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A Transparent Ultrasound Array for Real-Time Optical,Ultrasound,and Photoacoustic Imaging

Biomedical Engineering Frontiers 2022年第1期3卷 284-297页

作者： Haoyang Chen Sumit Agrawal Mohamed Osman Josiah Minotto Shubham Mirg Jinyun Liu Ajay Dangi Quyen Tran Thomas Jackson Sri-Rajasekhar Kothapalli Department of Biomedical Engineering The Pennsylvania State UniversityUniversity ParkPA 16802USA School of Electrical Engineering and Computer Science The Pennsylvania State UniversityUniversity ParkPA 16802USA Penn State Cancer Institute The Pennsylvania State UniversityHersheyPA 17033USA Graduate Program in Acoustics The Pennsylvania State UniversityUniversity ParkPA 16802USA

Objective and Impact *** imaging of ultrasound and optical contrasts can help map structural,functional,and molecular biomarkers inside living subjects with high spatial *** is a need to develop a platform to facilitate this multimodal imaging capability to improve diagnostic sensitivity and ***,combining ultrasound,photoacoustic,and optical imaging modalities is challenging because conventional ultrasound transducer arrays are optically *** a result,complex geometries are used to coalign both optical and ultrasound waves in the same field of *** elegant solution is to make the ultrasound transducer transparent to ***,we demonstrate a novel transparent ultrasound transducer(TUT)linear array fabricated using a transparent lithium niobate piezoelectric material for real-time multimodal *** TUT-array consists of 64 elements and centered at~6 MHz *** demonstrate a quad-mode ultrasound,Doppler ultrasound,photoacoustic,and fluorescence imaging in real-time using the TUT-array directly coupled to the tissue mimicking *** TUT-array successfully showed a multimodal imaging capability and has potential applications in diagnosing cancer,neurological,and vascular diseases,including image-guided endoscopy and wearable imaging.

关键词： Ultrasound specificity transparent

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A Novel Resource Allocation in Software-Defined Networks for IoT Application

A Novel Resource Allocation in Software-Defined Networks for...

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International Conference on Software, Telecommunications and computer Networks (SoftCOM)

作者： Alexandre Ladeira de Sousa Ogobuchi Daniel OKey Renata Lopes Rosa Muhammad Saadi Demostenes Zegarra Rodriguez Department of Computer Science Federal University of Lavras Lavras Minas Gerais Brazil Graduate Program in Information Engineering Federal University of ABC Santo Andre Sao Paulo Brazil Department of Electrical Enginnering University of Central Punjab Punjab Pakistan Computer Science Department Federal University of Lavras Lavras Minas Gerais Brazil

The rapid proliferation of Internet of Things (IoT) devices has posed significant challenges for network resource allocation and management. In this paper, we propose a novel methodology for efficient resource allocation in Software-Defined Networks (SDNs) to meet the unique requirements of IoT applications. Our approach leverages the flexibility and programmability of SDN to dynamically allocate network resources based on the specific needs of IoT devices. Our proposal combines reinforcement learning with dynamic resource allocation, demonstrating its effectiveness in optimizing resource allocation and enhancing network performance. The results highlight the potential of our approach to addressing the resource challenges in SDN-based IoT environments. The proposed method obtains results superior to 30% of throughput in comparison to a traditional resource allocation in SDN to IoT applications.

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An empirical model for branding oriented by the environmental sustainability of the Amazon Rainforest: a hybrid structural equation modeling with fsQCA approach

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Discover Analytics 2024年第1期2卷 1-21页

作者： Santos, Luiz Diego Vidal Holanda, Francisco Sandro Rodrigues Pedrotti, Alceu de Medeiros, Raphael Mario Bastos de Santana Oliveira, Catuxe Varjão Villwock, Ana Paula Schervinski Graduate Program in Intellectual Property Science Universidade Federal de Sergipe-UFS São Cristóvão Brasil Agronomy Engineering Department Universidade Federal de Sergipe-UFS São Cristóvão Brasil Administration Department Universidade da Amazônia Manaus Brasil Computer Science Department Instituto Federal de Sergipe-UFS Lagarto Brasil

Utilizing the Theory of Planned Behavior, a meaningful connection between an organization and its consumers can be established, transcending mere appearances and delving into emotions, relationships, and brand experiences. In line with these principles, this study aims to present an empirical model grounded in factual evidence derived from the perspectives of entrepreneurs and consumers of environmentally sustainable brands in the Brazilian Amazon. The proposed model adopts a mixed-modeling approach, utilizing data analysis techniques such as fuzzy-set qualitative comparative analysis (fsQCA). Two independent data sets were collected: one from 8 companies operating in the Amazon rainforest and another from 535 potential consumers. By analyzing these datasets, consumer motivations for the consumption of sustainable brands, entrepreneurial practices, and the role of traditional communities and the forest environment in shaping brand perceptions were identified. The results reveal that, from the consumer’s perspective, the key factors influencing a positive inclination to purchase sustainable brands include the brand’s engagement with traditional com munities, perceived product quality, brand innovation, and a transparently communicated positioning strategy. These constructs form the core components of the main solution. This article provides valuable information for entrepreneurs operating sustainable brands in tropical forests as well as for policymakers in community-based entrepreneurship. It is also of interest to researchers and individuals involved in brand management as a strategy for empowering small businesses engaged in sustainable practices and community collaboration. The study’s findings and recommendations have the potential to foster more sustainable and socially responsible business practices, thereby enhancing local economies and contributing to environmental preservation.

