nils-herrmann/clean_scirepeval huggingface dataset:
Scalable variable complexity approximate forward DCT

The discrete cosine transform (DCT) is one of the major components in most of image and video compression systems. The variable complexity algorithm framework has been applied successfully to achieve complexity savings in the computation of the inverse DCT in decoders. These gains can be achieved due to the highly predictable sparseness of the quantized DCT coefficients in natural image/video data. With the increasing demand for instant video messaging and two-way video transmission over mobile communication systems running on general-purpose embedded processors, the encoding complexity needs to be optimized. In this paper, we focus on complexity reduction techniques for the forward DCT, which is one of the more computationally intensive tasks in the encoder. Unlike the inverse DCT, the forward DCT does not operate on sparse input data, but rather generates sparse output data. Thus, complexity reduction must be obtained using different methods from those used for the inverse DCT. In the literature, two major approaches have been applied to speed up the forward DCT computation, namely, frequency selection, in which only a subset of DCT coefficients is computed, and accuracy selection, in which all the DCT coefficients are computed with reduced accuracy. These two approaches can achieve significant computation savings with minor output quality degradation, as long as the coding parameters are such that the quantization error is larger than the error due to the approximate DCT computation. Thus, in order to be useful, these algorithms have to be combined using an efficient mechanism that can select the "right" level of approximation as a function of the characteristics of the input and the target rate, a selection that is often based on heuristic criteria. In this paper, we consider two previously proposed fast, variable complexity, forward DCT algorithms, one based on frequency selection, the other based on accuracy selection. We provide an explicit analysis of the additional distortion that each scheme introduces as a function of the quantization parameter and the variance of the input block. This analysis then allows us to improve the performance of these algorithms by making it possible to select the best approximation level for each block and a target quantization parameter. We also propose a hybrid algorithm that combines both forms of complexity reduction in order to achieve overall better performance over a broader range of operating rates. We show how our techniques lead to scalable implementations where complexity can be reduced if needed, at the cost of small reductions in video quality. Our hybrid algorithm can speed up the DCT and quantization process by close to a factor of 4 as compared to fixed-complexity forward DCT implementations, with only a slight quality degradation in PSNR.
Sparse inverse bilateral filters for image processing

The bilateral filter (BF) is a prominent tool for adaptive, structure-preserving image filtering. It can be interpreted as a graph-based filter, where the nodes of the graph correspond to image pixels and link weights correspond to filter coefficients. Graphs associated to BFs of typical sizes used in practice are very dense. In this paper, we propose an efficient method for constructing a sparse graph for adaptive filtering of an image. The Laplacian matrix of the proposed sparse graph approximates the inverse of a dense BF kernel matrix. This is analogous to the idea of finding a sparse inverse covariance of a Gaussian Markov random field (GMRF) with a dense covariance matrix. The eigenvectors of the proposed graph Laplacian are approximately equal to the eigenvectors of the BF graph and allow for low frequency representation of the image similar to the BF eigenvectors. Filters in the form of polynomials of this sparse Laplacian offer a more flexible and less computationally complex alternative to a dense BF, with similar performance.
Efficient quantization for overcomplete expansions in RN

We study construction of structured regular quantizers for overcomplete expansions in /spl Ropf//sup N/. Our goal is to design structured quantizers which allow simple reconstruction algorithms with low complexity and which have good performance in terms of accuracy. Most related work to date in quantized redundant expansions has assumed that the same uniform scalar quantizer was used on all the expansion coefficients. Several approaches have been proposed to improve the reconstruction accuracy, with some of these methods having significant complexity. Instead, we consider the joint design of the overcomplete expansion and the scalar quantizers (allowing different step sizes) in such a way as to produce an equivalent vector quantizer (EVQ) with periodic structure. The construction of a periodic quantizer is based on lattices in /spl Ropf//sup N/ and the concept of geometrically scaled- similar sublattices. The periodicity makes it possible to achieve good accuracy using simple reconstruction algorithms (e.g., linear reconstruction or a small lookup table).
Geometric-guided label propagation for moving object detection

