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arXiv 2024年

作者： Huang, Xiaofei Chen, Wenting Liu, Jie Lu, Qisheng Luo, Xiaoling Shen, Linlin Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen China Department of Electrical Engineering City University of Hong Kong Kowloon Hong Kong National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Guangdong Provincial Key Laboratory of Intelligent Information Processing China

Medical report generation is crucial for clinical diagnosis and patient management, summarizing diagnoses and recommendations based on medical imaging. However, existing work often overlook the clinical pipeline involved in report writing, where physicians typically conduct an initial quick review followed by a detailed examination. Moreover, current alignment methods may lead to misaligned relationships. To address these issues, we propose DAMPER, a dual-stage framework for medical report generation that mimics the clinical pipeline of report writing in two stages. In the first stage, a MeSH-Guided Coarse-Grained Alignment (MCG) stage that aligns chest X-ray (CXR) image features with medical subject headings (MeSH) features to generate a rough keyphrase representation of the overall impression. In the second stage, a Hypergraph-Enhanced Fine-Grained Alignment (HFG) stage that constructs hypergraphs for image patches and report annotations, modeling high-order relationships within each modality and performing hypergraph matching to capture semantic correlations between image regions and textual phrases. Finally,the coarse-grained visual features, generated MeSH representations, and visual hypergraph features are fed into a report decoder to produce the final medical report. Extensive experiments on public datasets demonstrate the effectiveness of DAMPER in generating comprehensive and accurate medical reports, outperforming state-of-the-art methods across various evaluation metrics. Copyright © 2024, The Authors. All rights reserved.

关键词： Coarse-grained modeling

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Optimizing Heart Failure Predictive Accuracy: An Effective Approach Using SMOTE Techniques

Optimizing Heart Failure Predictive Accuracy: An Effective A...

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International Conference on Emerging Trends in Networks and computer Communications (ETNCC)

作者： Ahmed Baber Faizan Ahmed Attique Ur Rehman Sabeen Javaid Menwa Alshammeri Azka Mir Deepak Kumar Department of Software Engineering University of Sialkot Sialkot Pakistan Department of Computer Science College of Computer and Information Sciences Al Jouf University Saudia Arabia Division of Research and Development Lovely Professional University Phagwara KKR and KSR institute of technology and sciences Guntur Andhra Pradesh

ISBN: (数字)9798350353266

ISBN: (纸本)9798350353273

Heart failure occurs when the heart's lower cham-bers (ventricles) weaken and can't pump blood effectively. The heart contains 2 ventricles that are left and right. When left ventricular failure occurs, it causes shortness of breath and fatigue, and when right ventricular failure occurs, it causes peripheral and abdominal fluid accumulation. Heart failure is a disorder in breathing and oxygen supply to the whole body. As we know, Heart is responsible for circulating blood all over the body parts. When it stops pumping, blood can no longer circulate effectively causing fluid to build up in the lungs causing difficulty in breathing and in the legs, causing swelling. The fluid buildup can cause shortness of breath and swelling of the legs and feet. As a result, fluctuation in breathing takes place and, in the end, the patient dies. This study investigates the use of machine learning tools, like k-Nearest Neighbors, Decision Trees, Naïve Bayes, and Random Forests, to identify heart failure. We are proposing a model for Heart Failure Prediction using a machine learning algorithm and a dataset to determine the performance in terms of accuracy. As a result, the Random Forest classifier along with the SMOTE operator provides the highest accuracy of 91.12%, precision of 87.88%, recall of 95.39%, and F1 measure of 91.48% among the models considered. This research is a very important contribution to healthcare for effective predictions of this disease, as the number of patients is increasing day by day. The results hold significance for future modifications too in medical diagnostics and helping material for more accurate detection methodologies.

关键词： Heart Legged locomotion Accuracy Fluids Machine learning algorithms Pumps Nearest neighbor methods Cardiovascular diseases Random forests Blood

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TalkingGaussian: Structure-Persistent 3D Talking Head Synthesis via Gaussian Splatting

arXiv

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arXiv 2024年

作者： Li, Jiahe Zhang, Jiawei Bai, Xiao Zheng, Jin Ning, Xin Zhou, Jun Gu, Lin School of Computer Science and Engineering State Key Laboratory of Complex & Critical Software Environment Jiangxi Research Institute Beihang University China Institute of Semiconductors Chinese Academy of Sciences China School of Information and Communication Technology Griffith University Australia RIKEN AIP Japan The University of Tokyo Japan

