nils-herrmann/clean_scirepeval huggingface dataset:
Quality-optimized encoding of JPEG images using transform domain sparsification

To account for the unique characteristics and limitations of the human visual system (HVS) when perceiving images, a variety of perceptual quality metrics have been proposed in the literature. Tailoring rate-distortion (RD) optimization for each metric is cumbersome and time-consuming. In this paper, we propose a general RD-optimization strategy called “transform domain bounding box” (BB) that can easily adapt to different quality metrics for JPEG-like block-based encoding of images. First, we define an objective function that is a weighted sum of the l0-norm of the transform coefficients (a proxy for rate) and distortion from the transform domain representation. Next, for a given distortion target τ, we define a don't care region (DCR) that specifies a search region of representations with distortion ≤τ. We then show that the sparsest transform domain representation (lowest encoding rate) inside a BB that tightly contains the DCR can be constructed efficiently. Varying τ to induce different DCRs and corresponding BBs results in a set of constructed sparse representations of different sparsity counts, and the one that optimally trades off rate and distortion can be easily identified as solution to our objective. We show that our proposed BB strategy can be easily re-targeted for three common quality metrics: MSE, MSE-HVS-M and SSIM. Experimental results show that our BB strategy outperformed unoptimized JPEG compression by up to 1dB in PSNR when distortion metric is MSE, up to 2dB when metric is MSE-HVS-M, and up to 0.005 when metric is SSIM.
Delivery Improvement for Transport Companies

In this paper we propose a solution that improves the synchronization and effectiveness of sending and receiving of goods and merchandise by transport companies. We use a service-oriented architecture for the exchange of information related to the reception of products, such as the predicted time when the delivery should take place or the delay in the arrival. Our solution is based on fleet management systems widely used in the transport companies. The main objective of this paper is to improve the interoperability between providers and consumers, thereby creating a communication environment versed in delivery plans. Another feature revised here is the analysis of vehicle tracking information to obtain accurate knowledge of the movement pattern followed by drivers and usefulness for the companies.
Time series forecasting by means of SOM aided Fuzzy Inference Systems

The forecast of industrial process time series represents a critical factor in order to assure a proper operation of the whole manufacturing chain, as it allows to act against any process deviation before it affects the final manufactured product. In this paper, in order to take advantage from process relations and improve forecasting performance, a prediction method based in Adaptive Neuro Fuzzy Inference System (ANFIS) and Self-Organizing Maps is presented. The novelties of the proposed method are based on considering, as an input of an ANFIS model, the interrelations of process variables regarding the signal that wants to be forecasted, by means of topology preservation SOM. An experimental study performed with real industrial data from a cooper manufacturing plant indicated the suitability of the proposed method in time series forecasting applications.
Lifting Transforms on Graphs for Video Coding

We present a new graph-based transform for video signals using wavelet lifting. Graphs are created to capture spatial and temporal correlations in video sequences. Our new transforms allow spatial and temporal correlation to be jointly exploited, in contrast to existing techniques, such as motion compensated temporal filtering, which can be seen as "separable" transforms, since spatial and temporal filtering are performed separately. We design efficient ways to form the graphs and to design the prediction and update filters for different levels of the lifting transform as a function of expected degree of correlation between pixels. Our initial results are promising, with improvements in performance as compared to existing methods in terms of PSNR as a function of the percentage of retained coefficients of the transform.
Optimal bit allocation under multiple rate constraints

We present a new Lagrangian-based iterative technique for rate-distortion optimization under multiple rate constraints. We show how for sets of "linear" constraints this technique can be proven to be optimal up to a convex hull approximation. As an application we consider the problem of optimal buffer-constrained bit allocation. Our technique can be used to find an excellent approximation to the solutions achieved using dynamic programming. In cases where the buffer size is relatively large our approach shows a significant reduction in complexity as compared to dynamic programming.
Bipartite Graph Filter Banks: Polyphase Analysis and Generalization

