教師著作
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Item 以CEFR為基礎之數位華語文行動學習載具之研發(2010-12-17) 何宏發; 蔡忠霖在數位化時代語言學習機是學習第二語言不可或缺的重要工具，以往語言學習機大 部分所服務的對象為英語、日語學習者，而有鑑於全球化趨勢，華語成為廣大非華語系 國家之人士所學習的語言，為增進華語文學習者之學習效果，本研究是利用行動學習概 念，將數位化的華語文教材導入行動學習載具中。在本計劃中將以現有的開發平台，配 合軟體開發工具以及子計畫三之評量系統、子計畫一之教材，植入數位化華語文行動學 習工具軟體，以達到外國人士有效率學習華語文之目標。為了驗證華語文行動學習機之 效益，我們也會利用外國華語文學習者來進行相關實驗，取得數據回饋我們的行動學習 教材。利用現有的硬體架構整合華語文學習軟體，使得使用華語文學習機的學生能夠隨 時隨地而且更有效果、更有效率的學習。預期其系統架構能夠達成(1)字的學習；(2)詞 彙的學習；(3)句子的學習；(4)發音的學習；(5)會話的學習；(6)情境的學習，以及其 他能使用多媒體應用之華語文教材。Item WiMAX行動網路之智慧化資源要求分配技術(行政院國家科學委員會, 2011-07-31) 王嘉斌在本計畫中，我們研究在IEEE 802.16e無線都會廣域網路(Worldwide Interoperability for Microwave Access，WiMAX)中換手連線和新進連線之間頻寬分配問題、ertPS服務之頻寬要求分配和排程技術，以及WiMAX/WiFi整合網路中頻寬分配和計價等問題。本計畫第一年將對新進連線和換手連線頻寬分配問題作深入研究。我們將以理論推導的方式分析無線網路中新進連線與換手連線在不同比例下保留頻寬對於服務品質和系統效能的影響，並且以此理論模型為基礎提出我們的連線允入控制演算法(Connection Admission Control Algorithm)。我們設計的演算法將會考慮目前基地台的網路負擔情況來動態調整允入控制的規則和換手連線之保留頻寬，以提昇換手連線用戶的服務品質，並且同時增加頻寬使用效能。 針對VoWiMAX (Voice over WiMAX)服務，本計畫第二年將針對IEEE 802.16e無線都會廣域網路中延伸即時性輪詢服務(Eextended-real-Time Poling Service；ertPS)設計一動態的頻寬分配和排程機制。ertPS服務類型主要是用來用來提供即時的資料流傳輸，能週期性地傳送可變動大小的封包，其應用服務例如具無聲壓縮的VoIP服務。我們計畫研究及分析VoIP服務在IEEE 802.16e網路中的服務品質，並且同時研究設計ertPS的頻寬要求分配以及排程技術。 前兩年的計畫我們著重在WiMAX網路包括允入控制以及ertPS服務之動態頻寬要求分配方法等技術問題，而在第三年計畫中，我們將對於WiMAX與WiFi的網路整合進行探討。未來當WiMAX都會網路掘起時，未來如何將WiMAX與WiFi的網路做有效之整合將是一個重要的問題。我們計畫以賽局理論中動態賽局(Dynamic game)找出具有優勢equilibrium之研究，對此異質網路之間頻寬資源分配以及計價問題做深入探討，以增進WiMAX/WiFi整合網路之頻寬效能，增加服務提供商營收，同時維持使用者服務品質。Item 達成無線網路上下傳的比例公平性(行政院國家科學委員會, 2010-07-31) 王嘉斌在本計畫中，我們研究在IEEE 802.11 無線區域網路(WLAN)中上傳和下傳的公平 性問題。在基礎模式(infrastructure-mode)的無線網路中，資料傳輸分成上傳和下傳；其 中上傳是指資料由無線工作站(mobile stations)傳送至基地台(Access Point，AP)，而下 傳是指資料由基地台傳送至無線工作站。由於802.11 媒體存取控制層(Medium Access Control，MAC)所使用的分散協調式功能(Distributed Coordination Function，DCF)對於 每一個工作站(包括基地台)提供了相同的傳輸機會，將會導致無線網路中上傳和下傳吞 吐量(throughput)的不公平。而這個現象在無線工作站數量增加時將會是更為嚴重的問 題。 在本計畫中我們將會以理論推導的方式分析無線網路中上下傳吞吐量不公平的現 象，並且以此理論模型研究文獻中所提出方法之效能。同時我們也將會提出我們的類 神經網路自適性調整演算法(Neural-Network Based Adaptive Algorithm)，並且比較我們 的方法與文獻中提出方法的效能。我們將提出的演算法是使用類神經網路學習無線網 路系統參數和上下傳吞吐量之間非線性的函數關係，然後將所學到的知識，根據應用 層的服務品質(Quality of Service，QoS)需求調整系統參數，達成公平的上下傳吞吐量比 例。在本計畫中所提出的理論分析以及調整上下傳吞吐量的演算法，希望可作為未來 無線區域網路系統設計之參考。Item A Cross-Layer Link Adaptation Algorithm for IEEE 802.11 WLAN with Multiple Nodes(2008-12-12) Chiapin Wang; Tsungnan LinIEEE 802.11 wireless local area network (WLAN) physical layer (PHY) offers multiple data rates. In multi-rate WLANs, 802.11 distributed coordination function (DCF) presents the phenomenon so called ldquoperformance anomalyrdquo: when some hosts transmit at lower data rates, throughput of others at high rates will be restricted within the lowest rate, leading to the degradation of overall performance. Therefore, the link adaptation scheme for 802.11 WLAN should consider not only throughput of the observed host but also system throughput in order to optimize the overall performance. In this paper we propose a cross-layer link adaptation algorithm which improves system throughput by regarding the situation of PHY rate degradation more thoughtfully and critically, and meanwhile compensates the throughput of hosts with bad link qualities by applying differentiated channel access parameters. Simulation results demonstrate the effectiveness of the propose algorithm.Item A Cross-Layer Adaptive Handoff Algorithm in Wireless Multimedia Environments(2007-09-12) Tsungnan Lin; Chiapin WangProviding multimedia services in wireless networks is concerned about the performance of handoff algorithms because of the irretrievable property of real-time data delivery. To lessen unnecessary handoffs and handoff latencies which can cause media disruption perceived by users, we present in this paper a cross-layer handoff algorithm base on link quality. Neural networks are used to learn the cross-layer correlation between the link quality estimator such as packet success rate and the corresponding context metric indictors, e.g. the transmitting packet length, received signal strength, and signal to noise ratio. Based on a pre-processed learning of link quality profile, our approach makes handoff decisions intelligently and efficiently with the evaluations of link quality instead of the comparisons between relative signal strength. The experiment and simulation results show that the proposed method outperforms RSS-based handoff