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Detection of Escherichia coli Using Loop-Mediated Isothermal Amplification for a Novel Air Sampling System

Aerosol and Air Quality Research 2025年第1期25卷 1-13页

作者： Eraslan-Gültekin, Hülya Ergenc, Ali Fuat Department of Control and Automation Engineering Istanbul Technical University Istanbul34469 Turkey

Introduction: This paper presents an innovative air sampling and rapid detection system for airborne bacteria in the environment. Objective: The proposed system aims to enhance the efficiency and accuracy of airborne bacterial detection by integrating an improved air sampler with a rapid nucleic acid amplification method. Methods: The system incorporates an air sampler with a cutting-edge nucleic acid amplification method known as loop-mediated isothermal amplification to detect bacterial pathogens precisely and efficiently. The air sampling system comprises a specialized collection device capable of capturing airborne bacteria particles, and an automatic sample extraction system from this device. Subsequently, nucleic acid amplification is performed using the LAMP technique, which can amplify at a constant temperature (65 °C) without the need for thermal cycling as with polymerase chain reaction, providing a faster, less costly, isothermal, and highly sensitive method for detecting target bacterial DNA. In addition, a simple, easily portable, low-cost, high-efficiency heating device that can be operated at constant temperature for the desired time has been developed for the LAMP reactions. Results: The results demonstrate the capability of the system to detect a broad-spectrum bacteria concentration with high sensitivity and specificity, making it a valuable tool for real-time monitoring and surveillance. The limit of detection of the air sampling system is 65 CFU L-1, determined through a series of experiments. Conclusions: The proposed air sampling system, coupled with the LAMP technology, offers a promising solution for addressing the limitations associated with traditional bacterial detection methods in airborne environments, such as insufficient sensitivity, extended processing times, microbial drying, isolating target microorganisms from others, and challenges in detecting low concentrations, paving the way for enhanced public health and safety measures. © Th

关键词： Escherichia coli

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Size Matters: Exploring the Impact of Scaling on Quadruped Robot Dynamics 25th

Size Matters: Exploring the Impact of Scaling on Quadruped R...

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25th Annual Conference on Towards Autonomous Robotic Systems, TAROS 2024

作者： Rahvar, Faraz Yilmaz, Abdurrahman Department of Mechatronics Engineering Istanbul Technical University Istanbul Turkey Department of Control and Automation Engineering Istanbul Technical University Istanbul Turkey

ISBN: (纸本)9783031720611

Quadruped robots are indispensable for specialized tasks, particularly in disaster scenarios like earthquakes, where their mobility surpasses that of fixed robots. However, altering their dimensions significantly impacts their mechanical requirements and control systems. This study investigates and compares the principal mechanical parameters affecting the control systems of quadruped robots during scaling, aiming to understand how scalability influences their design across different sizes. Using a standard quasi-direct driven commercial quadruped robot1(Unitree A 1) model featuring dual coaxial motor design and serial link architecture actuators, simulations are conducted at four different sizes, each expanding the robot size by 30%. These simulations analyze static and dynamic forces as the robot walks on a flat surface at a constant speed. To fortify our findings, simulations employ three trajectory scenarios, in each scenario, four robots are scaled in length, increasing by a coefficient of 0.3 from robot 1 to robot 4. The first and second scenarios feature different trajectory lengths, while the third scenario increases the trajectory height while maintaining the length of the second scenario. Specifically, we examine the required torque, energy consumption of the robot motors, reaction forces between the robot feet and terrain, and the mechanical cost of transport. These outcomes encompass critical components of the robot from a mechanical analysis perspective, including joints, providing valuable references for various actuator architecture designs. Our findings reveal a logical pattern in the increase of torques, power consumptions, and reaction forces as the robot scales. These insights lay the groundwork for developing a scalable control architecture for legged robots. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Multipurpose robots

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Empowering Lab Education: Integrating a Vision-Based Monitoring System with Small-Scale Self-Driving Experiment Platforms

Empowering Lab Education: Integrating a Vision-Based Monitor...

