nils-herrmann/clean_scirepeval huggingface dataset:
Scalable proxy caching of video under storage constraints

Proxy caching has been used to speed up Web browsing and reduce networking costs. In this paper, we study the extension of proxy caching techniques to streaming video applications. A trivial extension consists of storing complete video sequences in the cache. However, this may not be applicable in situations where the video objects are very large and proxy cache space is limited. We show that the approaches proposed in this paper (referred to as selective caching), where only a few frames are cached, can also contribute to significant improvements in the overall performance. In particular, we discuss two network environments for streaming video, namely, quality-of-service (QoS) networks and best-effort networks (Internet). For QoS networks, the video caching goal is to reduce the network bandwidth costs; for best-effort networks, the goal is to increase the robustness of continuous playback against poor network conditions (such as congestion, delay, and loss). Two different selective caching algorithms (SCQ and SCB) are proposed, one for each network scenario, to increase the relevant overall performance metric in each case, while requiring only a fraction of the video stream to be cached. The main contribution of our work is to provide algorithms that are efficient even when the buffer memory available at the client is limited. These algorithms are also scalable so that when changes in the environment occur it is possible, with low complexity, to modify the allocation of cache space to different video sequences.
An input dependent algorithm for the inverse discrete wavelet transform

We propose a fast algorithm for computing the inverse discrete wavelet transform (IDWT). The method takes advantage of the large number of zero coefficients after quantization. A bit map of the wavelet coefficients is used to test for streams of zero coefficients and inverse transform filtering is omitted for these coefficients. The zero testing algorithm is a tree-like search operation. Information on the statistics of the wavelet transform coefficients of a few typical images, together with estimates of the cost of testing and the cost of filtering, are used to obtain optimal sizes of the root and leaves of the search tree. Results show that our algorithm is faster than the baseline inverse wavelet transform algorithm by about 20% to 50% for PSNRs in the range 35 dB to 29 dB.
Directional Transforms for Video Coding Based on Lifting on Graphs

In this paper, we describe and optimize a general scheme based on lifting transforms on graphs for video coding. A graph is constructed to represent the video signal. Each pixel becomes a node in the graph and links between nodes represent similarity between them. Therefore, spatial neighbors and temporal motion-related pixels can be linked, while nonsimilar pixels (e.g., pixels across an edge) may not be. Then, a lifting-based transform, in which filtering operations are performed using linked nodes, is applied to this graph, leading to a 3D (spatio-temporal) directional transform, which can be viewed as an extension of wavelet transforms for video. The design of the proposed scheme requires four main steps: 1) graph construction; 2) graph splitting; 3) filter design; and 4) extension of the transform to different levels of decomposition. We focus on the optimization of these steps in order to obtain an effective transform for video coding. Furthermore, based on this scheme, we propose a coefficient reordering method and an entropy coder leading to a complete video encoder that achieves better coding performance than a motion-compensated temporal filtering wavelet-based encoder, and a simple encoder derived from H.264/AVC that makes use of similar tools as our proposed encoder (reference software JM15.1 configured to use one reference frame, no subpixel motion estimation, and  $16 \times 16$  inter and  $4 \times 4$  intra modes).
Simplified grid message-passing algorithm with application to digital image halftoning

Based on message-passing techniques, a novel iterative grid algorithm for the general two-dimensional (2D) digital least metric (DLM) problem is proposed and applied to image halftoning. The algorithm attempts to achieve a globally optimal solution via a local-metric computation and message passing as opposed to other 2D iterative global-metric optimizations such as simulated annealing and toggle/swap scheme. A reduced-complexity version of the proposed digital image halftoning technique is demonstrated. Results show that the quality of the halftone images is comparable to that of the state-of-the-art toggle/swap algorithm. Since the algorithm is not constrained by the specific metric used, the proposed method is directly applicable to other digital image processing tasks (eg, optimal near-lossless coding or entropy-constrained halftoning).
Complexity-scalable transform coding using variable complexity algorithms

