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IEEE International Conference on Image Processing

作者： Ahmed Sharafeldeen Adel Khelifi Mohammed Ghazal Maha Yaghi Sohail Contractor Ayman El-Baz Department of Bioengineering University of Louisville USA Computer Science and Information Technology Department Abu Dhabi University UAE Electrical Computer and Biomedical Engineering Department Abu Dhabi University UAE Department of Radiology University of Louisville USA

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

This paper introduces an automatic segmentation system designed for precise outlining of the pulmonary area within 3D computed tomography (CT) scans, utilizing a combination of unsupervised and supervised models. Initially, an unsupervised model is utilized to depict the empirical distribution of Hounsfield units in both lung and chest regions within the 3D CT volume. This representation takes the form of a probability model based on a linear combination of Gaussians, determined through a modified expectation maximization (EM) algorithm. Subsequently, the LCG-based segmentation is refined by modeling it with a spatial probabilistic model using a 3D Markov Gibbs random field (MGRF) with analytically estimated potentials. Finally, a supervised deep learning model is introduced and integrated with the proposed unsupervised model to achieve superior segmentation results. The efficacy of the proposed method is assessed using 3D chest scans from 29 patients confirmed with varying degrees of severity in COVID-19. This evaluation employs four distinct metrics: Dice similarity coefficient (DSC), overlap coefficient, Hausdorff distance (HD), and absolute volume difference (AVD), achieving remarkable results of $97.35_{ \pm 1.51} \%, 94.89_{ \pm 2.80} \%$, $3.39_{ \pm 1.61}$, and $2.70_{ \pm 2.88}$, respectively. When compared to four state-of-the-art deep learning-based methods, the proposed system demonstrated outstanding performance in segmenting pathological lung tissues, highlighting its potential and efficacy.

关键词： Deep learning Solid modeling Image segmentation Analytical models Three-dimensional displays Shape Computed tomography

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Ionic Crosslinked Biopolymer-Ceramic Beads for Bone Tissue engineering 6th

Ionic Crosslinked Biopolymer-Ceramic Beads for Bone Tissue E...

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6th International Conference on Nanotechnologies and Biomedical engineering, ICNBME 2023

作者： Cojocaru, Florina Daniela Toader, Claudia Valentina Dodi, Gianina Gardikiotis, Ioannis Calistru, Anca Elena Rotaru, Aurelian Balan, Vera Verestiuc, Liliana Biomedical Sciences Department Faculty of Medical Bioengineering Grigore T. Popa University of Medicine and Pharmacy of Iasi Iasi Romania Grigore T. Popa University of Medicine and Pharmacy of Iasi Iasi Romania Department of Pedotechnics Iasi University of Life Sciences Iasi Romania Department of Electrical Engineering and Computer Science Stefan cel Mare University of Suceava Suceava Romania

ISBN: (纸本)9783031427749

In several situation when bone integrity is prejudiced, advanced regenerative medicine approaches are involved in order to get a good result, being designed and applied synthetic tissue engineered architectures. This rapidly evolving interdisciplinary domain, tissue engineering, is centered on developing three-dimensional scaffolds, which can be prepared from ceramics, polymers or the combination between those two, resulting a complex material mimicking the composition of the natural bone. Among polymers, polysaccharides are a remarkable class, due to the inexhaustible source, great biocompatibility and versatility in terms of processability. Alginate and guar gum, included in this class have the ability to crosslink in the presence of Ca2+ ions, resulting easy to handle beads. Due to the similarity with human inorganic matter, calcium phosphates are used in orthopedy, frequently combined with polymers to overcome one of the main disadvantages of ceramics: brittleness, which negatively influences mechanical behavior. All these aspects being considered, the aim of the study was to obtain alginate–calcium phosphate beads with inclusion of carboxymethyl guar gum nanoparticles as scaffolds for bone tissue engineering and analyze them in terms of morphology and composition (Field Emission Scanning Electron Microscope and Energy-dispersive X-ray Spectrometer), chemical structure (Fourier Transform Infrared Spectrometer) and behavior in simulated body fluids. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Calcium phosphate

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Cascaded multiparameter quantum metrology

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Physical Review A 2025年第1期111卷 012618-012618页

作者： Gregory Krueper Lior Cohen Juliet T. Gopinath Department of Physics University of Colorado Boulder Boulder Colorado 80309 USA Department of Electrical Computer and Energy Engineering University of Colorado Boulder Boulder Colorado 80309 USA Materials Science and Engineering Program University of Colorado Boulder Boulder Colorado 80309 USA

We present an innovative, platform-independent concept for multiparameter sensing where the measurable parameters are in series, or cascaded, enabling measurements as a function of position. With temporally resolved detection, we show that squeezing can give a quantum enhancement in sensitivity over that of classical states by a factor of e2r, where r≈1 is the squeezing parameter. As an example, we have modeled an interferometer that senses multiple phase shifts along the same path, demonstrating a maximal quantum advantage by combining a coherent state with squeezed vacuum. Further classical modeling with up to 100 phases shows linear scaling potential for adding nodes to the sensor. The approach can be applied to remote sensing, geophysical surveying, and infrastructure monitoring.

