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共 390 条记录，以下是21-30 订阅

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Personalized mmWave signal Synthesis for Human Sensing 18th

Personalized mmWave Signal Synthesis for Human Sensing

引用

18th International conference on Wireless Artificial Intelligent Computing Systems and Applications, WASA 2024

作者： Zhang, Jiaqi Liu, Hankai Xie, Xin Tong, Xinyu Liu, Xiulong Li, Keqiu College of Intelligence and Computing Tianjin University Tianjin China

ISBN: (纸本)9783031714665

the excellent sensing performance and privacy-preserving nature of millimeter-wave (mmWave) radar has led to its increasing prominence in the field of human sensing, enabling it to support a wide range of human sensing applications. However, the scarcity of mmWave datasets and the high cost of data collection limit the capability of deep learning. In this paper, we propose PmSyn, a system that synthesizes mmWave signals based on human body model provided by public human motion database and trains neural networks with signatures extracted from synthesized signals to achieve activity recognition and skeleton tracking. We analyze in depth the reflection of the human body model and the propagation principle of the signal. We propose to inject real user’s shape information into the public human body model to endow the synthesized signals with personalization. Experiments show that for activity recognition, PmSyn achieves 93.1%/98.6% accuracy under five activities when trained with synthesized data only/adding a small amount of real data. For skeleton tracking, PmSyn achieves an overall skeleton tracking error of 10.5 cm/6.2 cm when trained with synthesized data only/adding a small amount of real data. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Musculoskeletal system

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Sea Surface small Target Detection using Tri-median Normalized Doppler Spectrum Feature 16

Sea Surface Small Target Detection using Tri-median Normaliz...

引用

16th International conference on signal processing Systems, ICSPS 2024

作者： Guo, Zi-Xun Wu, Kang-Hui Bai, Xiao-Hui Su, Jia Fan, Yi-Fei Gong, Yan-Yun Wang, Ling Shui, Peng-Lang School of Electronics and Information Northwestern Polytechnical University Xi’an710072 China School of Information Xi’an University of Finance and Economics Shaanxi Xi’an710100 China The National Laboratory of Radar Signal Processing Xidian University Xi’an710071 China

ISBN: (纸本)9781510689251

this letter aims to develop a detector for detecting small targets on the sea surface based on feature utilization median normalization. At the long observation time, sea clutter and returns with target show differences of Doppler amplitude spectra (DAS). the median normalized DAS (MNDAS) is used to enhance the Doppler characteristics of returns with target, which is computed from the DAS of the cell under test and its neighboring reference cells. From the MNDAS, three salient features can be extracted for the detection, including the non-zero interval length, vector entropy and global accumulation in the MNDAS. the three features are combined into 3D feature vectors. Considering that the returns with target and their feature vectors cannot be used in the training, one semi-supervised classifier based on the convexhull learning algorithm is built by the feature vectors of sea clutter only. Finally, a new detector based on feature utilization median normalization which can control the false alarm rate precisely is constructed. through the experiments on the IPIX and CSIR radar databases and one offshore experimental data using an unmanned aerial vehicle with a corner reflector, the proposed detector attains competitive performance and lower computation cost with the TF-tri-feature-based detector. © 2025 SPIE.

关键词： Vectors

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EMG signal Classification Using Machine Learning for Augmentative Assistance in Cerebral Palsy 18th

EMG Signal Classification Using Machine Learning for Augment...

引用

18th International KES conference on Agents and Multi-Agent Systems: Technologies and Applications, KES-AMSTA 2024

作者： Ramirez-Lopez, Neri Marquez, Bogart Yail Alanis, Arnulfo Magdaleno-Palencia, Jose Sergio Departamento de Sistemas y Computación Maestría en Tecnologías de Información Tecnológico Nacional de México Campus Tijuana Tijuana Mexico

ISBN: (纸本)9789819764686

this article presents a research project that aims to assist individuals with cerebral palsy to improve their communication abilities and achieve greater autonomy using electromyography (EMG) technology. EMG records the electrical activity of muscles, providing insights into muscle physiology and activation. the proposed system would filter EMG signals, adapt them to the user's needs, store data, and present it through a graphical interface. the system targets those with cerebral palsy, facing challenges performing body movements and communication. the article provides background on EMG and its applications, especially for those with disabilities. It outlines common motor impairments in cerebral palsy. the methodology involves classified EMG signals over time. Results demonstrate the superior performance of random forests over single decision trees for prediction. Overall, the project leverages EMG technology to enhance communication and autonomy for cerebral palsy patients. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Electromyography

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Correlation ϑ Measure Neighborhood Constraint Features Generation small data targets Space Reconstruction 24th

Correlation ϑ Measure Neighborhood Constraint Features Gene...

