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An Electrochemical HbA1c Immunosensor With a Simple Electrode of Gold Nanoparticle Monolayer

IEEE Sensors Journal 2023年第15期23卷 16579-16584页

作者： Tseng, Chun-Chiang Lin, Ying-Ting Wang, Gou-Jen National Chung-Hsing University Department of Mechanical Engineering Taichung40227 Taiwan National Chung-Hsing University Program in Tissue Engineering and Regenerative Medicine Taichung40227 Taiwan National Chung-Hsing University Department of Mechanical Engineering The Graduate Institute of Biomedical Engineering Taichung40227 Taiwan

This study developed a novel electrochemical glycosylated hemoglobin (HbA1c) immunosensor. Polyethylene terephthalate (PET) was used as a substrate. After gold (Au) film sputtering on the PET surface, 1,6-hexanedithol with thiol groups on both terminals was used as an intermediary to attach a monolayer of uniformly distributed Au nanoparticles (AuNPs) to the Au thin film to form a nanostructured electrode. HbA1c antibodies were successfully immobilized on the AuNP monolayer via the self-assembled monolayer (SAM) approach. To mimic a real clinical detection environment, a calibration curve for HbA1c detection was performed in a mixture solution containing heparin sodium, red blood cell (RBC) lysis buffer, artificial human serum, and hemoglobin. The experimental results revealed that the proposed HbA1c immunosensor detected HbA1c in a linear range from 2500 to 10 000 ng/mL with a limit of detection of 2253 ng/mL. The developed HbA1c-sensing chip offers the advantages of simple operation, low cost, short detection time, and provides high feasibility for clinical application. © 2001-2012 IEEE.

关键词： Gold nanoparticles

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Real-Time Geometric Joint Uncertainty Tracking for Surgical Automation on the dVRK System

Real-Time Geometric Joint Uncertainty Tracking for Surgical ...

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2024 IEEE International Conference on robotics and Biomimetics, ROBIO 2024

作者： Li, Bin Lin, Hongbin Zhong, Fangxun Liu, Yunhui T Stone Robotics Institute The Chinese University of Hong Kong The Department of Mechanical and Automation Engineering Hong Kong

ISBN: (纸本)9781665481090

Accurate control of surgical robotics under uncertainty is crucial for achieving surgical autonomy. This uncertainty arises from sensor or model inaccuracies in surgical platforms, e.g., dVRK system, where joint bias in positioning and complex transmission effects caused by backlash and cable stretch. Previous approaches usually rely on depth sensors or additional offline calibration steps, making them difficult to deploy in real-world laparoscopy surgeries. In this paper, we propose a real-time geometric approach for calibrating joint errors on-the-fly combined with geometric features. An efficient visual detector is employed to identify the shaft mask and wrist keypoints. Based on the extracted features, we introduce a geometric model to recover the shaft axis pose and determine the first two joints uncertainty. Additionally, we develop a geometric model for wrist joints in projection space, calibrating remaining joint uncertainty through spatial geometry and the analytical structure of dVRK platform. Experimental results in simulation show that our approach significantly reduces joint error from 15° to 0.02°, with end-effector pose accuracy improving from centimeter to sub-millimeter level. This greatly enhances the accuracy and success rate of surgical automation. We also demonstrate robust control performance in diverse surgical tasks, highlighting the effectiveness of our geometric model in achieving accurate control despite joint offsets. © 2024 IEEE.

关键词： End effectors

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A 22nm CMOS PFM Buck Converter Used in a 32-Bit RISC-V Microcontroller for IoT Applications 16

A 22nm CMOS PFM Buck Converter Used in a 32-Bit RISC-V Micro...

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16th IEEE Latin American Symposium on Circuits and Systems, LASCAS 2025

作者： Oliveira, Luiz H. N. Amaral, Wellington A. Haddad, Sandro A. P. Munoz, Daniel M. Barcelos, Eduardo A. Sotero, Vinicius V. M. Jonhson, Emmanuel K. M. Campagnucci, Pamela R. A. N. Lima, Luis V. N. Electronics Engineering Undergraduate Program Faculty of Gama Brazil Mechatronics Graduate Program University of Brasilia Faculty of Technology Department of Mechanical Engineering DF Brasilia Brazil

ISBN: (纸本)9798331522124

The demand for efficient power management solutions in electronic devices has increased significantly, especially for microcontrollers operating in low-power modes. This article presents the design and implementation of a Pulse Frequency Modulation (PFM)-based buck converter for applications in 32-bit microcontrollers using 22nm CMOS technology. The methodology involves the development of a Constant On Time (COT) architecture that maintains a fixed duration for the activation of the high-side MOSFET, using a monostable multivibrator to generate the necessary timing signals. The converter's performance was simulated under various load conditions and process corners to evaluate its efficiency and output characteristics. The results show that the proposed buck converter achieves high efficiency, with peak values above 95%, while maintaining a low output ripple. Specifically, the converter exhibits an efficiency of 93.4% at a load of 1mA, with output ripple voltages as low as 2.20 mV under ideal conditions. The contributions of this work lie in the advancement of PFM converter technology for modern microcontroller applications, demonstrating the potential of 22nm CMOS processes to improve power efficiency and performance. © 2025 IEEE.

