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Acoustic-to-hyper-spectral: real-time perimeter intrusion detection system monitoring through learnable filters and hyper-spectral image generation from distributed acoustic sensing systems

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OPTICS EXPRESS 2025年第3期33卷 4109-4126页

作者： Pierau, Ruth-emely Katsifolis, Jim Meehan, Alaster Rezatofighi, Hamid Stuckey, Peter J. Monash Univ Clayton Vic 3800 Australia Univ Melbourne ARC Training Ctr Optimisat Technol Integrated Meth Parkville Vic 3052 Australia AVA Risk Grp Co Future Fibre Technol 10 Hartnett CL Mulgrave Vic 3170 Australia

This paper presents an integrated distributed acoustic sensing (DAS) system with artificial intelligence to provide real-time system monitoring for fence perimeter and buried system applications. The DAS system is a Rayleigh backscatter based fibre optic sensing system that has been deployed in two real-world, commercial applications to detect acoustic wave propagation and scattering along perimeter lines, and classify intrusions accurately. What we believe to be three novel signal processing methods are proposed to train filters for automatically selecting frequency bands from the power spectrum and generating hyper-spectral images from the data gathered by the DAS system without expert knowledge. The hyper-spectral images are analyzed by a neural network based object detection model. The system achieves 81.8% accuracy on a fence perimeter installation and 60.4% accuracy on a buried system application in detecting and classifying various intrusion events. The evaluation interval of the integrated DAS system framework between event sensing and detection does not exceed 5 s. (c) 2025 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement

关键词： Deep learning Hyperspectral imaging Machine learning neural networks Signal processing Wave propagation

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distributed wavelet-learning-based fusion estimation for multirate multisensor systems with integer and non-integer multiple sample ratios

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DIGITAL SIGNAL processing 2025年 162卷

作者： Wang, Rusheng Jin, Weihang Li, Han Chen, Bo Han, Yan Yu, Li Zhejiang Univ Technol Dept Automat Hangzhou 310023 Peoples R China Zhejiang Key Lab Intelligent Percept & Control Com Hangzhou 310023 Peoples R China Key Lab Intelligent Rehabil & Translat Neuroelect Hangzhou 310023 Peoples R China Zhe Jiang Nurotron Biotechnol Co Hangzhou 310023 Peoples R China

This paper explores the asynchronous fusion estimation problem of multirate multisensor systems under sensor time-varying sampling. First, the sensors are divided into integer and non-integer multiples based on the ratio of the sensor sampling rate to the state updating rate. Under the case of integer multiple, a synchronous state space model is established by state projection through wavelet transform, and then a Kalman-based multirate estimator is designed to calculate local estimation. Under the case of no-integer multiple, a measurement compensation mechanism is designed using the measurement information closest to the state updating instant, and then a back propagation neural network is constructed to obtain the corresponding estimation. Based on the above obtained local estimation, a learning-based fusion criterion is developed by taking into account the time difference between the state updating moment and the measurement sampling moment. Finally, the simulation results demonstrate the advantages and effectiveness of the proposed method.

关键词： Multirate sampling distributed fusion estimation Wavelet transform BP neural network

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FiberFlex: Real-time FPGA-based Intelligent and distributed Fiber Sensor System for Pedestrian Recognition

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ACM TRANSACTIONS ON RECONFIGURABLE TECHNOLOGY AND SYSTEMS 2024年第4期17卷 1-30页

作者： Li, Yuqi Zhao, Kehao Zhao, Jieru Wang, Qirui Zhong, Shuda Lalam, Nageswara Wright, Ruishu Zhou, Peipei Chen, Kevin P. Univ Pittsburgh Pittsburgh PA 15260 USA Natl Energy Technol Lab Pittsburgh PA USA Brown Univ Providence RI USA

In recent years, security monitoring of public places and critical infrastructure has heavily relied on the widespread use of cameras, raising concerns about personal privacy violations. To balance the need for effective security monitoring with the protection of personal privacy, we explore the potential of optical fiber sensors for this application. This article proposes FiberFlex, an intelligent and distributed fiber sensor system. real-time pedestrian detection by co-designing the entire pipeline of optical signal acquisition, processing, and recognition networks based on the principles of optical fiber sensing. As a promising alternative to traditional camera-based monitoring systems, FiberFlex achieves pedestrian detection by analyzing the vibration patterns caused by pedestrian footsteps, enabling security monitoring while preserving individual privacy. FiberFlex comprises three modules: First, fiber-optic sensing system: A fiber-optic distributed acoustic sensing (DAS) system is built and used to measure the ground vibration waves generated by people walking. Second, algorithms: We first collect the training data by measuring the ground vibration waves, label the data, and use the data to train the neural network models to perform pedestrian recognition. Third, hardware accelerators: We use HLS tools to design hardware modules on FPGA for data collection and pre-processing and integrate them with the downstream neural network accelerators to perform in-line real-time pedestrian detection. The final detection results are sent back from FPGA to the host CPU. We implement our system FiberFlex with the in-house built DAS system and AMD/Xilinx Kintex7 FPGA KC705 board and verify the whole system using the real-world collected data. We conduct recognition tests on five test subjects of varying ages, heights, and weights in a fixed sensing area. Each subject experienced 20 real-time recognition tests using their daily walking habits, and the subjects were given ade

