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IEEE Transactions on Audio, Speech and Language Processing 2025年 33卷 1949-1959页

作者： Zhikai Gong Yingsong Li Xinqi Huang Liping Li Zhixiang Huang Rodrigo. C. de Lamare Yingying Zhu College of Information and Communication Engineering Harbin Engineering University Harbin China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei China Centre for Telecommunications Research (CETUC) Pontifical Catholic University of Rio de Janeiro (PUC-Rio) Gávea Brazil School of Physics Engineering and Technology University of York York U.K.

In this work, we present a subband adaptive filtering (SAF) algorithm with good robustness for impulsive noise environments. The proposed squared sine normalized SAF (SS-NSAF) algorithm employs a squared sine function and is inspired by the idea of normalization. A statistical analysis of the proposed SS-NSAF algorithm is carried out under impulsive noise environments, which includes stability conditions for the convergence and analytical formulas to predict the mean-square deviation of the SS-NSAF algorithm. Numerical simulations for system-identification and echo cancellation applications are conducted to illustrate the performance of the SS-NSAF algorithm and competing techniques in impulsive noise environments.

关键词： Noise Prediction algorithms Signal processing algorithms filtering algorithms Nonlinear filters Maximum likelihood detection Convergence Vectors Mathematical models Information filters

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Detecting Integer Overflow Vulnerabilities in Binary Executables Based on Target filtering and Dynamic Taint Tracing

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Chinese Journal of Electronics 2025年第2期23卷 348-352页

作者： Baojiang Cui Xiaobiug Liang Bing Zhao Feng Zhai Jianxin Wang School of Computer Beijing University of Posts and Telecommunications Beijing China China Electric Power Research Institute Beijing China School of Information Science and Technology Beijing Forestry University Beijing China

The number of identified integer overflow vulnerabilities has been increasing rapidly in recent years. In this paper, a smart software vulnerability detection technology is presented, which is used for the identification of integer overflow vulnerabilities in binary executables. The proposed algorithm is combined with Target filtering and dynamic taint tracing (TFDTT). Dynamic taint tracing is used to reduce the mutation space and target filtering function is used to filter test cases during the process of test case generation. Theory analysis indicates that the efficiency of TFDTT is higher than NonTF-DTT and random Fuzzing technology. And the experiment results indicate that the detection technology based upon TFDTT can identify the possible integer vulnerabilities in binary program, meanwhile, it is more efficiency than other two technologies.

关键词： filtering Source coding Heuristic algorithms Software algorithms Prototypes Life estimation Fuzzing filtering algorithms Software filtering theory

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EFFICIENT ORDER RECURSIVE algorithms FOR MULTICHANNEL LEAST-SQUARES filtering

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 1992年第6期40卷 1354-1374页

作者： GLENTIS, GOA KALOUPTSIDIS, N INST INFORMAT & TELECOMMUN GR-15310 ATHENSGREECE

In this paper four efficient order recursive algorithms for least squares (LS) multichannel FIR filtering and multivariable system identification are developed. The need for such algorithms rises when the system model assigns an unequal number of delay elements to each input channel. All proposed schemes provide considerable improvements over over-parameterization, or the zero padding approach, which simply equalizes coefficients lengths of all channels by inserting zeros and then solves the extended problem via a fast least squares algorithm. First, a block-structured algorithm is derived. It operates on boxes, or blocks, whose dimensions successively increase until their size equals the number of input channels. As a result, it requires linear system solvers and matrix multiplications. The second algorithm manages to get free of block operations by proper decomposition of each block step involved in the first method, into a number of scalar steps equal to the size of the block. The third and the fourth algorithms provide highly concurrent alternatives, and if a parallel processing environment is available, they reduce processing time by an order of magnitude. All proposed methods include a primary module, a secondary module, and a management scheme. The primary module constitutes the main body of the algorithm and is responsible for all functions required to deal with a multichannel setup. The secondary module provides all operations needed to effect the passage from a multichannel problem of i input channels to the next case involving i + 1 channels. The management scheme integrates the primary and the secondary module to achieve overall efficiency. An illustrative example from multichannel autoregressive spectral estimation is supplied.

