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IEEE Global Conference on Consumer Electronics (GCCE)

作者： Nurul Asmi Amalia Aida Afira Maria Ulfa Kahlil Muchtar Department of Informatics Master of Artificial Intelligence Universitas Syiah Kuala Indonesia Department of Electrical and Computer Engineering Universitas Syiah Kuala

ISBN: (数字)9798350355079

ISBN: (纸本)9798350355086

This study presents an edge-based system for underwater image enhancement using CLAHE and its fusion with various techniques. Utilizing the NVIDIA Jetson Orin Nano and the Streamlit framework, the system processes images in real-time. The enhancement methods include CLAHE, Fusion of CLAHE and USM, Fusion of CLAHE and HEF, and Blending CLAHE with Percentile Techniques. Tested on real underwater images, these methods demonstrate significant improvements in UIQM and UCIQE scores while reducing LOE. This combination boosts image quality while maintaining natural lightness, making it suitable for marine exploration, underwater robotics, and environmental monitoring. A demo of our image enhancement web app can be found here: https://***/EDJxPKv1LSs.

关键词： Image quality Image edge detection Streaming media Real-time systems Environmental monitoring Image enhancement Robots Consumer electronics

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A Wideband Rotary Antenna Based on Gap Wavegudie Technology for mmWave Applications

A Wideband Rotary Antenna Based on Gap Wavegudie Technology ...

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European Conference on Antennas and Propagation, EuCAP

作者： Ali Farahbakhsh Davood Zarifi Michal Mrozowski Department of Microwave and Antenna Engineering Faculty of Electronics Telecommunications and Informatics Gdansk University of Technology Gdansk Poland Department of Electrical and Computer Engineering Graduate University of Advanced Technology Kerman Iran School of Electrical and Computer Engineering University of Kashan Kashan Iran

ISBN: (数字)9788831299107

ISBN: (纸本)9798350366327

This study introduces the design of a gap waveguide-based rotary antenna capable of 360° mechanical beam-steering for wideband mmWave applications. This innovative design overcomes the typical limitations of electronically steerable antennas, such as narrow bandwidth, restricted steering range, gain reduction, and high cost and complexity. The designed rotary antenna achieves a gain of 13.8 dBi without any scan loss across the entire 360° steering range from 45 to 60 GHz. The proposed rotary antenna is a promising candidate for a variety of mmWave mechanical beam scanning systems.

关键词： Steerable antennas Costs Electromagnetic scattering Gap waveguide Europe Broadband antennas Impedance Millimeter wave communication Electromagnetics Wideband

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Learning Robotic Cutting from Demonstration: Non-Holonomic DMPs using the Udwadia-Kalaba Method

Learning Robotic Cutting from Demonstration: Non-Holonomic D...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Artūras Straižys Michael Burke Subramanian Ramamoorthy School of Informatics University of Edinburgh Department of Electrical and Computer Systems Engineering Monash University

Dynamic Movement Primitives (DMPs) offer great versatility for encoding, generating and adapting complex end-effector trajectories. DMPs are also very well suited to learning manipulation skills from human demonstration. However, the reactive nature of DMPs restricts their applicability for tool use and object manipulation tasks involving non-holonomic constraints, such as scalpel cutting or catheter steering. In this work, we extend the Cartesian space DMP formulation by adding a coupling term that enforces a pre-defined set of non-holonomic constraints. We obtain the closed-form expression for the constraint forcing term using the Udwadia-Kalaba method. This approach offers a clean and practical solution for guaranteed constraint satisfaction at run-time. Further, the proposed analytical form of the constraint forcing term enables efficient trajectory optimization subject to constraints. We demonstrate the usefulness of this approach by showing how we can learn robotic cutting skills from human demonstration.

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Assessing the electrical Energy Consumption for Designing and Developing an Energy Management System in an Educational Building in Romania 58

Assessing the Electrical Energy Consumption for Designing an...

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58th International Universities Power engineering Conference, UPEC 2023

作者： Farkas, Timea Chicco, Gianfranco Czumbil, Levente Ceclan, Andrei Berciu, Alexandru-George Polycarpou, Alexis Micu, Dan D. Technical University of Cluj-Napoca Energy Transition Research Center Cluj-Napoca Romania Politecnico di Torino Dipartimento Energia 'Galileo Ferraris' Torino Italy Frederick University Department of Electrical Engineering Computer Engineering and Informatics Nicosia Cyprus

ISBN: (纸本)9798350316834

The building sector along with more and more inside or nearby-supplied EV mobility are a key pillar in the efforts of decarbonisation and achieving a Net Zero Emissions scenario by 2050 and even earlier. Without immediate action in the energy management and control automated systems, the building sector will not align with the decarbonisation goals. Energy management systems play a crucial role in managing energy usage and rely on forecasting algorithms and energy management schemes. A case study on a demo site pilot was conducted on the main building of the Faculty of Building engineering Services at the Technical University of Cluj-Napoca, Romania. The study aims to assess electrical energy consumption and bidirectional flows, and to identify key correlations between factors, using multilinear regression as well as consumption pattern identification. Two scenarios are proposed and assessed. © 2023 IEEE.

