nils-herrmann/clean_scirepeval huggingface dataset:
Joint Routing and 2D Transform Optimization for Irregular Sensor Network Grids Using Wavelet Lifting

We address the joint optimization of routing and compression for wireless sensor networks using a lifting-based 2D transform that can be computed along arbitrary routing trees. The proposed 2D transform allows for unidirectional computation, thereby eliminating costly backward transmissions often required by existing 2D transforms. We also propose a framework for optimizing the transform by selecting among a different set of coding schemes (i.e., different levels in the wavelet decomposition). Since our transform can operate on arbitrary routing trees, we focus on the problem of jointly optimizing routing trees based on inter-node data correlation and inter-node distance. The two extreme solutions would be i) to route data along paths that maximize inter-node data correlation (at the risk of increasing transport costs), corresponding to a minimum spanning tree (MST), or ii) to follow shortest path tree (SPT) routing (where inter-node data correlation may not be as high). We propose an optimization technique that exhaustively searches for the optimal tree over a set of combinations of MST and SPT. We also propose a heuristic approximation algorithm that is amenable for use on larger networks and with which we observe total cost reductions close to 10% for some of the data.
Stator Short Circuits Detection in PMSM by means of Higher Order Spectral Analysis (HOSA)

This paper presents and analyzed short circuit failures for Permanent Magnet Synchronous Motor (PMSM). The study includes stated and dynamic condition for experimental and simulation test. The stator current is analyzed by means of higher order spectral analysis (HOSA). HOSA techniques are used for stator current analysis under stable conditions is power frequency spectrum density (PSD); and Multiple Signal Classification (MUSIC), and bispectrum are used under dynamics conditions. Therefore, it is possible to improve the accuracy and efficiency of technique. Experimental results validate the analysis and demonstrate for HOSA can be applied to detect and identify short circuit failures in synchronous machines.
Detection of Stator Short Circuits in PMSM by mean of joint Time-Frequency Analysis

This paper presents and analyzes short circuit failures for permanent magnet synchronous motor (PMSM). The study includes speed transients in realistic experimental conditions. The stator current is analyzed by means of time-frequency spectral distribution (SP), Wigner Ville distribution (\WD) and Analytical Wavelet Transform (AWT); also, the ruts value of the local maximum for this distribution is calculated as indicated by Ridge-AWT algorithm. Experimental laboratory results validate the analysis and demonstrate that the time-frequency analysis can be applied to detect and identify short circuit failures in synchronous machines*.
Improved Internet traffic analysis via optimized sampling

Applications to evaluate Internet quality-of-service and increase network security are essential to maintaining reliability and high performance in computer networks. These applications typically use very accurate, but high cost, hardware measurement systems. Alternate, less expensive software based systems are often impractical for use with analysis applications because they reduce the number and accuracy of measurements using a technique called interrupt coalescence, which can be viewed as a form of sampling. The goal of this paper is to optimize the way interrupt coalescence groups packets into measurements so as to retain as much of the packet timing information as possible. Our optimized solution produces estimates of timing distributions much closer to those obtained using hardware based systems. Further we show that for a real Internet analysis application, periodic signal detection, using measurements generated with our method improved detection times by at least 36%.
Efficient Sampling Set Selection for Bandlimited Graph Signals Using Graph Spectral Proxies

We study the problem of selecting the best sampling set for bandlimited reconstruction of signals on graphs. A frequency domain representation for graph signals can be defined using the eigenvectors and eigenvalues of variation operators that take into account the underlying graph connectivity. Smoothly varying signals defined on the nodes are of particular interest in various applications, and tend to be approximately bandlimited in the frequency basis. Sampling theory for graph signals deals with the problem of choosing the best subset of nodes for reconstructing a bandlimited signal from its samples. Most approaches to this problem require a computation of the frequency basis (i.e., the eigenvectors of the variation operator), followed by a search procedure using the basis elements. This can be impractical, in terms of storage and time complexity, for real datasets involving very large graphs. We circumvent this issue in our formulation by introducing quantities called graph spectral proxies, defined using the powers of the variation operator, in order to approximate the spectral content of graph signals. This allows us to formulate a direct sampling set selection approach that does not require the computation and storage of the basis elements. We show that our approach also provides stable reconstruction when the samples are noisy or when the original signal is only approximately bandlimited. Furthermore, the proposed approach is valid for any choice of the variation operator, thereby covering a wide range of graphs and applications. We demonstrate its effectiveness through various numerical experiments.
Diagnosis of a Chopper Controlled DC Motor by Boosting

