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检索条件"任意字段=Conference on Radar Sensor Technology II"

共 103 条记录，以下是21-30 订阅

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Resource-Efficient sensor Fusion via System-Wide Dynamic Gated Neural Networks

Resource-Efficient Sensor Fusion via System-Wide Dynamic Gat...

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IEEE Communications Society conference on sensor and Ad Hoc Communications and Networks (SECON)

作者： C. Singhal Y. Wu F. Malandrino S. Ladron de Guevara Contreras M. Levorato C. F. Chiasserini INRIA France UC Irvine USA CNR-IEIIT Italy CNIT Italy Politecnico di Torino Italy Chalmers University of Technology Sweden

ISBN: (数字)9798331519186

ISBN: (纸本)9798331519193

Mobile systems will have to support multiple AI-based applications, each leveraging heterogeneous data sources through DNN architectures collaboratively executed within the network. To minimize the cost of the AI inference task subject to requirements on latency, quality, and - crucially - reliability of the inference process, it is vital to optimize (i) the set of sensors/data sources and (ii) the DNN architecture, (iii) the network nodes executing sections of the DNN, and (iv) the resources to use. To this end, we leverage dynamic gated neural networks with branches, and propose a novel algorithmic strategy called Quantile-constrained Inference (QIC), based upon quantile-Constrained policy optimization. QIC makes joint, high-quality, swift decisions on all the above aspects of the system, with the aim to minimize inference energy cost. We remark that this is the first contribution connecting gated dynamic DNNs with infrastructure-level decision making. We evaluate QIC using a dynamic gated DNN with stems and branches for optimal sensor fusion and inference, trained on the RADIATE dataset offering radar, LiDAR, and Camera data, and real-world wireless measurements. Our results confirm that QIC matches the optimum and outperforms its alternatives by over 80%.

关键词： Wireless communication Wireless sensor networks Costs Soft sensors Heuristic algorithms Neural networks Logic gates sensor fusion Dynamic scheduling Time measurement

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TC-LTIO: Tightly-coupled LiDAR Thermal Inertial Odometry for LiDAR and Visual Odometry Degraded Environments

TC-LTIO: Tightly-coupled LiDAR Thermal Inertial Odometry for...

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International conference on Control, Automation and Systems ( ICCAS)

作者： Junwoon Lee Taisei Ando Mitsuru Shinozaki Toshihiro Kitajima Qi An Atsushi Yamashita Department of Human and Engineered Environmental Studies The University of Tokyo Japan Technology Innovation R&D Dept.II Research & Development Headquarters KUBOTA Corporation Japan

ISBN: (数字)9788993215380

ISBN: (纸本)9798331517939

We propose tightly-coupled LiDAR thermal inertial odometry for LiDAR and visual odometry degraded environments to deal with LiDAR and RGB-based visual odometry degenerate environments. Our approach exhibits high robustness and accuracy by tightly-coupled sensor fusion between LiDAR edge/planar features and thermal camera point features. Furthermore, to mitigate challenges inherent in thermal vision such as low contrast, we employ a learning-based optical flow trained on both synthetic thermal images generated from RGB images and real-world thermal images. Experimental results demonstrate that our method effectively handles not only the degradation of LiDAR and visual odometry but also challenges inherent in thermal vision.

关键词： Degradation Laser radar Thermal factors Thermal sensors Feature extraction Robustness Odometry Optical flow Visual odometry Thermal degradation

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Identification and Classification of Electronic Devices Using Harmonic radar

Identification and Classification of Electronic Devices Usin...

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International conference on Distributed Computing in sensor Systems (DCOSS)

作者： Beatrice Perez Timothy J. Pierson Gregory Mazzaro David Kotz Department of Computer Science Dartmouth College Hanover NH USA Department of Electrical & Computer Engineering The Citadel The Military College of South Carolina Charleston SC USA

Smart home electronic devices invisibly collect, process, and exchange information with each other and with remote services, often without a home occupants' knowledge or consent. These devices may be mobile or fixed and may have wireless or wired network connections. Detecting and identifying all devices present in a home is a necessary first step to control the flow of data, but there exists no universal mechanism to detect and identify all electronic devices in a space. In this paper we present ICED (Identification and Classification of Electronic Devices), a system that can (i) identify devices from a known set of devices, and (ii) detect the presence of previously unseen devices. ICED, based on harmonic radar technology, collects measurements at the first harmonic of the radar's transmit frequency. We find that the harmonic response contains enough information to infer the type of device. It works when the device has no wireless network interface, is powered off, or attempts to evade detection. We evaluate performance on a collection of 17 devices and find that by transmitting a range of frequencies we correctly identify known devices with 97.6% accuracy and identify previously unseen devices as ‘unknown’ with 69.0% balanced accuracy.

