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arXiv 2020年

作者： Tu, Zhengzheng Li, Zhun Li, Chenglong Lang, Yang Tang, Jin Key Lab of Intelligent Computing and Signal Processing of Ministry of Education Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei230601 China

RGB-thermal salient object detection (SOD) aims to segment the common prominent regions of visible image and corresponding thermal infrared image that we call it RGBT SOD. Existing methods don’t fully explore and exploit the potentials of complementarity of different modalities and multi-type cues of image contents, which play a vital role in achieving accurate results. In this paper, we propose a multi-interactive dual-decoder to mine and model the multi-type interactions for accurate RGBT SOD. In specific, we first encode two modalities into multi-level multi-modal feature representations. Then, we design a novel dual-decoder to conduct the interactions of multi-level features, two modalities and global contexts. With these interactions, our method works well in diversely challenging scenarios even in the presence of invalid modality. Finally, we carry out extensive experiments on public RGBT and RGBD SOD datasets, and the results show that the proposed method achieves the outstanding performance against state-of-the-art algorithms. The source code has been released at: https://***/lz118/Multi-interactiveDual-decoder. Copyright © 2020, The Authors. All rights reserved.

关键词： Image segmentation

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Challenge-aware rgbt tracking

arXiv

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arXiv 2020年

作者： Li, Chenglong Liu, Lei Lu, Andong Ji, Qing Tang, Jin Key Lab of Intelligent Computing and Signal Processing Ministry of Education Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei230601 China

RGB and thermal source data suffer from both shared and specific challenges, and how to explore and exploit them plays a critical role to represent the target appearance in RGBT tracking. In this paper, we propose a novel challenge-aware neural network to handle the modality-shared challenges (e.g., fast motion, scale variation and occlusion) and the modality-specific ones (e.g., illumination variation and thermal crossover) for RGBT tracking. In particular, we design several parameter-shared branches in each layer to model the target appearance under the modality-shared challenges, and several parameter-independent branches under the modality-specific ones. Based on the observation that the modality-specific cues of different modalities usually contains the complementary advantages, we propose a guidance module to transfer discriminative features from one modality to another one, which could enhance the discriminative ability of some weak modality. Moreover, all branches are aggregated together in an adaptive manner and parallel embedded in the backbone network to efficiently form more discriminative target representations. These challenge-aware branches are able to model the target appearance under certain challenges so that the target representations can be learnt by a few parameters even in the situation of insufficient training data. From the experimental results we will show that our method operates at a real-time speed while performing well against the state-of-the-art methods on three benchmark datasets. Copyright © 2020, The Authors. All rights reserved.

关键词： Machine learning

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A conditional-probability zone transformation coding method for categorical features 19

A conditional-probability zone transformation coding method ...

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2019 ACM Turing Celebration Conference - China, ACM TURC 2019

作者： He, Liang Shen, Chao Li, Yun National Key Laboratory of Science and Technology on Blind Signal Processing Chengdu Sichuan China MOE Key Lab for Intelligent Networks and Network Security Xi'an Jiaotong University Shaanxi Xi'an China

ISBN: (纸本)9781450371582

It has been a key issue for solving problems efficiently by machine learning models with code categorical features. The state-of-the-art one-hot coding is a widely accepted method to convert the categorical features into numerical values. However, it attracts a sparse space and meaningless value after coding. We come up with a novel coding method based on conditional probability after dividing the features into zones, which is called Conditional-probability-based Zone Transformation (CZT) coding. CZT coding calculates the conditional probability of each feature, then divides the features into several zones according to the probability and finally codes the features in each zone. We mathematically prove that compared with the state-of-the-art method, CZT coding reduces the code length by at least the mean of feature space and the issue becomes into an easier one after CZT coding for the following machine learning model. Finally, using the same neuron network as the classifier, we compare the performance of CZT coding and one-hot coding by using the titanic dataset, where most of the features are categorical, and the result is that CZT coding makes the classifier performs better both on the accuracy and steadiness. © 2019 Association for Computing Machinery.

关键词： Probability

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Receiver Distortion Nornalization Method for Specific Emitter Identification

Receiver Distortion Nornalization Method for Specific Emitte...

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IEEE Joint International Information technology and Artificial Intelligence Conference (ITAIC)

作者： Mentkai Shi Yuanling Huang Guiliang Wang University School of Information System Engineering PLA Strategic Support Force Information Engineering University Zhengzhou China National Key Laboratory of Science and Technology on Blind Signal Processing Chengdu China

ISBN: (数字)9781728152448

ISBN: (纸本)9781728152455

Specific emitter identification (SEI) is an identification technique that identify the transmitting device with the hardware imperfection. However, the identification features are influenced by the receiver distortions and can be different when the receivers are changed. Previous SEI studies rarely concern the method to normalize the distortions among different receivers. To solve this problem, a SEI receiver normalization method is proposed. In the proposed method, a frequency-domain adaptive equalization system is contained to normalize the distortions in receivers. According to the experiment, this method can effectively eliminate the SEI feature difference caused by receivers. Then the robust of the proposed method is discussed.

