nils-herrmann/clean_scirepeval huggingface dataset:
A spatial exploration based blind DOA estimation algorithm for closely spaced sources

In this paper, a spatial extrapolation-based blind DOA estimation method is presented for closely spaced sources. The new method is based on the linear extrapolation of the correlation matrix using AR coefficients, which are first estimated through an initial DOA estimation. Both the initial and final DOA estimations are performed by using the ESPRIT algorithm. Unlike conventional AR coefficient estimation method which estimates AR coefficients on the snap-shot basis, our AR coefficient estimation is carried out in correlation domain once a block of snap-shots, thus significantly reducing the computational complexity of the antenna array. Computer simulations show that the proposed blind DOA estimation method outperforms the conventional method in terms of the mean square-error (MSE) when the angle of separation of DO As is very small.
Minimum redundancy linear sparse subarrays for direction of arrival estimation without ambiguity

This paper presents a new method of estimating the direction-of-arrival (DOA) for multiple signals using minimum redundancy linear sparse subarrays (MRLSS). The proposed method makes use of the array structure to obtain the extended correlation matrix that is constructed by Kronecker Steering Vectors (KSVs) of which each contains the ambiguous and unambiguous angle with a one-to-one relationship. Our method enjoys two advantages in comparison to the existing methods. First, the cyclic ambiguity can be resolved by the one-to-one mapping of unambiguous angle without requiring additional algorithms such as MUSIC and MODE. Second, the proposed method can deal with different unambiguous angles with the same ambiguous angles, which could not have been possible by using the traditional schemes due to the fact that our method obtains the ambiguous and unambiguous angles simultaneously.
Realization of 2-D linear-phase FIR filters based on the singular-value decomposition

In this paper, the singular-value decomposition (SVD) technique is investigated for the realization of 2-D linear-phase FIR filters with arbitrary magnitude responses. A parallel realization structure consisting of a number of 1-D FIR filters is obtained by applying the SVD to the impulse response of a 2-D filter. By using the symmetry property of the 2-D impulse response and by developing an appropriate unitary transform, a singular-value decomposition with symmetric and skew-symmetric singular vectors is obtained so that the constituent 1-D filters have linear-phase characteristics. It is shown that the number of parallel sections can be reduced significantly by neglecting the smaller singular values without introducing a large error in the magnitude response of the simplified 2-D filter.
Weighted residual minimization in PCA subspace for visual tracking

The success of sparse representation, in face recognition and visual tracking, has attracted much attention in computer vision in spite of its computational complexity. These sparse representation-based methods assume that the coding residual follows either Gaussian or Laplacian distribution, which may not be accurate enough to describe the coding residuals in real scenarios. In order to deal with such issues in visual tracking, a novel generative tracker is proposed in a Bayesian inference framework by exploiting both the robust sparse coding and the principle component analysis (PCA) algorithm. In contrast to the existing algorithms, the proposed method introduces weighted least squares into the PCA reconstruction avoiding the much complex l1-regularization. Further, it is proposed to generate an occlusion map based on weights, and is used to avoid updating the occlusion information during incremental subspace learning. The performance evaluation on the challenging image sequences demonstrates that the proposed method performs favorably when compared with the several state-of-the-art methods.
Cascade synthesis of a class of multivariable positive real functions

Necessary and sufficient conditions are developed for the realization of a multivariable positive real function (MPRF) as the driving-point function of an extracted single-variable lossless two-port with a termination whose driving-point function is an MPRF in the rest of the variables as the load. Conditions are also obtained for the case when an extraction of a lossless two-port is possible in either of the two variables P_{i} or p_{j} .
Interpolation of 2-D signals

A method based on the fast Fourier transform (FFT) is presented to estimate the values of signal samples between any two consecutive samples of an already sampled 2-D band-limited continuous signal. In other words, the effect is as if one increased sampling rate and obtained new samples at the higher rate. This will be useful when storing or transmitting an image sampled at a lower rate and reconstructing it later with better quality.
Efficient output-pruning of the 2-D FFT algorithm

In this paper, an efficient algorithm for pruning the output samples of the radix-(2 /spl times/ 2) two dimensional decimation-in-time FFT algorithm is presented. Comparisons with the existing algorithm show that substantial savings on the arithmetic operations, data transfers, address computations, and twiddle factor evaluations or accesses to the lookup table can be made. This is achieved by grouping in the radix-(2 /spl times/ 2) 2-D DIT FFT algorithm all the stages that involve unnecessary operations into a single stage and introducing a new recursive technique for computing the resulting stage. Due to this grouping and the efficient indexing process introduced in this paper, the implementation of the proposed algorithm requires a minimum number of stages; however, that of the existing algorithm uses all the stages required by the radix-(2 /spl times/ 2) 2-D DIT FFT. Therefore, the proposed algorithm also reduces the overall control and structural complexities.
Design of a multi-purpose silicon integrated optical sensor

