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Nature's Embrace: Development and Validation of a Pneumatic Soft Robotic Flower for Calming Interactions

Nature's Embrace: Development and Validation of a Pneumatic ...

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IEEE International Conference on Soft Robotics (RoboSoft)

作者： A. Castillo Ugalde Sharmayne Lim Zhiyu C. Carrasco Camara Wang Yixiao P. Valdivia Y Alvarado Engineering and Product Development Pillar Singapore University of Technology and Design Singapore Computer Science and Design Pillar Singapore University of Technology and Design Singapore School of Industrial Design at Georgia Tech GA USA

Exploring the intersection of soft robotics and well-being, this research aims to alleviate stress and create restorative environments. Against the backdrop of rising mental health concerns, we introduce a novel soft robotic flower based on an inflatable membrane mechanism. By replicating the gentle movements of a natural lotus flower, this innovation aims to induce calming and meditative interactions. Our design leverages its simplicity on the use of soft materials, providing a nuanced alternative to earlier prototypes. Through a comprehensive evaluation, we demonstrate that this soft robotic flower significantly reduces stress and enhances well-being, setting the stage for a new era of therapeutic human-robot interaction.

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Image-Guided Point Cloud Completion with Multi-modal Fusion Transformers

Image-Guided Point Cloud Completion with Multi-modal Fusion ...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Li, Zhaowen Lin, Shujin Zhou, Fan School of Computer Science and Engineering National Engineering Research Center of Digital Life China Guangdong Key Laboratory for Big Data Analysis and Simulation of Public Opinion China School of Communication and Design Sun Yat-sen University Guangzhou China

ISBN: (纸本)9798350337020

The task of image-guided point cloud completion aims to leverage information from images to address uncertainty issues in the completion inference of point clouds. The key challenge in this setting lies in how to effectively combine features extracted from both modalities. Due to the large domain discrepancy between the image and point cloud, existing methods that use cross-modal attention to directly fuse features have increased attention on redundant information and noise from different modalities, resulting in poor feature fusion performance. Hence, by introducing multi-modal fusion transformers that use bottleneck tokens, we enabled point cloud feature to learn image feature through information bridges, leading to improved point cloud completion performance. Our method can not only benefit from RGB images, but also from sketches with less feature information but more emphasis on edge information. Extensive experiments demonstrate that our proposed method enhances the quality of point cloud completion and outperforms other state-of-the-art methods. © 2023 IEEE.

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Research on Multi-Modal Artificial Intelligence Information Fusion Technology in Embedded computer Systems

Research on Multi-Modal Artificial Intelligence Information ...

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Electrical engineering, Big Data and Algorithms (EEBDA), IEEE International Conference on

作者： Qiongfei Wu Qubo Xie Yi Chen Department of Information Engineering Wuhan Institute of Design and Science Wuhan China School of Computer Science & Technology Huazhong University of Science and Technology Wuhan China

Multi-scale decomposition (MSD) has some problems, such as missing details and generating noise. This project aims to study multi-modal image based on texture decomposition model and using improved convolutional network and phase-shift consistency techniques. Aiming at the problem that MSD is easy to produce noise, low-pass filter optimization function and structure texture decomposition model are introduced to effectively solve the problem of decomposition technology. An adaptive filtering algorithm based on least square method is proposed, and the structure-texture analysis method based on image is combined to overcome the noise problem of traditional algorithms theoretically. Secondly, for structured texture, the fusion algorithm of convolutional neural network and Gaussian fairing is studied to enhance the effective acquisition of image details and remove noise. The phase-shift matching algorithm is used for high frequency band to achieve effective fusion of high frequency signals. Then the inversion is carried out to obtain the final composite image. The images are studied qualitatively and quantitatively from the perspectives of visual effect, mutual information, feature mutual information, structural similarity, information entropy, PSNR, etc.

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Emotion Recognition in Real Time for Personalized Healthcare Using Digital Twin Approach

Emotion Recognition in Real Time for Personalized Healthcare...