关键词： Receivers Adaptive equalizers Distortion Hardware Object recognition Information technology signal to noise ratio

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Geometric Correction of Spaceborne SAR Image Based on DEM and ICESat Database

Geometric Correction of Spaceborne SAR Image Based on DEM an...

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2019 IEEE International Conference on signal, Information and Data processing, ICSIDP 2019

作者： Xing, Chao Wang, Yuekun Wang, Zhibin Liao, Zhiqiang Tian, Feng Suo, Zhiyong Li, Zhenfang National Key Laboratory of Radar Signal Processing Xidian University Xi'an China Nanjing Research Institute of Electronics Technology Nanjing China Beijing Institute of Spacecraft System Engineering Beijing China Sichuan Aerospace Electronic Equipment Research Institute Sichuan China

ISBN: (纸本)9781728123455

The accuracy of geolocation is crucial in the application of remote sensing images. However, the presence of inevitable measurement errors consisting of antenna phase center error, slant range error, and reference height error of ground target, will result in degraded the geolocation accuracy of imaged ground targets. To improve the accuracy of geolocation, the approach of geometric correction for spaceborne SAR image is investigated in this paper. The well-known database contained digital elevation model(DEM) and ICESat data are adopted to estimate and calibrate the aforementioned errors for improving the geolocation accuracy. Validation of the proposed method is demonstrated with GF3 SAR data and high-precision GPS device. © 2019 IEEE.

关键词： Synthetic aperture radar

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A Real-Time Imaging processing Method Based on Modified RMA with Sub-Aperture Images Fusion for Spaceborne Spotlight SAR

A Real-Time Imaging Processing Method Based on Modified RMA ...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Fang Zhou Jun Yang Guangcai Sun Jiajia Zhang School of Computer and Information Hefei University of Technology Hefei China National Key Lab of Radar Signal Processing Xidian University Xi'an China Key Laboratory of Aperture Array and Space Application East China Research Institute of Electronic Engineering Hefei China

ISBN: (数字)9781728163741

ISBN: (纸本)9781728163758

The small satellite SAR has received increasing attention due to its flexibility and low cost. But limited by the data transmission technology, real-time transmission of a large amount of raw data generated by the spaceborne spotlight SAR can hardly be achieved. Meanwhile, the azimuth bandwidth of the spotlight mode is larger than the PRF, resulting in aliasing of the azimuth spectrum. Based on these, this paper proposes a real-time scheme for small satellite SAR with spotlight mode. The method can solve the problem of data transmission and eliminate spectrum overlap in Doppler domain by means of sub-aperture processing. The modified range migration algorithm (RMA) is used to perform range compression and range cell migration compensation (RCMC) on sub-aperture data. Then dechirp in the azimuth time domain is applied to obtain the low-resolution complex image focused in the range time-azimuth frequency domain. Finally, all theected onto a grid image with azimuth interval matching the azimuth full-resolution to complete image fusion.

关键词： Azimuth Spaceborne radar Real-time systems Synthetic aperture radar Doppler effect Imaging Bandwidth

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SAR Image Fusion Based on Mathematical Frame 6

SAR Image Fusion Based on Mathematical Frame

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6th Asia-Pacific Conference on Synthetic Aperture radar, APSAR 2019

作者： Liu, Yang Liao, Guisheng Li, Shidong Nie, Guoli Zeng, Cao Du, Peiju National Laboratory of Radar Signal Processing XiDian University Xi'an China San Francisco State University San Francisco United States Intl. Coop. Base of Integrated Electronic Information System of Ministry of Science and Technology XiDian University Xi'an China Imaging and Target Recognition Overseas Expertises Introduction Center for Discipline Innovation XiDian University Radar Cognitive Detection Xi'an China

ISBN: (纸本)9781728129129

In this paper, we proposed a frame-based approach to synthetic aperture radar (SAR) images fusion. Specifically, the method consists two parts: image fusion preprocessing and fusion reconstruction. Firstly, we fuse a set of low-resolution (LR) SAR images to form an aligned coarse SAR image which still has image blur caused by the point spread function (PSF) by fusion preprocessing. Then Frame fundamental iterative regularization is used to reconstruct high-resolution (HR) SAR image. Both simulated and real SAR images are used to verify the validity of the method. The results show that details and definition of the LR image processed with the method are effectively improved. © 2019 IEEE.

