Relevant bibliographies by topics / Circular Convolution

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers
  5. Reports

Academic literature on the topic 'Circular Convolution'

Author: Grafiati
Published: 3 June 2025
Last updated: 13 July 2025

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Journal articles on the topic "Circular Convolution"

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Cariow, Aleksandr, and Janusz P. Paplinski. "Algorithmic Structures for Realizing Short-Length Circular Convolutions with Reduced Complexity." Electronics 10, no. 22 (2021): 2800. http://dx.doi.org/10.3390/electronics10222800.

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A set of efficient algorithmic solutions suitable to the fully parallel hardware implementation of the short-length circular convolution cores is proposed. The advantage of the presented algorithms is that they require significantly fewer multiplications as compared to the naive method of implementing this operation. During the synthesis of the presented algorithms, the matrix notation of the cyclic convolution operation was used, which made it possible to represent this operation using the matrix–vector product. The fact that the matrix multiplicand is a circulant matrix allows its successful factorization, which leads to a decrease in the number of multiplications when calculating such a product. The proposed algorithms are oriented towards a completely parallel hardware implementation, but in comparison with a naive approach to a completely parallel hardware implementation, they require a significantly smaller number of hardwired multipliers. Since the wired multiplier occupies a much larger area on the VLSI and consumes more power than the wired adder, the proposed solutions are resource efficient and energy efficient in terms of their hardware implementation. We considered circular convolutions for sequences of lengths N= 2, 3, 4, 5, 6, 7, 8, and 9.
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Schluttenhofer, Sandra, and Jan Johannes. "Adaptive Minimax Testing for Circular Convolution." Mathematical Methods of Statistics 29, no. 2 (2020): 106–33. http://dx.doi.org/10.3103/s1066530720020039.

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MILENKOVIC, VICTOR, and ELISHA SACKS. "A MONOTONIC CONVOLUTION FOR MINKOWSKI SUMS." International Journal of Computational Geometry & Applications 17, no. 04 (2007): 383–96. http://dx.doi.org/10.1142/s0218195907002392.

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We present a monotonic convolution for planar regions A and B bounded by line and circular arc segments. The Minkowski sum equals the union of the cells with positive crossing numbers in the arrangement of the convolution, as is the case for the kinetic convolution. The monotonic crossing number is bounded by the kinetic crossing number, and also by the maximum number of intersecting pairs of monotone boundary chains, which is typically much smaller. We give a Minkowski sum algorithm based on the monotonic convolution. The running time is O (s + nα(n) log (n) + m2), versus O (s + n2) for the kinetic algorithm, with s the input size and with n and m the number of segments in the kinetic and monotonic convolutions. For inputs with a bounded number of turning points and inflection points, the running time is O (sα(s) log s), versus Ω(s2) for the kinetic algorithm. The monotonic convolution is 37% smaller than the kinetic convolution and its arrangement is 62% smaller based on 21 test pairs.
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Haneda, Yoichi, Ken'ichi Furuya, Shoichi Koyama, Kenta Niwa, and Kazunori Kobayashi. "Sound field simulation for circular array based on spatial circular convolution." Acoustical Science and Technology 35, no. 2 (2014): 99–107. http://dx.doi.org/10.1250/ast.35.99.

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Zheng, Rong. "Design of large point circular convolution algorithm." Computer Standards & Interfaces 20, no. 6-7 (1999): 459. http://dx.doi.org/10.1016/s0920-5489(99)90986-8.

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Lin, Cong, Zhoujian Chen, Yiquan Huang, Haoyu Jiang, Wencai Du, and Qiong Chen. "A Deep Neural Network Based on Circular Representation for Target Detection." Journal of Sensors 2022 (April 25, 2022): 1–10. http://dx.doi.org/10.1155/2022/4437446.

