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2023 IEEE International Conference on Network, Multimedia and Information Technology, NMITCON 2023

作者： Varadam, Deepak Agrawal, Yash Singh Rathore, Sumit Yashwanth Naik, Sugali Verma, Yash Pareta, Vishal M S Ramaiah University of Applied Sciences Faculty of Engineering and Technology Department of Computer Science and Engineering Bengaluru India M S Ramaiah University of Applied Sciences Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350300826

An Offline Navigation system to navigate the user without any connectivity is implemented by using smartphone sensors. The implementation of the system is carried out by extracting the relevant data from the smartphone sensors and discovering some meaningful insights from it. The offline navigation system is completely independent of GPS and any network connectivity such as Wi-Fi, Cellular Network, Bluetooth, etc. The system uses the Accelerometer sensor, Gyroscope sensor, and Magnetic sensor which are already present in the smartphone. The sensor fusion concept is used to combine the data from all the sensors and fuse it to give the device's orientation. The accelerometer sensor provides the acceleration of the device in all three directions. Similarly, the gyroscope sensor provides the angular velocity of the device, and the magnetometer sensor provides the magnetic fields. The data from all three sensors are combined to calculate the orientation angle such as pitch, roll, yaw, and azimuth of the device. The filtering algorithm used is known as Kalman Filter. It fuses the data from different sensors which results in less error on the combined data. The angles then use the position estimation algorithm and step detection algorithm, to find the next step and the coordinates of the device to track the user. The algorithm makes use of the previously determined coordinates of the device. © 2023 IEEE.

关键词： Accelerometers

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The Effectiveness of Advance Deep Learning Architectures for Classification of Stress using Raw EEG Data

The Effectiveness of Advance Deep Learning Architectures for...

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2024 Asia Pacific Conference on Innovation in Technology, APCIT 2024

作者： Tibrewal, Ayush Shikha Sethia, Divyashikha Delhi Technological University Department of Applied Physics Delhi India Delhi Technological University Science and Engineering Department of Computer Delhi India Delhi Technological University Department of Software Engineering Delhi India

ISBN: (纸本)9798350361537

Understanding and detecting stress in today's fast-paced world remains challenging. Previous research has explored various physiological signals, including EEG (Electroencephalography), to quantify stress. However, accurately assessing stress poses a significant challenge, particularly in preprocessing and feature extraction of EEG signals. Hence, this paper employs advanced deep learning algorithms, including ResNet18-1D, DenseNet-1D, and VGG16-1D, on raw EEG signals for automated stress classification. The study utilizes the publicly available EEG-based SAM40 dataset for stress classification and further validates the results on the EEG-based MAT dataset. The results demonstrate the efficacy of the ResNet18-1D model by achieving the highest of 98.73% and 97.24% on the SAM40 and MAT datasets, respectively. The findings pave the way for further exploration and utilization of raw EEG signals across diverse research domains beyond stress classification. © 2024 IEEE.

关键词： Electroencephalography

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Leveraging uncertainty quantification in adaptive multiphoton microscopy acquisition

Leveraging uncertainty quantification in adaptive multiphoto...

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Computational Optical Imaging and Artificial Intelligence in Biomedical sciences 2024

作者： Ye, Cassandra Tong Han, Jiashu Liu, Kunzan Angelopoulos, Anastasios Griffith, Linda Monakhova, Kristina You, Sixian Department of Electrical Engineering and Computer Science Mit United States Fu Foundation School of Engineering and Applied Science Columbia University United States Department of Eecs University of California Berkeley United States Department of Biological Engineering Mit United States

ISBN: (纸本)9781510669734

Multiphoton microscopy (MPM) provides high-resolution imaging of deep tissue structures while allowing for the visualization of non-labeled biological samples. However, photon generation efficiency of intrinsic biomarkers is low and this, coupled with inherent detection inaccuracies in the photoelectric sensors, leads to an introduction of noise in acquired images. Higher dwelling times can reduce noise but increase the likelihood of photobleaching. To combat this, deep learning methods are being increasingly employed to denoise MPM images, allowing for a more efficient and less invasive process. However, machine learning models can hallucinate information, which is unacceptable for critical scientific microscopy applications. Uncertainty quantification, which has been demonstrated for image-to-image regression tasks, can provide confidence bounds for machine learning-based image reconstruction tasks, adding confidence to predictions. In this work, we discuss incorporating uncertainty quantification into an optimized denoising model to guide adaptive multiphoton microscopy image acquisition. We demonstrate that our method is capable of maintaining fine features in the denoised image, while outperforming other denoising methods by adaptively selecting to reimage the most uncertain pixels in a human endometrium tissue sample. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Image reconstruction

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Recognition and Classification of Apple and Sugarcane Plant Leaf Diseases using SVM with DAE Models 2

Recognition and Classification of Apple and Sugarcane Plant ...

