nils-herrmann/clean_scirepeval huggingface dataset:
Dynamic Voltage Scaling Algorithms for Power Constrained Motion Estimation

In this paper, we apply dynamic voltage scaling (DVS) to the matching metric computation (MMC) used within motion estimation (ME) in typical video encoders. Our approach is based on "soft DSP" concepts. We analyze the effect of ME errors (due to DVS) in overall coding performance. We propose a model for the resulting rate increase (at a given fixed quantization parameter) as a function of input characteristics and input voltage, for given ME algorithm and MMC architecture. This model is validated using simulations. We then compare ME algorithms and MMC architectures, and propose a method for power saving of the ME process that depend on input characteristics and desired coding performance. As an illustration of the potential benefits of allowing computation errors, we show that allowing errors that lead to a small rate increase (about 3%) produces 37% power savings in the ME process, as compared to not using DVS. An essentially "error-free" DVS approach (no rate penalty) can achieve around 10% power savings.
Energy-efficient data representation and routing for wireless sensor networks based on a distributed wavelet compression algorithm

We address the problem of energy consumption reduction for wireless sensor networks, where each of the sensors has limited power and acquires data that should be transmitted to a central node. The final goal is to have a reconstructed version of the data measurements at the central node, with the sensors spending as little energy as possible, for a given data reconstruction accuracy. In our scenario, sensors in the network have a choice of different coding schemes to achieve varying levels of compression. The compression algorithms considered are based on the lifting factorization of the wavelet transform, and exploit the natural data flow in the network to aggregate data by computing partial wavelet coefficients that are refined as data flows towards the central node. The proposed algorithm operates by first selecting a routing strategy through the network. Then, for each route, an optimal combination of data representation algorithms i.e. assignment at each node, is selected. A simple heuristic is used to determine the data representation technique to use once path merges are taken into consideration. We demonstrate that by optimizing the coding algorithm selection the overall energy consumption can be significantly reduced when compared to the case when data is just quantized and forwarded to the central node. Moreover, the proposed algorithm provides a tool to compare different routing techniques and identify those that are most efficient overall, for given node locations. We evaluate the algorithm using both a second-order autoregressive (AR) model and empirical data from a real wireless sensor network deployment
Rotation-invariant features based on steerable transforms with an application to distributed image classification

In this paper, we propose a new rotation-invariant image retrieval system based on steerable pyramids and the concept of angular alignment across scales. First, we define energy-based texture features which are steerable under rotation, i.e., such that features corresponding to the rotated version of an image can be easily obtained from the features of the original (non-rotated) image. We also propose an approach to measure similarity between images that is robust to rotation; images are compared after being aligned in angle. The retrieval process is performed by means of a decision tree classifier where the angular alignment is performed at each node in the tree. To demonstrate the effectiveness of our system we consider a distributed image classification system, where the feature encoder and the classifier are physically apart and thus features are compressed before being transmitted. Our results of retrieval performance versus rate show a clear gain with respect to a wavelet transform (as an example, for the same rate, the retrieval precision is increased from 40% to 65%).
Joint Selection of Source and Channel Rate for VBR Video Transmission Under ATM Policing Constraints

Variable bit-rate (VBR) transmission of video over ATM networks has long been said to provide substantial benefits, both in terms of network utilization and video quality, when compared with conventional constant bit-rate (CBR) approaches. However, realistic VBR transmission environments will certainly impose constraints on the rate that each source can submit to the network. We formalize the problem of optimizing the quality of the transmitted video by jointly selecting the source rate (number of bits used for a given frame) and the channel rate (number of bits transmitted during a given frame interval). This selection is subject to two sets of constraints, namely, (1) the end-to-end delay has to be constant to allow for real-time video display and (2) the transmission rate has to be consistent with the traffic parameters negotiated by user and network. For a general class of constraints, including such popular ones as the leaky bucket, we introduce an algorithm to find the optimal solution to this problem. This algorithm allows us to compare VBR and CBR under the same end-to-end delay constraints. Our results indicate that variable-rate transmission can increase the quality of the decoded sequences without increases in the end-to-end delay. Finally, we show that for the leaky-bucket channel, the channel constraints can be combined with the buffer constraints, such that the system is identical to CBR transmission with an additional, infrequently imposed constraint. Therefore, video quality with a leaky-bucket channel can achieve the same quality of a CBR channel with larger physical buffers, without adding to the physical delay in the system.
Node clustering for data collection in wireless sensor networks using graph-transform and compressive sampling

In this paper, we address the problem of node clustering for compressed sensing (CS) based data collection in wireless sensor networks (WSNs). With consideration of recovery accuracy, communication cost and residual energy, two clustering strategies are proposed. Both strategies utilize Lapacian eigenvectors corresponding to the topology graph as a sparsifying basis, termed eigenbasis. The first clustering strategy is a centralized one, for which we treat the energy concentration of eigenbasis as sparsity feature vector and use traditional pattern clustering method to divide the nodes into clusters. The second one is a distributed heuristic strategy simultaneously considering residual power, communication cost, and basis energy distribution over clusters. By utilizing eigenbasis, both strategies are independent of the data to be collected and applicable in irregularly placed WSNs. Simulation results from both synthetic and real data are included to demonstrate the proposed strategies.
Compact Support Biorthogonal Wavelet Filterbanks for Arbitrary Undirected Graphs