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Convolutional neural network (CNN) model based on deep learning has excellent performance for target detection. However, the detection effect is poor when the object is circular or tubular because most of the existing object detection methods are based on the traditional rectangular box to detect and recognize objects. To solve the problem, we propose the circular representation structure and RepVGG module on the basis of CenterNet and expand the network prediction structure, thus proposing a high-precision and high-efficiency lightweight circular object detection method RebarDet. Specifically, circular tubular type objects will be optimized by replacing the traditional rectangular box with a circular box. Second, we improve the resolution of the network feature map and the upper limit of the number of objects detected in a single detect to achieve the expansion of the network prediction structure, optimized for the dense phenomenon that often occurs in circular tubular objects. Finally, the multibranch topology of RepVGG is introduced to sum the feature information extracted by different convolution modules, which improves the ability of the convolution module to extract information. We conducted extensive experiments on rebar datasets and used AB-Score as a new evaluation method to evaluate RebarDet. The experimental results show that RebarDet can achieve a detection accuracy of up to 0.8114 and a model inference speed of 6.9 fps while maintaining a moderate amount of parameters, which is superior to other mainstream object detection models and verifies the effectiveness of our proposed method. At the same time, RebarDet’s high precision detection of round tubular objects facilitates enterprise intelligent manufacturing processes.
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Lin, Cong, Zhoujian Chen, Yiquan Huang, Haoyu Jiang, Wencai Du, and Qiong Chen. "A Deep Neural Network Based on Circular Representation for Target Detection." Journal of Sensors 2022 (April 25, 2022): 1–10. http://dx.doi.org/10.1155/2022/4437446.

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Convolutional neural network (CNN) model based on deep learning has excellent performance for target detection. However, the detection effect is poor when the object is circular or tubular because most of the existing object detection methods are based on the traditional rectangular box to detect and recognize objects. To solve the problem, we propose the circular representation structure and RepVGG module on the basis of CenterNet and expand the network prediction structure, thus proposing a high-precision and high-efficiency lightweight circular object detection method RebarDet. Specifically, circular tubular type objects will be optimized by replacing the traditional rectangular box with a circular box. Second, we improve the resolution of the network feature map and the upper limit of the number of objects detected in a single detect to achieve the expansion of the network prediction structure, optimized for the dense phenomenon that often occurs in circular tubular objects. Finally, the multibranch topology of RepVGG is introduced to sum the feature information extracted by different convolution modules, which improves the ability of the convolution module to extract information. We conducted extensive experiments on rebar datasets and used AB-Score as a new evaluation method to evaluate RebarDet. The experimental results show that RebarDet can achieve a detection accuracy of up to 0.8114 and a model inference speed of 6.9 fps while maintaining a moderate amount of parameters, which is superior to other mainstream object detection models and verifies the effectiveness of our proposed method. At the same time, RebarDet’s high precision detection of round tubular objects facilitates enterprise intelligent manufacturing processes.
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Loulou, AlaaEddin, Juha Yli-Kaakinen, and Markku Renfors. "Advanced Low-Complexity Multicarrier Schemes Using Fast-Convolution Processing and Circular Convolution Decomposition." IEEE Transactions on Signal Processing 67, no. 9 (2019): 2304–19. http://dx.doi.org/10.1109/tsp.2019.2904015.

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Garg, H. K. "Skew circular convolution algorithms over finite integer rings." Electronics Letters 32, no. 24 (1996): 2213. http://dx.doi.org/10.1049/el:19961503.

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Lohar, G., D. P. Mukherjee, and D. Dutta Majumder. "On a decomposition of 2-D circular convolution." Pattern Recognition Letters 13, no. 10 (1992): 701–6. http://dx.doi.org/10.1016/0167-8655(92)90099-l.

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Dissertations / Theses on the topic "Circular Convolution"

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Christoffersson, Anton. "Real-time Depth of Field with Realistic Bokeh : with a Focus on Computer Games." Thesis, Linköpings universitet, Informationskodning, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-163080.

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Depth of field is a naturally occurring effect in lenses describing the distance between theclosest and furthest object that appears in focus. The effect is commonly used in film andphotography to direct a viewers focus, give a scene more complexity, or to improve aes-thetics. In computer graphics, the same effect is possible, but since there are no naturaloccurrences of lenses in the virtual world, other ways are needed to achieve it. There aremany different approaches to simulate depth of field, but not all are suited for real-time usein computer games. In this thesis, multiple methods are explored and compared to achievedepth of field in real-time with a focus on computer games. The aspect of bokeh is alsocrucial when considering depth of field, so during the thesis, a method to simulate a bokeheffect similar to reality is explored. Three different methods based on the same approachwas implemented to research this subject, and their time and memory complexity weremeasured. A questionnaire was performed to measure the quality of the different meth-ods. The result is three similar methods, but with noticeable differences in both quality andperformance. The results give the reader an overview of different methods and directionsfor implementing it on their own, based on which requirements suits them.
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Su, Ching-Yao, and 蘇敬堯. "Decoding the Tail-Biting Convolutional Codes with Pre-Decoding Circular Shift." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/60301194371731622238.

