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International Conference on Manipulation, Automation and Robotics at Small Scales (MARSS)

作者： Yuning Lei Sergej Fatikow Department of Computing Science Division Microrobotics and Control Engineering University of Oldenburg Oldenburg Germany

ISBN: (数字)9798350376807

ISBN: (纸本)9798350376814

The wetting phenomenons of surfaces, such as surface tension, are a critical concern for various micro- and nanoelectromechanical systems (MEMS/NEMS) due to scaling effects. In recent years, a lot of efforts have been made in research and industry to study wetting phenomena at the micron and nanometer scales, among which, the liquid metal droplet-based contact angle measurement (CAM) method allows for the characterization of surface wetting properties in a scanning electron microscope (SEM), thus obtaining a resolution that goes beyond the limits of an optical microscope. However, reproducible fabrication of specific micrometer-scale droplets is a key challenge towards the automation of this technique. In this paper, we provide a reliable and reproducible method for the fabrication of specific-sized liquid metal droplets. This includes the generation of droplets at specific locations based on electromigration techniques and the use of focused ion beam (FIB) induced sputtering techniques to fabricate droplets of specific sizes. Using Galinstan as an example, this paper reports its sputtering yield of 0.866 μm 3 .nC −1 , along with the process of determining it. We also demonstrated the reproducibility of fabricating liquid metal droplets with a radius of 10 μm and 5 μm, the results show standard deviations of 0.168 μm and 0.63 μm, respectively.

关键词： Fabrication Electromigration Scanning electron microscopy Liquids Automation Metals Process control

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Characterization of Tunable Multilayer Graphene Ink by a Microstrip Transmission Line

Characterization of Tunable Multilayer Graphene Ink by a Mic...

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International Conference on Manipulation, Automation and Robotics at Small Scales (MARSS)

作者： Shakir Ullah Warda Saeed Muhammad Yasir Department of Computing Sciences Division of Microrobotics and Control Engineering University of Oldenburg Oldenburg Germany

ISBN: (数字)9798350376807

ISBN: (纸本)9798350376814

This paper presents microwave characterization of multilayered graphene in the frequency range of 8–12 GHz. Thru, Reflect, Line calibration method has been used to shift calibration planes from the edge of a microstrip line to graphene. Transmission coefficient is measured for varying bias voltages. Values of complex impedance of graphene are derived from the transmission coefficient. Prototypes have been fabricated consisting of a microstrip line with different gaps on which multilayered graphene ink is deposited. By decreasing the gap size in the microstrip line, the resistance, reactance and the tuning range of graphene decreases.

关键词： Resistance Voltage measurement Graphene Prototypes Ink Transmission line measurements Microwave theory and techniques

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Artificial Enhanced Image Inpainting for Broadband SMM

Artificial Enhanced Image Inpainting for Broadband SMM

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International Conference on Manipulation, Automation and Robotics at Small Scales (MARSS)

作者： Markus F. Wieghaus Warda Saeed Muhammad Yasir Department of Computing Science Division Microrobotics and Control Engineering University of Oldenburg Oldenburg Germany

ISBN: (数字)9798350376807

ISBN: (纸本)9798350376814

Scanning probe microscopy techniques changed the understanding of micro and nanometer scaled objects. Since the invention of the scanning tunneling microscope and the atomic force microscope, these techniques were further developed and combined with different measurement methodologies such as scanning microwave microscopy. By introducing an electromagnetic signal in the microwave frequency range via the scanning probe microscope tip to the sample, electromagnetic characteristics of the specimen can be acquired. Using a series of frequencies in one scan, even more information like dopant concentration or different layers of material can be captured. The drawback of this method is the long data acquisition time, which cannot be neglected on nanometer scale. To address this issue, we are using reduced raster scan techniques with broadband scanning microwave microscopy and calculate the missing data information by using image inpainting algorithms afterwards. In this work we will present the development of a deep learning algorithm and compare the output of it with different inpainting algorithms, which can even be used in other scanning probe microscope setups.

关键词： Atomic measurements Atomic force microscopy Scanning probe microscopy Neural networks Training data Tunneling Broadband communication

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Improving graph collaborative filtering with network motifs

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Neural computing and Applications 2025年第16期37卷 9413-9432页

作者： Zhang, Yuqi Yu, Jian Liu, Zhizhong Wang, Guiling Nguyen, Minh Sheng, Quan Z. Wang, Nancy Department of Computer and Information Sciences Auckland University of Technology Auckland Central1010 New Zealand School of Computer and Control Engineering Yantai University Shandong Yantai264005 China School of Information Science and Technology North China University of Technology Beijing100144 China School of Computing Macquarie University SydneyNSW2109 Australia

Deep learning on graphs, specifically graph convolutional networks (GCNs), has exhibited exceptional efficacy in the domain of recommender systems. Most GCNs have a message-passing architecture that enables nodes to aggregate information from neighbours iteratively through multiple layers. This enables GCNs to learn from higher-order information, but the model does not allow for direct captions of the local structural patterns. Our rationale is to investigate the effectiveness of capturing such local patterns for graph-based collaborative filtering to enhance model’s learning ability per layer. This technique combines lower-order and higher-order interactions during layer-wise propagation. In this paper, we propose MotifGCN to aggregate both lower-order and higher-order information in each graph convolution layer. Specifically, we develop dedicated algorithms of generating motif adjacency matrices. The matrices are then used for motif-enhanced neighbourhood aggregation in each layer. As this paper focuses on recommender systems, MotifGCN is built on the basis of bipartite graphs. Our experiments on four real-world datasets show that MotifGCN has a superior performance compared to various state-of-the-art methods. © The Author(s) 2025.

