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Relevant bibliographies by topics / Convolution sum

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Journal articles
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Academic literature on the topic 'Convolution sum'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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Journal articles on the topic "Convolution sum"

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Kim, Aeran, Daeyeoul Kim, and Gyeong-Sig Seo. "CONVOLUTION SUM ∑k." Honam Mathematical Journal 34, no. 4 (2012): 519–31. http://dx.doi.org/10.5831/hmj.2012.34.4.519.

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Kim, Aeran. "The Convolution Sum." British Journal of Mathematics & Computer Science 4, no. 6 (2014): 774–89. http://dx.doi.org/10.9734/bjmcs/2014/7519.

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WILLIAMS, KENNETH S. "THE CONVOLUTION SUM $\sum\limits_{m." International Journal of Number Theory 01, no. 02 (2005): 193–205. http://dx.doi.org/10.1142/s1793042105000091.

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The evaluation of the sum ∑m<n/9σ(m)σ(n - 9m) is carried out for all positive integers n. This evaluation is used to detemine the number of solutions to [Formula: see text] in integers x1, x2, x3, x4, x5, x6, x7, x8.

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Aygin, Zafer Selcuk, and Nankun Hong. "Ramanujan’s convolution sum twisted by Dirichlet characters." International Journal of Number Theory 15, no. 01 (2019): 137–52. http://dx.doi.org/10.1142/s1793042119500027.

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We find formulas for convolutions of sum of divisor functions twisted by the Dirichlet character [Formula: see text], which are analogous to Ramanujan’s formula for convolution of usual sum of divisor functions. We use the theory of modular forms to prove our results.

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Alaca, Ayşe, Şaban Alaca та Kenneth S. Williams. "The Convolution Sum Σm". Canadian Mathematical Bulletin 51, № 1 (2008): 3–14. http://dx.doi.org/10.4153/cmb-2008-001-1.

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Williams, Kenneth. "The convolution sum ∑m." Pacific Journal of Mathematics 228, no. 2 (2006): 387–96. http://dx.doi.org/10.2140/pjm.2006.228.387.

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Kim, Aeran. "The Multinomial Combinatorial Convolution Sum." British Journal of Mathematics & Computer Science 4, no. 4 (2014): 487–94. http://dx.doi.org/10.9734/bjmcs/2014/6730.

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Cai, Jun, and Qihe Tang. "On max-sum equivalence and convolution closure of heavy-tailed distributions and their applications." Journal of Applied Probability 41, no. 1 (2004): 117–30. http://dx.doi.org/10.1239/jap/1077134672.

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In this paper, we discuss max-sum equivalence and convolution closure of heavy-tailed distributions. We generalize the well-known max-sum equivalence and convolution closure in the class of regular variation to two larger classes of heavy-tailed distributions. As applications of these results, we study asymptotic behaviour of the tails of compound geometric convolutions, the ruin probability in the compound Poisson risk process perturbed by an α-stable Lévy motion, and the equilibrium waiting-time distribution of the M/G/k queue.

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Cai, Jun, and Qihe Tang. "On max-sum equivalence and convolution closure of heavy-tailed distributions and their applications." Journal of Applied Probability 41, no. 01 (2004): 117–30. http://dx.doi.org/10.1017/s002190020001408x.

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In this paper, we discuss max-sum equivalence and convolution closure of heavy-tailed distributions. We generalize the well-known max-sum equivalence and convolution closure in the class of regular variation to two larger classes of heavy-tailed distributions. As applications of these results, we study asymptotic behaviour of the tails of compound geometric convolutions, the ruin probability in the compound Poisson risk process perturbed by an α-stable Lévy motion, and the equilibrium waiting-time distribution of the M/G/k queue.

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MILENKOVIC, VICTOR, and ELISHA SACKS. "TWO APPROXIMATE MINKOWSKI SUM ALGORITHMS." International Journal of Computational Geometry & Applications 20, no. 04 (2010): 485–509. http://dx.doi.org/10.1142/s0218195910003402.

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We present two approximate Minkowski sum algorithms for planar regions bounded by line and circle segments. Both algorithms form a convolution curve, construct its arrangement, and use winding numbers to identify sum cells. The first uses the kinetic convolution and the second uses our monotonic convolution. The asymptotic running times of the exact algorithms are increased by km log m with m the number of segments in the convolution and k the number of segment triples that are in cyclic vertical order due to approximate segment intersection. The approximate Minkowski sum is close to the exact

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Dissertations / Theses on the topic "Convolution sum"

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Ravindran, Hari Alangat. "On Shifted Convolution Sums Involving the Fourier Coefficients of Theta Functions Attached to Quadratic Forms." The Ohio State University, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=osu1406039690.