Moving object segmentation in video has uses in many applications and is a particularly challenging task when the video is acquired by a moving camera. Typical approaches that rely on principal component analysis (PCA) tend to extract scattered sparse components of the moving objects and generally fail in extracting dense object segmentations. In this paper, a novel label propagation framework based on motion vanishing point (MVP) analysis is proposed to address the challenges. A weighted graph is constructed with image pixels as nodes and the MVP-guided approach is used to define the graph weights. Label propagation is then performed by incorporating the graph Laplacian. In addition, a PCA result is used to initialize the foreground/background labels. Experiments on the Hopkins data set of outdoor sequences captured by a hand-held moving camera demonstrate that the proposed label propagation method outperforms state-of-the-art PCA and spectral clustering methods for a dense segmentation task. Moreover, the framework is capable of correcting mislabeled foreground pixels and thus does not require accurate initial label assignment.
Markov chain sparsification with independent sets for approximate value iteration

The ever-increasing size of wireless networks poses a significant computational challenge for policy optimization schemes. In this paper, we propose a technique to reduce the dimensionality of the value iteration problem, and thereby reduce computational complexity, by exploiting certain structural properties of the logical state transition network. Specifically, our method involves approximating the original Markov chain by a simplified one whose state transition graph contains an independent set of a prespecified size, thus resulting in a sparsification of the transition probability matrix. As a result, value iteration needs to be performed only on the vertex cover of the network, from which the value function on the independent set can be obtained in a one-step process via interpolation. The Markov chain approximation process presented in this paper, for a given choice of independent set, involves minimizing matrix distance defined in terms of Frobenius norm or the Kullback-Leibler distance. This minimum distance then helps us to define a cost that can be minimized through an iterative greedy algorithm to obtain an approximately optimal independent set. Our method provides a tradeoff between accuracy and complexity that one can exploit by choosing the size of the independent set. Numerical results show that for a class of “collision” networks the value function approximation is accurate, even with a large independent set.
Graph based transforms for depth video coding

In this paper a graph-based transform is proposed as an alternative to the discrete cosine transform. An image or video signal is represented as a graph signal, where the graph is generated so as not to cross an image edge in a local region, i.e., square block. Then, spectral representation of graph signal is used to form transform kernels by finding eigenvectors of Laplacian matrix of the graph. This method requires to include additional information, i.e., edge map or adjacency matrix, into a bitstream so that a decoder can regenerate the exactly same graph used at an encoder. The novelty of this paper includes finding the optimal adjacency matrix and compressing it using context-based adaptive binary arithmetic coding. Coding efficiency improvement can be achieved when an image block contains arbitrarily shaped edges by applying the transform not across the edges. The proposed transform is applied to coding depth maps used for view synthesis in a multi-view video coding system, and provides 14% bit rate savings on average.
On line measurement of the mould distribution temperatures during the casting cycle time

This research develops a temperature control that may be used for the design of an aluminium moulding machine taking into account the dynamics of the thermal behaviour. Therefore an implementation of a temperature's acquisition system in a foundry's mould is presented in order to obtain a more precise mathematic model. Thanks to this information we will have a deeper knowledge about alloy's liquid-to-solid changes. This transformation is critical in very short periods due to the necessity of an uniform cooling speed. Because of the lack of neither symmetry nor uniformity shape it is difficult to accomplish this. For a fast cooling a water cooling system inside the mould is required. The design of the mould is the most important part because the water cooling conduits must be very well calculated.
Design of Shading Coils for Minimizing the Contact Bouncing of AC Contactors

For many decades, electromechanical contactors have been widely used in the industry, and over 80% are equipped with AC powered coils. Electromechanical contactors have the drawback of the mechanical movement of some of their parts and the wear related with it. This movement causes the contacts to impact during closure, producing a bouncing of the contacts and consequently the estimated life of the contacts is dramatically reduced. The coil of the contactor can be powered by an AC or a DC supply. When dealing with DC powered coils, they generate a constant magnetic field that guarantees an appropriate closing of the contacts. However, AC powered coils generate sinusoidal magnetic fields which produce a force that drops to zero twice each cycle (50/60 Hz). This creates an undesired chatter or pulsation of the contacts. This harmful effect can be cancelled by using a shading ring placed in the contactor core. In a well- designed shaded pole contactor the magnetic force should never drop below the springs force in order to avoid the chattering of the contacts. In this work the influence of shading rings parameters on the behavior of the dynamic response of the contactor is studied and results are presented.
Adaptive quantization of image subbands with efficient overhead rate selection

