An investigation into the advanced time division multiple access (ATDMA) protocol for a personal communication network : a thesis presented in partial fulfilment of the requirements for the degree of Master of Technology in Information Engineering at Massey University

The performance of the Advanced Time Division Multiple Access (ATDMA) protocol in a microcell environment has been investigated in this thesis. The ATDMA protocol is a new generation protocol which can support both circuit switched and packet switched transmission modes. The protocol can also adapt in a varying propagation environment. This thesis examines the efficiency of the protocol in a microcell environment and also examines different access techniques for voice and data traffic to improve the efficiency of the protocol. To study the performance of the protocol a discrete event based simulation model has been developed which includes a microcell channel model of a city area. A data block reservation scheme has been developed in this work, which increase the traffic efficiency of the protocol. By combining the data block reservation scheme and capture effect, the ATDMA protocol's performance in transmitting mixed voice and data traffic in an urban microcell environment was investigated by means of computer simulation. The simulation model was used to find out the appropriate parameters for the optimum performance of the protocol and then to investigate the performance of the protocol. With consideration of the capture ratio, the effect of capture has also been evaluated in a more practical manner

Strategies to improve the clientele market of A & D Earthworks Limited

How well an indicator is doing in relation to its competitors can be defined as a company's market share. Simply speaking, market share is a comparison between the total sales of a company and the sales of that industry, usually in a specific region or area, over a period. The reality is that people are easily influenced by popularity. The more market share a company has, the greater the possibility they will grow fast and make more revenue without much effort. This project researches how a quite new excavation company, A&D Earthworks Limited, located in Hamilton, can improve its clientele market through its growing period. The company, which has been established for one and a half years, consists of 8 employees and 2 directors. The main purpose of this research is to investigate how to improve key customer satisfaction. In order to investigate this area, the study will look at which advertising methods are suitable for a small and new excavation company; an internal and external analysis will be carried out; it will look into the business culture and what people need to learn from it; it will also investigate how to build up a brand image and the importance of competitor analysis. As part of the research process, qualitative research analysis was carried out. In order to gather primary data, interviews were conducted with 6 competitors. Then, combined with the research results and literature review, a comprehensive discussion of the purpose is clarified. Some practical recommendations according to the real situation have been put forward for A&D Company. Social media, signs and billboards, business cards and a brochure need to be implemented in order to improve customer satisfaction. For branding image, a reliable, positive and principled impression should be set before the public, a logo design and slogan need to be designed as well. In this way, A&D Company would be able to identify their competitive advantage within the market

Attentive Adversarial Learning for Domain-Invariant Training

Adversarial domain-invariant training (ADIT) proves to be effective in suppressing the effects of domain variability in acoustic modeling and has led to improved performance in automatic speech recognition (ASR). In ADIT, an auxiliary domain classifier takes in equally-weighted deep features from a deep neural network (DNN) acoustic model and is trained to improve their domain-invariance by optimizing an adversarial loss function. In this work, we propose an attentive ADIT (AADIT) in which we advance the domain classifier with an attention mechanism to automatically weight the input deep features according to their importance in domain classification. With this attentive re-weighting, AADIT can focus on the domain normalization of phonetic components that are more susceptible to domain variability and generates deep features with improved domain-invariance and senone-discriminativity over ADIT. Most importantly, the attention block serves only as an external component to the DNN acoustic model and is not involved in ASR, so AADIT can be used to improve the acoustic modeling with any DNN architectures. More generally, the same methodology can improve any adversarial learning system with an auxiliary discriminator. Evaluated on CHiME-3 dataset, the AADIT achieves 13.6% and 9.3% relative WER improvements, respectively, over a multi-conditional model and a strong ADIT baseline.Comment: 5 pages, 1 figure, ICASSP 201

Adversarial Speaker Adaptation

We propose a novel adversarial speaker adaptation (ASA) scheme, in which adversarial learning is applied to regularize the distribution of deep hidden features in a speaker-dependent (SD) deep neural network (DNN) acoustic model to be close to that of a fixed speaker-independent (SI) DNN acoustic model during adaptation. An additional discriminator network is introduced to distinguish the deep features generated by the SD model from those produced by the SI model. In ASA, with a fixed SI model as the reference, an SD model is jointly optimized with the discriminator network to minimize the senone classification loss, and simultaneously to mini-maximize the SI/SD discrimination loss on the adaptation data. With ASA, a senone-discriminative deep feature is learned in the SD model with a similar distribution to that of the SI model. With such a regularized and adapted deep feature, the SD model can perform improved automatic speech recognition on the target speaker's speech. Evaluated on the Microsoft short message dictation dataset, ASA achieves 14.4% and 7.9% relative word error rate improvements for supervised and unsupervised adaptation, respectively, over an SI model trained from 2600 hours data, with 200 adaptation utterances per speaker.Comment: 5 pages, 2 figures, ICASSP 201

Structural Dynamics of Copper Nanomaterials for CO2 Electrocatalysis

Electrons are the currency of the future energy economy. With a host of renewable sources of electrical energy and steadily decreasing cost of generation, our ability to store that electricity via chemical bonds becomes increasingly paramount. Development of materials that allow the transformation of electrical energy to chemical energy, e.g. the electroreduction of CO2 to value-added chemicals and fuels, will push our energy infrastructure to new heights. Despite considerable progress in thermal gas-phase heterogeneous catalysis of CO2, the heterogeneous electrocatalysis of aqueous CO2 under room temperature and neutral pH has remained challenging. The key gating obstacle has been the development of catalysts that effect efficient and selective formation of higher order products such as methane or multicarbons, for which only copper has emerged as a candidate material. To design next-generation electrocatalysts for CO2 conversion to multicarbons (CO2-to-C2+), the relationship between activity/selectivity and catalyst structure must be better understood, to identify the structural motifs that define CO2-to-C2+ active sites. Recent work has consistently revealed that copper nanomaterials undergo considerable structural change under operating conditions. Thus, a central challenge to the understanding of these active surfaces is their dynamic nature under operation in electrochemical conditions, especially as applied to nanoscale electrocatalysts. Hence, this dissertation centers around structural dynamics of copper-based nanocatalysts under CO2 electroreducing conditions.After a brief introduction to the problem statement and fundamental concepts related to heterogeneous electrocatalysis of CO2 to value-added products, I discuss the prospects and existing work around Cu structural evolution in Chapter 1. In Chapter 2, I show how the link between structural evolution and catalytic activity change can be clearly shown on a Cu nanowire catalytic platform, and further show how strategies that mitigate structural change also impact selectivity retention. In Chapter 3, I move to a copper nanoparticle ensemble platform with intriguing activity for multicarbon formation, coupled with a striking structural evolution. I illustrate the dichotomy between the apparent evolution and active structure via conventional ex situ measurements, and the structural and catalytic insights revealed by more comprehensive investigation using preservation strategies. In Chapter 4, I discuss how in situ characterization techniques assist the illumination of such structural evolution under electrocatalytic conditions, and further explore what additional advances are needed to harness these insights. Finally, I close in Chapter 5 with an outlook on materials development for CO2 electrocatalysis as it stands at time of writing. Overall, this dissertation seeks to provide a narrative of the relationship between an electrochemical materials scientist and the concept of structural dynamics. Through the works to be described, this dissertation will take the reader on a journey from emphasizing stabilization and mitigating structural change, towards understanding it for the eventual purpose of leveraging such structural change as another dimension of electrocatalyst design