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A novel integrated radar sounder simulation technique for modelling large and small-scale surface scattering phenomena 27

A novel integrated radar sounder simulation technique for mo...

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Conference on Image and signal processing for Remote Sensing XXVII

作者： Sbalchiero, Elisa Thakur, Sanchari Cortellazzi, Marco Bruzzone, Lorenzo Univ Trento Dept Informat Engn & Comp Sci Via Sommar 5 I-38123 Trento Italy

ISBN: (数字)9781510645691

ISBN: (纸本)9781510645691;9781510645684

Radar sounders (RS) play an important role in planetary investigation. However, the complex tasks of predicting performance and interpreting RS data and predicting performance require to perform data simulations. In the literature, there are different methods for RS data simulation, including: i) numerical methods involving the exact solution of Maxwell's equations based on 3D modelling;and ii) methods based on ray-tracing and facet modelling. Among numerical methods, the Finite-Difference Time-Domain (FDTD) technique allows one to precisely model complex nonlinear targets, both geologically and geophysically, at the cost of high computational load. On the contrary, coherent ray-tracing methods reduce the computational cost by triangulating the targets into facets with size comparable to the wavelength. In this work, we combine the advantages of FDTD and ray-tracing methods into a novel integrated simulation technique for modelling scattering phenomena at two scales of wave interaction, i.e. large (facet) scale and small (sub-facet) scale. The coherent facet method is used to simulate facet- scale scattering phenomena, while FDTD is used to evaluate the sub-facet-scale scattering due to roughness on top of the single facets. We investigate the method's effectiveness by generating a one-layer synthetic DEM as a fractional Brownian motion (fBm) process superimposed by different values of RMS slope of the small-scale roughness and by analysing how the received signal changes in terms of signal-to-noise ratio. The results show the effectiveness of the method in representing small-scale roughness realistically.

关键词： Radar sounder simulations FDTD ray-tracing coherent simulator small-scale roughness planetary exploration

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Lightweight helmet detection algorithm based on improved YOLOv5 2

Lightweight helmet detection algorithm based on improved YOL...

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2nd International Conference on Advanced Algorithms and signal Image processing, AASIP 2022

作者： Li, Jianning Zhang, Junzhao Zhang, Xinyu Wang, Shuai College of Electrical and Power Engineering Taiyuan University of Technology Shanxi Taiyuan030024 China Software Institute Nanjing University Jiangsu Nanjing210093 China Shanxi Electric Drive and Internet of Things Engineering Research Center Shanxi Taiyuan030024 China Shanxi Province 1331 Project-Smart City Based on Big Data Lighting Data Sharing and Public Service Engineering Technology Research Center Shanxi Taiyuan030024 China Shanxi Intelligent Digital Lighting Union Laboratory Shanxi Taiyuan030024 China Shanxi Intelligent Digital Cultural Tourism Industry Research Institute Shanxi Taiyuan030024 China

ISBN: (纸本)9781510660571

In the construction process, wearing safety helmet can effectively reduce the injury caused by falling objects and hard objects. In order to detect the wearing of safety helmet during the construction process, a lightweight safety helmet wearing detection algorithm YOLOv5-CS is proposed and applied to the embedded equipment at the construction site. The algorithm is based on the improvement of YOLOv5 target detection algorithm. The improved CAShuffle module is used to form the backbone feature extraction network, which reduces the size of the original algorithm and ensures the detection accuracy of the algorithm;DIoU as the loss function, which makes the algorithm more conducive to detecting small targets such as hard hats;modify dataset, add the dataset on the basis of the original dataset and mark the safety helmet and the anchor frame of the person separately, and judge whether the worker wears the safety helmet by analyzing positional relationship of the identified anchor frame, use the algorithm combining K-means and genetic algorithm to predict the anchor frame of the dataset, and use Mosaic algorithm to enhance the data. The results show that the parameter quantity of YOLOv5-CS algorithm is 2.4M, less than one third of YOLOv5;The average precision decreased by only 4.2%;640x640 pictures can be recognized at 31 frames per second on Jetson Nano, a embedded platform. © 2022 SPIE.

关键词： signal detection

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Biosensors Based Controller for small Unmanned Aerial Vehicle Navigation

Biosensors Based Controller for Small Unmanned Aerial Vehicl...