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2nd International Conference on Distributed Computing and Optimization Techniques, ICDCOT 2024

作者： Sunil Kumar, V. Jyothi, A.P. Sumanth, S. Nagamani, H.S. Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India Ramaiah University of Applied Sciences Faculty of Engineering and Technology Department of Cse Peenya Bengaluru India Department of Computer Science and Computer Applications Kolar India Department of Computer Science Karnataka Bengaluru India

ISBN: (纸本)9798350382952

For this leaf disease study, apple and sugarcane plants are identified and classified using a mix of character features and in-depth automated coding. Farm sugar cane leaves were copied for the online apple Plant Village Dataset. Pre-process images to reduce size and improve HLG filters. Image segmentation using DWT and GLCM feature extraction follows. Like the Low-Low (LL) band, DWT images produce 4-orthogonal subbands. Deep Auto Encoder and SVM algorithms retrieved subgroup functions and identified diseases from training data. If classification findings are ignored, the division stage receives data with HL, LH, and HH bands to explain approach consistency. Deep Auto encoder classifies illness using training data after extracting sub-band features. The Auto Encoder and SVM classifier demonstrated that the proposed model outperformed alternatives in LL band. Other subbands performed worse than the LL band. This study found that LL sub band data provided fine image data for better sickness categorization than HH, HL, and LH sub band data. © 2024 IEEE.

关键词： Discrete wavelet transforms

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High Resolution Solar Image Generation Using Generative Adversarial Networks

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Annals of Data science 2024年第5期11卷 1545-1561页

作者： Dash, Ankan Ye, Junyi Wang, Guiling Jin, Huiran Department of Computer Science New Jersey Institute of Technology NewarkNJ07102 United States School of Applied Engineering and Technology New Jersey Institute of Technology NewarkNJ07102 United States

We applied Deep Learning algorithm known as Generative Adversarial Networks (GANs) to perform solar image-to-image translation. That is, from Solar Dynamics Observatory (SDO)/Helioseismic and Magnetic Imager (HMI) line of sight magnetogram images to SDO/Atmospheric Imaging Assembly (AIA) 0304-Å images. The Ultraviolet (UV)/Extreme Ultraviolet observations like the SDO/AIA 0304-Å images were only made available to scientists in the late 1990s even though the magnetic field observations like the SDO/HMI have been available since the 1970s. Therefore, by leveraging Deep Learning algorithms like GANs we can give scientists access to complete datasets for analysis. For generating high resolution solar images, we use the Pix2PixHD and Pix2Pix algorithms. The Pix2PixHD algorithm was specifically designed for high resolution image generation tasks, and the Pix2Pix algorithm is by far the most widely used image to image translation algorithm. For training and testing we used the data for the year 2012, 2013 and 2014. After model training, we evaluated the model on the test data. The results show that our deep learning models are capable of generating high resolution (1024 × 1024 pixels) SDO/AIA0304 images from SDO/HMI line of sight magnetograms. Specifically, the pixel-to-pixel Pearson Correlation Coefficient of the images generated by Pix2PixHD and original images is as high as 0.99. The number is 0.962 if Pix2Pix is used to generate images. The results we get for our Pix2PixHD model is better than the results obtained by previous works done by others to generate SDO/AIA 0304 images. Thus, we can use these models to generate AIA0304 images when the AIA0304 data is not available which can be used for understanding space weather and giving researchers the capability to predict solar events such as Solar Flares and Coronal Mass Ejections. As far as we know, our work is the first attempt to leverage Pix2PixHD algorithm for SDO/HMI to SDO/AIA0304 image-to-image translation. © Th

关键词： Generative adversarial networks

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Evaluate Effectiveness of NAO Robot to Train Children with Autism Spectrum Disorder (ASD) 5

Evaluate Effectiveness of NAO Robot to Train Children with A...

