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4th modeling, Estimation, and Control Conference (MECC)

作者： Elsberry, Allan H. Dawkins, Jeremy J. Mao, Annie M. Whitcomb, Louis L. US Naval Acad Dept Weap Robot & Control Engn Annapolis MD 21401 USA Johns Hopkins Univ Dept Mech Engn Baltimore MD 21218 USA

This paper reports a novel Nullspace Adaptive Identification (NSAID) algorithm to estimate plant and actuator model parameters for an underactuated ground vehicle with dynamics represented by a 3 degree-of-freedom second-order dynamic model, and reports an evaluation of its performance in simulation and experiments. Precise identification of ground vehicle plant and actuator model parameters is critical for physically realistic model-based simulation, control, navigation, and fault detection. However, ground vehicle plant model parameters, such as vehicle mass, moments of inertia, tire cornering stiffness and actuator model parameters such as motor torque constants are generally not possible to estimate a priori from first principles, and can change with varying payloads, configurations, and driving environments, and thus must be determined experimentally. Some parameter identification methods such as least squares regression depend on acceleration measurements, and most identification methods assume a priori knowledge of actuator parameters. In contrast, NSAID estimates plant and actuator parameters simultaneously, does not require acceleration measurements, can be utilized offline or online during vehicle operation, and can be applied with open or closed-loop control. Copyright (c) 2024 The Authors.

关键词： Nonlinear Adaptive Control Vehicle Dynamics Model Parameter Identification

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Analysis of the Influence of Noise in Reference signal on the performance of Non-Cooperative Passive Detection

Analysis of the Influence of Noise in Reference Signal on th...

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International Congress on image and signal processing, BioMedical Engineering and Informatics (CISP-BMEI)

作者： Tao Ying Ruoyu Zhang Cheng Zhou Xiaoyang Hou College of Electronic Engineering Naval University of Engineering Wuhan China

ISBN: (数字)9798331507398

ISBN: (纸本)9798331507404

In non-cooperative passive detection systems, reference signals are inevitably interfered by noise and will affect the detection performance of the system. This paper carries out in-depth analysis and research on this issue. First, the digital radio mondiale signal is used as the radiation source, the signal model and ambiguity function performance of digital radio mondiale signal are analyzed. Then, the influence of noise on the detection performance of the system is analyzed by theoretical modeling and formula deduction, based on the model of reference signal and surveillance signal. Finally, the simulation results verify the relevant conclusions of the theoretical derivation.

关键词： Analytical models Surveillance simulation Biological system modeling Noise Digital Radio Mondiale Informatics Biomedical engineering

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Deep-Learning Supervised Snapshot Compressive Imaging Enabled by an End-to-End Adaptive Neural Network

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IEEE JOURNAL OF SELECTED TOPICS IN signal processing 2022年第4期16卷 688-699页

作者： Marquez, Miguel Lai, Yingming Liu, Xianglei Jiang, Cheng Zhang, Shian Arguello, Henry Liang, Jinyang Univ Quebec Lab Appl Computat Imaging Ctr Energie Mat Telecommun Inst Natl Rech Sci Varennes PQ J3X 1P7 Canada Univ Ind Santander High Dimens Signal Proc Res Grp Santander 680006 Colombia East China Normal Univ State Key Lab Precis Spect Shanghai 200062 Peoples R China

Snapshot compressive imaging (SCI) is an advanced approach for single-shot high-dimensional data visualization. Deep learning is popularly used to improve SCI's performance. However, most existing methods are merely used as a replacement for analytical-modeling-based image reconstruction. Moreover, these models cling to the conventional random coded apertures and often presume a linear shearing operation. To overcome these limitations, we develop a new end-to-end convolutional neural network, termed deep high-dimensional adaptive net (D-HAN) that offers multi-faceted supervision to SCI by optimizing the coded aperture, sensing the shearing operation, and reconstructing three-dimensional datacubes. The D-HAN is implemented in two representative SCI systems for ultrahigh-speed imaging and hyperspectral imaging. The D-HAN is envisioned to benefit SCI in system design, image reconstruction, and performance evaluation.

