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Information Communication and Software engineering (ICICSE), IEEE International Conference on

作者： Jian Liu Shaohua Zhang Lai Wei Ruoting Xiong Wei Ren China Electronic Product Reliability Environmental Testing Research Institute Guangzhou China China Construction Eighth Engineering Division Shanghai China Department of Computing Science University of East Anglia Norfolk UK School of Computer Science China University of Geosciences Wuhan China Anhui Engineering Research Center of Intelligent Perception and Elderly Care Chuzhou University Chuzhou China

ISBN: (数字)9798350351699

ISBN: (纸本)9798350351705

Blockchain provides an publicly accessed and accepted ledger without centralized control among open participators. The anonymity in payments and transactions may results in possible misbehaviors, e.g., money laundry. Fortunately, the transaction data is open and can be analyzed. In this paper, we propose The major challenges are the data volume is large and the global view of transactions in terms of hops and time sequences. In this paper, we address the transactions with multiple intermediators, and graph dynamics with time-spans. The analyzing models and computing algorithms are proposed. The extensive and formal analysis justifies the soundness of our proposed methods.

关键词： Analytical models Computational modeling Heuristic algorithms Data models Blockchains Iterative methods Software engineering

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Digital Smart Structure: engineering a Sensor-to-Cloud Structural Health Monitoring Pilot

Digital Smart Structure: Engineering a Sensor-to-Cloud Struc...

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IEEE International Workshop on Measurements and Networking Proceedings (M&N)

作者： Ivan Zyrianoff Mattia Forlesi Lorenzo Gigli Soroosh Kamali Nicola Testoni Canio Mennuti Giuseppe Augugliaro Luca De Marchi Alessandro Marzani Luciano Bononi Marco Di Felice Department of Computer Science and Engineering University of Bologna Italy Advanced Research Center on Electronic Systems “Ercole De Castro” University of Bologna Italy Technological Innovations Department INAIL Roma Italy Department of Civil Chemical Environmental and Materials Engineering University of Bologna Bologna Italy

ISBN: (数字)9798350370539

ISBN: (纸本)9798350370546

Real-time monitoring of civil infrastructures is of great importance to ensure their integrity and safety. Achieving these goals requires a strong synergy between multiple tools, disciplines, and approaches, which can be realized through a joint hardware-software co-design of the different components based on the Internet of Things (IoT) paradigm. Deploying IoT-based systems for bridge monitoring presents many challenges, including difficulties in deploying sensors in real-world testbeds, the lack of reliability of IoT devices, constant network disconnections, power outages, and challenges in rapid on-site maintenance. To address these challenges, we propose an IoT-SHM architecture that incorporates mechanisms to ensure robustness across system infrastructure, software components, data flow, and data quality. Our system was deployed on the Volto Santo bridge in Naples, where it successfully collected data for over six months, capturing more than 3000 acoustic emission events and more than 250 million accelerometer data points. Despite several interruptions due to environmental hazards, the system was able to automatically restart data collection. Additionally, we present the specifics of the testbed and a processing of the collected data based on operational modal analysis (OMA).

关键词： Accelerometers Data integrity Bridge circuits Acoustic emission Software Robustness Sensors

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Tracking cellular and subcellular dynamics with quantitative oblique back-illumination microscopy 10

Tracking cellular and subcellular dynamics with quantitative...

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Quantitative Phase Imaging X 2024

作者： Filan, Caroline E. Bharadwaj, Srinidhi Casteleiro Costa, Paloma Davarzani, Amin Cappabianca, Dan Tommasi, Anna Sarko, Lauren La Vonne Denne, Nina Mao, Leidong Saha, Krishanu Karumbaiah, Lohitash Robles, Francisco E. George W. Woodruff School of Mechanical Engineering Georgia Institute of Technology 801 Ferst Dr. NW AtlantaGA United States Wallace H. Coulter Dept. of Biomedical Engineering Georgia Institute of Technology and Emory University 313 Ferst Dr. NW AtlantaGA United States School of Electrical and Computer Engineering Georgia Institute of Technology 266 Ferst Drive AtlantaGA United States School of Electrical and Computer Engineering College of Engineering University of Georgia AthensGA United States Regenerative Bioscience Center University of Georgia AthensGA United States Department of Biomedical Engineering University of Wisconsin-Madison MadisonWI United States Wisconsin Institute for Discovery University of Wisconsin-Madison MadisonWI United States Division of Neuroscience Biomedical and Health Sciences Institute University of Georgia AthensGA United States Edgar L. Rhodes Center for Animal and Dairy Science College of Agriculture and Environmental Science University of Georgia AthensGA United States