The work by Narang and Ortega [“Perfect reconstruction two-channel wavelet filter banks for graph structured data,” <italic> 2012], [“Compact support biorthogonal wavelet filterbanks for arbitrary undirected graphs,” <italic> 2013] laid the foundations for the two-channel critically sampled perfect reconstruction filter bank for signals defined on undirected graphs. This basic filter bank is applicable only to bipartite graphs but using the notion of separable filtering, the basic filter bank can be applied to any arbitrary undirected graphs. In this paper, several new theoretical results are presented. In particular, the proposed polyphase analysis yields filtering structures in the downsampled domain that are equivalent to those before downsampling and, thus, can be exploited for efficient implementation. These theoretical results also provide new insights that can be exploited in the design of these systems. These insights allow us to generalize these filter banks to directed graphs and to using a variety of graph base matrices, while also providing a link to the <inline-formula><tex-math notation="LaTeX">$\text{DSP}_G$</tex-math></inline-formula> framework of Sandryhaila and Moura [“Discrete signal processing on graphs,” <italic> 2013], [“Discrete signal processing on graphs: Frequency analysis,” <italic> 2014]. Experiments show evidence that better nonlinear approximation and denoising results may be obtained by a better selection of these base matrices.
Detecting low-rate periodic events in Internet traffic using renewal theory

In our previous work [1, 2] we studied detection of anomalies in packet arrival times for computer networks, most detection of denial-of-service (DoS) attacks in Internet traffic. In this paper we reformulate the detection method proposed in  using renewal theory, providing several useful extensions. This reformulation also leads to a method that would be applicable to numerous real life signals that exist as discrete events, e.g., biological signals. Most importantly renewal theory allows us to characterize the performance of our detector and determine theoretical bounds on the time-to-detection. Compared to alternative methods that use frequency spectra or event arrival rates for detection our method is shown to be superior in terms of time-to-detection. Further, unlike rate based techniques, our method can estimate the multiple periods when multiple periodic anomalies occur simultaneously.
Probabilistic partial-distance fast matching algorithms for motion estimation

Motion search is by far the most complex operation to be performed in a video encoder. This paper proposes a novel fast matching algorithm to help speed up the computation of the matching (distance) metric used in the search, e.g., the sum of absolute difference (SAD). Based on a partial distance technique, our algorithm reduces complexity by terminating the SAD calculation early once it becomes clear that, given the partial SAD, it is likely that the total SAD will exceed that of the best candidate encountered so far in the search. The key idea is to introduce models to describe the probability distribution of the total distance given a measured partial distance. These models enable us to evaluate the risk involved in "trusting" a distance estimate obtained from a partial distance. By varying the amount of risk we are willing to take, we ran increase the speed, but we may also eliminate some good candidates too early, and thus increase the distortion of the decoded sequence. Because our approach requires knowledge of the statistical characteristics of the input, we also propose two approaches that allow these models to be obtained online. Our experimental results (based on an actual software implementation of an MPEG encoder) demonstrate that significant gains can be achieved with this approach. For example, reductions in the motion estimation computation time as compared with the original partial-distance search (where computation stops if the partial SAD is already larger than the SAD of the best candidate so far) can be as high as 45% for 2-D log search and 65% for exhaustive full search with a small penalty of 0.1-dB degradation in PSNR of the reconstructed sequences.
Transform domain sparsification of depth maps using iterative quadratic programming

Compression of depth maps is important for “texture plus depth” format of multiview images, which enables synthesis of novel intermediate views via depth-image-based rendering (DIBR) at decoder. Previous depth map coding schemes exploit unique depth data characteristics to compactly and faithfully reproduce the original signal. In contrast, since depth map is only a means to the end of view synthesis and not itself viewed, in this paper we explicitly manipulate depth values, without causing severe synthesized view distortion, in order to maximize representation sparsity in the transform domain for compression gain — we call this process transform domain spar-sification (TDS). Specifically, for each pixel in the depth map, we first define a quadratic penalty function, with minimum at ground truth depth value, based on synthesized view's distortion sensitivity to the pixel's depth value during DIBR. We then define an objective for a depth signal in a block as a weighted sum of: i) signal's sparsity in the transform domain, and ii) per-pixel synthesized view distortion penalties for the chosen signal. Given that sparsity (70-norm) is non-convex and difficult to optimize, we replace the Zo-norm in the objective with a computationally inexpensive weighted 12-norm; the optimization is then an unconstrained quadratic program, solvable via a set of linear equations. For the weighted /2-norm to promote sparsity, we solve the optimization iteratively, where at each iteration weights are readjusted to mimic sparsity-promoting ZT-norm, 0 < r < 1. Using JPEG as an example transform codec, we show that our TDS approach gained up to 1.7dB in rate-distortion performance for the interpolated view over compression of unaltered depth maps.
Graph-wavelet filterbanks for edge-aware image processing

