Engineering Proceedings

10 pages, 1598 KiB

Open AccessProceeding Paper

Improving Magnetic Flux Density Fingerprint Map Matching by Mitigating AC-Induced Variability

by Peter J. Thompson, Paul D. Groves, Owen J. Griffiths, Robin J. Handley and David R. Selviah

Eng. Proc. 2025, 88(1), 59; https://doi.org/10.3390/engproc2025088059 - 20 May 2025

Magnetic flux density (MFD) map matching is a technique that can provide absolute position solutions by comparing a series of MFD measurements with a database. Map matching relies on the consistent measurement of the same physical phenomena during the surveying and positioning phases. [...] Read more.

Magnetic flux density (MFD) map matching is a technique that can provide absolute position solutions by comparing a series of MFD measurements with a database. Map matching relies on the consistent measurement of the same physical phenomena during the surveying and positioning phases. However, fluctuations in MFD due to alternating current (AC) electricity, influenced by dynamic power requirements, pose a challenge. This paper analyses the characteristics of the influences of AC sources on MFD measurements. It shows that employing spectral filtering can isolate magnetic perturbations from AC sources, which could be used to enhance the magnetic map matching’s resilience to this form of temporal change. Full article

(This article belongs to the Proceedings of European Navigation Conference 2024)

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9 pages, 1075 KiB

Open AccessProceeding Paper

Integrating Tiny Machine Learning and Edge Computing for Real-Time Object Recognition in Industrial Robotic Arms

by Nian-Ze Hu, Bo-An Lin, Yen-Yu Wu, Hao-Lun Huang, You-Xin Lin, Chih-Chen Lin and Po-Han Lu

Eng. Proc. 2025, 92(1), 74; https://doi.org/10.3390/engproc2025092074 - 19 May 2025

Abstract

By integrating visual recognition technology and multi-object recognition into robotic arms, the flexibility and automation of the production process were improved in this study. By applying tiny machine learning (TinyML) and machine vision algorithms, we integrated edge computing devices to control the robotic [...] Read more.

By integrating visual recognition technology and multi-object recognition into robotic arms, the flexibility and automation of the production process were improved in this study. By applying tiny machine learning (TinyML) and machine vision algorithms, we integrated edge computing devices to control the robotic arms and identified objects precisely on the production line, with ultra-low energy consumption. The developed system in this study included the SparkFun Edge development board and Raspberry Pi Camera Module 3, as edge devices for data processing, image recognition, and robotic arm control. By utilizing the Edge Impulse platform for data collection, model training, and optimization, edge devices and models for use in resource-limited environments were successfully generated. Using Edge Impulse’s automated toolchain, real-time image processing and object recognition were realized. The system achieved improved recognition accuracy and operational speed, demonstrating the potential of TinyML in enhancing the intelligence of robotic arms. MariaDB was chosen for data storage. Grafana was used to design a user-friendly web interface for real-time data monitoring and visualization and immediate data analysis and system monitoring. The developed system presented a success rate of 99% during actual operation. The feasibility of combining advanced image processing technology with robotic arms in intelligent manufacturing was verified in this study. The potential of integrating machine learning and automation technologies was also confirmed for the development of future manufacturing technologies. The model provides a technical reference and ideas for future factories that require high levels of automation and intelligence. Full article

(This article belongs to the Proceedings of 2024 IEEE 6th Eurasia Conference on IoT, Communication and Engineering)

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6 pages, 1163 KiB

Open AccessProceeding Paper

Real-Time Detection and Process Status Integration System for High-Pressure Gas Leakage

by Nian-Ze Hu, Hao-Lun Huang, Chun-Min Tsai, Yen-Yu Wu, You-Xin Lin, Chih-Chen Lin and Po-Han Lu

Eng. Proc. 2025, 92(1), 72; https://doi.org/10.3390/engproc2025092072 - 19 May 2025

Abstract

This study aims to develop a real-time gas leak detection system for application in gas cylinder filling machines. To promptly recover gas during leakage incidents, the efficiency of the gas filling process was improved by reducing resource wastage. The system utilized a Raspberry [...] Read more.