ISBN: (纸本)9781510669635

Quantitative oblique back-illumination microscopy (qOBM) is a label-free imaging technique that enables tomographic phase imaging of thick scattering samples with epi-illumination. Here, we propose the use of two forms of functional imaging with qOBM to study tissue and cell cultures. In doing so, we obtain the spatiotemporal and quantitative functional information associated with the phase values extrapolated from qOBM imaging. We have applied this process to study the efficacy of individual immune T cells to kill glioblastoma spheroid cultures in 3D spheroids. Data show that we can effectively distinguish between cell phenotypes and characterize the dynamic motion of these cells in 3D cultures. This work offers a distinct advantage in tracking 3D cellular dynamics in thick tissue as many function imaging modalities are limited to 2D samples. Further, this technology can be expanded to analyze a wide variety of cellular and subcellular dynamics non-invasively in thick tissue. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Tissue

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Meter-scale variation within a single transect demands attention to taxon accumulation curves in riverine microbiome studies

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Frontiers of environmental Science & engineering 2022年第5期16卷 115-125页

作者： Bingdi Liu Lin Zhang Jason H.Knouft Fangqiong Ling Department of Energy Environmental and Chemical EngineeringWashington University in St.LouisSt.LouisMO 63130USA Department of Biology Saint Louis UniversitySt.LouisMO 63103USA National Great Rivers Research and Education Center East AltonMO 62035USA Department of Computer Science and Engineering Washington University in St.LouisSt.LouisMO 63130USA Division of Biological and Biomedical Sciences Washington University in St.LouisSt.LouisMO 63110USA Division of Computational and Data Science Washington University in St.LouisSt.LouisMO 63130USA

Microbial communities inhabiting river ecosystems play crucial roles in global biogeochemical cycling and pollution *** variations in local microbial assemblages are important for detailed understanding of community assembly and developing robust biodiversity sampling ***,we intensely analyzed twenty water samples collected from a one-meter spaced transect from the near-shore to the near-center in the Meramec River in eastern Missouri,USA and examined the microbial community composition with 16S rRNA gene amplicon *** microbiomes across the transect exhibited extremely high similarity,with Pearson’s correlation coefficients above 0.9 for all pairwise community composition ***,despite the high similarity,PERMANOVA revealed significant spatial differences between near-shore and nearcenter communities(p=0.001).Sloan’s neutral model simulations revealed that within-transect community composition variation was largely explained by demographic stochasticity(R^(2)=0.89).Despite being primarily explained by neutral processes,LefSe analyses also revealed taxa from ten families of which relative abundances differed directionally from the bank to the river center,indicating an additional role of environmental ***,the local variations within a river transect can have profound impacts on the documentation of alpha ***-accumulation curves indicated that even twenty samples did not fully saturate the sampling effort at the genus level,yet four,six and seven samples were able to capture 80%of the phylum-level,family-level,and genus-level diversity,*** study for the first time reveals hyperlocal variations in riverine microbiomes and their assembly mechanisms,demanding attention to more robust sampling strategies for documenting microbial diversity in riverine systems.

