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2023 International Conference on Radar, Antenna, Microwave, electronics, and telecommunications, ICRAMET 2023

作者： Kurniawan, Erry Dwi Muthiah, Shafa Simbolon, David Erikson Taufiqqurrachman Research Center for Electronics National Research and Innovation Agency Bandung Indonesia Sumatera Institute of Technology Study Program of Physics South Lampung Indonesia National Research and Innovation Agency Research Center for Telecommunication Bandung Indonesia

ISBN: (纸本)9798350343892

In the digital age, the advancement of autonomous vehicles is currently extremely quick. The invention and development of retinomorphic sensors lend credence to this. This sensor's ability to function similarly to the human eye makes it handy for detecting the presence of objects in front of the sensor. It is necessary to use a tool known as an LED controller to test this sensor. To evaluate the voltage regulation on the LED controller both mechanically and digitally, this research will explore, design, and construct an LED controller. The LED Controller has voltage adjustment capabilities between 1.2 and 25 volts. However, the highest power the LED can withstand is somewhere between 15 and 16 volts. As a result, 16 Volts should be set as the input limit value in the microcontroller program. © 2023 IEEE.

关键词： Voltage regulators

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Using Robot-Assisting Personalized Learning for Children with Autism: A Pilot Study of Robot's Actions 15

Using Robot-Assisting Personalized Learning for Children wit...

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15th Biomedical Engineering International Conference, BMEiCON 2023

作者： Paengkumhag, Chatchai Jutharee, Wisanu Limpornchitwilai, Warissara Chamnongthai, Kosin Keawkamnerdpong, Boonserm King Mongkut's University of Technology Thonburi Faculty of Engineering Biological Engineering Program Bangkok Thailand King Mongkut's University of Technology Thonburi Institute of Field Robotics Bangkok Thailand King Mongkut's University of Technology Thonburi Faculty of Engineering Department of Electronics and Telecommunication Engineering Bangkok Thailand

ISBN: (纸本)9798350345247

There are a variety of symptoms of autism spectrum disorders. The appropriate interventions are selected according to the symptoms. Moreover, the key to successful intervention is to provide suitable therapy continuously. Unfortunately, the number of autism specialists is insufficient for the growing demands. Thus, for better intervention outcomes, the collaboration of parents/caregivers who help continue the intervention at home is crucial to the success of the intervention. However, it is not easy for inexperienced parents/caregivers. This study aimed to help parents/caregivers by using a robot to assist personalized learning for children with autism. We developed an artificial intelligence-based program to choose actions appropriate to the child user's learning style or preference so that the child user stays attended to the activities the robot offers. In this pilot study, we tested using a robot to assist personalized learning with six participants in the special education needs school. The result of this study revealed the possibility of using a robot to assist in personalized learning for children with autism, from the perspective of the participants' teacher. While interacting with the robot, the participants achieved a high average accuracy percentage of 83.36% and demonstrated a positive learning trend. © 2023 IEEE.

关键词： Human robot interaction

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2D Polynomial Regression Modeling for Predictive Analysis of Glucose Concentration in NP-Model Fractal Microwave Sensor Using Frequency and Transmission Coefficient Disparities

2D Polynomial Regression Modeling for Predictive Analysis of...

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13th International Electrical Engineering Congress, iEECON 2025

作者： Nantapanich, Nutyada Mahatthanajatuphat, Pisit Wongsin, Norakamon Lee, Wilaiporn Rajamangala University of Technology Thanyaburi Faculty of Engineering Department of Electronics and Telecommunication Engineering Pathum Thani Thailand Yothinburana School Regular Program in Mathematics-Science Bangkok Thailand King Mongkut's University of Technology North Bangkok Faculty of Engineering Department of Electrical and Computer Engineering Bangkok Thailand