This paper proposes a methodology to diagnose a transient state of a dynamic system using boosting. The methodology is composed by two steps: one off-line process and another on-line process. The off-line phase begins gathering data from the system, both when it is running free of fault and when the system is running in each fault mode. A segmentation and normalization algorithm is used to reduce the large amount of gathered data. The final step is the generation of a decision tree by a classification tool. The boosting technique is used with the aim of improving the classification results. The on-line process of the methodology consists of evaluating a new reading of the system sensors with the generated decision trees. The diagnosis of the system is the result of this evaluation which has very low computational cost due to the simplicity of the decision trees. Also, the implementation cost is very low due to this simplicity
Optimal rate control for video transmission over VBR channels based on a hybrid MMAX/MMSE criterion

In this paper, we consider the problem of rate control for video transmission over variable bit rate (VBR) channels. We focus on finding off-line optimal rate control for VBR transmission with a token bucket policing function. To ensure a maximum minimum quality is obtained over all data units, we introduce a minimum maximum distortion (MMAX) criterion in this channel-constrained problem. We show that, due to the channel constraints, a MMAX solution leads to a relatively low average distortion, because the total rate budget is not completely utilized. Therefore, after finding a MMAX solution, an additional minimization of average distortion (MMAX+) criterion is proposed to increase overall quality of the data sequence by using remaining resources. The proposed algorithms lead to an increase in average quality with respect to the MMAX solution, while providing a much more constant quality than MMSE solutions. Moreover we show how the MMAX+ approach can be implemented with low complexity.
High-power high-resolution pulser for air-coupled ultrasonic NDE applications

This paper describes the design and development of a high-power high-resolution pulser designed especially for air-coupled ultrasonic NDE applications requiring high material penetration capability. The pulser can deliver up to 800 V in a spike excitation type pulse to appropriate low/medium frequency transducers. Moreover, the pulser operates in three different modes: single pulse, double pulse (pulse cancellation technique) and external reference signal, where the last two operating modes are intended to enhance the resolution of ultrasonic inspections.
Enhanced standard compliant distributed speech recognition (Aurora encoder) using rate allocation

The paper proposes modifications to improve the recognition performance obtainable by the ETSI standard distributed speech recognition encoder, Aurora (ES 201 108, 2000). The proposed modifications are standard compliant, i.e., they require no algorithmic modifications to the Aurora operation. Performance improvements are achieved by distributing the available bit budget among Aurora's seven (different) 2-dimension vector quantizers (VQs) more efficiently. Improved bit-allocation to the different sub-vectors is achieved by incorporating the importance for recognition of each of the sub-vectors into the bit-allocation algorithm. The available bits are efficiently distributed among the sub-vectors by allocating a larger fraction of the available bits to the more important sub-vectors and hence maximizing recognition accuracy. The proposed bit-allocation algorithm is based on a novel mutual information (MI) measure. The MI measure quantifies the information content between a sub-vector and the class label and hence is a good indicator of the importance of the coefficient for recognition. It is shown that the proposed MI based method outperforms both the standard Aurora encoder and an encoder designed using traditional mean square error based bit-allocation. For the TIDIGITS connected digits recognition task, a 15.2% relative decrease in word error rate (WER) is possible with the proposed modified MI based Aurora encoder when compared to the recognition performance achieved using the standard Aurora encoder.
Image subband coding using context-based classification and adaptive quantization

Adaptive compression methods have been a key component of many proposed subband (or wavelet) image coding techniques. This paper deals with a particular type of adaptive subband image coding where we focus on the image coder's ability to adjust itself "on the fly" to the spatially varying statistical nature of image contents. This backward adaptation is distinguished from more frequently used forward adaptation in that forward adaptation selects the best operating parameters from a predesigned set and thus uses considerable amount of side information in order for the encoder and the decoder to operate with the same parameters. Specifically, we present backward adaptive quantization using a new context-based classification technique which classifies each subband coefficient based on the surrounding quantized coefficients. We couple this classification with online parametric adaptation of the quantizer applied to each class. A simple uniform threshold quantizer is employed as the baseline quantizer for which adaptation is achieved. Our subband image coder based on the proposed adaptive classification quantization idea exhibits excellent rate-distortion performance, in particular at very low rates. For popular test images, it is comparable or superior to most of the state-of-the-art coders in the literature.
Low-cost and minimal-conditioning interface for a PbSe photoconductor array