This study aims to develop a real-time gas leak detection system for application in gas cylinder filling machines. To promptly recover gas during leakage incidents, the efficiency of the gas filling process was improved by reducing resource wastage. The system utilized a Raspberry Pi with a camera for image-based detection and employed the dark channel prior method to detect the presence of gas. The message queue system was used for the real-time data transmission of gas leak status, temperature, and humidity data. The system sent data to a central server via message queuing telemetry transport (MTQQ). Node-RED was used for data visualization and anomaly alerts. Machine learning methods such as support vector machines (SVMs) and decision trees were applied to analyze the correlation between gas leaks and other environmental parameters to predict leak incidents. This system effectively detected gas leakage and transmitted and analyzed the data, significantly improving the operational efficiency of the gas cylinder filling process. Full article

(This article belongs to the Proceedings of 2024 IEEE 6th Eurasia Conference on IoT, Communication and Engineering)

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10 pages, 2559 KiB

Open AccessProceeding Paper

An End-To-End Solution Towards Authenticated Positioning Utilizing Open-Source FGI-GSRx and FGI-OSNMA

by Muwahida Liaquat, Mohammad Zahidul H. Bhuiyan, Toni Hammarberg, Saiful Islam, Mika Saajasto and Sanna Kaasalainen

Eng. Proc. 2025, 88(1), 58; https://doi.org/10.3390/engproc2025088058 - 19 May 2025

Abstract

This paper presents an end-to-end solution towards authenticated positioning using only Galileo E1B signal by utilizing the Open Service Navigation Message Authentication (OSNMA). One of the primary objectives of this work is to offer a complete OSNMA-based authenticated position solution by releasing FGI-GSRx-v2.1.0 [...] Read more.

This paper presents an end-to-end solution towards authenticated positioning using only Galileo E1B signal by utilizing the Open Service Navigation Message Authentication (OSNMA). One of the primary objectives of this work is to offer a complete OSNMA-based authenticated position solution by releasing FGI-GSRx-v2.1.0 (an open-source software-defined multi-constellation GNSS receiver) update. The idea is to bridge the gap between two open-source implementations by the Finnish Geospatial Research Institute (FGI): FGI-GSRx and FGI-OSNMA (an open-source Python software package). FGI-GSRx-v2.1.0 utilizes FGI-OSNMA as an OSNMA computation engine to generate the authentication events with the information of whether a tag is valid or not. FGI-GSRx computes the position authentication at the navigation layer with the Galileo E1B satellites that are OSNMA verified and have

C / N_{0}

greater than 30 dB-Hz. OSNMA-based position authentication is shown through the findings from two real-world open sky use cases: (i) a clean nominal scenario and (ii) a spoofing scenario recorded during the Jammertest 2023 in Andøya, Norway. In the case of the spoofing scenario, the software receiver stops offering an authenticated position solution. A detailed comparison between the authenticated and non-authenticated position solutions also highlights the damage spoofing could cause to the end user in deviating the user’s position on a target spoofed location. Full article

(This article belongs to the Proceedings of European Navigation Conference 2024)

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10 pages, 4421 KiB

Open AccessProceeding Paper

Geometric Analysis of LEO-Based Monitoring of GNSS Constellations

by Can Oezmaden, Omar García Crespillo, Michael Niestroj, Marius Brachvogel and Michael Meurer

Eng. Proc. 2025, 88(1), 57; https://doi.org/10.3390/engproc2025088057 - 19 May 2025

Abstract

The last decade has seen a surge in the development and deployment of low Earth orbit (LEO) constellations primarily serving broadband communication applications. These developments have also influenced the interest providing positioning, navigation, and timing (PNT) services from LEO. Potential services include new [...] Read more.

