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The conference program will consist of plenary lectures, symposia, workshops and invitedsessions of the latest significant findings and developments in all the major fields of biomedical engineering.Submitted full papers will be peer reviewed. Accepted high quality papers will be presented in oral and poster sessions,will appear in the Conference Proceedings and will be indexed in PubMed/MEDLINE.
The 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2020) will be held in Metro Toronto Convention Centre (MTCC), Toronto, Ontario, Canada. SMC 2020 is the flagship conference of the IEEE Systems, Man, and Cybernetics Society. It provides an international forum for researchers and practitioners to report most recent innovations and developments, summarize state-of-the-art, and exchange ideas and advances in all aspects of systems science and engineering, human machine systems, and cybernetics. Advances in these fields have increasing importance in the creation of intelligent environments involving technologies interacting with humans to provide an enriching experience and thereby improve quality of life. Papers related to the conference theme are solicited, including theories, methodologies, and emerging applications. Contributions to theory and practice, including but not limited to the following technical areas, are invited.
All areas of ionizing radiation detection - detectors, signal processing, analysis of results, PET development, PET results, medical imaging using ionizing radiation
The CDC is the premier conference dedicated to the advancement of the theory and practice of systems and control. The CDC annually brings together an international community of researchers and practitioners in the field of automatic control to discuss new research results, perspectives on future developments, and innovative applications relevant to decision making, automatic control, and related areas.
2019 IEEE International Symposium on Information Theory (ISIT)
Information theory and coding theory and their applications in communications and storage, data compression, wireless communications and networks, cryptography and security, information theory and statistics, detection and estimation, signal processing, big data analytics, pattern recognition and learning, compressive sensing and sparsity, complexity and computation theory, Shannon theory, quantum information and coding theory, emerging applications of information theory, information theory in biology.
The theory, design and application of Control Systems. It shall encompass components, and the integration of these components, as are necessary for the construction of such systems. The word `systems' as used herein shall be interpreted to include physical, biological, organizational and other entities and combinations thereof, which can be represented through a mathematical symbolism. The Field of Interest: shall ...
Broad coverage of concepts and methods of the physical and engineering sciences applied in biology and medicine, ranging from formalized mathematical theory through experimental science and technological development to practical clinical applications.
IEEE Communications Magazine was the number three most-cited journal in telecommunications and the number eighteen cited journal in electrical and electronics engineering in 2004, according to the annual Journal Citation Report (2004 edition) published by the Institute for Scientific Information. Read more at http://www.ieee.org/products/citations.html. This magazine covers all areas of communications such as lightwave telecommunications, high-speed data communications, personal communications ...
Theory, concepts, and techniques of science and engineering as applied to sensing the earth, oceans, atmosphere, and space; and the processing, interpretation, and dissemination of this information.
The development and application of electric systems, apparatus, devices, and controls to the processes and equipment of industry and commerce; the promotion of safe, reliable, and economic installations; the encouragement of energy conservation; the creation of voluntary engineering standards and recommended practices.
2018 Electric Electronics, Computer Science, Biomedical Engineerings' Meeting (EBBT), 2018
Attention Deficit hyperactivity disorder, which includes symptoms attention deficit, hyperactivity and impulsivity is brain disorder that affects millions of people .The ADHD specification differs from person to person. The method to be used in diagnosis should be objective and reliable. For this purpose, parameters obtained from brain imaging methods are an important component for diagnosis. As with many psychiatric disorders, ...
2016 IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES), 2016
There have been interest on white matter hyperintensity (WMH) and normal white matter (WM) changes reported but have not yet been fully characterized. Different image sequences of magnetic resonance imaging (MRI) scans may shows different gray scale intensity. However, it is difficult to differentiate the intensity of normal WM and WMH as their intensities are visually not much different. In ...
2017 International Conference on Electrical, Electronics and System Engineering (ICEESE), 2017
White matter hyperintensities (WMH) are small regions of high signal intensity that are observable on the white matter region of the brain through magnetic resonance imaging images. Generally, the medical expert conducts a white matter hyperintensities analysis to investigate brain tissue abnormality using manual or semi-automatic methods. However, those methods are prone to error and they establish unreliable results as ...
2018 IEEE 23rd International Conference on Digital Signal Processing (DSP), 2018
White matter hyperintensity (WMH) is commonly found in elder individuals and appears to be associated with brain diseases. U-net is a convolutional network that has been widely used for biomedical image segmentation. Recently, U-net has been successfully applied to WMH segmentation. Random initialization is usally used to initialize the model weights in the U-net. However, the model may coverage to ...
2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), 2017
White matter segmentation is an essential step to study whole-brain structural connectivity via diffusion MRI white matter tractography. One important goal of segmentation methods is to improve consistency of the white matter segmentations across multiple subjects. In this study, we quantitatively compare two popular white matter segmentation strategies, i.e., a cortical- parcellation-based method and a groupwise fiber clustering method, to ...
