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1st International Conference on Advanced engineering and Technologies, ICONNIC 2023

作者： Qasim, Osama A. Mahdi, Dania Mohammed Noori, Mohammed S. Ali, Rabei Raad Sari, Christy Atika Rachmawanto, Eko Hari Northern Technical University Department of Computer Engineering Technology Mosul Iraq Uniersiti Tenaga Nasional Department of Electrical and Electronics Engineering Selangor Malaysia University of Dian Nuswantoro Study Program in Informatics Engineering Semarang Indonesia

ISBN: (纸本)9798350306484

At present, ad hoc networks are getting a lot of attention, because they have many features that differ from the rest of the networks and because they are technically advanced. In routing protocols, the Vehicular Ad-hoc network (VANET) differ in performance because each protocol has different variables, such as variable density, speed, and traffic scenarios. In this work, we proposed topology-based routing namely Ad hoc On Demand Distance Vector (AODV) protocol. The AODV protocol has been simulated in case of the movement of vehicles at a fixed velocity at 60km/h, and in case of randomly changing the vehicle's locations in the network. There are three parameters used to investigate the performance of the proposed AODV protocol which are packet delivery ratio, and overheads. The Simulation results show a significant decrease from 0.55 to 0.82 in the packet delivery ratio. On the other hand, the performance of the AODV protocol demonstrates effective communication for the VANETs. © 2023 IEEE.

关键词： Vehicular ad hoc networks

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High Speed Miniaturized Multiphoton Microscopy with Elliptical Beam Excitation

High Speed Miniaturized Multiphoton Microscopy with Elliptic...

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Optics and the Brain, BRAIN 2023 - Part of Optica Biophotonics Congress: Optics in the Life Sciences 2023

作者： Mattison, Ben Liu, Shing-Jiuan Tian, Feng Yang, Weijian Department of Biomedical Engineering University of California Davis DavisCA95616 United States Department of Electrical and Computer Engineering University of California Davis DavisCA95616 United States Biomedical Engineering Graduate Group University of California Davis DavisCA95616 United States

We demonstrate elliptical beam shaping in a miniaturized two-photon microscope enabling cellular resolution fluorescence imaging at over three times faster frame rate than point scanning over a 400 µm diameter FO... 详细信息

ISBN: (纸本)9781957171210

关键词： Beam shaping CMOS cameras Cylindrical lenses Hollow core fibers Laser scanning Multiphoton microscopy

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A Realistic Surgical Simulator for Non-Rigid and Contact-Rich Manipulation in Surgeries with the da Vinci Research Kit

A Realistic Surgical Simulator for Non-Rigid and Contact-Ric...

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International Conference on Ubiquitous Robots (UR)

作者： Yafei Ou Sadra Zargarzadeh Paniz Sedighi Mahdi Tavakoli Department of Electrical and Computer Engineering University of Alberta Edmonton Alberta Canada Department of Electrical and Computer Engineering and the Department of Biomedical Engineering University of Alberta Edmonton Alberta Canada

ISBN: (数字)9798350361070

ISBN: (纸本)9798350361087

Realistic real-time surgical simulators play an increasingly important role in surgical robotics research, such as surgical robot learning and automation, and surgical skills assessment. Although there are a number of existing surgical simulators for research, they generally lack the ability to simulate the diverse types of objects and contact-rich manipulation tasks typically present in surgeries, such as tissue cutting and blood suction. In this work, we introduce CRESSim, a realistic surgical simulator based on PhysX 5 for the da Vinci Research Kit (dVRK) that enables simulating various contact-rich surgical tasks involving different surgical instruments, soft tissue, and body fluids. The real-world dVRK console and the master tool manipulator (MTM) robots are incorporated into the system to allow for teleoperation through virtual reality (VR). To showcase the advantages and potentials of the simulator, we present three examples of surgical tasks, including tissue grasping and deformation, blood suction, and tissue cutting. These tasks are performed using the simulated surgical instruments, including the large needle driver, suction irrigator, and curved scissor, through VR-based teleoperation.