The last decade has seen a surge in the development and deployment of low Earth orbit (LEO) constellations primarily serving broadband communication applications. These developments have also influenced the interest providing positioning, navigation, and timing (PNT) services from LEO. Potential services include new ranging signals from LEO, augmentation of global navigation satellite systems (GNSS), and monitoring of GNSS. The latter promises an advantage over existing ground-based monitoring due to the reception of observables with reduced atmospheric error contributions and the potential for lower costs. In this paper, we investigate the influence of LEO constellation design on the line-of-sight visibility conditions for GNSS monitoring. We simulate a series of Walker constellations in LEO with a varying number of total satellites, orbital planes, and orbital heights. From the simulated data, we gather statistics on the number of visible GNSS and LEO satellites, durations of visibility periods, and the quality of this visibility quantified by the dilution of precision (DOP) metric. Our findings indicate that increasing the total number of LEO satellites results in diminishing returns. We find that constellations with relatively few total satellites equally yield an adequate monitoring capability. We also identify orbital geometric constraints resulting in suboptimal performance and discuss optimization strategies. Full article

(This article belongs to the Proceedings of European Navigation Conference 2024)

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11 pages, 4185 KiB

Open AccessProceeding Paper

Enhancing GNSS PPP Algorithms with AI: Towards Mitigating Multipath Effects

by Álvaro Tena, Adrián Chamorro and Jesús David Calle

Eng. Proc. 2025, 88(1), 56; https://doi.org/10.3390/engproc2025088056 - 19 May 2025

Abstract

Nowadays, high precision and reliability of Global Navigation Satellite Systems are increasingly important in positioning applications. Machine learning is used to improve the performance of the GSHARP PPP algorithm by reducing the effect of multipath on GNSS measurements. The clustering analysis is conducted [...] Read more.

Nowadays, high precision and reliability of Global Navigation Satellite Systems are increasingly important in positioning applications. Machine learning is used to improve the performance of the GSHARP PPP algorithm by reducing the effect of multipath on GNSS measurements. The clustering analysis is conducted on the primary GNSS data points with the goal of discovering and analyzing patterns in the multipath interference. This study represents an early attempt to apply AI to the GSHARP PPP algorithm. Since Lightweight Machine Learning is used in this research, it is easier to integrate and might lay the groundwork for future integration of advanced deep learning methods. About 50 h of data collected from different environments (e.g., highways and urban areas) serves as the training data for these algorithms, which ensures their robustness and real-world applicability. The use of machine learning clustering inside the PPP algorithm serves as a way to improve its performance against multipath effects, as well as provide a platform for subsequent development of precision GNSS systems through AI technologies. Full article

(This article belongs to the Proceedings of European Navigation Conference 2024)

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10 pages, 1935 KiB

Open AccessProceeding Paper

Signal Enhancement and Interference Reduction with Minimum-Variance Distortionless Response Algorithm Using MATLAB and GNU Radio Simulations

by Tuan-Khanh Nguyen, Nguyen Do Nguyen, Huy Quang Nguyen and Khang Thai Viet Nguyen

Eng. Proc. 2025, 92(1), 2073; https://doi.org/10.3390/engproc2025092073 - 16 May 2025

Abstract

We improved signal reception by minimizing interference in dynamic communication environments with a minimum-variance distortionless response (MVDR) algorithm. The conditions of the MVDR algorithm were simulated using MATLAB and GNU Radio to enhance its capabilities in noise and interference suppression. Through a MATLAB [...] Read more.

We improved signal reception by minimizing interference in dynamic communication environments with a minimum-variance distortionless response (MVDR) algorithm. The conditions of the MVDR algorithm were simulated using MATLAB and GNU Radio to enhance its capabilities in noise and interference suppression. Through a MATLAB simulation, the adaptive beamforming performance of MVDR was examined and compared with that of conventional beamforming techniques to identify the advantages of beam steering for obtaining the desired signals. MVDR was effective in interference reduction and the improvement of signal clarity, with superiority over conventional approaches in cases with complex interference patterns. Based on the results of the MATLAB simulations, GNU Radio was used in a complete software-defined radio (SDR) environment that enabled the replication of real-world conditions to study MVDR. We simulated real-world applications by integrating GNU Radio to ensure the robustness and adaptability of the algorithm in live signal processing. The results from these two simulations prove the potential of MVDR as a strong dynamic interference suppressor that enables superior signal reception. The results enable the implementation of the MVDR algorithm in communication systems. Full article