关键词： CMOS integrated circuits

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Manufacturing process design for cellular structure used as impact energy absorbers 4

Manufacturing process design for cellular structure used as ...

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4th International Symposium on Advances and Innovation in mechanical engineering, ISAIME 2023

作者： Komara, Asep Indra Setiawan, Rachman Budiwantoro, Bagus Doctoral Program of Mechanical Engineering Faculty of Mechanical and Aerospace Engineering Institut Teknologi Bandung Bandung Indonesia Faculty of Mechanical and Aerospace Engineering Institut Teknologi Bandung Bandung Indonesia Manufacturing Design Engineering Department Politeknik Manufaktur Bandung Bandung Indonesia

The application of passive safety technology in vehicles is generally through impact-absorbing structures. This technology has been widely applied to trains, planes, and cars to protect passengers from fatal accidents. Cellular structures are defined by a unit cell that combines material, space, and unit cell repetition to obtain a large structure. It is selected as the base structure of the impact energy absorber for its potentially high specific energy absorption (SEA]. Such a structure poses a challenge in its manufacturing method. This study examines alternative manufacturing processes for cellular structures designed as impact absorbers, and then selects and develops an effective and efficient manufacturing method to produce these cellular structures. Several alternative manufacturing methods were chosen to exercise the use of a combined plaster molding technology and additive manufacturing for prototyping. Manufacturing process simulation studies were conducted using Altair Inspire Cast software to determine the success of the casting process. The final stage is to carry out the process of making cell structures and evaluating the results. This work results in an effective manufacturing process design to be implemented throughout the production stage. Furthermore, for mass production, mold patterns may be replaced by using injection molding. © 2025 Institute of Physics Publishing. All rights reserved.

关键词： Injection molding

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Coverage Path Planning of Agricultural Spraying Drones with Graph Convolutional Network 12

Coverage Path Planning of Agricultural Spraying Drones with ...

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12th RSI International Conference on robotics and Mechatronics, ICRoM 2024

作者： Khaloujini, Amirmasood Talaeizadeh, Amin Taheri, Alireza Alastya, Aria Advanced Research Lab for Control and Agricultural Robotics Sharif University of Technology Social and Cognitive Robotics Lab. Department of Mechanical Engineering Tehran Iran

ISBN: (纸本)9798331529734

Optimal route planning for autonomous robots is a crucial element of contemporary agriculture, especially for activities like spraying and pollination. This paper presents a Graph Convolutional Network (GCN) model especially developed to address the Coverage Path Planning (CPP). The aim of this paper is to convert the CPP problem into a Traveling Salesman Problem (TSP), so that by solving the TSP using the proposed neural network, an optimal route can be extracted for each agricultural field in the shortest possible time and with sufficient accuracy, ensuring full coverage of the field's area. The proposed model is a GCN, augmented with a transformer layer and a Multi-Layer Perceptron (MLP), was trained using datasets produced by a Genetic Algorithm (GA) Method. The model obtained a network error of 0.12 while solving TSP cases with 20 points. It showed robust generalization skills, as evidenced by validation and test errors of 0.1261 and 0.1264, respectively. Significantly, the GCN model reduces the solution time to approximately one-tenth compared to conventional approaches, a benefit that increases as the number of points increases. The effectiveness of the model in producing ideal coverage routes highlights its potential for wider use in optimization and coverage path planning, especially in dynamic and extensive agricultural environments. © 2024 IEEE.

关键词： Traveling salesman problem

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A holistic perspective of the sustainable manufacturing: a novel conceptual approach

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International Journal of Industrial and Systems engineering 2022年第2期41卷 135-167页

作者： Garbie, Ibrahim Garbie, Abdelrahman Industrial Engineering Program Department of Mechanical Engineering Helwan University Cairo Helwan Egypt Department of Mechanical Engineering University of Louisiana at Lafayette LafayetteLA United States

Sustainability is recently considered the buzzword in manufacturing environments. Analysis and investigation of sustainability in manufacturing becomes urgent not only from one stream but also from the whole manufacturing streams. The main goal of this paper is to explain how to classify sustainability in all streams of manufacturing as a whole (machine components design, manufacturing processes and manufacturing systems) through identifying the sustainability enablers. A framework to analyse the whole manufacturing streams will be illustrated and discussed to identify which enablers are significant in each manufacturing stream and which manufacturing stream is more significant towards whole manufacturing sustainability than others. A novel assessment for measuring the manufacturing sustainability will be presented. It seemed that the stream of the manufacturing system represented the highest turbulent one due to the diversity and numerous sustainable enablers. It was also observed from this analysis and investigation that understanding sustainable manufacturing as a whole is a comprehensive task and it is recommended as one of major pillars towards the Industry 4.0. © 2022 Inderscience Enterprises Ltd.

