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Note on a New Construction of Kantorovich Form q-Bernstein Operators Related to Shape Parameter λ

Computer Modeling in Engineering & Sciences 2022年第3期130卷 1479-1493页

作者： Qingbo Cai Resat Aslan Fujian Provincial Key Laboratory of Data-Intensive Computing Key Laboratory of Intelligent Computing and Information ProcessingSchool of Mathematics and Computer ScienceQuanzhou Normal UniversityQuanzhou362000China Department of Mathematics Faculty of Sciences and ArtsHarran UniversitySanlıurfa63300Turkey

The main purpose of this paper is to introduce some approximation properties of a Kantorovich kind q-Bernstein operators related to B′ezier basis functions with shape parameterλ∈[−1,1].Firstly,we compute some basic results such as moments and central moments,and derive the Korovkin type approximation theorem for these ***,we estimate the order of convergence in terms of the usual modulus of continuity,for the functions belong to Lipschitz-type class and Peetre’s K-functional,***,with the aid of Maple software,we present the comparison of the convergence of these newly defined operators to the certain function with some graphical illustrations and error estimation table.

关键词： q-calculus (λ,q)-Bernstein polynomials order of convergence Lipschitz-type function Peetre’s K-functional

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The application of optimized phase to generate holographic stereogram for a dual-view 3D display 3

The application of optimized phase to generate holographic s...

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3rd International computing Imaging Conference, CITA 2023

作者： He, Daofeng Wang, Bin Wang, Anlin Cheng, Hong Shen, Chuan Zhang, Cheng Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Anhui University Hefei230601 China

ISBN: (纸本)9781510671409

Holographic stereogram, as a marriage of holography and multi-view display technique, benefits of these two 3D display technologies. Recent research about the dual-view holographic stereogram 3D display technology shows an attractive way to present the visual information. However, due to the utilization of the random phase to smooth the spatial frequency spectrum, the troublesome of the quality of reconstructed images from holographic stereograms are those images containing speckle noise. In this paper, inspiring by the works about the computer-generated holograms, we introduced an optimized phase for the generation of dual-view holographic stereogram based on integral imaging to get a better suppression effect of speckle noise. The key goal is to determine the complex amplitude employed to calculate the holographic stereogram. Firstly, the pickup process of multiple parallax images corresponding to the left-view field and right-view field are finished by the virtual cameras. Then, the optimized phase is generated by the full-support iterative process, whose phase range is limited in the first time. Next, instead of the random phase, it is combined with corresponding parallax image to yield the complex amplitude. Holographic stereogram plane is segmented into many element holograms and each of them is generated by the Fourier transforming of the above complex amplitude. The phase of each complex element holograms is retained to generated the phase-only holographic stereogram, which have higher diffraction efficiency and no conjugate image than amplitude type holographic stereogram. Finally, the two set of holograms are combined together to reconstruct the corresponding dual-view. The optimized phase as the initial phase is demonstrated by simulations, the comparison of the simulated reconstructions shows that the method of this paper could improve the reproduction quality of the dual-view holographic stereogram, that extended its applicability and practicality. © 20

关键词： Holograms

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Design of a Miniaturised Wide-Stopband SIW Filter

Design of a Miniaturised Wide-Stopband SIW Filter

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2023 International Applied Computational Electromagnetics Society Symposium, ACES-China 2023

作者： Li, Chen Li, Minquan Qu, Xin Xiao, Shuang Anhui University Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Hefei230601 China

ISBN: (纸本)9781733509657

In this paper, we propose a miniaturized wide-stopband SIW dual-band filter. SIW limits the propagation of electromagnetic waves and thus enables the miniaturization of the filter. A passband below the waveguide cutoff frequency is generated by etching the CSRR structure on the surface of the SIW, while a modified CSRR-DGS structure is etched on the backside of the SIW structure, and the other passband is generated by coupling the CSRR etched on the surface of the SIW to the ground. In addition, the CSRR-DGS structure broadens the stopband without increasing the filter size. Through the circuit equivalent analysis and parameter optimization of the filter structure, the final simulation results are as follows: the filter has a passband of 3.5 GHz (WIMAX) and 5.2 GHz (WLAN), the 3dB bandwidths of the two bands are 130MHz and 180MHz respectively, the insertion loss is 0.5 dB and 0.65 dB, and the size is $0.18λ{\mathrm{g}}× 0.27λ{\mathrm{g}}$. Overall, it achieves high performance and also has a wide range of applications. © 2023 Applied Computational Electromagnetics Society (ACES).

关键词： Wireless local area networks (WLAN)

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A Bandstop Filter Based on Modified Defected Ground Structure with Compact Size

A Bandstop Filter Based on Modified Defected Ground Structur...

