nils-herrmann/clean_scirepeval huggingface dataset:
Bipartite Approximation for Graph Wavelet Signal Decomposition

To compactly represent a graph signal in the frequency domain, critically sampled biorthogonal wavelet filterbanks have been proposed to decompose signals on bipartite graphs. However, in practice graph signals often reside on general graph structures that are not bipartite. Thus, an original nonbipartite graph must be decomposed into a sequence of bipartite graph approximations, so that the filterbanks can be applied successively for signal decomposition. In this paper, unlike previous proposals that are heuristic in nature, we design new bipartite approximation strategies for model-based and empirically derived probability distributions. In the first case, a signal prior assumes a Gaussian Markov Random Field (GMRF) model parametrized by the original graph. In the second case, beyond the GMRF signal prior, empirical signal observations are available to compute a posterior probability distribution. In both cases, we optimize for energy compaction in the bipartite subgraphs with two criteria: the Kullback–Leibler divergence metric, which encourages preserving the spectral characteristics of the original graph; and multiplicity of eigenvalue at frequency 1 for graph Laplacian, which is the frequency with minimal energy discrimination. The relative importance between the two criteria is determined by a proposed measure that evaluates the degree of mismatch between the signal prior and posterior. Given these criteria, we first design a global numerical optimization, and then propose a local heuristic approach for fast implementation with simplifications leading to local metric computation. Experimental results show that our proposed bipartite subgraph decomposition outperforms competing proposals in terms of energy compaction.
On the short-circuiting Fault Detection in a PMSM by means of Stator Current Transformations

This paper examines winding faults as short circuit for permanent magnet synchronous motor (PMSM). The study is carried out by means of stator current harmonic analysis. dq0 current transformation is also used to diagnose the state of the machine, and Wavelet approach is proposed to extend the analysis to the transitory of the failure. Experimental results for the full range of speeds have been obtained, which demonstrate the claims of the paper.
Sparsity-based retinal layer segmentation of optical coherence tomography images

A novel method for optical coherence tomography retinal image segmentation utilizing sparsity constraints is demonstrated. Retinal images are sparse in the layer domain. The algorithm thus transforms an input retinal image into a layer-like domain, and then uses graph theory and dynamic programming to extract the retinal layers from the sparse representation. The number of identified boundaries is not fixed and is determined by the algorithm at run-time. Results show that this method can segment up to nine layer boundaries without making overly restrictive assumptions about anatomic structure.
An algorithm for low memory wavelet image compression

As wavelet-based image coding is set to became more widely used (e.g. with the completion of the JPEG2000 standard), memory efficiency for wavelet-based coding is becoming an increasingly important issue. In this paper we present a complete system to perform low memory wavelet image coding. Our approach is "line-based" in that the images are read line by line and only the minimum required number of lines is kept in memory. The line-based transform is combined with a low memory entropy coder that does not require any global image information. Our system achieves a large (two orders of magnitude) reduction in memory requirements compared to other available coders, with limited performance loss (e.g., less than 0.5 dB).
On-Line System for Fault Detection in Induction Machines based on Wavelet Convolution

Motor current signature analysis has been successfully used in induction machines for fault diagnosis. The method, however, does not always achieve good results when the load torque is not constant. For proper results, temporal analysis on certain frequency harmonics is needed. This paper proposes an automatic system for fault analysis based on wavelet functions, which allows on-line fault detection in the time domain, achieving good experimental results both on stationary and non-stationary states.
Analysis and testing for error tolerant motion estimation

We propose a novel system-level error tolerance approach specifically targeted for multimedia compression algorithms. In particular, we focus on the motion estimation process performed by most video encoders. While current manufacturing process classifies fabricated systems into two classes, namely, perfect and imperfect, our proposed scheme employs categories which are based on acceptable/unacceptable performance degradation. By enabling the use of systems that would otherwise have been discarded we seek to increase the overall yield rate in the system fabrication process. To achieve this, we propose testing algorithms that aim at determining if faults in a given chip produce acceptable performance degradation, and we propose a technique which can cancel the effect of those among the acceptable faults that can be compensated.
Object-Based High Contrast Traveltime Tomography