关键词： filtering algorithms Least squares methods Finite impulse response filter Signal processing algorithms Informatics Signal processing Signal design Image storage Student members MIMO

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FAST algorithms FOR 2-D FIR WIENER filtering AND LINEAR PREDICTION

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS 1987年第2期34卷 181-187页

作者： MANOLAKIS, DG INGLE, VK Department of Electrical and Computer Engineering Northeastern University Boston MA USA

This paper deals with the development of computationally efficient algorithms for 2-D finite-impulse response (FIR) Wiener filters and linear predictors which are optimum in the mean-square-error (MSE) sense. It turns out that the computational effort to determine the coefficients of the 2-D FIR restoration filter depends heavily on the statistical features of the input signal. It is shown that in the case of homogeneous signals, one can develop two very efficient algorithms which are, at least, 30-percent faster than other existing schemes. These algorithms are subsequently used for the efficient implementation of the introduced restoration techniques. Experimental results as well as performance evaluations of this technique are included.

关键词： filtering algorithms Finite impulse response filter Wiener filter Signal processing algorithms Image restoration Signal restoration Pixel Signal processing Very large scale integration Digital signal processing

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ADAPTIVE algorithms FOR JOINT TIME-DELAY ESTIMATION AND IIR filtering

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 1995年第4期43卷 841-851页

作者： LIM, TJ MACLEOD, MD Engineering Department University of Cambridge Cambridge UK

In this paper, a new class of gradient-based algorithms for joint adaptive time delay estimation and IIR filtering for the recursive identification of systems with long pure delays is introduced, The algorithms update an adaptive delay estimate and the coefficients of an IIR filter using familiar gradient-descent methods, In addition, interpolation of the input sequence is used to obtain delays that are not constrained to integer numbers of samples, Three algorithms using a direct form filter parametrization and one using a normalized lattice filter are derived, and computer simulations are used to demonstrate their convergence and tracking properties.

关键词： Adaptive algorithm Delay effects Delay estimation IIR filters filtering algorithms Recursive estimation Adaptive filters Interpolation Lattices Computer simulation

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QR-DECOMPOSITION BASED algorithms FOR ADAPTIVE VOLTERRA filtering

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-FUNDAMENTAL THEORY AND APPLICATIONS 1993年第6期40卷 372-382页

作者： SYED, MA MATHEWS, VJ UNIV UTAH DEPT ELECT ENGNSALT LAKE CITYUT 84112

A QR-RLS adaptive algorithm for nonlinear filtering is presented. The algorithm is based solely on Givens rotation. Hence the algorithm is numerically stable and highly amenable to parallel implementations. The computational complexity of the algorithm is comparable to that of the fast transversal Volterra filters. The algorithm is based on a truncated second-order Volterra series model;however, it can be easily extended to other types of polynomial nonlinearities. The algorithm is derived by transforming the nonlinear filtering problem into an equivalent multichannel linear filtering problem with a different number of coefficients in each channel. Such multichannel algorithms were not available in the past even for adaptive linear filtering applications. The derivation of the algorithm is based on a channel-decomposition strategy which involves processing the channels in a sequential fashion during each iteration. This avoids matrix processing and leads to a scalar implementation. Results of extensive experimental studies demonstrating the properties of the algorithm in finite and ''infinite'' precision environments are also presented. The results indicate that the algorithm retains the fast convergence behavior of the RLS Volterra filters and is numerically stable.

关键词： filtering algorithms Adaptive filters Nonlinear filters Transversal filters Resonance light scattering Adaptive algorithm Computational complexity Polynomials Maximum likelihood detection Convergence of numerical methods

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SOFTWARE AND VLSI algorithms FOR GENERALIZED RANKED ORDER filtering

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS 1987年第5期34卷 553-559页

作者： FITCH, JP Lawrence Livermore Laboratory University of California Livermore CA USA

The threshold decomposition architecture is known to provide a modular and parallel design for ranked order filtering. However, the chip area grows exponentially with the number of bits in the input. In this paper a new architecture which is equivalent to the threshold decomposition is shown to have linear growth in the size of the input words. filtering is accomplished by a pipelined bit by bit algorithm. Hardware modifications to the filtering scheme are accomplished by changing data paths and the number of cells to be replicated rather than modifying the functions performed by each module. Hardware redundancy to prevent single points of failure can be provided when space for extra modules is available. If external data flow control is provided, the chip can be programmed for many different filtering operations. The basic representation of the generalized rank order filter as a maximum of minimums also provides a fast software implementation and intuitive description of these filters as adaptive rank operations. Because the threshold description is extremely slow on a general-purpose computer, this is essential for investigating new applications of these robust signal and image processing filters.

