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8th International Conference on Speckle Metrology, Speckle 2023

作者： Lin, Angyi Chen, Rui Lin, Huixiang You, Jiangfeng Zhang, Fucai The Department of Electrical and Electronic Engineering Southern University of Science and Technology Shenzhen518055 China School of Control and Computer Engineering North China Electric Power University Baoding071003 China

ISBN: (数字)9781510674592

ISBN: (纸本)9781510674585

Currently, the dominant encoding method for computer-generated holograms is phase-only holography. The Gerchberg-Saxton (GS) algorithm is a commonly used approach for hologram generation. However, it suffers from issues such as poor convergence and low efficiency. In this paper, we introduce an iterative hologram algorithm that addresses these challenges. Our proposed algorithm divides the target object into segments with a certain degree of data redundancy. It computes the hologram iteratively, giving extra weight to each iteration. We also incorporate overlap constraints to enhance hologram quality and accelerate convergence by leveraging data redundancy. What sets our algorithm apart from others is that it varies the order in which segments are processed during iterations, resulting in different final holograms. By randomly selecting the order in each iteration, we can rapidly generate multiple diverse holograms. Importantly, objects reconstructed from these holograms exhibit consistency, allowing for the quick generation of multiple phase-only holograms that help mitigate the speckle effect. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Holograms

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Bio-Inspired Home Localization Using Event-Based Vision and Spiking Neural Networks in Simulated Environment

Bio-Inspired Home Localization Using Event-Based Vision and ...

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2025 IEEE International Conference on Simulation, Modeling, and Programming for autonomous Robots, SIMPAR 2025

作者： Kausar, Rizwana Zayer, Fakhreddine Sudevan, Vidya Viegas, Jaime Dias, Jorge Khalifa University Department of Electrical Engineering United Arab Emirates Khalifa University Center for Autonomous Robotic Systems United Arab Emirates Khalifa University Department of Computer and Information Engineering United Arab Emirates

ISBN: (纸本)9798331516857

In this work, we present a bio-inspired approach for home localization using event-based visual data and spiking convolutional neural networks (S-CNNs) in a simulated environment within NVIDIA Omniverse. Drawing inspiration from the navigational strategies of insects, which utilize visual cues and homing vectors to return to their nests, our approach leverages event cameras that mimic the asynchronous change-driven visual processing of insect eyes. To process this sparse, event-driven input, we employ spiking neural networks (SNNs), which mirror the spike-based transmission of information in biological neural systems. The proposed system utilizes a quadrotor equipped with an event camera to capture dynamic, asynchronous visual data and processes it using an S-CNN trained to estimate relative home vectors. This vector encoding method is inspired by the unit-circle representation of gaze directions observed in insect homing behavior, as outlined in recent studies. By integrating event-based vision and SNNs, our approach ensures energy-efficient computation, robustness to lighting variations, and adaptability to dynamic scenes. We validate the framework in a 3D environment modeled within ISAAC Sim, where the quadrotor autonomously navigates back to a designated "nest"location. Comparative analysis with conventional frame-based neural networks demonstrates the superiority of the proposed system in terms of efficiency, robustness, and accuracy. This work establishes a novel bio-inspired framework for integrating event-based data and spiking neural networks, paving the way for energy-efficient localization in robotics and smart home environments. Future work will explore the deployment of the system in a multi-quadrotor environment to coordinate collaborative tasks including the integration of multi-modal sensory inputs such as Camera, IMU, Gas sensors and GPS data, and the extension of the framework to real-world settings for further validation and scalability. © 2025 IE

关键词： Convolutional neural networks

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Distributed Event-Triggered Adaptive Cooperative Circumnavigation control of Networked Unmanned Aerial Vehicles 20

Distributed Event-Triggered Adaptive Cooperative Circumnavig...