IEEE Innovation Day 2011-Innovation Day Keynote Address
Do Fuzzy Sets Matter? An Interdisciplinary Point of View
APEC Speaker Highlights: Robert White, Chief Engineer, Embedded Power
Building IEEE Communities that Matter
Why Conferences Matter
IEEE N3XT @ SXSW 2016: Tanner Avery, Gray Matter
Sean White: Distinguished Experts Panel - TTM 2018
Why Conferences Matter: Connecting With My Community
Why Conferences Matter: The Global Technical Community
Where's my electric car?
ICRA 2020-Keynote: Cyrill Stachniss
Panel Session: 5G Test and Measurements - 5G Summit at IMS 2017
Patentable Subject Matter and Software Patents - IEEE USA
Day Two Opening Remarks by Megan Smith - Internet Inclusion: Global Connect Stakeholders Advancing Solutions, Washington DC, 2016
MicroApps: 802D11ac: Increased Throughput, but How Much? (National Instruments)
Annie Cannons presentation - Global Humanitarian Technology Conference, GHTC 2017
Options and Challenges in Providing Universal Access - IEEE Internet Initiative
MicroApps: Anatomy of PXI (National Instruments)
Vijayalata Yellasiri: Student Branch Affinity Group of the Year Winner - IEEE WIE ILC Awards 2017
Attention Deficit hyperactivity disorder, which includes symptoms attention deficit, hyperactivity and impulsivity is brain disorder that affects millions of people .The ADHD specification differs from person to person. The method to be used in diagnosis should be objective and reliable. For this purpose, parameters obtained from brain imaging methods are an important component for diagnosis. As with many psychiatric disorders, the change in the ratio of gray and white matter is also significant in ADHD. The increase or decrease in these regions brings with it many problems. Gray or white matter plays an important role on social skills as well as on the development of mental skills, thinking and learning. For this purpose, we developed a unique method for obtaining gray and white matter, an important task in the diagnosis of ADHD. This method was tested on brain MRI data obtained from NPIstanbul Neuropsychiatric Hospital and results are compared.
There have been interest on white matter hyperintensity (WMH) and normal white matter (WM) changes reported but have not yet been fully characterized. Different image sequences of magnetic resonance imaging (MRI) scans may shows different gray scale intensity. However, it is difficult to differentiate the intensity of normal WM and WMH as their intensities are visually not much different. In this study, normal WM and WMH changes were investigated based on their intensity to determine the correlation of WMH types and severity in brain of healthy subjects. The assessment was performed by using fully automatic WMH detection and computing algorithms. The main brain regions were segregated into gray matter (GM), normal WM, cerebrospinal fluid (CSF) and non-brain tissue. From the results, it shows that there was significant difference seen between normal appearing WM and hyperintense WM in terms of their intensity levels. The study shows that the development of WMH is prevalent to the occasion of normal WM changes. This is shows that WMH intensity reflects the level of WMH classes and severity; however, further investigations are needed to improve their efficiency.
White matter hyperintensities (WMH) are small regions of high signal intensity that are observable on the white matter region of the brain through magnetic resonance imaging images. Generally, the medical expert conducts a white matter hyperintensities analysis to investigate brain tissue abnormality using manual or semi-automatic methods. However, those methods are prone to error and they establish unreliable results as different in rating scales. In this paper, a fully automatic method is proposed to identify WMH using the multimodal technique which combining image segmentation and enhancement. This method is introduced as an unsupervised method to automatically segment WMH on MRI images of T2-weighted and FLAIR sequences. Subsequently, the processed sequences are integrated by overlying the mapping images in order to map the most precise WMH regions. The accuracy of the WMH regions identification is assessed through the similarity index between automated and manual approach. The experimental results show that the proposed method has achieved significant results to detect exact WMH area. The proposed method is suitable to be implemented in analyzing white matter hyperintensities identification and it may serves as a computer-aided tool for radiologists.
White matter hyperintensity (WMH) is commonly found in elder individuals and appears to be associated with brain diseases. U-net is a convolutional network that has been widely used for biomedical image segmentation. Recently, U-net has been successfully applied to WMH segmentation. Random initialization is usally used to initialize the model weights in the U-net. However, the model may coverage to different local optima with different randomly initialized weights. We find a combination of thresholding and averaging the outputs of U-nets with different random initializations can largely improve the WMH segmentation accuracy. Based on this observation, we propose a post-processing technique concerning the way how averaging and thresholding are conducted. Specifically, we first transfer the score maps from three U-nets to binary masks via thresholding and then average those binary masks to obtain the final WMH segmentation. Both quantitative analysis (via the Dice similarity coefficient) and qualitative analysis (via visual examinations) reveal the superior performance of the proposed method. This post-processing technique is independent of the model used. As such, it can also be applied to situations where other deep learning models are employed, especially when random initialization is adopted and pre-training is unavailable.
