Audio Jack Data Communication on Smartphones

By choosing adequate modulation and demodulation schemes on a smartphone and a client device, via the audio jack of the smartphone we can achieve a data transmitting rate of about 1k bits per second from the smartphone to the client device and 7.35k bit per second from the client device to the smartphone, which is sufficient for the smartphone to control and collect data from the client device

An Analysis of the Impact of RMB Depreciation on Hong Kong

Abstract. Hong Kong is one of the main economies operating a currency board system today. With its currency fixed to the U.S. dollar, the system has functioned successfully since it was restarted in 1983. The last time it faced severe challenges was during the East Asian financial crisis of 1997-98. However, with the comparatively large depreciation of renminbi (RMB, and sometimes referred to as Yuan) during the past two years, a rising question is how Hong Kong might be affected by a possible future crisis originating from China. In this paper, we examine the impact of RMB depreciation on Hong Kong, with a focus on three sectors of Hong Kong’s economy: foreign direct investment, external trade, and tourism.Keywords. RMB, China, Hong Kong, Asian financial crisis, FDI, Trade, Tourism, Retail sales.JEL. E39, O53

From 3D scan to body pressure of compression garments

Human bodies come under loads in sports. For safety or other purposes, athletes wear compression garments to help avoid wrong postures or movement. We assessed anthropometrics of elite rowers, and found significant differences with the general population, indicating compression garments would behave differently for the athletes. By combining 3D scanning technique and FEM modelling software, we were able to predict compression garment performance on part of the athlete bodies . Abaqus Explicit solver was applied to simulate movement of athletes actually putting on a compression garment, and to track stress distribution during the process

Modeling reinforcement structures in textile aimed at biomechanical purposes

While sporting, muscles, tendons and the body in general come under extreme loads which may lead to wrong movements and injuries which impact the performance or lead to mandatory rest. As athletes often wear compression garments, we investigate how reinforcement structures such as elastic bands, yarns or fabric strips with a given pretension, or rigid structures can be added to compression garments to prevent incorrect sport movements. This paper discusses how an existing simulation tool (DySiFil) can be adapted to be able to extract supportive forces and pressures and validates the findings for the case of overextension of the fingers and the thumb

Automatic Cardiac MRI Image Segmentation and Mesh Generation

Segmenting and reconstructing cardiac anatomical structures from magnetic resonance (MR) images is essential for the quantitative measurement and automatic diagnosis of cardiovascular diseases [1]. However, manual evaluation of the time-series cardiac MRI (CMRI) obtained during routine clinical care are laborious, inefficient, and tends to produce biased and non-reproducible results [2]. This thesis proposes an end-to-end pipeline for automatically segmenting short-axis (SAX) CMRI images and generating high-quality 2D and 3D meshes suitable for finite element analysis. The main advantage of our approach is that it can not only work as a stand-alone pipeline for the automatic CMR image segmentation and mesh generation but also functions effectively as a post-processing tool for improving the outcomes of deep learning methods. Our results indicate that the segmentation accuracy outperformed the traditional U-Net-based approach by as much as 82.5% (percent increase in Dice score) for 5 patient types. The mesh models generated from our contoured segmentations had minimized mean distance error of less than 1.3 pixels and optimized mesh quality with an average Kupp index greater than 0.8

BiRA-Net: Bilinear Attention Net for Diabetic Retinopathy Grading

Diabetic retinopathy (DR) is a common retinal disease that leads to blindness. For diagnosis purposes, DR image grading aims to provide automatic DR grade classification, which is not addressed in conventional research methods of binary DR image classification. Small objects in the eye images, like lesions and microaneurysms, are essential to DR grading in medical imaging, but they could easily be influenced by other objects. To address these challenges, we propose a new deep learning architecture, called BiRA-Net, which combines the attention model for feature extraction and bilinear model for fine-grained classification. Furthermore, in considering the distance between different grades of different DR categories, we propose a new loss function, called grading loss, which leads to improved training convergence of the proposed approach. Experimental results are provided to demonstrate the superior performance of the proposed approach.Comment: Accepted at ICIP 201

An Analysis of the Impact of RMB Depreciation on Hong Kong

Hong Kong is one of the main economies operating a currency board system today. With its currency fixed to the U.S. dollar, the system has functioned successfully since it was restarted in 1983. The last time it faced severe challenges was during the East Asian financial crisis of 1997-98. However, with the comparatively large depreciation of renminbi (RMB, and sometimes referred to as Yuan) during the past two years, a rising question is how Hong Kong might be affected by a possible future crisis originating from China. In this paper, we examine the impact of RMB depreciation on Hong Kong, with a focus on three sectors of Hong Kong’s economy: foreign direct investment, external trade, and tourism

Medical Image Understanding with Pretrained Vision Language Models: A Comprehensive Study

The large-scale pre-trained vision language models (VLM) have shown remarkable domain transfer capability on natural images. However, it remains unknown whether this capability can also apply to the medical image domain. This paper thoroughly studies the knowledge transferability of pre-trained VLMs to the medical domain, where we show that well-designed medical prompts are the key to elicit knowledge from pre-trained VLMs. We demonstrate that by prompting with expressive attributes that are shared between domains, the VLM can carry the knowledge across domains and improve its generalization. This mechanism empowers VLMs to recognize novel objects with fewer or without image samples. Furthermore, to avoid the laborious manual designing process, we develop three approaches for automatic generation of medical prompts, which can inject expert-level medical knowledge and image-specific information into the prompts for fine-grained grounding. We conduct extensive experiments on thirteen different medical datasets across various modalities, showing that our well-designed prompts greatly improve the zero-shot performance compared to the default prompts, and our fine-tuned models surpass the supervised models by a significant margin.Comment: 14 pages, 4 figures