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Communication Technology and Information Technology (ICCTIT), International conference on

作者： Chunhao Bao Pengfei He Yong Huang Lei Xu School of Physics and Electronic Information Yantai University Yantai China Shandong Data Open Innovation Application Laboratory of Smart grid Advanced Technology Yantai University Yantai China Naval Aviation University Yantai China Huaneng Shandong Power generation Co. LTD. Yantai power plant Yantai China

ISBN: (数字)9798331528973

ISBN: (纸本)9798331528980

In radar target detection, large targets often mask small targets, and the traditional constant false alarm (CFAR) detection method is difficult to set a suitable threshold and cannot cope with the detection of this type of target well. Aiming at this kind of problem, a residual LSTM detection method based on global channel attention mechanism (Global Channel Attention Mechanism- Long Short Term Memory, GCAMLSTM) is proposed. This method processes the radar echo data, establishes the time-order relationship of the radar signals through the sliding window method, and proposes a new Global Channel Attention Mechanism module according to the characteristics of the time-order signals, so as to extract the target echo features more accurately. The experimental results show that the detection performance of the proposed method is $\mathbf{6. 8 \%}$ higher than that of the traditional CFAR algorithm. It has strong robustness in the above scenario and can achieve effective detection of radar targets.

关键词： Attention mechanisms Time series analysis Radar detection Radar Object detection Feature extraction Radar signal processing Robustness Long short term memory Monitoring

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Extracting Sinusoidal Frequency Modulation signal Signature Using Fourier-Bessel Transform and Short-Time Fraction Fourier Transform

Extracting Sinusoidal Frequency Modulation Signal Signature ...

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IEEE International conference on Information Communication and signal processing (ICICSP)

作者： Xiaomeng Ma Meiguo Gao School of Information and Communication Beijing Institute of Technology Beijing China

A method using Fourier-Bessel transform (FBT) and short-time fraction Fourier transform (ST-FRFT), for accurate and robust parameter estimate and separation of sinusoidally frequency modulated (SFM) nonstationary signal, is presented. The rotation micro-motion coupling with the rigid body's motion provides an approximately sinusoidal phase modulation, which is known as the micro-Doppler (m-D) signal. According to priori research, the SFM signal shows the nearly linear frequency modulation characteristic in the local part and sparsity features in fraction domain. Firstly, in order to extract the slope of nearly FM and the rotation target's angular frequency, here using FBT method can decompose the rotation target's echo into stationary and nonstationary component. Then, using ST-FRFT method separate the target's signal into the fraction domain where through peak search to catch the SFM signal's slope and rotation angular frequency. This function shows enormous potential in solving the problem that nonstationary component always can't be extracted because of weak echo amplitude. The rotation angle frequency is an important characteristic of the rotating target, which shows great significance to the classification of low-altitude and low-speed small targets. This proposed method has been demonstrated by the air target's simulation data which shows effective results of estimating these parameters.

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FEW SHOT LEARNING FOR INFRA-RED OBJECT RECOGNITION USING ANALYTICALLY DESIGNED LOW LEVEL FILTERS FOR data REPRESENTATION

FEW SHOT LEARNING FOR INFRA-RED OBJECT RECOGNITION USING ANA...

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IEEE International conference on Image processing (ICIP)

作者： Arif, Maliha Mahalanobis, Abhijit Univ Cent Florida Ctr Res Comp Vis CRCV Orlando FL 32816 USA

ISBN: (纸本)9781665441155

It is well known that deep convolutional neural networks (CNNs) generalize well over large number of classes when ample training data is available. However, training with smaller datasets does not always achieve robust performance. In such cases, we show that using analytically derived filters in the lowest layer enables a network to achieve better performance than learning from scratch using a relatively small dataset. These class-agnostic filters represent the underlying manifold of the data space, and also generalize to new or unknown classes which may occur on the same manifold. This directly enables new classes to be learned with very few images by simply fine-tuning the final few layers of the network. We illustrate the advantages of our method using the publicly available set of infra-red images of vehicular ground targets. We compare a simple CNN trained using our method with transfer learning performed using the VGG-16 network, and show that when the number of training images is limited, the proposed approach not only achieves better results on the trained classes, but also outperforms a standard network for learning a new object class.

