nils-herrmann/clean_scirepeval huggingface dataset:
Quantization based nearest-neighbor-preserving metric approximation

To reduce the computational burden of the nearest neighbor search (NNS) problem, most existing algorithms focus on ‘preprocessing’ the data set to reduce the number of objects to be examined for each querying operation (e.g., efficient data structures, metric space transforms). In this paper we present a quantization based nearest-neighbor-preserving metric approximation algorithm (QNNM) that leads to further complexity reduction by simplifying the metric computation. The proposed algorithm is based on three observations: (i) the query vector is fixed during the entire search process, (ii) the-minimum distance exhibits an extreme value distribution, and (iii) there is high homogeneity of viewpoints. Based on these, QNNM approximates original/benchmark metric in terms of preserving the fidelity of NNS rather than the distance itself, while achieving significantly lower complexity using a query-dependent quantizer. We formulate a quantizer design problem where the goal is to minimize the average NNS error. We show how the query adaptive quantizers can be designed off-line without prior knowledge of the query and present an efficient and specifically tailored off-line optimization algorithm to find such optimal quantizer. Experimental results in a motion estimation (ME) application show minimal performance degradation (average 0.05dB loss) when using optimized 1-bit quantizer.
Plant-wide fault detection using graph signal processing

This paper introduces a graph signal processing (GSP) framework for industrial process monitoring. Through a graph built to represent the variables of an industrial plant and their relationships, we show how to transform each sample vector observation using the graph Fourier transform (GFT) in order to infer the status of the plant. To validate the proposed method we compare it to the state of the art PCA T2 and Q methods for the Tennessee Eastman Process (TEP), a widely used benchmark for validation of methods in control and process monitoring. The results show our method outperforms PCA in most cases.
Industrial process monitoring by means of recurrent neural networks and Self Organizing Maps

Industrial manufacturing plants often suffer from reliability problems during their day-to-day operations which have the potential for causing a great impact on the effectiveness and performance of the overall process and the sub-processes involved. Time-series forecasting of critical industrial signals presents itself as a way to reduce this impact by extracting knowledge regarding the internal dynamics of the process and advice any process deviations before it affects the productive process. In this paper, a novel industrial condition monitoring approach based on the combination of Self Organizing Maps for operating point codification and Recurrent Neural Networks for critical signal modeling is proposed. The combination of both methods presents a strong synergy, the information of the operating condition given by the interpretation of the maps helps the model to improve generalization, one of the drawbacks of recurrent networks, while assuring high accuracy and precision rates. Finally, the complete methodology, in terms of performance and effectiveness is validated experimentally with real data from a copper rod industrial plant.
A novel distributed fiber-optic strain sensor

The theory of operation of a fiber-optic-based strain sensor system, suitable for long structures, is described. The proposed sensor design is composed of several hundreds of sections separated by reflectors in order to monitor structures of more than 1 km of length. A quasi-distributed sensor structure accomplishes the best condition monitoring, where each section of the sensor integrates the measured magnitude, obtaining a value associated to the average shortening or elongation in this section. Tools for the design of the optical reflectors of the sensor are also provided, including a study of the optical noise due to multireflection within the system.
Adaptive distributed transforms for irregularly sampled Wireless Sensor Networks

We develop energy-efficient, adaptive distributed transforms for data gathering in wireless sensor networks. In particular, we consider a class of unidirectional transforms that are computed as data is forwarded to the sink along a given routing tree and develop a tree-based Karhunen-Loàve Transform (KLT) that is optimal in that it achieves maximum data de-correlation among this class of transforms. As an alternative to this KLT (which incurs communication overhead in order to learn second order data statistics), we propose a backward adaptive filter optimization algorithm for distributed wavelet transforms that i) achieves near optimal performance and ii) has no communication overhead in learning statistics.
Graph based Partially Supervised Learning of documents

We propose a novel graph-based algorithm, Graph-Partially Supervised Learning (Graph-PSL), to solve the problem of document classification with positive and unlabeled documents. The key characteristic of the problem is that labeled negative documents are missing. We present a graph-based method to identify reliable negative documents and theoretically explain it by lazy information transfer network. The documents are classified by Transductive Support Vector Machine (TSVM), which can explore the information contained in unlabeled data. We explain how the similarity matrix of the graph and the kernel matrix in TSVM are calculated. We apply Graph-PSL to 20 Newsgroup dataset. The experimental results demonstrate that Graph-PSL identifies negative documents accurately and classifies the unlabeled ones more effectively and more robustly compared to Bayesian based algorithms.
Erasure recovery in predictive coding environments using multiple description coding

