Results 31 to 40 of about 9,170,751 (397)
Venomave: Targeted Poisoning Against Speech Recognition
2023 IEEE Conference on Secure and Trustworthy Machine Learning (SaTML), 2023 Despite remarkable improvements, automatic speech recognition is susceptible to adversarial perturbations. Compared to standard machine learning architectures, these attacks against speech recognition are significantly more challenging, especially since the inputs to a speech recognition system are time series that contain both acoustic and linguistic ...
Aghakhani, Hojjat +6 more
openaire +2 more sources
Multi-spectral random illumination using a liquid crystal spatial light modulator
Journal of the European Optical Society-Rapid Publications, 2018 Background Using phase-only liquid crystal spatial light modulator (LC-SLM) to generate random illumination is always used in phase-retrieval and single pixel imaging as it have high energy efficiency.
Xiao Chen, Zhiguang Shi, Weidong Hu
doaj +1 more source
IET Radar, Sonar & Navigation, 2022 This paper focuses on how to obtain globally optimal multisensor data association results under the condition that the number of targets is unknown. This problem is likely to occur in scenarios where the sensors’ fields of view (FoVs) are limited.
Feng Ma +3 more
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Adversarial Patch Attack on Multi-Scale Object Detection for UAV Remote Sensing Images
Remote Sensing, 2022 Although deep learning has received extensive attention and achieved excellent performance in various scenarios, it suffers from adversarial examples to some extent. In particular, physical attack poses a greater threat than digital attack.
Yichuang Zhang +6 more
doaj +1 more source
Multi-target detection and recognition by UAVs using online POMDPs [PDF]
, 2013 This paper tackles high-level decision-making techniques for robotic missions, which involve both active sensing and symbolic goal reaching, under uncertain probabilistic environments and strong time constraints.
Lesire, Charles +2 more
core +1 more source
Deep convolution stack for waveform in underwater acoustic target recognition
Scientific Reports, 2021 In underwater acoustic target recognition, deep learning methods have been proved to be effective on recognizing original signal waveform. Previous methods often utilize large convolutional kernels to extract features at the beginning of neural networks.
Sheng-Zhao Tian +3 more
semanticscholar +1 more source
Independent Random Recurrent Neural Networks for Infrared Spatial Point Targets Classification
Applied Sciences, 2019 Exo-atmospheric infrared (IR) point target discrimination is an important research topic of space surveillance systems. It is difficult to describe the characteristic information of the shape and micro-motion states of the targets and to discriminate ...
Dongya Wu +3 more
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Underwater Target Recognition Based on Improved YOLOv4 Neural Network
Electronics, 2021 The YOLOv4 neural network is employed for underwater target recognition. To improve the accuracy and speed of recognition, the structure of YOLOv4 is modified by replacing the upsampling module with a deconvolution module and by incorporating depthwise ...
Lingyu Chen +4 more
semanticscholar +1 more source
Analysis and Design of M-Channel Hybrid Filter Bank With Digital Calibration
IEEE Access, 2018 Hybrid filter bank (HFB) is widely used in frequency-interleaved analog-to-digital converters, but traditional HFB structures suffer various degrees of imperfections. This paper presents a new-structure HFB based on power complementary pair. The proposed
Xiangyu Peng +4 more
doaj +1 more source
R2FA-Det: Delving into High-Quality Rotatable Boxes for Ship Detection in SAR Images
Remote Sensing, 2020 Recently, convolutional neural network (CNN)-based methods have been extensively explored for ship detection in synthetic aperture radar (SAR) images due to their powerful feature representation abilities.
Shiqi Chen, Jun Zhang, Ronghui Zhan
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