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24th conference on Man-Machine-Environment System Engineering, MMESE 2024

作者： Zhang, Haijun Chen, Yinghui Guangdong Provincial Key Laboratory of Conservation and Precision Utilization of Characteristic Agricultural Resources in Mountainous Areas Guangdong Meizhou514015 China School of Computing Jiaying University Guangdong Meizhou514015 China School of Mathematics Jiaying University Guangdong Meizhou514015 China

ISBN: (纸本)9789819771387

the process is detailed as follows. Firstly, the small data samples and their space points are obtained, and the quadratic elements are established to generate the projection transformation matrix with surface constraints. Secondly, a variety of construction factor transformations based on small data are obtained. thirdly, the planar points are inversely projected into a sparse stereo point set, based on the F-norm distance domain, the correlation ϑ measure factor is set and the surface is generated by infinite approximation. Finally, the seed points expend to generate dense sets and are iterated into exact models. the experimental results show that this method is effective, especially for small data samples, with low computation and high visibility of reconstructed objects. © Beijing KeCui Man-Machine-Environment System Engineering Technology Research Academy 2024.

关键词： Stereo image processing

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Customer Behavior Recognition Adaptable for Changing targets in Retail Environments 18

Customer Behavior Recognition Adaptable for Changing Targets...

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18th IEEE International conference on Advanced Video and signal Based Surveillance (AVSS)

作者： Wen, Jiahao Abe, Toru Suganuma, Takuo Tohoku Univ Grad Sch Informat Sci 6-3-09 Aramaki Aza AobaAoba ku Sendai 9808579 Japan Tohoku Univ Cybersci Ctr 6-3 Aramaki Aza AobaAoba ku Sendai 9808578 Japan

ISBN: (纸本)9781665463829

Recognizing Customer Behavior (CB) from videos of in-store cameras is important to smart retail solutions. Because of possible changes in retail needs and environments, a high degree of adaptability to different target CBs is required for Customer Behavior Recognition (CBR) methods. Existing CBR methods are mainly machine learning based models due to their remarkable recognition accuracy. However, trained models are not reusable for different target CBs. Consequently, existing CBR methods are hard to adapt to different target CBs because the necessary recollecting data and retraining models. In this paper, we propose a CBR method that recognizes CBs by combinations of primitives, each of which represents an object's motion or objects' relationship. Since primitives can be reused in combinations for various CBs, the proposed method is easily adaptable to changed target CBs. Experiments on two datasets indicate the good adaptability and sufficient recognition accuracy of our method.

关键词： Adaptation models Target recognition Surveillance Training data Symbols Machine learning Cameras

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Research on inertial navigation component measurement and control system 18

Research on inertial navigation component measurement and co...

引用

18th International conference on Computational Intelligence and Security, CIS 2022

作者： Xu, Yuan Tan, Tao Ma, Chao Macao Polytechnic University Macao China Dongguan City University Guangdong Dongguan China Xi'An Technological University Shaanxi Xi'an China

ISBN: (纸本)9798350346275

To improve the test efficiency of inertial navigation components and meet the measurement requirements of multi-type signal output. this paper proposes a dual-mode inertial navigation data acquisition system based on FPGA and USB. the system is based on FGPA EP4CE10F17 and USB CY7C68013 chips. which can realize seamless counting of 48 channels of pulse signals output by 4 channels of inertial navigation components, and can also collect output data of 8 channels of inertial navigation serial ports. FPGA controls data acquisition, USB realizes high-speed data transmission, measurement, and control computer realizes data processing, storage and display. the system has been tested on site for a long time, which has the characteristics of stable operation, high precision and small size. © 2022 IEEE.

关键词： data acquisition

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PERSONALIZED AUTOMATIC SPEECH RECOGNITION TRAINED ON small DISORDERED SPEECH dataSETS 47

PERSONALIZED AUTOMATIC SPEECH RECOGNITION TRAINED ON SMALL D...

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47th IEEE International conference on Acoustics, Speech and signal processing (ICASSP)

作者： Tobin, Jimmy Tomanek, Katrin Google Res Mountain View CA 94043 USA

ISBN: (纸本)9781665405409

this study investigates the performance of personalized automatic speech recognition (ASR) for recognizing disordered speech using small amounts of per-speaker adaptation data. We trained personalized models for 195 individuals with different types and severities of speech impairment with training sets ranging in size from <1 minute to 18-20 minutes of speech data. Word error rate (WER) thresholds were selected to determine Success Percentage (the percentage of personalized models reaching the target WER) in different application scenarios. For the home automation scenario, 79% of speakers reached the target WER with 18-20 minutes of speech;but even with only 3-4 minutes of speech, 63% of speakers reached the target WER. Further evaluation found similar improvement on test sets with conversational and out-of-domain, unprompted phrases. Our results demonstrate that with only a few minutes of recordings, individuals with disordered speech could benefit from personalized ASR.