We consider a traveltime tomography problem that involves detection of high-velocity structures in a homogeneous medium. If we only have limited measurements, this problem becomes an under-determined inverse problem and a common approach would be using prior information to guide the reconstruction process. We restrict the possible velocity into discrete values and model it as a discrete nonlinear inverse problem. However, typical iterative linearized reconstruction algorithms on grid-spacing model usually have very poor reconstruction results in the presence of high-contrast boundaries. The reason is that the travel path bends significantly near the boundary, making it very difficult to infer the travel path and velocity value from measured traveltime. To handle this scenario, we propose an object-based approach to model high-velocity structures by predefined convex objects. Compared to the typical grid-spacing model, which has variables that are proportional to the number of cells, our approach has an advantage that the number of unknown variables in the system is proportional to the number of objects, which greatly decreases the problem dimension. We have developed a fast algorithm to provide an estimate of the appearance probability of high-velocity structures in the region of interest. Simulations show that our method can efficiently sample the model parameter space, and provide more robust reconstruction results for the scenario where the number of measurements is limited.
Bilateral filter: Graph spectral interpretation and extensions

In this paper we study the bilateral filter proposed by Tomasi and Manduchi and show that it can be viewed as a spectral domain transform defined on a weighted graph. The nodes of this graph represent the pixels in the image and a graph signal defined on the nodes represents the intensity values. Edge weights in the graph correspond to the bilateral filter coefficients and hence are data adaptive. The graph spectrum is defined in terms of the eigenvalues and eigenvectors of the graph Laplacian matrix. We use this spectral interpretation to generalize the bilateral filter and propose new spectral designs of “bilateral-like” filters. We show that these spectral filters can be implemented with k-iterative bilateral filtering operations and do not require expensive diagonalization of the Laplacian matrix.
Redundant frame structure using M-frame for interactive light field streaming

A light field (LF) is a 2D array of closely spaced viewpoint images of a static 3D scene. In an interactive LF streaming (ILFS) scenario, a user successively requests desired neighboring viewpoints for observation, and in response the server must transmit pre-encoded data for correct decoding of the requested viewpoint images. Designing frame structures for ILFS is challenging, since at encoding time it is not known what navigation path a user will take, making differential coding very difficult to employ. In this paper, leveraging on a recent work on the merge operator - a new distributed source coding technique that efficiently merges differences among a set of side information (SI) frames into an identical reconstruction - we design redundant frame structures that facilitate ILFS, trading off expected transmission cost with total storage size. Specifically, we first propose a new view interaction model that captures view navigation tendencies of typical users. Assuming a flexible one-frame buffer at the decoder, we then derive a set of recursive equations that compute the expected transmission cost for a navigation lifetime of T views, given the proposed interaction model and a pre-encoded frame structure. Finally, we propose an algorithm that greedily builds a redundant frame structure, minimizing a weighted sum of expected transmission cost and total storage size. Experimental results show that our proposed algorithm generates frame structures with better transmission / storage tradeoffs than competing schemes.
Deconvolution problem to produce ultrasonic short pulses

The quality of a medical image is highly dependent on the axial resolution of the ultrasonic transducer, i.e. the duration of the transmitted and received ultrasonic pulses. The emission of short ultrasonic pulses can be accomplished by precompensating the electrical excitation applied to the transducer. To achieve this the impulse response of the transducer has to be determined beforehand. The paper presents a different approach to determine the impulse response of the transducer by using common deconvolution techniques. The main advantages of this approach are that it is easily programmable and that it avoids user interpretation, which is inherent in such deconvolution techniques. To prove the effectiveness of the method, the impulse response obtained is used to modify the electrical excitation applied to a piezoelectric ultrasonic transducer to produce a short emitted/received signal. Experimental results show that a transducer having more than one resonance peak may be compensated resulting in an increase of axial resolution.
DCT computation based on variable complexity fast approximations

We investigate input dependent, variable complexity algorithms to compute the DCT. The basic goal of these algorithms is to not compute those DCT coefficients that will be quantized to zero. These algorithms exploit the fact that for compression applications (i) most of the energy is concentrated in a few DCT coefficients and (ii) as the quantization step size increases an increased number of coefficients is set to zero and reduced precision computation of the DCT may be tolerable. Thus we propose two classes of algorithms, the first one selectively prunes the DCT computation while the second uses an approximate computation, without floating point multiplications, that is matched to the quantization level selected.
Stochastic rate-control of video coders for wireless channels

We study the transmission of real-time video over wireless channels, proposing a formulation of the problem that includes a priori stochastic models for both source and channel. Using techniques of stochastic dynamic programming, we obtain offline optimal policies for each system state that minimize the average expected frame distortion. The online complexity of our approach is only that required to identify the state of the system (source and channel). The state of the channel is obtained based on the ARQ error-control mechanism, and the source state is computed as a complexity measurement on each incoming frame. Simulation results based on this new approach are provided and compared to other proposed rate-control strategies. They show how our model-based optimal policies require negligible on-line computational cost while providing a transmission quality comparable to that achieved with more complex deterministic dynamic programming techniques, and better than for simpler algorithms such as TMN.
Computation Error Tolerance in Motion Estimation Algorithms

