Two Methods of Automatic Evaluation of Speech Signal Enhancement Recorded in the Open-Air MRI Environment

Přibil, Jiří; Přibilová, Anna; Frollo, Ivan

doi:10.1515/msr-2017-0031

Cited by 2 publications

(2 citation statements)

References 29 publications

(38 reference statements)

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“…While the cluster number is given, the data PDF in the 2D space will be estimated using any sophisticated machine learning estimator in Step (2). In this case, the Gaussian mixture model (GMM) [55][56][57] was adopted for the following algorithmic estimations. Then, the corresponding KEDF and PEDF can be estimated using Equations (3) and (4), respectively, in Step (3).…”

Section: Experimental Frameworkmentioning

confidence: 99%

Pattern Recognition of Human Postures Using the Data Density Functional Method

Huang

Chen

2018

Applied Sciences

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In this paper, we propose a new approach to recognize the motional patterns of human postures by introducing the data density functional method. Under the framework of the proposed method, sensed time signals will be mapped into specific physical spaces. The most probable cluster number within the specific physical space can be determined according to the principle of energy stability. Then, each corresponding cluster boundary can be measured by searching for the local lowest energy level. Finally, the configuration of the clusters in the space will characterize the most probable states of the motional patterns. The direction of state migration and the corresponding transition region between these states then constitute a significant motional feature in the specific space. Differing from conventional methods, only a single tri-axial gravitational sensor was employed for data acquirement in our hardware scheme. By combining the motional feature and the sensor architecture as prior information, experimental results verified that the most probable states of the motional patterns can be successfully classified into four common human postures of daily life. Furthermore, error motions and noise only offer insignificant influences. Eventually, the proposed approach was applied on a simulation of turning-over situations, and the results show its potential on the issue of elderly and infant turning-over monitoring.

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Section: Experimental Frameworkmentioning

confidence: 99%

Pattern Recognition of Human Postures Using the Data Density Functional Method

Huang

Chen

2018

Applied Sciences

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“…Automatic identification and segmentation of medical imageries benefit the planning and guidance of modern surgery [1][2][3][4][5], clinical investigations [6][7][8][9][10], rehabilitation [11][12][13], and so forth. High-quality reconstructive anatomical morphology provides convenience on surgery planning and the understanding between organ functionalities and pathological diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Medical Image Segmentation with Adjustable Computational Complexity Using Data Density Functionals

et al. 2019

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Techniques of automatic medical image segmentation are the most important methods for clinical investigation, anatomic research, and modern medicine. Various image structures constructed from imaging apparatus achieve a diversity of medical applications. However, the diversified structures are also a burden of contemporary techniques. Performing an image segmentation with a tremendously small size (<25 pixels by 25 pixels) or tremendously large size (>1024 pixels by 1024 pixels) becomes a challenge in perspectives of both technical feasibility and theoretical development. Noise and pixel pollution caused by the imaging apparatus even aggravate the difficulty of image segmentation. To simultaneously overcome the mentioned predicaments, we propose a new method of medical image segmentation with adjustable computational complexity by introducing data density functionals. Under this theoretical framework, several kernels can be assigned to conquer specific predicaments. A square-root potential kernel is used to smoothen the featured components of employed images, while a Yukawa potential kernel is applied to enhance local featured properties. Besides, the characteristic of global density functional estimation also allows image compression without losing the main image feature structures. Experiments on image segmentation showed successful results with various compression ratios. The computational complexity was significantly improved, and the score of accuracy estimated by the Jaccard index had a great outcome. Moreover, noise and regions of light pollution were mostly filtered out in the procedure of image compression.

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Two Methods of Automatic Evaluation of Speech Signal Enhancement Recorded in the Open-Air MRI Environment

Cited by 2 publications

References 29 publications

Pattern Recognition of Human Postures Using the Data Density Functional Method

Pattern Recognition of Human Postures Using the Data Density Functional Method

Medical Image Segmentation with Adjustable Computational Complexity Using Data Density Functionals

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