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检索条件"任意字段=IEEE International Conference on Signal Image Technology and Internet Based Systems"

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Direct Wave and Clutter Cancellation Algorithm based on Noise signal and Compressed Sensing 6

Direct Wave and Clutter Cancellation Algorithm Based on Nois...

引用

6th international conference on signal and image Processing, ICSIP 2021

作者： Zhang, Bowen Yang, Yu Yu, Bei Zhang, Yuping Institute China Shipbuilding Industry Systems Engineering Research Beijing100094 China

ISBN: (纸本)9780738133737

Direct wave and clutter suppression is one of the key issues in the bistatic noise FMCW (frequency modulation continuous wave) radar system. In the traditional compressed sensing method based on direct wave and clutter suppression algorithm, the fuzzy operation results in the high computational complexity. In this paper, the direct wave and clutter cancellation algorithm based on the noise signal and compressed sensing is proposed, which takes the advantage of the sparseness in the range-Doppler map and the random characteristics of the noise FMCW signal. The algorithm directly generates the sparse base matrix in the time domain by the delay and Doppler modulation of the reference signals. Moreover, it avoids the data redundancy in the process of obtaining the sensing matrix and measurement vector. In addition, the sparse representation of the direct wave and clutter, the construction of the measurement matrix are both demonstrated. Finally, by comparing other suppression algorithms, the real-life data processing verifies the validity of the proposed algorithm. © 2021 ieee.

关键词： Compressed sensing

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Data Enhancement technology based on I-GOS Algorithm

Data Enhancement Technology Based on I-GOS Algorithm

引用

2021 ieee international conference on Electronic Communications, internet of Things and Big Data, ICEIB 2021

作者： Li, Fei Deng, Xiaotong Shi, Feifei Zhou, Xiaoxue Xia, Kaijun Hu, Guangdi Mechanical Engineering Southwest Jiaotong University Chengdu China College of Nuclear Technology and Automation Engineering Chengdu University of Technology Chengdu China School of Nuclear Science and Technology Lanzhou University Lanzhou China Jiangsu Xintongda Electronic Technology Co. Ltd Jiangsu China Zhejiang Geely New Energy Commercial Vehicle Group Co. Ltd Zhejiang China

ISBN: (纸本)9781665437554

Although deep learning is developing rapidly and being applied on a large scale, its essence is still a black box model. When evaluating the credibility of the model, the judgment for the prediction of the model is important. based on the I-GOS algorithm in the field of visual interpretation, we effectively extract the effective information of the sample and then use the sample to retrain a new model to improve the accuracy of the model. Thus, a new data enhancement method is proposed to train the algorithm by using the imageNet dataset and retrain it by using the I-GOS algorithm to extract effective information. The experiment result shows that compared with the original model, the accuracy of the new model is improved by 5% in the top 1 and 3% in the top 5, which fully proves the effectiveness of this data enhancement method. © 2021 ieee.

关键词： image enhancement

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Analysis of Multi-ray Propagation for DSRC Communication on Street with RISs on Sidewalls 8

Analysis of Multi-ray Propagation for DSRC Communication on ...

引用

8th NAFOSTED conference on Information and Computer Science, NICS 2021

作者： Wu, Guilu Southeast University State Key Laboratory of Millimeter Waves Nanjing China Key Laboratory of Industrial Internet of Things and Networked Control Ministry of Education Chongqing University of Posts and Telecommunications Chongqing China School of Internet of Things Engineering Jiangnan University Wuxi China

ISBN: (纸本)9781665410014

Each element in the Reconfigurable Intelligent Sur-faces (RISs) can independently change the phase (or/and) amplitude of the incident signal, so the RISs can be used to enable users to better receive the signal transmitted by the transmitter. In this paper, a Multi-ray propagation model for RISs-assisted dedicated short-range communication (DSRC) system is presented and analyzed on street. The 3-D geometry and image techniques in ray tracing are combined to build the proposed model. This method could enhance the ray-tracing algorithm in RISs-assisted communication networks. In addition, the envelope strength of the received signal is analyzed and verified in our experiments by regulating phase of RISs in two cases. Different reflection times on transmitted signal wave bring different envelope strength of the received signal. The simulation results show that different reflection times and adjustment angles directly affect the received signal strength. © 2021 ieee.