关键词： Interferometers

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Neurocognitive assessment under various human-robot teaming environments

Neurocognitive assessment under various human-robot teaming ...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Anna L. Packy Jayesh Jayahankar Arya Teymourlouei Joshua Stone Hyuk Oh Garrett E. Katz James A. Reggia Rodolphe J. Gentili Neuroscience and Cognitive Science Program University of Maryland College Park USA Department of Computer Science University of Maryland College Park USA Department of Electrical & Computer Engineering University of Maryland College Park USA Department of Kinesiology University of Maryland College Park USA Department of Electrical Engineering & Computer Science Syracuse University Syracuse USA

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Human-robot teaming has become increasingly important with the advent of intelligent machines. Prior efforts suggest that performance, mental workload, and trust are critical elements of human-robot dynamics that can be altered by the robot’s behavior. Most prior human-robot teaming studies used behavioral analyses, but a limited number used neural markers, without the use of physical robots and complex tasks. Here we combine behavioral and EEG cortical dynamics to examine cognitive-motor processes when individuals complete a complex task under various team environments with a robot. The results revealed that altering the robot quality affected both behavioral and EEG dynamics. Task completion with an experienced robot led to greater team performance and human trust along with lower mental workload compared to an inexperienced teammate or when individuals performed alone. EEG changes suggest that different attentional processes were engaged when humans worked with the robot and performed alone, and that visual processing was more prominent when teaming with an inexperienced teammate. This work can inform human cognitive-motor processes and the design of robotic controllers in human-robot teams.

关键词： Visualization Process control Humanoid robots Collaboration Human factors Control systems Electroencephalography Behavioral sciences engineering in medicine and biology Robots

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Motion tracked 3D Visualization system for Segmented ROI in medical imaging 2021

Motion tracked 3D Visualization system for Segmented ROI in ...

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7th International Conference on Arab Women in Computing, ArabWIC 2021

作者： Yaghi, Maha Zia, Huma Hajjdiab, Hassan El-Baz, Ayman Ghazal, Mohammed Electrical Computer and Biomedical Engineering Department Abu Dhabi University United Arab Emirates Computer Science Department Concordia University Bioengineering Department University of Louisville

ISBN: (纸本)9781450384186

computer-aided diagnosis systems are increasingly used in the detection and segmentation of abnormalities in medical imaging. However, in many borderline cases, radiologists and physicians need to analyze the images to confirm the diagnosis. This paper proposes a novel analysis system for visualizing the output of 3D segmented abnormalities and the associated computer diagnoses. We use motion trackers to localize the user's viewing device and estimate its 3D orientation. We then use this tracking data to slice the segmented and labeled volume showing the abnormalities in real-time. The user can switch between the labeled and raw views to analyze the abnormalities' boundaries. To test our visualization, we use our brain tumor segmentation pipeline using MRI scans to produce a labeled image and slice and visualize the tumor from all angles. Our system has a low latency of 0.01 seconds and can identify the viewing device's 3D location and orientation within a maximum of 0.2 cm and 2 degrees of error, respectively. The accuracy of our system depends on the segmentation pipeline. Our proposed system is also useful for medical training and as a visual feedback tool for computer-aided system's researchers and developers. © 2021 Association for Computing Machinery.