关键词： Medical robotics Fluids Deformation Instruments Surgery Grasping Virtual reality

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Efficient pumping of atomic frequency combs in a Tm3+:YAG crystal for broadband quantum optical storage

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Physical Review Applied 2025年第5期23卷 054076-054076页

作者： Yisheng Lei Zongfeng Li Mahdi Hosseini Department of Electrical and Computer Engineering and Applied Physics Program Northwestern University Evanston Illinois 60208 USA Elmore Family School of Electrical and Computer Engineering Purdue University West Lafayette Indiana 47907 USA

Quantum memory devices with high storage efficiency and bandwidth are essential elements for future quantum networks. Here, we report a storage efficiency greater than 28% in a Tm3+:YAG crystal (where YAG is yttrium aluminum garnet) at elevated temperatures, achieving this milestone without relying on optical cavities and without compromising memory bandwidth. We introduce different pumping techniques for Tm-based memories that enable multifrequency-window storage and high memory bandwidth reaching 630 MHz, significantly surpassing previous Tm-based memory demonstrations, considering the operating temperature of 3.5 K. Furthermore, we propose a general method for large-bandwidth atomic frequency memory using non-Kramers rare-earth ions (REIs) in solids, paving the way for storage efficiencies and bandwidths approaching fundamental limits. The compatibility of Tm memories with neutral-atom quantum processors and their seamless integration with scalable photonic platforms, such as lithium niobate on insulator, underscore their potential for distributed quantum computing, hybrid quantum networks, and space-based quantum communication. Our study represents a significant advancement toward practical high-performance quantum memories based on REI-doped crystals, where we predict that gigahertz bandwidth and storage efficiencies exceeding 30% are achievable at lower temperatures.

关键词： Qubits

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Development of an Adaptive Artifact Subspace Reconstruction Based on Hebbian/Anti-Hebbian Learning Networks for Enhancing BCI Performance

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IEEE Transactions on Neural Networks and Learning Systems 2024年第1期35卷 348-361页

作者： Tsai, Bo-Yu Diddi, Sandeep Vara Sankar Ko, Li-Wei Wang, Shuu-Jiun Chang, Chi-Yuan Jung, Tzyy-Ping Institute of Biomedical Engineering and the Brain Research Center National Yang Ming Chiao Tung University Hsinchu300 Taiwan Hsinchu300 Taiwan College of Biological Science and Technology National Yang Ming Chiao Tung University Hsinchu300 Taiwan Institute of Electrical and Control Engineering Institute of Biomedical Engineering Brain Research Center College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Department of Electronics and Electrical Engineering Hsinchu300 Taiwan Kaohsiung Medical University Department of Biomedical Science and Environment Biology Drug Development and Value Creation Research Center Kaohsiung807 Taiwan Taipei Veterans General Hospital Taipei11217 Taiwan National Yang Ming Chiao Tung University Brain Research Center School of Medicine Taipei112 Taiwan University of California San Diego Swartz Center for Computational Neuroscience San DiegoCA92093 United States

Brain-computer interface (BCI) actively translates the brain signals into executable actions by establishing direct communication between the human brain and external devices. Recording brain activity through electroencephalography (EEG) is generally contaminated with both physiological and nonphysiological artifacts, which significantly hinders the BCI performance. Artifact subspace reconstruction (ASR) is a well-known statistical technique that automatically removes artifact components by determining the rejection threshold based on the initial reference EEG segment in multichannel EEG recordings. In real-world applications, the fixed threshold may limit the efficacy of the artifact correction, especially when the quality of the reference data is poor. This study proposes an adaptive online ASR technique by integrating the Hebbian/anti-Hebbian neural networks into the ASR algorithm, namely, principle subspace projection ASR (PSP-ASR) and principal subspace whitening ASR (PSW-ASR) that segmentwise self-organize the artifact subspace by updating the synaptic weights according to the Hebbian and anti-Hebbian learning rules. The effectiveness of the proposed algorithm is compared to the conventional ASR approaches on benchmark EEG dataset and three BCI frameworks, including steady-state visual evoked potential (SSVEP), rapid serial visual presentation (RSVP), and motor imagery (MI) by evaluating the root-mean-square error (RMSE), the signal-to-noise ratio (SNR), the Pearson correlation, and classification accuracy. The results demonstrated that the PSW-ASR algorithm effectively removed the EEG artifacts and retained the activity-specific brain signals compared to the PSP-ASR, standard ASR (Init-ASR), and moving-window ASR (MW-ASR) methods, thereby enhancing the SSVEP, RSVP, and MI BCI performances. Finally, our empirical results from the PSW-ASR algorithm suggested the choice of an aggressive cutoff range of c =1-10 for activity-specific BCI applications and a moderat

关键词： Electroencephalography

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Mutual Coupling Reduction of Miniaturized MM-wave ME-dipole Antennas using SRR Superstrate

Mutual Coupling Reduction of Miniaturized MM-wave ME-dipole ...