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5th IEEE International Conference on Cognitive Machine Intelligence, CogMI 2023

作者： Karim, Masud Mia, Md. Solaiman Tareeq, Saifuddin Md. Hasanuzzaman, Md. University of Dhaka Department of Computer Science and Engineering Dhaka1000 Bangladesh University of Bangladesh Department of Computer Science and Engineering Green Dhaka1207 Bangladesh Southern Arkansas University Department of Math of Computer Science MagnoliaAR71753 United States

ISBN: (纸本)9798350323832

Autism Spectrum Disorder (ASD) is a neurodevel-opmental disorder. Such disorders are found in childhood life. Children with ASD have less capabilities in communication and social skills. Therapies are used to develop communication and social skills. Recently researchers are trying to use robots in such therapies. In this paper, we have presented social skill learning test cases for children with ASD. Autism conditions are measured in 30 children in a special school. Among them, twelve children are selected who have similar ASD conditions. Then six children participated in training with humans and another six children participated in training with robots. The learning session continued for three alternative days for a child of a week and the duration of each session was two hours for each day. We have taken an assessment test before and after the learning sessions by a human trainer. We have found better performances from children who have participated in robotic sessions rather than the children who have participated in human sessions. © 2023 IEEE.

关键词： Human robot interaction

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DESIGN AND IMPLEMENTATION OF A VIRTUAL LAB PLATFORM FOR ACADEMIC TEACHING: EXPERIENCE IN THE DEVELOPMENT AND USE OF A NEW PLATFORM FOR THE DIGITIZATION OF LABORATORIES FOR computer science CLASSES 21

DESIGN AND IMPLEMENTATION OF A VIRTUAL LAB PLATFORM FOR ACAD...

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21st International Conference on applied Computing 2024, AC 2024 and 23rd International Conference on WWW/Internet 2024, ICWI 2024

作者： Baun, Christian Kappes, Martin Cocos, Henry-Norbert Koch, Malte Martin Petrozziello, Maurizio Frankfurt University of Applied Sciences Department of Computer Science and Engineering Nibelungenplatz 1 Frankfurt am MainD-60318 Germany

ISBN: (纸本)9789898704627

This paper presents a virtual platform for university teaching that simplifies the creation and use of complex IT setups, even for students with little to no experience. Setting up infrastructures for courses like computer networks or IT security has traditionally been challenging. However, the platform presented in this paper simplifies the setup by using virtualization techniques for compute, network, and storage resources, reducing the efforts in preparation for lecturers, researchers, and students. The platform gives access to complex IT setups utilizing free software components. The project employs Proxmox Virtual Environment and CEPH for virtualization and distributed storage, respectively, providing a flexible and practical solution. On top of that, a novel graphical web interface for the easy access and use of the virtual infrastructures is implemented using ReactFlow. © 2024 Proceedings of the International Conferences on applied Computing and WWW/Internet 2024. All rights reserved.

关键词： Students

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OFFLINE-FIRST STRATEGIES - A NOVEL CONCEPT FOR THE MIGRATION OF WORKLOADS USING VIRTUAL MACHINES OMITING LIMITATIONS OF TRADITIONAL SERVICE DEPLOYMENT CONCEPTS 21

OFFLINE-FIRST STRATEGIES - A NOVEL CONCEPT FOR THE MIGRATION...

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21st International Conference on applied Computing 2024, AC 2024 and 23rd International Conference on WWW/Internet 2024, ICWI 2024

作者： Cocos, Henry-Norbert Frankfurt University of Applied Sciences Department of Computer Science and Engineering Nibelungenplatz 1 Frankfurt am MainD-60318 Germany

ISBN: (纸本)9789898704627

The project aims to investigate the outsourcing of cloud services to clients by including heterogeneous, client-side resources and cooperation between cloud and client in the execution of services. For this purpose, an offline-first strategy shall be formulated, developed, and investigated to be used as a counterpart to the cloud-first strategy and enable the utilization of services without a permanent cloud connection. The scope of the offline-first strategy is to achieve autonomy for applications while cooperating with cloud services. To achieve the independence and simultaneous cooperation of applications in this context, methods of service migration are to be researched to enable the provision of services. In summary, this should increase resilience when using services, improve the stability of services along the cloud-to-edge supply, and allow transparent use for the end user. Furthermore, lab experiments were conducted to investigate the behavior of virtual machines during live migration in two setups, simulating a WAN link. The experiments show that migration over a WAN link takes five times longer for RAM-intensive tasks than in a LAN. This provides initial insights into selecting the technological basis for this project. © 2024 Proceedings of the International Conferences on applied Computing and WWW/Internet 2024. All rights reserved.