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SoutheastCon Conference

作者： McCoggle, Mylena Wilson, Shyra Rivera, Andrea Alba-Flores, Rocio Soloiu, Valentin Univ Michigan Dept Mech Engn Dearborn MI 48128 USA Kennesaw State Univ Dept Elect Engn Technol Kennesaw GA 30144 USA Interamer Univ Dept Mech Engn Bayamon PR USA Georgia Southern Univ Dept Elect Engn Statesboro GA USA Georgia Southern Univ Dept Mech Engn Statesboro GA USA

ISBN: (纸本)9781665406529

This work describes a system that uses electromyography (EMG) signals obtained from muscle sensors and an Artificial Neural Network (ANN) for signal classification and pattern recognition that is used to control a small unmanned aerial vehicle using specific arm movements. The main objective of this endeavor is the development an intelligent interface that allows the user to control the flight of a drone beyond direct manual control. The biosensors used in this work were the MyoWare Muscle sensors which contain two EMG electrodes and were used to collect signals from the posterior (extensor) and anterior (flexor) forearm, and the bicep. The collection of the raw signals from each sensor were performed using an Arduino Uno. data processing algorithms were developed with the purpose of classifying the signals generated by the arm's muscles when performing specific movements, namely: flexing, resting, arm-up, and arm motion from right to left. With these arm motions, roll control of the drone was achieved. MATLAB software was utilized to condition the signals and prepare them for the classification stage. To generate the input vector for the ANN and perform the classification, the root mean squared, and the standard deviation were processed for the signals from each electrode. The neuromuscular information was trained using an ANN with a single 10 neurons hidden layer to categorize the four targets. The result of the classification shows that an accuracy of 97.5% was obtained for a single user. Afterwards, classification results were used to generate the appropriate control signals from the computer to the drone through a Wi-Fi network connection. These procedures were successfully tested, where the drone responded successfully in real time to the commanded inputs.

关键词： Biosensors Electromyography Artificial Neural Network Arduino drone flight control Machine Learning

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Using data Analytics to Strengthen Monitoring and Surveillance of Routine Immunization Coverage for Children under One Year in Uganda 14

Using Data Analytics to Strengthen Monitoring and Surveillan...

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14th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC) / 14th Int Conf on Bio-inspired Systems and signal processing (BIOsignalS) / 14th Int Conf on Biomedical Electronics and Devices (BIODEVICES)

作者： Nantongo, Bartha Alexandra Nabukenya, Josephine Nabende, Peter Makerere Univ Coll Comp & Informat Syst Dept Informat Syst POB 7062 Kampala Uganda

ISBN: (纸本)9789897584909

Immunization coverage is a traditional key performance indicator that enables stakeholders to monitor child health, investigate gaps, and take remedial actions. It is continuously challenged by validity due to the neglect of unstructured data and process indicators that track small changes/milestones. While empirical evidence indicates digitalized immunization systems establish coverage from structured data, renowned administrative and household survey estimates are often inaccurate/untimely. Government instituted awareness, accessibility, and results-based performance approaches, but stakeholders are challenged by accurate monitoring of performance against Global Vaccination Action Plan coverage targets. This heightens inappropriate strategy implementation leading to persistent low coverage and declining trends. There is scanty literature substantiating the essence of comprehensive immunization indicators in monitoring evidence-based and timely interventions. For this reason, health workers failed to appreciate immunization process indicators and monitoring role. The study aims at developing a real-time immunization coverage monitoring framework that supports evidence-based strategy implementation using prescriptive analytics. The envisaged artifact analyzes a variety of data and monitors immunization performance against comprehensive indicators. It is a less resource-demanding strategy that prompts accurate and real-time insights to support intervention implementation decisions. This study will follow an explanatory research approach by first collecting quantitative data and later qualitative for in-depth analysis.

关键词： data Analytics Monitoring Routine Immunization Coverage Performance

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A Deep Learning-based Super-resolution Model for Bistatic SAR Image

A Deep Learning-based Super-resolution Model for Bistatic SA...