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Unified Approach to Video-Based AI Inference Tasks in Augmented Reality Systems Assisted by Mobile Edge Computing

Unified Approach to Video-Based AI Inference Tasks in Augmen...

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International Conference on Software, Telecommunications and computer Networks (SoftCOM)

In the rapidly evolving field of Augmented Reality (AR), delivering real-time, immersive experiences places a significant demand on computational resources, particularly in the context of video-based Artificial Intelligence (AI) inference tasks. Some solutions explorate optimize the tasks using the potential of Mobile Edge Computing (MEC), in which MEC brings the advantages of reduced latency and network congestion, which are critical for AR systems. In this work, we designed a novel algorithm named AR-MEC Optimization for the distribution of AI inference tasks among edge servers while considering several factors including task complexity, server capacity, and network conditions. Our approach not only ensures low-latency responses but also improves overall system efficiency and the user’s Quality of Experience (QoE). Experimental results demonstrate that our unified approach significantly outperforms traditional cloud-based methods in terms of response time and computational efficiency, thereby highlighting the potential of MEC-assisted AR systems for real-world applications.

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Hierarchical B-Frame Video Coding Using Two-Layer CANF Without Motion Coding

Hierarchical B-Frame Video Coding Using Two-Layer CANF Witho...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： David Alexandre Hsueh-Ming Hang Wen-Hsiao Peng Electrical Engineering and Computer Science International Graduate Program National Yang Ming Chiao Tung University Hsinchu Taiwan Institute of Electronics National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Computer Science National Yang Ming Chiao Tung University Hsinchu Taiwan

Typical video compression systems consist of two main modules: motion coding and residual coding. This general architecture is adopted by classical coding schemes (such as international standards H.265 and H.266) and deep learning-based coding schemes. We propose a novel B-frame coding architecture based on two-layer Conditional Augmented Normalization Flows (CANF). It has the striking feature of not transmitting any motion information. Our proposed idea of video compression without motion coding offers a new direction for learned video coding. Our base layer is a low-resolution image compressor that replaces the full-resolution motion compressor. The low-resolution coded image is merged with the warped high-resolution images to generate a high-quality image as a conditioning signal for the enhancement-layer image coding in full resolution. One advantage of this architecture is significantly reduced computational complexity due to eliminating the motion information compressor. In addition, we adopt a skip-mode coding technique to reduce the transmitted latent samples. The rate-distortion performance of our scheme is slightly lower than that of the state-of-the-art learned B-frame coding scheme, B-CANF, but outperforms other learned B-frame coding schemes. However, compared to B-CANF, our scheme saves 45% of multiply-accumulate operations (MACs) for encoding and 27% of MACs for decoding. The code is available at https://***.

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Mapping of digital innovation research themes: A 36-Year Review 6

Mapping of digital innovation research themes: A 36-Year Rev...

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6th International Conference on Information Management and Technology, ICIMTech 2021

作者： Purnomo, Agung Septianto, Andre Rosyidah, Elsa Ramadhani, Mirza Perdana, Mochamad Dafa Bina Nusantara University BINUS Business School Undergraduate Program Entrepreneurship Department Jakarta11480 Indonesia Universitas Nahdlatul Ulama Sidoarjo Institute for Research and Community Services Sidoarjo61218 Indonesia Universitas Nahdlatul Ulama Sidoarjo Environmental Engineering Department Sidoarjo61218 Indonesia Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781665449373

Research on digital innovation with an entrepreneurial spirit continues to develop but is limited to one country and/or one field. From a bibliometric review, this study purpose to visually study mapping and research trends in the field of digital innovation on an international scale. This study used bibliometric techniques with secondary data from Scopus. Analyze and visualize data using the VOSViewer program and the analyze search results function on Scopus. This study analyzed 821 academic documents published from 1983 to 2019. According to the research, the Universitetet I Oslo and Henfridsson, O. had the most active affiliated institutions and individual scientists in digital innovation research. computer science was the most studied of digital innovation. Based on the identification of a collection of knowledge accumulated from over thirty-six years of publication, this research proposes a grouping of digital innovation research themes: Health Care, Internet of Things, Conceptual Framework, Open System, Digital Platforms, Education, abbreviated as HICODE research themes. © 2021 IEEE.

关键词： Mapping

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Identification of Features in ANN's Black Box using Dimensional Reduction and Correlation Analysis: A Case Study of Cardiovascular Disease Dataset

Identification of Features in ANN's Black Box using Dimensio...

