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Ultra-Wideband Shaped-Chalice Microstrip Patch Antenna for Wireless communications Applications 3

Ultra-Wideband Shaped-Chalice Microstrip Patch Antenna for W...

引用

3rd International Conference on Emerging Smart Technologies and Applications, eSmarTA 2023

作者： Ali, Mohammed A. S. A. Shaddad, Redhwan Q. Taiz University Faculty of Engineering and It Dept. of Communication and Computer Engineering Taiz Yemen

ISBN: (纸本)9798350305333

The paper presents two compact ultra-wideband microstrip antennas for wireless communications applications. The antenna bandwidth is enhanced by sharping of upon part of ground and crop the two bottom edges of the patch sheet greatly improves the bandwidth. Two designs are of the same size and shape with a slight difference between them, both designs have the same size 30\times 26\times 1.6mm^{3} and are printed on FR-4 (flame resistant epoxy) material with height (h) of 1.6 mm, loss tangent (tan\delta) of 0.02 and dielectric constant (\varepsilon_{r}) of 4.4 and a 50-ohm feed line for the antenna is used. The first design that provides three bands, the first band of (3.3 - 11.88) GHz with resonant frequencies of 3.9/8.38/11.28 GHz, the second band of (15.9-19) GHz at 18 GHz resonant frequency, third band (21.47-25.44) GHz at 23.88 GHz resonant frequency. The second design provides three bands, the first band is (3.14-11.9) GHz with resonant frequencies of 3.8 / 5.3 / 8.4 / 11 GHz, the second band of(15.7-18.7) GHz at the resonant frequency of 17.9 GHz and the third band of (21-24.9) GHz at 23 GHz resonant frequency. The results and designed is applied by using HFSS software. © 2023 IEEE.

关键词： Natural frequencies

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Bidirectional Multi-Step Domain Generalization for Visible-Infrared Person Re-Identification

Bidirectional Multi-Step Domain Generalization for Visible-I...

引用

2025 IEEE/CVF Winter Conference on Applications of computer Vision, WACV 2025

作者： Alehdaghi, Mahdi Shamsolmoali, Pourya Cruz, Rafael M. O. Granger, Eric Livia Ills Dept. of Systems Engineering Ets Montreal Canada University of York Dept. of Computer Science United Kingdom

ISBN: (纸本)9798331510831

A key challenge in visible-infrared person re-identification (V-I ReID) is training a backbone model capable of effectively addressing the significant discrepancies across modalities. State-of-the-art methods that generate a single intermediate bridging domain are often less effective, as this generated domain may not adequately capture sufficient common discriminant information. This paper introduces Bidirectional Multi-step Domain Generalization (BMDG), a novel approach for unifying feature representations across diverse modalities. BMDG creates multiple virtual intermediate domains by learning and aligning body part features extracted from both I and V modalities. In particular, our method aims to minimize the cross-modal gap in two steps. First, BMDG aligns modalities in the feature space by learning shared and modality-invariant body part prototypes from V and I images. Then, it generalizes the feature representation by applying bidirectional multi-step learning, which progressively refines feature representations in each step and incorporates more prototypes from both modalities. Based on these prototypes, multiple bridging steps enhance the feature representation. Experiments11Our code is available at: ***/BMDG conducted on V-I ReID datasets indicate that our BMDG approach can outperform state-of-the-art part-based and intermediate generation methods, and can be integrated into other part-based methods to enhance their V-I ReID performance. © 2025 IEEE.

关键词： Multi-task learning

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Internet of Things - Internet of Coffee Machines 5

Internet of Things - Internet of Coffee Machines

引用

5th IEEE International Conference on communications, Information, Electronic and Energy systems, CIEES 2024

作者： Genkov, Delyan Manchev, Nikolay Technical University - Gabrovo Dept. of Computer Systems and Technologies Gabrovo Bulgaria Technical University - Gabrovo Dept. of Communication Equipment and Technologies Gabrovo Bulgaria

ISBN: (纸本)9798350352863

The Internet of Things concept introduces remote monitoring and management of different devices, collecting data through sensors, connected to controllers and sending commands to actuators. This paper presents a real-life example - a project developed for remote monitoring and management of street coffee machines, located at different places, collecting data for available products inside the machine, errors and warnings generated by the machine and remote controls. © 2024 IEEE.

关键词： Coffee machine IoT remote control remote monitoring

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Accessing Visual Information for Divyangjan 4

Accessing Visual Information for Divyangjan

引用

4th IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Rani, M. S. Divya Ghodke, Hrishikesh Vivekanand Manikandan, M. Manaswini, R. Shivani, K. Dhara, Sri Sai Venkata Gayathri Shreya Presidency University Dept. of Electronics and Communication Engineering Bengaluru560064 India Presidency University Dept. of Computer Science Engineering Bengaluru560064 India

ISBN: (纸本)9798331518981

Approximately 1.3 billion people worldwide suffer from a visual impairment. Typically, they must use Braille to read printed materials. However, when the content is not printed in Braille, these individuals have difficulties. Even though there are a lot of electronic reading aids available, the costs remain prohibitive. Therefore, our work suggests a low-cost document reader that uses a webcam to take pictures of printed materials. The Optical Character Recognition (OCR) framework's image-to-text conversion is then used to turn the acquired image into text. Finally, the Text to Speech (TTS) framework's text-to-speech conversion will be used to read the text aloud in speech format. Consequently, a person with a visual impairment can use hearing rather than touch to comprehend printed materials that are not written in Braille. Users may view documents printed in five different languages. The choice of different document sizes is this work's greatest novelty. The system is equipped with a Raspberry Pi, Arduino Uno, NEMA 17 stepper motors, a web camera and speaker. The Raspberry Pi, Arduino Uno, NEMA 17 stepper motors, webcam, and speaker are all part of the system. © 2024 IEEE.

