nils-herrmann/clean_scirepeval huggingface dataset:
Online learning in wireless networks via directed graph lifting transform

Due to the complexity of wireless network operations, estimation of cost-to-go functions requires a large number of observations and is impractical in many real-world networks. In this paper, a novel framework for the online learning of cost-to-go functions using a local wavelet transform is presented. The proposed technique allows a considerable reduction in the number of observations needed for accurate estimation. The approach is based on the representation of the trajectory of the logical state of the network as a graph. The observed state trajectory (and thus cost trajectory) is projected onto a subset of the nodes to construct a small graph summarizing paths of the overall graph. Low-complexity local lifting transform operations, then, are used to recover the cost-to-go function on the whole graph. Numerical results for a wireless network with ~ 1000 states show that the estimation error of the proposed technique is decreased by ~ 50% in the early stages of learning with respect to standard Q-learning.
Bipartite subgraph decomposition for critically sampled wavelet filterbanks on arbitrary graphs

The observation of frequency folding in graph spectrum during down-sampling for signals on bipartite graphs-analogous to the same phenomenon in Fourier domain for regularly sampled signals-has led to the development of critically sampled wavelet filterbanks such as GraphBior. However, typical graph-signals live on general graphs that are not necessarily bipartite. To decompose a non-bipartite graph into a series of bipartite subgraphs so that two-channel filterbanks can be applied iteratively, we propose a new algorithm based on two criteria easily computed in the vertex domain aiming at compact signal representation in the wavelet domain. Given that filterbanks have minimal frequency discrimination at 1, the first criterion aims to minimize the multiplicity of mid graph frequency 1. The second criterion aims to preserve the edge structure of the original graph, which may reflect correlations among signal samples, so that a signal projected on approximated bipartite subgraphs can nonetheless be well represented using low frequency components. Experimental results show that our proposed bipartite subgraph decomposition outperforms competing proposals in terms of energy compaction.
Intra-Prediction and Generalized Graph Fourier Transform for Image Coding

Intra-prediction is employed in block-based image coding to reduce energy in the prediction residual before transform coding. Conventional intra-prediction schemes copy directly from known pixels across block boundaries as prediction. In this letter, we first cluster differences between neighboring pixel pairs. Then, for each pixel pair, we add the cluster mean to the known pixel for prediction of the neighboring unknown pixel. The cluster indices are transmitted per block, allowing the decoder to mimic the same intra-prediction. We then propose an optimized transform for the prediction residual, based on a generalized version of previously developed Graph Fourier Transform (GFT). Experimental results show that our generalized intra-prediction plus transform coding outperforms combinations of previous intra-prediction and ADST coding by 2.5 dB in PSNR on average.
A Dynamic Programming Approach to Distortion-Energy Optimization for Distributed Wavelet Compression with Applications to Data Gathering Inwireless Sensor Networks

We address a scenario where energy-constrained sensors in a wireless sensor network can choose among different distributed coding schemes to encode their data. We propose a framework where the network is described as a graph, with sensors representing the nodes, and where communication and processing costs are associated to edge weights and the coding schemes associated to states of operation. After describing data transitions and edge costs, we show that a shortest-path algorithm can be used to find the optimum network configuration, i.e., the one that leads to the lowest overall energy consumption
Unidirectional graph-based wavelet transforms for efficient data gathering in sensor networks

We design lifting-based wavelet transforms for any arbitrary communication graph in a wireless sensor network (WSN). Since transmitting raw data bits along the routing trees in WSN usually requires more bits than transmitting encoded data, we seek to minimize raw data transmissions in the network. We especially focus on unidirectional transforms which are computed as data is forwarded towards the sink on a routing tree. We formalize the problem of minimizing the number of raw data transmitting nodes as a weighted set cover problem and provide greedy approximations. We compare our method with existing distributed wavelet transforms on communication graphs. The results validate that our proposed transforms reduce the total energy consumption in the network with respect to existing designs.
Localization bounds for the graph translation

The graph translation operator has been defined with good spectral properties in mind, and in particular with the end goal of being an isometric operator. Unfortunately, the resulting definitions do not provide good intuitions on a vertex-domain interpretation. In this paper, we show that this operator does have a vertex-domain interpretation as a diffusion operator using a polynomial approximation. We show that its impulse response exhibit an exponential decay of the energy way from the impulse, demonstrating localization preservation. Additionally, we formalize several techniques that can be used to study other graph signal operators.
Edge-adaptive transforms for efficient depth map coding