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2022 United States National Committee of URSI National Radio Science Meeting, USNC-URSI NRSM 2022

作者： Kakhki, Mehri Borhani Denidni, Tayeb A. Dadgarpour, Abdolmehdi Sebak, Abdel-Razik Antoniades, Marco A. University of Quebec Montréal Canada Concordia University Department of Electrical and Computer Engineering Montréal Canada Ryerson University Department of Electrical Computer & Biomedical Engineering Toronto Canada

ISBN: (数字)9781946815156

ISBN: (纸本)9781946815156

In this paper, the effect of Split Ring Resonator (SRR) loading on mutual coupling reduction of Magneto Electric (ME)-dipole antennas fed through printed ridge gap waveguide (PRGW) is presented for millimeter-wave 5G applications. A miniaturized ME-dipole antenna with a -10 dB impedance bandwidth of 27 to 37 GHz and a peak gain of 10 dBi at 33 GHz is designed as the elementary antenna. As a proof of concept, a two-port ME-dipole antenna with half-wavelength inter-element spacing is loaded with a single SRR layer in the elevation plane. The simulated results demonstrate that the SRR superstrate reduces the mutual coupling by up to 12 dB while maintaining the impedance bandwidth of the antenna. Moreover, the realized gain of the antenna is improved in the upper bandwidth by up to 2.4 dBi due to the SRR's phase compensation on the antenna aperture. © 2022 USNC-URSI.

关键词： Bandwidth

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Virtual Staining,Segmentation,and Classification of Blood Smears for Label-Free Hematology Analysis

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biomedical engineering Frontiers 2022年第1期3卷 309-322页

作者： Nischita Kaza Ashkan Ojaghi Francisco E.Robles School of Electrical and Computer Engineering Georgia Institute of TechnologyAtlantaGeorgiaUSA Wallace H.Coulter Department of Biomedical Engineering Georgia Institute of Technology and Emory UniversityAtlantaGeorgiaUSA

Objective and Impact *** present a fully automated hematological analysis framework based on single-channel(single-wavelength),label-free deep-ultraviolet(UV)microscopy that serves as a fast,cost-effective alternative to conventional hematology *** analysis is essential for the diagnosis and monitoring of several diseases but requires complex systems operated by trained personnel,costly chemical reagents,and lengthy ***-free techniques eliminate the need for staining or additional preprocessing and can lead to faster analysis and a simpler *** this work,we leverage the unique capabilities of deep-UV microscopy as a label-free,molecular imaging technique to develop a deep learning-based pipeline that enables virtual staining,segmentation,classification,and counting of white blood cells(WBCs)in single-channel images of peripheral blood *** train independent deep networks to virtually stain and segment grayscale images of *** segmented images are then used to train a classifier to yield a quantitative five-part WBC *** virtual staining scheme accurately recapitulates the appearance of cells under conventional Giemsa staining,the gold standard in *** trained cellular and nuclear segmentation networks achieve high accuracy,and the classifier can achieve a quantitative five-part differential on unseen test *** proposed automated hematology analysis framework could greatly simplify and improve current complete blood count and blood smear analysis and lead to the development of a simple,fast,and low-cost,point-of-care hematology analyzer.

关键词： networks classifier Blood

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Inductively-Coupled MEMS Pressure Sensor

Inductively-Coupled MEMS Pressure Sensor

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2023 IEEE biomedical Circuits and Systems Conference, BioCAS 2023

作者： Patel, Sneh Darbasi, Sima Antoniades, Marco A. Valente, Virgilio Computer and Biomedical Engineering Toronto Metropolitan University Department of Electrical TorontoM5B 2K3 Canada

ISBN: (纸本)9798350300260

This research paper introduces a novel design of an inductive sensor based on a MEMS (Micro-Electro-Mechanical System) inductive link. The sensor comprises two identical Al inductor coils micromachined on each side of a thinned silicon wafer. The sensing coil is suspended over a 30μm cavity etched in the silicon wafer. The sensor was designed, modeled, and simulated using the ANSYS Electronics 3D electromagnetic High-Frequency Simulation Software (HFSS). Simulated results showed that the impedance of the primary coil varied from approximately 2310 Ω to 2775 Ω over a deflection range of 20 μm, indicating an average sensitivity of 23.25 Ω/μm. © 2023 IEEE.