In applications where compression has to be performed under varying complexity constraints (e.g., with hardware having to operate in reduced power mode) it is beneficial to design compression algorithms that allow some degree of complexity scalability. In this paper we explore complexity scalability for transform coding algorithms. We show that a variable complexity algorithm (VCA), which uses energy thresholds to determine the number of coefficients to be computed for each input, is preferable to other alternatives such as a pruned transform, where the same number of coefficients is computed for the whole image. We show that the benefits include not only a higher degree of scalability, but also increased compression performance, as we take advantage of the energy classification that is needed for VCA operation and design quantizers that match each class. We provide expressions for the average complexity as well as rate/distortion relations for a generic N-point VCA transform. For a two point case, we present closed-form relations describing the variance changes in two classes. In addition, rate-distortion-complexity relations are also empirically obtained. We apply VCA to eight-point KLT and 8/spl times/8 DCT in the JPEG framework and experiments show that the VCA approach is superior in rate/distortion performance at low rates compared to the standard transform coding techniques.
Dynamic characterization and position estimation of electromechanical contactors

This paper presents a rigorous model to simultaneously solving the mechanic and electromagnetic coupled differential equations which govern the dynamic response of the contactor. The model is validated with data obtained experimentally and therefore the accuracy of the model is validated. On the other hand, an estimator of the position and the velocity of the contacts, as well as of the moving core is developed. This estimator only uses available electrical variables, such as the voltage across the coil terminals and the current flowing through the coil of the contactor. Hence, by this way, a low cost microcontroller could be able to implement a control algorithm in order to reduce the undesirable phenomenon of the contact bounce, which causes severe erosion of the contacts and, as a consequence, the electrical life and reliability of the contacts are greatly reduced.
Feed-Back Active Control for Half-Bridge Class-D audio amplifiers

In this paper is presented a new technique to avoid the distortion generated due to mid point voltage variations on DC-bus of class-D half bridge audio amplifiers. An usual distortion source on class-D amplifier is the bus-pumping effect. Bus-pumping causes characteristic distortion and introduces attenuation on output gain. Due to this effect the amplifier efficiency decreases. By including distortion factors on the Half-Bridge amplifier model a new feed-back active control (FBAC) is implemented. The FBAC minimizes distortion due to mid point DC-Bus voltage variations. Simulation and experimental results confirm the theoretical approach of parameter influence on Bus-Pumping, and the effectiveness of FBAC implemented control. Results show a clear reduction of generated distortion, which allows incremented audio amplifier efficiency.
Efficient memory management control for H.264

Multiframe motion compensated prediction can achieve high prediction gain with additional frame buffer memory requirement. For mobile-telephony applications which utilize mobile processor and relatively small memory space, reducing the memory requirement is very important. In this paper, we propose an efficient memory management control technique, which reduces memory requirement for storing reference frames at the decoder side. Our proposed technique is a frame-based memory management scheme which discards the reference frames that are least important in terms of prediction gain performance. The optimal solution for this problem requires checking all the rate performance degradation for all the combination of discarded reference frames and therefore we propose a greedy search that checks a subset of reference frame combinations.
Transform-Based Distributed Data Gathering

A general class of unidirectional transforms is presented that can be computed in a distributed manner along an arbitrary routing tree. Additionally, we provide a set of conditions under which these transforms are invertible. These transforms can be computed as data is routed towards the collection (or sink) node in the tree and exploit data correlation between nodes in the tree. Moreover, when used in wireless sensor networks, these transforms can also leverage data received at nodes via broadcast wireless communications. Various constructions of unidirectional transforms are also provided for use in data gathering in wireless sensor networks. New wavelet transforms are also proposed which provide significant improvements over existing unidirectional transforms.
Stochastic rate-control of video coders for wireless channels

We introduce a new approach to deal with the transmission of real-time video over wireless channels, based on a priori stochastic models for both source and channel. This new problem formulation captures in a natural way the stochastic nature of the channel as well as the uncertainty regarding the properties of the video sequence. Our formulation leads to an optimal control problem that can be solved off-line, employing standard stochastic dynamic programming techniques. The outcome of this optimization is an off-line control policy that is optimal in the sense of minimizing the average coding distortion. The on-line computational cost of the new approach is thus very low: all that is required during run-time is to identify the state of the system (source and channel). Unlike other optimization-based rate control techniques, which require a search for the optimal operating point, the operating points here for each allowable state of the system have been precalculated. We consider wireless packet-based transmission with Automatic Repeat reQuest (ARQ) error control. While a standard model has been adopted to characterize the channel behavior, a new model based on the concept of coding complexity has been devised in order to characterize the video source. Simulation results based on this new approach are provided and compared to other proposed rate-control strategies. They show how the use of model-based optimal policies has negligible on-line computational cost while providing a transmission quality comparable to that achieved with more costly deterministic dynamic programming techniques, and significantly better than for simpler algorithms that do not explicitly take into account the channel state.
Fault detection of eccentricity and bearing damage in a PMSM by means of wavelet transforms decomposition of the stator current