ISBN: (纸本)9798331543952

The article introduces a two-dimensional polynomial regression model for the predictive analysis of glucose concentration in a fractal microwave sensor NP model, utilizing frequency and transmission coefficient differences to examine their correlation with glucose concentration levels. The sensor employed in this investigation is fabricated on an economical FR-4 substrate to minimize costs. The sensor structure is developed with the fractal structure reduction method established by the NP model to operate at a frequency of roughly 2.97 GHz. The CST Studio software is utilized to analyze the findings from the simulation of the suggested sensor structure. Furthermore, the proposed sensor is integrated into a test apparatus comprising a sensor platform and a liquid tube constructed from PPA material. The liquid under examination is a glucose and water mixture at varying concentrations. It was analyzed using a VNA instrument to evaluate its frequency response and the magnitude of the alteration in the transmission coefficient. The values are utilized to create a two-dimensional polynomial regression model. The experiments demonstrated that the proposed sensor model possesses an Adjusted R² value of 0.99499 and an average error of approximately 3.57% for glucose concentration analysis throughout the range of 5% to 30%, with sensitivities of 0.00879 dB/% for insertion loss and 0.092 MHz/% for frequency variation, facilitating accurate glucose concentration prediction analysis. © 2025 IEEE.

关键词： Polynomial regression

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Hybrid Model Approaches Toward Movie Recommendation Systems and Their Comparisons

Hybrid Model Approaches Toward Movie Recommendation Systems ...

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International Conference on Data Analytics and Management, ICDAM 2023

作者： Parikh, Jolly Antony, Saji M. Jain, Rachna Chawla, Naman Mishra, Ashish de Albuquerque, Victor Hugo C. Neto, Aloísio Vieira Lira Department of Computer Science and Engineering Bharati Vidyapeeth’s College of Engineering New Delhi India Department of Electronics and Communication Engineering Bharati Vidyapeeth’s College of Engineering New Delhi India Department of Information Technology Bhagwan Parshuram Institute of Technology New Delhi India Department of Computer Science and Engineering Bhagwan Parshuram Institute of Technology New Delhi India Department of CSE Gyan Ganga Institute of Technology and Sciences Madhya Pradesh Jabalpur India Federal Institute of Education Science and Technology of Ceara Fortaleza Brazil Federal University of Ceará Fortaleza Ceará Fortaleza Brazil Graduation Program in Telecommunication Engineering Federal University of Ceará Fortaleza Ceará Fortaleza Brazil

ISBN: (纸本)9789819965465

In the contemporary digital era, individuals face an overwhelming challenge of searching for personalized content from a seemingly infinite pool of available options such as books, videos, articles, and movies. Simultaneously, digital content providers strive to attract and retain as many users on their platforms as possible for extended periods. This has led to the evolution of recommender systems, which aim to enhance user engagement and satisfaction by recommending content based on their preferences and interests. These systems employ sophisticated algorithms and data mining techniques to analyze user behavior and feedback, enabling them to make personalized recommendations to users, thereby improving their overall experience. In this paper, an investigation is conducted in which different models in machine learning with their combinations are evaluated to evaluate which model could give the best accuracy with actual ratings given by users. The models used are Lasso regression, XGBoost, K-nearest neighbor, surprise baseline only, SVD, SVDpp, and their combinations. The proposed ensembled model of XGBoost + surprise baseline + surprise KNN baseline + SVD + SVDpp, with the minimum value of RMSE as 1.111410 and MAPE value as 39.261054, is our best model and can serve as a reference for any future recommender system. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Singular value decomposition

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2D Polynomial Regression Modeling for Predictive Analysis of Glucose Concentration in NP-Model Fractal Microwave Sensor Using Frequency and Transmission Coefficient Disparities

2D Polynomial Regression Modeling for Predictive Analysis of...

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International Electrical Engineering Congress (iEECON)

作者： Nutyada Nantapanich Pisit Mahatthanajatuphat Norakamon Wongsin Wilaiporn Lee Department of Electronics and Telecommunication Engineering Faculty of Engineering Rajamangala University of Technology Thanyaburi Pathum Thani Thailand Regular Program in Mathematics-Science Yothinburana School Bangkok Thailand Department of Electrical and Computer Engineering Faculty of Engineering King Mongkut's University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9798331543952

ISBN: (纸本)9798331543969

关键词： Analytical models Adaptation models Correlation Liquids Predictive models Microwave sensors Polynomials Fractals Glucose Predictive analytics

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ICU Bed Capacity Analysis with Transformer-Based Length of Stay Prediction and Erlang Loss Formula

ICU Bed Capacity Analysis with Transformer-Based Length of S...