关键词： Synthetic aperture radar

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An Audio-Visual Whisper Database in Chinese 4

An Audio-Visual Whisper Database in Chinese

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2019 4th International Conference on Intelligent Computing and signal processing, ICSP 2019

作者： Zhou, Jian Hu, Yuting Lian, Hailun Pang, Cong Wang, Huabin Tao, Liang Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei AnHui230039 China School of Computer Science and Technology Anhui University Hefei AnHui230601 China

Converting whisper to normal vocalized speech has been a hot research topic in speech signal processing area. A complete and large scale whisper database is a major basis for this task. In this paper, we propose a multimodal whisper database in Chinese mandarin. A total of 103 syllables and 100 sentences were carefully selected. 5 male and 5 female participants pronounced the syllables and sentences in whisper and normal styles respectively, result in 4096 parallel speech utterances and 263, 849 frames of voicing face and lip image sequences. The beginning and ending sample point of each syllable were labeled both for speech signal and voicing face video. The lip region of interest were also extracted and provided in the proposed database. Experiments in various speech conversion tasks in different speech database show the effectiveness of the proposed multimodal whisper speech database. © 2019 IOP Publishing Ltd. All rights reserved.

关键词： Database systems

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Simultaneous Prediction of SOH and RUL for Lithium-ion Batteries Using Transferable Knowledge Sharing Network

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IEEE Transactions on Power Electronics 2025年

作者： Zhong, Kai Liu, Zhihao Tian, Jiaqiang Fan, Chao Li, Mince Wang, Yujie Liu, Xinghua Wang, Peng Anhui University Institutes of Physical Science and Information Technology Key Laboratory of Intelligent Computing and Signal Processing of the Ministry of Education Hefei230601 China Anhui University School of Eelectrical Engineering and Automation Anhui230039 China Hefei Comprehensive National Science Center Institute of Energy Hefei230601 China University of Science and Technology of China Department of Automation Hefei230027 China Xi'an University of Technology School of Electrical Engineering Xi'an710048 China Nanyang Technological University School of Electrical and Electronic Engineering 639798 Singapore

The state of health (SOH) and remaining useful life (RUL) prediction of lithium-ion batteries are critical for the safe operation and maintenance optimization of electrical systems. However, existing methods face challenges in generalization ability and prediction accuracy, including the limitations of single-task learning for joint SOH and RUL prediction, sensitivity to complex operating conditions, and the impact of cross-domain data distribution differences. To address these challenges, this paper proposes a dual-representation transferable knowledge sharing network (DRTKSN) to predict SOH and RUL for batteries. The proposed algorithm is based on the framework of multi task learning (MTL) and Transfer Learning. By extracting and fusing dual-representation data features, the framework effectively captures the latent patterns of battery aging characteristics. It employs parameter and network structure sharing mechanisms to enhance the adaptability and generalization of cross-domain features. The proposed model achieves information collaboration between SOH and RUL tasks through a shared network, effectively reducing computational costs and improving prediction accuracy. Experimental results demonstrate that the proposed framework exhibits excellent robustness and transferability in complex degradation scenarios, significantly outperforming baseline models. © 1986-2012 IEEE.

关键词： Multi-task learning

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Improved Model-Based Channel Tracking for Underwater Acoustic Communications

Improved Model-Based Channel Tracking for Underwater Acousti...

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Sensor Array and Multichannel signal processing Workshop

作者： Yuxing Wang Jun Tao Le Yang Fei Yu Chunguo Li Xiao Han Key Laboratory of Underwater Acoustic Signal Processing of the Ministry of Education School of Information Science and Engineering Southeast University Nanjing China Department of ECE University of Canterbury Christchurch New Zealand Acoustic Science and Technology Laboratory Harbin Engineering University Harbin China

ISBN: (数字)9781728119465

ISBN: (纸本)9781728119472

For tracking time-varying underwater acoustic (UWA) channels, a state-space model based scheme generally outperforms a direct adaptive method. The success for the former depends on the choice of a proper state transition model as well as accurate estimation of the model parameters. The autoregressive (AR) transition model has proven to be useful and the key is to determine the AR coefficients so as to achieve a good channel tracking performance. In this paper, we revisit the problem of determining the AR coefficients via Yule-Walker equation, for which the required autocorrelations are estimated as an ensemble average of estimated channel impulse responses (CIRs). Different from existing scheme employing least squares (LS) channel estimation, we propose to obtain a sequence of CIR estimations via adaptive schemes. The advantage is twofold: first, complexity reduction is achieved and the saving can be significant for a UWA channel with extensive delay spread; second, improved tracking performance is achieved as the implicit assumption by the LS method that the channel remains constant over a block is not required. We also propose to dynamically update the autocorrelations and AR coefficients as the channel tracking progresses, such that the variation in the channel statistical property can be captured. Both simulations and experimental results verify the performance gain of the proposed model-based channel tracking scheme.

关键词： Adaptation models Correlation Computational modeling Channel estimation Estimation Delays Complexity theory

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