This paper presents the study of permanent magnet synchronous machines (PMSM) running with eccentricity and bearings damage. The objective is to detect and identify the fault through the current signature analysis. The stator current has been analyzed by means of both Fourier (FFT) and Discrete Wavelet (DWT) transforms. Simulations have been carried out with a two-dimensional (2-D) finite element analysis (FEA), and they have been also compared with experiments. The results prove that the method proposed can be used to identify mechanical faults in a PMSM.
A low-cost Webcam & personal computer opens doors

Face recognition is one of the most successful ways for biometric recognition. One of its advantages is the high social acceptance by users, because it does not have the criminal stigma associated to other biometric traits, such as fingerprints. In addition, the cost of a webcam can be below 20 USD, which is certainly lower than other biometric acquisition devices. For this reason, the authors have developed a low-cost door-opening system, which is installed in our university premises for lab control access.
Pre-demosaic light field image compression using graph lifting transform

A plenoptic light field (LF) camera places an array of microlenses in front of an image sensor, in order to separately capture different directional rays arriving at an image pixel. Using a Bayer pattern, data captured at each pixel is a single color component (R, G or B). The sensed data then undergoes demosaicking (interpolation of RGB components per pixel) and conversion to a series of subaperture images. In this paper, we propose a novel LF image coding scheme based on graph lifting transform, where the acquired sensor data are coded in their original form without pre-processing. Specifically, demosaicking is not performed, and instead we first map raw sensed color data directly to subaperture image 2D grids, then encode the color pixels, which are sparse in spatial distribution, via a graph lifting transform. Our method avoids redundancies stemming from demosaicking, and operates in the original RGB domain without color conversion and sub-sampling. The graph lifting transform efficiently encodes irregularly spaced pixels in each subaperture image, resulting in compact representations. Experiments show that at high PSNRs — important for archiving and instant storage scenarios — our method outperforms demosaicking followed by intra-only High Efficiency Video Coding (HEVC) significantly.
Constrained bit allocation for error resilient JPEG coding

Variable-rate bit streams generated under the current DCT-based JPEG standard are vulnerable to propagation of error across the data units, i.e., DCT blocks. A traditional approach to combat this problem has been to introduce resynchronization markers in the transmitted bitstream. We propose an alternative, and more efficient, approach based on constraining the data length for the blocks or groups of blocks in the compressed image. In the proposed scheme the number of coded bits in each block, or group of blocks, is sent to the decoder as side information and thus the decoder can introduce resynchronization markers. The required overhead is significantly less than that of typical explicit synchronization schemes (e.g., those in JPEG). In addition, it is possible to further constrain the allowable rates for the blocks in order to reduce the overhead. We show experimental results where the constrained rate allocation is performed using thresholding techniques on images encoded by the baseline JPEG.
Multi-dimensional separable critically sampled wavelet filterbanks on arbitrary graphs

In our previous work, we observed an “aliasing” phenomenon for functions defined on bipartite graphs which is analogous to aliasing occurring in the downsampling of regular 1-dimensional signals. We exploited these concepts to design critically sampled two-channel wavelet filterbanks for any bipartite graph. For arbitrary graphs, we proposed a bipartite subgraph decomposition scheme to decompose the graph into edge-disjoint bipartite subgraphs and apply filtering and downsampling separately on each subgraph. This leads to the design of multi-dimensional separable filterbanks on graphs. In this paper, we study these bipartite decompositions in more detail. In particular, we describe the meaning of dimensionality in the subgraph decomposition of arbitrary graphs and define some graph based metrics based on this understanding. Subsequently, we propose a heuristics based algorithm for bipartite subgraph decomposition and compare it with other non-optimized algorithms. The results show both qualitative and quantitative improvements in the decomposed bipartite subgraphs with the proposed heuristics.
GTT: Graph template transforms with applications to image coding

The Karhunen-Loeve transform (KLT) is known to be optimal for decorrelating stationary Gaussian processes, and it provides effective transform coding of images. Although the KLT allows efficient representations for such signals, the transform itself is completely data-driven and computationally complex. This paper proposes a new class of transforms called graph template transforms (GTTs) that approximate the KLT by exploiting a priori information known about signals represented by a graph-template. In order to construct a GTT (i) a design matrix leading to a class of transforms is defined, then (ii) a constrained optimization framework is employed to learn graphs based on given graph templates structuring a priori known information. Our experimental results show that some instances of the proposed GTTs can closely achieve the rate-distortion performance of KLT with significantly less complexity.
Maximum a Posteriori (MAP)-Based Algorithm For Distributed Source Localization Using Quantized Acoustic Sensor Readings