In this paper we study the computation error tolerance properties of motion estimation algorithms. We are motivated by two scenarios where hardware systems may introduce computation errors. First, we consider hardware faults such as those arising in a typical fabrication process. Second, we consider "soft" errors due to voltage scaling, which can arise when operating at a lower voltage than specified for the system. Current practice is to discard all faulty systems. However there is an increasing interest in tools that can identify faulty systems which provide acceptable performance. We show that motion estimation (ME) algorithms exhibit significant error tolerance in these two scenarios. We propose simple error models and use these to provide insights into what features in these ME algorithms lead to increased error tolerance. Our comparison of the full search ME and a state of the art fast ME approach in the context of H.264/AVC shows that while both techniques are error tolerant, the faster algorithm is in fact more robust to computation errors.
Rate-complexity-distortion optimization for quadtree-based DCT coding

We study rate-complexity-distortion (R-C-D) tradeoffs for video coding, where we focus on the complexity of computing the inverse DCT. A quadtree coding approach is used and the quadtree is optimized based on the constraints of not only a rate budget but also decoding complexity budget. We employ a variable complexity algorithm (VCA) for the IDCT in order to obtain better complexity result. The main novelty of this work is to demonstrate that VCA approaches combined with R-C-D optimization can provide better results (e.g., lower rate at same distortion and complexity) than approaches proposed in the past, which relied on fixed blocksize and fixed complexity techniques.
Application-optimized quantizers for time delay estimation in sensor networks

Sensor networks have emerged as a fundamentally new tool for monitoring inaccessible environments. They are distinguished from traditional sensors by strict limitations on system bandwidth and sensor energy resources. These constraints motivate the use of data compression at each sensor. Location finding is an important application of sensor networks, and estimation of the time delay between data from different sensors is a key step in localization. In this work, new quantizer designs specific to the time-delay estimation problem in sensor networks are presented. The goal for these new application-specific encoders is to achieve the best time delay estimate at a given bandwidth budget or latency bound, or minimize the rate required to reach an estimate with desired accuracy.
Efficient data-gathering using graph-based transform and compressed sensing for irregularly positioned sensors

In this work, we propose a decentralized approach for energy efficient data-gathering in a realistic scenario. We address a major limitation of compressed sensing (CS) approaches proposed to data for wireless sensor network (WSN), namely, that they work only on a regular grid tightly coupled to the sparsity basis. Instead, we assume that sensors are irregularly positioned in the field and do not assume that sparsifying basis is known a priori. Under the assumption that the sensor data is smooth in space, we propose to use a graph-based transform (GBT) to sparsify the sensor data measured at randomly positioned sensors. We first represent the random topology as a graph then construct the GBT as a sparsifying basis. With the GBT, we propose a heuristic design of the data-gathering where aggregations happen at the sensors with fewer neighbors in the graph. In our simulations, our proposed approach shows better performance in terms of total power consumption for a given reconstruction MSE, as compared to other CS approaches proposed for WSN.
Bayesian detection of recurrent copy number alterations across multiple array samples

Copy number alterations (CNA) affecting small portions of chromosomes are difficult to identify. Advances in microarray technology now allow very high resolution scans of large cohorts of samples but at the price of severe noise degradation. Our proposed genome alteration detection algorithm (GADA) has been shown to be a highly accurate and efficient approach to analyze a single array sample. In this paper, the sparse Bayesian learning (SBL) used in GADA is extended to find CNA on multiple samples that share breakpoint positions but may have different magnitude of alteration. Our model is especially well suited to analyze sample replicates, i.e., multiple arrays from the same specimen. Our results show that replicates greatly improve the accuracy and robustness in detection. In some cases, a single replicate sample offers an accuracy equivalent to a 2-fold increase in the signal to noise ratio, while reducing by up to a 50% the detection of false CNA caused by outliers. The computational cost of the algorithm is essentially linear O(NM) in the number of the microarray probes M and samples N. In conclusion, the multiple sample GADA (N-GADA) presented here appears to be a promising tool for finely locating small CNAs that are shared across multiple samples.
3-D video quality improvement using depth transition data

To improve rendered view quality in a 3-D video system, we propose to encode and transmit depth transition data, which represents, for each pixel in a frame, the location in between two existing views where the depth corresponding to that pixel changes. Given the highly localized and non-linear characteristics of rendered view distortion, it is possible to achieve better coding performance by providing this depth transition data only for subjectively important regions. In this paper, a method to apply the depth transition data to the view rendering procedure is proposed. Experimental results verify that improvements in subjective quality can be achieved by the proposed method.
Comopact image representation using wavelet lifting along arbitrary trees

