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检索条件"任意字段=18th Conference on Signal and Data Processing of Small Targets"

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Machine learning classifier for marine radar small targets based on normalization features 16

Machine learning classifier for marine radar small targets b...

引用

16th International conference on signal processing Systems, ICSPS 2024

作者： Zhao, Zi-Jian Shui, Peng-Lang National Key Laboratory of Radar Signal Processing Xidian University China

ISBN: (纸本)9781510689251

targets on the sea surface are of great concern, especially the classification of small targets, which is crucial for marine safety. therefore, we propose a machine learning classifier for marine radar small targets based on normalization features, which can effectively classify targets with small radar cross section (RCS) and signal-to-clutter ratio (SCR) on the sea surface. We standardize the radar parameters because different radars have different system parameters, resulting in limited generalization ability of the trained classifier. We extract 9 features from multiple dimensions of the signal such as time domain, frequency domain and time-frequency domain and then classify targets using decision tree (DT) classifier, K-nearest neighbors (KNN) classifier, bootstrap aggregation (Bagging) classifier and adaptive boosting (AdaBoost) classifier. Experimental validation is performed on publicly available IPIX, self-measured radar data and data provided by the South African Council for Scientific and Industrial Research (CSIR), which contains four types of targets: floating ball, floating boats, low-altitude unmanned aerial vehicles (UAVs) and moving speedboats, and the results of the experiments show that the proposed classifier achieves a correct classification accuracy of 0.8537. © 2025 SPIE.

关键词： Marine radar

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LOCAL INFORMATION GUIDED GLOBAL INTEGRATION FOR INFRARED small TARGET DETECTION 49

LOCAL INFORMATION GUIDED GLOBAL INTEGRATION FOR INFRARED SMA...

引用

49th IEEE International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Li, Qiang Mao, Qianchen Liu, Wenjie Wang, Jinbao Wang, Wenmin Wang, Bingshu Northwestern Polytech Univ Sch Software Xian Peoples R China Shenzhen Univ Natl Engn Lab Big Data Syst Comp Technol Shenzhen Peoples R China Macau Univ Sci & Technol Fac Innovat Engn Macau Peoples R China

ISBN: (纸本)9798350344868;9798350344851

Infrared small targets often exhibit small scale and weak semantic features, which makes it a great challenge to their detection. To address this situation, we propose a novel network for infrared small target detection that combines local details information and global contextual information. To preserve the local and high-frequency details present in infrared images, we introduce a High-frequency Aware Encoder. To extract contextual information from multi-scale feature maps, we propose a Multi-scale Context Learning Bottleneck that incorporates contextual information repeatedly and performs cross-level fusion, which enables the recognition of small targets based on their surroundings. Finally, a lightweight Transformer Decoder is employed to restore the feature map, while placing attention on the target pixels. Experimental results on the IRSTD-1k dataset demonstrate that our method outperforms other state-of-the-art approaches.

关键词： infrared small target detection local inductive bias information context and cross-level fusion Swin Transformer

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RF Solder Ball Break Detection in Automotive FMCW Radars Using Isolated targets' Phase 18

RF Solder Ball Break Detection in Automotive FMCW Radars Usi...

引用

18th International conference on Ph.D Research in Microelectronics and Electronics (PRIME)

作者： Ghafi, Esmaeil Kavousi Lang, Oliver Wagner, Matthias Melzer, Alexander Huemer, Mario Johannes Kepler Univ Linz Inst Signal Proc Linz Austria Infineon Technol Linz GmbH & Co KG Linz Austria

ISBN: (纸本)9798350303209

Embedded wafer level ball grid array (eWLB) packaging is a common technology for radar transceiver monolithic microwave integrated circuits (MMICs) in the automotive radar industry. One of the challenging issues with the eWLB packages is the radio frequency solder ball breaks (BBs), due to its adverse effects on the radar performance. In this paper, a method for BB detection (BBD) based on digital signal processing is proposed. this method exploits the collected data from the environment during the sensing task of the radar receiver to detect BBs in real-time. To the best of the authors' knowledge, this work is the first one that addresses the BBD problem solely by digital signal processing, meaning that no additional analog hardware is required for the detection. To validate the performance of the proposed method, simulation results for measured radar data are provided.

关键词： Automotive radar embedded wafer level ball grid array FMCW monolithic microwave integrated circuit solder ball break detection

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small Language Models Improve Giants by Rewriting their Outputs 18

Small Language Models Improve Giants by Rewriting Their Outp...