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2021 International Conference on Electronics, Circuits and Information Engineering, ECIE 2021

作者： Cen, Xi Song, Xuan Li, Yachao Wu, Chunfeng Xidian University School of Life Science and Technology Xi'An China Xidian University Key Laboratory for Radar Signal Processing Xi'An China China Sanjiang Space Group Wu Han China

ISBN: (纸本)9781665418690

Target detection and recognition of bistatic SAR image has been widely studied in recently. However, how to accurately detect and recognize targets with low-resolution and small size in the image is still a problem. Image super-resolution reconstruction technology can increase image resolution and expand target size to improve detection and recognition performance. Thus, we in this paper bring deep learning onto the topic of bistatic SAR image super-resolution reconstruction and propose a novel super-resolution reconstruction model (named FSRCNN) for bistatic SAR images. The proposed model is characterized by a feature extractor with different structures, a feedback feature enhancement block, and a feature fusion module. During the experiments of bistatic SAR measured data, our method is proved to perform clearer visual effects than other image super-resolution reconstruction method. Moreover, our model achieves the best value on peak signal to noise ratio and structural similarity, which indicates that our model can effectively super-resolution reconstruct bistatic SAR images. © 2021 IEEE.

关键词： Synthetic aperture radar

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Development of a Computer Simulator of the Visual N2 Event-Related Potential Component for the Study of Cognitive Processes 15th

Development of a Computer Simulator of the Visual N2 Event-R...

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15th Mediterranean Conference on Medical and Biological Engineering and Computing (MEDICON)

作者： Marturano, Francesca Brigadoi, Sabrina Doro, Mattia Dell'Acqua, Roberto Sparacino, Giovanni Univ Padua I-35131 Padua PD Italy

ISBN: (纸本)9783030316358;9783030316341

The importance of visual N2 Event-Related Potential (ERP) component in the study of cognitive processes lies in its interpretation as a measure of the allocation of visual attention to possible targets. Unfortunately, the N2 component has a small amplitude and the domain of validity of methods used for its estimation is difficult to assess if real data are considered. Here, we develop a computer simulator of ERP measurements emulating the variability of the visual N2 component and use the synthetic data to evaluate the performance of four popular literature ERP estimation methods. Results confirmed that a solid simulation framework could allow identifying a reliable method to detect small-amplitude ERP components and quantifying its accuracy.

关键词： Visual N2 ERP modeling EEG signal processing Gaussian mixture models

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small Infrared Target Detection via Iteratively-Reweighted-Nuclear-Norm

Small Infrared Target Detection via Iteratively-Reweighted-N...

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International Symposium on Artificial Intelligence and Robotics

作者： Gao, Haoran Ren, Jinlei He, Chunming Deng, Lizhen Nanjing Univ Posts & Tele Commun Coll Telecommun & Informat Engn Nanjing Peoples R China China Acad Launch Vehicle Technol CALT Beijing Peoples R China Wuhan Univ Technol Sch Informat Engn Wuhan Peoples R China Nanjing Univ Posts & Telecommun Bell Honor Sch Nanjing Peoples R China

ISBN: (数字)9781510639690

ISBN: (纸本)9781510639690

The process of infrared images via computer-based algorithms for better application is a frontier field integrating physical technology with computer science. One of the key techniques in infrared image processing is the detection of infrared targets. This technique is extensively applied in security and defense systems and search and tracking systems. However, due to their small size, dim light and lack of texture, the detection of infrared targets is a technical problem. One strategy to address this problem is to transform the detection work into a non-convex optimization problem of recovering a low-rank matrix (background) and a sparse matrix (target) from a patch-image matrix (original image) based on IPI (infrared patch-image) model. When targets are clear and recognizable, the APG (accelerated proximal gradient) algorithm works effectively to solve it. However, when targets become much dimmer and are screened by the intricate texture of background, the experimental detection results degrade dramatically. In order to solve this problem, a novel method via IRNN (iteratively reweighted nuclear norm) is proposed in this paper. Experimental results show that under different complicated backgrounds, targets with higher SCRG (signal-to-clutter ratio gain) values and BSF (background suppression factor) values can be acquired through IRNN algorithm compared with the APG algorithm, which means that our method performs better.

关键词： small target detection Iteratively reweighted nuclear norm infrared patch-image model

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Position Monitoring of Multiple Moving targets Based on ZigBee and GPS 4

Position Monitoring of Multiple Moving Targets Based on ZigB...