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20th International Multi-Conference on Systems, Signals and Devices, SSD 2023

作者： Ghommam, Jawhar Fareh, Raouf Al Naimi, Ibrahim Sultan Qaboos University College of Engineering Department of Electrical and Computer Engineering Oman Laboratory of Control Energy Management ENIS Tunisia University of Sharjah Electrical Engineering Dept. United Arab Emirates

ISBN: (纸本)9798350332568

In this paper, the objective for a group of unmanned aerial vehicle agents (UAVs) to achieve three dimensional circumnavigation around a moving target which information is made available to all agents in the group. The cooperative circumnavigation is to drive the UAVs to orbit around the target according to a given elliptical desired spatial formation. Due to the thrust limitation needed to fly the drone, existing cyclic pursuit algorithms cannot be extended directly to achieve this objective. Thus the proposed algorithm is worked out to take into account this constraint in order to achieve such objective. The drones are subject to unknown external disturbance, also the masses of those agent drones are assumed to be unknown. Furthermore, the communication cost can be decreased and the Zeno behavior is shown to be excluded. The proposed controller guarantees the bounded control effort irrespective of the external disturbance and model uncertainties of the drone. Numerical simulations are conducted to illustrate the efficacy of the approach. © 2023 IEEE.

关键词： Drones

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On a novel tracking differentiator design based on iterative learning in a moving window

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control Theory and Technology 2023年第1期21卷 46-55页

作者： Xiangyang Li Rafal Madonski Zhiqiang Gao Senping Tian Key Laboratory of Autonomous Systems and Network Control Ministry of EducationSchool of Automation Science and EngineeringSouth China University of TechnologyGuangzhou 510641China Energy and Electricity Research Center Jinan UniversityZhuhai 519070China Department of Electrical and Computer Science Cleveland State UniversityClevelandOH 44115USA

Differential signals are key in control engineering as they anticipate future behavior of process variables and therefore are critical in formulating control laws such as proportional-integral-derivative(PID).The practical challenge,however,is to extract such signals from noisy measurements and this difficulty is addressed first by *** in the form of linear and nonlinear tracking differentiator(TD).While improvements were made,TD did not completely resolve the conflict between the noise sensitivity and the accuracy and timeliness of the *** two approaches proposed in this paper start with the basic linear TD,but apply iterative learning mechanism to the historical data in a moving window(MW),to form two new iterative learning tracking differentiators(IL-TD):one is a parallel IL-TD using an iterative ladder network structure which is implementable in analog circuits;the other a serial IL-TD which is implementable digitally on any computer *** algorithms are validated in simulations which show that the proposed two IL-TDs have better tracking differentiation and de-noise performance compared to the existing linear TD.

关键词： Tracking differentiator(TD) Iterative learning Iterative learning tracking differentiator(IL-TD) Active disturbance rejection control(ADRC)-Two-dimensional system(2-D system)

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PrivShape: Extracting Shapes in Time Series under User-Level Local Differential Privacy 40

PrivShape: Extracting Shapes in Time Series under User-Level...

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40th IEEE International Conference on Data engineering, ICDE 2024

作者： Mao, Yulian Ye, Qingqing Hu, Haibo Wang, Qi Huang, Kai Southern University of Science and Technology Department of Computer Science and Engineering China The Hong Kong Polytechnic University Department of Electrical and Electronic Engineering Hong Kong Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology China School of Computer Science and Engineering Macau University of Science and Technology China

ISBN: (纸本)9798350317152

Time series have numerous applications in finance, healthcare, IoT, and smart city. In many of these applications, time series typically contain personal data, so privacy infringement may occur if they are released directly to the public. Recently, local differential privacy (LDP) has emerged as the state-of-the-art approach to protecting data privacy. However, existing works on LDP-based collections cannot preserve the shape of time series. A recent work, PatternLDP, attempts to address this problem, but it can only protect a finite group of elements in a time series due to ?-event level privacy guarantee. In this paper, we propose PrivShape, a trie-based mechanism under user-level LDP to protect all elements. PrivShape first transforms a time series to reduce its length, and then adopts trie-expansion and two-level refinement to improve utility. By extensive experiments on real-world datasets, we demonstrate that PrivShape outperforms PatternLDP when adapted for offline use, and can effectively extract frequent shapes. © 2024 IEEE.

关键词： Time series

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Visual Tracking Nonlinear Model Predictive control Method for autonomous Wind Turbine Inspection 21

Visual Tracking Nonlinear Model Predictive Control Method fo...