关键词： Ray tracing

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Fine-Grained Recognition Framework For Ship Targets In SAR images based On Component Architecture

Fine-Grained Recognition Framework For Ship Targets In SAR I...

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signal, Information and Data Processing (ICSIDP), ieee international conference on

作者： Yongchen Pan Can Su Hongcheng Zeng Yamin Wang Wei Yang Chunsheng Li Jiaqi Yu Dongliang Li School of Electronic Information Engineering Beihang University Beijing China Beijing Institute of Control and Electronics Technology Beijing China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

Applying deep learning network models in SAR image processing has significantly improved speed of target recognition performance. Nevertheless, due to the SAR imaging principle distinct from optical images, these models struggle to distinguish strong scattering characteristics from clutter, resulting in a high false alarm rate in applications of target recognition. To ensure correctness practically, the expert analysis for discern various structures is strongly necessary for fine-grained recognition after possessing through integrating intelligent models, with extra time and manpower cost. This study introduces a fine-grained recognition framework for ship targets in SAR images based on component architecture. A ship targets dataset from Gaofen-3 SAR images, with three classified ship types is designed to train the model, including meticulous analysis and labeling for target categorization, axis orientation, and key component localization. Features of different dimensions extracted via pre-trained ResNet-50 to construct a weight dossier, and a multi-task joint detection mode are embedded into the framework to integrate the experts’ ship recognition method built on component architecture into the intelligent network model. This yields an accuracy rate surpassing 89% for ship identification, eclipsing conventional intelligence models by a significant margin. The framework successfully bolsters the ability of target awareness affiliated with SAR ocean monitoring.

关键词： Component architectures Intelligent networks Analytical models image recognition Accuracy Target recognition Object detection Feature extraction Radar polarimetry Marine vehicles

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Demo Abstract: Bio-inspired Tactile Sensing for MAV Landing with Extreme Low-cost Sensors

Demo Abstract: Bio-inspired Tactile Sensing for MAV Landing ...

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international Symposium on Information Processing in Sensor Networks (IPSN)

作者： Chenyu Zhao Ciyu Ruan Shengbo Wang Jirong Zha Haoyang Wang Jiaqi Li Yuxuan Liu Xuzhe Wang Xinlei Chen Shenzhen International Graduate School Tsinghua University Shenzhen China Pengcheng Laboratory Shenzhen China RISC-V International Open Source Laboratory Shenzhen China

ISBN: (数字)9798350362015

ISBN: (纸本)9798350362022

MAV (Micro Aerial Vehicle) requires landing on a docking platform for recharging during or after missions due to their limited energy capacity. Inspired by biological tactile sensing, we propose a proprioceptive sensing system that allows MAV to "touch", recognize, and locate the landing platform even when visual or other positioning systems are not functioning properly. We leverage a physical phenomenon: as the MAV approaches a beneath obstacle, it experiences attitude disturbances caused by the airflow generated by the rotor’s reflections from the ground. By employing traditional signal processing and learning-based techniques to analyze signals from the IMU (Inertial Measurement Unit) and motors, the MAV can sense the edges of the platform and further calculate the precise landing coordinates. With a power consumption of less than 40 mW, our system achieves an edge detection error of less than 2 cm and a landing success rate exceeding 90%.CCS CONCEPTS• Applied computing → Aerospace; • Computing methodologies → Machine learning approaches; • Computer systems organization → Sensors and actuators.

关键词： Visualization Power demand image edge detection Propioception Organizations signal processing Sensor systems

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Environment and Person-independent Gesture Recognition with Non-static RFID Tags Leveraging Adaptive signal Segmentation

Environment and Person-independent Gesture Recognition with ...