关键词： FPGA architecture Recognition Fiber-optic distributed

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Event-Triggered Data-Driven distributed LFC Using Controller-Dynamic-Linearization Method

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IEEE TRANSACTIONS ON SIGNAL AND INFORMATION processing OVER networkS 2025年 11卷 85-96页

作者： Bu, Xuhui Zhang, Yan Zeng, Yiming Hou, Zhongsheng Henan Polytech Univ Sch Elect Engn & Automat Jiaozuo 454003 Peoples R China Qingdao Univ Sch Automat Qingdao 266071 Peoples R China

This paper is concerned with an event-triggered distributed load frequency control method for multi-area interconnected power systems. Firstly, because of high dimension, nonlinearity and uncertainty of the power system, the relevant model information cannot be fully obtained. To realize the design of LFC algorithm under the condition that the model information is unknown, the equivalent functional relationship between the control signal and the area-control-error signal is established by using a dynamic linearization technique. Secondly, a novel distributed load frequency control algorithm is proposed based on controller dynamic-linearization method and the controller parameters are tuned online by constructing a radial basis function neural network. In addition, to reduce the computation and communication burden on the system, an event-triggered mechanism is also designed, in which whether the data is transmitted at the current instant is completely determined by a triggering condition. Rigorous analysis shows that the proposed method can render the frequency deviation of the power system to converge to a bounded value. Finally, simulation results in a four-area power system verify the effectiveness of the proposed algorithm.

关键词： Power systems Power system stability Power system dynamics Frequency control Mathematical models Event detection Information processing Load modeling Nonlinear dynamical systems Uncertainty Multi-area power systems load frequency control distributed controller-dynamic-linearization method event-triggered strategy data-driven

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Pattern recognition in distributed fiber-optic acoustic sensor using an intensity and phase stacked convolutional neural network with data augmentation

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OPTICS EXPRESS 2021年第3期29卷 3269-3283页

作者： Wu, Huan Zhou, Bin Zhu, Kun Shang, Chao Tam, Hwa-Yaw Lu, Chao Hong Kong Polytech Univ Dept Elect & Informat Engn Kowloon Hong Kong Peoples R China South China Normal Univ South China Acad Adv Optoelect Guangdong Prov Key Lab Opt Informat Mat & Technol Guangzhou Peoples R China Beijing Jiaotong Univ Key Lab Luminescence & Opt Informat Beijing Peoples R China Hong Kong Polytech Univ Photon Res Ctr Dept Elect Engn Hung HomKowloon Hong Kong Peoples R China Hong Kong Polytech Univ Shenzhen Res Inst Shenzhen 518057 Peoples R China

distributed acoustic sensors (DASs) have the capability of registering faint vibrations with high spatial resolution along the sensing fiber. Advanced algorithms are important for DAS in many applications since they can help extract and classify the unique signatures of different types of vibration events. Deep convolutional neural networks (CNNs), which have powerful spectro-temporal feature learning capability, are well suited for event classification in DAS. Generally, these data-driven methods are highly dependent on the availability of large quantities of training data for learning a mapping from input to output. In this work, to fully utilize the collected information and maximize the power of CNNs, we propose a method to enlarge the useful dataset for CNNs from two aspects. First, we propose an intensity and phase stacked CNN (IP-CNN) to utilize both the intensity and phase information from a DAS with coherent detection. Second, we propose to use data augmentation to further increase the training dataset size. The influence of different data augmentation methods on the performance of the proposed CNN architecture is thoroughly investigated. The experimental results show that the proposed 1P-CNN with data augmentation produces a classification accuracy of 88.2% on our DAS dataset with lkm sensing length. This indicates that the usage of both intensity and phase information together with the enlarged training dataset after data augmentation can greatly improve the classification accuracy, which is useful for DAS pattern recognition in real applications. (C) 2021 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

关键词： Fiber optic sensors Heterodyne detection neural networks Pattern recognition Signal processing Spatial resolution

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A high-precision distributed neural processor for efficient computation of a new distributed FxSMAP-L algorithm applied to real-time active noise control systems

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NEUROCOMPUTING 2023年 518卷 545-561页