A low cost conditioning structure for PbSe photoconductor arrays with few components is described. We use a PbSe array of 64 pixels in a simultaneous atmospheric pollutant detector of several gases. The sensor system is based on infrared optical absorption spectroscopy. The authors present a second version of the sensor. From the first version they deduced: i) it is necessary to work at high chopper frequencies to reduce the effect of 1/f noise, ii) it is necessary to provide a conditioning circuit for each channel, and iii) the conditioning interface must be near the array. Therefore, they propose to amplify and mix the incoming signal to produce a signal at a fixed intermediate frequency (IF), in their case 1 Hz. The fact that the circuit has few components makes possible to place 64 conditioners in a small volume (10/spl times/4.9/spl times/7.6 cm/sup 3/) using the with SMT technique. The conditioner is divided into eight boards. Each board has eight channels and an analog multiplexor. These characteristics permit to approach the circuitry to the array. Each channel conditioner has up to 100 dB voltage gain. The low IF technique makes it possible to have high-performance circuits with low-cost components.
Processing-aware compression for sensor networks

In sensor networks, where power and bandwidth are at a premium, there is a clear need to use compression to limit the amount of information exchanged by the sensors. We study the signal compression problem in situations where signals are being processed for the purpose of source localization. In these scenarios, compression should be optimized for the accuracy of source localization, rather than to provide a reproduction of the signals with some desired fidelity. We show how this leads to novel design techniques that have clear advantages over standard quantizer design approaches.
Remaining useful life estimation of ball bearings by means of monotonic score calibration

The estimation of remaining useful life applied to industrial machinery and its components is one of the current trends in the advanced manufacturing field. In this context, this work presents a reliable methodology applied to ball bearings health monitoring. First, the proposed methodology analyses the available vibration and temperature data by means of the Spearman coefficient. This step allows the identification of the most significant monotonic relationship between features and the evolution of the remaining useful life. The method is complemented by means of the application of one-class support vector machine in order to obtain the remaining useful life indication trough the mapping of the classification scores. The proposed scheme shows a significant accuracy and reliability of the degradation detection due to the coherent management of the information. This fact is experimentally demonstrated by a run-to-failure test bench and the comparison with classical approaches.
Towards a definition of local stationarity for graph signals

In this paper, we extend the recent definition of graph stationarity into a definition of local stationarity. Doing so, we present a metric to assess local stationarity using projections on localized atoms on the graph. Energy of these projections defines the local power spectrum of the signal. We use this local power spectrum to characterize local stationarity and identify sources of non-stationarity through differences of local power spectrum. Finally, we take advantage of the knowledge of the spectrum of the atoms to give a new power spectrum estimator.
Expansion hole filling in depth-image-based rendering using graph-based interpolation

Using texture and depth maps of a single reference viewpoint, depth-image-based rendering (DIBR) can synthesize a novel viewpoint image by translating texture pixels of the reference view to a virtual view, where synthesized pixel locations are derived from the associated depth pixel values. When the virtual viewpoint is located much closer to the 3D scene than the reference view (camera movement in the z-dimension), objects closer to the camera will increase in size in the virtual view faster than objects further away. A large increase in object size means that a patch of pixels sampled from an object surface in the reference view will be scattered to a larger spatial area, resulting in expansion holes. In this paper, we investigate the problem of identification and filling of expansion holes. We first propose a method based on depth histogram to identify missing or erroneously translated pixels as expansion holes. We then propose two techniques to fill in expansion holes with different computation complexity: i) linear interpolation, and ii) graph-based interpolation with a sparsity prior. Experimental results show that proper identification and filling of expansion holes can dramatically outperform inpainting procedure employed in VSRS 3.5 (up to 4.25dB).
An optimization framework for combining multiple graphs

This paper introduces a novel framework for combining multiple weighted graphs into a single optimized weighted graph. In our framework, we first develop a statistical formulation for the graph combining problem with a maximum likelihood criterion, and derive its optimality conditions. We then use these conditions to formulate the deterministic graph combining problem and propose a solution. Our experimental results show that the proposed solution provides better modeling compared to the commonly used averaging method. The introduced framework has various applications in signal processing and machine learning.
Depth map distortion analysis for view rendering and depth coding

Video representations that support view synthesis based on depth maps, such as multiview plus depth (MVD), have been recently proposed, raising interest in efficient tools for depth map coding. In this paper, we derive a new distortion metric that takes into consideration camera parameters and global video characteristics in order to quantify the effect of lossy coding of depth maps on synthesized view quality. In addition, a new skip mode selection method is proposed based on local video characteristics. Experimental results with the proposed mode selection scheme show coding gains of up to 2 dB for the synthesized views, as well as better subjective quality.
Face detection from a video camera image sequence

