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A binary coding method for half-period sinusoidal stripes in focused projection

MEASUREMENT SCIENCE AND TECHNOLOGY 2025年第2期36卷 025011-025011页

作者： Yan, Fei Xiang, Qianhuai Zhu, Rui Ma, Xingyu Liu, Yinping Nanjing Univ Informat Sci & Technol Sch Automat Nanjing 210044 Peoples R China Jiangsu Collaborat Innovat Ctr Atmospher Environm Nanjing 210044 Peoples R China Nanjing Univ Informat Sci & Technol Sch Emergency Management Nanjing 210044 Peoples R China

During sinusoidal streak projection measurements, the nonlinear responses inherent in projectors and cameras can significantly alter the characteristics of projection and imaging. Traditional techniques require precise out-of-focus levels to achieve accurate depth measurements, which inherently limit the depth range that can be effectively measured. Addressing this limitation, this paper introduces an innovative binarization coding method for half-period sinusoidal stripes in focused projection. Our approach begins with a binarization process that encodes the half-period sinusoidal stripes. We then capture these stripes as they are modulated by the height of the object under test. The subsequent synthesis of the half-period sinusoidal stripes is achieved through a method of superposition calculation. To enhance the experimental procedure, we employ a four-step phase-shifting technique, complemented by an assisted phase unfolding using the gray code method. For the validation of our method, a standard ball with a diameter of 50.8140 mm was measured. The results are compelling, demonstrating a root mean square error of merely 0.0285 mm. Comparative experimental results prove that the proposed method can effectively avoid the influence of nonlinearity, and also obtain high-precision reconstruction results for discontinuous scenes.

关键词： three-dimensional measurement binary coding nonlinearity focusing projection gray code

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A Novel Robust Digital Subarray Partition Algorithm With binary coding

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IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION 2023年第7期71卷 6126-6131页

作者： Zhao, Yang Wu, Jianxin Zhang, Lei Sun Yat Sen Univ Sch Elect & Commun Engn Shenzhen Campus Shenzhen 518107 Peoples R China

In large-scale phased-array radar signal processing, the digital subarray partition technique would reduce the system size and computational complexity effectively. However, increasing intersubarray spacing will lead to the grating lobes, which degrades the beamforming performance. In this communication, by merging the concepts of encoding technology and subarray division, a novel digital subarray structure based on binary coding is proposed to suppress the grating lobes and lower the sidelobe level. The core of the algorithm is optimally accommodating the binary coding so that the array is divided into multiple layers of subarrays, including uniform and interleaved structures with various intersubarray spacing. Thus, the diversity of subarray phase centers is guaranteed, and the periodicity of the phase centers is broken. Simulation results are given to demonstrate the superiorities of the proposed algorithm in beamforming and efficiency performance.

关键词： binary coding digital subarray partition grating lobe

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Spatial-Temporal 3-D Directional binary coding Method for Fringe Projection Profilometry

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2025年 74卷

作者： Wu, Haitao Cao, Yiping Dai, Yongbo Qin, Jiayi Sichuan Univ Dept Optoelect Chengdu 610065 Peoples R China AVIC Chengdu Caic Elect Co Ltd Chengdu 611130 Peoples R China

Fringe projection profilometry (FPP) is a leading optical technique for high-speed and efficient 3-D measurements, crucial in automation for enhancing efficiency, quality, productivity, and reliability. Traditional FPP methods face challenges with limited pattern quantity and coding efficiency. This article introduces a novel spatial-temporal 3-D directional binary coding (STDBC) method, enhancing the conventional binary coding approach by incorporating the temporal dimension. Compared to the traditional four codewords in 1-D (x) and nine codewords in 2-D (x and y) methods, this integration expands codewords in the 3-D (x, y, and t) space, achieving up to 81 ultralarge codewords. To address decoding difficulties caused by synchronization circuit delays or memory optimization, the article introduces a directional coding method to ensure the correct decoding position of coded patterns. Additionally, a region contraction method was developed to suppress the percentage shift problem caused by static defocusing and motion blur, respectively. Under the optimized projection-decoding paradigm, the proposed method can achieve the same reconstruction efficiency as conventional single-frame coded maps with guaranteed reconstruction accuracy. Experimental results demonstrate that this method significantly advances high-precision and high-efficiency 3-D imaging as well as paves the way for further research and practical applications in various automated dynamic measurement environments.