In our recent work, we proposed the construction of critically-sampled wavelet filterbanks for analyzing functions defined on the vertices of arbitrary undirected graphs. These graph based functions, referred to as graph-signals, provide a flexible model for representing many datasets with arbitrary location and connectivity. An application area considered in that work is image-processing, where pixels can be connected with their neighbors to form undirected graphs. In this paper, we propose various graph-formulations of images, which capture both directionality and intrinsic edge-information. The proposed graph-wavelet filterbanks provide a sparse, edge-aware representation of image-signals. Our preliminary results in non-linear approximation and denoising using graphs show promising gains over standard separable wavelet filterbank designs.
A graph-based approach for latency modeling and optimization in multiview video encoding

We present a novel framework for encoding latency analysis of arbitrary multiview video coding prediction structures. This framework avoids the need to consider an specific encoder architecture for encoding latency analysis by assuming an unlimited processing capacity on the multiview encoder. Under this assumption, only the influence of the prediction structure and the processing times have to be considered, and the encoding latency is solved systematically by means of a graph model. The results obtained with this model are valid for a multiview encoder with sufficient processing capacity and serve as a lower bound otherwise. Furthermore, with the objective of low latency encoder design with low penalty on rate-distortion performance, the graph model allows us to identify the prediction relationships that add higher encoding latency to the encoder. Experimental results for JMVM prediction structures illustrate how low latency prediction structures with a low rate-distortion penalty can be derived in a systematic manner using the new model.
A novel packet loss recovery technique for multimedia communication

In this paper a novel loss recovery technique is proposed for multimedia communications over lossy packet networks. The proposed technique uses a combination of recent results on multiple description coding and erasure recovery codes in channel coding. The uniqueness of the proposed technique lies in its ability to recover not only the data carried in lost packets, but also the decoding state for successive packets. Experimental results on image and speech coding show that the proposed technique has excellent coding performance compared to some of the best results published and it can also significantly reduce the error propagation in successive packets due to packet losses.
Multiple description coding via scaling-rotation transform

We propose a two-stage transform design technique for multiple description transform coding. The first stage is the structure design in which we enforce a scaling-rotation factorization of the transform and we further constrain the transform for specific channel conditions using the knowledge of the input correlation matrix and the desired output correlation matrix. In the second stage, magnitude design, we find the optimal transform from all admissible transforms given by the structure design using the numerical algorithm proposed by Goyal et al. ). Such a design enables a structured transform framework which reduces both the design and implementation complexities compared to an exhaustive search through the whole space of nonorthogonal transforms. We give two examples to illustrate the design idea, the scaling-Hadamard transform for equal rate channels and the scaling-DST transform for sequential protection channels.
Overlapped Tiling for Fast Random Oblique Plane Access of 3D Object Datasets

Volume visualization with random data access poses significant challenges. While tiling techniques lead to simple implementations, they are not well suited for cases where the goal is to access arbitrarily located subdimensional datasets (e.g., being able to display an arbitrary 2D planar “cut” from a 3D volume). Significant effort has been devoted to volumetric data compression, with most techniques proposing to tile volumes into cuboid subvolumes to enable random access. In this paper we show that, in cases where subdimensional datasets are accessed, this leads to significant transmission inefficiency. As an alternative, we propose novel serverclient based data representation and retrieval methods which can be used for fast random access of oblique plane from 3D volume datasets. In this paper, 3D experiments are shown but the approach may be extended to higher dimensional datasets. We use multiple redundant tilings of the 3D object, where each tiling has a different orientation.We discuss the 3D rectangular tiling scheme and two main algorithm components of such 3D system, namely, (i) a search algorithm to determine which tiles should be retrieved for a given query and (ii) a mapping algorithm to enable efficient encoding without interpolation of rotated tiles. In exchange for increased server storage, we demonstrate that significant reductions in average transmission rate can be achieved relative to conventional cubic tiling techniques, e.g., nearly 40% reduction in average transmission rate for less than a factor of twenty overhead in storage before compression. Note that, as shown in our earlier work on the 2D case, the storage overhead will be lower after compression (e.g., in 2D the relative increase in storage in the compressed domain was at least a factor of two lower than in the uncompressed domain.
Contact Bounce Elimination by Means of a Sensorless Closed-Loop Current Controller in DC Core Contactors

The undesirable phenomenon of the contact bounce causes severe erosion of the contacts and, as a consequence, their electrical life and reliability are greatly reduced. On the other hand, the bounce of the armature can provoke re-opening of the contacts, even when they have already closed. This paper deals with the elimination of the bounce in both, contacts and armature, of a commercial DC core contactor. This is achieved by means of a current closed-loop controller, which only uses as input the current and voltage of the magnetizing coil of the contactor. The logic control has been implemented in a low cost microcontroller. Moreover, the board control can be fed by either DC or AC, and either in 50 Hz or 60 Hz so as to extend its applicability. A set of data is obtained from the measurement of the position and velocity of the movable parts for different operating voltages, so that the dynamic behavior of the contactor is discussed.
Localized iterative methods for interpolation in graph structured data

