Liers, Frauke, Pardella, Gregor · Computer Science Publication Server (University of Cologne) · 2010
Maximum-flow problems occur in a wide range of applications. Although already well-studied, they are still an area of active research. The fastest available implementations for determining maximum flows in graphs are either based on augmenting-path or on push-relabel algorithms. In this work, we present two ingredients that, appropriately used, can considerably speed up these methods. On the one hand, we present flow-conserving conditions under which subgraphs can be contracted to a single node. These rules are in the same spirit as presented by Padberg and Rinaldi (Math. Programming (47), 1990) for the minimum cut problem in graphs. On the other hand, we propose a two-step max-flow algorithm for solving the problem on instances coming from physics and computer vision. In the two-step algorithm flow is first sent along augmenting paths of restricted lengths only. Starting from this flow, the problem is then solved to optimality using some known max-flow methods. By extensive experiments on random instances and on instances coming from applications in theoretical physics and in computer vision, we show that a suitable combination of the proposed techniques speeds up traditionally used methods.
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Ignacio Cordón, Salvador García, Alberto Fernández et al. · 2017
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D. V. Ravindra, Y. N. Srikant · NOT FOUND REPOSITORY (Indian Institute of Science Bangalore) · 2002
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Dan Gusfield · Cambridge University Press eBooks · 1997
Almost all discussions of exact matching begin with the naive method, and we follow this tradition. The naive method aligns the left end of P with the left end of T and then compares the characters of P and T left to right until either two unequal characters are found or until P is exhausted, in which case an occurrence of P is reported. In either case, P is then shifted one place to the right, and the comparisons are restarted from the left end of P. This process repeats until the right end of P shifts past the right end of T.
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Anjan Ghosh, Paparao Palacharla · Annual Meeting Optical Society of America · 1989
A new and efficient split-step polynomial preconditioning algorithm is discussed. In this algorithm, basic polynomial preconditioning iterations are repeated several times, each time with an improved matrix. With minor modifications this algorithm can be used for matrix inversion and condition number estimation. The split-step algorithms consist of two nested iterative loops involving matrix multiplications and thus are attractive for parallel optical processors. The details of realization of splitstep algorithms on three different optical matrix multiplier architectures are outlined. The condition number after m outer loop iterations is given by1/{1−[1−1/ c (a)] pm }, where p is the number of inner loop iterations and C (A) is the initial condition number. 1 This strategy reduces the condition number approximately at the rate O ( p −m ), much faster compared to other preconditioning algorithms. The results of numerical experiments on the split-step algorithm applied to a case study of an optimum phased array antenna design problem show that this new preconditioning algorithm is a viable tool for improving the performance of analog optical processors. Preliminary analyses of the split-step algorithm demonstrate its robustness with regard to the spatial errors and noise present in most optical processors. Using these split-step algorithms an interconnected optical processor capable of computing solutions with a specified accuracy can be designed.
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D. V. Ravindra, Y. N. Srikant · Lecture notes in computer science · 2002
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Finn Meissner · SSRN Electronic Journal · 2024
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Rodrigo Gonçalves, Ronaldo dos Santos Mello · Lecture notes in computer science · 2007
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Laurent Robert, R. Nadarajan · IET Software · 2009
Lossless data compression researchers have developed highly sophisticated approaches, such as Huffman encoding, arithmetic coding, the Lempel–Ziv family, prediction by partial matching and Burrow–Wheeler transform based algorithms. One approach for attaining better compression is to develop generic, reversible transformation that can be applied to a source text that improves an existing compression algorithm's ability to compress. A few reversible transformation techniques that give better compression ratios are presented. A method, which transforms a text file into intermediate file with minimum possible byte values, is proposed. An attempt has been made to reduce the number of possible bytes that appear after every byte in the source file. This increases backend algorithm's compression performance.
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Zegar Chouki · Journal of Electrical Systems · 2024
Speech Enhancement and noise reduction have wide applications in speech processing. They are often employed as pre-processing stage in various applications. The work to be presented in this paper is denoising a single-channel speech signal at the presence of a highly non-stationary background noise in order to improve the perceptible quality and intelligibility of the speech. Real world noise is mostly highly non-stationary and does not affect the speech signal uniformly over the spectrum. This paper investigates various Discrete Fourier Transform-based algorithms as single-channel pre-processing techniques consisting of: Spectral Subtraction using over-subtraction and spectral floor, Multi-Band Spectral Subtraction (MBSS), Wiener Filter, MMSE of Short-Time Spectral Amplitude (MMSE-STSA) estimator with, and without using SPU modifier, MMSE Log-Spectral Amplitude Estimator with, and without using SPU modifier, Optimally-Modified Log-Spectral Amplitude estimator (OM-LSA). The processed speeches from these algorithms are compared at the same set of conditions using visual examinations of signals in the time domain and the spectrograms, and also the objective and subjective tests for quality and perceptual evaluation. All the implemented algorithms provide considerable, different degrees of flexibility and control on noise elimination levels that reduces artifacts in the enhanced speech, resulting in the improved quality, and intelligibility.