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18th conference of the European-Chapter of the Association-for-Computational-Linguistics (EACL)

作者： Vernikos, Giorgos Brazinskas, Arthur Adamek, Jakub Mallinson, Jonathan Severyn, Aliaksei Malmi, Eric Ecole Polytech Fed Lausanne Lausanne Switzerland HEIG VD HES SO Yverdon Switzerland Google Res Mountain View CA USA Google Mountain View CA USA

ISBN: (纸本)9798891760882

Despite the impressive performance of large language models (LLMs), they often lag behind specialized models in various tasks. LLMs only use a fraction of the existing training data for in-context learning, while task-specific models harness the full dataset for fine-tuning. In this work, we tackle the problem of leveraging training data to improve the performance of LLMs without fine-tuning. Our approach directly targets LLM predictions without requiring access to their weights. We create a pool of candidates from the LLM through few-shot prompting and we employ a compact model, the LM-corrector (LMCOR), specifically trained to merge these candidates to produce an enhanced output. Our experiments on four natural language generation tasks demonstrate that even a small LMCOR model (250M) substantially improves the few-shot performance of LLMs (62B), matching and even outperforming standard fine-tuning. Furthermore, we illustrate the robustness of LMCOR against different prompts, thereby minimizing the need for extensive prompt engineering. Finally, we show that LMCOR can be seamlessly integrated with different LLMs at inference, serving as a plug-and-play module to improve their performance.

关键词： Computational linguistics

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Multistatic OFDM Radar Fusion of MUSIC-based Angle Estimation 18

Multistatic OFDM Radar Fusion of MUSIC-based Angle Estimatio...

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18th European conference on Antennas and Propagation (EuCAP)

作者： Willame, Martin Yildirim, Hasan Can Storrer, Laurent Horlin, Francois Louveaux, Jerome Catholic Univ Louvain ICTEAM UCLouvain Louvain La Neuve Belgium Univ Libre Bruxelles ULB OPERA Brussels Belgium

ISBN: (纸本)9788831299091;9798350394436

this study investigates the problem of angle-based localization of multiple targets using a multistatic OFDM radar. Although the maximum likelihood (ML) approach can be employed to merge data from different radar pairs, this method requires a high complexity multi-dimensional search process. the multiple signal classification (MUSIC) algorithm simplifies the complexity to a two-dimensional search, but no framework is derived for combining MUSIC pseudo-spectrums in a multistatic configuration. this paper exploits the relationship between MUSIC and ML estimators to approximate the multidimensional ML parameter estimation with a weighted combination of MUSIC pseudo-spectrum. this enables the computation of a likelihood map on which a peak selection is applied for target detection. In addition to reducing the computational complexity, the proposed method relies only on transmitting the estimated channel covariance matrices of each radar pair to the central processor. A numerical analysis is conducted to assess the benefits of the proposed fusion.

关键词： Multistatic data Fusion Maximum Likelihood MUSIC OFDM radar

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18th International conference on Artificial Intelligence and Natural Language processing and International conference on Artificial Intelligence and Internet of things, iSAI-NLP 2023

18th International Conference on Artificial Intelligence and...

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18th International Joint Symposium on Artificial Intelligence and Natural Language processing, iSAI-NLP 2023 and 4th International conference on Artificial Intelligence and Internet of things, AIoT 2023

ISBN: (纸本)9798350371215

the proceedings contain 43 papers. the topics discussed include: solar-powered IoT charging system for ESP32 in organic rice field;STAT intelligence: data management and monitoring platform for BCG economy model;comment usefulness classification on YouTube using artificial neural networks;vehicle telematics system design for real-time applications using mobile networks;using knowledge graphs to enhance keyword search efficiency of the online encyclopedia and taxonomy system of the office of the royal society;decode brain signal into thai word using EEG and L-SVM;cloud stateless server failover prediction using machine learning on proactive system metrics;generative AI for self-healing systems;machine learning to examine the foraging periods of bees;spoof detection using voice contribution on LFCC features and ResNet-34;and Simple2In1: a simple method for fusing two sequences from different captioning systems into one sequence for a small-scale thai dataset.

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GASS: GENERALIZING AUDIO SOURCE SEPARATION WIth LARGE-SCALE data 49

GASS: GENERALIZING AUDIO SOURCE SEPARATION WITH LARGE-SCALE ...

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49th IEEE International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Pons, Jordi Liu, Xiaoyu Pascual, Santiago Serra, Joan Dolby Labs San Francisco CA 94103 USA

ISBN: (纸本)9798350344868;9798350344851

Universal source separation targets at separating the audio sources of an arbitrary mix, removing the constraint to operate on a specific domain like speech or music. Yet, the potential of universal source separation is limited because most existing works focus on mixes with predominantly sound events, and small training datasets also limit its potential for supervised learning. Here, we study a single general audio source separation (GASS) model trained to separate speech, music, and sound events in a supervised fashion with a large-scale dataset. We assess GASS models on a diverse set of tasks. Our strong in-distribution results show the feasibility of GASS models, and the competitive out-of-distribution performance in sound event and speech separation shows its generalization abilities. Yet, it is challenging for GASS models to generalize for separating out-of-distribution cinematic and music content. We also fine-tune GASS models on each dataset and consistently outperform the ones without pre-training. All fine-tuned models (except the music separation one) obtain state-of-the-art results in their respective benchmarks.

