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IEEE Transactions on Radar Systems 2024年 2卷 778-790页

作者： Tsinos, Christos G. Arora, Aakash Tsiftsis, Theodoros A. National and Kapodistrian University of Athens Department of Digital Industry Technologies Evripus Campus Euboea 34400 Greece University of Twente Radio Systems Group Faculty of Electrical Engineering Mathematics and Computer Science AE Enschede7500 Netherlands Indian Institute of Technology Delhi Centre for Applied Research in Electronics New Delhi110016 India University of Thessaly Department of Informatics and Telecommunications Lamia35100 Greece Jinan University Zhuhai Campus School of Intelligent Systems Science and Engineering Zhuhai519070 China

In this article, the problem of linear precoding and radar receive beamforming design for joint radar-communication (JRC) systems is studied. A multiple antenna base station (BS) that serves multiple single-antenna user terminals on the downlink is assumed. Furthermore, the BS employs a simultaneous radar function in the form of point-like target detection from the reflected return signals in a signal-dependent interference environment. In this work, we jointly design the JRC linear precoder and the radar receive beamformer, thus aiming to optimize the performance of the radar part while maintaining a desired quality of service (QoS) for the communication one subject to a total transmit power constraint. To that end, we formulate a challenging fractional nonconvex optimization problem via which the optimal precoder and radar receive beamformer are derived. Then, we develop algorithmic solutions based on the majorization-minimization (MM) principle and the semidefinite relaxation (SDR) methodology for the formulated optimization problem. The performance of both the proposed solutions is examined and compared to the one of a system that supports only the radar functionality via numerical results. © 2023 IEEE.

关键词： Signal to noise ratio

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The Recommendation System for Increasing the Independence of Micro, Small and Medium Enterprises (MSMEs) Using the Normalized Rating Frequency (NRF) Method 4

The Recommendation System for Increasing the Independence of...

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4th International Conference on Information and Communications Technology, ICOIACT 2021

作者： Lestari, Sri Yulmaini Aswin Kurniawan, Rio Sulyono Pratama, Yan Aditiya Faculty of Computer Science Informatics Engineering Study Program Bandar Lampung Indonesia Faculty of Business Economics Management Study Program Bandar Lampung Indonesia

ISBN: (纸本)9781665433945

Micro, Small, and Medium Enterprises (MSMEs) have an important role in improving the economy of small communities and the stability of the Indonesian economy. However, they still face various problems such as capital, raw materials, distribution, licensing, and others. This study developed a special treatment recommendation system to increase the independence of MSMEs by adopting a ranking-based method to produce recommendations related to problem-solving steps from MSMEs. The ranking method used Normalized Rating Frequency (NRF). This method performed an aggregation process of the ratings given by MSMEs in looking at the priority level of problem-solving based on previous experience. The result of the experiment showed that an average NDCG value was 0.7916. This explained that the quality of the ranking was good so that it was feasible to be recommended in solving problems in the form of special treatment to acquire to the independence of MSMEs. © 2021 IEEE

关键词： Recommender systems

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Cooperative, Connected and Autonomous Mobility: A Simulation Framework for Evaluating Coordination Mechanisms

Cooperative, Connected and Autonomous Mobility: A Simulation...

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International Conference on Intelligent Transportation

作者： Francisco Filipe Zafeiris Kokkinogenis Pedro M. D'Orey Rosaldo J. F. Rossetti Faculty of Engineering University of Porto Porto Portugal Department of Informatics Engineering Laboratory of Artificial Intelligence and Computer Science (LIACC) Faculty of Engineering University of Porto Porto Portugal CISTER ISEP Polytechnic Institute of Porto Portugal Department of Computer Sciences Faculty of Sciences University of Porto Porto Portugal

Autonomous driving and vehicular networking have gained traction during the last decade. Simulation is one of the major tools for assessing coordination mechanisms (e.g. intersection control), especially when considering autonomous vehicles. This study proposes a simulation framework for researching MAS-based coordination mechanisms. The framework includes three main components for accurately replicating the scenario, namely a traffic simulator for modeling vehicle dynamics and interactions, a network simulator modeling vehicular networks and a BDI-based agent simulator for modeling cognitive decision-making abilities. We perform a preliminary analysis of the framework to assess its capability to cope with average complexity coordination mechanisms in connected and autonomous vehicle settings.

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Manufacturing A Low-Cost Telegram and Optical Character Recognition-Based Indoor Air Quality Monitoring Data Logger 8

Manufacturing A Low-Cost Telegram and Optical Character Reco...