关键词： Microbiome Freshwater Taxon accumulation curve Community assembly

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Ultrathin-shell epitaxial Ag@Au core-shell nanowires for high-performance and chemically-stable electronic, optical, and mechanical devices

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Nano Research 2021年第11期14卷 4294-4303页

作者： Yangzhi Zhu Sanggon Kim Xuezhi Ma Peter Byrley Ning Yu Qiushi Liu Xiaoming Sun Da Xu Sangshan Peng Martin C.Hartel Shiming Zhang Vadim Jucaud Mehmet R.Dokmeci Ali Khademhosseini Ruoxue Yan Department of Chemical and Environmental Engineering Bourns College of EngineeringUniversity of California-RiversideRiversideCA92521USA Terasaki Institute for Biomedical Innovation Los AngelesCA90064USA Department of Electrical and Computer Engineering University of California-RiversideRiversideCA92521USA State Key Laboratory of Chemical Resource Engineering Beijing University of Chemical TechnologyBeijing100029China State Key Laboratory of Chemo/Biosensing and Chemometrics College of Chemistry and Chemical EngineeringHunan UniversityChangsha410082China Department of Bioengineering University of California-Los AngelesLos AngelesCA90095USA Department of Electrical and Electronic Engineering The University of Hong KongHong KongChina Material Science and Engineering Program University of California-RiversideRiversideCA92521USA

Silver nanowires (AgNWs) hold great promise for applications in wearable electronics, flexible solar cells, chemical and biological sensors, photonic/plasmonic circuits, and scanning probe microscopy (SPM) due to their unique plasmonic, mechanical, and electronic properties. However, the lifetime, reliability, and operating conditions of AgNW-based devices are significantly restricted by their poor chemical stability, limiting their commercial potentials. Therefore, it is crucial to create a reliable oxidation barrier on AgNWs that provides long-term chemical stability to various optical, electrical, and mechanical devices while maintaining their high performance. Here we report a room-temperature solution-phase approach to grow an ultra-thin, epitaxial gold coating on AgNWs to effectively shield the Ag surface from environmental oxidation. The Ag@Au core-shell nanowires (Ag@Au NWs) remain stable in air for over six months, under elevated temperature and humidity (80 °C and 100% humidity) for twelve weeks, in physiological buffer solutions for three weeks, and can survive overnight treatment of an oxidative solution (2% H2O2). The Ag@Au core-shell NWs demonstrated comparable performance as pristine AgNWs in various electronic, optical, and mechanical devices, such as transparent mesh electrodes, surface-enhanced Raman spectroscopy (SERS) substrates, plasmonic waveguides, plasmonic nanofocusing probes, and high-aspect-ratio, high-resolution atomic force microscopy (AFM) probes. These Au@Ag core-shell NWs offer a universal solution towards chemically-stable AgNW-based devices without compromising material property or device performance.

关键词： epitaxial growth core-shell nanowire plasmonic waveguides atomic force microscopy(AFM)probe transparent electrode wearable electronics

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Blending of Resolution-Jumping and Beamforming for Enhanced Tomographic Reconstruction

Blending of Resolution-Jumping and Beamforming for Enhanced ...

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International Conference on Intelligent Systems and Networks, ICISN 2024

作者： Theu, Luong Thi Huy, Tran Quang Phung, Viet Bac T. Nguyen, Tien-Anh Duc-Tan, Tran Hoa Binh University My Dinh Nam Tu Liem Hanoi100000 Viet Nam Faculty of Physics Hanoi Pedagogical University 2 Hanoi100000 Viet Nam Center for Environmental Intelligence and College of Engineering and Computer Science VinUniversity Hanoi100000 Viet Nam Department of Physics Le Quy Don Technical University 236 Hoang Quoc Viet Street Bac Tu Liem District Hanoi100000 Viet Nam Faculty of Electrical and Electronic Engineering Phenikaa University Hanoi100000 Viet Nam

ISBN: (纸本)9789819755035

Due to its widespread use, safety, low cost, capacity to provide real-time images and ongoing image quality improvements, ultrasound imaging is frequently chosen over other medical imaging modalities for diagnostic purposes. The inverse acoustic scattering issue was linearized to form the basis of ultrasonic tomography (UT). This makes it possible to detect small fluctuations in a reference medium. This work suggests employing resolution-jumping and beamforming techniques for tomographic ultrasound imaging. The beamformed technique employs multiple probe transmitting elements concurrently to generate a narrow beam for noise reduction, while also employing resolution-jumping to enhance the reconstruction’s quality and speed. Numerical simulation results demonstrate a substantial improvement in image recovery quality, along with a considerable reduction in total imaging runtime when the proposed method is applied. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： beamforming DBIM inverse scattering Resolution-jumping ultrasonic tomography

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Enhancing Spectral Efficiency in IoT Networks using Deep Deterministic Policy Gradient and Opportunistic NOMA

Enhancing Spectral Efficiency in IoT Networks using Deep Det...