关键词： manifold eigen representation few shot learning sparse learning infra-red datasets

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Transformer-in-Transformer Network with A Complex-Valued Mixup Module for SAR Automatic Target Recognition

Transformer-in-Transformer Network with A Complex-Valued Mix...

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signal, Information and data processing (ICSIDP), IEEE International conference on

作者： Zhenghua Ze Jianghong Han Qiyuan Lab Beijing China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

Convolutional neural network (CNN) has been demonstrated an effective approach for synthetic aperture radar (SAR) automatic target recognition (ATR) in recent years. However, CNNs usually extract characteristics through local receptive fields, resulting in the loss of global feature information of targets. Moreover, satisfactory target recognition performance using CNN generally depends on sufficient labeled sample images, which are difficult to obtain in SAR ATR conditions. To address these issues, we propose a Complex-Valued Mixup (CVM) module- aided Transformer-in-Transformer (TNT) network (CVM-TNT) for SAR ATR. The CVM module generates new samples through linear interpolation and utilizes both amplitude and phase information to extract richer electromagnetic scattering feature. The TNT network takes the advantages of global and local features to compensate the deficiency of traditional Transformer network that only extracts global features. Firstly, we divide the input images including amplitude and phase into several local patches as "visual sentences". Then, we use the outer conventional Transformer block to calculate global features and their global attentions of visual sentences. Subsequently, dividing the local patches into sub-patches as "visual words" and embedding an inner sub-Transformer block into the network excavates the local phase and amplitude features of visual words. Finally, both word-level and sentence-level features will be combined to boost the representation capability. Experiments implemented on the moving and stationary target acquisition and recognition (MSTAR) dataset have shown that the proposed CVM-TNT model outperforms the state-of-the-art (SOTA) methods in the case of small datasets.

关键词： Visualization Interpolation Target recognition Transformers Feature extraction Excavation Radar polarimetry Convolutional neural networks data mining Synthetic aperture radar

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A Tobacco Pest Detection System Based on the Improved YOLO v5 Model

A Tobacco Pest Detection System Based on the Improved YOLO v...

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6th International conference on Intelligent Computing and signal processing (ICSP)

作者： Shaohui Zhou Jinchao Zhao Yong Gan Yuefeng Wang College of Computer Science and Technology Zhengzhou University of Light Industry Zhengzhou China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

During the storage process, tobacco leaves are vulnerable to infestation by Lasioderma serricorne. To achieve precise detection of this pest within stored tobacco leaves and provide real-time updates on pest activity, we propose a detection system based on an improved YOLOv5 model. Initially, an open-source hardware system is established to capture images of the pests, which are then transmitted to a computer terminal via a Wi-Fi network. Random data augmentation techniques, including translation, rotation, and the addition of Gaussian noise, are applied to expand the training set of Lasioderma serricorne images. Subsequently, the BiFormer attention mechanism is integrated to enhance the model's accuracy in detecting small targets such as Lasioderma serricorne. By combining the improved YOLOv5 model with hardware facilities and the detection system, a comprehensive device is formed. The results demonstrate that the improved YOLOv5 network model achieves an accuracy rate of 93.26% and an average detection precision of 92.40% for Lasioderma serricorne. Compared to models such as YOLO v3, Retinanet, Faster R-CNN, the original YOLOv5, and SSD, the improved YOLOv5 model exhibits superior detection performance, with efficiency improvements of 2.76% and 2.46% over the existing YOLOv5 algorithm, respectively. The integration of the improved YOLOv5 model with hardware and the detection system into a comprehensive device fully harnesses the latest advancements in modern computer vision and artificial intelligence technologies. This device not only offers efficient object detection capabilities but also adapts well to various environments and scenarios, opening up a wide range of application possibilities. In this study, the device accurately identifies and counts Lasioderma serricorne, providing technical support for early warning and pest control in tobacco storage systems.

