Abnormality Detection in Mammography using Deep Convolutional Neural Networks

Breast cancer is the most common cancer in women worldwide. The most common screening technology is mammography. To reduce the cost and workload of radiologists, we propose a computer aided detection approach for classifying and localizing calcifications and masses in mammogram images. To improve on conventional approaches, we apply deep convolutional neural networks (CNN) for automatic feature learning and classifier building. In computer-aided mammography, deep CNN classifiers cannot be trained directly on full mammogram images because of the loss of image details from resizing at input layers. Instead, our classifiers are trained on labelled image patches and then adapted to work on full mammogram images for localizing the abnormalities. State-of-the-art deep convolutional neural networks are compared on their performance of classifying the abnormalities. Experimental results indicate that VGGNet receives the best overall accuracy at 92.53\% in classifications. For localizing abnormalities, ResNet is selected for computing class activation maps because it is ready to be deployed without structural change or further training. Our approach demonstrates that deep convolutional neural network classifiers have remarkable localization capabilities despite no supervision on the location of abnormalities is provided.Comment: 6 page

Semi-autonomous vehicles as a cognitive assistive device for older adults

Losing the capacity to drive due to age-related cognitive decline can have a detrimental impact on the daily life functioning of older adults living alone and in remote areas. Semi-autonomous vehicles (SAVs) could have the potential to preserve driving independence of this population with high health needs. This paper explores if SAVs could be used as a cognitive assistive device for older aging drivers with cognitive challenges. We illustrate the impact of age-related changes of cognitive functions on driving capacity. Furthermore, following an overview on the current state of SAVs, we propose a model for connecting cognitive health needs of older drivers to SAVs. The model demonstrates the connections between cognitive changes experienced by aging drivers, their impact on actual driving, car sensors' features, and vehicle automation. Finally, we present challenges that should be considered when using the constantly changing smart vehicle technology, adapting it to aging drivers and vice versa. This paper sheds light on age-related cognitive characteristics that should be considered when developing future SAVs manufacturing policies which may potentially help decrease the impact of cognitive change on older adult drivers

Novel Coronavirus Cough Database: NoCoCoDa

The current pandemic associated with the novel coronavirus (COVID-19) presents a new area of research with its own set of challenges. Creating unobtrusive remote monitoring tools for medical professionals that may aid in diagnosis, monitoring and contact tracing could lead to more efficient and accurate treatments, especially in this time of physical distancing. Audio based sensing methods can address this by measuring the frequency, severity and characteristics of the COVID-19 cough. However, the feasibility of accumulating coughs directly from patients is low in the short term. This article introduces a novel database (NoCoCoDa), which contains COVID-19 cough events obtained through public media intervi

An Acoustic Echo Cancellation Structure For Synthetic Surround Sound

This paper proposes an acoustic echo cancellation structure for hands-free synthetic surround sound applications, such as multiple participant conferencing, and virtual reality applications. Voice over Internet protocol (VoIP) and other virtual reality applications can benefit from the addition of 3D spatial audio generated by more than two loudspeakers. When full-duplex audio is present in a system, however, acoustic echo cancellation is required to eliminate the feedback echo path. The acoustic echo cancellation structure proposed by this paper is based on the acoustic echo canceller per spatial region allocation scheme previously introduced by the authors for two channel synthetic stereo. This paper will show that the spatial region allocation scheme is extensible to any number of channels which makes it extremely versatile and flexible, especially for surround sound audio. Microsoft Direct X 7, a commonly used application programmer interface (API), was used in our simulations to generate the 3D spatial audio on a PC

On the Efficiency of Using Multiple Hops in Fixed Relay Based Wireless Networks

The undersigned hereby recommends to the Faculty of Graduate Studies and Researc

A perceptual performance measure for adaptive echo cancellers in packet-based telephony

This paper investigates performance measures of adaptive echo cancellers for packet-based telephony. It is shown that steady-state echo return loss enhancement (ERLE) does not accurately reflect perceived echo canceller convergence when background noise is present. An upper bound is derived for the maximum perceivable ERLE achievable in practice, and an algorithm is introduced for calculating ERLE that incorporates these masking effects based on a perceptual hearing model. Simulation and informal listening test results show a clear correspondence between the new performance measure and the perceptual upper bound induced by background noise