作者： Maya, Xochitl Garcia, Luis Vazquez, Angel Pichardo, Eduardo Sanchez, Juan-Carlos Perez, Hector Avalos, Juan -Gerardo Sanchez, Giovanny Inst Politecn Nacl ESIME Culhuacan Ave Santana 1000 Mexico City 04440 Mexico

During the last decade, there has been growing interest in developing active noise cancellation (ANC) sys-tems since they have emerged as a potential solution in the noise reduction of indoor or outdoor sources. Regarding the last point, if the users need to reduce the noise in some extended areas, a very-large amount of microphones and loudspeakers are required. As a consequence, the ANC system demands a large amount of computation. One potential solution can be given if the information is partitioned and distributed into several computing systems since the use of a centralized computing system could be insufficient. However, current distributed strategies demand a huge computationally cost. Therefore, the development of an efficient distributed ANC system to be applied in practical real-time ANC applica-tions is a challenging task. Here, we present two contributions, which involve the development of a new variant of the filtered-x set membership affine projection-like algorithm to save a large amount of com-putational cost and the design of a FPGA-based distributed neural processor to efficiently simulate the proposed algorithm. Specifically, we improve two aspects to create a compact and high-performance dis-tributed neural processor. The first aspect is linked to the improvement of the processing system. In par-ticular, we make extraordinary efforts to optimize existing spiking neural arithmetic circuits, which are highly demanded in the computation of the proposed algorithm. The second improvement is related to the development of a new communication scheme based on cutting-edge variants of spiking neural P (SN P) systems to efficiently perform the data distribution between multiple FPGAs. To demonstrate the computational capabilities of the proposed FPGA-based distributed neural processor, we develop an acoustic sensor network as proof-of-concept. Our results have demonstrated that the proposed dis-tributed FPGA-based neural processor can be used in practical r

关键词： ANC system D-FxSMAP-L algorithm Spiking neural P system Spike-based communication system neural processor FPGA

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SHEATH: Defending Horizontal Collaboration for distributed CNNs Against Adversarial Noise

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IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 2025年 20卷 4353-4368页

作者： Asif, Muneeba Kazmi, Mohammad Kumail Rahman, Mohammad Ashiqur Hasan, Syed Rafay Homsi, Soamar Florida Int Univ Analyt Cyber Def ACyD Lab Miami FL 33199 USA Tennessee Technol Univ Dept Elect & Comp Engn Cookeville TN 38505 USA Air Force Res Lab Informat Warfare Div Rome NY 13441 USA

As edge computing and the Internet of Things (IoT) expand, horizontal collaboration (HC) emerges as a distributed data processing solution for resource-constrained devices. In particular, a convolutional neural network (CNN) model can be deployed on multiple IoT devices, allowing distributed inference execution for image recognition while ensuring model and data privacy. Yet, this distributed architecture remains vulnerable to adversaries who want to make subtle alterations that impact the model, even if they lack access to the entire model. Such vulnerabilities can have severe implications for various sectors, including healthcare, military, and autonomous systems. However, security solutions for these vulnerabilities have not been explored. This paper presents a novel framework for Secure Horizontal Edge with Adversarial Threat Handling (SHEATH) to detect adversarial noise and eliminate its effect on CNN inference by recovering the original feature maps. Specifically, SHEATH aims to address vulnerabilities without requiring complete knowledge of the CNN model in HC edge architectures based on sequential partitioning. It ensures data and model integrity, offering security against adversarial noise in diverse HC environments. Our evaluations demonstrate SHEATH's adaptability and effectiveness across diverse CNN configurations.

关键词： Noise Internet of Things Computational modeling Security Image edge detection Collaboration Data models Convolutional neural networks Privacy Edge computing Secure horizontal collaboration adversarial noise detection decentralized CNNs

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Packet Format Detection and Modulation Classification of Wireless LAN Using distributed Convolutional neural network

Packet Format Detection and Modulation Classification of Wir...

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2021 International Symposium on Intelligent Signal processing and Communication Systems, ISPACS 2021

作者： Putra, Dody Ichwana Pratama, Muhammad Harry Bintang Lanante, Leonardo Grad, Hiroshi Ochi Kyushu Institute of Technology School of Computer Science and System Engineering Iizuka Japan

ISBN: (纸本)9781665419512

This paper proposes a method to detect the packet format and classify signal modulation type of wireless LAN signals using the distributed model of Convolutional neural network (CNN). The main advantages of this method compared to the conventional are the high accuracy of the model and the flexibility for lowering the complexity. These are achieved because the CNN models are trained to perform multiple small classification tasks instead of a single big classification task. This method makes retraining much easier. The five-fold cross-validation is applied to assess the performance for training and testing the model. To validate the model, actual Wi-Fi signals are used to test the model using Software Define Radio (SDR). The result shows the proposed method can accurately classify different packet formats and signal modulation types above 90%, even when different timing offsets occur. © 2021 IEEE.