In this paper, we present two localized graph filtering based methods for interpolating graph signals defined on the vertices of arbitrary graphs from only a partial set of samples. The first method is an extension of previous work on reconstructing bandlimited graph signals from partially observed samples. The iterative graph filtering approach very closely approximates the solution proposed in the that work, while being computationally more efficient. As an alternative, we propose a regularization based framework in which we define the cost of reconstruction to be a combination of smoothness of the graph signal and the reconstruction error with respect to the known samples, and find solutions that minimize this cost. We provide both a closed form solution and a computationally efficient iterative solution of the optimization problem. The experimental results on the recommendation system datasets demonstrate effectiveness of the proposed methods.
Feature Extraction of Demagnetization Faults in Permanent-Magnet Synchronous Motors Based on Box-Counting Fractal Dimension

This paper presents a methodology for feature extraction of a new fault indicator focused on detecting demagnetization faults in a surface-mounted permanent-magnet synchronous motors operating under nonstationary conditions. Preprocessing of transient-current signals is performed by applying Choi-Williams distribution to highlight the salient features of this demagnetization fault. In this paper, fractal dimension calculation based on the computation of the box-counting method is performed to extract the optimal features for diagnosis purposes. It must be noted that the applied feature-extraction process is autotuned, so it does not depend on the severity of the fault and is applicable to a wide range of operating conditions of the motor. The performance of the proposed system is validated experimentally. According to the obtained results, the proposed methodology is reliable and feasible for diagnosing demagnetization faults in industrial applications.
Sequential diagonal linear discriminant analysis (SeqDLDA) for microarray classification and gene identification

In microarray classification we are faced with a very large number of features and very few training samples. This is a challenge for classical Linear Discriminant Analysis (LDA), since reliable estimates of the covariance matrix cannot be obtained. Alternative techniques based on Diagonal LDA (DLDA) combined with an independent gene selection (filtering) have been proposed. In this paper we propose a novel sequential DLDA (SeqDLDA) technique that combines gene selection and classification. At each iteration, one gene is sequentially added and the linear discriminant (LD) recomputed using the DLDA model (i.e., a diagonal co-variance matrix). Classical DLDA will add the gene with highest t-test score without checking the resulting model. In contrast, SeqDLDA will find the one gene that better improves class separation after recomputing the model measured using a robustified t-test score. We evaluate the new method in several 2-class datasets (Neuroblastoma, Prostate, Leukemia, Colon) using 10-fold cross-validation. For example, for the Neuroblastoma data set, the average misclassification rate of DLDA (16.91%) is significantly reduced to 13.87% using SeqDLDA.
Dedicated hierarchy of neural networks applied to bearings degradation assessment

Condition monitoring schemes, able to deal with different sources of fault are, nowadays, required by the industrial sector to improve their manufacturing control systems. Pattern recognition approaches, allow the identification of multiple system's scenarios by means the relations between numerical features. The numerical features are calculated from acquired physical magnitudes, in order to characterize its behavior. However, only a reduced set of numerical features are used in order to avoid computational performance limitations of the artificial intelligence techniques. In this sense, feature reduction techniques are applied. Classical approaches analyze the features significance from a global data discrimination point of view. This paper, however, proposes a novel and reliable methodology to exploit the information contained in the original features set, by means a dedicated hierarchy of neural networks.
Adaptive Filtering for Video Coding with Focus Change

We propose an adaptive filtering approach for coding video that exhibits localized camera focus changes, e.g., such that different portions of a video frame can undergo different blurriness/sharpness changes with respect to corresponding areas in frames used for prediction. First we obtain, for each macroblock, the filter that provides maximum reduction in residual energy, when applied to the reference macroblock before to motion compensation. Then, starting from the block-wise filter parameters, we divide the macroblocks into classes, by clustering macroblocks that have been assigned "similar" filters. Finally, for each class, a two-dimensional filter is designed to minimize the average residual energy for all macroblocks in the class. The resulting filters are applied to the reference frames to generate better matches for motion compensation. Simulation results shows that the proposed method provides up to 1 dB gain over current H.264 for certain sequences. As compared to H.264 with multiple reference frames, the coding gain is about 0.5 dB.