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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Binh Nguyen Michael Nigro Alice Rueda Venkat Bhat Sri Krishnan Department of Electrical Computer and Biomedical Engineering Toronto Metropolitan Univerity Toronto ON Interventional Psychiatry Program St. Michael’s Hospital Toronto

Digital phenotyping (DP) is a multidisciplinary field of science that quantifies the individual level phenotype through active and passive data. Although DP is a multidisciplinary field, there lacks a technical and a systematic approach to representing DP. This work proposes the development of digital phenotype profile (DPP) to represent a user’s physical and behavioural health baseline through systematic investigations with an emphasis on robustness and explainability. To achieve this, a Statistical, Information Theory, and data-driven (SID) pipeline will develop the foundation of the DPP. SID evaluates the non-linearity of the signal to offer inference for domain-specific feature extraction, evaluates the information theory to rank the DPP parameters, and imputes missing data for robust analysis, respectively. SID was applied to a 24-hr Multi-Level dataset and was able to represent individual DPPs. The respective DPPs were visualized and clusters of awake and asleep were used for individual specific modelling.

关键词： Systematics Pipelines data visualization Signal processing Feature extraction Robustness Acoustics

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Engineering Large Wearable Sensor data towards Digital Measures

Engineering Large Wearable Sensor Data towards Digital Measu...

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ieee International Conference on Big data Analysis (ICBDA)

作者： Hui Zhang Guangchen Ruan Regan Giesting Leah Miller Neel Patel Ju Ji Yi Lin Yang Jian Yang Eli Lilly & Company Indianapolis Digital Health Office Indiana USA Eli Lilly & Company Indianapolis Advanced Analytics and Data Science Indiana USA Carmel High School Carmel Indiana USA

Developing digital biomarkers requires handling unprecedented quantities of digital data generated from digital health technologies that utilize a combination of computing platforms, connectivity, software, and sensors. These collected data need to be transformed and transported into a meaningful and useful format before further being derived into health indicators for understanding disease state and life quality. The unique challenges for this class of data engineering tasks are due to the complexity and volume of the digital data we are handling, the data quality and fidelity required to enable subsequent analysis, and the repeated cycles of trial and error to achieve the desired results. This paper presents a family of systems, pipelines, and methods we have designed and built to facilitate these tasks in a typical digital data engineering lifecycle in the context of digital biomarker development.

关键词： data integrity Pipelines data visualization Biomarkers data engineering Software Electronic healthcare

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Key Technologies, Applications, Trends of Visual Analysis and Prediction in Healthcare System

Key Technologies, Applications, Trends of Visual Analysis an...

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ieee Globecom Workshops

作者： Md Altab Hossin M. M. Kamruzzaman Ibrahim Alrashdi Abdulaziz Marzouq Alsharari Ayman Almjnoony School of Innovation and Entrepreneurship Chengdu University Chengdu Sichuan P.R.China Department of Computer Science Jouf University Sakaka Al-Jouf KSA

With increasing disease-related complexities, the challenges in the healthcare sector are growing at an unprecedented rate. The implications of visual tools and prediction models are playing a significant role in the field of medical and health to overcome the challenges in this globalized technological era, and promote clinical decision-making and personalized care delivery plans. The aim of the present research is to systematically assess the analysis and assessment of the key technologies, applications and trends of visual analysis and prediction in medical and healthcare data making use of machine learning (ML), deep learning (DL), virtual reality (VR), augmented reality (AR), and 3D visualization.

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Supporting Exploratory Programming in Domain-Specific Applications

Supporting Exploratory Programming in Domain-Specific Applic...

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ieee Symposium on Visual Languages and Human Centric Computing (VL/HCC)

作者： Daniel Manesh Department of Computer Science Virginia Tech Blacksburg VA

The act of computer programming can take many different forms. For example, a software engineer might rewrite C++ code to speed up some process; an analyst might use a spreadsheet program to gain insights into their data; or a creative coder might write a program to create an abstract visualization that is synchronized with music. While each of these tasks is a complex activity involving creative problem-solving, the first example has a well-defined goal, while the second and third examples are more open-ended. Generally, programming activities involving data analysis or artistic creative practice can be seen as exploratory programming, characterized by rapid experimentation and an evolving set of goals [1]. In the creative coding example, the coder might explore several options using different combinations of colors, shapes, textures, and animation speeds. There is no “correct” answer for what the final product should look like, and in fact, what they envision at the outset might be completely different from what they ultimately create.