Face detection is a key problem in building automated systems that perform face recognition. In this sense, a very attractive approach for face detection based on motion detection is presented. Motivated by the simplicity of this approach, a rule-based face detection algorithm in frontal views is developed. The proposed algorithm has been applied to frontal views extracted from our own database that contains the video sequences of 30 different persons, (at this time) taken by a digital camera. The image is obtained, by the reflecting light of the human face, when illuminated with a weak light source.
Fault Detection in Induction Machines Using Power Spectral Density in Wavelet Decomposition

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 speed or the load torque is not constant, because this causes variations on the motor-slip and fast Fourier transform problems appear due to a nonstationary signal. This paper proposes a new method for motor fault detection, which analyzes the spectrogram based on a short-time Fourier transform and a further combination of wavelet and power-spectral-density (PSD) techniques, which consume a smaller amount of processing power. The proposed algorithms have been applied to detect broken rotor bars as well as shorted turns. Besides, a merit factor based on PSD is introduced as a novel approach for condition monitoring, and a further implementation of the algorithm is proposed. Theoretical development and experimental results are provided to support the research.
Embedded image-domain adaptive compression of simple images

Popular transform coding techniques provide excellent lossy compression for natural images but fall short with "simple" images, i.e., images represented by only a small portion of the available intensity values. In this paper we propose a novel image-domain compression technique aiming at simple images, or, more generally, simple regions within a compound image. The proposed method is a context-adaptive bit-plane coder based on a binary arithmetic coder. The context information allows us to exploit the strong correlation across the bit-planes of simple images. Given the bit-plane coding structure our coder produces an SNR-scalable embedded bit stream which can be lossless when completely decoded.
Downsampling graphs using spectral theory

In this paper we present methods for downsampling datasets defined on graphs (i.e., graph-signals) by extending downsampling results for traditional N-dimensional signals. In particular, we study the spectral properties of k-regular bipartite graphs (K-RBG) and prove that downsampling in these graphs is governed by a Nyquist-like criteria. The results are useful for designing critically sampled filter-banks in various data-domains where the underlying relations between data locations can be represented by undirected graphs. In order to illustrate our results we represent images as a set of k-RBG graphs and apply our downsampling results to them. The results show that common 2-D lattice downsampling methods can be seen special cases of (k-RBG) based downsampling. Further we demonstrate new downsampling schemes for images with non-rectangular connectivity.
High-power high-resolution pulser for air-coupled ultrasonic NDE applications

This paper describes the design and development of a high-power high-resolution pulser designed especially for air-coupled ultrasonic nondestructive evaluation applications requiring high material penetration capability. The pulser can deliver up to 800 V in a spike excitation type pulse to appropriate low/medium frequency transducers. Moreover, the pulser operates in three different modes: single pulse, double pulse (pulse cancellation technique), and external reference signal, where the last two operating modes are intended to enhance the axial resolution of ultrasonic inspections. Experimental results show that the received pulse duration can be significantly reduced, and reduction factors of 2 or 3 in the pulse duration can be easily achieved.
Fault detection of eccentricity by means of joint time-frequency analysis in PMSM under dynamic conditions

This paper presents a study of eccentricity fault detection in permanent magnet synchronous machines (PMSM), under dynamic conditions. The fault simulation was made by means of two-dimensional (2-D) finite element analysis (FEA). Joint time - frequency transforms, as Wigner Ville distribution (WVD) and Zao-Atlas-Marks distribution, were proposed for signal analysis. Simulations carried out were compared with experimental results.
Short-Circuit Detection by Means of Empirical Mode Decomposition and Wigner–Ville Distribution for PMSM Running Under Dynamic Condition

This paper presents and analyzes a method for short-circuit fault detection in a permanent-magnet synchronous motor (PMSM). The study includes steady-state condition and speed transients in motor operation. The stator current is decomposed by empirical mode decomposition (EMD), which generates a set of intrinsic mode functions (IMFs). Quadratic time-frequency (TF) distributions such as smoothed pseudo-Wigner-Ville and Zhao-Atlas-Marks are applied on the more significant IMFs for fault detection. Simulations and experimental laboratory tests validate the algorithms and demonstrate that this kind of TF analysis can be applied to detect and identify short-circuit failures in PMSM.