(This article belongs to the Proceedings of 2024 IEEE 6th Eurasia Conference on IoT, Communication and Engineering)

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9 pages, 3787 KiB

Open AccessProceeding Paper

Powder-Mixed Micro Electrical Discharge Machining-Assisted Surface Modification of Ti-35Nb-7Zr-5Ta Alloy in Biomedical Applications

by Altair Kossymbayev, Shahid Ali, Didier Talamona and Asma Perveen

Eng. Proc. 2025, 92(1), 71; https://doi.org/10.3390/engproc2025092071 - 16 May 2025

Abstract

One of the most popular alloys for biomedical applications is TiAl6V4. Even though TiAl6V4 is widely used, it faces several challenges. Firstly, TiAl6V4 is prone to stress shielding caused by the difference in Young’s moduli of the alloy (110 GPa) and human bones [...] Read more.

One of the most popular alloys for biomedical applications is TiAl6V4. Even though TiAl6V4 is widely used, it faces several challenges. Firstly, TiAl6V4 is prone to stress shielding caused by the difference in Young’s moduli of the alloy (110 GPa) and human bones (20–30 GPa). Secondly, there is the presence of cytotoxic elements, aluminum and vanadium. Researchers have proposed Ti-35Nb-7Zr-5Ta (TNZT) alloy to overcome these disadvantages, an excellent substitute for natural human bones. This alloy offers a lower elastic modulus (up to 81 GPa), much closer to human bones than TiAl6V4 alloy. Also, TNZT alloy contains no cytotoxic elements and has excellent biocompatibility and high corrosion resistance. Given the positive outcomes on powder-mixed micro electro-discharge machining (PM-μ-EDM) of Ti alloy using hydroxyapatite (HA) powder, we studied the machinability of TNZT alloy using HA powder mixed-μ-EDM by changing the HA powder concentration (0, 5, and 10 g/L), gap voltage (90, 100, and 110 V), and capacitance (10, 100, and 400 nF) according to the Taguchi L9 method. Machining performance metrics such as material removal rate (MRR), overcut, and circularity were examined using a tungsten carbide tool of 237 µm diameter. The results showed an overcut of 10.33 µm, circularity of 8.47 µm, and MRR of 6030.89 µm³/s for the lowest energy setup. Full article

(This article belongs to the Proceedings of 2024 IEEE 6th Eurasia Conference on IoT, Communication and Engineering)

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7 pages, 5843 KiB

Open AccessProceeding Paper

Solvothermal Synthesis of Nanomagnetite-Coated Biochar for Efficient Arsenic and Fluoride Adsorption

by Diego-Antonio Corona-Martinez, Lourdes Díaz-Jiménez, Audberto Reyes-Rosas, Alejandro Zermeño-González, Luis Samaniego-Moreno and Sasirot Khamkure

Eng. Proc. 2025, 87(1), 67; https://doi.org/10.3390/engproc2025087067 - 16 May 2025

Abstract

Arsenic contamination in water demands effective, low-cost removal methods. This study introduces nanomagnetite-coated biochar derived from pecan nutshells for efficient arsenic adsorption. Utilizing a solvothermal method, uniform magnetite crystals were grown on biochar in a controlled process at 200 °C. The resulting bioadsorbent, [...] Read more.

