, we shift the focus to the gains that D2D communications could bring if coupled with multicast wireless networks. We demonstrate that by employing a wise balance of multicast and D2D communications we can improve both the spectral efficiency and the load in cellular networks. In order to let the network adapt to current conditions, we devise a learning strategy based on the multi-armed bandit algorithm to identify the best mix of multicast and D2D communications. Finally, we investigate the cost models for operators wanting to reward users who cooperate in D2D offloading. We propose separating the notion of seeders (users that carry content but do not distribute it) and forwarders (users that are tasked to distribute content). With the aid of the analytic framework based on Pontryagin's Maximum Principle, we develop an optimal offloading strategy. Results provide us with an insight on the interactions between seeders, forwarders, and the evolution of data dissemination.

京都大学 / 0048 / 新制・課程博士 / 博士(医学) / 甲第21620号 / 医博第4426号 / 新制||医||1033(附属図書館) / 京都大学大学院医学研究科医学専攻 / (主査)教授 平井 豊博, 教授 宮本 享, 教授 福田 和彦 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DFAM

Distance education is the fastest growing educational modality because of advances information technology has made over the past 25 years. Adult learners have become the fastest growing population in distance education. Adult learners, through technical tools and devices they use on the job, have become more digitally literate and mobile, making the ability to access class work on the go a necessity. Mobile learning or m-learning (learning that uses wireless, portable, mobile computing, and communication devices) is becoming an extension of distance learning, providing a channel for students to learn, communicate, and access educational material outside the traditional classroom environment. For adult learners, this modality allows them to take advantage of accessing material using mobile devices they use for job related activities. Despite the portability and readiness to information mobile devices provide its users, cognitive and physical ergonomic issues may impact learner performance. These issues may stem from information overload and physical discomfort from extended use of the mobile device which may negatively affect the overall success and satisfaction of m-learning environments. The purpose of this study was to examine the relationship between physical ergonomic discomfort, subjective workload, physiological response, and the impact on student performance while using mobile technology to read course material. Activity Theory was used as the theoretical foundation that guided the study. Eighty-four research participants, all over the age of 25, read a passage using one of two distance education modalities: desktop computer or mobile device. While reading the passage, one of three task load levels was imposed on participants: none, low or high. Each participant endured three trials, repeating the same task for each trial. After each trial, participants completed an achievement test and the NASA-TLX assessment. The results from this study provided evidence that mobile learning technologies with increased levels of task load introduced physical ergonomic discomfort and affected perceptions of mental workload in participants. The study also provided evidence that mobile learning technologies with increased levels of task load affected the performance (reading and learning) of participants. Study results provided insight into capabilities and limitations of students in their use of mobile devices for educational purposes. The limitations identified need to be further examined to aid in building successful m-learning environments with the goal of mobile device usage not affecting student performance.