关键词： Stepping motors

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Enhancing Mortality Prediction in ICU: A Deep Learning Framework for Multivariate Time Series Classification

Enhancing Mortality Prediction in ICU: A Deep Learning Frame...

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2024 International Conference on Integration of Emerging Technologies for the Digital World, ICIETDW 2024

作者： Sathia Bhama, Ponsy R K Sarah R, Roshina Shobia, T.R. Vinothini, R. Anna University Dept. of Computer Technology MIT Campus Chennai India Sri Sairam Institute of Technology Dept. of Computer and Communication Engineering Chennai India

ISBN: (纸本)9798331541170

Relying on human diagnosis is potentially prone to errors, time-consuming and leading to treatment mistakes. Early Intensive Care Unit (ICU) mortality prediction is important for determining which patients are most likely to die and for offering appropriate interventions that will preserve their lives. However, conventional diagnostic approaches often face challenges in fully harnessing the extensive information contained within Electronic Health Record (EHR) data, particularly in capturing the intricate temporal dynamics of patient health. In this research paper, a hybrid model leverages the power of Temporal Convolutional Networks (TCN) in conjunction with Bidirectional Gated Recurrent Units (BiGRU) to effectively capture the complex temporal dependencies present in ICU time series data, before constructing the model, the data is pre-processed by cutting-edge Generative Adversarial Network (GAN)-based imputation techniques to handle missing values within the EHR data. Through this comprehensive framework, the goal is to significantly enhance mortality prediction accuracy, empower clinicians with actionable insights into patient prognosis, and ultimately improve patient outcomes in critical care settings. © 2024 IEEE.

关键词： Electronic health record

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Fuzzy Based Intelligent Transportation systems for Smart Cities to Mitigate Road Traffic Congestion

Fuzzy Based Intelligent Transportation Systems for Smart Cit...

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2024 International Conference on Innovations in Science, engineering and Technology, ICISET 2024

作者： Kabir, Md. Humayun Islam, Md. Saiful Hoque, Md. Jiabul Chittagong University of Engineering and Technology Dept. of Electronics and Telecommunication Engineering Chittagong Bangladesh International Islamic University Chittagong Dept. of Computer and Communication Engineering Chittagong Bangladesh

ISBN: (纸本)9798350355499

Traffic congestion in smart cities is a growing concern. This paper explores the application of Fuzzy Logic in Intelligent Transportation systems (ITS) to address this challenge. Fuzzy logic can handle imprecise and dynamic traffic data and offers advantages over traditional fixed-time signal control. The research proposes a Fuzzy-based ITS framework for dynamically traffic signal control. This framework utilizes real-time sensor traffic data to adjust signal timings, dynamically prioritizing congested areas. The system also incorporates emergency vehicle detection using siren sensors. This ensures priority for emergency vehicles by granting them the right of way. This paper discusses the design of the fuzzy inference system, including the selection of fuzzy variables and membership functions. The effectiveness of the proposed system is evaluated through simulations, demonstrating its potential to reduce traffic congestion and improve traffic flow in smart cities. © 2024 IEEE.

关键词： Traffic congestion

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Detecting Holes in Fish Farming nets: A Two-Method approach

Detecting Holes in Fish Farming nets: A Two-Method approach

引用

2023 International Conference on Control, Automation and Diagnosis, ICCAD 2023

作者： Paraskevas, Konstantinos Kavallieratou, Ergina University of the Aegean Dept. Information and Communication Systems Engineering Samos Greece

ISBN: (纸本)9798350347074

This paper presents two different techniques for detecting holes in fish farming nets using image processing. The first technique utilizes the OpenCV library, while the second employs artificial neural networks (ANNs). A real-world dataset of fish farming net images, captured by an ROV with various hole types, was used to evaluate the performance of both techniques. The results indicate that the OpenCV-based approach outperforms the ANN-based method in terms of accuracy and robustness, despite the fact that both methods performed poorly. However, additional research can significantly improve the proposed techniques, ultimately enhancing the efficiency and reliability of hole detection in fish farming nets. This is particularly important for maintaining the health and productivity of fish in aquaculture operations. © 2023 IEEE.

关键词： computer vision

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Non-Invasive Oral Cancer Detection Using Hyperspectral Imaging and Advanced Spectral Unmixing Models

Non-Invasive Oral Cancer Detection Using Hyperspectral Imagi...