关键词： YOLO Training Adaptation models Accuracy Computational modeling signal processing algorithms signal processing Pest control Real-time systems Wireless fidelity

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MKD-YOLO: Multi-Scale and Knowledge-Distilling YOLO for Efficient PPE Compliance Detection

MKD-YOLO: Multi-Scale and Knowledge-Distilling YOLO for Effi...

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International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Juntao Zan Yang Fang Qilie Liu Uswah Khairuddin Yan Li Kaiwei Sun School of Communications and Information Engineering Chongqing University of Posts and Telecommunications Chongqing China Chongqing Key Laboratory of Image Cognition Chongqing University of Posts and Telecommunications Chongqing China Malaysia-Japan International Institute of Technology University of Technology Malaysia Kuala Lumpur Malaysia Department of Electrical and Computer Engineering Inha University Incheon South Korea Key Laboratory of Data Engineering and Visual Computing Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

YOLO-based models are widely used for personal protective equipment (PPE) compliance detection due to their excellent detection performance and efficiency. However, most YOLO models are not competent for detection tasks in complex industrial scenarios such as remote surveillance and extremely small targets. In addition, there is a lack of effective model lightweighting and knowledge transfer approaches for industrial deployment. To this end, this paper proposes a Multi-scale and Knowledge-Distilling YOLO (MKD-YOLO) based on YOLOv8n for efficient PPE compliance detection. Specifically, in backbone stage, we design an Efficient Multi-Scale Enhanced Convolution (C2f-EMSEC) module and Large Spatial Pyramid Pooling-Fast (LSPPF) module for multi-scale and global-contextual feature learning as well as reducing model complexity. Then, in neck stage, a refined Bidirectional feature Pyramid Network (BPNet) is designated to capture fine-grained details for extremely small object detection. Moreover, we apply channel-wise knowledge distillation to facilitate model lightweighting and domain-specific knowledge transfer learning. Experiments on our proposed dataset and public datasets show that the proposed MKD-YOLO achieves a new state-of-the-art (SOTA) detection performance and efficiency for practical PPE compliance detection tasks. Codes and the dataset are available at https://***/z1Zjt/MKD-YOLO.

关键词： Personal protective equipment YOLO Representation learning Convolution Surveillance Speech enhancement Feature extraction Neck Kernel Knowledge transfer

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Multiple Moving targets Heartbeat Estimation and Recovery using Multi-Frequency Radars

Multiple Moving Targets Heartbeat Estimation and Recovery us...

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IEEE Radar conference (RadarConf)

作者： Rong, Yu Mishra, Kumar Vijay Bliss, Daniel W. Arizona State Univ Sch Elect Comp & Energy Engn Ctr Wireless Informat Syst & Computat Architectur Tempe AZ 85281 USA US Army Res Lab Adelphi MD 20783 USA

ISBN: (纸本)9781728176093

In this paper, we present a novel non-contact and privacy preserving approach on recovering physiological measurements like heart rate when multiple subjects are slowly walking in the scene using a ultra-wideband (UWB) multi-frequency radar system. We employ joint probabilistic data association (JPDA) algorithm to separate detection from multiple moving objects. Additionally, we exploit a recently developed multichannel variational mode decomposition (MVMD) algorithm to extract heartbeat information from the range aligned samples. Our contribution lies in demonstration the possibility of a novel signal processing approach on heartbeat detection while multiple human targets are moving on the order of a few meters with a relatively simple setup while prior works requires a more complex setting and only considers small body movement on centimeter levels.

关键词： Bio-radars data association remote sensing tracking vital sign detection

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Research on Spatial Angle Super-Resolution Algorithm Based on Adaptive Layered Sparse

Research on Spatial Angle Super-Resolution Algorithm Based o...