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2025 IEEE/SICE International Symposium on System Integration, SII 2025

作者： Ren, Junru Fisker-Bodker, Nis Guldenring, Ronja Chang, Jin Hyun Vegge, Tejs Ravn, Ole Nalpantidis, Lazaros Technical University of Denmark Automation and Control Group Department of Electrical and Photonics Engineering Denmark Technical University of Denmark Department of Energy Conversion and Storage Denmark

ISBN: (纸本)9798331531614

The rise of self-driving laboratories has seen significant growth across various research domains, particularly in chemistry, materials science and life science. However, a major challenge persists - the majority of self-driving systems are costly due to the use of highly precise lab equipment, robotic platforms, and case-specific algorithms, rendering these systems less accessible for educational purposes. This paper takes a multidisciplinary approach;we first introduce a small-scale self-driving experiment platform tailored for educational use, focusing on liquid materials mixing tasks commonly seen in chemistry and life sciences. To understand the operational status in real-time while maintaining self-driving capability and efficiency, we propose a novel system concept: employing a mobile robot as the lab supervisor to monitor the experiment process across multiple identical self-driving platforms. Specifically, this paper focuses on implementing a vision-based monitoring system. A deep learning architecture with a new training strategy is presented to jointly address two tasks: (a) vessel and content material segmentation and (b) volume estimation. The two tasks can be trained independently but can be inferred end-to-end by integrating them into the Mask R-CNN framework. Through evaluating the monitoring module on a real dataset, the results showcase promising detection capabilities, good real-time performance, and compatibility with the self-driving platform, indicating the feasibility of our proposed system. © 2025 IEEE.

关键词： Crystalline materials

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Reinforcement Learning Based Optimization of Autonomous Aircraft Performance 11

Reinforcement Learning Based Optimization of Autonomous Airc...

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11th International Conference on Mechatronics and Robotics engineering, ICMRE 2025

作者： Sari, Metin Caliskan, Fikret Istanbul Technical University Department of Mechatronics Engineering Istanbul Turkey Turkish Aerospace Industries Ankara Turkey Istanbul Technical University Department of Control and Automation Istanbul Turkey

ISBN: (纸本)9798331509293

This research introduces an advanced flight control system for optimizing autonomous aircraft performance, leveraging deep reinforcement learning (DRL) to address the complexities of nonlinear flight dynamics. Using a six-degree-of-freedom (6-DoF) rigid aircraft flight dynamics model, we develop a Deep Deterministic Policy Gradient (DDPG) controller tailored for waypoint navigation and attitude stabilization tasks. A custom reward framework and extensive hyperparameter tuning enable effective training within a high-fidelity MATLAB/Simulink environment, achieving high rewards and precise control. Although computationally intensive, the simulations demonstrate robust performance across diverse flight conditions, with potential for real-world applications and future extensions to multi-agent scenarios. © 2025 IEEE.

关键词： Deep reinforcement learning

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Video Streaming Protocols Comparison in Field Experiments for Aerial Teleoperation 9

Video Streaming Protocols Comparison in Field Experiments fo...

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9th International Conference on Mechanical engineering and Robotics Research, ICMERR 2025

作者： Fernandez, Manuel J. Franceschini, Riccardo Becerra, Julian Cayero Fumagalli, Matteo Technical University of Denmark Department of Electrical Engineering Automation and Control Kongens Lyngby2800 Denmark Robotics and Automation Unit Eurecat Technology Center of Catalunya Cerdanyola Del Vallès Barcelona Spain

ISBN: (纸本)9798331509262

As aerial robots evolve to perform complex tasks such as manipulation and visual inspection, maintaining the pilot at the center of decision-making is critical for mission safety and leveraging human expertise. A key enabler of this collaboration is reliable video streaming between the robot and the operator. This paper presents a comparative analysis of four video streaming protocols RTP (Real-time Transport Protocol), RTSP (Real-Time Streaming Protocol), SRT (Secure Reliable Transport), and ROSTCP (ROS Transport control Protocol) based on extensive field experiments. The evaluation focuses on CPU usage, network throughput, latency, streaming quality, and packet integrity across varying distances. Tools such as iperf, ping, GStreamer, and Wireshark were employed for detailed performance measurements. Results reveal significant differences: SRT and RTP deliver superior network stability and resilience to packet loss, while RTSP and ROSTCP show advantages in CPU efficiency. These findings provide actionable insights for optimizing video streaming in aerial robotics, emphasizing the trade-offs and advantages of each protocol in real-world scenarios. © 2025 IEEE.