Describes the design of a multi-purpose optical sensor that can realize a variety of optical signal sensing and processing functions. The silicon space required for implementing the sensor is about 50 /spl mu/m/spl times/50 /spl mu/m with a fill-factor greater than 40%, using a single-poly dual-metal 1.0-/spl mu/m CMOS technology. One version of the proposed sensor has been fabricated using a single-poly CMOS technology without any extra process and is currently being tested in the laboratory.
A spatio-temporal stacking approach for estimating two-dimensional direction-of-arrival

A number of 2-D DOA estimation techniques based on L-shaped array have received much attention. In general, these methods require division of the L-shaped array into two independent uniform linear array (ULA) to obtain the azimuth and elevation angles independently, and then use additional pair matching technique to achieve 2-D DOA estimation. Therefore, these methods have some drawbacks such as 1) requirement of pair matching which may increase the computational burden significantly and 2) estimating the azimuth and elevation angles by independently using each ULA half of the L-shaped array. The purpose of this paper is to develop a spatio-temporal stacking approach (STSA) to deal with these shortcomings. The STSA method first partitions the many lag cross-correlation matrices into a lot of submatrices based on the assumption on the second-order temporal structure (SOTS) of the source signals, and then stacks these submatrices according to some special structure to form a spatio-temporal stacking matrix. Finally, the joint singular value decomposition (JSVD) technique is exploited to extract out the 2-D DOAs one by one.
A fast 8×8 integer Tchebichef transform and comparison with integer cosine transform for image compression

Presently, there is an undeniable need for novel transform coding techniques promising improved reconstruction and reduced computational complexity in the field of image and data compression. Discrete Tchebichef transform (DTT), though possessing valuable properties like energy compaction, is a potentially unexploited polynomial-based orthogonal transform, compared to the much popular Discrete Cosine transform (DCT). Specifically, integer DCT is widely used in the field of video compression in view of its ease of computation and acceptable performance. However, specific features of the image, such as the structure and content, profoundly influence the quality of the reconstructed image after decompression. This paper focuses on identifying areas where integer DTT might be an alternative to integer DCT for image compression. An integer-transform for the fast computation of 8×8 DTT that reduces the computational complexity significantly is proposed. The image compression performance of integer DTT and integer DCT are evaluated and compared.
Approximation design of 2-D digital filters with elliptical magnitude response of arbitrary orientation

This paper presents an approximation technique which resuits in simple formulas for fast calculation of the McClellan transform coefficients, and should be useful for real-time and adaptive 2-D digital filtering. The coefficients are calculated by fitting the power series expansion of the transform to the analytical description of the iso-potential contours of the filter. Formulas are also derived for the coefficients of the scaling-free transform, recently proposed by the authors.
BI-CosampSE: Block identification based compressive sampling matching pursuit for speech enhancement

The conventional compressive sampling matching pursuit (CoSaMP) algorithm often produces isolated components (IFC) in the frequency domain, when it is used to recover sparse or compressible signals from their downsampled data. The IFCs in the frequency domain are perceived as musical noise, when CoSaMP is used for speech enhancement (SE). In this paper, we propose a novel method to tackle this problem by using a block based identification strategy (BIS) to seek the most prominent components in the observed data to update the sparse estimate of CoSaMP. The proposed method has been found to be very effective to reduce musical noise in speech enhancement, in combination with some time-frequency smoothing techniques (TFSTs). The perceptual quality of the enhanced signals is significantly improved.
Bit-serial window partitioning algorithm for stack filtering

The authors introduce a new bit-serial algorithm for stack filtering, designated as the bit-serial window partitioning algorithm. It is shown that the proposed algorithm can achieve very important savings over the conventional bit-serial binary-tree search algorithm, in terms of the computational speed. This improved efficiency is obtained by evaluating the Boolean function at thresholds corresponding to the sample values within the filter window, and by taking advantage of the ordering information associated with the threshold sequences.
A novel unsupervised spike sorting algorithm for intracranial EEG