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8th International Conference on Information Management and Technology, ICIMTech 2023

作者： Pramono, Andi Budiman, Satrio Arif Farchan, Muchammad Mappaturi, Andi Baso School of Design Bina Nusantara University Interior Design Department Jakarta11480 Indonesia Brawijaya University Information System Computer Science Faculty Malang65145 Indonesia Institut Teknologi Sepuluh Nopember Faculty of Intelligent Electrical and Informatics Technology Department of Electrical Engineering Surabaya60111 Indonesia Islamic State University of Maulana Malik Ibrahim Faculty of Science and Technology Architecture Department Malang Indonesia

ISBN: (纸本)9798350326093

Urban air is frequently contaminated with CO, CO2, VOC, HCHO, PM 2.5, and PM 10. Rural regions are at a lower risk than those near roads and industrial areas that produce emissions. Air pollutants negatively affect its inhabitants' health, particularly the respiratory system. Most people will spend more time indoors, requiring a method for monitoring indoor air quality (IAQ). Urban communities need IAQ monitoring instruments as an early warning system for their health. This research seeks to produce inexpensive IAQ that can be utilized by all levels of society, particularly urban communities. This research utilizes a qualitative, design-thinking-based methodology. The research diverged from Empatize, which asserts that urban communities require a method for determining the local air quality. Moreover, it is determined at the define stage that IAQ monitoring, which includes a data log feature, is still comparatively expensive, so a solution is developed at the ideate stage. Creating a prototype is the next step in realizing the specified solution. In this investigation, they decided to use IAQ monitoring, which is relatively inexpensive but lacks log data storage. The author uses the ESP32-cam microcontroller to capture the screen display device, and the resulting image is sent to a telegram and processed with optical character recognition (OCR) to generate a data log. The results of prototype testing are utilized for prototype development. © 2023 IEEE.

关键词： Industrial emissions

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Design And Implementation Of A PID Control System On A UAV Tracker Antenna

Design And Implementation Of A PID Control System On A UAV T...

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International Conference on Advanced Mechatronics, Intelligent Manufacture, and Industrial Automation (ICAMIMIA)

作者： Akhmad Jayadi Faisal Dharma Adhinata Jaka Persada Sembiring Canggih Gelar Setyo Adhi Wendel Herman Selsily Ahmad Amiruddin Faculty of Engineering and Computer Science Universitas Teknokrat Indonesian Lampung Indonesia Faculty of Informatics Institut Teknologi Telkom Purwokerto Jawa Tengah Indonesia Faculty of Computer Science Universitas Bina Nusantara Semarang Indonesia Faculty of Computer Science Universitas Kristen Indonesia Maluku Maluku Indonesia

Antennas are essential components in communication systems, serving as both transmitters and receivers. Their efficient operation depends on precise alignment with the strongest signal source, facilitated by tracker antennas. A recent research study focused on enhancing the responsiveness of these tracker antennas. By manually inputting PID (Proportional-Integral-Derivative) values for direction control, the study achieved notable *** results demonstrated that by fine-tuning PID values, particularly setting Kp=5, Ki=0.21812, and Kd=12, and targeting an angle of 90°, the response time was reduced to 1.2 seconds with an overshoot of 28.8% and 1.6 seconds of oscillation. This optimized control mechanism significantly improved antenna direction accuracy, aligning it with the desired setpoint. Furthermore, the importance of antenna alignment and polarization matching in communication systems was emphasized. Ensuring alignment between the receiving antenna's polarization and the incoming wave polarization is crucial for maintaining a strong signal reception power level. This alignment enables accurate tracking of antenna positions, supporting real-time GPS data communication. Additionally, the study involving horizontal azimuth angle measurements with tracker antennas demonstrated their capability to transmit real-time data from multiple positions, achieving 360° horizontal azimuth angles, along with varying elevations. These precise measurements play a vital role in object monitoring and effective communication.

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Depth Based Region Proposal: Multi-Stage Real- Time Object Detection

SSRN

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SSRN 2022年

作者： Ayman, Shehab Eldeen Hussein, Walid Karam, Omar H. Department of Software Engineering Faculty of Informatics and Computer Science The British University in Egypt Cairo Egypt Department of Computer Science Faculty of Informatics and Computer Science The British University in Egypt Cairo Egypt Department of Information Systems Faculty of Informatics and Computer Science The British University in Egypt Cairo Egypt

The majority of real-time object recognition systems operate on two-dimensional images, degrading the influence of the involved objects' third-dimensional (i.e., depth) information. The depth information of a captured scene provides a thorough understanding of an object in full-dimensional space. During the last decade, several region proposal techniques have been integrated into object detection. scenes’ objects are then localized and classified but only in a twodimensional space. Such techniques exist under the umbrella of two-dimensional object detection models such as YOLO and SSD. However, these techniques have the issue of being uncertain that an object's boundaries are properly specified into the particular scene. This paper proposes a unique region proposal and object detection strategy based on retrieving depth information for localization and segmentation of the scenes’ objects in real-time manner. The obtained results on different datasets show superior accuracy in comparison to the commonly implemented techniques with regards to not only detection but also a pixel-bypixel accurate localization of objects. © 2022, The Authors. All rights reserved.