关键词： Very large scale integration Software algorithms filtering algorithms Computer architecture Hardware Adaptive filters Application software Robustness Signal processing Image processing

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EFFICIENT TRANSVERSAL AND LATTICE algorithms FOR LINEAR-PHASE MULTICHANNEL filtering

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS 1987年第7期34卷 804-813页

作者： KALOUPTSIDIS, N Department of Physics Division of Applied Physics University of Athens (NKUA) Athens Greece

A new family of fast algorithms for batch and adaptive computation of multichannel linear- and affine-phase filters is developed. Both lattice and transversal realizations in ordinary and suitable normalized forms are studied. An important feature of the proposed recursive theme (besides its notable computational advantages) is that it offers a remarkable symmetry and unification of expressions and thus greatly facilitates the implementation process. Moreover, it demonstrates the extensive commonalities between adaptive and batch methods that result from the coincidences of their internal structure.

关键词： Lattices filtering algorithms Transversal filters Finite impulse response filter Adaptive filters Nonlinear filters Frequency Signal processing algorithms Speech processing Harmonic distortion

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REGULARIZED FAST RECURSIVE LEAST-SQUARES algorithms FOR FINITE MEMORY filtering

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 1992年第4期40卷 758-769页

作者： HOUACINE, A Institute of Electronics University of Sciences and Technology of Algiers U. S. T. H. B. Algiers Algeria

New fast recursive least squares algorithms are developed here, for finite memory filtering, by using a sliding data window. These algorithms allow the use of statistical priors about the solution, and they allow maintaining a balance between the a priori and the data informations. They are then well suited to compute a regularized solution, which has better numerical stability properties than the conventional least squares solution. An important advantage of the algorithms presented in this paper lies in the fact that they have a general matrix formulation, such that the same equations are suitable for the prewindowed as well as the covariance case, independently of the used a priori information. Only the initialization step and the numerical complexity change through the dimensions of the intervening matrix variables. The lower bound of O(16m) is achieved in the prewindowed case when the estimated coefficients are assumed to be uncorrelated, m being the order of the estimated model. It is shown that a saving of 2m multiplications per recursion can always be obtained. The lower bound of the resulting numerical complexity becomes O(14m), but then the general matrix formulation is lost. Simulation results are given to illustrate the interest of incorporating priors in the estimation problem.

关键词： Least squares methods filtering algorithms Covariance matrix Partitioning algorithms Transversal filters Equations Least squares approximation Finite impulse response filter Numerical stability Adaptive estimation

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STABLE AND EFFICIENT LATTICE algorithms FOR ADAPTIVE IIR filtering

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 1992年第2期40卷 375-388页

作者： REGALIA, PA Départment Electronique et Communications Institut National de Télécommunications Evry France

Conventional direct-form adaptive IIR filters suffer from potential instability problems, and as such there is a general interest in adaptive filters based on alternate parametrizations. The normalized lattice filter is one example of a filter structure whose stability can be ensured theoretically in a time-varying environment, and thus the structure is well suited for adaptive IIR filtering. Previous attempts at applying lattice structures to adaptive IIR filtering have met with gradient computations of O(N2) complexity. To overcome this computational burden, two new lattice-based algorithms are proposed for adaptive IIR filtering and system identification, with both algorithms of O(N) complexity. The first algorithm is a reinterpretation of the Steiglitz-McBride method, while the second is a variation on the output error method. State space models are employed to make the derivations transparent, and the methods can be extended to other parametrizations if desired. The set of possible stationary points of the algorithms are shown to be consistent with the convergent points obtained from the direct-form versions of the Steiglitz-McBride and output error methods, whose properties are well studied. The derived algorithms are as computationally efficient as existing direct-form based algorithms, while overcoming the stability problems associated with time-varying direct-form filters.

关键词： Lattices filtering algorithms Adaptive filters IIR filters Finite impulse response filter Stability System identification Signal processing algorithms State-space methods Modeling

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