In this paper, we propose a distributed source localization algorithm based on the maximum a posteriori (MAP) criterion, where the observations generated by each of the distributed sensors are quantized before being transmitted to a fusion node for localization. If the source signal energy is known, each quantized sensor reading corresponds to a region in which the source can be located. Aggregating the information obtained from multiple sensors corresponds to generating intersections between the regions. In our previous work we developed quantizer design techniques aimed at optimizing localization accuracy for a given aggregate rate. In this paper we develop localization algorithms based on estimating the likelihood of each of the intersection regions. This likelihood can incorporate uncertainty about the source signal energy as well as measurement noise. We show that the computational complexity of the algorithm can be significantly reduced by taking into account the correlation of the received quantized data. We also propose a technique, based on a weighted average of estimators, to address the case when the signal energy is unknown. Our simulation results show that our localization algorithm achieves good performance with reasonable complexity as compared with minimum mean square error (MMSE) estimation
Modeling of contours in wavelet domain for generalized lifting image compression

This paper introduces the design of context-based models of contours in the wavelet domain, which are used to construct generalized lifting (GL) mappings for image compression. The GL context-based mapping may significantly reduce the signal energy and the resulting bitrate. Here, we propose a strategy to define a reduced set of structured models to design the GL. The models capture the contour structures and are contrast-invariant. Initial experimental results applying the strategy on a wavelet subband exhibit potential gains. Iterations of the GL scheme as well as an adaptive entropy coding strategy may increase the coding gain.
Mapping data on a rotated grid in high-dimensions for lossless compression

Interactive navigation of large high-dimensional media datasets aims at allowing viewers to freely navigate content, selecting a subset of the high-dimensional visual data of interest for display. An example application would be remote visualization of an arbitrary 2-D planar cut from a large volumetric dataset with random access. In our previous work, we proposed a server-client based data representation and retrieval system using overlapping rotated tiles to represent the dataset, which lower the bandwidth required for accessing a random plane from large volume data. This leads to the question of how best to represent these rotated tiles for compression. In this paper we present a non-interpolated symmetric mapping algorithm, which maps each voxel in the original image to a rotated Cartesian grid point. We will show that this approach outperforms tile representation methods based on interpolation and non-symmetric mapping. In particular, the lack of interpolation means that complexity is significantly lower. Moreover, especially at high rates, remapping without interpolation will be shown to lead to overall better RD performance and the more symmetric the mapping is, the better RD performance will be achieved. Furthermore, a metric is proposed for automatically checking the mapping symmetry and measuring the percentage of the non-symmetric mapped points in the non-symmetric mapping algorithms.
Applications of Graph Theory

Graph-theoretical methods are being increasingly used in areas of interest within the IEEE and beyond. Graphs are mathematical abstractions that can be used to represent networks of various types: physical (e.g., the internet or electrical networks), biological (e.g., brain networks), or social (e.g., online social networks). Furthermore, graphs can provide tools for flexible representation of data sets in which data points have irregular positions with respect to each other. Common examples of this include data sets acquired by a sensor network, where uniform sensor placement may not be possible, or machine learning data sets, where training samples are not uniformly distributed in feature space. In some instances, a graph representation arises as a natural way to describe the problem, while in other areas, e.g., image processing, they are being used to develop powerful, content-dependent alternatives to conventional processing tools.
Quantizer design for source localization in sensor networks

In this paper, we propose a quantizer design algorithm that is optimized for source localization in sensor networks. For these applications, the goal is to minimize the amount of information that the sensor nodes have to exchange in order to achieve a certain source localization accuracy. We show that to achieve this goal requires the use of "application-specific" quantizers. Our proposed quantizer design algorithm uses a cost function that takes into account the distance between the actual source position and the position estimated based on quantized data. We apply this algorithm to a system where an acoustic sensor model is employed for localization. For this case we introduce the equally distance-divided quantizer (EDQ), designed so that quantizer partitions correspond to a uniform partitioning in terms of distance. Our simulations show the improved performance of our quantizer over traditional quantizer designs. They also show that an optimized bit allocation leads to significant improvements in localization performance with respect to a bit allocation that uses the same number of bits for each node.