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Sarkar, Sanjoy. "Exotic Properties of Metal Organic Systems: Single Molecule Studies." Ohio University / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1627990825304104.

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Málková, Eliška. "Monitorování pohybu více objektů užitím křížové korelace." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2019. http://www.nusl.cz/ntk/nusl-400456.

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Tato práce popisuje metodu analýzy translačního pohybu užitím křížové korelace. Ukazujeme, jakým způsobem se chová funkce křížové korelace obrazů s navzájem posunutými objekty, a jak nám to umožňuje nacházet jejich vektory posunu. Pro následnou implementaci je nalezena efektivní metoda pro hledání pouze požadovaného počtu lokálních maxim funkce.

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Ekström, Marcus. "Road Surface Preview Estimation Using a Monocular Camera." Thesis, Linköpings universitet, Datorseende, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-151873.

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Recently, sensors such as radars and cameras have been widely used in automotives, especially in Advanced Driver-Assistance Systems (ADAS), to collect information about the vehicle's surroundings. Stereo cameras are very popular as they could be used passively to construct a 3D representation of the scene in front of the car. This allowed the development of several ADAS algorithms that need 3D information to perform their tasks. One interesting application is Road Surface Preview (RSP) where the task is to estimate the road height along the future path of the vehicle. An active suspension cont

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Lin, Guan-Henry, and 林冠亨. "Improved Survivor Memory Unit (SMU) Designs of Viterbi Decoder for Convolutional Coding." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/23131374462736974366.

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碩士<br>國立交通大學<br>電信工程系所<br>93<br>In the thesis, three new approaches are proposed to improve the drawbacks of three corresponding methods that are used conventionally in the realization of survivor memory unit (SMU) in Viterbi Algorithm (VA) as convolution decoders. For trace-back management (TBM) method, it has owned the virtues as low power consumption and small circuit area, but suffers long decoding latency. Corresponding to TBM, Stage-Hopping TBM (SH-TBM) is developed. The decoding efficiency can be raised approaching to the performance of register exchange algorithm (REA) as constraint

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Розов, Богдан Олегович. "Особливості розробки крос-платформного мобільного застосунку для SMM із використанням нейронних мереж". Магістерська робота, 2021. https://dspace.znu.edu.ua/jspui/handle/12345/5674.

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Розов Б. О. Особливості розробки крос-платформного мобільного застосунку для SMM із використанням нейронних мереж : кваліфікаційна робота магістра спеціальності 121 "Інженерія програмного забезпечення" / наук. керівник А. І. Безверхий. Запоріжжя : ЗНУ, 2021. 92 с.<br>UA : Завданням науково-дослідницької роботи є дослідження процесу розробки маркетингового застосунку для мобільних платформ. Для досягнення мети створений мобільний застосунок, що використовує нейронні мережі для обробки та класифікації світлин користувача, а також їх просування у соціальних мережах. Мета роботи — створення мобі

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Croon, Dennis Gerardus. "The outperformance of the semantic learning machine, against commonly used algorithms, for binary and multi-class medical image classification: combined with the usage of feature extraction by several convolutional neural networks." Master's thesis, 2020. http://hdl.handle.net/10362/103901.

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Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics<br>Extensive recent research has shown the importance of innovation in medical healthcare, with a focus on Pneumonia. It is vital and lifesaving to predict Pneumonia cases as fast as possible and preferably in advance of the symptoms. An online database source managed to gather Pneumonia-specific image data, with not just the presence of the infection, but also the nature of it, divided in bacterial- and viral infection. The first achievement is extracting valuable inf

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Cruz, Maria Quintela. "Classificação de imagens de interior/exterior de imóveis e a sua qualidade representativa." Master's thesis, 2019. http://hdl.handle.net/10071/22191.

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A aprendizagem automática tem vindo a progredir cada vez mais rapidamente, especialmente na área do reconhecimento de imagens. Apesar das redes neuronais convolucionais estarem presentes na classificação de imagens há muito tempo, apenas nos últimos anos têm sido reconhecidas pelo seu bom desempenho. A principal vantagem destas redes neuronais é a flexibilidade em transpor conhecimento entre problemas, i.e. usar uma rede treinada para um determinado tipo de problema num outro problema com pequenas adaptações. Por outro lado, as redes neuronais convolucionais têm uma grande exigência computaci

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Book chapters on the topic "Convolution sum"

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Sobot, Robert. "Discrete Convolution Sum." In Engineering Mathematics by Example. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-79545-0_18.