Arsenic contamination in water demands effective, low-cost removal methods. This study introduces nanomagnetite-coated biochar derived from pecan nutshells for efficient arsenic adsorption. Utilizing a solvothermal method, uniform magnetite crystals were grown on biochar in a controlled process at 200 °C. The resulting bioadsorbent, characterized by XRD, SEM, and FTIR, exhibited a narrow size distribution and consistently high arsenic removal rates (97.30–98.76%). Biochar with varied particle sizes, synthesized at a short reaction time (6 h), showed the highest removal efficiency of arsenic (98.76%) and adsorption capacity (7.974 mg/g). This approach offers a sustainable for arsenic remediation, and ease of magnetic separation. Full article

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11 pages, 11179 KiB

Open AccessProceeding Paper

GNSS Jamming Observed on Sounding Rocket Flights from Northern Scandinavia

by Benjamin Braun, Oliver Montenbruck, Markus Markgraf, Marcus Hörschgen-Eggers and Rainer Kirchhartz

Eng. Proc. 2025, 88(1), 55; https://doi.org/10.3390/engproc2025088055 - 16 May 2025

Abstract

Since 2022, DLR’s Mobile Rocket Base (MORABA) has observed jamming of GNSS signals on sounding rockets launched from Esrange in northern Sweden and Andøya Space Center (ASC) in northern Norway. The jamming primarily affected the GPS L1, Galileo E1 and BeiDou B1C and [...] Read more.

Since 2022, DLR’s Mobile Rocket Base (MORABA) has observed jamming of GNSS signals on sounding rockets launched from Esrange in northern Sweden and Andøya Space Center (ASC) in northern Norway. The jamming primarily affected the GPS L1, Galileo E1 and BeiDou B1C and B1I signals on the L1 frequency band and was noticeable through a pronounced reduction in the carrier-to-noise ratio of the received GNSS signals. Jamming was observed in northern Sweden at an altitude above 22 km and in northern Norway at an altitude above 36 km. Geometric considerations made it possible to roughly localize the source of the jamming signals from the points of the flight path marking the start and end of interference. Full article

(This article belongs to the Proceedings of European Navigation Conference 2024)

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9 pages, 2822 KiB

Open AccessProceeding Paper

Performance Analysis of CUDA-Based Galileo Signal Quality Monitoring

by Florian Binder, Daniel J. Bauer, Thomas Pany and Torben Schüler

Eng. Proc. 2025, 88(1), 54; https://doi.org/10.3390/engproc2025088054 - 15 May 2025

Abstract

The aim of this study was to develop basic findings for a continuous Signal Quality Monitoring system based on a measurement campaign. Four Galileo satellites were repeatedly recorded, using a dish antenna, and their metrics were analyzed. Due to the stable course, thresholds [...] Read more.

The aim of this study was to develop basic findings for a continuous Signal Quality Monitoring system based on a measurement campaign. Four Galileo satellites were repeatedly recorded, using a dish antenna, and their metrics were analyzed. Due to the stable course, thresholds for the detection of threat models can be determined. These values were tested against simulated signals and the sensitivity of the detection was found to be satisfactory. Based on the convergence behavior of the data, a measurement duration of 180–200 s can be recommended. Finally, the influence of the GPU and memory clock on the performance of predefined conditions close to the receiver was tested. The core clock of the GPU was identified as the bottleneck of the processing. Full article

(This article belongs to the Proceedings of European Navigation Conference 2024)

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16 pages, 612 KiB

Open AccessProceeding Paper

Factors and Method of Preventing Construction Site Incidents

by Ameir Mohamed Medani, Ismail Bin Abdul Rahman and Nor Aziati Binti Abdul Hamid

Eng. Proc. 2025, 91(1), 18; https://doi.org/10.3390/engproc2025091018 - 15 May 2025

Abstract

Construction accidents cause property damage and harm the environment. The construction industry in the UAE has recorded high fatalities and injuries. However, there has been limited research to prevent accidents on construction sites. Hence, this study aims to uncover the factors causing accidents [...] Read more.