关键词： Social robots

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Resilient fractional-order fuzzy-based tilt control of renewable-dominated hybrid power system

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Neural Computing and Applications 2025年 1-17页

作者： Sah, Sneha V. Prakash, Vivek Pathak, Pawan Kumar Yadav, Anil Kumar School of Automation Banasthali Vidyapith Rajasthan Tonk India Government Polytechnic College Tonk Department of Technical Education Government of Rajasthan Jodhpur India Department of Instrumentation and Control Engineering Dr B R Ambedkar National Institute of Technology Jalandhar Punjab Jalandhar144008 India

The research conducted for this work has focused on the design and implementation of resilient fractional-order fuzzy integral tilt derivative with filter (FOF-ITDF) controller for renewable-dominated hybrid power system (HPS). The considered HPS employs solar thermal, wind power, diesel engine, aqua electrolyzer, and fuel cell as the distributed generation system. Moreover, flywheel, battery, and ultra-capacitor (UC) are chosen as the energy storage components. The parameters of FOF-ITDF controller are tuned via a hybrid optimization termed as artificial gorilla troops optimizer (GTO) with gradient-based optimizer (GBO). This proposed controller shows enhanced performance over fuzzy proportional–integral–derivative (PID), fuzzy ITDF, and FOF-PID controllers in terms of dynamic as well as steady state. Proposed GTO-GBO: FOF-ITDF controller provides highly robust operation against rate constraint non-linearities, parameter variations, and communication time delays (CTDs). Finally, the performance assessment of the proposed control architecture is also benchmarked over real-power system data via a standard IEEE-14 bus system with its stability evaluation by bode plot. By utilizing the proposed control design, the maximum frequency deviation Δf in the worst situation, i.e., CTD of 0.3 s, is − 0.024 Hz, which is much less and under the admissible range of IEEE standard. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Bode diagrams

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Balancing Precision and Speed: Introducing The Performance Efficiency Evaluation Ratio (PEER) in Visual Odometry

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IEEE Access 2025年 13卷 89874-89904页

作者： Atilgan, Cem Mercimek, Muharrem Kırklareli University Department of Mechatronics Engineering Kırklareli Turkey Yildiz Technical University Department of Control and Automation Engineering Istanbul Turkey

Visual odometry is extensively utilized in robotics, autonomous navigation, and augmented reality to estimate motion by analyzing sequential images. Provides high precision without relying on external sensors such as GPS. However, real-time visual odometry systems must strike a balance between computational correctness and speed, a critical yet often overlooked trade-off in existing evaluation methods. Traditional metrics such as ATE, RPE, RMSE, FPS, and RTF typically assess precision or efficiency in isolation and lack adaptability to real-time constraints. To address this limitation, we propose the Performance Efficiency Evaluation Ratio (PEER), a novel, adaptive, and lightweight metric that jointly evaluates algorithm performance based on both fidelity and computation time. PEER incorporates a tunable weighting parameter to prioritize performance, speed, or a balanced trade-off, and employs normalization techniques to ensure comparability across different algorithms and systems. We evaluated PEER using the KITTI dataset with various feature extraction algorithms (SIFT, ORB, BRISK, KAZE, AKAZE) and matching algorithms (BF, FLANN). The findings indicate that PEER not only distinguishes performance effectively across various scenarios but also shows strong alignment with established MCDM methods such as TOPSIS and VIKOR, all while maintaining lower computational complexity and greater suitability for real-time deployment. Overall, PEER provides a robust and flexible framework for optimizing visual odometry systems. © 2013 IEEE.

关键词： Augmented reality

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Empowering Lab Education: Integrating a Vision-Based Monitoring System with Small-Scale Self-Driving Experiment Platforms

Empowering Lab Education: Integrating a Vision-Based Monitor...

