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2023 IEEE Global Conference on Information Technologies and Communications, GCITC 2023

作者： Vas, Aveen Dsouza, Glenn Aston St. Joseph Engineering College Department of Intelligent Computing and Business Systems Vamanjoor Mangalore India

ISBN: (纸本)9798350308167

This study proposes a unique method for object tracing with OpenCV (Open Source Computer Vision Library) using colour range filtering in the HSV (Hue Saturation Value) colour space and object following with ROS2. The suggested approach combines ROS2 (Robot Operating System 2) with OpenCV on a Raspberry Pi as the primary processing platform, allowing for effective communication and coordination between the two frameworks. Objects are identified based on colour range filtering, by utilizing the robust image processing capabilities of OpenCV using HSV colour space. The data of the detected object is then smoothly sent to ROS2 for additional processing and tracing. As the central node, it receives input from a camera, uses OpenCV to perform real-time object tracing and transmits the outcome to ROS2 nodes. Through this integration, OpenCV and ROS2 can work effectively together to give reliable solutions for object tracing and following. The feasibility and efficiency of the suggested strategy are demonstrated by the experimental evaluations carried out in a Raspberry Pi platform. The presented approach opens significant potential for many applications, including robotics, surveillance and interactive systems, where real-time object following are essential. © 2023 IEEE.

关键词： Colour range filtering HSV Colour space Image Processing Object Following Object Tracing OpenCV Raspberry Pi ROS2

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Cross-Modality Gait Recognition: Bridging LiDAR and Camera Modalities for Human Identification 18

Cross-Modality Gait Recognition: Bridging LiDAR and Camera M...

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18th IEEE International Joint Conference on Biometrics, IJCB 2024

作者： Wang, Rui Shen, Chuanfu Marin-Jimenez, Manuel J. Huang, George Q. Yu, Shiqi Southern University of Science and Technology Department of Computer Science and Engineering China The University of Hong Kong Department of Industrial and Manufacturing Systems Engineering Hong Kong University of Cordoba Department of Computing and Numerical Analysis Spain The Hong Kong Polytechnic University Department of Industrial and Systems Engineering Hong Kong

ISBN: (纸本)9798350364132

Current gait recognition research mainly focuses on identifying pedestrians captured by the same type of sensor, neglecting the fact that individuals may be captured by different sensors in order to adapt to various environments. A more practical approach should involve cross-modality matching across different sensors. Hence, this paper focuses on investigating the problem of cross-modality gait recognition, with the objective of accurately identifying pedestrians across diverse vision sensors. We present CrossGait inspired by the feature alignment strategy, capable of cross retrieving diverse data modalities. Specifically, we investigate the cross-modality recognition task by initially extracting features within each modality and subsequently aligning these features across modalities. To further enhance the cross-modality performance, we propose a Prototypical Modality-shared Attention Module that learns modality-shared features from two modality-specific features. Additionally, we design a Cross-modality Feature Adapter that transforms the learned modality-specific features into a unified feature space. Extensive experiments conducted on the SUSTech1K dataset demonstrate the effectiveness of CrossGait: (1) it exhibits promising cross-modality ability in retrieving pedestrians across various modalities from different sensors in diverse scenes, and (2) CrossGait not only learns modality-shared features for cross-modality gait recognition but also maintains modality-specific features for single-modality recognition. © 2024 IEEE.

关键词： Unattended sensors

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Calligraphy Alphabet Perception Using Artificial Intelligence 9

Calligraphy Alphabet Perception Using Artificial Intelligenc...

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9th International Conference on Science, Technology, engineering and Mathematics, ICONSTEM 2024

作者： Venkatesh, S. Jeevitha, D. Gnanaselvi, J. Anitha Srm Institute of Science And Technology Department of Data Science And Business Systems School of Computing Chennai Kattankulathur India Jeppiaar Engineering College Department of Computer Science And Engineering Chennai India

ISBN: (纸本)9798350365092

The human brain has a simple time analyzing and processing images. The brain is able to rapidly deconstruct and distinguish an image's various components when the eye perceives it. With the Convolutional Neural Network (CNN) as its foundation, this research suggests deep learning conceptual models. When the algorithms are compared, it becomes clear that CNN-based classification of handwritten alphabets performs better than other algorithms in terms of accuracy. The Manual Net, Alex Net, and LeNet Architectures are among the CNN algorithms employed in this research. The convulational layer, max pooling, flattening, feature assortment, rectifier lined unit, and completely linked softmaxx layers are each components of the aforementioned designs. The proposed network is tested using an image dataset comprising 530 training photos and 2756 testing images. The top precision and cost-efficient model will be used in the Django context to build a handler line for supplying the appeal to be recognized and obtaining the productivity outcome of recognized appeal. © 2024 IEEE.

