30,776 research outputs found
\u3ci\u3eBioinformatics and Biomedical Engineering\u3c/i\u3e
Editors: Francisco Ortuño, Ignacio Rojas
Chapter, Identification of Biologically Significant Elements Using Correlation Networks in High Performance Computing Environments, co-authored by Kathryn Dempsey Cooper, Sachin Pawaskar, and Hesham Ali, UNO faculty members.
The two volume set LNCS 9043 and 9044 constitutes the refereed proceedings of the Third International Conference on Bioinformatics and Biomedical Engineering, IWBBIO 2015, held in Granada, Spain in April 2015. The 134 papers presented were carefully reviewed and selected from 268 submissions. The scope of the conference spans the following areas: bioinformatics for healthcare and diseases, biomedical engineering, biomedical image analysis, biomedical signal analysis, computational genomics, computational proteomics, computational systems for modelling biological processes, eHealth, next generation sequencing and sequence analysis, quantitative and systems pharmacology, Hidden Markov Model (HMM) for biological sequence modeling, advances in computational intelligence for bioinformatics and biomedicine, tools for next generation sequencing data analysis, dynamics networks in system medicine, interdisciplinary puzzles of measurements in biological systems, biological networks, high performance computing in bioinformatics, computational biology and computational chemistry, advances in drug discovery and ambient intelligence for bio emotional computing.https://digitalcommons.unomaha.edu/facultybooks/1323/thumbnail.jp
Ultrasound IMT measurement on a multi-ethnic and multi-institutional database: Our review and experience using four fully automated and one semi-automated methods
Automated and high performance carotid intima-media thickness (IMT) measurement is gaining increasing importance in clinical practice to assess the cardiovascular risk of patients. In this paper, we compare four fully automated IMT measurement techniques (CALEX, CAMES, CARES and CAUDLES) and one semi-automated technique (FOAM). We present our experience using these algorithms, whose lumen-intima and media-adventitia border estimation use different methods that can be: (a) edge-based; (b) training-based; (c) feature-based; or (d) directional Edge-Flow based. Our database (DB) consisted of 665 images that represented a multi-ethnic group and was acquired using four OEM scanners. The performance evaluation protocol adopted error measures, reproducibility measures, and Figure of Merit (FoM). FOAM showed the best performance, with an IMT bias equal to 0.025 ± 0.225 mm, and a FoM equal to 96.6%. Among the four automated methods, CARES showed the best results with a bias of 0.032 ± 0.279 mm, and a FoM to 95.6%, which was statistically comparable to that of FOAM performance in terms of accuracy and reproducibility. This is the first time that completely automated and user-driven techniques have been compared on a multi-ethnic dataset, acquired using multiple original equipment manufacturer (OEM) machines with different gain settings, representing normal and pathologic case
Distributed computing methodology for training neural networks in an image-guided diagnostic application
Distributed computing is a process through which a set of computers connected by a network is used collectively to solve a single problem. In this paper, we propose a distributed computing methodology for training neural networks for the detection of lesions in colonoscopy. Our approach is based on partitioning the training set across multiple processors using a parallel virtual machine. In this way, interconnected computers of varied architectures can be used for the distributed evaluation of the error function and gradient values, and, thus, training neural networks utilizing various learning methods. The proposed methodology has large granularity and low synchronization, and has been implemented and tested. Our results indicate that the parallel virtual machine implementation of the training algorithms developed leads to considerable speedup, especially when large network architectures and training sets are used
Quantum Robot: Structure, Algorithms and Applications
A kind of brand-new robot, quantum robot, is proposed through fusing quantum
theory with robot technology. Quantum robot is essentially a complex quantum
system and it is generally composed of three fundamental parts: MQCU (multi
quantum computing units), quantum controller/actuator, and information
acquisition units. Corresponding to the system structure, several learning
control algorithms including quantum searching algorithm and quantum
reinforcement learning are presented for quantum robot. The theoretic results
show that quantum robot can reduce the complexity of O(N^2) in traditional
robot to O(N^(3/2)) using quantum searching algorithm, and the simulation
results demonstrate that quantum robot is also superior to traditional robot in
efficient learning by novel quantum reinforcement learning algorithm.
Considering the advantages of quantum robot, its some potential important
applications are also analyzed and prospected.Comment: 19 pages, 4 figures, 2 table
Smart nanotextiles: materials and their application
Textiles are ubiquitous to us, enveloping our skin and
surroundings. Not only do they provide a protective
shield or act as a comforting cocoon but they also
serve esthetic appeal and cultural importance. Recent
technologies have allowed the traditional functionality
of textiles to be extended. Advances in materials
science have added intelligence to textiles and created
âsmartâ clothes.
Smart textiles can sense and react to environmental
conditions or stimuli, e.g., from mechanical, thermal,
chemical, electrical, or magnetic sources (Lam Po
Tang and Stylios 2006). Such textiles find uses in many
applications ranging from military and security to
personalized healthcare, hygiene, and entertainment.
Smart textiles may be termed ââpassiveââ or ââactive.ââ A
passive smart textile monitors the wearerâs physiology
or the environment, e.g., a shirt with in-built
thermistors to log body temperature over time. If
actuators are integrated, the textile becomes an active,
smart textile as it may respond to a particular stimulus,
e.g., the temperature-aware shirt may automatically
roll up the sleeves when body temperature rises.
The fundamental components in any smart textile
are sensors and actuators. Interconnections, power
supply, and a control unit are also needed to complete
the system. All these components must be integrated
into textiles while still retaining the usual
tactile, flexible, and comfortable properties that we
expect from a textile. Adding new functionalities to
textiles while still maintaining the look and feel of the
fabric is where nanotechnology has a huge impact on
the textile industry. This article describes current developments
in materials for smart nanotextiles and
some of the many applications where these innovative
textiles are of great benefit
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