关键词： Collaborative filtering

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User Input Search - Custom Motion Estimation Algorithm Optimized for UAVs

User Input Search - Custom Motion Estimation Algorithm Optim...

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Proceedings of the International Convention MIPRO

作者： J. Benjak D. Hofman Department of Control and Computer Engineering Faculty of Electrical Engineering and Computing Zagreb Croatia

Unmanned aerial vehicle technology is growing rapidly as it finds its application in the various industries, including military & defense, agriculture, logistics, transportation, healthcare, entertainment and many others. One of the fastest growing industries including drones is the entertainment industry. More specifically, First Person View (FPV) piloting has become a popular sport which attracts huge masses. In FPV systems, the pilot wears FPV goggles and controls the drone using a controller, and the drone transmits video data to the goggles in real time. Since drones usually fly very quickly, the video quality in terms of resolution, compression artefacts and end-to-end delay must be maintained. This paper explores the idea of optimizing the Motion Estimation (ME) algorithm of existing High Efficiency Video Coding (HEVC) algorithms by utilizing user input from the controller. For example, if the drone is directed to hover to the left, then it would make sense to search for the most similar blocks in the previous video frame only on the left side of the referent block. For the purpose of this research, a HEVC video coder was customized to receive additional input besides the video data – the user input from the controller, or in other words, drone movement directions. We call this ME algorithm User Input Search (UIS). Our UIS algorithm is compared with the standard ME algorithms and its efficiency is tested.

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A novel analytical model for estimating vehicle delay at isolated signalised intersections

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International Journal of Simulation and Process Modelling 2022年第1-2期19卷 71-85页

作者： Qiao, Feng Liu, Huixin Luo, Dan Sun, Haochen Chen, Yinong School of Information and Control Engineering Shenyang Institute of Technology Fushun113122 China Faculty of Information and Control Engineering Shenyang Jianzhu University Shenyang100168 China School of Information Engineering Henan University of Science and Technology Henan Luoyang471000 China School of Computing Informatics and Decision Systems Engineering Arizona State University TempeAZ85287-8809 United States

This paper proposes a novel analytical model to estimate the average vehicle delay at signalised intersections under saturated or oversaturated conditions based on the investigation and analysis of the existing methods to deal with the problems arising in the process of acceleration, deceleration, and the transmissibility of the cycle-by-cycle average vehicle delay. The proposed model employs and combines the operating and queuing characteristics of vehicles to produce the analytic formula. To verify the effectiveness of the proposed model, simulation experiments are conducted, and the error rates and the correlation coefficients are investigated, which confirm that the proposed model possesses certain significant advantages over the existing models in saturated and oversaturated conditions. The results of research work show that the proposed model can provide transportation engineers or professionals with an effective tool for analysing, timing and managing the saturated or oversaturated signalised intersections. © 2022 Inderscience Enterprises Ltd.

关键词： Analytical models

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A spintronic memristive circuit on the optimized RBF-MLP neural network

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Chinese Physics B 2022年第11期31卷 272-283页

作者： Yuan Ge Jie Li Wenwu Jiang Lidan Wang Shukai Duan School of Artificial Intelligence Southwest UniversityChongqing 400715China Chongqing Brain Science Collaborative Innovation Center Chongqing 400715China Brain-inspired Computing and Intelligent Control of Chongqing Key Laboratory Chongqing 400715China National&Local Joint Engineering Laboratory of Intelligent Transmission and Control Technology Chongqing 400715China

A radial basis function network(RBF)has excellent generalization ability and approximation accuracy when its parameters are set ***,when relying only on traditional methods,it is difficult to obtain optimal network parameters and construct a stable model as *** view of this,a novel radial basis neural network(RBF-MLP)is proposed in this *** connecting two networks to work cooperatively,the RBF’s parameters can be adjusted adaptively by the structure of the multi-layer perceptron(MLP)to realize the effect of the backpropagation updating ***,a genetic algorithm is used to optimize the network’s hidden layer to confirm the optimal neurons(basis function)number *** addition,a memristive circuit model is proposed to realize the neural network’s operation based on the characteristics of spin *** is verified that the network can adaptively construct a network model with outstanding robustness and can stably achieve 98.33%accuracy in the processing of the Modified National Institute of Standards and Technology(MNIST)dataset classification *** experimental results show that the method has considerable application value.