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IEEE Conference on Vehicular Technology (VTC)

作者： Neha Mazhar Syed Asad Ullah Haejoon Jung Qurrat-Ul-Ain Nadeem Syed Ali Hassan School of Electrical Engineering & Computer Science (SEECS) National University of Sciences & Technology (NUST) Islamabad Pakistan Department of Electronic Engineering Kyung Hee University (KHU) Yongin South Korea Engineering Division New York University (NYU) Abu Dhabi Abu Dhabi United Arab Emirates NYU Tandon School of Engineering Brooklyn United States of America

ISBN: (数字)9798331517786

ISBN: (纸本)9798331517793

Amidst the ongoing debate about limited spectral availability, there remains a persistent demand for the development of spectrally efficient self-sustainable network (SSN) models. This paper addresses this challenge by optimizing spectral efficiency (SE) in uplink transmissions for an energy harvesting (EH)-enabled secondary user (SU) that operates opportunistically among multiple primary users (PUs) in an Internet-of-things (IoT) network. The PUs are assumed to employ a rotational time division multiple access (TDMA) scheme for transmissions, where the signals are divided into time slots for each PU to transmit data in a cyclic manner, while the SU uses an opportunistic non-orthogonal multiple access (NOMA) technique to transmit data without interfering with the PU transmissions, such that, at any given time slot, a PU and a SU share the same frequency band simultaneously. The SE of the system is maximized jointly by employing convex optimization and a deep reinforcement learning (DRL) model, specifically the deep deterministic policy gradient (DDPG) algorithm. Simulations demonstrate that the proposed approach significantly improves the SE of the considered IoT network, highlighting its potential for efficient spectrum management in IoT networks. We present a comprehensive SE analysis of the system, which further underscores the robustness and adaptability of our approach in optimizing SE under diverse operational conditions.

关键词： NOMA Time division multiple access Adaptation models Vehicular and wireless technologies Time-frequency analysis Spectral efficiency Benchmark testing Approximation algorithms Convex functions Uplink

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Automatic classification of 10 blood cell subtypes using transfer learning via pre-trained convolutional neural networks

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Informatics in Medicine Unlocked 2024年 49卷

作者： Asghar, Rabia Kumar, Sanjay Hynds, Paul Environmental Sustainability and Health Institute Technological University (TU) Dublin Ireland Department of Electronic and Computer Engineering University of Limerick Ireland Spatiotemporal Environmental Epidemiology Research (STEER) Group Technological University (TU) Dublin Ireland

Human blood is primarily composed of plasma, red blood cells, white blood cells, and platelets. It plays a vital role in transporting oxygen and nutrients to all organs, and stores essential health-related data about the human body. Blood cells are utilized to defend the body against infections and disease. Hence, analysis of blood permits physicians to assess an individual's physiological condition. Blood cells are sub-classified into eight groups: Neutrophils, eosinophils, basophils, lymphocytes, monocytes, immature granulocytes (promyelocytes, myelocytes, and metamyelocytes), erythroblasts, and platelets (thrombocytes) on the basis of their nucleus, shape and cytoplasm. Traditionally, pathologists and hematologists have identified and examined these via microscopy prior to manual classification, with this manual approach being slow and prone to human error. Therefore, it is essential to automate this process. In the current study, transfer learning with a series of pre-trained Convolutional Neural Network (CNN) models—VGG16, VGG19, ResNet-50, ResNet-101, ResNet-152, InceptionV3, MobileNetV2 and DenseNet-201 was applied to the normal peripheral blood cells dataset (PBC). The overall accuracy achieved with individual CNNs ranged from 91.4 % to 94.7 %. Based on these pre-trained architectures, a CNN-based architecture has been developed to automatically classify all ten blood cell types. The proposed transfer learning CNN model was tested on blood cell images from the PBC, Kaggle and LISC datasets. Achieved accuracy was 99.91 %, 99.68 % and 98.79 %, respectively, across these three datasets. The presented CNN architecture outperforms all previous results reported in the scientific/medical literature with a high capacity for framework generalization in future applications of blood cell classification. © 2024 The Authors