In this work a new set of edge-adaptive transforms (EATs) is presented as an alternative to the standard DCTs used in image and video coding applications. These transforms avoid filtering across edges in each image block, thus, they avoid creating large high frequency coefficients. These transforms are then combined with the DCT in H.264/AVC and a transform mode selection algorithm is used to choose between DCT and EAT in an RD-optimized manner. These transforms are applied to coding depth maps used for view synthesis in a multi-view video coding system, and provides up to 29% bit rate reduction for a fixed quality in the synthesized views.
Short circuit fault detection in PMSM by means of empirical mode decomposition (EMD) and wigner ville distribution (WVD)

This paper presents and analyzes short circuit failures for Permanent Magnet Synchronous Motor (PMSM). The study includes stable condition and speed transients in simulation and realistic experimental conditions. The stator current is analyzed by the empirical mode decomposition (EMD) method, which will generate a collection of intrinsic mode functions (IMF). Finally, the Hilbert-Huang transform (HHT) is used to compute the instantaneous frequencies resulting from the IMFs obtained from the stator currents. Moreover, the IMF 1 and IMF 2 have been analyzed by means of Wigner Ville distribution (WVD). Experimental laboratory results validate the analysis and demonstrate that this kind of time-frequency analysis can be applied to detect and identify short circuit failures in synchronous machines.
Broken Bearings and Eccentricity Fault Detection for a Permanent Magnet Synchronous Motor

The paper presents an experimental study of the broken bearings and eccentricity faults detection for a permanent magnet synchronous motor (PMSM). Conclusions are obtained from the analysis of the stator current harmonic spectrum. The analysis is spread to a zero component and q axis component of the stator current, for the whole range of rotor speeds
Fault detection techniques for induction motors

Motor Current Signature Analysis (MCSA) is being the most widely used method to identify faults in Induction Motors. MCSA focuses its effort in the spectral analysis of the stator current, by analysis of the resulting harmonics when the machine is working under failure state. Motors faults such as broken rotor bars, bearing damage and eccentricity of the rotor axis can be detected. However, the method presents some problems at low speed and low torque, mainly due to the proximity between the frequencies to detect and the small amplitude of the resulting harmonics respectively. In both cases, the problem of frequency accuracy is very tricky since the side band harmonic is closed to the fundamental. Since the accuracy is reverse proportional to the data acquisition time, the spectrum read could be meaningless. Therefore, it is difficult to evaluate the correct magnitude of the side band harmonics even when windowing. This paper proposes to inject into the machine under test an additional voltage at frequency different from the fundamental, and then to study the resulting harmonics around the new frequencies appearing due to composition between injected and main frequencies. Fault detection, Induction motor, Electrical drives
Call admission: dimensioning a single VBR video source using transition probability matrix

This paper describes a novel and simple comparison method for variable bit rate (VBR) video sources according to their average excess rate/frame. We present the parameter average excess rate/frame (/spl alpha/) as an alternative to the traditional parameters used in call admission, e.g. long term rate, variance, peak rate. We propose a way of calculating /spl alpha/ through the use of transition probability matrices (TPM) of VBR sources, specifically using their holding times and asymptotic mean occupancies. A useful property of the proposed algorithm is that it allows comparisons between TPMs of different sizes and VBR video sequences of different durations. This algorithm can serve as an aid in call admission for bandwidth allocation purposes.
Reconstruction algorithm for high contrast velocity travel time tomography

We consider travel time tomography problems involving detection of high contrast, discrete high velocity lines. This results in a discrete nonlinear inverse problem, for which traditional least-squares reconstruction algorithms are not suitable, as they tend to result in oscillations in the estimated values of the ray-path matrix. We propose a new algorithm that provides a more stable reconstruction for high contrast velocity scenarios. Our approach is based on using multiple candidate discrete high velocity lines along with a probabilistic mixture model that captures the likelihood of each of the lines. We propose an iterative algorithm based on a graphical model that successively updates the length and probability of these line structures. Preliminary simulation results show that exact reconstruction can be achieved in cases when the ground-truth lines are a subset of candidate structures.
Fast implementation for symmetric non-separable transforms based on grids

When a line graph is symmetric, the associated graph Fourier transform has a fast implementation. In this paper, we extend this idea to the 2D non-separable case, where the graph of interest is a square-shaped grid. We investigate a number of symmetry types for 2D grids. Then, for each type of symmetry we derive a block-diagonalization form of the graph Laplacian matrix, based on which fast implementations with reduced number of multiplications can be obtained. We show that for moderate block sizes, certain types of grid symmetry enable us to design non-separable block transforms that have computational complexities comparable to those of separable ones.
A distributed wavelet compression algorithm for wireless sensor networks using lifting