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Xianquan Han, Hui Zhang, Yu Jiang et al. · DEStech Transactions on Engineering and Technology Research · 2017
Filter of airborne LiDAR data is a primary step of data processing.This paper reviews the literatures of filter algorithms from aspects of theory and performance. The moving plane fitting algorithm is introduced and improved especially. In addition, the Experiments show the method is effective and lays a foundation for the subsequent regularization of complex building boundaries.
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Mehran Rostamnia, Sahar Kianian · 2019 5th Conference on Knowledge Based Engineering and Innovation (KBEI) · 2019
The Influence maximization problem is a fundamental problem in social networks analysis which aims to select a small set of nodes, as a seed set, and maximizes influence spread through the seed set under a specific propagation model. In this paper, the effect of performing vertex cover, as a preprocess, has been investigated on the influence maximization algorithms under the independent cascade model. Proposed method eliminates ineffective nodes from the main calculation process to find the most influential nodes. Based on the results, combining the proposed algorithm with several methods on real-world datasets confirms the efficiency of the method.
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Affaan Shaikh · ECS Meeting Abstracts · 2025
Neuroimaging technologies play a crucial role in diagnosing and understanding neurological disorders by capturing brain structure and function through various modalities such as MRI, CT, PET, EEG, and MEG. Integrating these imaging techniques with machine learning (ML) and deep learning (DL) enables efficient classification and prediction of neurological conditions. However, the diversity in data formats necessitates robust preprocessing techniques to enhance data quality and usability for computational models. This paper reviews neuroimaging modalities, their applications in diagnosing neurodegenerative diseases, preprocessing strategies, and the application of ML and DL methods in neuroimaging.
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Andrew V. Goldberg · Lecture notes in computer science · 2007
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Bella Specktor‐Fadida · Journal of Digital Imaging · 2017
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Cano Aparicio, Ramon · QRU Quaderns de Recerca en Urbanisme · 2022
Many real-life problems can be expressed as a set of restrictions. If those restrictions can be satisfied, then there's a solution to the problem, otherwise, the problem can be classified as not solvable. The set of those problems that can be codified using boolean formulas, that is to say, where the variables of the problems can take the values 0 and 1, are wildly known as SATISFIABILITY (SAT) problems. Therefore, if a program is capable of assigning values to the variables such that all the restrictions of a given problem are satisfied, then the solution to the problem is found. Although many problems can be easily represented this way, deciding whether there's a solution to the problem or giving a solution if it exists is not easy to compute. SAT is the first family of problems proven to be NP-Complete by Stephen Cook and Leonid Levin. Therefore, there's no known algorithm that runs in polynomial time capable of solving this kind of problem. This project tries to transform the SAT problem into an LP problem, as well as applying preprocessing techniques to express the information in a more optimized way for the LP solver.
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Yuhao Jiang · 2025
This paper focuses on the application of clustering technology in data preprocessing to enhance the comprehensive leap of data analysis efficiency and accuracy. This experiment introduces the K-means clustering algorithm, standardizes the wholesale customer data set, and adopts the principal component analysis dimensionality reduction method to implement clustering process improvement. The experimental results show that, relying on the detailed cluster analysis method, the data resources are successfully subdivided into three major customer categories, the consistency within the data group is significantly enhanced, and the recognition between clusters is greatly improved. The research results show that clustering technology has significantly innovated the means of data preprocessing and effectively supported the subsequent data mining and decision-making implementation. We will continue to deepen the research, explore ways to integrate multiple machine learning strategies, and discuss technical paths to deal with complex data environments.
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Andrew V. Goldberg, Haim Kaplan, Renato F. Werneck · DIMACS series in discrete mathematics and theoretical computer science · 2009
We study the point-to-point shortest path problem with preprocessing. Given an input graph, we preprocess it so as to be able to answer a series of source-to-destination queries efficiently. Our work is motivated by an algorithm of Gutman [ALENEX’04], based on the notion of reach, which measures how important each vertex is with respect to shortest paths. We present a simplified version of his algorithm that does not require explicit lower bounds during queries. We also show how the addition of shortcuts to the graph greatly improves the performance of both preprocessing and queries. Finally, we combine a reach-based algorithm with landmark-based A search to obtain a wide range of space-time trade-offs. For our motivating application, driving directions for road networks, the resulting algorithm is very efficient and practical. The road networks of the USA and Western Europe have roughly 20 million vertices, but on average our algorithm must visit fewer than a thousand to find the distance between two points. Our algorithm also works reasonably well on 2-dimensional grid graphs with random arc weights.
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Suleyman Suleymanzade · 2018
Estimating the text preprocessing algorithms in machine learning classification task helps to select the most accurate combination of word embedding text processing algorithms to map the test data samples to appropriate labels. Increasing the accuracy in classification of text data improves afterwards the response to queries in search engines and gives more relevant results. This work includes several preprocess text data techniques and their combinations for following sentence classification by using support vector machine (SVM).
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Velunde, Pratik · Zenodo (CERN European Organization for Nuclear Research) · 2025
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