关键词： Convolutional neural networks

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Pansharpening using a guided image filter based on dual-scale detail extraction

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Journal of Ambient Intelligence and Humanized computing 2024年第3期15卷 1849-1863页

作者： Jian, Lihua Yang, Xiaomin Wu, Wei Ahmad, Awais Sangaiah, Arun Kumar Jeon, Gwanggil College of Electronics and Information Engineering Sichuan University Sichuan Chengdu610064 China Department of Information and Communication Engineering Yeungnam University Gyeongsan Korea Republic of School of Computing Science and Engineering VIT University Vellore India Department of Embedded Systems Engineering Incheon National University Incheon Korea Republic of

High spatial resolution multispectral (HMS) images can provide sufficient information for researchers to analyze the potential disasters in the living environment. However, an original multispectral (MS) image is with low-space-resolution and high-spectrum-resolution, while an original panchromatic (PAN) image has the opposite property. Pansharpening aims at obtaining HMS image by retaining spectrum of the MS image and injecting details of the PAN image simultaneously. In this paper, we present a new pansharpening method. First, we use a bilateral filter (BF) to obtain the low-frequency-component (LFC) of PAN and MS images, respectively. Then the high-frequency-component (HFC) of PAN and MS images are readily obtained. Second, an adaptive intensity-hue-saturation (AIHS) based method is applied to generate the HFC of intensity. Finally, a dual-scale guided image filter (GIF) is utilized to calculate the difference between HFC of intensity and PAN to get the detail images. And then, these detail images are injected into the original MS image to achieve the HMS image. The proposed method is applied into testing various satellite data sets, and performs better effect on both visual quality and objective indictors than the existing methods. © Springer-Verlag GmbH Germany, part of Springer Nature 2018.

关键词： Nonlinear filtering

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Bridging the gap between theory and practice in local energy sharing - Energy communities in Croatia

Bridging the gap between theory and practice in local energy...

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2024 IEEE PES Innovative Smart Grid Technologies Europe Conference, ISGT EUROPE 2024

作者： Antić, Mirna Gržanić Capuder, Tomislav Medved, Tomi Faculty of Electrical Engineering and Computing University of Zagreb Department of Energy and Power Systems Croatia Faculty of Electrical Engineering University of Ljubljana Laboratory of Energy Policy Slovenia

ISBN: (纸本)9789531842976

The European directives focus on unlocking the potential of final customers in the clean energy transition. With the recent trend of rising electricity and petrol prices, coupled with reduced investment costs in low-carbon technologies, the installation of photovoltaic solar panels for end customers has become more profitable compared to previous years, leading to a shorter payback period. Additionally, to enhance citizen engagement in the integration of renewable energy sources, various forms of local energy-sharing models are discussed in the literature. This paper elaborates on the regulatory framework and policies at the European level concerning renewable energy communities, citizen energy communities, and collective self-consumption, with a particular focus on Croatia's perspective. It highlights the steps needed to accelerate the realization of energy communities in practice. Under current Croatian regulations, the profit from selling surplus community energy to the supplier cannot be directly remunerated to community members. The results indicate that community members could achieve 20% lower electricity costs annually if the surplus energy were directly remunerated. To avoid losing potential profits from excess energy, the paper models an investment in a community-owned battery storage system, aiming to increase community self-consumption and minimize the amount of energy sold to suppliers. It is shown that with a static sharing scheme, individual community members' costs can decrease by 11% to 16%, and by up to 20% with a dynamic scheme compared to cases where profit from excess energy cannot be directly remunerated to community members. © 2024 IEEE.