algorithms in a transmission scenario of VoIP applications.Item A Cross-Layer Design for Per-Flow and Weighted Fairness between Uplink and Downlink in WLANs(2010-11-19) Chiapin Wang; Hao-Kai Lo; Kueihsiang Liang; Chuyuan HsuIn this paper, we investigate a fairness issue between uplink and downlink flows in IEEE 802.11 Wireless Local Area Networks (WLANs). We propose a cross-layer adaptive algorithm which dynamically adjusts the minimum contention window size of Access Point (AP) according to the amount of downlink users and channel environments to achieve per-flow fairness. In case that uplink and downlink transmissions are with different bandwidth demands for various applications, our algorithm can efficiently find the optimal contention window size which provides weighted fairness based on their resource requirements. The simulation results demonstrate that our scheme can effectively provide both per-flow fairness and weighted fairness in a varying heterogeneous WLAN environment.Item Improving 802.11 QoS in Heterogeneous Channels Using an Adaptive Scheme(2006-01-01) Chiapin Wang; Tsungnan LinItem A Context-Aware Approach for Multimedia Performance Optimization using Neural Networks in Wireless LAN Environments(2006-07-12) Po-Chiang Lin; Chiapin Wang; Tsungnan LinPacket size is one of the most important factors that would affect the user-perceived multimedia QoS in the wireless LAN environments. The time-varying channel characteristics make it difficult to find the exact relationship between the packet size and the throughput and decide an optimal packet size in advance. Furthermore, every node would suffer different channel conditions. In this paper, we tackle this problem by an optimization approach. A context-aware framework is designed to optimize the packet size adaptively in order to maximize the throughput. In this approach each node abstracts its specific context via the throughput from the time-varying wireless environments. The obtained throughput information is the instantaneous integrated effect of all contexts in wireless LAN environments. This approach adopts neural networks to learn the complex nonlinear function between the packet size and the throughput and adaptively adjusts the packet size. Simulation results show that out method can cope with the time-varying wireless channel conditions and improve the perceived QoS of wireless multimedia servicesItem Adaptive Admission Control Algorithm in IEEE 802.16e Broadband Wireless Access Networks(2010-01-01) Chiapin Wang; Hsin-Chi Lin; Hao-Kai LoThe emerging IEEE 802.16e Broadband Wireless Access (BWA) network is one of the most promising solutions to provide ubiquitous wireless access with high data rates, high mobility, and wide coverage. In the paper we develop a novel Connection Admission Control (CAC) algorithm for IEEE 802.16e mobile BWA networks to simultaneously improve the utilization efficiency of network resources and guarantee Quality of Services (QoS) for handoff connections. The proposed CAC scheme dynamically adjusts the amount of reserved bandwidth for handoff users according to the arrival distributions of handoff and new coming connections. Simulation results demonstrate that the proposed CAC algorithm can increase the number of admitted connections and also provide handoff QoS.Item A Neural Network Based Adaptive Algorithm for Multimedia Quality Fairness in WLAN Environments(2006-07-12) Chiapin Wang; Tsungnan LinThis paper investigates multimedia quality fairness in wireless LAN environments where channel are error-prone due to mobility and fading. The experimental results show that using fixed MAC arguments for nodes in heterogeneous channel conditions leads to unequal throughput performance and that may incur the degradation of multimedia QoS. To overcome the unfairness problem for provisioning QoS, we propose a cross-layer adaptation scheme by on-line adapting the multidimensional MAC-layer backoff parameters depending on the application-layer QoS requirements and PHY-layer channel conditions. Our solution is based on an optimization approach which utilizes neural networks to learn the cross-layer function. Simulation results demonstrate that our adaptive scheme can tackle a variety of channel condition to provide fair throughput for nodes in heterogeneous channel conditions