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2023 International Applied Computational Electromagnetics Society Symposium, ACES-China 2023

作者： Zhang, Boyan Li, Minquan Xiao, Shuang Li, Chen Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Hefei230601 China

ISBN: (纸本)9781733509657

This paper reports a compact size bandstop filter using Defected Ground Structure (DGS). The proposed filter consists of squared shaped defect and U-shaped defect sticked on the ground plane. This design is based on a parallel LC circuit model and designed to work at 1 GHz. The filter is implemented on a FR-4 dielectric plate and its size is 0.049λg× 0.049λg (where λg is the wavelength in waveguide). © 2023 Applied Computational Electromagnetics Society (ACES).

关键词： Notch filters

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Iterative phase hologram generation with adaptive weighted constraints for color holographic display 13

Iterative phase hologram generation with adaptive weighted c...

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Holography, Diffractive Optics, and Applications XIII 2023

作者： Ding, YuXin Wang, Bin Wang, AnLin Shen, Chuan Cheng, Hong Zhang, Cheng Wei, Sui Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Anhui University Hefei230601 China

ISBN: (纸本)9781510667853

Compared with monochromatic holographic displays, color holographic displays have rich depth and color information, which can give observers a more pleasant visual effect. Inspiring by the adaptive weighted GS method, we introduced an optimized iterative method based on exponential weighted constraint to calculate phase-only holograms of color images. After channel separation of the target image to obtain each component image, the input complex amplitude is formed with quadratic phase and iterated. During the iteration, subtract the amplitude of the reconstructed image from the amplitude of the target image to construct an exponential constraint. This exponential constraint is applied to the signal area of the image plane. After calculating the three-channel phase hologram, a color holographic reconstruction can be obtained using the time multiplexing method. Simulated and optical experiments verify the effectiveness of this method. © 2023 SPIE.

关键词： Holograms

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Exploring the Compositional Deficiency of Large Language Models in Mathematical Reasoning Through Trap Problems

Exploring the Compositional Deficiency of Large Language Mod...

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2024 Conference on Empirical Methods in Natural Language processing, EMNLP 2024

作者： Zhao, Jun Tong, Jingqi Mou, Yurong Zhang, Ming Zhang, Qi Huang, Xuanjing School of Computer Science Fudan University China Shanghai Key Laboratory of Intelligent Information Processing Fudan University China

ISBN: (纸本)9798891761643

Human cognition exhibits systematic compositionality, the algebraic ability to generate infinite novel combinations from finite learned components, which is the key to understanding and reasoning about complex logic. In this work, we investigate the compositionality of large language models (LLMs) in mathematical reasoning. Specifically, we construct a new dataset MATHTRAP‡ by introducing carefully designed logical traps into the problem descriptions of MATH and GSM8K. Since problems with logical flaws are quite rare in the real world, these represent "unseen" cases to LLMs. Solving these requires the models to systematically compose (1) the mathematical knowledge involved in the original problems with (2) knowledge related to the introduced traps. Our experiments show that while LLMs possess both components of requisite knowledge, they do not spontaneously combine them to handle these novel cases. We explore several methods to mitigate this deficiency, such as natural language prompts, few-shot demonstrations, and fine-tuning. Additionally, we test the recently released OpenAI o1 model and find that human-like 'slow thinking' helps improve the compositionality of LLMs. Overall, systematic compositionality remains an open challenge for large language models. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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MDFNet: Multimodal Feature Decomposition and Fusion Network for Multimodal Remote Sensing Image Semantic Segmentation 2

MDFNet: Multimodal Feature Decomposition and Fusion Network ...

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2nd IEEE International Conference on Signal, information and Data processing, ICSIDP 2024

作者： Wei, Tianyu Chen, He Wang, Jue Liu, Wenchao Beijing Institute of Technology National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Beijing China

ISBN: (纸本)9798331515669

The semantic segmentation of multimodal remote sensing (RS) images, utilizing optical and synthetic aperture radar (SAR) data, has raised attention in recent studies. Advanced studies adaptively modeled and fused modality-share and modality-specific information beneficial for segmentation and achieved competitive performance. However, in challenging scenarios such as low image contrast or blurred textures in optical images and speckle noises or foreshortening in SAR images, modality-specific information may lost. This study proposed a multimodal feature decomposition and fusion network (MDFNet) designed for multimodal RS image semantic segmentation using optical and SAR images. By decomposing multimodal features into modality-share and modality-difference features and applying gradient descent on modality-difference features, modality-specific information beneficial for segmentation can be retained. Specifically, we designed a MDF decoder with MDF blocks. MDF block maps multimodal features into the same feature space and calculates the difference of multimodal features to obtain modality-share and modality-difference features respectively, then fuses these features. MDF decoder optimizes modality-difference features by gradient descent using adaptive modeling, thereby retaining modality-specific information that is beneficial for segmentation. Comprehensive experiments conducted on the DFC20 dataset demonstrated that the proposed MDFNet surpasses representative methods. © 2024 IEEE.