关键词： Cloud platforms

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Information-Centric IoT-Based Smart Farming with Dynamic Data Optimization

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computers, Materials & Continua 2023年第2期74卷 3865-3880页

作者： Souvik Pal Hannah VijayKumar D.Akila N.Z.Jhanjhi Omar A.Darwish Fathi Amsaad Department of Computer Science and Engineering Sister Nivedita UniversityKolkataIndia Post Doctoral Researcher Sambalpur UniversitySambalpurIndia Department of Computer Science Anna Adarsh College for WomenChennaiIndia Department of Information Technology Vels Institute of ScienceTechnology and Advanced StudiesChennaiIndia School of Computer Science and Engineering SCETaylor’s UniversitySubang Jaya47500SelangorMalaysia FITTaylor’s UniversitySubang Jaya47500SelangorMalaysia Department of Information Security and Applied Computing Eastern Michigan UniversityUSA School of Information Security and Applied Computing Eastern Michigan UniversityUSA

Smart farming has become a strategic approach of sustainable agriculture management and monitoring with the infrastructure to exploit modern technologies,including big data,the cloud,and the Internet of Things(IoT).Many researchers try to integrate IoT-based smart farming on cloud platforms *** define various frameworks on smart farming and monitoring system and still lacks to define effective data management *** IoT-cloud systems involve massive structured and unstructured data,data optimization comes into the ***,this research designs an Information-Centric IoT-based Smart Farming with Dynamic Data Optimization(ICISF-DDO),which enhances the performance of the smart farming infrastructure with minimal energy consumption and improved ***,a conceptual framework of the proposed scheme and statistical design model has beenwell *** information storage and management with DDO has been expanded individually to show the effective use of membership parameters in data *** simulation outcomes state that the proposed ICISF-DDO can surpass existing smart farming systems with a data optimization ratio of 97.71%,reliability ratio of 98.63%,a coverage ratio of 99.67%,least sensor error rate of 8.96%,and efficient energy consumption ratio of 4.84%.

关键词： Smart farming data optimization information management internet of things cloud

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3GPP SEAL as an Edge Application: The Ultimate Enabler of Flexible and Universal Communication between Vulnerable Road Users and Autonomous Vehicles 100

3GPP SEAL as an Edge Application: The Ultimate Enabler of Fl...

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100th IEEE Vehicular Technology Conference, VTC 2024-Fall

作者： Charpentier, Vincent Leyendeckers, Amaryllis Camelo, Miguel Marquez-Barja, Johann Slamnik-Kriještorac, Nina University of Antwerp-imec IDLab Faculty of Applied Engineering Belgium University of Antwerp-imec IDLab Department of Computer Science Belgium

ISBN: (纸本)9798331517786

Enhancing communication between Vulnerable Road Users (VRUs) and Unmanned Automated Vehicles (UAVs) has significant potential to improve road safety. The need for this communication is due to the fact that VRUs will no longer be able to establish physical eye contact with UAVs, given the absence of a human driver behind the steering wheel. However, a challenge in the state-of-the-art technologies for Connected, Cooperative, and Automated Mobility (CCAM), i.e. ITS-G5 (IEEE 802.11p) and Cellular Vehicle-to-Everything (C-V2X), is the lack of a unified communication stack that connects all types of users. This is because the current generation of CCAM communication technologies requires dedicated hardware devices that cannot be easily installed on devices carried by VRUs (such as phones or wearables). This paper aims to address this challenge by providing a real-life, sophisticated solution that offers the CCAM communication stack as a Network-as-a-Service (NaaS) in the 5G and Beyond ecosystem. Integration is achieved by relying on the Service Enabler Architecture Layer (SEAL) principles standardised by the 3rd Generation Partnership Project (3GPP). These architectural principles are embedded in the design of Network-Aware Edge Applications (EdgeApps), which are the building blocks of vertical services in 5G and Beyond. This way, any device or user with the capability to connect to 5G will also be able to retrieve important CCAM services from the network by using EdgeApps. In addition, no dedicated CCAM hardware is needed. Furthermore, this paper provides key lessons learned from the challenges encountered in connecting VRUs and UAVs by integrating CCAM into the 5G and Beyond ecosystem. Moreover, we have conducted real-life experiments to evaluate the system-level latency characteristics of the proposed solution and compared them with those of ITS-G5 and C-V2X. © 2024 IEEE.

关键词： 5G mobile communication systems

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