关键词： radial basis function network(RBF) genetic algorithm spintronic memristor memristive circuit

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Memristor-based multi-synaptic spiking neuron circuit for spiking neural network

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Chinese Physics B 2022年第4期31卷 225-233页

作者： Wenwu Jiang Jie Li Hongbo Liu Xicong Qian Yuan Ge Lidan Wang Shukai Duan College of Artificial Intelligence Southwest UniversityChongqing 400715China National&Local Joint Engineering Laboratory of Intelligent Transmission and Control Technology Chongqing 400715China Brain-inspired Computing and Intelligent Control of Chongqing Key Laboratory Chongqing 400715China Chongqing Brain Science Collaborative Innovation Center Chongqing 400715China

Spiking neural networks(SNNs) are widely used in many fields because they work closer to biological ***,due to its computational complexity,many SNNs implementations are limited to computer ***,this paper proposes a multi-synaptic circuit(MSC) based on memristor,which realizes the multi-synapse connection between neurons and the multi-delay transmission of pulse *** synapse circuit participates in the calculation of the network while transmitting the pulse signal,and completes the complex calculations on the software with ***,a new spiking neuron circuit based on the leaky integrate-and-fire(LIF) model is designed in this *** amplitude and width of the pulse emitted by the spiking neuron circuit can be adjusted as *** combination of spiking neuron circuit and MSC forms the multi-synaptic spiking neuron(MSSN).The MSSN was simulated in PSPICE and the expected result was obtained,which verified the feasibility of the ***,a small SNN was designed based on the mathematical model of *** the SNN is trained and optimized,it obtains a good accuracy in the classification of the IRIS-dataset,which verifies the practicability of the design in the network.

关键词： memristor multi-synaptic circuit spiking neuron spiking neural network(SNN)

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Weakly supervised target detection based on spatial attention

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Visual Intelligence 2024年第1期2卷 1-11页

作者： Zhao, Wenqing Xu, Lijiao School of Control and Computer Engineering North China Electric Power University Hebei Baoding 071000 China Department of Hebei Key Laboratory of Knowledge Computing for Energy & Power North China Electric Power University Hebei Baoding 071000 China

Due to the lack of annotations in target bounding boxes, most methods for weakly supervised target detection transform the problem of object detection into a classification problem of candidate regions, making it easy for weakly supervised target detectors to locate significant and highly discriminative local areas of objects. We propose a weak monitoring method that combines attention and erasure mechanisms. The supervised target detection method uses attention maps to search for areas with higher discrimination within candidate regions, and then uses an erasure mechanism to erase the region, forcing the model to enhance its learning of features in areas with weaker discrimination. To improve the positioning ability of the detector, we cascade the weakly supervised target detection network and the fully supervised target detection network, and jointly train the weakly supervised target detection network and the fully supervised target detection network through multi-task learning. Based on the validation trials, the category mean average precision (mAP) and the correct localization (CorLoc) on the two datasets, i.e., VOC2007 and VOC2012, are 55.2% and 53.8%, respectively. In regard to the mAP and CorLoc, this approach significantly outperforms previous approaches, which creates opportunities for additional investigations into weakly supervised target identification algorithms. © The Author(s) 2024.

关键词： Erasure concept Multi instance learning Object detection Spatial attention Weakly supervised learning

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Deep Forest-Based Fall Detection in Internet of Medical Things Environment

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Computer Systems Science & engineering 2023年第6期45卷 2377-2389页

作者： Mohamed Esmail Karar Omar Reyad Hazem Ibrahim Shehata College of Computing and Information Technology Shaqra UniversityP.O.Box 33Shaqra11961Saudi Arabia Department of Industrial Electronics and Control Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Computer Science Department Faculty of Computers and Artificial IntelligenceSohag UniversitySohag82524Egypt Department of Computer and Systems Engineering Faculty of EngineeringZagazig UniversityZagazig44519Egypt

This article introduces a new medical internet of things(IoT)framework for intelligent fall detection system of senior people based on our proposed deep forest *** cascade multi-layer structure of deep forest classifier allows to generate new features at each level with minimal hyperparameters compared to deep neural ***,the optimal number of the deep forest layers is automatically estimated based on the early stopping criteria of validation accuracy value at each generated *** suggested forest classifier was successfully tested and evaluated using a public SmartFall dataset,which is acquired from three-axis accelerometer in a *** includes 92781 training samples and 91025 testing samples with two labeled classes,namely non-fall and *** results of our deep forest classifier demonstrated a superior performance with the best accuracy score of 98.0%compared to three machine learning models,i.e.,K-nearest neighbors,decision trees and traditional random forest,and two deep learning models,which are dense neural networks and convolutional neural *** considering security and privacy aspects in the future work,our proposed medical IoT framework for fall detection of old people is valid for real-time healthcare application deployment.

关键词： Elderly population fall detection wireless sensor networks internet of medical things deep forest

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