关键词： Encoding Three-dimensional displays Decoding Optical variables measurement Reflective binary codes Phase measurement Optimization Optical imaging Optical distortion Mirrors Automated measurement binary coding fringe projection profilometry (FPP) projection-decoding paradigm

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Multivariate Time Series Retrieval with binary coding from Transformer 29th

Multivariate Time Series Retrieval with Binary Coding from T...

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29th International Conference on Neural Information Processing

作者： Tan, Zehan Zhao, Mingyu Wang, Yun Yang, Weidong Fudan Univ Sch Comp Sci Shanghai Peoples R China Zhuhai Fudan Innovat Inst Zhuhai Guangdong Peoples R China

ISBN: (纸本)9789819916382;9789819916399

Deep learning to binary coding improves multivariate time series retrieval performance by end-to-end representation learning and binary codes from training data. However, it is fair to say that exist deep learning retrieval methods, e.g., Encoder-Decoder based on recurrent or Convolutional neural network, failed to capture the latent dependencies between pairs of variables in multivariate time series, which results in substantial loss of retrieval quality. Furthermore, supervised deep learning to binary coding failed to meet the requirements in practice, due to the lack of labeled multivariate time series datasets. To address the above issues, this paper presents Unsupervised Transformer-based binary coding Networks (UTBCNs), a novel architecture for deep learning to binary coding, which consists of four key components, Transformer-based encoder (T-encoder), Transformer-based decoder (T-decoder), an adversarial loss, and a hashing loss. We employ the Transformer encoder-decoder to encode temporal dependencies, and inter-sensor correlations within multivariate time series by attention mechanisms solely. Meanwhile, to enhance the generalization capability of deep network, we add an adversarial loss based upon improved Wasserstein GAN (WGAN-GP) for real multivariate time series segments. To further improve of quality of binary code, a hashing loss based upon Convolutional encoder (C-encoder) is designed for the output of T-encoder. Extensive empirical experiments demonstrate that the proposed UTBCNs can generate high-quality binary codes and outperform state-of-the-art binary coding retrieval models on Twitter dataset, Air quality dataset, and Quick Access Recorder (QAR) dataset.

关键词： Multivariate Time Series Retrieval binary coding Transformer Adversarial Loss Hashing Loss

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Inverse stimulation enables ultrasonic binary coding for NDE using a custom linear testing system

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ULTRASONICS 2024年 141卷 107341页

作者： Schaefer, Marius W. Fischer, Sarah C. L. Fraunhofer IZFP Campus E3 1 D-66123 Saarbrucken Saarland Germany

Ultrasonic testing is an established method of non-destructive evaluation. The increasing complexity of material systems requires an extension of conventional methods. In related fields such as radar and medical ultrasound, signal optimisation and coded stimulation are successfully used and offer great potential for optimising stateof-the-art measurements and extending applications. In our work, we highlight the difference between using a coded sequence to stimulate an ultrasonic testing system and the actual performance of the digital code to motivate the exploration of inverse stimulation. In order to study inverse stimulation, a custom-built ultrasonic system was designed. As a first step, the transfer function was obtained by testing pulse and chirp stimulation. In the next step, inverse stimulation was performed based on the linear transfer function to engineer the ultrasonic echoes to have shapes similar to the target code. Finally, the auto -correlation function of the ultrasonic echoes resulting from the inverse stimulation is compared with the function of the original code sequence and the agreement of the recorded ultrasonic echo with the spectrally limited code sequence. With this work we propose an integrated, low -voltage, fully linear ultrasonic testing system where the recording of a linear transfer function allows echo engineering even for a binary coded excitation sequence. We have demonstrated that inverse stimulation enables the generation of binary ultrasonic echoes with performance equal to the digital code.