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IEEE/SICE International Symposium on System Integration

作者： Junru Ren Nis Fisker-Bødker Ronja Güldenring Jin Hyun Chang Tejs Vegge Ole Ravn Lazaros Nalpantidis Department of Electrical and Photonics Engineering Automation and Control Group Technical University of Denmark Department of Energy Conversion and Storage Technical University of Denmark

ISBN: (数字)9798331531614

ISBN: (纸本)9798331531621

The rise of self-driving laboratories has seen significant growth across various research domains, particularly in chemistry, materials science and life science. However, a major challenge persists—the majority of self-driving systems are costly due to the use of highly precise lab equipment, robotic platforms, and case-specific algorithms, rendering these systems less accessible for educational purposes. This paper takes a multidisciplinary approach; we first introduce a small-scale self-driving experiment platform tailored for educational use, focusing on liquid materials mixing tasks commonly seen in chemistry and life sciences. To understand the operational status in real-time while maintaining self-driving capability and efficiency, we propose a novel system concept: employing a mobile robot as the lab supervisor to monitor the experiment process across multiple identical self-driving platforms. Specifically, this paper focuses on implementing a vision-based monitoring system. A deep learning architecture with a new training strategy is presented to jointly address two tasks: (a) vessel and content material segmentation and (b) volume estimation. The two tasks can be trained independently but can be inferred end-to-end by integrating them into the Mask R-CNN framework. Through evaluating the monitoring module on a real dataset, the results showcase promising detection capabilities, good real-time performance, and compatibility with the self-driving platform, indicating the feasibility of our proposed system.

关键词： Training Materials science and technology Chemistry Volume measurement System integration Rendering (computer graphics) Real-time systems Life sciences Mobile robots Monitoring

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A Fractional-Order Nonlinear Backstepping controller Design for Current-controlled Maglev System

arXiv

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arXiv 2025年

作者： Astekin, Dorukhan Adigüzel, Fatih Yıldız Technical University Electric-Electronic Faculty Department of Control and Automation Engineering İstanbul Turkey

The magnetic levitation system (Maglev) is a nonlinear system by which an object is suspended with no support other than magnetic fields. The main control perspective of the Maglev system is to levitate a steel ball in air by the electromagnetic force. However, the Maglev system has highly nonlinear dynamics which is inconvenient in the sense of sensitive control/regulation of its nonlinear dynamics. In this paper, the nonlinear backstepping controller based on the fractional-order derivative is proposed for the control of the nonlinear current-controlled Maglev system. After, the system dynamics and fractional-order backstepping controller design are given, the asymptotic stability of the closed-loop system is proved by employing the Lyapunov theory. Some computer-based numerical experiments are carried out to show the effectiveness of the proposed controller for the control of Maglev system. © 2025, CC BY.

关键词： Magnetic levitation

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Capturing Aerodynamic Characteristics of ATTAS Aircraft with Evolving Intelligent System

arXiv

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arXiv 2025年

作者： Soylu, Aydoğan Kumbasar, Tufan Control and Automation Engineering Department Istanbul Technical University Istanbul Turkey AI and Intelligent Systems Laboratory Istanbul Technical University Istanbul Turkey

Accurate modeling of aerodynamic coefficients is crucial for understanding and optimizing the performance of modern aircraft systems. This paper presents the novel deployment of an Evolving Type-2 Quantum Fuzzy Neural Network (eT2QFNN) for modeling the aerodynamic coefficients of the ATTAS aircraft to express the aerodynamic characteristics. eT2QFNN can represent the nonlinear aircraft model by creating multiple linear submodels with its rule-based structure through an incremental learning strategy rather than a traditional batch learning approach. Moreover, it enhances robustness to uncertainties and data noise through its quantum membership functions, as well as its automatic rule-learning and parameter-tuning capabilities. During the estimation of the aerodynamic coefficients via the flight data of the ATTAS, two different studies are conducted in the training phase: one with a large amount of data and the other with a limited amount of data. The results show that the modeling performance of the eT2QFNN is superior in comparison to baseline counterparts. Furthermore, eT2QFNN estimated the aerodynamic model with fewer rules compared to Type-1 fuzzy counterparts. In addition, by applying the Delta method to the proposed approach, the stability and control derivatives of the aircraft are analyzed. The results prove the superiority of the proposed eT2QFNN in representing aerodynamic coefficients. © 2025, CC BY-NC-SA.

关键词： Fuzzy rules

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