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碩士<br>國立交通大學<br>電信工程系所<br>97<br>By noting that the convolutional tail-biting code (CTBC) can be represented by a circular-free trellis structure, pre-decoding circular shift (together with the post-decoding shift back) will not change its decoding procedure. Simulations in the literature have already shown that the decoding performance as well as decoding complexity can be apparently improved by a proper pre-decoding circular shift. In this thesis, we proposed the shifting Viterbi algorithm and the shifting circular decoding algorithm using equal-weight and unequal-weight pre-decoding shift methods. We then show empirically that our methods can reduce the forward and backward training window sizes required for near maximum-likelihood performance. We also provide an intuitive analytical approach to determine the training window sizes required for near optimal performance.
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Book chapters on the topic "Circular Convolution"

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Vo, Xuan-Thuy, Duy-Linh Nguyen, Adri Priadana, and Kang-Hyun Jo. "Dynamic Circular Convolution for Image Classification." In Communications in Computer and Information Science. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-4914-4_4.

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Zhang, Haokui, Wenze Hu, and Xiaoyu Wang. "ParC-Net: Position Aware Circular Convolution with Merits from ConvNets and Transformer." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-19809-0_35.

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Moreno, Carlos D., Pilar Martínez, Francisco J. Bellido, Javier Hormigo, Manuel A. Ortiz, and Francisco J. Quiles. "Convolution Computation in FPGA Based on Carry-Save Adders and Circular Buffers." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-32304-1_20.

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Tajanpure, Rupali, and Akkalakshmi Muddana. "Overlapped Circular Convolution Based Feature Extraction Algorithm for Classification of High Dimensional Datasets." In Communications in Computer and Information Science. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-81462-5_20.

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Huang, Rujin, Genhou Wang, Jiahao Tian, and Quanping Zhang. "Open-Pit Image Detection Based on Improved Faster – RCNN." In Lecture Notes in Civil Engineering. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-8401-1_23.

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AbstractRemote sensing image open-pit mine monitoring is usually affected by speckle noise, multi-scale and other factors due to the limitations of landform and other conditions, and faces the problem of low availability of monitoring area effect. Therefore, this paper introduces an improved regional convolution neural network method. The network thinning process and the improved conditional random field are proposed respectively as a circular neural network, and the accurate classification and coordinate positioning of the target are completed by establishing the network thinning process in the output part to increase the classification and regression thinning of the target features; Remove the influence of color vector for the fully connected conditional random field and improve it and construct a recurrent neural network for the conditional random field. The experiment shows that the target detection accuracy of the improved Faster-RCNN network has achieved a breakthrough in the mine image detection details, with the overall recognition accuracy of 94.67% and the detection speed of 24.03 fps. Compared with Faster-RCNN, SPP-NET and YOLOV7, it effectively improves the accuracy and provides technical support for ecological restoration monitoring of open pit.
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Averbuch, Amir Z., Pekka Neittaanmaki, and Valery A. Zheludev. "Mixed Circular Convolutions and Zak Transforms." In Spline and Spline Wavelet Methods with Applications to Signal and Image Processing. Springer Netherlands, 2014. http://dx.doi.org/10.1007/978-94-017-8926-4_3.

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Teo, Choon Hui, and Yong Haur Tay. "Invariant Object Recognition Using Circular Pairwise Convolutional Networks." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/978-3-540-36668-3_167.

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Xu, Xuran, Tong Zhang, Chunyan Xu, and Zhen Cui. "Circulant Tensor Graph Convolutional Network for Text Classification." In Lecture Notes in Computer Science. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-02375-0_3.

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Yang, Dengjie, Junjie Cao, YingZhi Ma, Jiawei Yu, Shikun Jiang, and Liang Zhou. "Circular FC: Fast Fourier Transform Meets Fully Connected Layer for Convolutional Neural Network." In Communications in Computer and Information Science. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-8126-7_38.

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Rüttgers, Mario, Seong-Ryong Koh, Jenia Jitsev, Wolfgang Schröder, and Andreas Lintermann. "Prediction of Acoustic Fields Using a Lattice-Boltzmann Method and Deep Learning." In Lecture Notes in Computer Science. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59851-8_6.