We propose an algorithm for erasure recovery in predictive coding schemes, where erasures can cause catastrophic error propagation. The recovery algorithm is based on sending multiple descriptions of the source and using a deterministic distance measure to find the most likely estimate for the lost data, given the received data and the side information. Results show that we can recover from short burst erasures and that for long bursts (more than 10% of the samples are lost) we can recover to within 0.4 dB of the original DPCM performance.
A novel adaptive Nyström approximation

We propose a novel perspective on the Nyström approximation method. Sampling the columns of the kernel matrix can be interpreted as projecting the data onto the subspace spanned by the corresponding columns. Thus, the quality of Nyström approximation can be quantified by the distance between the subspace spanned by the sampled columns and the subspace spanned by the data mapped to the eigenvectors corresponding to the top eigenvalues of the kernel matrix. Based on this interpretation, we design a novel adaptive Nyström approximation algorithm, BoostNyström. BoostNyström is efficient in terms of both time and space complexity. Experiments on benchmark data sets show that BoostNyström is more effective than the state-of-art algorithms.
New Coding Tools for Illumination and Focus Mismatch Compensation in Multiview Video Coding

We propose new tools for multiview video coding (MVC) that aim to compensate for mismatches between video frames corresponding to different views. Such mismatches could be caused by different shooting positions of the cameras and/or heterogeneous camera settings. In particular, we consider illumination and focus mismatches across views, i.e., such that different portions of a video frame can undergo different illumination and blurriness/sharpness changes with respect to the corresponding areas in frames from the other views. Models for illumination and focus mismatches are proposed and new coding tools are developed from the models. We propose a block-based illumination compensation (IC) technique and a depth-dependent adaptive reference filtering (ARF) approach for cross-view prediction in multiview video coding. In IC, disparity field and illumination changes are jointly computed as part of the disparity estimation search. IC can be adaptively applied by taking into account the rate-distortion characteristics of each block. For ARF, the disparity fields are used to estimate scene depth, such that video frames are first divided into regions with different scene-depth levels. A 2-D filter is then selected for each scene-depth level. These filters are chosen to minimize residual energy, with the goal of compensating for focus mismatches. The resulting filters are applied to the reference frames to generate better matches for cross-view prediction. Furthermore, we propose a coding system that combines IC and ARF. Adjustments are made so as to maximize the gains achieved by using both coding tools, while reducing the complexity of the final integrated system. We analyze the complexity of all proposed methods and present simulation results of IC, ARF and combined system for different multiview sequences based on the H.264/AVC reference codec. When applying the proposed tool to cross-view coding we observe gains of up 1.3 dB as compared to directly using an H.264/AVC codec to perform predictive coding across views.
Bit-rate control using piecewise approximated rate-distortion characteristics

Digital video's increased popularity has been driven to a large extent by a flurry of international standards (MPEG-1, MPEG-2, H.263, etc). In most standards, the rate control scheme, which plays an important role in improving and stabilizing the decoding and playback quality, is not defined, and thus different strategies can be implemented in each encoder design. Several rate-distortion (R-D)-based techniques have been proposed aimed at the best possible quality for a given channel rate and buffer size. These approaches are complex because they require the R-D characteristics of the input data to be measured before making quantization assignment decisions. We show how the complexity of computing the R-D data can be reduced without significantly reducing the performance of the optimization procedure. We propose two methods which provide successive reductions in complexity by: (1) using models to interpolate the rate and distortion characteristics, and (2) using past frames instead of current ones to determine the models. Our first method is applicable to situations (e.g., broadcast video) where a long encoding delay is possible, while our second approach is more useful for computation-constrained interactive video applications. The first method can also be used to benchmark other approaches. Both methods can achieve over 1 dB peak signal-to-noise rate (PSNR) gain over simple methods like the MPEG Test Model 5 (TM5) rate control, with even greater gains during scene change transitions. In addition, both methods make few a priori assumptions and provide robustness in their performance over a range of video sources and encoding rates. In terms of complexity, our first algorithm roughly doubles the encoding time as compared to simpler techniques (such as TM5). However, the complexity is greatly reduced as compared to methods which exactly measure the R-D data. Our second algorithm has a complexity marginally higher than TM5 and a PSNR performance slightly lower than that of the first approach.
Jointly optimized quantization and time delay estimation for sensor networks

