Local Analysis of Human Cortex in MRI Brain Volume

This paper describes a method for subcortical identification and labeling of 3D medical MRI images. Indeed, the ability to identify similarities between the most characteristic subcortical structures such as sulci and gyri is helpful for human brain mapping studies in general and medical diagnosis in particular. However, these structures vary greatly from one individual to another because they have different geometric properties. For this purpose, we have developed an efficient tool that allows a user to start with brain imaging, to segment the border gray/white matter, to simplify the obtained cortex surface, and to describe this shape locally in order to identify homogeneous features. In this paper, a segmentation procedure using geometric curvature properties that provide an efficient discrimination for local shape is implemented on the brain cortical surface. Experimental results demonstrate the effectiveness and the validity of our approach

Multi-attention bottleneck for gated convolutional encoder-decoder-based speech enhancement

Convolutional encoder-decoder (CED) has emerged as a powerful architecture, particularly in speech enhancement (SE), which aims to improve the intelligibility and quality and intelligibility of noise-contaminated speech. This architecture leverages the strength of the convolutional neural networks (CNNs) in capturing high-level features. Usually, the CED architectures use the gated recurrent unit (GRU) or long-short-term memory (LSTM) as a bottleneck to capture temporal dependencies, enabling a SE model to effectively learn the dynamics and long-term temporal dependencies in the speech signal. However, Transformers neural networks with self-attention effectively capture long-term temporal dependencies. This study proposes a multi-attention bottleneck (MAB) comprised of a self-attention Transformer powered by a time-frequency attention (TFA) module followed by a channel attention module (CAM) to focus on the important features. The proposed bottleneck (MAB) is integrated into a CED architecture and named MAB-CED. The MAB-CED uses an encoder-decoder structure including a shared encoder and two decoders, where one decoder is dedicated to spectral masking and the other is used for spectral mapping. Convolutional Gated Linear Units (ConvGLU) and Deconvolutional Gated Linear Units (DeconvGLU) are used to construct the encoder-decoder framework. The outputs of two decoders are coupled by applying coherent averaging to synthesize the enhanced speech signal. The proposed speech enhancement is examined using two databases, VoiceBank+DEMAND and LibriSpeech. The results show that the proposed speech enhancement outperforms the benchmarks in terms of intelligibility and quality at various input SNRs. This indicates the performance of the proposed MAB-CED at improving the average PESQ by 0.55 (22.85%) with VoiceBank+DEMAND and by 0.58 (23.79%) with LibriSpeech. The average STOI is improved by 9.63% (VoiceBank+DEMAND) and 9.78% (LibriSpeech) over the noisy mixtures

A Novel Optimization for GPU Mining Using Overclocking and Undervolting

Cryptography and associated technologies have existed for a long time. This field is advancing at a remarkable speed. Since the inception of its initial application, blockchain has come a long way. Bitcoin is a cryptocurrency based on blockchain, also known as distributed ledger technology (DLT). The most well-known cryptocurrency for everyday use is Bitcoin, which debuted in 2008. Its success ushered in a digital revolution, and it currently provides security, decentralization, and a reliable data transport and storage mechanism to various industries and companies. Governments and developing enterprises seeking a competitive edge have expressed interest in Bitcoin and other cryptocurrencies due to the rapid growth of this recent technology. For computer experts and individuals looking for a method to supplement their income, cryptocurrency mining has become a big source of anxiety. Mining is a way of resolving mathematical problems based on the processing capacity and speed of the computers employed to solve them in return for the digital currency incentives. Herein, we have illustrated benefits of utilizing GPUs (graphical processing units) for cryptocurrency mining and compare two methods, namely overclocking and undervolting, which are the superior techniques when it comes to GPU optimization. The techniques we have used in this paper will not only help the miners to gain profits while mining cryptocurrency but also solve a major flaw; in order to mitigate the energy and resources that are consumed by the mining hardware, we have designed the mining hardware to simultaneously run longer and consume much less electricity. We have also compared our techniques with other popular techniques that are already in existence with respect to GPU mining.publishedVersio

Design and study of an mmWave wearable textile based compact antenna for healthcare applications

In this study, the design of a compact and novel millimeter wave cotton textile-based wearable antenna for body-centric communications in healthcare applications is presented. The free space and on-body antenna performance parameters for the proposed antenna at 60 GHz are investigated and analyzed. The antenna is based on a 1.5 mm thick cotton substrate and has an overall dimension of 7.0 × 4.5 ×1.5 mm3. In free space, the antenna is resonant at 60 GHz and achieves a wide impedance bandwidth. The maximum gain at this resonant frequency is 6.74 dBi, and the radiation efficiency is 93.30%. Parametric changes were carried out to study the changes in the resonant frequency, gain, and radiation efficiency. For body-centric communications,the antenna was simulated at 5 different distances from a three-layer human torso-equivalent phantom. The radiation efficiency dropped by 24% and gradually increased with the gap distance. The antenna design was also analyzed by using 10 different textile substrates for both free space and on-body scenarios. The major benefits of the antenna are discussed as follows. Compared to a previous work, the antenna is very efficient, compact, and has a wide bandwidth. In BCWCs for e-health applications, the antenna needs to be very compact due to the longer battery life, and it has to have a wide bandwidth for high data rate communication. Since the antenna will be wearable with a sensor system, the shape of the antenna needs to be planar, and it is better to design the antenna on a textile substrate for integration into clothes. The antenna also needs to show high gain and efficiency for power-efficient communication. This proposed antenna meets all these criteria, and hence, it will be a good candidate for BCWCs in e-health applications