关键词： Object detection

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Reassessing Glove Embeddings in Deep Learning: A Comparative Study with Classical ML Approaches

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Procedia computer Science 2024年 251卷 740-745页

作者： Zakia Labd Said Bahassine Khalid Housni Fatima Zahrae Aithamou Aadi Laboratory of research in informatics L@RI Department of Computer Science Faculty of Sciences Ibn Tofail University Kenitra Morocco Laboratory of Artificial Intelligence and Complex Systems Engineering Department of Computer Science National Higher School of Arts and Crafts Hassan II University Casablanca Morocco

The importance of text classification algorithms has increased due to the growing availability of large-scale data. This has led to a greater demand for efficient classification techniques and encoding algorithms. Word embedding techniques, like Glove, have shown significant success in encoding semantic relationships between words. This research paper aims to reassess the effectiveness of Glove embeddings coupled with deep learning algorithms. The impact of Glove embedding on two widely used deep learning models: Recurrent Neural Networks (RNN) and Recurrent Convolutional Neural Networks (RCNN) is analyzed. The results highlight the impact of Glove embeddings on deep learning models, showcasing significant performance enhancements in some cases while having minimal effects in others. By examining the impact of Glove embeddings on traditional ML algorithms in a previous study, valuable context for understanding the performance differences between the two approaches is obtained.

关键词： Glove Text Classification, Deep Learning, Natural Language Processing (NLP), Recurrent Convolutional Neural Networks (RCNN)

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Depth Based Region Proposal: Towards Robust Two-Stage Real-Time Object Detection

SSRN

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SSRN 2022年

The majority of real-time object recognition systems operate on two-dimensional images, degrading the influence of the involved objects' third-dimensional (i.e., depth) information. The depth information of a captured scene provides a thorough understanding of an object in full-dimensional space. During the last decade, several region proposal techniques have been integrated into object detection. scenes’ objects are then localized and classified but only in a two-dimensional space. Such techniques exist under the umbrella of two-dimensional object detection models such as YOLO and SSD. However, these techniques have the issue of being uncertain that an object's boundaries are properly specified into the particular scene. This paper proposes a unique region proposal and object detection strategy based on retrieving depth information for localization and segmentation of the scenes’ objects in real-time manner. The obtained results on different datasets show superior accuracy in comparison to the commonly implemented techniques with regards to not only detection but also a pixel-by-pixel accurate localization of objects. © 2022, The Authors. All rights reserved.

关键词： Object recognition

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Bipartite Graph-Based Recommendation Systems: Introducing the Enhanced Graph Convolutional Neural Network Method for Indonesian Tourism

Bipartite Graph-Based Recommendation Systems: Introducing th...

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Conference on New Media Studies (CoNMedia)

作者： Felix Indra Kurniadi Rudi Sutomo Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Information Systems Faculty of Engineering & Informatics Universitas Multimedia Nusantara Banten Indonesia

Indonesia's tourism sector, a cornerstone of its economy, has seen significant growth with both domestic and international tourists, drawn by its diverse landscapes and cultural sites. In 2022, the country welcomed around 10 million international visitors, indicating the industry's potential. In this context, employing artificial intelligence for personalized travel recommendations is crucial. This study proposes an innovative approach, converting tourism data into a bipartite format focused on the user-place relationship. We introduce the Enhanced Graph Convolutional Neural Network (Enhanced GCN), tailored for the tourism sector's complexities. Compared to the traditional Graph Convolutional Network (GCN), the Enhanced GCN shows superior predictive accuracy, reducing the Mean Squared Error (MSE) to 2.38. This advance represents a significant step in providing more accurate, customized travel suggestions. However, the current MSE suggests room for further improvement. Future research will aim to refine the Enhanced GCN, enhancing its ability to meet and predict tourists' needs in Indonesia's dynamic tourism environment. Ultimately, the goal is to use artificial intelligence to transform how visitors experience Indonesia's cultural and natural offerings, thereby bolstering the tourism industry's growth..

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Morphology study of Agave fiber surface by using scanning electron microscopy 10

Morphology study of Agave fiber surface by using scanning el...

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10th International Conference on Theoretical and Applied Physics, ICTAP 2020

作者： Mulyaningsih, N.N. Widiyatun, F. Dwitiyanti, N. Physics Education Faculty of Mathematics and Natural Sciences Indraprasta PGRI University Indonesia Informatics Faculty of Engineering and Computer Science Indraprasta PGRI University Indonesia

Agave plants can grow well in all weather conditions, so that they are very abundant in Indonesia. The appropriate processing technique is needed to develop the utilization of Agave plants, so that they have a higher value. The main purpose of this research is to conduct material engineering of Agave americana species, so that the Agave fiber has good quality when used as a natural composite material. The research method was used by giving 5% alkaline sodium hydroxide (NaOH) treatment for 30 minutes against Agave fiber (called T samples) and the results were compared with normal Agave fiber/not treated with alkaline (called UT samples). The parameters observed were the surface morphology of the fibers which were analyzed using Scanning Electron Microscopy (SEM) at magnifications of 250x, 500x and 1000x. SEM characterization results showed that Agave fiber treated with 5% NaOH alkaline had a denser surface morphology with an average cavity percentage of 9.015%, while untreated Agave fiber had a higher average cavity percentage of 29.795%. In addition, soaking with 5% alkaline NaOH also causes dirt that sticks to the fibers more easily, so the fibers were cleaner. The conclusion of the research shows that 5% alkaline NaOH treatment on Agave fiber could increase the strength and quality of Agave fiber. © Published under licence by IOP Publishing Ltd.

关键词： Sodium hydroxide

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