关键词： Ultrasound Inverse stimulation Coded excitation Linear system model binary coding

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SOM-based binary coding for single sample face recognition

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2021年第12期13卷 5861-5871页

作者： Liu, Fan Wang, Fei Ding, Yuhua Yang, Sai Hohai Univ Coll Comp & Informat Nanjing Peoples R China Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing Peoples R China Nantong Univ Sch Elect Engn Nantong Peoples R China

Due to the semantic gap between the insufficient facial features and facial identifying information, the single sample per person (SSPP) problem has always been a significant challenge in the field of facial recognition. To address this problem, this paper proposes a Self-Organizing Map (SOM)-based binary coding (SOM-BC) method, which extracts the middle-level semantic features by merging the SOM network with the Bag-of-Features (BoF) model. First, we extract the local features of the facial images using the SIFT descriptor. Next, inspired by human visual perception, we utilize a SOM neural network to obtain a visual words dictionary capable of reflecting the intrinsic structure of facial features in semantic space. Subsequently, a binary coding method is further proposed to map the local features into semantic space. Finally, we propose a simple but effective similarity measure method for classification. Experimental results on three public databases not only demonstrate the effectiveness of the proposed method, but also its high computational efficiency.

关键词： Single sample Semantic gap BoF SOM binary coding

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Reconfigurable Fixed-Frequency Beam Steering Leaky-Wave Antenna Based on binary coding

Reconfigurable Fixed-Frequency Beam Steering Leaky-Wave Ante...

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IEEE MTT-S International Microwave Workshop Series on Advanced Materials and Processes for RF and THz Applications (IMWS-AMP)

作者： Zhao, Dan Wang, Huiyi Rao, Zhenzhong Nanjing Univ Sci & Technol Zijin Coll Nanjing 210023 Peoples R China Nanjing Univ Sci & Technol Sch Elect & Opt Engn Nanjing 210094 Peoples R China

ISBN: (纸本)9781665428194

This work contributes a novel method to design a reconfigurable circularly polarized leaky-wave antenna (LWA) which can alter the main beam angle by loading binary switches. The states of the shunt stubs that loaded on the proposed leaky-wave antenna unit could be switched by utilizing p-i-n diodes, which can influence the propagation constant of the LWA and thus alter the main beam angle. Main beam positions are steered according to various switching states utilizing an iterative method. For demonstration, an antenna prototype that operates at 3.0 GHz has been designed and fabricated. Experimental results show that the radiation angle varies from - 15 degrees to +13 degrees. A great agreement has been obtained between simulated and measured performances.

关键词： Reconfigurable antenna fixed-frequency leakywave antenna (LWA) circular polarization binary coding

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ADVERSARIAL binary coding FOR EFFICIENT PERSON RE-IDENTIFICATION

ADVERSARIAL BINARY CODING FOR EFFICIENT PERSON RE-IDENTIFICA...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Liu, Zheng Qin, Jie Li, Annan Wang, Yunhong Van Gool, Luc Beihang Univ Beijing Beijing Adv Innovat Ctr Big Data & Brain Comp Beijing 100191 Peoples R China Incept Inst Artificial Intelligence Abu Dhabi U Arab Emirates Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland

ISBN: (纸本)9781538695524

Person re-identification (ReID) aims at associating persons with the same identity across different views/scenes. Most existing methods improve matching accuracy by proposing high-dimensional real-valued features to represent person images comprehensively. However, considering the increasing data scale in real-world applications, the storage and matching efficiencies should be paid attention to as well. In this paper, we propose a binary coding approach for efficient ReID, inspired by the recent advances in adversarial learning. Specifically, the proposed Adversarial binary coding (ABC) implicitly fits the feature distribution to the expected binary one by optimizing the Wasserstein distance. To further enhance the semantic discriminability of binary codes, we seamlessly embed the ABC into a similarity measuring deep neural network. By end-to-end learning the framework, compact and discriminative binary features are generated for efficient and accurate ReID. Extensive experiments on large-scale benchmarks demonstrate the superiority of our approach over the state-of-the-art methods in both efficiency and accuracy.