Subband image coding techniques owe much of their success to an effective use of adaptive quantization and adaptive entropy coding. It is often the case that adaptive quantization is achieved by defining a discrete set of quantizers from which one is chosen for a given set of coefficients. This type of forward adaptation thus requires that overhead information (the choice of quantizer) be sent to the decoder. Then, the quantized coefficients are transmitted using adaptive entropy coding, typically through backward adaptive arithmetic coding. We show that a combination of forward and backward adaptation methods can be used to update the quantizers thus reducing the overhead requirements while still providing good performance. Specifically, we present an algorithm where each coefficient is classified into several classes based on the past quantized data and where the quantizer to be used for each class can itself be adapted on the fly.
Block Diagonal Linear Discriminant Analysis with Sequential Embedded Feature Selection

Model selection and feature selection are usually considered two separate tasks. For example, in a linear discriminant analysis (LDA) setting, a modeling assumption is typically made first (e.g., a full or a diagonal covariance matrix can be chosen) and then with this model the feature subset providing the best prediction performance is selected. If limited training data is available, then the number of parameters of a model that can be reliably estimated will also be limited. In the context of LDA, model selection basically entails simplifying the covariance matrix by setting to zero some of this components. This leads to different block diagonal matrix structures (e.g., full/diagonal) which involve different sets of features and require different parameters to be estimated. In this paper we argue that LDA feature and parameter selection should be done jointly; and we propose a greedy algorithm for joint selection of features and of a block diagonal structure for the covariance matrix. To the best of our knowledge this is the first time such a joint design has been proposed in the context of LDA. The choice of a block diagonal structure is motivated by microarray classification problems, where we have a very large amount of features, i.e., genes, that are expected to be corregulated in small groups. Results obtained with artificial datasets show that the algorithm can flexibly choose an adequate covariance matrix structure according to the size of the training set and the generating distribution. Our results consistently outperform those achieved with other LDA based techniques
Bearing Fault Detection by a Novel Condition-Monitoring Scheme Based on Statistical-Time Features and Neural Networks

Bearing degradation is the most common source of faults in electrical machines. In this context, this work presents a novel monitoring scheme applied to diagnose bearing faults. Apart from detecting local defects, i.e., single-point ball and raceway faults, it takes also into account the detection of distributed defects, such as roughness. The development of diagnosis methodologies considering both kinds of bearing faults is, nowadays, subject of concern in fault diagnosis of electrical machines. First, the method analyzes the most significant statistical-time features calculated from vibration signal. Then, it uses a variant of the curvilinear component analysis, a nonlinear manifold learning technique, for compression and visualization of the feature behavior. It allows interpreting the underlying physical phenomenon. This technique has demonstrated to be a very powerful and promising tool in the diagnosis area. Finally, a hierarchical neural network structure is used to perform the classification stage. The effectiveness of this condition-monitoring scheme has been verified by experimental results obtained from different operating conditions.
Treelets as feature transformation tool for block diagonal linear discrimination

The main novelty of this paper is to apply treelets as a feature transformation tool prior to using block diagonal linear discriminant analysis (BDLDA). Using pairwise feature transformations, treelets seek to approximate the decorrelation behavior of principal component analysis (PCA) without requiring identifying the eigenvectors of the full covariance matrix. Instead, PCA is successively applied to pairs of features, which makes it possible to reduce the magnitude of off-diagonal terms of the resulting covariance matrix, even in scenarios where the number of available training samples is small relative to the feature vector dimension. BDLDA seeks to find a block diagonal approximation to the covariance matrix that can capture the most discriminant sets of features and their correlations. Treelet-based preprocessing facilitates the BDLDA search primarily because each treelet coefficient is a linear combination of several original features. Blocks obtained after applying BDLDA to treelet coefficients are in effect larger (a larger number of original features are used). Thus treelets combined with BDLDA makes it possible to find block diagonal structures with larger effective blocks, so that the correlations between a larger number of features can be taken into account. Our experiments demonstrate that this leads to better classification performance for real DNA expression data than state of the art LDA-based techniques.
Lifting factorization-based discrete wavelet transform architecture design

In this paper, two new system architectures, overlap-state sequential and split-and-merge parallel, are proposed based on a novel boundary postprocessing technique for the computation of the discrete wavelet transform (DWT). The basic idea is to introduce multilevel partial computations for samples near data boundaries based on a finite state machine model of the DWT derived from the lifting scheme. The key observation is that these partially computed (lifted) results can also be stored back to their original locations and the transform can be continued anytime later as long as these partial computed results are preserved. It is shown that such an extension of the in-place calculation feature of the original lifting algorithm greatly helps to reduce the extra buffer and communication overheads, in sequential and parallel system implementations, respectively. Performance analysis and experimental results show that, for the Daubechies , p.247-69, 1998) (9,7) wavelet filters, using the proposed boundary postprocessing technique, the minimal required buffer size in the line-based sequential DWT algorithm is 40% less than the best available approach. In the parallel DWT algorithm we show 30% faster performance than existing approaches.
Motor fault classification system including a novel hybrid feature reduction methodology