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International Conference on Advances in Information Technology (ICAIT)

作者： B. Karthikeyani D. Yuvaraj T. Parimalam P. Parthasarathi Nandha Arts and Science College & Assistant Professor @ Shree Venkateshwara Arts and Science (Co Education) CollegeGobi Department of Computer Science and Design Kongu Engineering College Perundurai Department of Computer Science Nandha Arts and Science College Perundurai Department of Computer Science and Engineering Bannari Amman Institute of Technology Sathyamangalam

ISBN: (数字)9798350383867

ISBN: (纸本)9798350383874

Facial Emotional Recognition (FER) in healthcare can help to monitoring the patient by analyzing the emotional state of the patient through various emotional states. In medical industry it can help to trace the emotional state changes and diagnosis the pattern which are mapped respective to the patient's history. Digital twin technology is transforming healthcare systems and improving patient care, clinical operations, predictive analytics, and training and simulation by integrating virtual simulations and emerging analytic mechanism through ML and DL techniques. By allocating resources, monitoring workflow, and providing individualized patient care, these algorithms reduce healthcare operations and assist in determining an individual's risk. Since FER has lot of technical challenges like illumination, obstruction issues also high implementation cost. To solve these issues, the FER system will embed with the digital twin technology to provide a better result from real time sensing cameras also analyzing the result and guiding for a better treatment of an individual. We propose a system of end to end framework which produces best results in minimum amount of time with maximum accuracy. This real time implementation will help all the healthcare centers/industry to study an individual's health status as well as early prediction of diseases with effective treatment steps.

关键词： Training Emotion recognition Face recognition Medical services Speech recognition Cameras Real-time systems Digital twins Medical diagnostic imaging Monitoring

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Artificial Intelligence-Based Control Strategies for Unmanned Aerial Vehicles 10

Artificial Intelligence-Based Control Strategies for Unmanne...

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10th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and computer engineering, UPCON 2023

作者： Prabagar, S. Al-Jiboory, Ali Khudhair Nair, Prabha Shreeraj Mandal, Pawan Garse, Komal Mohan Natrayan, L. Alliance College of Engineering and Design Alliance University Department of Computer Science and Engineering Karnataka Bangalore India University of Diyala Mechanical Engineering Baqubah Iraq Noida Institute of Engineering and Technology Department of It Greater Noida India Rimt University Forensic Science Gobindgarh India Sinhgad College of Engineering Maharashtra Pune India Saveetha School of Engineering Simats Department of Mechanical Engineering Tamil Nadu Chennai602105 India

ISBN: (纸本)9798350382471

Drones, also known as Unmanned Aerial Vehicles, or UAVs, are becoming more and more well-liked as a multifunctional tool that can be used in a variety of industries, including environmental monitoring, search and rescue, agricultural, and surveillance. In this work, we explore the potential for significantly increased UAV efficiency, versatility, and durability with AI-based control techniques. The research technique combines simulation, experimental validation, and mathematical modelling to fully investigate AI-based control systems. We develop a mathematical model of UAV dynamics and use a deep reinforcement learning (DRL) technique based on Proximal Policy Optimisation (PPO) to control the drones. Both theoretical analysis and real-world testing on a DJI Matrice 210 RTK platform validate the method's efficacy. The algorithm's capacity to provide precise and accurate answers is shown by the average location error's steady decline over time. It is very resilient, remaining stable in the face of wind gusts, and remarkably flexible, with quick response times. These patterns are shown graphically. According to the study's findings, AI-based control algorithms might greatly advance UAV technology by increasing its precision, adaptability, and dependability. Future priorities will include advanced machine learning techniques, multi-agent systems, safety measures, ethical frameworks, human-AI collaboration, environmental impact assessments, and urban integration. Through the creation of new capabilities and the resolution of urgent concerns, these activities have significant potential to impact UAV development in the future. © 2023 IEEE.

关键词： AI-based control Artificial Intelligence Human-AI Collaboration Position Tracking Proximal Policy Optimization Reinforcement Learning UAV Dynamics UAVs Velocity Control Wind Disturbances

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Archer: Adaptive Memory Compression with Page-Association-Rule Awareness for High-Speed Response of Mobile Devices 23

Archer: Adaptive Memory Compression with Page-Association-Ru...