Sensor networks have emerged as a fundamentally new tool for monitoring inaccessible environments. Strict limitations on system bandwidth and sensor energy resources motivate the use of data compression at each sensor. Localization of unknown sources is a key application of sensor networks, requiring as an initial step, estimation of the time delay between signals received at different sensors. In this work, joint designs for quantizer-time delay estimator structures are presented. The goal for these new application-specific encoders/estimators 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. For white sources, the optimal structure is shown to be a maximum-likelihood detector coupled with a maximum mutual information quantizer. Variations of this system are also considered: sequential detection schemes, empirical methods for unknown signal models, and rate- constrained methods. The proposed designs offer gains over those based on classical compression criteria.
A distributed wavelet compression algorithm for wireless multihop 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 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 compression algorithm for multihop, distributed sensor networks based on the lifting factorization of the wavelet transform that exploits the natural data flow in the network to aggregate data by computing partial wavelet coefficients that are refined as the data flows towards the central node. A key result of our work is that by performing partial computations we greatly reduce unnecessary transmission, significantly reducing the overall energy consumption.
Power Efficient Motion Estimation using Multiple Imprecise Metric Computations

In this paper, we propose power efficient motion estimation (ME) using multiple imprecise sum of absolute difference (SAD) metric computations. We extend the recent work of Varatkar et al (2006) by providing analytical solutions based on modelling of computation errors due to voltage overscaling (VOS) and sub-sampling (SS). Results show that our solutions provide significantly better performance in the sense of rate increase for fixed QP, e.g., less than 5% increase, while in Varatkar et al (2006) the rate increase could be as high as 20%. It allows us to apply lower voltage which leads to additional power saving. Our analysis also allows us to compare different ME algorithms (e.g., full search vs. a fast algorithm) and SAD computation architectures (parallel vs. serial) in terms of their robustness to imprecise metric computations and their power efficiency. Finally, we demonstrate that additional power savings can be achieved by removing redundancy between the various computations.
Intra predictive transform coding based on predictive graph transform

In this paper, we propose a new intra-frame coding approach using the predictive graph transform (PGT). The predicted block together with the reference pixels are modeled as a normal distributed random vector with respect to a graph whose edges represent the correlations between pixels. This model is more flexible than the Gaussian Markov random field (GMRF) model in the sense that it enables us to adapt the graph both before and after the collection of the statistics. The optimal prediction and the transform of the prediction residual are then derived jointly. Two PGT based intra coding schemes are proposed: one is based on global image statistics and the other is mode-adaptive, i.e., the graph is adaptive to different directional modes defined in H.264/AVC. The simulations show the advantage of our proposed approach over standard intra predictive transform coding in terms of both prediction quality and coding gain assuming the model parameters are known at decoder.
Fault Detection Analysis in Induction Motors by Injecting Additional Test Signal

Condition monitoring methods for the dynamic performance analysis of electrical drives received considerable attention in recent years. Many of these methods use vibration sensors based on proximity probes. However, it has been suggested that stator current monitoring can provide the same indications without requiring access to the motor. Motor current signature analysis (MCSA) is now going to be a widely used method for identifying faults in induction motors. MCSA utilizes results of spectral analysis of the stator current to spot an existing or incipient failure of the motor or the drive system. Motor faults such as broken rotor bars, bearings damage and eccentricities of the rotor axis can be detected. However, when the motor is working at both low speed and low torque some problems appear, due to the small amplitude and the proximity between the frequencies to be detected. This paper proposes to inject to the machine an additional voltage test at a different frequency to the fundamental one, and then to study the resulting harmonics around the new frequencies appearing due to the composition between injected and main frequencies. Theoretical development and experimental results are presented to support the research
Learning graphs with monotone topology properties

Learning graphs with topology properties is a non-convex optimization problem. We propose a tractable algorithm that finds the generalized Laplacian matrix of a graph with the desired type of topology. Given the covariance/similarity matrix, our algorithm first solves a combinatorial optimization problem to find a graph topology that satisfies the desired structural property, and second, it estimates a generalized Laplacian matrix by solving a sparsity constrained Log-Determinant divergence minimization problem. The proposed method is guaranteed to find a near optimal generalized Laplacian matrix if the graph family is monotone, i.e. it is closed under edge removal operations. We derive an specific instance of our algorithm to learn bipartite graphs and we evaluate the performance of our graph learning method via numerical experiments with synthetic and image data.
Tree-based wavelets for image coding: Orthogonalization and tree selection