关键词： Autoimmune diseases Blood cell subtypes Classification Deep learning Feature extraction Image analyses Machine learning Pre-trained models

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OdCFU-Net for Brain Tumor Segmentation Based on MRI

OdCFU-Net for Brain Tumor Segmentation Based on MRI

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IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications

作者： Shang Wu Sijing Yang Boyue Li Shimin Yin Jianming Zhu Shiyong Li Zhencheng Chen Guilin University of Electronic Technology School of Life and Environmental Science Guilin China School of Computer and Information Security Guilin University of Electronic Science and Technology Guilin University of Electronic Technology School of Electronic Engineering and Automation Guilin China Guangxi Colleges and Universities Key Laboratory of Biomedical Sensors and Intelligent Instruments Guilin China Guangxi Engineering Technology Research Center of Human-Physiological Information Noninvasive Detection Guilin China

ISBN: (数字)9798350322996

ISBN: (纸本)9798350323009

In the field of medical image analysis, the accurate segmentation of brain tumors plays a crucial role in the diagnosis and treatment of brain-related diseases. This study aims to address key challenges in this domain, with a particular focus on improving feature extraction, information integration, and model generalization capabilities. By concurrently introducing multi-dimensional attention mechanisms in convolutional operations, a method has been developed to dynamically generate convolution kernels in spatial, input channel, and output channel dimensions. This approach optimizes the limitations of traditional static convolutional kernels, maintaining high-performance efficiency while reducing the model’s parameter count. To overcome the deficiencies of traditional convolutional networks in capturing global information, channel and spatial attention mechanisms have been integrated between convolutional layers. This enhances adaptability and global information retrieval capabilities, allowing the model to dynamically adjust the weight of feature maps based on different task requirements. Leveraging the advantages of Vision Transformer in handling global information and addressing its limitations in capturing details and training on small datasets, the algorithm aligns and learns feature representations effectively by incorporating channel attention and normalizing attention weights within its multi-head attention. When applied to the BraTs2021 multi-modal brain tumor image database, the proposed algorithm accurately delineates brain tumor regions, demonstrating excellent performance.

关键词： Training Adaptation models Attention mechanisms Semantic segmentation Virtual environments Transformers Kernel

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CITR: A Coordinate-Invariant Task Representation for Robotic Manipulation

CITR: A Coordinate-Invariant Task Representation for Robotic...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Peter So Rafael I. Cabral Muchacho Robin Jeanne Kirschner Abdalla Swikir Luis Figueredo Fares J. Abu-Dakka Sami Haddadin Munich Institute of Robotics and Machine Intelligence TUM Germany Division of Robotics Perception and Learning KTH Royal Institute of Technology Sweden Department of Electrical and Electronic Engineering Omar Al-Mukhtar University (OMU) Libya School of Computer Science University of Nottingham UK Electronic and Informatics Department Faculty of Engineering Mondragon Unibertsitatea Spain

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

The basis for robotics skill learning is an adequate representation of manipulation tasks based on their physical properties. As manipulation tasks are inherently invariant to the choice of reference frame, an ideal task representation would also exhibit this property. Nevertheless, most robotic learning approaches use unprocessed, coordinate-dependent robot state data for learning new skills, thus inducing challenges regarding the interpretability and transferability of the learned *** this paper, we propose a transformation from spatial measurements to a coordinate-invariant feature space, based on the pairwise inner product of the input measurements. We describe and mathematically deduce the concept, establish the task fingerprints as an intuitive image-based representation, experimentally collect task fingerprints, and demonstrate the usage of the representation for task classification. This representation motivates further research on data-efficient and transferable learning methods for online manipulation task classification and task-level perception.

关键词： Learning systems Coordinate measuring machines Robot kinematics Taxonomy Fingerprint recognition Mathematical models Libraries

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