Radiance fields have demonstrated impressive performance in synthesizing lifelike 3D talking heads. However, due to the difficulty in fitting steep appearance changes, the prevailing paradigm that presents facial motions by directly modifying point appearance may lead to distortions in dynamic regions. To tackle this challenge, we introduce TalkingGaussian, a deformation-based radiance fields framework for high-fidelity talking head synthesis. Leveraging the point-based Gaussian Splatting, facial motions can be represented in our method by applying smooth and continuous deformations to persistent Gaussian primitives, without requiring to learn the difficult appearance change like previous methods. Due to this simplification, precise facial motions can be synthesized while keeping a highly intact facial feature. Under such a deformation paradigm, we further identify a face-mouth motion inconsistency that would affect the learning of detailed speaking motions. To address this conflict, we decompose the model into two branches separately for the face and inside mouth areas, therefore simplifying the learning tasks to help reconstruct more accurate motion and structure of the mouth region. Extensive experiments demonstrate that our method renders high-quality lip-synchronized talking head videos, with better facial fidelity and higher efficiency compared with previous methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Gaussian distribution

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Exploring Sensory Conflict Effect Due to Upright Redirection While Using VR in Reclining & Lying Positions 22

Exploring Sensory Conflict Effect Due to Upright Redirection...

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35th Annual ACM Symposium on User Interface software and Technology, UIST 2022

作者： Luo, Tianren He, Zhenxuan Cai, Chenyang Han, Teng Pan, Zhigeng Tian, Feng Institute of Software Chinese Academy of Sciences Beijing China College of Information Engineering Capital Normal University Beijing China College of Information Technology Beijing University of Technology Beijing China College of Artifcial Intelligence Nanjing University of Information Science and Technology Jiangsu Nanjing China College of Computer Science and Technology University of Chinese Academy of Sciences China College of Artifcial Intelligence University of Chinese Academy of Sciences China

ISBN: (纸本)9781450393201

When users use Virtual Reality (VR) in nontraditional postures, such as while reclining or lying in relaxed positions, their views lean upwards and need to be corrected, to make sure they see upright contents and perceive the interactions as if they were standing. Such upright redirection is excepted to cause visual-vestibular-proprioceptive conflict, affecting users' internal perceptions (e.g., body ownership, presence, simulator sickness) and external perceptions (e.g., egocentric space perception) in VR. Different body reclining angles may affect vestibular sensitivity and lead to the dynamic weighting of multi-sensory signals in the sensory integration. In the paper, we investigated the impact of upright redirection on users' perceptions, with users' physical bodies tilted at various angles backward and views upright redirected accordingly. The results showed that upright redirection led to simulator sickness, confused self-awareness, weak upright illusion, and increased space perception deviations to various extents when users are at different reclining positions, and the situations were the worst at the 45° conditions. Based on these results, we designed some illusion-based and sensory-based methods, that were shown effective in reducing the impact of sensory conflict through preliminary evaluations. © 2022 ACM.

关键词： Virtual reality

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SCAF-DG: A Multi-site Medical Image Denoising With A Domain-Generalized Spatial-Channel Attention Fusion

SCAF-DG: A Multi-site Medical Image Denoising With A Domain-...

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Pattern Recognition and Artificial Intelligence (PRAI), International Conference on

作者： Linda Delali Fiasam Yunbo Rao Collins Sey Benjamin Klugah-Brown Obed Nartey Tettey Esther Stacy E.B. Aggrey Isaac Osei Agyemang Isaac Adjei-Mensah Yang Yang School of Information and Software Engineering University of Electronic Science Technology of China Chengdu China School of Life Science and Technology University of Electronic Science Technology of China Chengdu China School of Information and Communication Engineering University of Electronic Science Technology of China Chengdu China Sichuan Institute of Computer Sciences Chengdu China

Images from multiple medical sites usually contain varying noise levels that can affect the generalization performance of the denoising models. Domain generalization (DG), which seeks to learn a model that can generalize to an unseen test domain, has not been extensively explored in medical image denoising for multi-site images. We present an approach for multi-site medical image denoising, which addresses the issue of varying noise levels across different imaging sites. Our proposed method, called SCAF-DG (Spatial-Channel Attention Fusion for Domain Generalization), employs two attention units to extract both domain-specific channel features and mutually-invariant spatial features from the noisy images. These features are then combined to form a rich feature space. Additionally, we utilized skip connections to preserve structural information during the denoising process. We evaluate SCAF-DG on publicly available brain magnetic resonance image (MRI) datasets from three different imaging sites, and demonstrate an average increase of 1.01 in peak signal-to-noise ratio (PSNR) and 0.016 in structural similarity index measure (SSIM) for denoising images. Our results demonstrate that SCAF-DG has the potential to generalize well to new, unseen test domains, and outperforms existing state-of-the-art denoising models on multi-site medical images.