关键词： automatic speech recognition speech disorders personalized models

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Extracting Sinusoidal Frequency Modulation signal Signature Using Fourier-Bessel Transform and Short-Time Fraction Fourier Transform 6

Extracting Sinusoidal Frequency Modulation Signal Signature ...

引用

6th International conference on Information Communication and signal processing, ICICSP 2023

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

ISBN: (纸本)9798350339994

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. © 2023 IEEE.

关键词： Fourier-Bessel transform (FBT) fraction Fourier transform (FRFT) sinusoidal frequency modulation (SFM) unmanned aerial vehicle (UAV)

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the Simons Observatory: Design, Integration, and Current Status of small Aperture Telescopes 18

The Simons Observatory: Design, Integration, and Current Sta...

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18th International conference on Topics in Astroparticle and Underground Physics (TAUP)

作者： Mangu, Aashrita Corbett, Lance Bhimani, Sanah Carl, Fred Day-Weiss, Samuel DiGia, Brooke Errard, Josquin Galitzki, Nicholas Hazumi, Masashi Henderson, Shawn W. Kabra, Varun Miller, Amber Moore, Jenna Song, Xue Tsan, Tran Wang, Yuhan Zonca, Andrea Univ Calif Berkeley Berkeley CA 94720 USA Yale Univ New Haven CT USA Princeton Univ Princeton NJ 08544 USA Univ Paris Cite CNRS Paris France Univ Texas Austin Austin TX 78712 USA KEK QUP Tsukuba Ibaraki Japan Stanford Univ Stanford CA 94305 USA SLAC Natl Accelerator Lab Menlo Pk CA USA Univ Southern Calif Los Angeles CA 90007 USA Arizona State Univ Tempe AZ USA Univ Calif San Diego La Jolla CA 92093 USA

the Simons Observatory (SO) is a cosmic microwave background (CMB) survey experiment located in the Atacama Desert in Chile at an elevation of 5200 meters, nominally consisting of an array of three 0.42-meter small aperture telescopes (SATs) and one 6-meter large aperture telescope (LAT). SO will make accurate measurements of the CMB temperature and polarization spanning six frequency bands ranging from 27 to 280 GHz, fielding a total of 68,000 detectors covering angular scales between one arcminute to tens of degrees. In this paper, we focus on the SATs, which are tailored to search for primordial gravitational waves, with the primary science goal of measuring the primordial tensor-to-scalar ratio r at a target level of sigma (r) approximate to 0.003. We discuss the design drivers, scientific impact, and current deployment status of the three SATs, which are scheduled to start taking data in the coming year. the SATs aim to map 10% of the sky at a 2 mu K-arcmin noise level observing at mid-frequencies (93/145 GHz), with additional ultra-high-frequency (225/280 GHz) and low-frequency (27/39 GHz) targets to yield galactic foreground-subtracted measurements.

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Attention-based Dual-Branch Network for Micro-Expression Recognition with Global-Local Feature Fusion 18

Attention-based Dual-Branch Network for Micro-Expression Rec...

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18th IEEE International Joint conference on Biometrics, IJCB 2024

作者： Qi, Yupeng Ibrayim, Mayire Hamdulla, Askar Xinjiang University School of Computer Science and Technology Urumqi830017 China Xinjiang Key Laboratory of Signal Detection and Processing Urumqi830017 China

ISBN: (纸本)9798350364132

Micro-expression(ME) is an uncontrollable muscle movement that appears on the face when people try to hide or inhibit their real emotions, which has the problems of short duration, small movement amplitude and uneven distribution. In order to solve the problem of localization and asymmetry in the distribution of ME features, this paper proposes a new two-branch attention network to recognize MEs, which utilizes the attention mechanism to capture global and local ME features. the network is mainly divided into three parts: data preprocessing, ME feature learning and feature fusion classification. First, the data preprocessing first extracts the ME optical flow features and then divides them into four regions, which are used as inputs to the two-branch network respectively. Second, the two-branch network uses the Inception-MSFE global multi-scale feature extraction network incorporating the attention mechanism (CBAM) and the Swin Transformer-based local feature extraction network for feature learning, respectively. Finally, MEs are predicted by fusing global and local features of MEs. Experimental validation is carried out on three datasets, CASME II, SAMM, and SMIC, which proves that Acc, UAR, and UF1 are 0.797, 0.702, and 0.698 on the SAMM dataset, respectively;Acc, UAR, and UF1 are 0.734, 0.723, and 0.729 on SMIC, respectively;and on the CASME II dataset, Acc, UAR and UF1 are 0.865, 0.872, and 0.889, respectively, which are competitive with other state-of-the-art methods. © 2024 IEEE.

关键词： Feature extraction

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