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International Conference on Electrical Engineering/electronics, Computer, telecommunications and Information technology, ECTI-CON

作者： Sahawat Tanutsiriteeradet Wuttipong Kumwiaisak Jisue Youn Kumwilaisak Ratchapon Thammacharo Kanya Kumwilaisak Phornlert Chatkaew Electronics and Telecommunication Engineering King Mongkut's University of Technology Thonburi Bangkok Thailand Gifted Math Program Triam Udom Suksa School Bangkok Thailand Gifted Math Program Mahidol Wittayanusorn School Nakorn Pathom Thailand Department of Anesthesiology King Chulalongkorn Memorial Hospital Bangkok Thailand

ISBN: (数字)9798350381559

ISBN: (纸本)9798350381566

This paper introduces a new method for predicting the length of stay in the ICU using a transformer-based architecture. The predicted results are used for analyzing ICU bed capacity with the Erlang loss formula. The prediction architecture comprises two transformer layers, one global average pooling layer, and two fully connected layers. The proposed model classifies the length of stay into four classes depending on how long patients are admitted to the ICU based on patient data. In addition, the model estimates the length of stay in days of each patient. We then utilize the estimated length of stay to analyze ICU resources with the Erlang loss model. Given ICU blocking probabilities, the required ICU bed numbers are estimated with Erlang-B. Patient data from the MIMIC-III dataset, including various vital signs and patient health conditions, and the actual statistics from the Pediatric ICU at King Chulalongkorn Memorial Hospital are deployed to evaluate the proposed method. Experimental results show that the transformed-based architecture surpasses previous works regarding length of stay prediction accuracy. In addition, our analysis of ICU resources provides close results to those from the actual statistics from the Pediatric ICU at King Chulalongkorn Memorial Hospital.

关键词： Analytical models Accuracy Hospitals Computational modeling MIMICs Predictive models Probability

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Development of Controllable LED Light Source for Retinomorphic Sensor Measurement Test

Development of Controllable LED Light Source for Retinomorph...

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International Conference on Radar, Antenna, Microwave, electronics, and telecommunications (ICRAMET)

作者： Erry Dwi Kurniawan Shafa Muthiah David Erikson Simbolon Taufiqqurrachman Research Center for Electronics National Research and Innovation Agency Bandung Indonesia Study Program of Physics Sumatera Institute of Technology South Lampung Indonesia Research Center for Telecommunication National Research and Innovation Agency Bandung Indonesia

In the digital age, the advancement of autonomous vehicles is currently extremely quick. The invention and development of retinomorphic sensors lend credence to this. This sensor’s ability to function similarly to the human eye makes it handy for detecting the presence of objects in front of the sensor. It is necessary to use a tool known as an LED controller to test this sensor. To evaluate the voltage regulation on the LED controller both mechanically and digitally, this research will explore, design, and construct an LED controller. The LED Controller has voltage adjustment capabilities between 1.2 and 25 volts. However, the highest power the LED can withstand is somewhere between 15 and 16 volts. As a result, 16 Volts should be set as the input limit value in the microcontroller program.

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MMA-Net: Multi-Modal Attention Network for 2-D Object Detection in Autonomous Driving

MMA-Net: Multi-Modal Attention Network for 2-D Object Detect...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Abhilash Gaur Shubh Goel Kanishk Goel Seshan Srirangarajan Po-Hsuan Tseng Kai-Ten Feng IIT Delhi-NYCU Taiwan Joint Doctoral Program Bharti School of Telecommunication Technology and Management Indian Institute of Technology Delhi (IIT Delhi) New Delhi India International College of Semiconductor Technology National Yang Ming Chiao Tung University (NYCU) Hsinchu Taiwan Department of Electrical Engineering IIT Delhi New Delhi India Department of Electrical Engineering Bharti School of Telecommunication Technology and Management IIT Delhi New Delhi India Department of Electronic Engineering National Taipei University of Technology Taipei Taiwan Department of Electronics and Electrical Engineering NYCU Hsinchu Taiwan

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Autonomous driving technology relies heavily on sensor data for environment perception. Heterogeneous sensors such as lidar, radar, and camera have their own strengths and limitations. Therefore, relying on any single sensor would restrict the effectiveness of autonomous driving technology. However, integrating data from such heterogeneous sensors poses challenges due to differences in their representations. This article outlines a deep learning network aimed at designing modality-agnostic multi-modal fusion architecture. We study sensor data from different modalities and learn fine-grained representations using modality-specific feature encoders independently. Then, a multimodal attention-based network (MMA-Net) is proposed to fuse the data from heterogeneous modalities. The proposed MMA-Net fuses multi-modal sensor data by jointly exploiting the inter-modality and intra-modality relationships among camera, lidar, and radar sensors. The effectiveness of the proposed multi-modal fusion architecture is demonstrated using 2-D object detection metrics through extensive experiments on a dataset generated using the CARLA simulator.