关键词： Energy efficiency

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Colorectal Polyp Localization: From Image Restoration to Real-time Detection with Deep Learning 17

Colorectal Polyp Localization: From Image Restoration to Rea...

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17th IEEE International Symposium on Applied Computational Intelligence and informatics, SACI 2023

作者： Manshadi, Mahsa Dehghan Mousavi, Milad Golzarian, Arian Soltani, Madjid Mosavi, Amir K.N. Toosi University of Technology Department of Mechanical Engineering Tehran Iran University of Waterloo Department of Electrical and Computer Engineering Ontario Canada Obuda University John von Neumann Faculty of Informatics Budapest Hungary University of Public Service Budapest Hungary

ISBN: (纸本)9798350321104

Increasing demands for artificial intelligence tools in the medical sector show the huge interest of physicians. In recent years, one of the most effective assistant systems in this field is the real-time detection of early-stage colorectal polyps during colonoscopy. Since even experienced physicians may miss polyps during colonoscopy, the real-time assistance system is designed to prevent this and hence contributes to diminish the number of missed critical cases. One challenge in this field is the detection of false positives. These systems are prone to mistake artifacts for colorectal polyps. This review provides an overview over current quality assessment and restoration techniques to make a high-quality training dataset for training deep neural network algorithms. Furthermore, four of the latest, fastest, and most accurate methods are introduced and analyzed in the rest of the review. Our main contribution is to provide an analysis of current methods used to detect colorectal polyps. We present a list of available datasets and present a range of challenges colorectal cancer detection systems face. © 2023 IEEE.

关键词： Image reconstruction

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Load Balancing on Named Data Networking, Case Study: UIN Topology in Indonesia 16

Load Balancing on Named Data Networking, Case Study: UIN Top...

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16th International Conference on Telecommunication Systems Services and Applications, TSSA 2022

作者： Zaki Hamidi, Eki Ahmad Ahdan, Syaiful Jupriyadi Sucinto, Adi Arifin, Hasan Nur Syambas, Nana Rachmana School of Electrical Engineering and Informatics Instsitut Teknologi Bandung Bandung Indonesia Uin Sunan Gunung Djati Bandung Electrical Engineering Department Bandung Indonesia Universitas Teknokrat Indonesia Faculty of Engineering and Computer Science Bandarlampung Indonesia

ISBN: (纸本)9781665455947

Named Data Networking (NDN) is a network with a future internet architecture that changes the point of view in networking from host-centric to data-centric. Named data networking provides a network system where the routing system is no longer dependent on traditional IP. Network packets are routed through nodes by name. When many manufacturers produce packages with different names for several consumers, routing with load balancing is necessary. The case study carried out is to conduct a simulation by connecting all UIN campuses into a topology with the name UIN Topology in Indonesia, using several scenarios to describe the effectiveness of the load balancer on the UIN topology in Indonesia. This study focuses on load balancer applications to reduce delays in Named Data Networking (NDN), the topology of UIN in Indonesia. © 2022 IEEE.

关键词： Topology

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DCA: A Dual-layer Cache Architecture for Providing High Lookup Performance in KV Stores

DCA: A Dual-layer Cache Architecture for Providing High Look...

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International Conference on Big Data and Smart Computing (BIGCOMP)

作者： Jeongho Lee Yongju Song Young Ik Eom Department of Electrical and Computer Engineering Sungkyunkwan University Suwon South Korea Department of Electrical and Computer Engineering / College of Computing and Informatics Sungkyunkwan University Suwon South Korea

Recent advances in memory technology have expedited the adoption of a large-capacity memory layer for key-value stores. RocksDB, which is one of the most popular key-value stores, utilizes the memory layer effectively with an LRU cache for supporting fast lookup queries. Also, RocksDB ensures the consistency of the LRU cache using mutex locks. But, this design brings a contention issue, resulting in performance degradation, when multiple threads access the LRU cache simultaneously. To tackle this problem, we propose a novel LRU cache architecture called DCA that exploits an additional hash table to enable parallel lookups. Experimental results show that DCA outperforms Vanilla RocksDB by up to $2. 75 \times$ on read-intensive workloads.

关键词： Degradation Concurrent computing Instruction sets computer architecture Big Data

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Fusion of Infrared and Visible Images Using Fuzzy Based Siamese Convolutional Network

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computers, Materials & Continua 2022年第3期70卷 5503-5518页

作者： Kanika Bhalla Deepika Koundal Surbhi Bhatia Mohammad Khalid Imam Rahmani Muhammad Tahir Department of Electrical Engineering National Taipei University of TechnologyTaipei10608Taiwan Department Virtualization School of Computer ScienceUniversity of Petroleum&Energy StudiesDehradunIndia College of Computer Science and Information technology King Faisal University36362Saudi Arabia College of Computing and Informatics Saudi Electronic UniversityRiyadh11673Saudi Arabia