引用

international conference on Emerging Technologies and Factory Automation (ETFA)

作者： Sahar Golipoor Stephan Sigg Department of Information and Communications Engineering Aalto University Finland

ISBN: (数字)9798350361230

ISBN: (纸本)9798350361247

Gesture recognition for human machine interaction enhances the efficiency, safety, and usability of industrial and factory automation systems. We investigate hand-gesture recognition using battery-less body-worn reflective tags. Particularly, we propose two methods for hand gesture recognition using radio frequency identification (RFID). From backscattered signals we utilize in-phase and quadrature (IQ) constellation, as well as the phase. We convert extracted IQ samples into images and interprete them for gestures using a pre-trained VGG16. As a second approach we alternatively conduct pre-processing on the phase of the backscattered signals and propose Zero Crossing-Modified Derivative (ZCMD) for signal segmentation. Through signal resampling and wavelet denoising we mitigate undesired fluctuations introduced during this process, while retaining crucial signal characteristics. Subsequently, we integrate time-domain and frequency-domain features of the signals and train a random forest classifier based on these features to identify different gestures. Utilizing battery-free body-worn RFID tags, we are able to outperform a state-of-the art method and recognize four gestures with an accuracy of 81 % with the VGG16-based model. Employing phase, we achieve an accuracy of 94 %.

关键词： image segmentation Adaptation models Accuracy Gesture recognition Safety RFID tags Usability Time-domain analysis Random forests Manufacturing automation

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ADVERSARIAL CROSS-SCALE ALIGNMENT PURSUIT FOR SERIOUSLY MISALIGNED PERSON RE-IDENTIFICATION

ADVERSARIAL CROSS-SCALE ALIGNMENT PURSUIT FOR SERIOUSLY MISA...

引用

ieee international conference on image Processing (ICIP)

作者： He, Yuanhang Yang, Hua Chen, Lin Shanghai Jiao Tong Univ Dept Elect Engn Inst Image Commun & Network Engn Shanghai Peoples R China Shanghai Jiao Tong Univ Shanghai Key Lab Digital Media Proc & Transmiss Shanghai Peoples R China Shanghai Jiao Tong Univ AI Inst MoE Key Lab Artificial Intelligence Shanghai Peoples R China

ISBN: (纸本)9781665441155

Person re-identification is still a challenging task in actual application due to serious misalignment caused by large scale variation, serious occlusion, or variably truncated body. Conventional holistic methods usually lack the cross-scale aligning ability. Segmentation-based partial methods achieve better aligning performance but generally suffer from the instability of part segmentation. To explicitly address these issues, we define the seriously misaligned Re-ID task and propose a novel framework called adversarial cross-scale alignment pursuit (ACSAP). Instead of dynamically segmenting the feature map for part alignment, the model incorporates the stability of holistic methods and adversarially generates aligned feature maps for similarity metrics. Especially, the spatial reconstruction (SR) module in the generator is proposed for feature filtering and aligning. Then a part visibility calculation (PVC) algorithm is proposed to distinguish the credibility of different generated areas. We propose a novel dataset called Seriously-Misaligned-REID and achieve 10.0% rank-1 out-performance compared to state-of-the-art methods on it. Extensive performance results demonstrate the effectiveness of our framework.

关键词： Cross-scale feature alignment Seriously misaligned person Re-ID GAN

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SAROD: EFFICIENT END-TO-END OBJECT DETECTION ON SAR imageS WITH REINFORCEMENT LEARNING

SAROD: EFFICIENT END-TO-END OBJECT DETECTION ON SAR IMAGES W...