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Abstract Using traditional computational fluid dynamics and aeroacoustics methods, the accurate simulation of aeroacoustic sources requires high compute resources to resolve all necessary physical phenomena. In contrast, once trained, artificial neural networks such as deep encoder-decoder convolutional networks allow to predict aeroacoustics at lower cost and, depending on the quality of the employed network, also at high accuracy. The architecture for such a neural network is developed to predict the sound pressure level in a 2D square domain. It is trained by numerical results from up to 20,000 GPU-based lattice-Boltzmann simulations that include randomly distributed rectangular and circular objects, and monopole sources. Types of boundary conditions, the monopole locations, and cell distances for objects and monopoles serve as input to the network. Parameters are studied to tune the predictions and to increase their accuracy. The complexity of the setup is successively increased along three cases and the impact of the number of feature maps, the type of loss function, and the number of training data on the prediction accuracy is investigated. An optimal choice of the parameters leads to network-predicted results that are in good agreement with the simulated findings. This is corroborated by negligible differences of the sound pressure level between the simulated and the network-predicted results along characteristic lines and by small mean errors.
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Conference papers on the topic "Circular Convolution"

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Pei, Soo-Chang, and Kuo-Wei Chang. "Fast Sparse DFT Computation for Arbitrary Length by Circular Convolution." In ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2025. https://doi.org/10.1109/icassp49660.2025.10890724.

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Imai, Takeshi, Hirohito Nishi, and Kyoji Matsushima. "Large Background Produced Using Circular Convolution in Full-Parallax High-Definition Computer Holography." In Digital Holography and Three-Dimensional Imaging. Optica Publishing Group, 2024. http://dx.doi.org/10.1364/dh.2024.w4a.2.

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A novel technique is proposed to expand the background in 3D scenes of full-parallax high-definition computer-generated holograms without increasing the calculation time. In this technique, the periodic backdrop’s wavefield is calculated using the circular convolution.
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Chang, Feng-Yu, Min-Che Hsieh, and Chia-Han Lee. "Joint Channel Estimation and Equalization Using Circular Convolution and DNN for ZP-OTFS." In 2025 International Conference on Information Networking (ICOIN). IEEE, 2025. https://doi.org/10.1109/icoin63865.2025.10992924.

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Adeniyi, Jide Kehinde, Abidemi Emmanuel Adeniyi, Oluwatobi Halleluyah Aworinde, Tunde Taiwo Adeniyi, Odunayo Olanloye, and Deborah Olufemi Ninan. "A Circular Local Binary Pattern and Convolutional Neural Network Approach to Mutilated Nigerian banknotes Recognition." In 2024 International Conference on Science, Engineering and Business for Driving Sustainable Development Goals (SEB4SDG). IEEE, 2024. http://dx.doi.org/10.1109/seb4sdg60871.2024.10629799.

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Li, Changli, Hon Keung Kwan, and Xinxin Qin. "Revisiting Linear Convolution, Circular Convolution and Their Related Methods." In 2020 13th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI). IEEE, 2020. http://dx.doi.org/10.1109/cisp-bmei51763.2020.9263607.

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Stasinski, R. "Selective nesting of circular convolution algorithms." In [Proceedings] ICASSP-92: 1992 IEEE International Conference on Acoustics, Speech, and Signal Processing. IEEE, 1992. http://dx.doi.org/10.1109/icassp.1992.226673.

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Garcia, Kleber. "Circular separable convolution depth of field." In SIGGRAPH '17: Special Interest Group on Computer Graphics and Interactive Techniques Conference. ACM, 2017. http://dx.doi.org/10.1145/3084363.3085022.

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Xuan, Donghua, Ho-Hsuan Chang, Canbin Li, and Weixuan Xie. "Construction of Zero Circular Convolution Sequences." In 2022 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME). IEEE, 2022. http://dx.doi.org/10.1109/iceccme55909.2022.9988686.

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Hossain, M. S., G. N. Milford, and M. C. Reed. "Convolution constrained based broadband circular antenna array." In 2012 37th International Conference on Infrared, Millimeter, and Terahertz Waves (IRMMW-THz 2012). IEEE, 2012. http://dx.doi.org/10.1109/irmmw-thz.2012.6380285.

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Nishikawa, K., H. Kiya, and M. Sagawa. "Property of circular convolution for subband image coding." In [Proceedings] ICASSP-92: 1992 IEEE International Conference on Acoustics, Speech, and Signal Processing. IEEE, 1992. http://dx.doi.org/10.1109/icassp.1992.226431.

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Reports on the topic "Circular Convolution"

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Champion, Daniel. Exhaustively searching PDV waveforms using fast circular-convolution/cross-correlation to perform improved extraction of dynamic surface velocity. Office of Scientific and Technical Information (OSTI), 2023. http://dx.doi.org/10.2172/1922912.

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Champion, Daniel J., and Caleb C. Monoran. Enhanced PDV waveform search and analysis method using parallel circular-convolution / cross-correlation for improved dynamic surface velocity extraction. Office of Scientific and Technical Information (OSTI), 2024. http://dx.doi.org/10.2172/2458222.

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