Computers

1338 KiB

Open AccessArticle

On the Use of Voice Signals for Studying Sclerosis Disease

by Patrizia Vizza, Giuseppe Tradigo, Domenico Mirarchi, Roberto Bruno Bossio and Pierangelo Veltri

Computers 2017, 6(4), 30; https://doi.org/10.3390/computers6040030 - 28 Nov 2017

Cited by 2 | Viewed by 6440

Multiple sclerosis (MS) is a chronic demyelinating autoimmune disease affecting the central nervous system. One of its manifestations concerns impaired speech, also known as dysarthria. In many cases, a proper speech evaluation can play an important role in the diagnosis of MS. The [...] Read more.

Multiple sclerosis (MS) is a chronic demyelinating autoimmune disease affecting the central nervous system. One of its manifestations concerns impaired speech, also known as dysarthria. In many cases, a proper speech evaluation can play an important role in the diagnosis of MS. The identification of abnormal voice patterns can provide valid support for a physician in the diagnosing and monitoring of this neurological disease. In this paper, we present a method for vocal signal analysis in patients affected by MS. The goal is to identify the dysarthria in MS patients to perform an early diagnosis of the disease and to monitor its progress. The proposed method provides the acquisition and analysis of vocal signals, aiming to perform feature extraction and to identify relevant patterns useful to impaired speech associated with MS. This method integrates two well-known methodologies, acoustic analysis and vowel metric methodology, to better define pathological compared to healthy voices. As a result, this method provides patterns that could be useful indicators for physicians in identifying patients affected by MS. Moreover, the proposed procedure could be a valid support in early diagnosis as well as in monitoring treatment success, thus improving a patient’s life quality. Full article

(This article belongs to the Special Issue Biomedical and Bioinformatics Challenges for Computer Science)

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485 KiB

Open AccessArticle

Application of Machine Learning Models in Error and Variant Detection in High-Variation Genomics Datasets

by Milko Krachunov, Maria Nisheva and Dimitar Vassilev

Computers 2017, 6(4), 29; https://doi.org/10.3390/computers6040029 - 10 Nov 2017

Cited by 2 | Viewed by 4986

Abstract

For metagenomics datasets, datasets of complex polyploid genomes, and other high-variation genomics datasets, there are difficulties with the analysis, error detection and variant calling, stemming from the challenges of discerning sequencing errors from biological variation. Confirming base candidates with high frequency of occurrence [...] Read more.

For metagenomics datasets, datasets of complex polyploid genomes, and other high-variation genomics datasets, there are difficulties with the analysis, error detection and variant calling, stemming from the challenges of discerning sequencing errors from biological variation. Confirming base candidates with high frequency of occurrence is no longer a reliable measure because of the natural variation and the presence of rare bases. The paper discusses an approach to the application of machine learning models to classify bases into erroneous and rare variations after preselecting potential error candidates with a weighted frequency measure, which aims to focus on unexpected variations by using the inter-sequence pairwise similarity. Different similarity measures are used to account for different types of datasets. Four machine learning models are implemented and tested. Full article

(This article belongs to the Special Issue Biomedical and Bioinformatics Challenges for Computer Science)

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6252 KiB

Open AccessFeature PaperReview

3D NAND Flash Based on Planar Cells

by Andrea Silvagni

Computers 2017, 6(4), 28; https://doi.org/10.3390/computers6040028 - 24 Oct 2017

Cited by 12 | Viewed by 22576

Abstract

In this article, the transition from 2D NAND to 3D NAND is first addressed, and the various 3D NAND architectures are compared. The article carries out a comparison of 3D NAND architectures that are based on a “punch-and-plug” process—with gate-all-around (GAA) cell devices—against [...] Read more.

In this article, the transition from 2D NAND to 3D NAND is first addressed, and the various 3D NAND architectures are compared. The article carries out a comparison of 3D NAND architectures that are based on a “punch-and-plug” process—with gate-all-around (GAA) cell devices—against architectures that are based on planar cell devices. The differences and similarities between the two classes of architectures are highlighted. The differences between architectures using floating-gate (FG) and charge-trap (CT) devices are also considered. Although the current production of 3D NAND is based on GAA cell devices, it is suggested that architectures with planar cell devices could also be viable for mass production. Full article

(This article belongs to the Special Issue 3D Flash Memories)

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Computers, Volume 6, Issue 4 (December 2017) – 3 articles

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