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2024 IEEE International Conference on Intelligent Computing and Emerging communication Technologies, ICEC 2024

作者： Ayyapa, Valluri Kothamasu, Kesava Sriram Killaru, Priyusha Muddana, Saadhivik Aala, Suresh Gutha, Vamsi Kumar, Mukkara Prasana Chinnadurai, Sunil SRM University Dept.of Electronics and Communication Engineering AP India SRM University Dept. of Computer Science and Engineering AP India SRM University Dept. of Computer Science AP India

ISBN: (纸本)9798331508432

Oral cancer is a significant global health concern, often leading to high mortality rates due to late-stage diagnosis and the lack of effective early detection methods. Despite advances in medical science, the absence of reliable early diagnostic tools remains a critical challenge. Hyperspectral imaging (HSI) has emerged as a powerful noninvasive technology, capturing detailed spectral information across a wide range of wavelengths. This allows for accurate differentiation between cancerous and healthy tissues, improving early detection and enhancing treatment outcomes. In this study, we propose the use of HSI for early oral cancer diagnosis. To address the scarcity of labeled data, we developed a synthetic hyperspectral dataset that includes spectral signatures of both normal and cancerous tissues. The dataset was generated using a bilinear mixing model, with key spectral features extracted through Vertex Component Analysis (VCA) and abundances computed using Non-Negative Least Squares (NNLS). The model's performance was evaluated using Spectral angle distance (SAD) and Root mean square error (RMSE) metrics. Our findings demonstrate that HSI significantly improves the accuracy of early oral cancer detection, outperforming traditional methods. This work highlights the potential of advanced imaging technologies in revolutionizing cancer diagnosis, offering a robust framework for non-invasive detection and showcasing the effectiveness of synthetic datasets in medical imaging research. © 2024 IEEE.

关键词： Deep Learning Hyperspectral Imaging (HSI) Non-Negative Least Squares (NNLS) Oral Cancer Detection Root Mean Square Error (RMSE) Spectral Angle Distance (SAD) Synthetic Dataset Vertex Component Analysis (VCA)

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Support Vector Regression Based Traffic Prediction Machine Learning Model∗ 8

Support Vector Regression Based Traffic Prediction Machine L...

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8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Srivastava, Ansh Singh, Mrigaannkaa Nandi, Somesh Rv College of Engineering Dept. of Computer Science and Engineering Bengaluru India Rv College of Engineering Dept. of Electronics and Communication Bengaluru India Rv College of Engineering Dept. of AI/ML Bengaluru India

ISBN: (纸本)9798331505462

To combat the rising issue of urban traffic congestion, optimization of traffic light timings at intersections is crucial. Traditional methods show stagnancy in managing traffic flow dynamically. This study introduces Support Vector Regression (SVR) as an unconventional solution for traffic signal optimization. The model predicts the traffic conditions and alters the green light to turn on and off accordingly. Utilization of a comprehensive dataset consisting of vehicle distributions and critical flow ratios have been implemented to training a machine learning model. The statistics and performance is checked by usage of Mean Squared Error, R Squared, Mean Absolute Error demonstrating that SVR can effectively enhance traffic signal control. This approach pledges significant improvements in reduction in congestion and streamlined traffic flow. Our findings majorly highlight the capability of the SVR Machine Learning model in improving urban traffic management systems and call for further enhancement and collaboration with experts to address dynamic traffic issues. © 2024 IEEE.

关键词： Mean square error

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AirWave: Enhancing UAV Connectivity in Cellular Networks through an Integrated Simulation Framework

AirWave: Enhancing UAV Connectivity in Cellular Networks thr...

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2024 IEEE International Conference on Big Data and Machine Learning, ICBDML 2024

作者： Zor, Emirhan Taskiran, Yusuf Dael, Fares A. Shayea, Ibraheem Rzayeva, Leila Syzdykova, Zuleikha Istanbul Technical University Electronic and Communication Engineering Istanbul Turkey İzmir Bakirçay University Management Information Systems İzmir Turkey Astana IT University Dept. of Intelligent Systems and Cybersecurity Astana Kazakhstan Astana IT University Dept. of Computer Engineering Astana Kazakhstan

ISBN: (纸本)9798350374100

This study presents AirWave, an innovative modeling framework tailored to address the challenges associated with integrating Unmanned Aerial Vehicles (UAVs) into 5G and future cellular networks. With the rising prominence of UAVs across various industries, ensuring uninterrupted connectivity and efficient mobility management within cellular networks becomes paramount. AirWave offers a comprehensive simulation environment, amalgamating network and physics simulations using ns-3 for network simulation, Gazebo for physics simulation, and integrating PX4 autopilot for realistic UAV flight dynamics. This framework facilitates in-dept. investigations into UAV mobility and handover management, including a trajectory optimization technique based on reinforcement learning for cellular-connected drones. Extensive simulations demonstrate the versatility and effectiveness of AirWave, showcasing notable improvements in UAV range, connectivity, and management within cellular networks. These findings hold significant promise for advancing research and practical applications in UAV communications. © 2024 IEEE.

关键词： Reinforcement learning

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