关键词： image reconstruction Apertures Shearing Imaging Optical sensors Deep learning Optical imaging Snapshot compressive imaging end-to-end neural networks coded aperture design shearing estimation high-dimensional imaging

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simulation and evaluation of Deep Learning Autoencoders for image Compression in Multi-UAV Network systems 20

Simulation and Evaluation of Deep Learning Autoencoders for ...

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20th Latin American Robotics Symposium (LARS) / 15th Brazilian Symposium on Robotics (SBR) / 14th Workshop on Robotics in Education (WRE)

作者： Ramos, Gabryel Silva de Lima, Amaro Azevedo Almeida, Luciana F. Lima, Jose Pinto, Milena Faria CEFET RJ Elect Engn Dept Rio De Janeiro Brazil Inst Politecn Braganca Res Ctr Digitalizat & Intelligent Robot CeDRI Braganca Portugal

ISBN: (纸本)9798350315387

Mobile multi-robot systems are versatile alternatives for improving single-robot capacities in many applications, such as logistics, environmental monitoring, search and rescue, photogrammetry, etc. In this sense, this kind of system must have a reliable communication network between the vehicles, ensuring that information exchanged within the nodes has little losses. This work simulates and evaluates the use of autoencoders for image compression in a multi-UAV simulation with ROS and Gazebo for a generic surveillance application. The autoencoder model was developed with the Keras library, presenting good training and validation results, with training and validation accuracy of 70%, and a Peak signal Noise Ratio (PSNR) of 40dB. The use of the CPU for the simulated UAVs for processing and sending compressed images through the network is 25% faster. The results showed that this compression methodology is a good choice for improving the system's performance without losing too much information.

关键词： component formatting style styling insert

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Efficiency of Double-Barrier Magnetic Tunnel Junction-Based Digital eNVM Array for Neuro-Inspired Computing

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IEEE TRANSACTIONS ON CIRCUITS AND systems II-EXPRESS BRIEFS 2023年第3期70卷 1254-1258页

作者： Moposita, Tatiana Garzon, Esteban Crupi, Felice Trojman, Lionel Vladimirescu, Andrei Lanuzza, Marco Univ Calabria Dept Comp Engn Modeling Elect & Syst I-87036 Arcavacata Di Rende Italy Inst Suprieur Elect Paris Lab Informat Signal & Image Elect & Telecom F-75006 Paris France Sorbonne Univ F-75006 Paris France Univ Calif Berkeley Dept Elect Engn & Comp Sci Berkeley CA 94720 USA Delft Univ Technol NL-2628 CD Delft Netherlands

This brief deals with the impact of spin-transfer torque magnetic random access memory (STT-MRAM) cell based on double-barrier magnetic tunnel junction (DMTJ) on the performance of a two-layer multilayer perceptron (MLP) neural network. The DMTJ-based cell is benchmarked against the conventional single-barrier MTJ (SMTJ) counterpart by means of a comprehensive evaluation carried out through a state-of-the-art device-to-algorithm simulation framework. The benchmark is based on the MNIST handwritten dataset, Verilog-A MTJ compact models developed by our group, and 0.8 V FinFET technology. Our results point out that the use of DMTJ-based STT-MRAM cells to implement digital embedded non-volatile memory (eNVM) synaptic core allows write/read energy and latency improvements of about 53%/61% and 66%/17%, respectively, as compared to the SMTJ-based equivalent design. This is achieved by ensuring a reduced area footprint and a learning accuracy of about 91%. Such results make the DMTJ-based STT-MRAM cell a good eNVM option for neuro-inspired computing.