Construction accidents cause property damage and harm the environment. The construction industry in the UAE has recorded high fatalities and injuries. However, there has been limited research to prevent accidents on construction sites. Hence, this study aims to uncover the factors causing accidents and prevention measures. All the factors and prevention measures were identified through a literature review and verified in a questionnaire survey. A total of 50 incident causative factors were identified in two groups, and direct and underlying causes and six main preventive measures were determined. The questionnaire survey involved 30 experts who had 10 years of working experience in the UAE construction industry. The experts assessed each of the causative factors and the preventative measures based on a 5-point Likert scale. Reliability was tested on the collected data using Cronbach’s alpha value, and the value was 0.977. The most severe relevant factors of direct causes included violation, taking shortcuts, inadequate leadership/supervision, and human errors. The probability and severity were moderate, and the hazardous activities included unsafe working at height and unsafe lifting. This study shows that workers with experience from 1 to 5 years were engaged in the most accidents. In total, 26 preventive measures were determined. The results benefit the construction industry of the UAE in preventing or avoiding potential accidents at construction sites. Full article

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6 pages, 944 KiB

Open AccessProceeding Paper

Improving Facial Expression Recognition with a Focal Transformer and Partial Feature Masking Augmentation

by Liang-Ying Ke, Chia-Yu Liao and Chih-Hsien Hsia

Eng. Proc. 2025, 92(1), 70; https://doi.org/10.3390/engproc2025092070 - 14 May 2025

Abstract

With the advancement of deep learning (DL) and computer vision (CV) technologies, significant progress has been made in facial expression identification FER for real-world applications. However, FER still faces challenges such as occlusion and head pose variations, which make it difficult for FER [...] Read more.

With the advancement of deep learning (DL) and computer vision (CV) technologies, significant progress has been made in facial expression identification FER for real-world applications. However, FER still faces challenges such as occlusion and head pose variations, which make it difficult for FER models to maintain stability and accuracy. In this study, we introduced a focal vision transformer (FViT) with partial feature masking (PFM) into FER. This method was found to efficiently simulate the challenges posed by occlusion and head pose variations by introducing PFM data augmentation. Parts of the image were randomly masked while preserving key facial expressions. The proposed FViT showed an accuracy of 89.08% on the real-world affective faces database, which includes scenarios with occlusion and head pose variations. PFM enhanced the model’s performance, too. The developed method effectively addresses the challenges of occlusion and head pose variations in FER. Full article

(This article belongs to the Proceedings of 2024 IEEE 6th Eurasia Conference on IoT, Communication and Engineering)

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6 pages, 904 KiB

Open AccessProceeding Paper

Lightweight Depthwise Pooling Transformer for Enhanced Coffee Bean Recognition

by Liang-Ying Ke, Pin-Feng Lin and Chih-Hsien Hsia

Eng. Proc. 2025, 92(1), 69; https://doi.org/10.3390/engproc2025092069 - 14 May 2025

Abstract

As global trade networks rapidly expand, coffee production and consumption have increased globally, profoundly influencing modern lifestyles. However, the coffee production process still demands substantial labor, especially in the selection and processing of coffee beans. The high implementation costs have impeded its widespread [...] Read more.

As global trade networks rapidly expand, coffee production and consumption have increased globally, profoundly influencing modern lifestyles. However, the coffee production process still demands substantial labor, especially in the selection and processing of coffee beans. The high implementation costs have impeded its widespread adoption. Therefore, we developed a defect detection and roasting level recognition method using a lightweight vision transformer (ViT) based on the deep learning (DL) method to extract features from coffee bean images. The developed method effectively reduces the overall cost of the coffee production process, showing a recognition accuracy of 98.49% for the Coffee Cobra database and 99.68% for the Roasting Coffee Bean database. The number of the model parameters was only 0.13 M, making it appropriate to deploy to low-cost embedded platforms. Full article

(This article belongs to the Proceedings of 2024 IEEE 6th Eurasia Conference on IoT, Communication and Engineering)

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7 pages, 214 KiB

Open AccessProceeding Paper

Platform-Based Design of a Smart 12-Lead Electrocardiogram Device by Using Multiple Criteria Decision-Making Methods

by Chi-Yo Huang, Ping-Jui Chen and Jeng-Chieh Cheng

Eng. Proc. 2025, 92(1), 68; https://doi.org/10.3390/engproc2025092068 - 14 May 2025

Abstract

Smart telemedicine represents an innovative application of information and communication technology within the healthcare sector, encompassing healthcare delivery, disease management, public health surveillance, education, and research. The commercialization of 5G and the extensive adoption of the Internet of Things (IoT) enable smart telemedicine [...] Read more.