In this work, we present a method for achieving a compact image representation by applying wavelet lifting along an arbitrary combination of trees. This is done by extending a lifting transform along a single tree to a series of lifting transforms along a number of different trees, each of which exploits directionality differently. The trees are constructed in ways that force tree edges to follow the geometric flows inherent in images. In particular, we propose a method for constructing trees that trace out geometric flows using edges in the image. Our preliminary results demonstrate promising performance in terms of PSNR as a function of the percentage of retained coefficients, motivating its potential for image coding.
A graph-based joint bilateral approach for depth enhancement

Depth images are often presented at a lower spatial resolution, either due to limitations in the acquisition of the depth or to increase compression efficiency. As a result, upsampling low-resolution depth images to a higher spatial resolution is typically required prior to depth image based rendering. In this paper, depth enhancement and up-sampling techniques are proposed using a graph-based formulation. In one scheme, the depth is first upsampled using a conventional method, then followed by a graph-based joint bilateral filtering to enhance edges and reduce noise. A second scheme avoids the two-step processing and upsamples the depth directly using the proposed graph-based joint bilateral upsampling. Both filtering and interpolation problems are formulated as regularization problems and the solutions are different from conventional approaches. Further, we also studied operations on different graph structures such as star graph and 8-connected graph. Experimental results show that the proposed methods produce slightly more accurate depth at the full resolution with improved rendering quality of intermediate views.
Generation of redundant frame structure for interactive multiview streaming

While multiview video coding focuses on the rate-distortion performance of compressing all frames of all views, we address the problem of designing a frame structure to enable interactive multiview streaming, where clients can interactively switch views during video playback. Thus, as a client is playing back successive frames (in time) for a given view, it can send a request to the server to switch to a different view while continuing uninterrupted temporal playback. Noting that standard tools for random access (i.e., I-frame insertion) can be inefficient for this application, we propose a technique where redundant representations of some frames can be stored to facilitate view switching. We first present an optimal algorithm with exponential running time that generates such a frame structure so that the expected transmission rate is optimally traded off with total storage. We then present methods to reduce the algorithm complexity for practical use. We show in our experiments that we can generate redundant frame structures offering a range of tradeoff points with transmission and storage, including ones that outperformsimple I-frame insertion structures by up to 48%in terms of bandwidth efficiency for similar storage costs.
Ups and Downs in Buzzes: Life Cycle Modeling for Temporal Pattern Discovery

In social media analysis, one critical task is detecting burst of topics or buzz, which is reflected by extremely frequent mentions of certain key words in a short time interval. Detecting buzz not only provides useful insights into the information propagation mechanism, but also plays an essential role in preventing malicious rumors. However, buzz modeling is a challenging task because a buzz time-series usually exhibits sudden spikes and heavy tails, which fails most existing time-series models. To deal with buzz time-series sequences, we propose a novel time-series modeling approach which captures the rise and fade temporal patterns via Product Life Cycle (PLC) models, a classical concept in economics. More specifically, we propose a mixture of PLC models to capture the multiple peaks in buzz time-series and furthermore develop a probabilistic graphical model (K-MPLC) to automatically discover inherent life cycle patterns within a collection of buzzes. Our experiment results show that our proposed method significantly outperforms existing state-of-the-art approaches on buzzes clustering.
Image coding based on multiple projections and multistage vector quantization

The vast majority of practical image coding systems used today are based on the transform coding paradigm, where image blocks are projected into a series of basis functions, and the expansion coefficients are subsequently quantized. In this paper we introduce a novel constrained vector quantizer (VQ), which we call seg-VQ. As an extension of the transform coding framework, in our approach codevectors are constrained to be located on a series of line segments in the multidimensional space. These segments are designed sequentially based on a training set. The advantages of seg-VQ are twofold: first, the encoding complexity is proportional to the number of segments rather than to the number of codevectors, and second, it can efficiently exploit the directional preferences (correlations) in sources such as images. For image sources, at low dimensions (e.g., 4 by 4 blocks), at the same encoding complexity with TSVQ, seg-VQ outperforms TSVQ by 0.5 dB at 0.4375 bpp achieving a performance close to unconstrained VQ obtained by pairwise nearest neighbor (PNN) initialized GLA. At higher dimensions (e.g., 8 by 8 blocks) we use multistage seg-VQ where the input block (as in transform coding) is projected into a series of segments in order to be quantized.