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GA-Net: Gated Attention Mechanism Based Global Refinement Network for Image Inpainting

GA-Net: Gated Attention Mechanism Based Global Refinement Ne...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Ran Qiu Shengrong Zhao Hu Liang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

Underwater images are often affected by problems such as light attenuation, color distortion, noise and scattering, resulting in image defects. A novel image inpainting method is proposed to intelligently predict and fill damaged areas for complete and continuous visualization of the image. First, in order to effectively solve the problem of color distortion caused by light refraction in underwater environments, the improved gated attention mechanism is used. This mechanism improves the local details by learning and weighting the important features of the image. Second, gated convolution automatically determines the degree of restoration for each pixel based on local features of the original image. It eliminates distractions such as low contrast and scattering, retaining more original detailed information. By doing so, image inpainting techniques improve the quality and visualization of underwater images.

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PAM-4 data Transmission Quality Evaluation Using Two- and Three-Dimensional Mapping of Received Symbols

PAM-4 Data Transmission Quality Evaluation Using Two- and Th...

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International Symposium on Multiple-Valued Logic

作者： Yasushi Yuminaka Kazuharu Nakajima Yosuke Iijima Graduate School of Science and Technology Gunma University Kiryu Japan National Institute of Technology (KOSEN) Oyama College Oyama Japan

Based on four-level pulse-amplitude modulation (PAM-4) symbol transition, this study investigated a two-dimensional (2D) symbol mapping technique that evaluates the quality of transmission data to set equalizer parameters adaptively. The 2D symbol transition mapping enables visualization of the degree of intersymbol interference (ISI). The simulation results demonstrate that the 2D symbol mapping can evaluate the PAM-4 data transmission quality degraded by ISI and can visualize the effect of equalization. The applications of 2D mapping and the extension of 2D mapping to 3D mapping are also discussed.

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Geometric Registration of Benthic Imagery for Learning Appearance-Based Place Recognition over Multiple Sessions

Geometric Registration of Benthic Imagery for Learning Appea...

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International Symposium on Underwater Technology

作者： Martin Kvisvik Larsen Oscar Pizarro Martin Ludvigsen Department of Marine Technology Norwegian University of Science and Technology Trondheim Norway Australian Centre for Field Robotics University of Sydney NSW Australia

This paper present a geometric registration method tailored to benthic imagery under significant appearance change, motivated by the need to create training data for learning-based visual place recognition models. We demonstrated that the method can register imagery from benthic sites over multiple sessions spanning up to four years. The registered imagery is used to automatically create training and test data for a place recognition model based on contrastive learning. Our results show that the performance of the model is significantly improved by training on the registered imagery, and that it can successfully recognize places over multiple years. However, additional work in setting up the learning problem is required to achieve high reliability.

关键词： Training visualization Image recognition Three-dimensional displays Training data Cameras data models

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An Extended System for External Sensors data Acquisition and Validation During Conducting Polysomnography

An Extended System for External Sensors Data Acquisition and...

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ieee/SP Workshop on Statistical Signal Processing (SSP)

作者： Tanut Choksatchawathi Thitikorn Kaewlee Guntitat Sawadwuthikul Busarakum Chaitusaney Nattapong Jaimchariyatam Theerawit Wilaiprasitporn Thapanun Sudhawiyangkul School of Information Science and Technology Vidyasirimedhi Institute of Science and Technology Rayong Thailand Sense AI Company Limited Thailand School of Computing KAIST Daejeon South Korea Excellence Center for Sleep Disorders King Chulalongkorn Memorial Hospital Thai Red Cross Society Bangkok Thailand

This paper proposes an extended system for acquiring external sensor data during polysomnography (PSG) tests. The proposed method intends to provide means for integrating external sensor devices with existing PSG systems in the sleep clinic without modification to the original system. The acquired external sensor data is temporally synchronized with the PSG data through a trigger signal and can be used in subsequent applications. Using our proposed system, we acquire ten additional channels via Polar Verity Sense. We validate the acquired data qualitatively through signal visualization. Then, we compare the derived heart rate (HR) from PPG with those from an electrocardiogram (ECG) in the PSG device to validate our system quantitatively. The result reveals a fair error between PPG and ECG HR, demonstrating an acceptable performance. In addition to the PPG data acquisition, the proposed method can be employed on different external sensors to produce various databases for sleep studies.