关键词： Machine design

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Deep Learning-Based Classification of Areca Nut Yellow Leaf Disease with ResNet-50 CNN

Deep Learning-Based Classification of Areca Nut Yellow Leaf ...

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2024 IEEE International Conference on Recent Advances in Science and engineering Technology, ICRASET 2024

作者： Veeresha, R.K. Lathish Kumar, N.D. Shetty, Samarth S. Prasad, Shrajan G. Poojary, Swaroop S. Karegoudra, Shilpa Koten, Hasan Department of Robotics and Artificial Intelligence Udupi574110 India Department of Computer Science and Engineering Udupi574110 India Istanbul Medeniyet University Faculty of Engineering and Pure Sciences Department of Mechanical Engineering Istanbul Turkey

ISBN: (纸本)9798350388602

Detecting healthy arecanut leaves, yellow leaf disease in arecanut, and differentiating these from other types of leaves using deep learning involves designing an advanced neural network model for precise image classification. The model is trained on a dataset comprising images of healthy arecanut leaves, arecanut leaves affected by yellow leaf disease, and various other leaves from different plant species. Convolution Neural Networks (CNNs) are leveraged to extract and analyze intricate patterns in the images, enabling the model to effectively classify each leaf type. Transfer learning techniques might be utilized to improve the model's performance and adaptability. The model's effectiveness is evaluated using accuracy, precision, recall, and F1 score on a dedicated test dataset. This paper explores the methodology and results of employing image detection for the identification of healthy arecanut leaves, yellow leaf disease, and other leaf types, with potential applications in agricultural diagnostics, disease management, and crop monitoring. © 2024 IEEE.

关键词： Convolutional neural networks

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Light-driven high-precision cell adhesion kinetics

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Light(Science & Applications) 2022年第10期11卷 2256-2257页

作者： Zhiyuan Zhang Daniel Ahmed Acoustic Robotics Systems Laboratory Institute of Robotics and Intelligent SystemsDepartment of Mechanical and Process EngineeringETH ZurichS?umerstrasse 4CH-8803 ZurichSwitzerland

Existing single-cell adhesion kinetics methods are performed under conditions highly unlike the physiological cell adhesion ***,researchers have developed a new optical technique for high-precision measurement of cell... 详细信息

关键词： conditions. kinetics formed

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Recent advances on monolithic perovskite-organic tandem solar cells

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Interdisciplinary Materials 2024年第1期3卷 113-132页

作者： Guanshui Xie Huan Li Longbin Qiu Shenzhen Key Laboratory of Intelligent Robotics and Flexible Manufacturing Systems Department of Mechanical and Energy EngineeringSUSTech Energy Institute for Carbon NeutralitySouthern University of Science and TechnologyShenzhenChina

Perovskite-organic tandem solar cells(TSCs)have emerged as a groundbreaking technology in the realm of photovoltaics,showcasing remarkable enhancements in efficiency and significant potential for practical ***-organic TSCs also exhibit facile fabrication surpassing that of all-perovskite or all-organic TSCs,attributing to the advantageous utilization of orthogonal solvents enabling sequential solution process for each *** perovskite-organic TSCs capitalize on the complementary light absorption characteristics of perovskite and organic *** is a promising prospect of achieving further enhanced power conversion efficiencies by covering a broad range of the solar spectrum with optimized perovskite absorber,organic semiconductors as well as the interconnecting layer's optical and electrical *** review comprehensively analyzes the recent advancements in perovskite-organic TSCs,highlighting the synergistic effects of combining perovskite with a low open-circuit voltage deficit,organic materials with broader light absorption,and interconnecting layers with reduced optical and electrical ***,the underlying device architecture design,regulation strategies,and key challenges facing the high performance of the perovskite-organic TSCs are also discussed.

关键词： interconnecting layer narrow‐bandgap organic solar cells recombination layer tandem solar cells wide‐bandgap perovskite solar cells

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Histogram of Oriented Gradients (HOG) and Haar Cascade with Convolutional Neural Network (CNN) Performance Comparison in the Application of Edge Home Security System 1

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International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications, CITISIA 2022

作者： Asy’ari, Muhammad Zacky Filbert, Sebastian Sukra, Zener Lie Automotive and Robotics Program Computer Engineering Department BINUS ASO of Engineering Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9783031290787

ISBN: (纸本)9783031290770

In recent years, security has played a significant role in our daily lives. House security has become more popular with the enhancement of the Internet of Things (IoT) in intelligent home automation. This paper aims to build a system that can detect faces and the presence of unknown people using computer vision methods combined with the Internet of things. Two algorithms, Histogram of Oriented Gradients (HOG) and Haar Cascade classifiers with the enhancement of Convolutional Neural Network (CNN), were used to compare their performance. The system utilizes Nvidia Jetson Nano as an edge device, HD camera, Light Emitting Diode (LED), and sound buzzer as peripheral hardware. Telegram messenger application was used to notify the homeowner when uninvited guests passed through the pre-defined security area. In the result, the HOG method has 100% accuracy compared to 75% Haar Cascade with 720p video quality in the proposed developed system. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Internet of things

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