关键词： Clean energy

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Phylogenetic-Informed Generative Adversarial Network for Predicting Mutations in SARS-CoV-2 2

Phylogenetic-Informed Generative Adversarial Network for Pre...

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2nd IEEE International Conference on Networking and Communications, ICNWC 2024

作者： Chandar, Vijay Nair, Vishnu G. Rajeswari, D. SRM Institute of Science and Technology School of Computing College of Engineering and Technology Department of Data Science and Business Systems Chennai603203 India

ISBN: (纸本)9798350365269

In the pursuit of developing more effective vaccines against viruses like SARS-CoV-2, understanding their potential mutations is crucial. In order to generate protein sequences, this study presents a unique framework that synergistically blends phylogenetic data with Generative Adversarial Networks (GANs). The proposed method combines the strengths of two models: TEMPO, a transformer-based model proficient in leveraging phylogenetic data for mutation detection in SARS-CoV-2, and MutaGAN, a GAN-based model adept in generating synthetic protein sequences. The phylogenetic data from TEMPO is used in this novel design as the input for the MutaGAN generator, replacing the conventional random noise input used in GANs. The generator may now create synthetic protein sequences that are impacted by the evolutionary past included in the phylogenetic data resulting in the creation of artificial protein sequences that are more biologically significant and lifelike. The discriminator portion of the model, similar to MutaGAN, is still in charge of determining whether or not the input sequences comprise a valid parent-child pair and uses sequences produced by the first GAN as extra negative cases. This prevented the model from going too far off its planned route. Additional investigation into this fascinating confluence of bioinformatics and AI may be possible as a result of this study's conclusions. This approach has tremendous promise in many applications, such as the production of more powerful vaccinations, protein engineering, medication design, and improving our knowledge of biological systems, by forecasting how SARS-CoV-2 could change. A precision of 0.662 and an accuracy of around 0.671, together with a Matthews Correlation Coefficient (MCC) score of 0.313, show that the framework performs very well. These scores show that the framework performs better than a number of cutting-edge baseline techniques. The synthetic protein sequences generated by the framework not only bea

关键词： Coronavirus

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Towards Accurate RPM Detection Using Primary Ignition Pulses 49

Towards Accurate RPM Detection Using Primary Ignition Pulses

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49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023

作者： Wijayaraja, Jaliya L. Abhayasinghe, Nimsiri Wellalage, Sasini Sliit Faculty of Computing Department of Computer Systems Engineering Malabe Sri Lanka Sliit Faculty of Engineering Department of Electrical and Electronic Engineering Malabe Sri Lanka

ISBN: (纸本)9798350331820

Enhancing the performance and extending the life-cycle of older vehicles through retrofitting with improved systems presents an alluring opportunity. However, achieving accurate sensing of crucial parameters, such as engine revolutions per minute (RPM), poses challenges due to the need for mechanical manipulation and some designs, which may result in diminished accuracy in adverse settings. This study proposes using the primary ignition pulse signal to accurately determine engine speed without mechanical manipulation. We investigate the effectiveness of signal processing methods, including the RC filter, 2nd and 6th order Butterworth filter, to improve the accuracy of the readings. Our proposed method successfully collects data from conventional internal combustion engines, with simulated signal processing methods producing accurate detection of RPM and reduced fluctuations. The proposed method eliminated the noise, leading to accurate RPM measurements, while the maximum standard deviation observed for the RC filter implementation was 38.74. By accurately sensing key parameters, older vehicles can be retrofitted with improved systems, leading to enhanced performance and an extended lifecycle. The introduced methods can be practically implemented using custom electronics or embedded systems and can contribute to future research on automotive retrofitting. © 2023 IEEE.

关键词： Life cycle

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Understanding Productivity of Colombian Development Teams Working on Remote Environments 27

Understanding Productivity of Colombian Development Teams Wo...

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27th Ibero-American Conference on Software engineering, CIbSE 2024

作者： Ahogado, Alejandro Hoyos, Juan Bocanegra, José Gómez, Viviola Garcés, Kelly School of Engineering Department of Systems and Computing Engineering Universidad de los Andes Bogotá Colombia School of Social Sciences Department of Psychology Universidad de los Andes Bogotá Colombia

This project is a collaborative effort to understand and address productivity challenges in remote development teams, employing a multifaceted approach that integrates software engineering, psychology, and design thinking methodologies. The initial focus has been on developing a tool for mining GitHub repositories, laying the groundwork for subsequent phases of the research. © 2024 27th Ibero-American Conference on Software engineering, CIbSE 2024. All rights reserved.