Design and analysis of a compact superwideband millimeter wave textile antenna for body area network

The advancement of wireless technology has led to an exponential increase in the usage of smart wearable devices. Current wireless bands are getting more congested, and we are already seeing a shift towards millimeter wave bands. This paper proposes a design for a millimeter wave textile antenna for body-centric communications. The antenna has a quasi-self-complementary (QSC) structure. The radiating patch is a semicircular disc with a radius of 1.855 mm and is fed by a 5.07 mm long, 0.70 mm wide microstrip feedline. A complementary leaf-shaped slot is etched in the ground plane. The radiating disc and the ground plane are attached to a 1.5 mm thick nonconducting 100% polyester substrate. The antenna has an overall dimension of 10 mm × 7:00 mm. In free space, the antenna achieved a superwideband impedance bandwidth that covers the Ka, V, and W bands designated by IEEE. At 60 GHz, the antenna’s radiation efficiency was 89.06%, with a maximum gain of 5.7 dBi. Millimeter waves are easily blocked by obstacles and have low skin penetration depth. On-body investigations were carried out by placing the antenna on a human phantom at five different distances. No significant amount of back radiation was observed. The radiation efficiency decreased to 67.48% at 2 mm away from the phantom, while the maximum gain slightly increased. The efficiency and radiation patterns improved as the distance between the antenna and the phantom gradually increased. Ten different textile substrates were also used to test the antenna. With a few exceptions, the free space and on-body simulation results were very similar to polyester. The design and simulation of the antenna were carried out using the CST microwave studio

Bayesian Learning of Shifted-Scaled Dirichlet Mixture Models and Its Application to Early COVID-19 Detection in Chest X-ray Images

Early diagnosis and assessment of fatal diseases and acute infections on chest X-ray (CXR) imaging may have important therapeutic implications and reduce mortality. In fact, many respiratory diseases have a serious impact on the health and lives of people. However, certain types of infections may include high variations in terms of contrast, size and shape which impose a real challenge on classification process. This paper introduces a new statistical framework to discriminate patients who are either negative or positive for certain kinds of virus and pneumonia. We tackle the current problem via a fully Bayesian approach based on a flexible statistical model named shifted-scaled Dirichlet mixture models (SSDMM). This mixture model is encouraged by its effectiveness and robustness recently obtained in various image processing applications. Unlike frequentist learning methods, our developed Bayesian framework has the advantage of taking into account the uncertainty to accurately estimate the model parameters as well as the ability to solve the problem of overfitting. We investigate here a Markov Chain Monte Carlo (MCMC) estimator, which is a computer–driven sampling method, for learning the developed model. The current work shows excellent results when dealing with the challenging problem of biomedical image classification. Indeed, extensive experiments have been carried out on real datasets and the results prove the merits of our Bayesian framework

Online Learning of Finite and Infinite Gamma Mixture Models for COVID-19 Detection in Medical Images

The accurate detection of abnormalities in medical images (like X-ray and CT scans) is a challenging problem due to images’ blurred boundary contours, different sizes, variable shapes, and uneven density. In this paper, we tackle this problem via a new effective online variational learning model for both mixtures of finite and infinite Gamma distributions. The proposed approach takes advantage of the Gamma distribution flexibility, the online learning scalability, and the variational inference efficiency. Three different batch and online learning methods based on robust texture-based feature extraction are proposed. Our work is evaluated and validated on several real challenging data sets for different kinds of pneumonia infection detection. The obtained results are very promising given that we approach the classification problem in an unsupervised manner. They also confirm the superiority of the Gamma mixture model compared to the Gaussian mixture model for medical images’ classification

Bayesian Learning of Shifted-Scaled Dirichlet Mixture Models and Its Application to Early COVID-19 Detection in Chest X-ray Images

Early diagnosis and assessment of fatal diseases and acute infections on chest X-ray (CXR) imaging may have important therapeutic implications and reduce mortality. In fact, many respiratory diseases have a serious impact on the health and lives of people. However, certain types of infections may include high variations in terms of contrast, size and shape which impose a real challenge on classification process. This paper introduces a new statistical framework to discriminate patients who are either negative or positive for certain kinds of virus and pneumonia. We tackle the current problem via a fully Bayesian approach based on a flexible statistical model named shifted-scaled Dirichlet mixture models (SSDMM). This mixture model is encouraged by its effectiveness and robustness recently obtained in various image processing applications. Unlike frequentist learning methods, our developed Bayesian framework has the advantage of taking into account the uncertainty to accurately estimate the model parameters as well as the ability to solve the problem of overfitting. We investigate here a Markov Chain Monte Carlo (MCMC) estimator, which is a computer–driven sampling method, for learning the developed model. The current work shows excellent results when dealing with the challenging problem of biomedical image classification. Indeed, extensive experiments have been carried out on real datasets and the results prove the merits of our Bayesian framework