We address the problem of compression for wireless sensor networks, where each of the sensors has limited power, and acquires data that should be sent to a remote central node. The final goal is to have a reconstructed version of the sampled field at the central node, with the sensors spending as little energy as possible. We propose a distributed wavelet algorithm, based on the lifting scheme, as a means to decorrelate data at the nodes by exchanging information between neighboring sensors. A key result of our work is that by using a locally adaptive distributed transform it is possible to optimize overall power consumption by operating at the right trade-off point between local processing and transmission costs.
Overlapped block disparity compensation with adaptive windows for stereo image coding

We propose a modified overlapped block-matching (OBM) scheme for stereo image coding. OBM has been used in video coding but, to the best of our knowledge, it has not been applied to stereo image coding to date. In video coding, OBM has proven useful in reducing blocking artifacts (since multiple vectors can be used for each block), while also maintaining most of the advantages of fixed-size block matching. There are two main novelties in this work. First, we show that OBM techniques can be successfully applied to stereo image coding. Second, we take advantage of the smoothness properties typically found in disparity fields to further improve the performance of OBM in this particular application. Specifically, we note that practical OBM approaches use noniterative estimation techniques, which produce lower quality estimates than iterative methods. By introducing smoothness constraints into the noniterative DV computation, we improve the quality of the estimated disparity as compared to standard noniterative OBM approaches. In addition, we propose a disparity estimation/compensation approach using adaptive windows with variable shapes, which results in a reduction in complexity. We provide experimental results that show that our proposed hybrid OBM scheme achieves a PSNR gain (about 1.5-2 dB) as compared to a simple block-based scheme, with some slight PSNR gains (about 0.2-0.5 dB) in a reduced complexity, as compared to an approach based on standard OBM with half-pixel accuracy.
Construction of low complexity regular quantizers for overcomplete expansions in R/sup N/

We study the construction of structured regular quantizers for overcomplete expansions in R/sup N/. Our goal is to design structured quantizers allowing simple reconstruction algorithms with low (memory and computational) complexity and having good performance in terms of accuracy. Most related work to date in quantized redundant expansions has assumed that uniform scalar quantization with the same stepsize was used on the redundant expansion and then has dealt with more complex methods to improve the reconstruction. Instead, we consider the design of scalar quantizers with different stepsizes for each coefficient of an overcomplete expansion in such a way as to produce an equivalent vector quantizer with periodic structure. The periodicity makes it possible to achieve good accuracy using simple reconstruction algorithms from the quantized coefficients of the overcomplete expansion.
Fast adaptive media scheduling based on expected run-time distortion

In delay-constrained multimedia communications over the Internet, scalable compressed media formats are useful in that they allow the rate and quality of video delivery to adapt to the changes in the available bandwidth. Previously, we introduced the expected run-time distortion based scheduling (ERDBS) algorithm (Miao and Ortega, Proc.  2002), which allowed the media source to select in what order packets should be transmitted, where packets contain different "layers" in the scalable representation of the transmitted signal. The basic idea of ERDBS was to calculate at any given time the "importance" of each candidate packet in the transmission buffer, and then proceed to send the most important packet. We now present techniques to reduce the complexity of the ERDBS approach, which could be significant when the set of candidate packets is large. Our proposed algorithm decomposes the packet importance calculation into several stages. We show how, through a proper decomposition of these calculations, the results obtained for some stages during past iterations can be re-used, with updates based on the new information received in the meantime. With this recursive definition of the packet importance calculation, we show that the complexity of ERDBS is reduced without sacrificing its performance.
Generalized Laplacian precision matrix estimation for graph signal processing

Graph signal processing models high dimensional data as functions on the vertices of a graph. This theory is constructed upon the interpretation of the eigenvectors of the Laplacian matrix as the Fourier transform for graph signals. We formulate the graph learning problem as a precision matrix estimation with generalized Laplacian constraints, and we propose a new optimization algorithm. Our formulation takes a covariance matrix as input and at each iteration updates one row/column of the precision matrix by solving a non-negative quadratic program. Experiments using synthetic data with generalized Laplacian precision matrix show that our method detects the nonzero entries and it estimates its values more precisely than the graphical Lasso. For texture images we obtain graphs whose edges follow the orientation. We show our graphs are more sparse than the ones obtained using other graph learning methods.
Depth Map Coding Optimization Using Rendered View Distortion for 3D Video Coding

In order to improve 3D video coding efficiency, we propose methods to estimate rendered view distortion in synthesized views as a function of the depth map quantization error. Our approach starts by calculating the geometric error caused by the depth map error based on the camera parameters. Then, we estimate the rendered view distortion based on the local video characteristics. The estimated rendered view distortion is used in the rate-distortion optimized mode selection for depth map coding. A Lagrange multiplier is derived using the proposed distortion metric, which is estimated based on an autoregressive model. Experimental results show the efficiency of the proposed methods, with average savings of 43% in depth map bitrate as compared with encoding the depth maps using the same coding tools but with the rate-distortion optimization based on the conventional distortion metric.
Adaptive quantization without side information using scalar-vector quantization and trellis coded quantization