关键词： Semantic Segmentation

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Image Editing based on Diffusion Model for Remote Sensing Image Change Captioning 2

Image Editing based on Diffusion Model for Remote Sensing Im...

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2nd IEEE International Conference on Signal, information and Data processing, ICSIDP 2024

作者： Cai, Miaoxin Chen, He Li, Can Gan, Shuyu Chen, Liang Zhuang, Yin Beijing Institute of Technology National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Beijing China

ISBN: (纸本)9798331515669

Remote Sensing Image Change Captioning (RSICC) is a task that utilizes natural language to describe changes in remote sensing images of the same area captured at different times. However, the significant temporal intervals between multi-temporal images, the infrequency of observable changes, and the limitations on observation locations make it difficult to acquire and annotate a large and diverse dataset for analyzing change in multi-temporal images. The scarcity of labeled data hinders the training of RSICC models, leading to poor generalization. Compared to annotated registered bi-temporal image, single-temporal data is easier to obtain. Therefore, to tackle the issue of poor generalization of RSICC models under limited annotated sample conditions, a text-guided image pairs generation (TGIPG) method is proposed to create synthetic RSICC datasets from single-temporal data and randomly sampled text instructions via a diffusion-based controllable image editing model. This approach generates more valid pairs of multi-temporal samples to address the constraints of limited change information. Specifically, this method utilizes language instructions to introduce change information into the diffusion process, gradually transforming the pre-phase image into the post-phase image. Our experiments on the LEVIR-CC dataset show that synthetic data can significantly enhance the performance of any RSICC model, with a restricted number of training samples, by employing this plug-and-play TGIPG method. © 2024 IEEE.

关键词： Spatio-temporal data

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Efficient On-board Remote Sensing Scene Classification Using FPGA With Ternary Weight 2

Efficient On-board Remote Sensing Scene Classification Using...

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2nd IEEE International Conference on Signal, information and Data processing, ICSIDP 2024

作者： Qi, Guijie Qiao, Tingting Yu, Jingchi Xie, Yizhuang Beijing Institute of Technology National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Beijing China

ISBN: (纸本)9798331515669

Remote Sensing Scene Classification (RSSC) is essential for applications such as environmental monitoring and catastrophe management, which often have stringent time constraints requiring real-time processing. On-board processing significantly enhances real-time performance but is challenged by limited computational resources and strict power requirements. To address these challenges, this study proposes an FPGA-based accelerator designed for Ternary Weight Networks (TWNs), which use ternary values {+1, 0, -1} for weights. By adopting TWNs, the multiplication operations in convolution are eliminated, resulting in a significant reduction in computing and power needs. Experimental results show that TWNs significantly reduce network parameters while retaining classification accuracy comparable to Full-precision Weight Networks (FPWNs). The FPGA-based accelerator achieves an energy-efficiency ratio of 434.11 GOP/W, outperforming most existing CNN accelerators, hence meeting the requirements for on-board RSSC. © 2024 IEEE.

关键词： Image thinning

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Spatiality-Aligned Graph Convolution Hashing for Unsupervised Cross-Modal Retrieval 2

Spatiality-Aligned Graph Convolution Hashing for Unsupervise...

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2nd IEEE International Conference on Signal, information and Data processing, ICSIDP 2024

作者： Li, Fang Dai, Liulian Lan, Rushi Yang, Rui Luo, Xiaonan Guangxi Key Laboratory of Image and Graphic Intelligent Processing School of Computer Science and Information Security Guilin541004 China

ISBN: (纸本)9798331515669

Unsupervised cross-modal retrieval leveraging hash learning has attracted a lot of interest from academics, primarily due to its minimal storage requirements, swift retrieval speeds, and label-free nature, making it a prime candidate for real-world applications. Nevertheless, the inherent "heterogeneous differences"and "semantic gap"within cross-modal data features pose significant challenges. Despite various attempts by scholars to address these issues, the existing solutions often fall short in terms of effectiveness. This can be attributed to several reasons: Firstly, image features, in contrast to text features, possess a plethora of attributes, making it challenging to extract features that possess strong expressive capabilities. Secondly, each modality of data exhibits not only inter-node correlations but also intricate relationships between individual nodes and their neighboring nodes. Consequently, a straightforward semantic correlation matrix is inadequate in capturing the intricate structural relationships within the data. Thirdly, the neglect of internal structural associations results in insufficient representation capabilities of binary hash codes in the Hamming space, hampering the learning of effective hash functions. To address these limitations, we introduce a Spatiality-Aligned Graph Convolution Hashing (SAGCH) algorithm. Extensive experiments demonstrate that our SAGCH method excels in both "image-to-text retrieval"and "text-to-image retrieval"tasks. © 2024 IEEE.

关键词： Semantics

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