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Jointly Optimized Spatial Histogram UNET Architecture(JOSHUA)for Adipose Tissue Segmentation

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biomedical engineering Frontiers 2022年第1期3卷 248-261页

作者： Joshua K.Peeples Julie F.Jameson Nisha M.Kotta Jonathan M.Grasman Whitney L.Stoppe Alina Zare Department of Electrical and Computer Engineering University of FloridaGainesvilleFL 32611USA Department of Chemical Engineering University of FloridaGainesvilleFL 32611USA J.Crayton Pruitt Family Department of Biomedical Engineering University of FloridaGainesvilleFL 32611USA Department of Biomedical Engineering New Jersey Institute of TechnologyUniversity HeightsNewarkNJ 07102USA

*** aim to develop a machine learning algorithm to quantify adipose tissue deposition at surgical sites as a function of biomaterial *** *** our knowledge,this study is the first investigation to apply convolutional neural network(CNN)models to identify and segment adipose tissue in histological images from silk fibroin biomaterial *** designing biomaterials for the treatment of various soft tissue injuries and diseases,one must consider the extent of adipose tissue *** this work,we analyzed adipose tissue accumulation in histological images of sectioned silk fibroin-based biomaterials excised from rodents following subcutaneous implantation for 1,2,4,or 8 *** strategies for quantifying adipose tissue after biomaterial implantation are often tedious and prone to human bias during *** used CNN models with novel spatial histogram layer(s)that can more accurately identify and segment regions of adipose tissue in hematoxylin and eosin(H&E)and Masson’s trichrome stained images,allowing for determination of the optimal biomaterial *** compared the method,Jointly Optimized Spatial Histogram UNET Architecture(JOSHUA),to the baseline UNET model and an extension of the baseline model,attention UNET,as well as to versions of the models with a supplemental attention-inspired mechanism(JOSHUA+and UNET+).*** inclusion of histogram layer(s)in our models shows improved performance through qualitative and quantitative *** results demonstrate that the proposed methods,JOSHUA and JOSHUA+,are highly beneficial for adipose tissue identification and *** new histological dataset and code used in our experiments are publicly available.

关键词： injuries implantation Spatial

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A Multimodal MRI-based Framework for Thyroid Cancer Diagnosis Using eXplainable Machine Learning 27th

A Multimodal MRI-based Framework for Thyroid Cancer Diagnosi...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Sharafeldeen, Ahmed Balaha, Hossam Magdy Mahmoud, Ali Khaled, Reem Taman, Saher Hussein, Manar Mansour Ghazal, Mohammed El-Baz, Ayman Bioengineering Department J.B. Speed School of Engineering University of Louisville LouisvilleKY United States Department of Radiology Faculty of Medicine Mansoura University Mansoura Egypt Electrical Computer and Biomedical Engineering Department Abu Dhabi University Abu Dhabi United Arab Emirates

ISBN: (纸本)9783031783975

Diagnosing thyroid cancer is notably challenging because of its diverse manifestations and the rising number of cases worldwide. Early detection and diagnosis of thyroid nodules’ malignancy is crucial for reducing their progression. This paper introduces a novel computer-aided diagnosis (CAD) system that utilizes T2 and diffusion-weighted (DWI) magnetic resonance imaging (MRI) modalities to help diagnose thyroid cancer. First, the thyroid nodules are delineated from T2 and DWI modalities. Then, various features are extracted from these nodules, such as first order statistics (FOS), gray level co-occurrence matrix (GLCM), and gray level run length matrix (GLRLM), to capture texture and spatial information. To improve both the performance and interpretability of the model, outlier detection methods, such as the cluster-based local outlier factor (CBLOF), are utilized to identify deviations in the data. Finally, the extracted features from T2 and DWI modalities are fed into a multilayer perceptron (MLP) and LightGBM (LGBM) classifiers, respectively. Subsequently, the classifiers’ outputs are integrated using a majority fusion approach for final diagnosis. The proposed system is evaluated on 55 thyroid nodule patients using a 10-folds cross-validation approach, achieving an accuracy of 99.48%. The reported results, based on integrating decisions from each MRI modality using a majority fusion approach, clearly demonstrate the effectiveness of the proposed framework compared to the performance of well-known pretrained convolutional neural networks (CNNs). © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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