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Overview of the Adaptive Camouflage for the Soldier ii (ACAMSii) 7

Overview of the Adaptive Camouflage for the Soldier II (ACAM...

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conference on Target and Background Signatures Vii Part of SPIE Security + Defence conference

作者： Kariis, Hans Akerlind, Christina Swedish Def Res Agcy FOI Div Electroopt Syst Linkoping Sweden

ISBN: (数字)9781510645752

ISBN: (纸本)9781510645752;9781510645745

The development of new sensing abilities, such as hyperspectral sensors, small man-portable infrared sensors, battlefield radars and faster AI-based data processing requires new capabilities in camouflage materials. Military systems also have to work in changing environments, which calls for adaptivity. ACAMSii is an EU-funded project within the Preparatory Action for Defence Research (PADR) programme under grant agreement No 800871. Scientists from Sweden, Germany, Portugal, France, Lithuania and the Netherlands cooperate in the project. ACAMSii conducts applied research on improved adaptive camouflage for the soldier, with the purpose to provide increased survivability in diversified environments. The ACAMSii multifunctional camouflage demonstrator consists of three main parts: IR adaptivity realized by integrating phase changing and low emissivity materials in the printed camouflage patterned textile. Adaption to background colours and lighting conditions in the visible range, managed by operating and driving light emitting diodes (LEDs) embedded in a panel of clothing textile material. The LEDs are controlled by a camera and a light sensor placed in the back. Thermochromic materials provide further adaptation. Microwave shielding using woven fabric coated with absorbing materials to reduce the radar signature. For a soldier combat system, not only camouflage properties are of importance. The comfort properties of the system are also evaluated. This paper emphasizes military needs (top-down or market pull), scientific aspects and technical readiness level of available technology and materials (bottom-up or technology push), on how to build a consortium in an international collaboration during a pandemic. The concept of the EDA Overarching Strategic Research Agenda has been introduced and the project's relation to this concept has been explained. Only general aspects and results from the extended three-year project, ending in 2022, are presented here whereas speci

关键词： Camouflage Light emitting diodes radar sensors Defense and security Infrared sensors Electronic design automation Infrared radiation Infrared signatures Visualization

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STAGE: Scalable and Traversability-Aware Graph based Exploration Planner for Dynamically Varying Environments

STAGE: Scalable and Traversability-Aware Graph based Explora...

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IEEE International conference on Robotics and Automation (ICRA)

作者： Akash Patel Mario A. V. Saucedo Christoforos Kanellakis George Nikolakopoulos Department of Computer Electrical and Space Engineering Robotics & AI Team Luleå University of Technology Luleå Sweden

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

In this article, we propose a novel navigation framework that leverages a two layered graph representation of the environment for efficient large-scale exploration, while it integrates a novel uncertainty awareness scheme to handle dynamic scene changes in previously explored areas. The framework is structured around a novel goal oriented graph representation, that consists of, i) the local sub-graph and ii) the global graph layer respectively. The local sub-graphs encode local volumetric gain locations as frontiers, based on the direct pointcloud visibility, allowing fast graph building and path planning. Additionally, the global graph is build in an efficient way, using node-edge information exchange only on overlapping regions of sequential sub-graphs. Different from the state-of-the-art graph based exploration methods, the proposed approach efficiently re-uses sub-graphs built in previous iterations to construct the global navigation layer. Another merit of the proposed scheme is the ability to handle scene changes (e.g. blocked pathways), adaptively updating the obstructed part of the global graph from traversable to not-traversable. This operation involved oriented sample space of a path segment in the global graph layer, while removing the respective edges from connected nodes of the global graph in cases of obstructions. As such, the exploration behavior is directing the robot to follow another route in the global re-positioning phase through path-way updates in the global graph. Finally, we showcase the performance of the method both in simulation runs as well as deployed in real-world scene involving a legged robot carrying camera and lidar sensor.

