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

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

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

Conditional Teacher-Student Learning

The teacher-student (T/S) learning has been shown to be effective for a variety of problems such as domain adaptation and model compression. One shortcoming of the T/S learning is that a teacher model, not always perfect, sporadically produces wrong guidance in form of posterior probabilities that misleads the student model towards a suboptimal performance. To overcome this problem, we propose a conditional T/S learning scheme, in which a "smart" student model selectively chooses to learn from either the teacher model or the ground truth labels conditioned on whether the teacher can correctly predict the ground truth. Unlike a naive linear combination of the two knowledge sources, the conditional learning is exclusively engaged with the teacher model when the teacher model's prediction is correct, and otherwise backs off to the ground truth. Thus, the student model is able to learn effectively from the teacher and even potentially surpass the teacher. We examine the proposed learning scheme on two tasks: domain adaptation on CHiME-3 dataset and speaker adaptation on Microsoft short message dictation dataset. The proposed method achieves 9.8% and 12.8% relative word error rate reductions, respectively, over T/S learning for environment adaptation and speaker-independent model for speaker adaptation.Comment: 5 pages, 1 figure, ICASSP 201