Traditional techniques based on image fusion are arduous in integrating complementary or heterogeneous infrared(IR)/visible(VS)*** in various kind of features in these images are vital to preserve in the single fused ***,simultaneous preservation of both the aspects at the same time is a challenging ***,most of the existing methods utilize the manual extraction of features;and manual complicated designing of fusion rules resulted in a blurry artifact in the fused ***,this study has proposed a hybrid algorithm for the integration of multi-features among two heterogeneous ***,fuzzification of two IR/VS images has been done by feeding it to the fuzzy sets to remove the uncertainty present in the background and object of interest of the ***,images have been learned by two parallel branches of the siamese convolutional neural network(CNN)to extract prominent features from the images as well as high-frequency information to produce focus maps containing source image ***,the obtained focused maps which contained the detailed integrated information are directly mapped with the source image via pixelwise strategy to result in fused *** parameters have been used to evaluate the performance of the proposed image fusion by achieving 1.008 for mutual information(MI),0.841 for entropy(EG),0.655 for edge information(EI),0.652 for human perception(HP),and 0.980 for image structural similarity(ISS).Experimental results have shown that the proposed technique has attained the best qualitative and quantitative results using 78 publically available images in comparison to the existing discrete cosine transform(DCT),anisotropic diffusion&karhunen-loeve(ADKL),guided filter(GF),random walk(RW),principal component analysis(PCA),and convolutional neural network(CNN)methods.

关键词： Convolutional neural network fuzzy sets infrared and visible image fusion deep learning

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Optimizing NOMA Transmissions to Advance Federated Learning in Vehicular Networks

Optimizing NOMA Transmissions to Advance Federated Learning ...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Ziru Chen Zhou Ni Peiyuan Guan Lu Wang Lin X. Cai Morteza Hashemi Zongzhi Li Department of Electrical and Computer Engineering Illinois Institute of Technology Chicago USA Department of Electrical Engineering and Computer Science University of Kansas Lawrence USA Department of Informatics University of Oslo Oslo Norway Department of Civil Engineering Illinois Institute of Technology Chicago USA

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

Diverse critical data, such as location information and driving patterns, can be collected by IoT devices in vehicular networks to improve driving experiences and road safety. However, drivers are often reluctant to share their data due to privacy concerns. The Federated Vehicular Network (FVN) is a promising technology that tackles these concerns by transmitting model parameters instead of raw data, thereby protecting the privacy of drivers. Nevertheless, the performance of Federated Learning (FL) in a vehicular network depends on the joining ratio, which is restricted by the limited available wireless resources. To address these challenges, this paper proposes to apply Non-Orthogonal Multiple Access (NOMA) to improve the joining ratio in a FVN. Specifically, a vehicle selection and transmission power control algorithm is developed to exploit the power domain differences in the received signal to ensure the maximum number of vehicles capable of joining the FVN. Our simulation results demonstrate that the proposed NOMA-based strategy increases the joining ratio and significantly enhances the performance of the FVN. Index Terms—Federated Vehicular Network, NOMA

关键词： Wireless communication NOMA Data privacy Federated learning Simulation Power control Road safety Real-time systems Resource management Vehicles

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Implementation Augmented Intelligence on Drug Inventory Management Forecasting 24

Implementation Augmented Intelligence on Drug Inventory Mana...

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24th International Electronics Symposium, IES 2022

作者： Satrio, Naufal Adi Sukaridhoto, Sritrusta Al Rasyid, Muhammad Udin Harun Budiarti, Rizqi Putri Nourma Fajrianti, Evianita Dewi Al Hafidz, Ilham Achmad Haz, Amma Liesvarastranta Politeknik Elektronika Negeri Surabaya Informatics and Computer Engineering Indonesia Universitas Nahdlatul Ulama Surabaya Department of Information Systems Indonesia Politeknik Elektronika Negeri Surabaya Electrical Engineering Indonesia Institut Teknologi Sepuluh Nopember Department of Electrical Engineering Indonesia

ISBN: (数字)9781665489713

ISBN: (纸本)9781665489713

The administration of medications at many hospitals is not governed by an inventory forecasting system, which is common in the management of many hospitals. This might either result in an excessive amount of pharmaceuticals being available in the hospital or a serious shortage of them. Because of this, it is essential for the hospital to have inventory forecasting for the management of drugs;this will assist the hospital in determining when the medicine will run out of supply. As a result of this, it is essential for the hospital to have inventory forecasting for the management of drugs. The author of this study builds an augmented intelligence with the help of a stacked LSTM model. Augmented intelligence is a kind of intelligence that supports stakeholders in getting a broad grasp of inventory projections for the foreseeable future. Using event-driven architecture, the systems are developed independently so that they will not interfere with the workings of the current hospital administration system. We test the model on 19 different drug stocks using a total of 1000 days of data for each medication, and the results reveal that stacked LSTM is able to correctly predict with average values for the R2 coefficient, RMSE, MSE, and MAE being 0.9972, 1.95, 3.81, and 1.26 respectively. The results also show that the proposed system can work. © 2022 IEEE.

关键词： Forecasting

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