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Human-Machine Pilot Navigation Method Using Nonlinear Model Predictive Control, Simulation and Inflight Testing 43

Human-Machine Pilot Navigation Method Using Nonlinear Model ...

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43rd AIAA DATC/IEEE Digital Avionics systems Conference, DASC 2024

作者： Bertoncini, Jeremy Dudek, Adrian Ostler, Johannes Russ, Martin Gerdts, Matthias Institute of Applied Mathematics and Scientific Computing Institute of Flight Systems University of the Bundeswehr Munich Department of Aerospace Engineering Neubiberg85577 Germany

ISBN: (纸本)9798350349610

In military or civil aviation, depending on the mission to be accomplished, an aircraft may need to avoid prohibited or dangerous areas that are not always within line of sight. However, these areas can be identified by external information and communicated from a ground station. The aim of this work is to provide an optimal control method that generates trajectories and assists the pilot in navigating. Using a human-machine interface, the pilot has access to a digital twin of the actual airspace, where the route is modeled using a tunnel-in-the-sky method. The research can be applied to airborne mission management, airport maneuver management or military operations. The path planning task is formulated as an optimal control problem and solved on a fixed time horizon using a nonlinear model predictive controller (NMPC) [5]. © 2024 IEEE.

关键词： Motion planning

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SMART AGRICULTURE: INTEGRATING AIR QUALITY MONITORING WITH DEEP LEARNING FOR PROCESS OPTIMIZATION*

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Scalable computing 2025年第3期26卷 1005-1016页

作者： Shobana, J. Subramanian, A Venkata Balamurugan, P. Perumal, Sivakumar Sankari, V. Eldho, K.J. Nareshkumar, R. Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Tamilnadu Kattankulathur India Department of Computer Science and Engineering GITAM University Bengaluru India Department of Computer Science and Engineering J N N Institute of Enginering India Saveetha university Chennai India Department of Computer Science and Engineering Sasi Institute of Technology and Engineering Andhra Pradesh Tadepalligudem India Department of Artificial Intelligence and Data Science K.Ramakrishnan College of Engineering Kariyamanickam Rd Tamil Nadu Samayapuram India PG and Research Department of Computer Science Mary Matha Arts and Science College Kerala Mananthavady India Department of Networking and Communications School of Computing College of Engineering and Technology SRM Institute of Science and Technology Tamilnadu Kattankulathur India

Modernization and intense industrialization have led to a substantial improvement in people’s quality of life. However, the aspiration for achieving an improved quality of life results in environmental contamination. A primary consequence of environmental degradation is air pollution, resulting from rising levels of poisonous chemicals in the atmosphere, which may induce detrimental health conditions in humans. It is harmful to both humans and agriculture. Given that the effects of air pollution on plants may not be readily apparent, it is important to analyse the necessary data and compute the outcomes. Farmers prioritise on pests and plant diseases, frequently neglecting the detrimental impacts of air pollution. Some plant species can withstand high amounts of pollution from suspended particulate matter and accumulated gases, while others are more susceptible to harm. Therefore, plants’ reaction to air pollution is influenced by the kind of harmful compounds, their levels, and the plant’s susceptibility to them. The LSTM +CNN Proposed Ensemble method may be used to analyse the impact of air pollution on agriculture by examining trends in crop production over time and predicting which crop is more resistant based on the pollution data. The initiative created for this aim may assist farmers in determining the most suitable crop to cultivate in their fields to minimize the impact of air pollution on agricultural yield. The findings show deep learning algorithms correctly predict hourly pollutant concentrations such as carbon monoxide, sulphur dioxide, nitrogen dioxide, ground-level ozone, and particulate matter 2.5, along with the hourly Air Quality Index (AQI) for California. A proposed model used test RMSE values as a measure to evaluate prediction performance, achieving the best possible results. © 2025 SCPE.

关键词： Nitrogen oxides

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