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Asadi, Farzin. "Convolution Integral and Sum." In Signals and Systems with MATLAB® and Simulink®. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-45622-0_2.

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Stoian, Mihail. "Approximate Min-Sum Subset Convolution." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-81396-2_14.

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Yu, Dongxiao, Yuexuan Wang, Qiang-Sheng Hua, and Francis C. M. Lau. "Exact Parameterized Multilinear Monomial Counting via k-Layer Subset Convolution and k-Disjoint Sum." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22685-4_7.

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Khamidullin, Sergey, and Liudmila Mikhailova. "One Problem of the Sum of Weighted Convolution Differences Minimization, Induced by the Quasiperiodic Sequence Recognition Problem." In Mathematical Optimization Theory and Operations Research. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58657-7_6.

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Bobkov, Sergey, Gennadiy Chistyakov, and Friedrich Götze. "Supremum and Infimum Convolutions." In Concentration and Gaussian Approximation for Randomized Sums. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-31149-9_8.

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Kamimura, Ryotaro. "Compressing and Interpreting SOM-Based Convolutional Neural Networks." In Trends in Artificial Intelligence Theory and Applications. Artificial Intelligence Practices. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-55789-8_63.

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Fend, Chiara, Ali Moghiseh, Claudia Redenbach, and Katja Schladitz. "Machine learning for reconstruction of highly porous structures from FIB-SEM nano-tomographic data." In Machine Learning for Cyber Physical Systems. Springer Berlin Heidelberg, 2020. http://dx.doi.org/10.1007/978-3-662-62746-4_13.

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AbstractReconstruction of highly porous structures from FIB-SEM image stacks is a difficult segmentation task. Supervised machine learning approaches demand large amounts of labeled data for training, that are hard to get in this case. A way to circumvent this problem is to train on simulated images. Here, we report on segmentation results derived by training a convolutional neural network solely on simulated FIB-SEM image stacks of realizations of a variety of stochastic geometry models.

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Gepperth, Alexander, Ayanava Sarkar, and Thomas Kopinski. "An Energy-Based Convolutional SOM Model with Self-adaptation Capabilities." In Artificial Neural Networks and Machine Learning – ICANN 2018. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01421-6_41.

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Yoo, Jaemin, and Lee Sael. "Transition Matrix Representation of Trees with Transposed Convolutions." In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM). Society for Industrial and Applied Mathematics, 2022. http://dx.doi.org/10.1137/1.9781611977172.18.

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Conference papers on the topic "Convolution sum"

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Huang, M. Jerry, Stephanie Loveless, and Jonas Braasch. "Cenotaph and Elegy of Contagion: Collective Immersion of National-Level Covid-19 Fatality Data in the United States." In ICAD 2024: The 29th International Conference on Auditory Display. International Community for Auditory Display, 2024. http://dx.doi.org/10.21785/icad2024.018.

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Effective representation of epidemiological data is essential for informing the general public about the extent of disease contagion, yet hard to achieve. On the one hand, the expressiveness of statistical visualization is often limited. On the other hand, explicit data sonification is lost in translation, failing to capture the meaning of the data at hand. The ongoing COVID-19 pandemic, which has caused excessive fatalities in the world and the United States, has presented us with a timely opportunity to address both challenges at once. In this work, we use the room-oriented immersive system

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Jothi, B., and J. Jeyasudha. "Expression of Concern for: Traffic Signs Recognition using Convolutional Neural Networks." In 2022 IEEE 2nd Mysore Sub Section International Conference (MysuruCon). IEEE, 2022. http://dx.doi.org/10.1109/mysurucon55714.2022.10703498.

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Kosenko, Roman, Alexander Mikhailov, Vasiliy Pchelko, Pavel Burundukov, Vladislav Kholkin, and Timur Karimov. "Investigation of Convolutional Autoencoder for Noise Reduction in ECG Signal." In 2025 XXVIII International Conference on Soft Computing and Measurements (SCM). IEEE, 2025. https://doi.org/10.1109/scm66446.2025.11060431.