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21st International Conference on Advanced Robotics, ICAR 2023

作者： Amer, Abdelhakim Mehndiratta, Mohit Le Fevre Sejersen, Jonas Pham, Huy Xuan Kayacan, Erdal Department of Electrical and Computer Engineering Aarhus C8000 Denmark Gim Robotics Espoo Finland Automatic Control Group Paderborn University Department of Electrical Engineering and Information Technology Paderborn Germany

ISBN: (纸本)9798350342291

Automated visual inspection of on- and off-shorewind turbines using aerial robots provides several benefits, namely, a safe working environment by circumventing the need for workers to be suspended high above the ground, reduced inspection time, preventive maintenance, and access to hard-to-reach areas. A novel nonlinear model predictive control (NMPC) framework alongside a global wind turbine path planner is proposed to achieve distance-optimal coverage for wind turbine inspection. Unlike traditional MPC formulations, visual tracking NMPC (VT-NMPC) is designed to track an inspection surface, instead of a position and heading trajectory, thereby circumventing the need to provide an accurate predefined trajectory for the drone. An additional capability of the proposed VT-NMPC method is that by incorporating inspection requirements as visual tracking costs to minimize, it naturally achieves the inspection task successfully while respecting the physical constraints of the drone. Multiple simulation runs and real-world tests demonstrate the efficiency and efficacy of the proposed automated inspection framework, which outperforms the traditional MPC designs, by providing full coverage of the target wind turbine blades as well as its robustness to changing wind conditions. The implementation codes11https://***/open-airlab/VTNMPC-autonomous-Wind-Turbine-Inspection are open-sourced. © 2023 IEEE.

关键词： Inspection

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Privacy-Preservation for Distributed Quantized k-means Clustering 59

Privacy-Preservation for Distributed Quantized k-means Clust...

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59th Annual Allerton Conference on Communication, control, and Computing, Allerton 2023

作者： Rikos, Apostolos I. Manitara, Nicolas E. Oliva, Gabriele Hadjicostis, Christoforos N. Boston University Division of Systems Engineering Department of Electrical and Computer Engineering BostonMA02215 United States University of Cyprus Department of Electrical and Computer Engineering Nicosia1678 Cyprus Università Campus Bio-Medico di Roma Unit of Automatic Control Department of Engineering Rome00128 Italy

ISBN: (纸本)9798350328141

In this paper, we enhance a distributed version of the well known k-means algorithm with privacy-preservation features. While ensuring that sensitive or confidential information remains undisclosed to unauthorized entities (in our case these entities are curious nodes within the network), we maintain the desirable features of the distributed algorithm: the transmitted values are quantized, which optimizes bandwidth utilization and alleviates communication bottlenecks, and nodes possess the ability to collectively determine when to terminate the algorithm, which enables the conservation of valuable resources. We introduce a novel privacy-preserving protocol that not only preserves the privacy of a node's state, but it also ensures confidentiality about its cluster affiliation (i.e., it does not reveal to curious nodes whether a node participates in the update calculation for a specific centroid value). Moreover, we precisely characterize topological conditions that guarantee privacy preservation for individual nodes. Our distributed algorithm allows the formation of exclusive clusters within a finite time frame on any static and strongly connected directed graph. © 2023 IEEE.

关键词： Directed graphs

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A blockchain based privacy-preserving federated learning scheme for Internet of Vehicles

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Digital Communications and Networks 2024年第1期10卷 126-134页

作者： Naiyu Wang Wenti Yang Xiaodong Wang Longfei Wu Zhitao Guan Xiaojiang Du Mohsen Guizani School of Control and Computer Engineering North China Electric Power UniversityChina Department of Mathematics&Computer Science Fayetteville State UniversityFayettevilleNCUSA Department of Electrical and Computer Engineering Stevens Institute of TechnologyHobokenNJ07030USA Department of Computer Science and Engineering Qatar UniversityDohaQatar

