Directional speech acquisition using a MEMS cubic acoustical sensor microarray cluster.

This thesis presents the design of a directional speech acquisition system using a MEMS cubic acoustical sensor microarray cluster to improve speech intelligibility in a noisy reverberant acoustical environment. In the proposed system, five identical acoustical sensor arrays constitute the five sides of a cubic geometry whereas the other side of the cube is to be used for interconnection and packaging purposes. Each of the sensor microarrays is associated with two beam shapes: a main beam to acquire speech signal from a particular direction and a scanning beam to locate and track a potential speech source. A microelectronics based beam synthesis engine controls the selection of a main beam to acquire speech signals from a particular direction based on the output level of the five scanning beams. In this way the developed system provides an improved reduced noise dynamic directional speech acquisition system covering a 3-D space. (Abstract shortened by UMI.)Dept. of Electrical and Computer Engineering. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2006 .H8. Source: Masters Abstracts International, Volume: 45-01, page: 0411. Thesis (M.A.Sc.)--University of Windsor (Canada), 2006

Image annotation with discriminative model and annotation refinement by visual similarity matching

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.Cataloged from PDF version of thesis.Includes bibliographical references (p. 65-67).A large percentage of photos on the Internet cannot be reached by search engines because of the absence of textual metadata. Such metadata come from description and tags of the photos by their uploaders. Despite of decades of research, neither model based and model-free approaches can provide quality annotation to images. In this thesis, I present a hybrid annotation pipeline that combines both approaches in hopes of increasing the accuracy of the resulting annotations. Given an unlabeled image, the first step is to suggest some words via a trained model optimized for retrieval of images from text. Though the trained model cannot always provide highly relevant words, they can be used as initial keywords to query a large web image repository and obtain text associated with retrieved images. We then use perceptual features (e.g., color, texture, shape, and local characteristics) to match the retrieved images with the query photo and use visual similarity to rank the relevance of suggested annotations for the query photo.by Rong Hu.M.Eng

Natural Dyeing with Madder: Exploring Traditional Techniques and Color Characteristics

The color red has a significant presence in cultures around the world. The organic dye madder, derived from plants, has been used throughout human civilization and remains an important natural dye. The research reviews five traditional madder dyeing techniques from China, Europe, Turkey, and Japan. The techniques are applied to cotton, linen, silk, and wool fabrics, and their similarities and differences are compared and analyzed. Additionally, the environmental sustainability, resource conservation, and process efficiency of these dyeing techniques are also evaluated in this article. The chromatic value of dyed textile color was tested by means of an American Hunterlab spectrophotometer, the influence of different dyeing techniques on textile color characteristics was analyzed, and the artistic features of textile color were analyzed from the perspective of visual art

I&S-ViT: An Inclusive & Stable Method for Pushing the Limit of Post-Training ViTs Quantization

Albeit the scalable performance of vision transformers (ViTs), the dense computational costs (training & inference) undermine their position in industrial applications. Post-training quantization (PTQ), tuning ViTs with a tiny dataset and running in a low-bit format, well addresses the cost issue but unluckily bears more performance drops in lower-bit cases. In this paper, we introduce I&S-ViT, a novel method that regulates the PTQ of ViTs in an inclusive and stable fashion. I&S-ViT first identifies two issues in the PTQ of ViTs: (1) Quantization inefficiency in the prevalent log2 quantizer for post-Softmax activations; (2) Rugged and magnified loss landscape in coarse-grained quantization granularity for post-LayerNorm activations. Then, I&S-ViT addresses these issues by introducing: (1) A novel shift-uniform-log2 quantizer (SULQ) that incorporates a shift mechanism followed by uniform quantization to achieve both an inclusive domain representation and accurate distribution approximation; (2) A three-stage smooth optimization strategy (SOS) that amalgamates the strengths of channel-wise and layer-wise quantization to enable stable learning. Comprehensive evaluations across diverse vision tasks validate I&S-ViT' superiority over existing PTQ of ViTs methods, particularly in low-bit scenarios. For instance, I&S-ViT elevates the performance of 3-bit ViT-B by an impressive 50.68%