In this work we consider the design of the lifting filters and trees used in a separable tree-based wavelet transform. We first consider the use of improved prediction filters, optimized to represent more efficiently smooth signals for arbitrary tree structures. We then consider the design of update filters that are orthogonal to neighboring prediction operators. While the corresponding decomposition is not fully orthogonal, near orthogonality between prediction and update operators leads to significant improvements in energy compaction. Finally we consider the design of trees that (i) avoid filtering across discontinuities in an image to reduce the amount of high frequency energy, while (ii) maintaining some regularity in the downsampled grids over multiple levels of decomposition in order to achieve good spatial localization of filtering.
Gesture dynamics modeling for attitude analysis using graph based transform

Gesture dynamic pattern is an essential indicator of emotions or attitudes during human communication. However, there might exist great variability of gesture dynamics among gesture sequences within the same emotion, which form a major obstacle to detect emotion from body motion in general interpersonal interactions. In this paper, we propose a graph-based framework for modeling gesture dynamics towards attitude recognition. We demonstrate that the dynamics derived from a weighted graph based method provide a better separation between distinct emotion classes and maintain less variability within the same emotion class. This helps capture salient dynamic patterns for specific emotions by removing interaction-dependent variations. In this framework, we represent each gesture sequence as an undirected graph of connected gesture units and use the graph-based transform to generate features to describe gesture dynamics. In our experiments, we apply the graph-based dynamics for attitude recognition, i.e., classifying the attitude of an individual as friendly or conflictive. Experimental results verify the effectiveness of our approach.
Moving object segmentation using depth and optical flow in car driving sequences

Segmentation of moving objects in a scene is difficult for non-stationary cameras, and especially challenging in the presence of fast and unstable egomotion, e.g., as encountered with car-mounted cameras or wearable devices. Based on an analysis of motion vanishing points of the scene and estimated depth, a geometric model that relates extracted 2D motion to a 3D motion field relative to the camera is derived. Observing that the 3D motion field is piece-wise smooth, a constrained optimization problem that considers group sparsity is formulated to recover the 3D motion field from the 2D motion. The recovered 3D motion field is then clustered to provide the segmentation of moving objects. Experiments are performed using the KITTI Vision Benchmark Suite and demonstrate that the proposed framework provides a dense segmentation of moving objects that is robust to the challenging conditions inherent with car driving sequences.
Simulation and Fault Detection of Short Circuit Winding in a Permanent Magnet Synchronous Machine (PMSM) by means of Fourier and Wavelet Transform

This paper presents permanent magnet synchronous machines with short circuit of turns. Two-dimensional (2-D) finite element analysis (FEA) is used to simulation of PMSM under fault. PMSM is working at nominal condition and speed change of 5000 rpm. Relationships between stator-current-induced harmonics were investigated by means of Fourier (FFT) and discrete wavelet transforms (DWT). The simulation is also compared with experimental results.
Low complexity motion estimation algorithm by multiresolution search for long-term memory motion compensation

We propose a new low complexity motion estimation (ME) algorithm using multiresolution motion search for long term memory motion compensation (LTMC). While multiresolution motion search has been used for standard single-frame motion compensation, here we introduce several novel techniques to exploit it efficiently in the context of LTMC. The proposed algorithm compute a coarse motion vector from a lower resolution sequence to locate motion search windows (MSW). Because the coarse motion vectors are good approximations to the actual vectors, small MSW whose sizes are chosen based on the error surface smoothness information can be used without significant loss in quality, leading to significant reduction in complexity. Our approach also incorporates temporal reduction of the motion search range to reduce the complexity further. The proposed algorithm provides particularly significant gains for video sequences which contain large motions, where directed search also results in promising gains in PSNR over non-directed search.
Rate control for video coding over variable bit rate channels with applications to wireless transmission

Video transmission over wireless links is an emerging application which involves a time-varying channel. In this paper we propose that rate control algorithms should be used at the video encoders, along with models of the channel behavior, to improve the performance of such systems. Rather than letting information be lost as the channel conditions change, in our scheme channel state information is fed back to the encoder. We propose a method, based on dynamic programming, to compute the rate-distortion performance for a given channel and source realization. We show how the rate-distortion performance changes with end-to-end delay and feedback delay.