关键词： Person re-identification binary coding adversarial learning deep learning

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binary coding by Matrix Classifier for Efficient Subspace Retrieval 18

Binary Coding by Matrix Classifier for Efficient Subspace Re...

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8th ACM International Conference on Multimedia Retrieval (ACM ICMR)

作者： Zhou, Lei Bai, Xiao Liu, Xianglong Zhou, Jun Beihang Univ Sch Comp Sci & Engn Beijing Peoples R China Beihang Univ Beijing Adv Innovat Ctr Big Data & Brain Comp Beijing Peoples R China Griffith Univ Sch Informat & Commun Technol Nathan Qld Australia

ISBN: (纸本)9781450350464

Fast retrieval in large-scale database with high-dimensional subspaces is an important task in many applications, such as image retrieval, video retrieval and visual recognition. This can be facilitated by approximate nearest subspace (ANS) retrieval which requires effective subspace representation. Most of the existing methods for this problem represent subspace by point in the Euclidean space or the Grassmannian space before applying the approximate nearest neighbor (ANN) search. However, the efficiency of these methods can not be guaranteed because the subspace representation step can be very time consuming when coping with high dimensional data. Moreover, the transforming process for subspace to point will cause subspace structural information loss which influence the retrieval accuracy. In this paper, we present a new approach for hashing-based ANS retrieval. The proposed method learns the binary codes for given subspace set following a similarity preserving criterion. It simultaneously leverages the learned binary codes to train matrix classifiers as hash functions. This method can directly binarize a subspace without transforming it into a vector. Therefore, it can efficiently solve the large-scale and high-dimensional multimedia data retrieval problem. Experiments on face recognition and video retrieval show that our method outperforms several state-of-the-art methods in both efficiency and accuracy.

关键词： Large-Scale Multimedia Data Retrieval Subspace Retrieval binary coding Hashing Matrix Classifier

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binary coding for Partial Action Analysis with Limited Observation Ratios 30

Binary Coding for Partial Action Analysis with Limited Obser...

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30th IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Qin, Jie Liu, Li Shao, Ling Ni, Bingbing Chen, Chen Shen, Fumin Wang, Yunhong Beihang Univ Beijing Adv Innovat Ctr Big Data & Brain Comp Beijing Peoples R China Beihang Univ State Key Lab Virtual Real Technol & Syst Beijing Peoples R China Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland Malong Technol Co Ltd Shenzhen Peoples R China Univ East Anglia Norwich Norfolk England Shanghai Jiao Tong Univ Shanghai Peoples R China Univ Cent Florida Ctr Res Comp Vis Orlando FL 32816 USA Univ Elect Sci & Technol China Chengdu Sichuan Peoples R China

ISBN: (纸本)9781538604571

Traditional action recognition methods aim to recognize actions with complete observations/executions. However, it is often difficult to capture fully executed actions due to occlusions, interruptions, etc. Meanwhile, action prediction/recognition in advance based on partial observations is essential for preventing the situation from deteriorating. Besides, fast spotting human activities using partially observed data is a critical ingredient for retrieval systems. Inspired by the recent success of data binarization in efficient retrieval/recognition, we propose a novel approach, named Partial Reconstructive binary coding (PRBC), for action analysis based on limited frame glimpses during any period of the complete execution. Specifically, we learn discriminative compact binary codes for partial actions via a joint learning framework, which collaboratively tackles feature reconstruction as well as binary coding. We obtain the solution to PRBC based on a discrete alternating iteration algorithm. Extensive experiments on four realistic action datasets in terms of three tasks (i.e., partial action retrieval, recognition and prediction) clearly show the superiority of PRBC over the state-of-the-art methods, along with significantly reduced memory load and computational costs during the online test.

关键词： binary coding Partial Action Analysis Limited Observation Ratios binary code on line detection Action Search engines Iterative algorithms Occlusion

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