关键词： General audio source separation deep learning

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data-Driven Optimization of an Array of Steered Sub-Arrays for Enhanced Fairness in MU-MIMO 18

Data-Driven Optimization of an Array of Steered Sub-Arrays f...

引用

18th European conference on Antennas and Propagation (EuCAP)

作者： Kanters, Noud Glazunov, Andres Alayon Univ Twente Dept Elect Engn Enschede Netherlands Linkoping Univ Dept Sci & Technol Norrkoping Campus Linkoping Sweden

ISBN: (纸本)9788831299091;9798350394436

this paper investigates arrays of steered sub-arrays (SAs) for multi-user MIMO applications. We optimize SA steering angles based on rate-based and gain-based objective functions which naturally balance aggregate system performance and user fairness. Numerical simulations show that, compared to a full-digital array with the same number of RF-chains, rate-optimized arrays increase the array gain in small sector scenarios and compensate for low element gains in large sector scenarios. An array with 4-element SAs achieves the highest rate at all sectors at both low and high signal-to-noise ratios (SNRs). Improvements vary from 2% (120-degree sector, high SNR) to 113% (30-degree sector, low SNR). Hence, adjusting the steering angles to the long-term channel statistics yields significant rate improvements if users are confined to a small sector, without sacrificing performance if this is not the case. Similar results are obtained for gain-based optimized arrays, which is important and convenient for array design.

关键词： Antenna Arrays Multi-User MIMO Optimization

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Infrared small target detection based on long-short term frame difference algorithm(LSTFD) 6

Infrared small target detection based on long-short term fra...

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6th International conference on Intelligent Control, Measurement and signal processing, ICMSP 2024

作者： Zhu, Bing Shi, Jiahui Yu, Ruixing Gao, Weixin Wang, Siyu Xi'an Shiyou University School of Electronic Engineering Xi'an China Northwestern Polytechnical University School of Astronautics Xi'an China

ISBN: (纸本)9798331516550

According to the characteristic that the position change of small infrared target in sequence images is more obvious than background and noise, an infrared small target detection algorithm based on multi-frame time domain information is proposed, called the Long-Short Term Frame Difference (LSTFD) algorithm. the algorithm first adopts the SURF (Speeded Up Robust Feature) alignment strategy to eliminate the relative motion of background pixels caused by camera shake and achieve global motion compensation. then, the image sequence is constructed based on the current frame, and the computational results of long-interval difference and short-interval difference are combined to detect the infrared small targets. the experimental results show that the LSTFD algorithm can accurately detect moving targets with high detection accuracy and low false alarm rate under the condition of low signal-to-noise ratio. And the average detection rate can reach over 90% on five sets of experimental data. © 2024 IEEE.

关键词： Motion compensation

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Long-time Adaptive Coherent Detection of small targets in Sea Clutter using Inversion of Block Tridiagonal Covariance Matrices 15

Long-time Adaptive Coherent Detection of Small Targets in Se...

引用

15th International conference on signal processing Systems, ICSPS 2023

作者： Zhang, Xiaojun Xue, Yufan Shui, Penglang National Key Laboratory of Radar Signal Processing Xidian University China

ISBN: (纸本)9781510675056

Low-velocity small target detection in maritime surveillance radars is always a challenging task. Low signal-to-clutter ratio requires long-time coherent integration to obtain enough gain of target returns. However, long-time coherent integration encounters insufficient secondary data due to the spatial inhomogeneity of sea clutter. In this paper, considering the decorrelation time of speckle component of sea clutter short up to a dozen of milliseconds, the spherical invariant random vector (SIRV) model with block tridiagonal speckle covariance matrix and the inverse Gamma distributed texture is proposed to model sea clutter sequences in several tenths of a second. In this model, a long-time adaptive generalized likelihood ratio test with linear threshold detector (GLRT-LTD) is constructed. Owing to the block tridiagonal structure of speckle covariance matrices, the adaptive detection requires much less reference cells for speckle covariance matrix estimation and much lower computational cost for its inversion. the proposed detector is verified by an X-band CSIR radar database with low-velocity small boats as test targets. the experimental result shows that it obtains competitive detection performance in comparison with the state-of-the-art detectors. © 2024 SPIE.

关键词： Covariance matrix

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