Hybrid biomedical intelligent systems

"Copyright © 2012 Maysam Abbod et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited."The purpose of this special issue is to promote research and developments of the best work in the field of hybrid intelligent systems for biomedical applications

Generic Subsequence Matching Framework: Modularity, Flexibility, Efficiency

Subsequence matching has appeared to be an ideal approach for solving many problems related to the fields of data mining and similarity retrieval. It has been shown that almost any data class (audio, image, biometrics, signals) is or can be represented by some kind of time series or string of symbols, which can be seen as an input for various subsequence matching approaches. The variety of data types, specific tasks and their partial or full solutions is so wide that the choice, implementation and parametrization of a suitable solution for a given task might be complicated and time-consuming; a possibly fruitful combination of fragments from different research areas may not be obvious nor easy to realize. The leading authors of this field also mention the implementation bias that makes difficult a proper comparison of competing approaches. Therefore we present a new generic Subsequence Matching Framework (SMF) that tries to overcome the aforementioned problems by a uniform frame that simplifies and speeds up the design, development and evaluation of subsequence matching related systems. We identify several relatively separate subtasks solved differently over the literature and SMF enables to combine them in straightforward manner achieving new quality and efficiency. This framework can be used in many application domains and its components can be reused effectively. Its strictly modular architecture and openness enables also involvement of efficient solutions from different fields, for instance efficient metric-based indexes. This is an extended version of a paper published on DEXA 2012.Comment: This is an extended version of a paper published on DEXA 201

Thermoelastic Damping in Micro- and Nano-Mechanical Systems

The importance of thermoelastic damping as a fundamental dissipation mechanism for small-scale mechanical resonators is evaluated in light of recent efforts to design high-Q micrometer- and nanometer-scale electro-mechanical systems (MEMS and NEMS). The equations of linear thermoelasticity are used to give a simple derivation for thermoelastic damping of small flexural vibrations in thin beams. It is shown that Zener's well-known approximation by a Lorentzian with a single thermal relaxation time slightly deviates from the exact expression.Comment: 10 pages. Submitted to Phys. Rev.

Special Issue on Advanced Signal Processing in Intelligent Systems for Health Monitoring

Copyright © 2019 by the authors. Recently, significant developments have been achieved in the field of artificial intelligence, in particular the introduction of deep learning technology that has improved the learning and prediction accuracy to unpresented levels, especially when dealing with big data and high-resolution images. Significant developments have occurred in the area of medical signal processing, measurement techniques, and health monitoring, such as vital biological signs for biomedical systems and noise and vibration of mechanical systems, which are carried out by instruments that generate large data sets. These big data sets, ultimately driven by high population growth, would require Artificial Intelligence techniques to analyse and model. In this Special Issue, papers are presented on the latest signal processing and deep learning techniques used for health monitoring of biomedical and mechanical systems

Fastpass: A Centralized “Zero-Queue” Datacenter Network

An ideal datacenter network should provide several properties, including low median and tail latency, high utilization (throughput), fair allocation of network resources between users or applications, deadline-aware scheduling, and congestion (loss) avoidance. Current datacenter networks inherit the principles that went into the design of the Internet, where packet transmission and path selection decisions are distributed among the endpoints and routers. Instead, we propose that each sender should delegate control—to a centralized arbiter—of when each packet should be transmitted and what path it should follow. This paper describes Fastpass, a datacenter network architecture built using this principle. Fastpass incorporates two fast algorithms: the first determines the time at which each packet should be transmitted, while the second determines the path to use for that packet. In addition, Fastpass uses an efficient protocol between the endpoints and the arbiter and an arbiter replication strategy for fault-tolerant failover. We deployed and evaluated Fastpass in a portion of Facebook’s datacenter network. Our results show that Fastpass achieves high throughput comparable to current networks at a 240 reduction is queue lengths (4.35 Mbytes reducing to 18 Kbytes), achieves much fairer and consistent flow throughputs than the baseline TCP (5200 reduction in the standard deviation of per-flow throughput with five concurrent connections), scalability from 1 to 8 cores in the arbiter implementation with the ability to schedule 2.21 Terabits/s of traffic in software on eight cores, and a 2.5 reduction in the number of TCP retransmissions in a latency-sensitive service at Facebook.National Science Foundation (U.S.) (grant IIS-1065219)Irwin Mark Jacobs and Joan Klein Jacobs Presidential FellowshipHertz Foundation (Fellowship

GBM heterogeneity as a function of variable epidermal growth factor receptor variant III activity.