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International Conference on Advanced Mechatronics, Intelligent Manufacture, and Industrial Automation (ICAMIMIA)

作者： Boby Siswanto Yaya Heryadi Wayan Suparta Antoni Wibowo Haryono Soeparno Lukas Computer Science Department BINUS Graduate Program-Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Cognitive Engineering Research Group (CERG) Atma Jaya University Jakarta Indonesia

ISBN: (纸本)9781665499705

Artificial Neural Network (ANN) is a machine learning algorithm that can perform classification. ANN has limitations; namely, it has a black box working principle, which is unsure which feature is the most influential. This study is to identify the most influential features inside the ANN's black box using a classification model by applying Principal Component Analysis (PCA) dimension reduction combined with Pearson correlation analysis. The result of the proposed model can identify the name of the main features of the data inside the ANN's black box. This study uses two public Kaggle cardiovascular datasets. The first dataset consists of 13 features, and the second dataset consists of 12 features. The result is height and gender are the most influential features in the first dataset with the correlation value of 0.734; sex and smoking are the most influential features in the second dataset with the correlation value of 0.728. Black box model result with 2 PCA's features against a model with height and gender features in the first dataset resulting from the same accuracy on the test dataset of the classification prediction results with the value of 49.90%, while on the second dataset 58.30%.

关键词： Dimensionality reduction Analytical models Correlation Mechatronics Machine learning algorithms Artificial neural networks Predictive models

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Sport Field Reservation Based on Mobile Application 7

Sport Field Reservation Based on Mobile Application

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7th International Conference on ICT for Smart Society, ICISS 2020

作者： Claudinus, Tonto Prayoga Wicaksana, Made Kornelius Sitorus, Nicolas Ariqi Gustiandza, Marsa Oktavia, Tanty Lumban Gaol, Ford Hosoda, Takaaki Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University School of Information Systems Information Systems Department Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program - Doctor of Computer Science Computer Science Department Jakarta11480 Indonesia Advanced Institute of Industrial Technology Decision Science Department Tokyo Japan

ISBN: (纸本)9781665404228

This paper presents the sports apps based on a mobile application that has a function to help the provider or the owner of the sports field and sports vendor to promote their business in the apps. Due to the popularity and convenience that mobile applications had and high interest from many people in having a healthy life, it is a great opportunity to combine both of them. Therefore, this study focusses to build mobile sports application using object-oriented analysis and design method, that could meet the demand of lots of peoples that wants to do a sport especially a ball sport. Otherwise, the application can help and support the community and public, especially for ball sports fans, to make reservations for the sports field easier without having to come to the location and also meet their sporting needs. © 2020 IEEE.

关键词： Sports

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Interactive Facial Expression Editing with Non-linear Blendshape Interpolation

Interactive Facial Expression Editing with Non-linear Blends...

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43rd Annual Conference on European Association for computer Graphics, EUROGRAPHICS 2022

作者： Roh, Ji Hyun Kim, Seong Uk Jang, Hanyoung Seol, Yeongho Kim, Jongmin Department of Computer Science and Engineering Kangwon National University Korea Republic of Interdisciplinary Graduate Program in Medical Bigdata Convergence Kangwon National University Korea Republic of Game AI Lab NC Korea Republic of NVIDIA Korea Republic of

ISBN: (纸本)9783038681694

The ability to manipulate facial animations interactively is vital for enhancing the productivity and quality of character animation. In this paper, we present a novel interactive facial animation editing system that can express the naturalness of non-linear facial movements in real-time. The proposed system is based on a fully automatic algorithm that maintains all positional constraints while deforming the facial mesh as realistic as possible. Our method is based on direct manipulation with non-linear blendshape interpolation. We formulate the facial animation editing as a two-step quadratic minimization and solve it efficiently. From our results, the proposed method produces the desired and realistic facial animation better compared to existing mesh deformation methods, which are mainly based on linear combination and optimization. © 2022 The Author(s).

关键词： Interpolation

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Bridging Accuracy and Efficiency: The Role of PINNs in Immune Response Simulations

Bridging Accuracy and Efficiency: The Role of PINNs in Immun...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Thiago E. Fernandes Rodrigo W. Dos Santos Marcelo Lobosco Graduate Program on Computational Modelling Federal University of Juiz de Fora Juiz de Fora Brazil Department of Computer Science Institute of Exact Sciences Federal University of Juiz de Fora Juiz de Fora Brazil

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Myocarditis, characterized by inflammation of the heart muscle, has seen a notable 62.2% increase in incidence over the past three decades, leading to 324,490 deaths in 2019. Despite advancements in understanding its etiology, several critical questions remain unresolved, highlighting the need for improved patient treatment strategies. Computational methods play a crucial role in unraveling the complex interactions between pathogens and the immune system. This study explores the use of Physics-Informed Neural Networks (PINNs) to accelerate a newly developed model for myocardial edema formation in acute infectious myocarditis. The model describes the concentrations of pathogens and leukocytes using a nonlinear system of ordinary differential equations (ODEs). Our findings demonstrate a mean computational speedup of 5.93, while maintaining a Root Mean Squared Error of 0.0015. These results indicate that PINNs can accurately replicate the ODE system’s solutions, offering substantial computational efficiency for this application.

关键词： Training Pathogens Accuracy Computational modeling Artificial neural networks Myocardium Mathematical models Numerical models Computational efficiency Biological neural networks

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