White matter segmentation is an essential step to study whole-brain structural connectivity via diffusion MRI white matter tractography. One important goal of segmentation methods is to improve consistency of the white matter segmentations across multiple subjects. In this study, we quantitatively compare two popular white matter segmentation strategies, i.e., a cortical- parcellation-based method and a groupwise fiber clustering method, to investigate their performance on consistency. Our experimental results indicate that the groupwise fiber clustering generated more consistent segmentations with lower variability across subjects. This suggests that the fiber clustering strategy could provide a potential alternative to the traditional cortical-parcellation-based brain connectivity modeling methods.
We are interested in investigating white matter connectivity using a novel computational framework that does not use diffusion tensor imaging (DTI) but only uses T1-weighted magnetic resonance imaging. The proposed method relies on correlating Jacobian determinants across different voxels based on the tensor-based morphometry (TBM) framework. In this paper, we show agreement between the TBM-based white matter connectivity and the DTI-based white matter atlas. As an application, altered white matter connectivity in a clinical population is determined.
Diffusion tensor imaging (DTI) technique, which can be used to research the white matter of human brain noninvasively, provides more valuable information in the study of white matter abnormality, especially using fractional anisotropy (FA) images calculated from DTI data. Although there are many different DTI data analysis software, most of them are inconvenient to use and qualitative via visual inspection. To provide assistance to physicians in improving the facilitation and sensitivity of the white matter abnormality analysis, we developed an automated FA analysis method of an individual in comparison with a group of normal controls, which was based on the principle of voxel based analysis (VBA), and contained preprocessing, database, and statistical analysis procedures. Only using FA and B<sub>o</sub> images, the final results about the white matter structure difference would be displayed automatically and interactively. Some data generated during the processing were stored to corresponding path and could be checked when necessary. We applied this methods to Alzheimer's disease (AD). The final result supplied the brain regions where FA values of AD were reduced. DTI data from fifteen AD and sixteen elderly healthy subjects was used to analyse and the brain regions which affected by AD were consonant in the main with the previous study, such as corpus callosum, cingulate regions, and so on. This method could be valuable tool to help physicians in making their clinical decisions.
We propose a new white matter atlas creation method that learns a model of the common white matter structures present in a group of subjects. We demonstrate that our atlas creation method, which is based on group spectral clustering of tractography, discovers structures corresponding to expected white matter anatomy such as the corpus callosum, uncinate fasciculus, cingulum bundles, arcuate fasciculus, and corona radiata. The white matter clusters are augmented with expert anatomical labels and stored in a new type of atlas that we call a high-dimensional white matter atlas. We then show how to perform automatic segmentation of tractography from novel subjects by extending the spectral clustering solution, stored in the atlas, using the Nystrom method. We present results regarding the stability of our method and parameter choices. Finally we give results from an atlas creation and automatic segmentation experiment. We demonstrate that our automatic tractography segmentation identifies corresponding white matter regions across hemispheres and across subjects, enabling group comparison of white matter anatomy.
Long-term outcomes for Tetralogy of Fallot (TOF) have improved dramatically in recent years, but survivors are still afflicted by cerebral damage. In this paper, we characterized the prevalence and predictors of cerebral silent infarction (SCI) and their relationship to cerebral blood flow (CBF) in 46 adult TOF patients. We calculated both whole brain and regional CBF using 2D arterial spin labeling (ASL) images, and investigated the spatial overlap between voxel-wise CBF values and white matter hyperintensities (WMHs) identified from T2-FLAIR images. SCIs were found in 83% of subjects and were predicted by the year of the patient's first cardiac surgery and patient's age at scanning (combined r<sup>2</sup> 0.44). CBF was not different in brain regions prone to stroke compared with healthy white matter.
Since the invention of diffusion magnetic resonance imaging (dMRI), currently the only established method for studying white matter connectivity in a clinical environment, there has been a great deal of interest in the effects of various pathologies on the connectivity of the brain. As methods for in vivo tractography have been developed, it has become possible to track and segment specific white matter structures of interest for particular study. However, the consistency and reproducibility of tractography-based segmentation remain limited, and attempts to improve them have thus far typically involved the imposition of strong constraints on the tract reconstruction process itself. In this work we take a different approach, developing a formal probabilistic model for the relationships between comparable tracts in different scans, and then using it to choose a tract, a posteriori, which best matches a predefined reference tract for the structure of interest. We demonstrate that this method is able to significantly improve segmentation consistency without directly constraining the tractography algorithm.
IEEE Standard for Information Technology--Telecommunications and information exchange between systems--Local and metropolitan area networks--Specific requirements Part 22.1: Standard to Enhance Harmful Interference Protection for Low-Power Licensed Devices Operating in TV Broadcast Bands
This standard specifies methods to provide enhanced protection to protected devices such as those used in the production and transmission of broadcast programs [e.g., devices licensed as secondary under Title 47 of the Code of Federal Regulations (CFR) in the USA and equivalent devices in other regulatory domains] from harmful interference caused by license-exempt devices (e.g., IEEE P802.22) that also ...
Unless the logic inside embedded cores can be merged with the surrounding user-defined logic (UDL), the SoC test requires reuse of test data and test structures specific to individual cores (designs) when integrated into larger systems. This standard defines language constructs sufficient to represent the context of a core and of the integration of that core into a system, to ...