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Hybrid of Ant Colony and Artificial Bee Colony Optimization for Resource Allocation in Cognitive Radio Networks

Hybrid of Ant Colony and Artificial Bee Colony Optimization ...

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Distributed Computing and Optimization Techniques (ICDCOT), International Conference on

作者： P Lakshmiramana Mohammed Al-Farouni P Senthil Shashidhara K S Pramod Mutalik Computer Science and Technology Madanapalle Institute of Technology and Science Angallu Madanapalle India The Islamic university Najaf Iraq Department of Information Technology S.A. Engineering College Chennai India Dept. of ECE Nitte Meenakshi Institute of Technology Bengaluru India Software Engineer Visvesvaraya Technological University Raichur Karnataka India

The resource allocation in heterogeneous cognitive radio networks (CRN) system has been considered a key technology in wireless networks. However, the main challenge of the CRN system is resource allocation with minimized and maximum transmission in the spectrum band, enhancing optimum throughput increase, load balance and global optimization. The proposed method is hybrid optimization, comprising Ant Colony Optimization (ACO) and Artificial Bee Colony (ABC), allocating resources while considering power, throughput and equal to distributed the allocation. The load balancing technique should ensure virtual independent task scheduling approaches of the hybrid optimization and optimize scheduling and resource utilization to maximize throughput and create load balancing. Performance analysis of the proposed method was compared using resource allocation with existing load balancing and scheduling algorithms. Although the proposed ACO-ABC method exhibits high performance compared to existing methods such as Dynamic Fuzzy-based Primary User Aware Clustering (DFPC), non-orthogonal multiple access (NOMA) and Hybrid Tabu Search-based Stimulated Algorithm (HTSA), experimental results demonstrate high performance achieving a Packet Delivery Ratio (PDR) of 97%, Delay of 0.009s, Energy consumed of 0.01 (nJ), and Throughput of 2.0 (Mpbs).

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DNGaussian: Optimizing Sparse-View 3D Gaussian Radiance Fields with Global-Local Depth Normalization

arXiv

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arXiv 2024年

Radiance fields have demonstrated impressive performance in synthesizing novel views from sparse input views, yet prevailing methods suffer from high training costs and slow inference speed. This paper introduces DNGaussian, a depth-regularized framework based on 3D Gaussian radiance fields, offering real-time and high-quality few-shot novel view synthesis at low costs. Our motivation stems from the highly efficient representation and surprising quality of the recent 3D Gaussian Splatting, despite it will encounter a geometry degradation when input views decrease. In the Gaussian radiance fields, we find this degradation in scene geometry primarily lined to the positioning of Gaussian primitives and can be mitigated by depth constraint. Consequently, we propose a Hard and Soft Depth Regularization to restore accurate scene geometry under coarse monocular depth supervision while maintaining a fine-grained color appearance. To further refine detailed geometry reshaping, we introduce Global-Local Depth Normalization, enhancing the focus on small local depth changes. Extensive experiments on LLFF, DTU, and Blender datasets demonstrate that DNGaussian outperforms state-of-the-art methods, achieving comparable or better results with significantly reduced memory cost, a 25× reduction in training time, and over 3000× faster rendering speed. Code is available at: https://***/Fictionarry/DNGaussian. Copyright © 2024, The Authors. All rights reserved.

关键词： Geometry

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Multi-class multi-object detection and tracking system in 3 dimensions using YOLOv5

Multi-class multi-object detection and tracking system in 3 ...