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23rd USENIX Conference on File and Storage Technologies, FAST 2025

作者： Li, Changlong Zhu, Zongwei Wang, Chao Liu, Fangming Xu, Fei Sha, Edwin H.-M. Zhou, Xuehai School of Computer Science and Technology East China Normal University China Jianghuai Advance Technology Center Hefei230026 China MoE Engineering Research Center of Hardware/Software Co-Design Technology and Application China School of Software Engineering University of Science and Technology of China Hefei230026 China Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou215123 China Huazhong University of Science and Technology China Peng Cheng Laboratory China

ISBN: (纸本)9781939133458

In mobile systems, memory can be compressed page-by-page to save space. This approach is widely adopted because memory data is accessed by page. However, this paper shows that the system response speed is significantly limited by page-grained compression. In this paper, we observe that approximately a quarter of anonymous memory pages are highly correlated, even though the association is implicit. Inspired by this, we propose Archer, an association-rule-aware memory compression framework in mobile systems. Archer demonstrates that memory in mobile devices should be compressed using flexible granularity, rather than relying solely on traditional page compression. To further integrate association-rule mining techniques into system design, we redesign the LRU mechanism and propose an adaptive memory compression region. Experimental results show that the average app launching speed is 1.55x faster when enabling Archer, and the average photographic speed and frame rate increase by 1.42x and 1.31x, respectively, compared to the state-of-the-art. © 2025 FAST. All Rights Reserved.

关键词： Portable equipment

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Investigation of PCM thermal effectiveness towards an optimised design of cooling building envelope 16

Investigation of PCM thermal effectiveness towards an optimi...

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16th International engineering and Computing Research Conference, EURECA 2021

作者： Al-Obaidi, A.S.M. Chew, H.J. Gunasagaran, S. Hoi, J.W. School of Computer Science and Engineering Taylor's University Malaysia School of Architecture Building and Design Taylor's University Malaysia Axial Design Works Sdn. Bhd. Malaysia

The buildings are responsible to 40% of the global greenhouse gas emission. To tackle the climate change, the development of sustainable building is urgent. Phase Change Material (PCM) as a potential material was suggested to integrate in building envelope to maintain a constant room temperature. This paper investigated the thermal performance of the PCM in building envelope at tropics and optimizes the cooling effect by manipulating melting temperature, thickness, position, and the type of PCM. ANSYS FLUENT software is used to model the 2D cross-sectional area of the steel roof and brick wall. With the input of 2 normal days outdoor temperature, the results of simulated indoor surface temperature are compared with the control set to determine the optimum setting while monitoring on its cost-effectiveness. The results showed that the optimum melting temperature of PCM is 29 °C for brick wall application and 31 °C for steel roof application. In normal day, PCM can reduce the peak indoor surface temperature up to 4 °C. Although in the evening, PCM delays the time for the room to cool off, but for the normal warehouse operating hours this is not affecting the thermal comfortability of the workers. © Published under licence by IOP Publishing Ltd.

关键词： Melting point

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Reconstruction of Medical Images from Sparse Data: A Deep Learning Approach 5

Reconstruction of Medical Images from Sparse Data: A Deep Le...

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5th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2024

作者： Saravanan, S. Bruntha, P.Malin Joseph S, Iwin Thanakumar Subramanian, Suresh Sundar, G. Naveen Narmadha, D. VIT School of Design Vellore Institute of Technology Department of Multimedia Vellore India Karunya Institute of Technology and Sciences Department of Electronics and Communication Engineering Coimbatore India Koneru Lakshmaiah Education Foundation Department of CSE Andhrapradesh Vaddeswaram India Saveetha School of Engineering Saveetha Institute of Medical and Technical Science Department of Electronics and Communication Engineering Chennai India Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350395235