关键词： Magnetic resonance imaging

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Label-free hyperspectral multiphoton microscopy

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Optics Letters 2025年第5期50卷 1484-1487页

作者： De La Cadena, Alejandro Renteria, Carlos A. Aksamitiene, Edita Boppart, Stephen A. Beckman Institute for Advanced Science and Technology University of Illinois Urbana-Champaign UrbanaIL61801 United States Department of Bioengineering University of Illinois Urbana-Champaign UrbanaIL61801 United States Department of Electrical and Computer Engineering University of Illinois Urbana-Champaign UrbanaIL61801 United States Interdisciplinary Health Science Institute University of Illinois Urbana-Champaign UrbanaIL61801 United States University of Illinois Urbana-Champaign NIH/NIBIB P41 Center for Label-Free Imaging and Multiscale Biophotonics United States

Label-free multiphoton microscopy is a powerful tool for investigating pristine biological specimens. This imaging modality leverages optical signals originating from the nonlinear response of native biomolecules to intense optical radiation, nonlinear signals that allow localizing and quantifying the constituents of specimens, driving applications in biology and medicine. However, since its inception over three decades ago, this approach has operated with a narrowband detection scheme, relying on narrow bandwidths from the entire spectra to derive imaging contrast. This detection scheme hinders the analytical power of the nonlinear microscope, preventing the rigorous unmixing of co-localized constituents with spectral overlap. In this Letter, we shift from the narrowband paradigm to the broadband, demonstrating label-free hyperspectral multiphoton imaging of biological specimens. We validate this configuration by disentangling the constituents of fresh murine tissues by virtue of nonlinear spectra. © 2025 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.

关键词： Nonlinear optics

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The Effect of Physical Structural Properties on Electrochemical Properties of Ruthenium Oxide for Neural Stimulating and Recording Electrodes 46

The Effect of Physical Structural Properties on Electrochemi...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Wu, Yupeng Hernández, Miguel Figueroa Lei, Tian Jayakumar, Siddarth Lalapet, Rohan R. Joshi-Imre, Alexandra Orazem, Mark E. Otto, Kevin J. Cogan, Stuart F. The University of Texas at Dallas Department of Material Science Engineering RichardsonTX United States The University of Texas at Dallas Department of Bioengineering RichardsonTX United States The University of Texas at Dallas Department of Electrical & Computer Engineering RichardsonTX United States The University of Texas at Dallas Office of Research and Innovation RichardsonTX United States University of Florida Department of Chemical Engineering GainesvilleFL United States University of Florida J. Crayton Pruitt Family Department of Biomedical Engineering GainesvilleFL United States

ISBN: (纸本)9798350371499

Recently, there has been a growing interest in ruthenium oxide (RuOx) as an alternative mixed-conductor oxide to SIROF as an electrode coating. RuOx is recognized as a faradic charge-injection coating with high CSCc, long-term pulsing stability, and low impedance. We examined how the structural properties of sputter-deposited RuOx influence its electrochemical performance as an electrode coating for neural stimulation and recording. Thin film RuOx was deposited under various pressures: 5 mTorr, 15 mTorr, 30 mTorr, and 60 mTorr on wafer-based planar test structures. Electrochemical characterizations, including electrochemical impedance spectroscopy (EIS), cyclic voltammetry (CV), and voltage transient (VT), were employed. The structure of RuOx films was characterized by scanning electron microscope (SEM). Our findings revealed that the sputtering pressure significantly influences the growth of the RuOx film, subsequently affecting its electrochemical performance. The results indicate that the electrochemical performance of RuOx can be optimized by adjusting the deposition conditions to achieve a favorable balance between electronic and ionic *** Relevance - This research underscores the potential for optimizing the structural properties of RuOx to enhance its electrochemical capabilities for neural stimulation and recording. © 2024 IEEE.

关键词： Cyclic voltammetry

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Sum-Rate Maximization for Intelligent Reflecting Surface Assisted MIMO SWIPT Systems 6

Sum-Rate Maximization for Intelligent Reflecting Surface Ass...

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6th IEEE Symposium on Future Telecommunication Technologies, SOFTT 2022

作者： Liong, H.J. Chiong, C.W.R. Gopal, Lenin Juwono, Filbert H. Curtin University Malaysia Department of Electrical and Computer Engineering Miri Sarawak98009 Malaysia University of Southampton Malaysia Eko Galleria Department of Electrical and Electronic Engineering Nusajaya Johor79100 Malaysia University of Southampton Malaysia Eko Galleria Computer Science Program Nusajaya Johor79100 Malaysia

ISBN: (纸本)9781665455978

Intelligent reflecting surface (IRS) has recently stimulated an upsurge of research interest due to its capability of enhancing the spectral and energy efficiencies for future sixth generation (6G) wireless communication systems. This motivates the integration of IRS into simultaneous wireless information and power transfer (SWIPT) systems to improve the performance of energy harvesting and information de-coding. To unleash the full potential of IRS and achieve high sum-rate, the optimal joint transmitter precoding and reflective beamforming design is crucial yet challenging to solve. The issue has not been well investigated and the solution remains elusive. Hence, this research aims to jointly optimize the transmitter precoding and reflective beamforming matrices for IRS-Assisted multi-user multiple-input multiple-output (MIMO) SWIPT systems under quality of service (QoS) constraints. Simulation results demonstrate the effectiveness of IRS in enhancing the system performance. © 2022 IEEE.