关键词： Computer architecture Microprocessors Magnetic tunneling Neurons Training Synapses Switches STT-MRAM double-barrier magnetic tunnel junction (DMTJ) multilayer perceptron (MPL) online classification MNIST dataset energy-efficiency

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MultiRAT DYMO: Enhanced Routing Protocols for VANETs 9

MultiRAT DYMO: Enhanced Routing Protocols for VANETs

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9th International Conference on Communication, image and signal processing, CCISP 2024

作者： Suthaputchakun, Chakkaphong Li, Tianru Foh, Chuan Sun, Zhili Chatzimisios, Periklis Bangkok University Electrical and Computer Engineering Phathumthani Thailand Institute for Communication Systems University of Surrey Surrey United Kingdom School of Science & Technology International Hellenic University Thessaloniki Greece

ISBN: (纸本)9798350356656

This paper describes our design of multihop multiRAT routing protocol based on the traditional DYMO (Dynamic MANET On Demand) routing protocol. Since the tradition routing protocols including DYMO only support singleRAT communications. With the increasing demand of multiRAT communications, there is a need for a multihop multiRAT routing protocol. Among the well-accepted routing protocols, DYMO is one of the popular routing protocols, which is also a successor of the widely-used AODV (Ad-Hoc on Demand Distance Vector). To support multiRAT, a route discovery process in DYMO is enhanced. A route request message RREQ will be broadcasted from the source to its neighbors through multiRAT instead of singleRAT as done traditionally. Then, the intermediate hosts also perform the same procedure by broadcasting the received RREQ to the next hop via all RATs they have in hands. The process continues until reaching the destination. The destination then sends a route reply message RREP back to the source through the reverse order of the intermediate hosts and the specified RATs. Therefore, a routing table stored on each host has be enhanced to include the information regarding the RAT used for each route entry, so that the RREP can be successfully sent through the right RATs along the discovered route back to the source. With the performance evaluation based on the simulation in OMNET++, the result shows that the proposed enhanced multiRAT DYMO achieves high QoS in terms of reception rate and end-to-end delay, and successfully create routes in multiRAT environment with the mix use of different RATs in each data route. © 2024 IEEE.

关键词： Routing protocols

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performance evaluation of Median Filter Hardware Chip for image and signal processing

Performance Evaluation of Median Filter Hardware Chip for Im...

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Cognitive Computing in Engineering, Communications, Sciences and Biomedical Health Informatics (IC3ECSBHI), International Conference on

作者： Sreesh Gaur Akash Goel Pawan Kumar Pal Amit Kumar Singh Sanger Department of Computer Science KIET Group of Institutions Ghaziabad India

ISBN: (数字)9798331518523

ISBN: (纸本)9798331518530

The median filter is a valuable image processing tool that can be used in many applications to advance picture quality, and inferior noise, and acquire data ready for more analysis. The median filter, a non-linear image processing technique, has excessive application in many diverse fields because of its capacity to remove noise while preserving edges. The filter is significant for both simple and complex imageprocessing occupations since it is together easy to comprehend and fast to use. An active method to attain real-time performance is to devise a median filter on a FieldProgrammable Gate Array (FPGA) for image signal processing and machine vision applications. FPGAs are well-suited for photo filtering due to their capability to achieve parallel processing, which is one of their prominent advantages. The research article meets with the design and simulation of the average filter in HDL and synthesis on the FPGA for assessing the performance indices.

关键词： performance evaluation Filtering Machine vision Noise Parallel processing Logic gates Hardware Real-time systems Delays Field programmable gate arrays

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Novel Test Bench for End-to-End Validation of Monocular Depth Estimation Under the Influence of Glaring Situations 27

Novel Test Bench for End-to-End Validation of Monocular Dept...