For diagnosis, medical imaging technology plays a significant role in today's scenario. Accordingly, enormous amounts of medical data are generated daily on research centers and hospitals. Storing the medical images needs a lot of space where in 3D (Three dimensional) medical data acquires a huge space over it. All the medical image details need to be stored digitally for efficient diagnosis. The Digital Imaging and Communications in Medicine (DICOM) standard was established as the global norm for digitally storing patient images from diverse medical imaging modalities. Efficient compression over medical images is required to maintain efficacy in storage space. Compression over images like JPEG, PNG, etc. may not be efficient in bringing an effective lossless image compression in all the types of imaging modalities. Hence, an efficient methodology of Image compression over medical images are required in today's scenario. This research study compares the performance of different methods of compression standard for medical images with the proposed method called DL Based Compressive sensing on Medical Image compression. The primary aim of Compressive Sensing (CS) is to achieve compression of a sparse signal at a sampling rate below the Nyquist rate. Results are analyzed using performance metrics like PSNR, MSE, BPP and CR. And it proves that the proposed algorithm achieves a higher PSNR value and states that it is efficient in bringing a lossless medical image compression. © 2024 IEEE.

关键词： Image compression

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Research on YOLO model optimization of multi-modal data processing attention mechanism based on deep learning

Research on YOLO model optimization of multi-modal data proc...

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Electronic Technology, Communication and Information (ICETCI), IEEE International Conference on

作者： Qiongfei Wu Qubo Xie School of Intelligent Engineering Wuhan Institute of Design and Science Wuhan China School of Computer Science & Technology Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9798350361643

ISBN: (纸本)9798350361650

In this study, a YOLO model method combining LightGBM and multi-modal data processing attention mechanism optimization is proposed to enhance the effectiveness and accuracy of object detection. The traditional YOLO model has the problem of insufficient utilization of multi-modal data when processing image data, while LightGBM has significant advantages in processing structured data. Therefore, this paper designs and implements an optimization framework by embedding CBAM module into the extraction of features part of YOLOv5 model to improve the capability of feature representation. Concurrently, LightGBM is employed to handle structured data, and a fusion module is used to combine the features from both sources with appropriate weighting.

关键词： YOLO Attention mechanisms Accuracy Computational modeling Feature extraction Data processing Data models

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Hybrid Machine Learning Model for Predicting Damage Scale in Sunflower Leaves affected by Aster Yellow Disease

Hybrid Machine Learning Model for Predicting Damage Scale in...

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Recent Advances in Information Technology for Sustainable Development (ICRAIS), International Conference on

作者： Anuj Kumar Jain G S Pradeep Ghantasala Rahul Bhandari Gaganpreet Kaur Rajesh Sharma R Akey Sungheetha Department of Computer Science and Engineering Chandigarh University Mohali Punjab India Department of Computer Science and Engineering Alliance College of Engineering and Design Alliance University Bengaluru India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (数字)9798350354461

ISBN: (纸本)9798350354522

Through this work a thorough summary of a predictive model specifically designed to evaluate Aster Yellows infections in sunflower leaves is presented. Exhibiting remarkable accuracy are the model's performance measures, which span from 97.74% to 97.89% for various Damage Scale categories and include precision, recall, and F1-Score. These measurements demonstrate how well the model performs in accurately categorizing cases within each group and highlight how consistently it measures the severity of Aster Yellows disease. The support values clarify the significant representation of cases on every Damage Scale, demonstrating the model's resilience to a variety of situations. This work trains and evaluates the model using the 6880-picture dataset, yielding an astonishing 97.81% overall accuracy, recorded repeatedly at 97.82% on average due to the high averages (of macro, weighted, or micro numbers within each kind). It not only shows high performance across the dataset but also its consistency across Damaged Scales is further supported. And not just the model's reliability, modest variations in support rates also increase its dependability: highlighting its capacity to manage multiple diverse Aster Yellows infection scenarios. Through the model, they can clearly distinguish between different levels of damage with an accuracy of 99 percent overall, which betokens its potential application in precision farming. Now, thanks to these compelling test numbers, farmers and agricultural experts will no longer have to guess at how to distinguish and treat sunflower plants suffering from Aster Yellows infection. In summary: The abstract deftly depicts this model's quantitative performance, general versatility, and possible pragmatic utility, making it a bright spot in the history of precision farming.

关键词： Measurement Training Accuracy Predictive models Reliability Sustainable development Farming Diseases Resilience Pragmatics

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