Abnormal activation of the epidermal growth factor receptor (EGFR) due to a deletion of exons 2-7 of EGFR (EGFRvIII) is a common alteration in glioblastoma (GBM). While this alteration can drive gliomagenesis, tumors harboring EGFRvIII are heterogeneous. To investigate the role for EGFRvIII activation in tumor phenotype we used a neural progenitor cell-based murine model of GBM driven by EGFR signaling and generated tumor progenitor cells with high and low EGFRvIII activation, pEGFRHi and pEGFRLo. In vivo, ex vivo, and in vitro studies suggested a direct association between EGFRvIII activity and increased tumor cell proliferation, decreased tumor cell adhesion to the extracellular matrix, and altered progenitor cell phenotype. Time-lapse confocal imaging of tumor cells in brain slice cultures demonstrated blood vessel co-option by tumor cells and highlighted differences in invasive pattern. Inhibition of EGFR signaling in pEGFRHi promoted cell differentiation and increased cell-matrix adhesion. Conversely, increased EGFRvIII activation in pEGFRLo reduced cell-matrix adhesion. Our study using a murine model for GBM driven by a single genetic driver, suggests differences in EGFR activation contribute to tumor heterogeneity and aggressiveness

Pain and Stress Detection Using Wearable Sensors and Devices—A Review

© 2021 by the authors. Pain is a subjective feeling; it is a sensation that every human being must have experienced all their life. Yet, its mechanism and the way to immune to it is still a question to be answered. This review presents the mechanism and correlation of pain and stress, their assessment and detection approach with medical devices and wearable sensors. Various physiological signals (i.e., heart activity, brain activity, muscle activity, Electrodermal activity, respiratory, blood volume pulse, skin tempera-ture) and behavioral signals are organized for wearables sensors detection. By reviewing the wearable sensors used in the healthcare domain, we hope to find a way for wearable healthcare-monitoring system to be applied on pain and stress detection. Since pain leads to multiple consequences or symptoms such as muscle tension and depression that are stress related, there is a chance to find a new approach for chronic pain detection using daily life sensors or de-vices. Then by integrating modern computing techniques, there is a chance to handle pain and stress management issue.Ministry of Science and Technology (MOST) of Taiwan (grant number: MOST 107-2221-E-155-009-MY2)

Superconductivity from D3/D7: Holographic Pion Superfluid

We show that a D3/D7 system (at zero quark mass limit) at finite isospin chemical potential goes through a superconductor (superfluid) like phase transition. This is similar to a flavored superfluid phase studied in QCD literature, where mesonic operators condensate. We have studied the frequency dependent conductivity of the condensate and found a delta function pole in the zero frequency limit. This is an example of superconductivity in a string theory context. Consequently we have found a superfluid/supercurrent type solution and studied the associated phase diagram. The superconducting transition changes from second order to first order at a critical superfluid velocity. We have studied various properties of the superconducting system like superfluid density, energy gap, second sound etc. We investigate the possibility of the isospin chemical potential modifying the embedding of the flavor branes by checking whether the transverse scalars also condense at low temperature. This however does not seem to be the case.Comment: 18 pages, 8 figures, revtex

Nonlinear and conventional biosignal analyses applied to tilt table test for evaluating autonomic nervous system and autoregulation

Copyright © Tseng et al.; Licensee Bentham Open. This is an open access article licensed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/ by-nc/3.0/) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, provided the work is properly cited.Tilt table test (TTT) is a standard examination for patients with suspected autonomic nervous system (ANS) dysfunction or uncertain causes of syncope. Currently, the analytical method based on blood pressure (BP) or heart rate (HR) changes during the TTT is linear but normal physiological modulations of BP and HR are thought to be predominately nonlinear. Therefore, this study consists of two parts: the first part is analyzing the HR during TTT which is compared to three methods to distinguish normal controls and subjects with ANS dysfunction. The first method is power spectrum density (PSD), while the second method is detrended fluctuation analysis (DFA), and the third method is multiscale entropy (MSE) to calculate the complexity of system. The second part of the study is to analyze BP and cerebral blood flow velocity (CBFV) changes during TTT. Two measures were used to compare the results, namely correlation coefficient analysis (nMxa) and MSE. The first part of this study has concluded that the ratio of the low frequency power to total power of PSD, and MSE methods are better than DFA to distinguish the difference between normal controls and patients groups. While in the second part, the nMxa of the three stages moving average window is better than the nMxa with all three stages together. Furthermore the analysis of BP data using MSE is better than CBFV data.The Stroke Center and Department of Neurology, National Taiwan University, National Science Council in Taiwan, and the Center for Dynamical Biomarkers and Translational Medicine, National Central University, which is sponsored by National Science Council and Min-Sheng General Hospital Taoyuan

Developing a biotic index for Colorado stream quality

November 1994.Also listed online under Open file reports list as no. 8.Includes bibliographical references (pages 114-118).Financed in part by the U.S. Dept. of the Interior, Geological Survey, grant no. 14-08-0001-G2008/3 11