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Sensing, Measurement & data Analytics in the era of Artificial Intelligence (ICSMD), International conference on

作者： Min Xue Yuexin Gao Zhixin Wu National Key Laboratory of Radar Signal Processing Xidian University Xi'an China School of Aerospace Science and Technology Xidian University Xi'an China

Compressed Sensing (CS) theory is based on the sparsity of signals and involves compressively sampling high-dimensional data to obtain a small number of linear observations that contain the complete information of the signals. By solving an optimization problem, the original signals can be recovered from these compressed linear observations. This paper combines CS theory with the sparse characteristics of targets in the spatial domain and proposes an adaptive layered sparse spatial angle super-resolution algorithm. This algorithm converts the target spatial angle super-resolution into the problem of using orthogonal basis to reconstruct sparse signals. Then, the target spatial angle super-resolution is carried out by using single snapshot data through optimization solution, and the target spatial angle domain is continuously reduced through layered orthogonal matching pursuit (LOMP) solution to complete the target spatial angle reconstruction. The effectiveness and robustness of the algorithm are verified by using the proposed algorithm to process the measured data.

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Imaging Ultrasound Scattering targets using Density-Enhanced Chaotic Compressive Sampling

Imaging Ultrasound Scattering Targets using Density-Enhanced...

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Asia-Pacific signal and Information processing Association Annual Summit and conference (APSIPA)

作者： Luong Thi Theu Tran Quang Huy Tran Thi Thuy Quynh Duc-Tan Tran Hoa Binh University Hanoi Vietnam Faculty of Physics Hanoi Pedagogical University 2 Hanoi Vietnam Faculty of Electronics & Telecommunications VNU University of Engineering & Technology Hanoi Vietnam Faculty of Electrical and Electronic Engineering Phenikaa University Hanoi Vietnam

The ultrasound imaging method is a highly promising technique used in various fields. The utilization of ultrasound technique's backscattering could also allow humans to observe objects as small as the size of the incident wavelength. However, the employing backscattering approach requires high computational calculations and poses a significant obstacle to the production of equipment using the ultrasonic method. In this work, we utilized the pseudo-random compression sampling method in the extended DBIM model, which takes tissue density factor. This is necessary because tissue density provides quantitative information about soft tissue and serves as an image contrast source. This method combines pseudo-random measurement and compressed sensing data recovery technique. The simulation results have proved the effectiveness of the proposed CCS-DBIM method. In terms of imaging quality, at compression ratios as low as 0.5, the large difference in imaging quality is evident. After 5 iterations, the normalization error of the DBIM and CCS-DBIM methods was 0.8828 and 0.5281 respectively, i.e. a 40% reduction in normalization errors when the number of measurements was only half of the variables. The disadvantage of the proposed CCS-DBIM method is that the imaging time is significantly greater than that of the DBIM method. However, with today's parallel computing technology, large amounts of data and computational time will no longer be a big problem.

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Search for supersymmetric sleptons and charginos with the ATLAS detector

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JOURNAL OF INSTRUMENTATION 2023年第8期18卷 C08009页

作者： Ballabene, Eric Univ Bologna Dept Phys & Astron Augusto Righi Viale Berti Pichat 6-2 Bologna Italy Ist Nazl Fis Nucl Sez Bologna Viale Berti Pichat 6-2 Bologna Italy

A new search for the electroweak production of supersymmetric particles decaying into two leptons with missing transverse momentum is presented. Two simplified models are considered: direct pair production of sleptons decaying into the lightest neutralinos through leptons of the Standard Model (SM) and direct pair production of the lightest charginos decaying into the lightest neutralinos through bosons of the SM. The analysis targets phase space regions where the difference in mass between the slepton or the lightest chargino and the lightest neutralino is close to or below the mass of the boson. Such regions with compressed mass spectra have not been covered by any searches conducted so far due to the low cross-section of the supersymmetric signal. Therefore, improved analysis strategies are crucial to separate the supersymmetric signal from the SM backgrounds. A search for an excess of same-flavour lepton pairs in opposite-sign lepton events is made in the direct slepton pair production analysis while a machine learning approach using boosted decision trees is exploited in the chargino pair production analysis and considering both the same-flavour and different-flavour channels. No significant excesses over the expected background are observed using proton-proton collisions data collected by the ATLAS experiment at root = 13 TeV and exclusion limits at 95% confidence level are set for each considered model. Exclusion limits are also set for selectrons and smuons separately and portions of the region excluded by the search of smuons pair production are expected to be compatible with the - 2 anomaly for small tan values.

关键词： Analysis and statistical methods data processing methods

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