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Nowadays, the IEEE 802.11 wireless local area network (WLAN) has been widely used for Internet access services around the world. Then, the unfairness or insufficiency in meeting the throughput request can appear among concurrently communicating hosts with the same access point (AP), which should be solved by sacrificing advantageous hosts. Previously, we studied the fairness control method by adopting packet transmission delay at the AP. However, it suffers from slow convergence and may not satisfy different throughput requests among hosts. In this paper, we propose a throughput request satisfaction method for providing fair or different throughput requests when multiple hosts are concurrently communicating with a single AP. To meet the throughput request, the method (1) measures the single and concurrent throughput for each host, (2) calculates the channel occupying time from them, (3) derives the target throughput to achieve the given throughput request, and (4) controls the traffic by applying traffic shaping at the AP. For evaluations, we implemented the proposal in the WLAN testbed system with one Raspberry Pi AP and up to five hosts, and conducted extensive experiments in five scenarios with different throughput requests. The results confirmed the effectiveness of our proposal.

Full Text, Thesis (PhD Doctorate), Doctor of Philosophy (PhD), School of Eng & Built Env, Science, Environment, Engineering and Technology, Speech corrupted by background noise (or noisy speech) can reduce the efficiency of communication between man-man and man-machine. A speech enhancement algorithm (SEA) can be used to suppress the embedded background noise and increase the quality and intelligibility of noisy speech. Many applications, such as speech communication systems, hearing aid devices, and speech recognition systems, typically rely upon speech enhancement algorithms for robustness. This dissertation focuses on single-channel speech enhancement using Kalman filtering with machine learning methods. In Kalman filter (KF)-based speech enhancement, each clean speech frame is represented by an auto-regressive (AR) process, whose parameters comprise the linear prediction coefficients (LPCs) and prediction error variance. The LPC parameters and the additive noise variance are used to form the recursive equations of the KF. In augmented KF (AKF), both the clean speech and additive noise LPC parameters are incorporated into an augmented matrix to construct the recursive equations of AKF. Given a frame of noisy speech samples, the KF and AKF give a linear MMSE estimate of the clean speech samples using the recursive equations. Usually, the inaccurate estimates of the parameters introduce bias in the KF and AKF gain, leading to a degradation in speech enhancement performance. The research contributions in this dissertation can be grouped into three focus areas. In the first work, we propose an iterative KF (IT-KF) to offset the bias in KF gain for speech enhancement through utilizing the parameters in real-life noise conditions. In the second work, we jointly incorporate the robustness and sensitivity metrics to offset the bias in the KF and AKF gain - which address speech enhancement in real-life noise conditions. The third focus area consists of the deep neural network (DNN) and whitening filter assisted KF and AKF for speech enhancement. Specifically, DNN and whitening filter-based approaches utilize

Delay-Tolerant Networks (DTNs) are part of Opportunistic networks. In the case of opportunistic networks, the joined node of a network can have zero or partial knowledge about other nodes in a network. For this reason, the evident information towards the nodes in the existing network is most difficult to collect for forwarding the message. The application of Opportunistic networks is where have a high tolerance for long delays, high error rate, etc. DTNs are also sparse dynamic Ad-hoc networks were source to destination path does not present all-time for successfully message transmission. As DTN has no end-to-end path for message transmission source to destination node so, the routing design is so sophisticated. The social-based routing protocol is developed to improve the routing mechanism by focusing on social behavior and the interaction with the nodes of a network. Consequently, the performance analysis of existing several DTN routing protocols represents a significant role in designing or developing a new routing protocol for a specific scenario. This article investigates the execution of ordinary routing protocols of DTNs such as Epidemic, Binary Spray and Wait (BSNW), including two social-based routing protocols such as Scorp and dLife using Opportunistic Network Environment (ONE) simulator. The performance of these routing protocols is measured based on delivery ratio and average hop count with inevitable simulation settings. From the simulation result, it is condensed that for higher delivery ratio, BSNW is best, and for average hop count, dLife is the best routing protocol. &nbsp