FORWARD ADAPTIVE SPEECH CODING WITH LOW BIT RATES AND VARIABLE WORD LENGTH

In this paper, two scalar quantizers for the memoryless Laplacian source with low number of levels are designed and discussed. The nonuniform quantizer is designed according to the Lloyd-Max’s algorithm since it can provide an optimal performance in the minimum distortion sense. Two variants of the uniform dead-zone quantizer are designed according to the criterion of minimal distortion and the simultaneous criterion of minimal distortion and minimal bit rate. Joint design of quantizer and Huffman encoder is considered in all proposed solutions. In addition, forward adaptation of the observed quantizers is performed on frame-by-frame basis. The best performance from the point of practical implementation is obtained using a uniform dead-zone quantizer that satisfies the criterion of minimal distortion and minimal bit rate at the same time. Moreover, the theoretical results are verified via the experimental results obtained on a real speech signal

Fast embedding for image classification & retrieval and its application to the hostel industry

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonContent-based image classification and retrieval are the automatic processes of taking an unseen image input and extracting its features representing the input image. Then, for the classification task, this mathematically measured input is categorized according to established criteria in the server and consequently shows the output as a result. On the other hand, for the retrieval task, the extracted features of an unseen query image are sent to the server to search for the most visually similar images to a given image and retrieve these images as a result. Despite image features could be represented by classical features, artificial intelligence-based features, Convolutional Neural Networks (CNN) to be precise, have become powerful tools in the field. Nonetheless, the high dimensional CNN features have been a challenge in particular for applications on mobile or Internet of Things devices. Therefore, in this thesis, several fast embeddings are explored and proposed to overcome the constraints of low memory, bandwidth, and power. Furthermore, the first hostel image database is created with three datasets, hostel image dataset containing 13,908 interior and exterior images of hostels across the world, and Hostels-900 dataset and Hostels-2K dataset containing 972 images and 2,380 images, respectively, of 20 London hostel buildings. The results demonstrate that the proposed fast embeddings such as the application of GHM-Rand operator, GHM-Fix operator, and binary feature vectors are able to outperform or give competitive results to those state-of-the-art methods with a lot less computational resource. Additionally, the findings from a ten-year literature review of CBIR study in the tourism industry could picturize the relevant research activities in the past decade which are not only beneficial to the hostel industry or tourism sector but also to the computer science and engineering research communities for the potential real-life applications of the existing and developing technologies in the field