Smart telemedicine represents an innovative application of information and communication technology within the healthcare sector, encompassing healthcare delivery, disease management, public health surveillance, education, and research. The commercialization of 5G and the extensive adoption of the Internet of Things (IoT) enable smart telemedicine devices to mitigate geographical and transmission delays, hence enhancing the quality of treatment provided to individuals. Although intelligent medicine is significant, previous studies emphasize the implementation and adoption of systems or technologies with few studies conducted on the platform of smart telemedicine equipment. This study aims to address the research gap by forecasting future developments and delineating smart telemedicine device designs utilizing platform-based design. We introduce a hybrid multi-criteria model that delineates the components of the intelligent medical platform. A portable 12-lead electrocardiogram (ECG) system is used by a global telemedicine technology company to assess the viability of the suggested framework. The portable 12-lead ECG device integrates artificial intelligence (AI), cloud computing, and 6G technology. The results of this study provide a basis for product creation by other smart telemedicine companies, while the platform-based analytical methodology can be employed for future product design. Full article

(This article belongs to the Proceedings of 2024 IEEE 6th Eurasia Conference on IoT, Communication and Engineering)

7 pages, 858 KiB

Open AccessProceeding Paper

A Model-Based Analysis of Direct Methanol Production from CO₂ and Renewable Hydrogen

by Azizbek Kamolov, Zafar Turakulov, Botir Shukurillaevich Usmonov, Khayrulla Pulatov, Abdulaziz Bakhtiyorov, Bekjon Urunov and Adham Norkobilov

Eng. Proc. 2025, 87(1), 66; https://doi.org/10.3390/engproc2025087066 - 14 May 2025

Abstract

Methanol synthesis from CO₂ is a key strategy for carbon capture and utilization, offering a viable solution to mitigate climate change. The direct synthesis of methanol not only reduces greenhouse gases but also produces valuable chemicals for industrial applications. The aim of [...] Read more.

Methanol synthesis from CO₂ is a key strategy for carbon capture and utilization, offering a viable solution to mitigate climate change. The direct synthesis of methanol not only reduces greenhouse gases but also produces valuable chemicals for industrial applications. The aim of this study is to model and optimize the methanol synthesis process from CO₂, focusing on maximizing methanol yield while minimizing CO₂ content in the product stream. In this work, a detailed methanol synthesis process simulation was developed using the Soave–Redlich–Kwong equation of state in the Aspen Plus V11 commercial software environment. Pure CO₂ streams, which are produced from the post-combustion carbon capture process, and renewable hydrogen streams were used. The results are compared with open literature sources. In addition, a sensitivity analysis was employed to evaluate the effects of the pressure, temperature, and recirculation fraction on process efficiency. The results showed that the highest methanol yield of 76,838 kg/h was obtained at 80 bar, 276 °C, and a recirculation fraction of 0.9. The lowest CO₂ content in the final product (73 kg/h) occurred at 80 bar, 220 °C, and a recirculation fraction of 0.6. These findings demonstrate the trade-off between maximizing methanol output and reducing unreacted CO₂. In conclusion, optimal operating conditions for both the high yield and low CO₂ content were identified, providing a foundation for further process refinement. Future work will involve developing a more complex multi-reactor model and conducting economic assessments for large-scale industrial implementation. Full article

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13 pages, 6636 KiB

Open AccessProceeding Paper

Estimation of the Effect of Single Source of RF Interference on an Airborne Global Navigation Satellite System Receiver: A Theoretical Study and Parametric Simulation

by Ahmad Esmaeilkhah and Rene Jr Landry

Eng. Proc. 2025, 88(1), 53; https://doi.org/10.3390/engproc2025088053 - 14 May 2025

Abstract

This paper addresses the critical issue of unwanted interference in airborne GNSS receivers, crucial for navigational safety. Previous studies often simplified the problem, but this work offers a comprehensive approach, considering factors like Earth’s reflective properties, 3D calculations, and distinct radiation patterns. It [...] Read more.