The application of artificial intelligence technology in Internet of Vehicles(lov)has attracted great research interests with the goal of enabling smart transportation and traffic ***,concerns have been raised over the security and privacy of the tons of traffic and vehicle *** this regard,Federated Learning(FL)with privacy protection features is considered a highly promising ***,in the FL process,the server side may take advantage of its dominant role in model aggregation to steal sensitive information of users,while the client side may also upload malicious data to compromise the training of the global *** existing privacy-preserving FL schemes in IoV fail to deal with threats from both of these two sides at the same *** this paper,we propose a Blockchain based Privacy-preserving Federated Learning scheme named BPFL,which uses blockchain as the underlying distributed framework of *** improve the Multi-Krum technology and combine it with the homomorphic encryption to achieve ciphertext-level model aggregation and model filtering,which can enable the verifiability of the local models while achieving ***,we develop a reputation-based incentive mechanism to encourage users in IoV to actively participate in the federated learning and to practice *** security analysis and performance evaluations are conducted to show that the proposed scheme can meet the security requirements and improve the performance of the FL model.

关键词： Federated learning Blockchain Privacy-preservation Homomorphic encryption Internetof vehicles

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Enhancing Pneumonia Detection using Vision Transformer with Dynamic Mapping Re-Attention Mechanism 13

Enhancing Pneumonia Detection using Vision Transformer with ...

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13th International Conference on computer and Knowledge engineering, ICCKE 2023

作者： Labbaf Khaniki, Mohammad Ali Mirzaeibonehkhater, Marzieh Manthouri, Mohammad K.N. Toosi University of Technology Department of Control Engineering Tehran Iran Indiana University-Purdue University Department of Electrical and Computer Engineering United States Shahed University Department of Electrical and Electronic Engineering Tehran Iran

ISBN: (纸本)9798350330151

Pneumonia is a prevalent respiratory infection with potentially life-threatening consequences. In this research, we propose a novel deep learning approach to enhance pneumonia detection using the Vision Transformer (ViT) architecture with a dynamic mapping re-attention mechanism. The re-attention mechanism in the ViT architecture facilitates the flow of information between patches of the input image, enabling contextual understanding through self-attention. This mechanism captures long-range dependencies and enhances the ViT's ability to effectively recognize and interpret images. The dynamic mapping attention mechanism allows the model to dynamically assign attention weights to different regions of the input data based on their relevance to pneumonia detection. By adaptively adjusting the attention weights, the model can capture both local details and global dependencies, enabling more accurate diagnosis. The ViT utilizes the dynamic mapping re-attention mechanism with attention dropout. Here the performance of this ViT compared to the conventional ViT and the ViT incorporating the re-attention mechanism. This comparative analysis offers valuable insights into the effectiveness of the proposed dynamic mapping re-attention mechanism. Additionally, it highlights the added benefits of attention dropout in enhancing the overall performance of the model. © 2023 IEEE.

关键词： Image enhancement

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Enhancing Fault Detection in Induction Motors using LSTM-Attention Neural Networks 9

Enhancing Fault Detection in Induction Motors using LSTM-Att...

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9th International Conference on control, Instrumentation and Automation, ICCIA 2023

作者： Khaniki, Mohammad Ali Labbaf Mirzaeibonehkhater, Marzieh Manthouri, Mohammad K.N. Toosi University of Technology Department of Control Engineering Tehran Iran Indiana University-Purdue University Department of Electrical and Computer Engineering United States Shahed University Department of Electrical and Electronic Engineering Tehran Iran

ISBN: (纸本)9798350366013

Induction motors carry notable importance in modern machinery and industrial equipment. Hence, the imperative to establish an early fault detection system for discerning operational states and potential faults in these motors is evident. This paper introduces a pioneering methodology for fault detection in induction motors by leveraging the power of Long Short-Term Memory (LSTM) networks and attention mechanisms within a neural network framework. By combining the temporal sequence analysis capabilities of LSTMs with the selective focus feature of attention mechanisms, the proposed architecture enhances the accuracy and timeliness of fault detection, paving the way for improved machinery health monitoring and predictive maintenance. The proposed approach is rigorously evaluated alongside established methods, including simple Artificial Neural Networks (ANN) and LSTM networks to showcase the strengths of the LSTM-Attention approach. This comprehensive analysis reveals the additional advantages of attention mechanisms and positions the LSTM-Attention architecture as a robust solution for detecting faults in induction motors. © 2023 IEEE.

关键词： Long short-term memory

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