关键词： Laser radar Fuses Multimodal sensors Radar detection Object detection Signal processing Cameras Speech processing Autonomous vehicles Meteorology

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Analysis of Mumbai Floods in recent Years with Crowdsourced Data

arXiv

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arXiv 2023年

作者： Tripathy, Shrabani Sailaja Chaudhuri, Sautrik Murtugudde, Raghu Mharte, Vedant Parmar, Dulari Pinto, Manasi Zope, P.E. Dixit, Vishal Ghosh, Subimal Interdisciplinary Program in Climate Studies Indian Institute of Technology Bombay Powai Mumbai400076 India Department of Civil Engineering Indian Institute of Technology Bombay Powai Mumbai400076 India Electronics and Telecommunication Engineering Vidyalankar Institute of Technology Mumbai400 037 India Kharghar Navi Mumbai410201 India

Mumbai, a densely populated city, experiences frequent extreme rainfall events leading to floods and waterlogging. However, the lack of real-time flood monitoring and detailed past flooding data limits the scientific analysis to extreme rainfall assessment. To address this, we explore the usability of crowdsourced data for identifying flood hotspots and extracting reliable flood information from the past. Through an automated program, we filter and retrieve flood-related data from Twitter, using location information to generate flood maps for past heavy rainfall events. The validity of the retrieved data is confirmed by comparing it with volunteered geographic information (VGI) which is more accurate but less abundant. In the absence of direct flood information, Twitter data is cross-verified with the Height above the Nearest Drainage (HAND) map, which serves as a proxy for elevation. Interestingly, while extreme rainfall events are increasing in frequency, recent Twitter-based information shows a decrease in flood reporting, attributed to effective mitigation measures implemented at various flood hotspots. Local surveys support this finding and highlight measures such as underground storage tanks and pumping stations that have reduced flood severity. Our study demonstrates the value of crowdsourced data in identifying urban flood hotspots and its potential for real-time flood monitoring and forecasting. This approach can be adapted for data-sparse urban regions to generate location-specific warnings, contributing to improved early warnings and mitigating the impact on lives and property. © 2023, CC BY.

关键词： Floods

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Using Robot-Assisting Personalized Learning for Children with Autism: A Pilot Study of Robot's Actions

Using Robot-Assisting Personalized Learning for Children wit...

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Biomedical Engineering International Conference (BMEiCON)

作者： Chatchai Paengkumhag Wisanu Jutharee Warissara Limpornchitwilai Kosin Chamnongthai Boonserm Keawkamnerdpong Biological Engineering Program Faculty of Engineering King Mongkut’s University of Technology Thonburi Bangkok Thailand Institute of Field Robotics King Mongkut’s University of Technology Thonburi Bangkok Thailand Department of Electronics and Telecommunication Engineering Faculty of Engineering King Mongkut’s University of Technology Thonburi Bangkok Thailand

There are a variety of symptoms of autism spectrum disorders. The appropriate interventions are selected according to the symptoms. Moreover, the key to successful intervention is to provide suitable therapy continuously. Unfortunately, the number of autism specialists is insufficient for the growing demands. Thus, for better intervention outcomes, the collaboration of parents/caregivers who help continue the intervention at home is crucial to the success of the intervention. However, it is not easy for inexperienced parents/caregivers. This study aimed to help parents/caregivers by using a robot to assist personalized learning for children with autism. We developed an artificial intelligence-based program to choose actions appropriate to the child user’s learning style or preference so that the child user stays attended to the activities the robot offers. In this pilot study, we tested using a robot to assist personalized learning with six participants in the special education needs school. The result of this study revealed the possibility of using a robot to assist in personalized learning for children with autism, from the perspective of the participants’ teacher. While interacting with the robot, the participants achieved a high average accuracy percentage of 83.36% and demonstrated a positive learning trend.

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