This paper presents a novel, unsupervised spike classification algorithm for intracranial EEG. The method combines template matching and principal component analysis (PCA) for building a dynamic patient-specific codebook without a priori knowledge of the spike waveforms. The problem of misclassification due to overlapping classes is resolved by identifying similar classes in the codebook using hierarchical clustering. Cluster quality is visually assessed by projecting inter- and intra-clusters onto a 3D plot. Intracranial EEG from 5 patients was utilized to optimize the algorithm. The resulting codebook retains 82.1% of the detected spikes in non-overlapping and disjoint clusters. Initial results suggest a definite role of this method for both rapid review and quantitation of interictal spikes that could enhance both clinical treatment and research studies on epileptic patients.
Noise spectrum estimation with improved minimum controlled recursive averaging based on speech enhancement residue

The conventional soft-decision based noise estimation algorithms normally assume that noise exists, only when speech is absent. Consequently, the estimated noise spectra are not updated in the segments of speech presence, but only in those of speech absence. This assumption often results in several problems such as delay and bias of noise spectrum estimates. In this paper, we propose a solution by using speech enhancement residue (SER) to compensate the estimation bias in the presence of speech. The proposed method can be naturally combined with the improved minimum controlled averaging (IMCRA) method to consistently update noise spectra. The experimental results show that the SER-based IMCRA can reduce the relative segmental estimation errors for various types of noise at different SNR levels, especially for car internal noise.
Minimum mean square error design of stack filters

This paper addresses the problem of designing optimal stack filters under the most commonly used error criterion in filter design, namely, the minimum mean square error (MSE) between the desired signal and the estimated signal. It is shown that the MSE can be expressed as a linear combination of the responses of positive Boolean filters to different binary input vectors. As a result, a stack filter which is optimal for signal estimation in the MSE sense can be determined as the solution of a linear program.
Symmetry and its Applications in Multidimensional Signal Processing

Symmetry is an important aspect of nature. It has been widely studied and used to simplify the analysis and design of physical systems as well as to add beauty and balance to them. This concept has been applied to abstract entities, as well as in the fields of quantum mechanics and crystallography. One- dimensional systems can only have a limited number of symmetries. Two-and higher dimensional systems may exhibit many types of symmetry. These may be used to advantage in reducing the complexity of design and implementation of such systems. In this talk, we will be concerned with various types of symmetries that may be present in two- or three- dimensional filters. Since the frequency and impulse response functions of these filters are related, symmetry in one function induces certain form of symmetry in the other. We will discuss these interdependencies and explore their applications. In particular, we will discuss the design of 2-D FIR and IIR filters by employing various types of symmetries in the frequency response of these filters. The usefulness of symmetry in reducing the complexity in 2-D FFT algorithm will also be considered. In addition, the question of stability of two- and higher dimensional systems, both with and without nonessential singularities of the second kind, will be examined.
Tracking Biological Cells in Time-Lapse Microscopy: An Adaptive Technique Combining Motion and Topological Features

This paper presents a vision-based method for automatic tracking of biological cells in time-lapse microscopy by combining the motion features with the topological features of the cells. The automation of tracking frequently faces problems of segmentation error and of finding correct cell correspondence in consecutive frames, since the cells are of varying size and shape, and may have uneven movement; these problems become more acute when the cell population is very high. To reduce the segmentation error, we introduce a cell-detection method based on h-maxima transformation, followed by the fitting of an ellipse for the nucleus shape. To find the correct correspondence between the detected cells, the topological features, namely, color compatibility, area overlap and deformation are combined with the motion features of skewness and displacement. This reduces the ambiguity of matching and constructs accurately the trajectories of the cell proliferation. Finally, a template-matching-based backward tracking procedure is employed to recover any break in a cell trajectory that may occur due to the segmentation errors or the presence of a mitosis. The tracking procedure is tested using a number of different cell sequences with nonuniform illumination, or uneven cell motion, and is shown to provide high accuracy both in the detection and the tracking of the cells.
Design of least asymmetric compactly supported orthogonal wavelets via optimization

The compactly supported orthogonal wavelets have found wide applications in practice. A disadvantage of these wavelets is that they are not symmetric. Thus they do not have a linear phase which is a property often desired in some applications such as signal processing. In this paper, degrees of freedom are introduced in the design to minimize the asymmetry of the wavelets. The results indicate that the compactly supported orthogonal wavelets designed via optimization are much less asymmetric and have an approximate linear phase.
Driving-point-function synthesis using nonuniform lines