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Range Technology (ICORT), International Conference on

作者： Aditi Bhateja Sanyam Sharma Abhishek Krishna Himanshu Chaudhary Brejesh Lall Prem K Kalra Computer Science Indian Institute of Technology Delhi India Software Engineering Delhi Technological University Delhi India Information Technology Delhi Technological University Delhi India Securities and Exchange Board of India India Electrical Engineering Indian Institute of Technology Delhi India

It is of great significance to detect and track multiple objects belonging to multiple classes in 3 dimensions and the same can be applied to various research domains. One such application pertains to multi-fish detection and tracking The problem is of significance to marine researchers who want to study the behaviour of fish in a constrained environment i.e. in an aquarium, using different sensors. For this study, a data set consisting of different fishes in an aquarium was constructed and Microsoft Kinect was used to capture the movements in 3 dimensions. The data set was cleaned, aligned, and calibration was done to make it suitable for object detection and tracking In this paper, YOLOv5s model was improvised, hereafter referred as YOLOv5-With Attention Mechanism (YOLOv5-WAM), and an object detection and tracking pipeline was designed to detect and track underwater objects (fishes in our work) belonging to multiple classes in 3D. YOLOv5-WAM can be adapted for use on devices having low compute availability, making it suitable for real time object detection and tracking using low cost experimental setup. YOLOv5-WAM showed about 1% improvement in mAP for our custom dataset while for Fish4Knowkdge and COCO dataset, the improvements were to the tune of 7% when compared to YOLOv5s. The results and analysis of the approach are presented in this paper.

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Transforming 6G Mobile Edge Intelligence with Large Models

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IEEE Network 2025年

作者： He, Xiaoming Jiang, Yunzhe Liu, Yinqiu Cui, Huajun Pan, Heng Mao, Yingchi Nanjing University of Posts and Telecommunications College of Internet of Things Nanjing China University of Electronic Science and Technology of China College of Communication and Information Engineering Chengdu China Nanyang Technological University College of Computing and Data Science Singapore PowerChina Beijing Engineering Corporation Limited Digital Intelligence Research Institute Beijing China Zhongyuan University of Technology Zhengzhou China Hohai University College of Computer Science and Software Engineering Nanjing China

Large models (LMs) exhibit transformative power in 6G Mobile-Edge Intelligence (MEI) scenarios, which can effectively address key challenges regarding network complexity, resource optimization, intelligent decision-making, and user experience. In this article, we first introduce the basics of LM and explain the potential applications and existing solutions for integrating LMs into 6G MEI. Afterward, we conduct a case study oriented to LM-assisted 6G MEI scenarios, focusing on how to use LMs to predict the size of traffic. Specifically, we employ two attention modules named Dartboard Spatial Multi-head Self-Attention (DS-MSA) and Causal Temporal Multi-head Self-Attention (CT-MSA) to extract spatio-temporal features. A Large Language Model (LLM) named GPT-2 is utilized as the backbone model. To fit time-series data, the original token encoding layer is replaced by the convolutional encoding layer. Additionally, to handle multi-scale temporal information, we adopt an additional temporal encoding layer. During training, the majority of the pre-trained parameters are frozen, with only the input/output layers and the fully connected layer involved in parameter updates. The simulation results demonstrate that, compared to the traditional AI-based baselines, the proposed LM-based framework acquires a lower training loss and converges faster. Moreover, it achieves better prediction accuracy with less resource consumption. © 1986-2012 IEEE.

关键词： Spatio-temporal data

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Compact Millimeter-Wave Antenna Operating in 28/38 GHz Spectrum

Compact Millimeter-Wave Antenna Operating in 28/38 GHz Spect...

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2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio science Meeting, AP-S/URSI 2022

作者： Rizvi, Syed Naheel Raza Sarfraz, Muhammad Alibakhshikenari, Mohammad Dalarsson, Mariana Falcone, Francisco Chungbuk National University Department of Information and Communication Engineering Cheongju Korea Republic of Virtual University of Pakistan Department of Computer Science Lahore Pakistan Universidad Carlos III de Madrid Department of Signal Theory and Communications Madrid Leganés28911 Spain KTH Royal Institute of Technology School of Electrical Engineering and Computer Science StockholmSE 100-44 Sweden Public University of Navarre Electric Electronic and Communication Engineering Department Pamplona31006 Spain Public University of Navarre Institute of Smart Cities Pamplona31006 Spain

ISBN: (数字)9781665496582

ISBN: (纸本)9781665496582

We present a compact wideband antenna operating in the 28 GHz and 38 GHz millimeter-wave spectrum. The proposed antenna is designed on the Rogers RT/duroid 5880 substrate. The proposed antenna has a simple geometry and compact dimensions of 17 mm × 12 mm × 0.79 mm. The antenna design is simulated using the ANSYS HFSS software. The features of the proposed antenna are also compared to similar designs from the literature, to demonstrate the potential of our antenna for use in the targeted frequency band. The results in the present paper indicate that our design is a good candidate for use in future wireless communication systems. © 2022 IEEE.

关键词： Microwave antennas

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