关键词： Legged locomotion Uncertainty Laser radar Navigation Robot vision systems Memory management Cameras

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ESTIMATING ABOVEGROUND FOREST CARBON STOCKS USING MULTI-sensor SATELLITE DATA and FIELD MEASUREMENTS in the PHILIPPINES 42

ESTIMATING ABOVEGROUND FOREST CARBON STOCKS USING MULTI-SENS...

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42nd Asian conference on Remote Sensing, ACRS 2021

作者： De Alban, Jose Don T. Veridiano, Rizza Karen A. Pang, Sean E.H. Estomata, Mari Trix L. Jamaludin, Johanness Jaojoco, Angelica Kristina M. Parinas, Margie T. Reyes, Sheryl Rose C. Tumaneng, Roven D. Verma, Akash Department of Biological Sciences National University of Singapore Singapore117558 Singapore Centre for Nature-based Climate Solutions Department of Biological Sciences National University of Singapore Singapore117558 Singapore Center for Development Research University of Bonn Bonn53113 Germany FORLIANCE GmbH Bonn53119 Germany GmbH Makati1209 Philippines PS241 Consulting Inc. City of San Pedro Laguna4203 Philippines National Mapping and Resource Information Authority Fort Andres Bonifacio Taguig1634 Philippines United Nations University Institute for the Advanced Study of Sustainability 5-53-70 Jingumae Shibuya-ku Tokyo150-8925 Japan Manila Observatory Ateneo de Manila University Campus Quezon1108 Philippines Philippine Council for Industry Energy and Emerging Technology Research and Development Department of Science and Technology Bicutan Taguig1631 Philippines Fauna & Flora International Philippines Programme Pembroke Street CambridgeCB2 3QZ United Kingdom

ISBN: (纸本)9781713843818

Quantifying the amount of carbon stored in tropical forests is needed to inform climate mitigation mechanisms such as Reducing Emissions from Deforestation and Forest Degradation (REDD+). Remote sensing is a cost-effective tool to accurately estimate aboveground forest carbon stocks. Studies that use data from multiple sensors for estimating aboveground forest carbon, however, remain limited despite their tremendous potential. We estimated aboveground forest carbon stocks using multiple linear regression modeling between forest inventory data and satellite data, specifically L-band synthetic aperture radar (SAR) and Landsat, in three study sites in the Philippines. Models built using data from individual sensors versus combined multi-sensor data were assessed to determine which produced more accurate estimates of aboveground forest carbon. Subsequently, the accuracy of a global model developed from forest plot data across sites, was compared against site-specific models. Across sites, results showed that models using combined multi-sensor data performed better than those using individual sensors, with higher accuracies obtained for larger plot size (R2=0.82-0.93;RMSE=27.20-42.92 Mg C ha-1). Textural attributes made up the majority of predictors in these combined sensor models and contributed to improved accuracies and reduced uncertainties in carbon estimates. The global model did not perform better than site-specific models, regardless of sensor data or plot size, which suggests that the predictive power of the global model may be influenced by inherent site-specific variabilities (e.g., forest characteristics and forest inventory methods). Combining multi-sensor satellite data offers an improved approach for estimating aboveground forest carbon, with larger forest plot sizes contributing to this improvement, thus leading to higher accuracies and lower uncertainties for spatially explicit carbon mapping in dense tropical forest regions. © ACRS *** right reserv

关键词： Landsat

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A Low-Cost radar-based Domain Adaptive Breast Cancer Screening System

A Low-Cost Radar-based Domain Adaptive Breast Cancer Screeni...

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IEEE MIT Undergraduate Research technology conference (URTC)

作者： Samuel Claflin Mohammad Arif Ul Alam CUBICS Lab University of Massachusetts Lowell

ISBN: (纸本)9781728175720

Over the past three decades, the advancements of breast cancer screening technologies such mammography, ultrasound, Magnetic resonance imaging (MRI) saved countless lives. The invention of mammography screening of breast cancer in the 1990s led a technological revolution which is, now-a-days, coupled with MRI and/or ultrasound to achieve diagnoses of much greater accuracy than previously attainable. However, these technologies (mammography, ultrasound, MRI) are not as widely available to patients as one might assume less fortunate countries (such as Bangladesh) often cannot afford the potentially enormous price tag that several ultrasound machines of sufficient quality for accurate diagnoses incurs. In this paper, we present a low-cost (<100 USD) millimeter Wave (mmWave) radar sensor array (3– 10 GHz) imaging technology and a deep learning domain adaptation model-based breast cancer screening system. More specifically, (i) we develop a mmWave radar sensor array (18 sensor antennas) based 2D imaging system; (ii) we develop a deep learning based domain adaptation model that can learn breast segmentation and cancer detection from expensive source data (mammography, ultrasound) and transfer the knowledge to less expensive target data (radar images), (iii) we validated our system and methods by utilizing our existing mammography and ultrasound breast cancer screening data as well as 14 patients' radar images collected from a third world country (Bangladesh).