We combine backward adaptive quantization with the scalar-vector quantizer (SVQ) and the trellis coded quantizer (TCQ) both of which have an underlying scalar quantizer (USQ) in their structure. The resulting adaptive scalar-vector quantizer (ASVQ) and adaptive trellis coded quantizer (ATCQ) redesign the USQ based on the past quantized outputs. The adaptive quantizers require no side information and also outperform the SVQ and the TCQ, respectively, when the input signal is non-stationary. For an input sequence from a bimodal source switching infrequently between two Gaussian distributions with the same mean and different variances, both adaptive quantizers achieve performance gains of more than 1.3 dB over the non-adaptive quantizers designed on the training set from the same bimodal source. Also the adaptive quantizers demonstrate minimal performance degradation due to adaptation when stationary inputs are considered.
An iterative algorithm for two-dimensional digital least metric problems with applications to digital image compression

A correspondence between the problem of two-dimensional digital least metric (DLM) fitting and data detection in serially concatenated systems in digital communication theory is described. Nearly optimal detection algorithms based on previous advances in iterative detection/decoding are applied to the DLM problem for two applications in digital image compression. The first application is least squares halftoning of digital images. The second is near-lossless (i.e., error constrained) minimum-entropy image compression. In both applications the use of the iterative algorithm yields significant improvements, measured in terms of residual metric, relative to previously suggested approaches to the DLM problem.
Fault Detection by Means of Hilbert–Huang Transform of the Stator Current in a PMSM With Demagnetization

This paper presents a novel method to diagnose demagnetization in permanent-magnet synchronous motor (PMSM). Simulations have been performed by 2-D finite-element analysis in order to determine the current spectrum and the magnetic flux distribution due to this failure. The diagnostic just based on motor current signature analysis can be confused by eccentricity failure because the harmonic content is the same. Moreover, it can only be applied under stationary conditions. In order to overcome these drawbacks, a novel method is used based upon the Hilbert-Huang transform. It represents time-dependent series in a 2-D time-frequency domain by extracting instantaneous frequency components through an empirical-mode decomposition process. This tool is applied by running the motor under nonstationary conditions of velocity. The experimental results show the reliability and feasibility of the methodology in order to diagnose the demagnetization of a PMSM.
Fault detection by means of Hilbert Huang Transform of the stator current in a PMSM with demagnetization

This paper presents a study of the permanent magnet synchronous motor (PMSM) running under demagnetization. The simulation has been carried out by means of two dimensional (2-D) finite element analysis (FEA), and simulations results were compared with experimental results. The demagnetization fault is analyzed by means of decomposition of stator currents obtained at different speeds. The Hilbert Huang transform (HHT) is used as processing tool. This transformation represents time-dependent series in a two-dimensional (2-D) time-frequency domain by extracting instantaneous frequency components within the signal through an Empirical Mode Decomposition (EMD) process.
Distributed transforms for efficient data gathering in arbitrary networks

In this paper we present a simple distributed transform for data-gathering applications for arbitrary networks that achieves significant gains over raw data transmission, while requiring minimal coordination between nodes. In most spatial compression schemes some nodes (i.e., raw nodes) need to transmit raw data before spatial compression can be performed. Nodes that receive raw data (i.e., aggregating nodes) can then perform spatial compression. Thus, most spatial compression schemes require some raw-aggregating node assignment (RANA) to enable compression. Since transmitting raw data usually requires more bits than transmitting compressed data, we seek to find RANAs that select raw nodes in order to minimize overall energy consumption in the network. We formulate the problem of optimally selecting raw nodes as a set cover problem and propose distributed solutions for a variety of scenarios, including single-sink, multi-sink and gossip-based networks.
A novel approach of image compression in digital cameras with a Bayer color filter array

We propose a new approach for image compression in digital cameras, where the goal is to achieve better quality at a given rate by using the characteristics of a Bayer color filter array. Most digital cameras produce color images by using one CCD plate and each pixel in an image has only one color component, so an interpolation method is needed to produce a full color image. After finishing an image processing stage, in order to reduce the memory requirements of the camera, a lossless or lossy compression stage, using a coder such as JPEG, often follows. Before decreasing redundancy in a compression stage, redundancy is increased in an interpolation stage. We propose an algorithm for image compression, in which the order of the compression and interpolation stages is reversed to avoid increasing redundancy before compression. We introduce the image transform to compress uninterpolated images with JPEG. Our simulations show that the result of our algorithm is better than conventional methods for all compression ranges. This proposed algorithm provides not only better quality but also lower complexity because the number of luminance data of our method is only half of that of conventional methods.