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CAMP: A Cross-View Geo-Localization Method Using Contrastive Attributes Mining and Position-Aware Partitioning

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ieee TRANSACTIONS ON GEOscience AND REMOTE SENSING 2024年 62卷 1页

作者： Wu, Qiong Wan, Yi Zheng, Zhi Zhang, Yongjun Wang, Guangshuai Zhao, Zhenyang Wuhan Univ Sch Remote Sensing & Informat Engn Wuhan 430079 Peoples R China Chinese Univ Hong Kong Dept Geog & Resource Management Shatin Hong Kong Peoples R China China Railway Design Grp Co Ltd Tianjin 300308 Peoples R China

Cross-view geo-localization (CVGL) task aims to utilize geographic data, such as maps or high-resolution satellite images, as reference to estimate the positions of a ground- or near-ground- captured query image. This task is particularly challenging due to the significant changes in visual appearance resulting from the extreme viewpoint variations. To address this challenge, a range of innovative methods have been proposed. However, intra-scene geometric information and inter-scene discriminative representation are not fully explored. In this article, we propose a novel CVGL method using contrastive attributes mining and position-aware partitioning (CAMP), which incorporates a position-aware partition branch (PPB) and a contrastive attributes mining (CAM) strategy. PPB learns fine-grained local features of different parts and captures their spatial information, providing a comprehensive understanding of scenes from both textual and spatial perspectives. CAM establishes supervision of the negative samples based on the images from the same platform, empowering the model to better discern differences between distinct scenes without extra memory cost. The proposed CAMP surpasses existing methods, achieving state-of-the-art results on the satellite-drone CVGL datasets University-1652 and SUES-200. Additionally, our method also outperforms existing methods in cross-dataset generalization, achieving an 8.85% increase in R@1 when trained on the University-1652 dataset and tested on the SUES-200 dataset at a height of 150 m. Our code and model are available at https://***/Mabel0403/CAMP.

关键词： Feature extraction Contrastive learning Task analysis Drones Satellite images data mining visualization Cross-view geo-localization (CVGL) image retrieval remote sensing satellite image unmanned aerial vehicles (UAVs)

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Comparing 2D and Augmented Reality visualizations for Microservice System Understandability: A Controlled Experiment

Comparing 2D and Augmented Reality Visualizations for Micros...

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ieee/ACM International Conference on Program Comprehension (ICPC)

作者： Amr S. Abdelfattah Tomas Cerny Davide Taibi Sira Vegas Computer Science of Baylor University Waco TX USA Tampere University Tampere Finland University of Oulu Oulu Finland Universidad Politecnica de Madrid Madrid Spain

Microservice-based systems are often complex to understand, especially when their sizes grow. Abstracted views help practitioners with the system understanding from a certain perspective. Recent advancement in interactive data visualization begs the question of whether established software engineering models to visualize system design remain the most suited approach for the service-oriented design of microservices. Our recent work proposed presenting a 3D visualization for microservices in augmented reality. This paper analyzes whether such an approach brings any benefits to practitioners when dealing with selected architectural questions related to system design quality. For this purpose, we conducted a controlled experiment involving 20 participants investigating their performance in identifying service dependency, service cardinality, and bottlenecks. Results show that the 3D enables novices to perform as well as experts in the detection of service dependencies, especially in large systems, while no differences are reported for the identification of service cardinality and bottlenecks. We recommend industry and researchers to further investigate AR for microservice architectural analysis, especially to ease the onboarding of new developers in microservice projects.

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