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Campbell, Andrew, Hang Liu, Anna Scaglione, and Tong Wu. "A Federated Learning Approach for Graph Convolutional Neural Networks." In 2024 IEEE 13th Sensor Array and Multichannel Signal Processing Workshop (SAM). IEEE, 2024. http://dx.doi.org/10.1109/sam60225.2024.10636596.

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Krol, Matt, Rakib Hyder, Michael Peechatt, Ashley Prater-Bennette, M. Salman Asif, and Panos P. Markopoulos. "Continual Learning in Convolutional Neural Networks with Tensor Rank Updates." In 2024 IEEE 13th Sensor Array and Multichannel Signal Processing Workshop (SAM). IEEE, 2024. http://dx.doi.org/10.1109/sam60225.2024.10636545.

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Hu, Liyun, Yuexing Su, Yunlong Li, Shuang Zheng, and Minming Zhang. "Nonvolatile PCM-driven Photonic Computing Using Programmable Sub-wavelength Metasurfaces." In Optical Fiber Communication Conference. Optica Publishing Group, 2025. https://doi.org/10.1364/ofc.2025.m3g.1.

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We demonstrate a programmable nonvolatile convolutional core based on sub-wavelength phase-change metasurfaces, offering advanced computing capacities for its zero static power consumption and multi-level reconfigurable weights.

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Kumari, C. Meenu, R. Bhavani, S. Padmashree, and R. Priya. "Expression of Concern for: Detection and Identification of Cervical Cancer on Elephant Herding Optimization on Convolutional Neural Network." In 2022 IEEE 2nd Mysore Sub Section International Conference (MysuruCon). IEEE, 2022. http://dx.doi.org/10.1109/mysurucon55714.2022.10703499.

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Ren, Bin, and De-yong Yu. "Evaluate the Convolution Integral and Convolution Sum Use Compact Formula." In 2011 7th International Conference on Wireless Communications, Networking and Mobile Computing (WiCOM). IEEE, 2011. http://dx.doi.org/10.1109/wicom.2011.6039951.

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Kim, Daeyeoul, and Aeran Kim. "A property of convolution sum for divisor functions." In NUMERICAL ANALYSIS AND APPLIED MATHEMATICS ICNAAM 2012: International Conference of Numerical Analysis and Applied Mathematics. AIP, 2012. http://dx.doi.org/10.1063/1.4756143.

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Jian Jun Yin, Jian Qiu Zhang, and Ze Sen Zhuang. "The Gaussian sum convolution probability hypothesis density filter." In 2008 9th International Conference on Signal Processing (ICSP 2008). IEEE, 2008. http://dx.doi.org/10.1109/icosp.2008.4697125.

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Reports on the topic "Convolution sum"

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Naseem, Shahid. Hand written digits classification and recognition using convolutional neural networks by implementing the techniques of MLP and SVM. Peeref, 2023. http://dx.doi.org/10.54985/peeref.2303p8226220.

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Mosalam, Khalid, Issac Pang, and Selim Gunay. Towards Deep Learning-Based Structural Response Prediction and Ground Motion Reconstruction. Pacific Earthquake Engineering Research Center, 2025. https://doi.org/10.55461/ipos1888.

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This research presents a novel methodology that uses Temporal Convolutional Networks (TCNs), a state-of-the-art deep learning architecture, for predicting the time history of structural responses to seismic events. By leveraging accelerometer data from instrumented buildings, the proposed approach complements traditional structural analysis models, offering a computationally efficient alternative to nonlinear time history analysis. The methodology is validated across a broad spectrum of structural scenarios, including buildings with pronounced higher-mode effects and those exhibiting both line

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SUPER-RESOLUTION RECONSTRUCTION AND HIGH-PRECISION TEMPERATURE MEASUREMENT OF THERMAL IMAGES UNDER HIGH- TEMPERATURE SCENES BASED ON NEURAL NETWORK. The Hong Kong Institute of Steel Construction, 2024. http://dx.doi.org/10.18057/ijasc.2024.20.2.9.

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Accurate temperature readings are vital in fire resistance tests, but conventional thermal imagers often lack sufficient resolution, and applying super-resolution algorithms can disrupt the temperature and color correspondence, leading to limited efficiency. To address these issues, a convolutional network tailored for high-temperature scenes is designed for image super-resolution with the internal joint attention sub-residual blocks (JASRB) efficiently integrating channel, spatial attention mechanisms, and convolutional modules. Furthermore, a segmented method is developed for predicting ther

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