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4th Annual International Conference on data Science and Business Analytics, ICDSBA 2020

作者： Dai, Yu Wang, Xuan Long, Wenjia Yu, Peng Ma, Junyu Zhixing College of Hubei University Wuhan China

ISBN: (纸本)9781728181646

Moving target monitoring is widely used in many fields such as industrial control, military, and security. Traditional moving target monitoring generally uses infrared, ultra-sonic and other technologies, which have the disadvantages of complex implementation technology, high hardware cost and high environmental requirements. This paper proposes a low-cost, easy-to-deploy, and small-scale environment with little environmental impact by collecting GPS monitoring signals of multi-target speed and location and signal transmission and processing methods based on ZigBee wireless ad hoc network. signal acquisition and real-time data storage, transmission and processing methods have high application value and promotion significance. © 2020 IEEE.

关键词： Global positioning system

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Design of a Hyperspectral Imager Using COTS Optics for small Satellite Applications

Design of a Hyperspectral Imager Using COTS Optics for Small...

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2020 International Conference on Space Optics, ICSO 2020

作者： Prentice, Elizabeth Frances Grøtte, Mariusz Eivind Sigernes, Fred Johansen, Tor Arne Department of Engineering Cybernetics Norwegian University of Science and Technology Trondheim7491 Norway University Centre in Svalbard Svalbard Science Centre LongyearbyenN-9171 Norway

This paper describes the design of a pushbroom hyperspectral imager built for small satellite applications. Its design allows for low weight, low cost, and flexible configuration making it accessible to both smaller projects and supplemental to larger ones. The imager is constructed from commercial off-the-shelf (COTS) optical and sensor components, along with a machined mechanical structure to enable rapid development times. The presented configuration was selected based on a 6U CubeSat ocean color mission developed at the Norwegian University of Science and Technology. The optical design includes three 50 mm lens objectives, a precision-cut 50 μm slit, a blazed transmission grating, and a detecting CMOS sensor. It has the ability to record wavelengths in the spectral range of 300−1000 nm, e.g. in the visible and near infrared spectrum. The calculated bandpass of typically about 5 nm can be configured or binned for the specific application needs. Since targets, such as the ocean surface, are dark and non-Lambertian, it is challenging to reach the necessary sensitivity that ensures a high signal-to-noise ratio (SNR) in the image. The imaging system presented in this paper is built for obtaining that sufficient SNR for a given resolution, both in the spatial and spectral domains. The satellite will be launched into a 500 km altitude sun-synchronous orbit. While in orbit, the imager utilizes the pushbroom concept of sequentially gathering lines of pixels, of all wavelengths in range, as it passes over its target. The pushbroom concept, combined with the optical design, yields a swath width of up to 70 km per scan line with a sampling distance of 49 × 60 m on ground. A final consideration must be made due to the large size of raw hyperspectral data cubes and the constraints this sets on satellite power consumption for downlink. This can be significantly improved through onboard image processing (e.g. correction, classification, anomaly detection, feature extraction, an

关键词： Orbits

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Space Debris Tracking Via Generalized Labeled Multi-Bernoulli Random Finite Sets

Space Debris Tracking Via Generalized Labeled Multi-Bernoull...

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IEEE International Conference on signal processing, Communications and Computing (ICSPCC)

作者： Sun Quan Ding Ding Niu Zhaodong Natl Univ Def Technol Coll Elect Sci & Technol Natl Key Lab Sci & Technol ATR Changsha 410073 Peoples R China Natl Univ Def Technol Ctr Teaching & Res Serv Changsha 410073 Peoples R China

ISBN: (纸本)9781728117089

Y In this paper we present an algorithm for detection and tracking space debris under complex space background via delta-generalized labeled multi-Bernoulli (delta-GLMB) random finite sets (RFS). It includes two parts, position extractor for suspected targets and tracking filter. The core strategy of this position extractor is morphological method. The filter is implemented by Sequential Monte Carlo (SMC) method and each iteration of delta-GLMB multi-target tracking filter includes two operations: update and prediction. The processing results of measured data show that the algorithm has strong detection and tracking ability for dim and small targets under complex space background. To the best of our knowledge, this is the first time that the delta-GLMB multi-target tracking filter has been successfully applied in the field of space debris target detection and tracking.

关键词： space debris detection GLMB tracking filter complex space background dim and small targets

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