Necessary and sufficient conditions for the synthesis of a driving-point function using any two kinds of one-port impedances are presented, and the result is used to develop a procedure for synthetising a driving-point function using tapered lines. The result also provides a short proof for O'Shea's necessary and sufficient conditions for the realisation of a driving-point function using symmetric structures. It is shown that any impedance which can be realised using symmetric lines may always be realised using the one-port impedances of the nonuniform transmission lines used in the construction of the symmetric lines. It is shown that all the existing synthesis procedures for the realisation of a driving-point function using any two one-port impedances are special cases of the general procedure advanced in this paper. Necessary and sufficient conditions for realising a driving-point function as a cascade of symmetric structures have been advanced. These are shown to be equivalent to those for realising the same driving-point function using the open- and short-circuit impedances of a symmetric structure. A procedure for such a cascade synthesis is advanced. If the structure is a uniform line, it provides a simple alternative to Wyndrum's cascade-synthesis procedure. An illustrative example is also included. By using known transformations, all the results may be applied to other types of lines such as lossless lines.
Wavelet-Based Video Denoising Using Gauss-Hermite Density Function

A new wavelet-domain video denoising scheme is proposed that exploits the Gauss-Hermite probability density function (p.d.f.) for spatial filtering of the noisy frame wavelet coefficients. It is observed that the proposed p.d.f. matches the empirical one very well as compared to other conventional density functions such as the generalized Gaussian and Bessel K-form densities. The proposed p.d.f. is used in an approximate minimum mean square error estimator for spatial filtering. Temporal filtering of a video sequence is performed by a motion detector and recursive time-averaging. Simulation results on standard video sequences show improved performance both in visual quality and in terms of peak signal-to-noise ratio as compared to other recent video denoising methods.
On global stability of a linear time-varying system in the presence of two kinds of local instabilities

In this paper, we introduce two new types of local instabilities which exclusively pertain to a linear time-varying (LTV) system. Investigation of these local instabilities has lead to new necessary and sufficient conditions for a LTV system to be globally stable. It is shown that a direct extension of the stability conditions of a linear time-invariant (LTI) system is not applicable to a LTV system. The admissible area for a set of coefficients of a globally stable LTV system has been established and this area is found to be much larger than its LTI system counterpart.
Structural local DCT sparse appearance model for visual tracking

The success of sparse representation in face recognition has motivated the development of sparse representation-based appearance models for visual tracking. These sparse representation-based trackers show state-of-the-art performance, but at the cost of computationally expensive l1-norm minimization. As the computational cost prevents the tracker from being used in real-time systems such as real-time surveillance and military operations, it has become a very important issue. With the aim of reducing the computational complexity of l1-norm minimization, a structural local DCT sparse appearance model is proposed in a particle filter framework. Application of DCT on local patches helps to reduce the dimensions of the dictionary as well as candidate samples by using low-pass filtered DCT coefficients. This in turn helps to remove the information relating to occlusion and background clutter thereby reducing the ambiguity created while computing the confidences of the target samples. The proposed method is evaluated on the challenging image sequences available in the literature and its performance compared with three recent state-of-the-art methods. It is shown that the proposed method provides superior/similar performance for most of the sequences with reduced computational complexity in l1-norm minimization.
A new two-stage method for single-microphone speech dereverberation

Single-microphone speech dereverberation is a challenging problem of de-convolving the reverberation produced by the room impulse response from the speech signal, when only one observation of the reverberant signal (one microphone) is available. By using linear prediction (LP)-residuals and spectral subtraction as two promising tools for dereverberation, a new technique is proposed. The first stage of the proposed technique consists of pre-whitening followed by a delayed long-term LP filtering whose kurtosis or skewness of LP-residuals is maximized to control the weight updates of the inverse filter. A nonlinear spectral subtraction scheme is the second stage of the proposed technique. It is shown that that the proposed algorithms outperform the existing major single-microphone methods in terms of a number of qualitative and quantitative measures.
A theorem in the theory of determinants and the number of spanning trees in a graph

A network-theoretic approach for counting the number of spanning trees of a graph is proposed. This approach is based on a theorem in the theory of determinants. Following this approach, a recurrence relation for counting Γ<sub>n</sub>, the number of spanning trees in a multigraph ladder having (<i>n</i>+1) nodes, is established. A recurrence relation is obtained connecting the sequences {<i>W</i><sub>n</sub>} and {Γ<sub>n</sub>} where <i>W</i><sub>n</sub> is the number of spanning trees in a multigraph wheel having (<i>n</i>+1) nodes. The significance of the approach is further illustrated by giving simple proofs of certain well-known results, in particular, the formula for counting the number of spanning trees in a cascade of 2-port networks.
Comments on "Minimal multiplier realization of 2-D all-pass digital filters" by K. Manivannan and C. Eswaran

Points out an error in the above paper (see ibid., vol.CAS-35, p.480-482, 1988) and also shows that the realization as such is not valid for filters of order greater than 1. The comments show that the method can be made to work for any order N, provided the delay network is modified by introducing a few more delay elements.