This paper addresses the critical issue of unwanted interference in airborne GNSS receivers, crucial for navigational safety. Previous studies often simplified the problem, but this work offers a comprehensive approach, considering factors like Earth’s reflective properties, 3D calculations, and distinct radiation patterns. It introduces Spatial Interference Distribution Expression Heat-map and Operation Efficacy Plot graphs to visualize interference distribution along flight paths. The results highlight the significance of physical configuration and distance from interference sources on receiver performance. The algorithm developed can assess interference effects on GNSS receivers and aid in selecting optimal flight paths for minimal interference. This research enhances understanding and management of unintentional interference in airborne navigation systems. Full article

(This article belongs to the Proceedings of European Navigation Conference 2024)

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10 pages, 803 KiB

Open AccessProceeding Paper

A Novel Navigation Message for Future LCNS Satellites

by Filipe De Oliveira Salgueiro, Floor Thomas Melman, Richard Swinden, Yoann Audet, Pietro Giordano and Javier Ventura-Traveset

Eng. Proc. 2025, 88(1), 52; https://doi.org/10.3390/engproc2025088052 - 14 May 2025

Abstract

With the renewed interest in the Moon, several countries are launching projects to explore the Moon (at both institutional and private level). As part of the Moonlight Programme, the European Space Agency (ESA) is developing Lunar Communication and Navigation Services (LCNS) with its [...] Read more.

With the renewed interest in the Moon, several countries are launching projects to explore the Moon (at both institutional and private level). As part of the Moonlight Programme, the European Space Agency (ESA) is developing Lunar Communication and Navigation Services (LCNS) with its industrial partners. The Moon orbits, specifically the Elliptical Lunar Frozen Orbits (ELFO), are quite different compared to the GNSS orbits. This work presents a novel orbit model for the LCNS that can support different ELFOs and other orbits. The performance of the new model is measured in terms of accuracy and the number of bits (required to broadcast the information) against other available models. Such a model could be used to broadcast the ephemeris of the LCNS satellites within the navigation message of the LunaNet Augmented Forward Signal (AFS). Full article

(This article belongs to the Proceedings of European Navigation Conference 2024)

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11 pages, 1729 KiB

Open AccessProceeding Paper

On the Edge Model-Aided Machine Learning GNSS Interference Classification with Low-Cost COTS Hardware

by Simon Kocher, David Contreras Franco, Antonia Dietz and Alexander Rügamer

Eng. Proc. 2025, 88(1), 51; https://doi.org/10.3390/engproc2025088051 - 14 May 2025

Abstract

Interference signals can disrupt global navigation satellite system (GNSS) receivers and degrade or deny a position-velocity-time (PVT) solution. After detecting an interference signal, classifying its type can provide insight into its cause and help determine the necessary next steps to counteract it. In [...] Read more.

Interference signals can disrupt global navigation satellite system (GNSS) receivers and degrade or deny a position-velocity-time (PVT) solution. After detecting an interference signal, classifying its type can provide insight into its cause and help determine the necessary next steps to counteract it. In this paper, we present a method for interference detection and a resource-efficient model-aided on-the-edge machine learning (ML) model for interference signal classification running on low-cost commercial-off-the-shelf (COTS) hardware, enabling a highly cost-effective spectral monitoring solution. The choice of detection metrics is justified based on real-world spectral monitoring data from a German highway and the capability of the ML model to generalize across different environments is demonstrated in an outdoor field test. Overall, we present an operationally ready GNSS interference detection and classification system. Full article

(This article belongs to the Proceedings of European Navigation Conference 2024)

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Engineering Proceedings

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Vol. 92

IEEE ECICE 2024

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EASN 2024

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IEEE ICKII 2024

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