关键词： Orthogonal frequency division multiplexing

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SJFO: Sail Jelly Fish Optimization enabled VM migration with DRNN-based prediction for load balancing in cloud computing

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network-COMPUTATION IN neural SYSTEMS 2024年第4期35卷 403-428页

作者： Rathinam, Rajesh Sivakumar, Premkumar Sigamani, Sivakumar Kothandaraman, Ishwarya Vellore Inst Technol Sch Comp Sci & Engn Chennai 600127 Tamil Nadu India Galgotias Univ Dept Comp Sci & Engn Greater Noida Uttar Pradesh India SRM Inst Sci & Technol Dept Comp Sci & Engn Tiruchirappalli Tamil Nadu India Sathyabama Inst Sci & Technol Dept Comp Sci & Engn Chennai Tamil Nadu India

The dynamic workload is evenly distributed among all nodes using balancing methods like hosts or VMs. Load Balancing as a Service (LBaaS) is another name for load balancing in the cloud. In this research work, the load is balanced by the application of Virtual Machine (VM) migration carried out by proposed Sail Jelly Fish Optimization (SJFO). The SJFO is formed by combining Sail Fish Optimizer (SFO) and Jellyfish Search (JS) optimizer. In the Cloud model, many Physical Machines (PMs) are present, where these PMs are comprised of many VMs. Each VM has many tasks, and these tasks depend on various parameters like Central processing Unit (CPU), memory, Million Instructions per Second (MIPS), capacity, total number of processing entities, as well as bandwidth. Here, the load is predicted by Deep Recurrent neural network (DRNN) and this predicted load is compared with a threshold value, where VM migration is done based on predicted values. Furthermore, the performance of SJFO-VM is analysed using the metrics like capacity, load, and resource utilization. The proposed method shows better performance with a superior capacity of 0.598, an inferior load of 0.089, and an inferior resource utilization of 0.257.

关键词： Cloud computing Virtual Machine (VM) Deep Recurrent neural network (DRNN) SailFish Optimizer (SFO)

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Deep learning model for efficient traffic forecasting in intelligent transportation systems

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neural Computing and Applications 2024年 1-14页

作者： Khan, Shakir Alghayadh, Faisal Yousef Ahanger, Tariq Ahamed Soni, Mukesh Viriyasitavat, Wattana Berdieva, Uguloy Byeon, Haewon Riyadh Saudi Arabia University Centre for Research and Development Department of Computer Science and Engineering Chandigarh University Mohali140413 India Computer Science and Information Systems Department College of Applied Sciences AlMaarefa University Riyadh Saudi Arabia Department of Management Information Systems CoBA Prince Sattam Bin Abdulaziz University Al-Kharj Saudi Arabia Dr. D. Y. Patil Vidyapeeth Pune Pune India Dr. D. Y. Patil School of Science and Technology Tathawade Pune India Chulalongkorn Business School Faculty of Commerce and Accountancy Chulalongkorn University Bangkok Thailand The Department of Tax and Taxation Tashkent Institute of Finance Tashkent Uzbekistan Department of Digital Anti-Aging Healthcare Inje University Gimhae50834 Korea Republic of

In this paper, a novel intelligent transportation framework called graph convolutional neural network for distributed machine learning (GCN-DML) is presented. Its primary application is traffic forecasting in large-scale, dynamically-evolving traffic scenarios. One unique method in traffic forecasting, called GCN-DML, is that it allows user data to be processed locally on edge devices, removing the requirement to send all data to CS (CS). Designed specifically for edge computing, this distributed machine learning framework makes it easier to train neural networks on a variety of edge devices. In addition to protecting user privacy, this method reduces the difficulties related to centralised neural network training, minimises communication delays, lowers data transfer quantities, and improves overall data processing efficiency. GCN-LSTM, the composite graph convolutional networks used in this framework, combine edge-enhanced attention mechanisms with the feature transmission capabilities of graph convolutional neural networks to effectively handle the challenging task of traffic forecasting. Even as the number of cars in the network rises, they perform exceptionally well at quickly extracting and sending interaction information between vehicles, while preserving high prediction accuracy and low time complexity. The versatility of GCN-DML is extended to a range of traffic forecasting application scenarios, whereby edge devices can adjust the kind and scale of the neural network according to available compute and storage resources, all without sacrificing performance. The usefulness of GCN-DML in traffic forecasting is demonstrated by experimental evaluations on the NGSIM public dataset, where it outperforms other models with better computation time and prediction performance. It obtains noteworthy F1 scores of 0.9473, 0.9557, and 0.9391, respectively, for recall, precision, and precision. Even when there is an increase in the number of cars, the accuracy remains high,

关键词： Convolutional neural networks

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