关键词： Deep learning Ultrasonic imaging Magnetic resonance imaging Biological system modeling Transfer learning radar detection radar imaging

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Design Simulation of MEMS Based Shockwave Pressure sensor for Test Evaluation Applications 1

Design Simulation of MEMS Based Shockwave Pressure Sensor fo...

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1st International conference on Range technology, ICORT 2019

作者： Dey, Prithu Padhy, Sankarsan Bose, Aniruddha Proof Experimental Establishment Defense Research Development Organizatiomn Chandipur Balasore Odisha India

ISBN: (纸本)9781728113531

During dynamic testing evaluation of different armaments stores, recording of different parameters related to internal, external and terminal ballistics precisely plays a crucial role for the qualitative assessment of the different ammunitions and ordnances. This paper deals with design and simulation of MEMS based pressure sensor which will be used to measure shockwave pressure. Shockwave pressure is used to compute the velocity of the projectile. Three different configurations such as I. Single turn piezoresistor, ii. Four turn meander shaped piezoresistor and iiI. Bossed diaphragm structure have been presented here to design the pressure sensor. The simulation has been performed using FEM based CoventorWare tool. The measurement range of the pressure sensor is up to 10 psi. © 2019 IEEE.

关键词： Pressure sensors

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MIMO radar techniques: Two-stage constant false alarm rate detection for fast-fluctuating targets

MIMO radar techniques: Two-stage constant false alarm rate d...

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IET International radar conference 2018, IRC 2018

作者： Li, Zhihua Su, Hongtao Hu, Qinzhen Zhou, Shenghua National Laboratory of Radar Signal Processing Xidian University Xi'an710071 China

Here, the authors analyse a two-step constant false alarm rate (CFAR) detection problem in distributed sensor system (e.g. statistical multiple-input multiple-output (MIMO) radar) for fast-fluctuating targets (TS-AMF-ii). The scheme arises when a data rate restriction forces each of the distributed systems to censor their detection statistics before sharing and is motivated by the fact that the clutter plus noise power and the signal to clutter plus noise ratio may be both unknown in practice. The proposed detector has two thresholds: the first one called local threshold controls the communication rate and the second one called global threshold adjusts the global probability of false alarm. Numerical results illustrate that the proposed detector has good detection performance at a low communication rate. © 2019 Institution of Engineering and technology. All rights reserved.

关键词： MIMO radar

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Offshore Oil Slicks Detection From SAR Images Through The Mask-RCNN Deep Learning Model

Offshore Oil Slicks Detection From SAR Images Through The Ma...

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International Joint conference on Neural Networks (IJCNN)

作者： Amri Emna Benoit Alexandre Philippe Bolon Migebielle Véronique Conche Bruno Oppenheim Georges LISTIC laboratory USMB/Total company Annecy France LISTIC laboratory USMB Annecy France Total company hyperref Pau France Total company Pau France University of Paris-Est Paris France

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169279

This paper introduces a method for offshore oil slick detection. At present, Synthetic Aperture radar (SAR) is an image acquisition technology useful for oil slick detection in all weather conditions. It is used to carry out the detection, with notable limitations under certain conditions (surfaces, weather conditions). Manual SAR images analysis is expensive and, given the increasing amount of data collected from available sensors, automation becomes mandatory. To achieve this objective, instance object detection relying on deep neural networks is interesting to adapt to the data variability. Relying on such an approach, this article explores the capabilities of generalizing the detection of slicks on large datasets using the Mask-RCNN model. A detailed performance analysis is established in two complementary directions: (i) the impact of the SAR image characteristics(sensor, geographical areas, lookalike presence), (ii) the impact of the neural network architecture, transferred capabilities and training procedures. The main findings of this analysis show that Mask-RCNN features promising performance for pollution detection.

关键词： Oils Synthetic aperture radar sensors Feature extraction Machine learning Neural networks Sea surface

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