引用

ieee international conference on image Processing (ICIP)

作者： Kang, Junhyung Jeon, Hyeonseong Bang, Youngoh Woo, Simon S. Sungkyunkwan Univ Dept Artificial Intelligence Suwon South Korea AIRS Co Hyundai Motor Grp Seoul South Korea

ISBN: (纸本)9781665441155

Generally, object detection on Synthetic-Aperture Radar (SAR) images is known to be more challenging than that in Electro-Optical (EO) satellite images because SAR images have non-negligible speckle noise and require extensive data pre-processing. Nevertheless, object detection in SAR images is important, as SAR imagery can be obtained under severe weather and time conditions. While many recent object detection approaches on SAR imagery focus on improving detection accuracy, few studies focus on improving processing efficiency. In fact, there are significant challenges and trade-offs to achieve both high accuracy and efficiency at the same time. In this work, we introduce SAROD, a novel efficient end-to-end object detection framework on SAR images based on Reinforcement Learning (RL) to balance the trade-offs. Our proposed model consists of two detectors, coarse and fine-grained detectors, with an RL agent, where RL has not yet been utilized for object detection on SAR imagery. Our model was evaluated on a challenging SAR imagery dataset, achieving performance comparable to state-of-the-art detectors while maintaining high efficiency of source data usage.

关键词： SAR image Object Detection Reinforcement Learning Multi-resolution Efficient Learning

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Spatial Frequency-Aware Self-Distillation for Weakly-Supervised Semantic Segmentation

Spatial Frequency-Aware Self-Distillation for Weakly-Supervi...

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international conference on Acoustics, Speech, and signal Processing (ICASSP)

作者： Jingyuan Fang Yang Ning Xiushan Nie School of Computer Science and Technology Shandong Jianzhu University Jinan P.R. China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Weakly-supervised semantic segmentation (WSSS) aims to achieve pixel-level classification under image-level supervision. Recent class activation map (CAM)-based methods seek to expand foreground activation while suppressing background. However, they often overlook the uncertainty of CAM, where non-salient activation in some regions complicates semantic classification. These regions are typically dismissed as noise, resulting in inappropriate activations due to inadequate regularization. To resolve this, we introduce a Spatial Frequency-Aware Self-Distillation strategy (SFS). Firstly, to enhance the perception of high-frequency spatial information in uncertain regions, we propose a boundary self-distillation and uncertain region reconstruction strategy, which captures high-frequency boundary information and fine-grained spatial context in these regions. Secondly, to enhance the discrimination of low-frequency semantic features, we propose a contrastive attention mechanism that guides the Vision Transformer (ViT) to focus more on the foreground, thereby improving the distinction between foreground and background. Finally, our SFS demonstrates outstanding performance on both the VOC 2012 and COCO 2014 datasets, attributed to its superior spatial frequency perception capabilities. The code is available at https://***/fjoybest/SFS.

关键词： Uncertainty Semantic segmentation Semantics Noise Frequency conversion Transformers Data mining Speech processing Spatial resolution image reconstruction

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GBA-Net: A Method for 3D Brain Tumor Segmentation based on Multi-scale Gaussian Boundary Attention

GBA-Net: A Method for 3D Brain Tumor Segmentation Based on M...

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international conference on Acoustics, Speech, and signal Processing (ICASSP)

作者： Wei Wang Longrun Wang Xin Wang Changsha University of Science and Technology Changsha China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The complex nature of brain tumors, characterized by their individual shapes, sizes, and locations, as well as the presence of indistinct boundaries, presents a challenging task for precise automatic segmentation. While U-Net has been a top performer in medical image segmentation, it struggles with capturing multi-scale details, preserving information across layers, and focusing on critical features. To address these issues, a new 3D brain tumor segmentation network called GBA-Net is proposed, which introduces - 1) A multi-scale Gaussian boundary attention (GBA) module with the ability to automatically focus on boundary features, 2) Efficient inverted bottleneck convolution upsample (Up-IBC) and inverted bottleneck convolution downsample (Down-IBC) modules that enhance the richness of cross-scale information, 3) The high-low feature fusion (HLFF) module that mitigates information loss during the decoder’s restoration of full spatial resolution. The proposed GBA-Net achieves state-of-the-art performance on the 3D brain tumor dataset BraTS 2021. Cross-validation on the BraTS 2018 and BraTS 2019 datasets indicates that GBA-Net generalizes well on the external datasets.

关键词： image segmentation Three-dimensional displays Convolution Shape Magnetic resonance imaging Brain tumors Liver Speech processing Spatial resolution Biomedical imaging

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