关键词： Beamforming

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Analysis of Gait Data Based on Deep Learning to Predict Multiple Balance Scale Scores

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IEEE Sensors Journal 2024年第1期24卷 920-930页

作者： Lin, Chueh-Ho Peng, Chih-Wei Lee, I-Jung Wu, Chi-Ming Lin, Bor-Shing Taipei Medical University International Ph.D. Program in Gerontology and Long-Term Care Taipei City110 Taiwan Taipei Medical University Research Center in Nursing Clinical Practice Wan Fang Hospital Taipei City110 Taiwan Taipei Medical University School of Biomedical Engineering College of Biomedical Engineering Taipei City110 Taiwan Taipei Medical University School of Gerontology and Long-Term Care College of Nursing Taipei City110 Taiwan National Taipei University Department of Computer Science and Information Engineering New Taipei City237 Taiwan National Taipei University College of Electrical Engineering and Computer Science New Taipei City237 Taiwan

Unbalanced walking is increasingly common among older adults;therefore, routinely assessing the balance of older adults is crucial. The traditional method of assessing balance uses scales, requires the supervision of a physical therapist (PT), and is time-consuming. The present study, therefore, proposes a deep learning (DL) model architecture that combines a convolutional neural network (CNN) and a long short-term memory (LSTM) network model to predict the scores of three scales, namely the Berg balance scale (BBS), timed up and go (TUG), and single-leg stance (SLS). The gait data of 15 m of walking were collected from seven inertial measurement units (IMUs) and input into the CNN-LSTM model for evaluation. The BBS and TUG only require the participants to wear two IMUs on the left and right thighs, respectively, for accurate predictions. The mean absolute errors (MAEs) of predicting the scores of the BBS and TUG are 1.2562 and 1.4016 s, respectively;however, the MAE of score predictions of SLS is higher than that of the BBS and TUG, indicating that gait data cannot be used for assessing SLS;moreover, participants only wear one IMU on the right calf for BBS and TUG evaluations, which yield MAEs of 1.4334 and 1.5229 s, respectively. The proposed system can quickly and accurately predict the scores of the BBS and TUG. The proposed model can assist PTs with making clinical decisions. © 2001-2012 IEEE.

关键词： Long short-term memory

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Improving Candle Direction Classification in Forex Market Using Support Vector Machine with Hyperparameters Tuning 7

Improving Candle Direction Classification in Forex Market Us...

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7th International Conference on Informatics and Computing, ICIC 2022

作者： Oetama, Raymond Sunardi Heryadi, Yaya Lukas Suparta, Wayan Computer Science Bina Nusantara University Jakarta Indonesia Binus Graduate Program Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia Cognitive Engineering Research Group Universitas Katolik Indonesia Atma Jaya Jakarta Indonesia Institut Teknologi Nasional Faculty of Industrial Technology Department of Electrical Engineering Yogyakarta Indonesia

ISBN: (纸本)9798350345711

Foreign Exchange market is the world's largest daily currency turnover. Two of the popular currencies Euro and Pound sterling traded against the US Dollar. Since the Russia and Ukraine war started in February 2022, their exchange rates decrease to the lowest rate ever. Even though the general trend is bearish, several daily candles increase for some days making challenges for forex analysts. To solve this problem, classification is applied. The data is labeled downward and upward. By utilizing Linear Kernel and Radial Basis Function (RBF) Kernel-based Support Vector Machines (SVM), the candle direction can be classified and optimized by tuning the Hyperparameters. The accuracy of candle direction classifications are highly improved. After tuning, in general, classification using Linear Models can outperform RBF Models. The best accuracy found on the Pound sterling against US Dollar by using the Linear model is 98.11% and the accuracy becomes 100% on data testing at a ratio of 70:30. Whilst for the Euro against the US Dollar, the best accuracy found the same for both Linear and RBF models on a ratio of 80:20 at 97.53%. However, on data testing, it decreases to 94.51% for Linear Model and 93.41% using RBF Model. The implication of this study is SVM can successfully classify candle direction on pairs in the Forex Market that are affected by a big event that comes for such a long period as long as the hyperparameter is tuned. © 2022 IEEE.

关键词： Support vector machines

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