The fault diagnosis field is in a continuous movement towards the generation of more reliable and powerful machine health monitoring schemes. Improved data processing methodologies are required to reach high diagnosis demands. For that reason, a contribution in motor fault classification methodology is presented. Different physical magnitudes such as phase currents, voltages and vibrations, are acquired from an electromechanical system based on Brushless DC motor. Statistical features, from time and frequency domains, are calculated to supply a classification algorithm based on Neural Network and enhanced by Genetic Algorithm. The significance of feature space dimensionality, related with the number of used features, for classification success is analyzed. The combination of a feature selection technique (by Sequential Floating Forward Selection), with a feature extraction technique (by Principal Component Analysis), is proposed as a novel hybrid feature reduction methodology to improve the classification performance in electrical machine fault diagnosis. The proposed methodology is validated experimentally and compared with classical feature reduction strategies.
Buffer control for variable complexity Fano decoders

Fano sequential decoders are variable complexity convolutional decoders, which have the desirable property of operating with very low computation at high SNR. In portable mobile communications, it is often desirable to trade BER with decoder complexity/power consumption. However, the variable complexity nature of the Fano algorithm means that buffers are required for the Fano decoder due to large variations in processing delays. In this paper, we formulate the buffer control problem as one that seeks to minimize the overall probability of block loss, subject to a finite buffer size constraint. The overall probability of block loss is comprised of two terms, corresponding to loss due to excessive bit errors and decoder buffer overflow, respectively. This leads to an interesting trade-off, as faster decoding often means higher bit error rate. Based on the joint distribution of decoding complexity and BER, at each decoding stage, we find an optimal Fano decoder parameter (/spl Delta/) to minimize block loss. In addition, we propose a simple real-time table lookup algorithm that implements the /spl Delta/ control policy. Simulation results demonstrate the superior performance of the proposed algorithm.
Manifold denoising based on spectral graph wavelets

We propose a new framework for manifold denoising using the Spectral Graph Wavelet transform, which enables non-iterative denoising directly in the graph frequency domain, an approach inspired by conventional wavelet-based signal denoising methods. We theoretically justify our approach, based on the fact that for smooth manifolds the coordinate information tends to create energy in the low spectral graph wavelet coefficients, while the noise affects all frequency bands in a similar way. Experimental results show that our suggested manifold frequency denoising (MFD) approach significantly outperforms the state of the art manifold denosing methods, and is robust to a wide range of parameter selections, e.g., the choice of k nearest neighbor connectivity of the graph.
Optimized distributed 2D transforms for irregularly sampled sensor network grids using wavelet lifting

We address the design and optimization of an energy-efficient lifting-based 2D transform for wireless sensor networks with irregular spatial sampling. The 2D transform is designed to allow for unidirectional computation found in existing path-wise transforms, thereby eliminating costly backward transmissions often required by existing 2D transforms, while simultaneously achieving greater data decorrelation than those path-wise transforms. We also propose a framework for optimizing the 2D transform via an extension of standard dynamic programming (DP) algorithms, where a selection is made among alternative coding schemes (e.g., different number of levels in the wavelet decomposition). A recursive DP formulation is provided and an algorithm is given that finds the minimum cost coding scheme assignment for our proposed 2D transform.
Perceptually based video rate control using pre-filtering and predicted rate-distortion characteristics

In digital video coding, the rate control scheme is essential to regulate the output data rate and maintain the output quality. The control scheme defined in MPEG Test Model 5 provides a solution with very light computational overhead, but the results are not guaranteed to be good for all video sources and channel rates without resorting to a manual "tweaking" of the control parameters, with many trial-and-error encoding tests. In this paper, we propose a rate-distortion based control scheme where we use pre-filtering and block classification to achieve higher quality at low rates. In particular, we show how to efficiently include the pre-filtering parameters as part of the rate control optimization process. Our coded sequences are compatible with standard MPEG decoders and our method is suitable for channel rates lower than those normally used for MPEG-1 sequences (around 1 Mbps), but which may be more appropriate for Internet applications. In addition, our algorithm is generic and can be readily be extended for H.263/MPEG-4 encoders. Our results show significant reductions of the blockiness usually encountered at low rates, when compared to schemes such as TM5.