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27th IEEE International Conference on Intelligent Transportation systems, ITSC 2024

作者： Ulreich, Fabian Kaup, Andre Ebert, Martin Friedrich-Alexander-Universität Erlangen-Nürnberg Chair of Multimedia Communications and Signal Processing Erlangen91058 Germany Technische Hochschule Ingolstadt Ingolstadt85049 Germany Technische Hochschule Ingolstadt Faculty of Electrical Engineering and Information Technology Ingolstadt85049 Germany

ISBN: (纸本)9798331505929

The black-box nature of deep learning models employed in automated driving functions requires suitable evaluation tools. Efforts are being made to increase the validity of testing environments for real-world operations. Understanding the impact of the sensor characteristics and degradation on the downstream task of perception is another field of research. We propose a test environment for vision-based autonomous driving functions in which a real camera and a deep learning model can be evaluated jointly. Our approach enables the validation under real-world brightness conditions through projector technology. To demonstrate its applicability, we employed a Vision Transformer to perform monocular depth estimation. Our experimental setup included a challenging scenario involving glare to assess the differences in performance between the testing environments: camera test bench and simulation. We quantified the gap by contrasting image quality metrics of partly-synthetic and pure synthetic data with real-world data contained in the KITTI depth dataset. With our approach, we were able to produce images that are 37 % closer to real-world than synthetic image data. Also, the gap in data variability is 18 % less than with synthetic data. In addition, we found that clipping in glare situations does not necessarily lead to large errors in depth prediction. © 2024 IEEE.

关键词： Depth perception

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Lightweight design and deployment of object detection model for low-light scenes 4

Lightweight design and deployment of object detection model ...

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4th International Conference on Computer Vision, Application, and Algorithm, CVAA 2024

作者： Sun, Kangjian Huo, Ju Jiang, Xiaona Gao, Yichao Cai, Chen School of Electrical Engineering and Automation Harbin Institute of Technology Harbin China National Key Laboratory of Modeling and Simulation for Complex Systems Harbin China China FAW Corporation Limited Changchun China College of Design and Engineering National University of Singapore Singapore Signal and Communication Research Institute China Academy of Railway Sciences Beijing China

ISBN: (数字)9781510687622

ISBN: (纸本)9781510687615

Low-light scenes are common in fields such as autonomous driving, which tests the robustness of intelligent systems. This paper tests the performance of object detection in low-light scenes based on the YOLOv8n model. In order to solve the constraint of limited computing power of embedded devices, the YOLOv8n model is adjusted through model pruning and knowledge distillation. The complexity of the model is reduced through these lightweight operations. While ensuring the detection performance as much as possible, the multiply-accumulate operations (MACs) and parameters of the model decreased by about 40% and 27% respectively. The proposed model is evaluated on the Exclusively Dark (ExDARK) dataset and achieves a mean average precision (mAP) value of 0.672. In addition, the proposed model is migrated to the embedded platform NVIDIA Jetson Xavier NX. The deployment process is optimized by multi-process scheduling and multi-thread scheduling. Experiments are also conducted on real data, and the results show that the proposed model can provide a feasible solution for vision-based nighttime autonomous driving. © 2025 SPIE.

关键词： Object detection Deep learning image processing

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Design and Analysis of Ring Resonator Embedded with High Contrast Optical Bragg Grating in Z-Domain for Optical Filtering

Design and Analysis of Ring Resonator Embedded with High Con...

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Smart and Sustainable Developments in Electrical Engineering (SSDEE), IEEE International Conference on

作者： Aman Shekhar Sanjoy Mandal Department of Electrical Engg. Indian Institute of Technology (ISM) Dhanbad Jharkhand India

ISBN: (数字)9798331542108

ISBN: (纸本)9798331542115

The integration of ring resonators with Bragg gratings have emerged as promising optical filters because of their compact size and good spectral selectivity. A comprehensive mathematical modeling and analysis of such a system is presented to precisely capture the resonance characteristics and transmission spectra of the system using delay-line signal processing and Z-domain analysis, followed by a discussion on related potential application as well as challenges in modern optical systems. The simulation results indicate improved filtering precision and sharp resonance peaks, indicating excellent performance.

关键词： Optical filters Bragg gratings performance evaluation Filtering simulation Resonance Optical sensors Optical modulation Optical ring resonators Optical signal processing

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