This paper extends previous results on wavelet filterbanks for data defined on graphs from the case of orthogonal transforms to more general and flexible biorthogonal transforms. As in the recent work, the construction proceeds in two steps: first we design “one-dimensional” two-channel filterbanks on bipartite graphs, and then extend them to “multi-dimensional” separable two-channel filterbanks for arbitrary graphs via a bipartite subgraph decomposition. We specifically design wavelet filters based on the spectral decomposition of the graph, and state sufficient conditions for the filterbanks to be perfect reconstruction and orthogonal. While our previous designs, referred to as graph-QMF filterbanks, are perfect reconstruction and orthogonal, they are not exactly k-hop localized, i.e., the computation at each node is not localized to a small k-hop neighborhood around the node. In this paper, we relax the condition of orthogonality to design a biorthogonal pair of graph-wavelets that are k-hop localized with compact spectral spread and still satisfy the perfect reconstruction conditions. The design is analogous to the standard Cohen-Daubechies-Feauveau's (CDF) construction of factorizing a maximally-flat Daubechies half-band filter. Preliminary results demonstrate that the proposed filterbanks can be useful for both standard signal processing applications as well as for signals defined on arbitrary graphs.
Optimizing frame structure for interactive multiview video streaming with viewsynthesis

Traditional multiview video coding schemes compress all captured video frames exploiting all possible inter-view and temporal frame correlation for coding gain, creating complex inter-frame dependencies in the process. In contrast, interactive multiview video streaming (IMVS) demands data navigation flexibility in the frame structure design, so that server can send only a single periodically selected video view for decoding and display at client, saving transmission bandwidth. In this paper, we generalize previous IMVS frame structure optimization to allow a client to request an arbitrary virtual view; i.e., the server sends two adjacent coded views for the client to synthesize the desired virtual view. Since existing IMVS schemes transmit only one view at a time, they employ only cross-time prediction; i.e., the frame of previous time instant from which the client switches is used as predictor for the requested view. In our new scenario, two coded views are transmitted, thus within-time prediction can also be used, where the coded frame of one transmitted view is used to predict the frame of the other view of same time instant. Using I-frames, P-frames and Merge (M-) frames as building blocks, we formulate a Lagrangian problem to find the optimal frame structure for a desired storage/streaming rate tradeoff, with the right mixture of cross-time / within-time prediction types. Experiments show that for the same storage cost, the expected streaming rate of the proposed structure can be 40% lower than that of the I-frame-only structure, and 9% lower than that of the structure using M-frames but with cross-time prediction only.
Flexible Video Decoding: A Distributed Source Coding Approach

We investigate video compression techniques to address problems that require flexible video decoding. In these, the encoder has access to a number of candidate predictors that allow it to exploit source signal correlation, but only a subset of these predictors will be available at the decoder. Crucially, the encoder does not know which predictors will be available. Flexible decoding is important in a number of applications including frame-by-frame forward and backward video playback, multiview video, bitstreams switching, robust video transmission, etc. The main challenge to support flexible decoding is that the encoder needs to compress a current frame under the uncertainty on the predictor at decoder. An approach based on conventional "closed loop" prediction, e.g., motion-compensated predictive (MCP) coding in the case of video, could be developed by including multiple possible prediction residues in the bitstream, but this would lead to a considerable coding performance penalty, if all possible predictor combinations are supported, or to drifting, if only some combinations are. Moreover, it is not possible in general to guarantee that decoded versions under different prediction scenarios will be identical. In this paper, we propose a distributed source coding (DSC) based algorithm to tackle the problem. The main novelties of the proposed algorithm are that it incorporates different macroblock modes and significance coding within the DSC framework. This, combined with a judicious exploitation of correlation statistics, allows us to achieve competitive coding performance. Using forward/backward video playback as an example, we demonstrate the proposed algorithm can outperform a solution based on MCP coding.
Energy-efficient graph-based wavelets for distributed coding in Wireless Sensor Networks

This work presents a class of unidirectional lifting-based wavelet transforms for an arbitrary communication graph in a wireless sensor network. These transforms are unidirectional in the sense that they are computed as data is forwarded towards the sink on a routing tree. We derive a set of conditions under which a lifting transform is unidirectional, then find the full set of those transforms. Among this set, we construct a unidirectional transform that allows nodes to transform their own data using data forwarded to them from their descendants in the tree and data broadcasted to them from their neighbors not in the tree. This provides a higher quality data representation than existing methods for a fixed communication cost.
Broken Bearings Fault Detection for a Permanent Magnet Synchronous Motor under non-constant working conditions by means of a Joint Time Frequency Analysis

This work is an approach to develop new and reliable tools in order to diagnose mechanical faults in PMSM motors under non constant working conditions. These kinds of faults (especially damaged bearings) are more present in the industry. The paper presents a theoretical overview of the different techniques for a joint time frequency analysis and an experimental study of detection and diagnosis of damaged bearings on a permanent magnet synchronous motor (PMSM). The experiments were performed with variable rotor speed in such a way that the conventional methods such as MCSA do not work well. The stator current is analysed by means of STFT and Gabor spectrogram. Both results are presented and discussed.
Optimized frame structure using distributed source coding for interactive multiview video streaming

While multiview video coding typically focuses on the rate-distortion performance of compressing all frames of all views, we address the problem of designing a pre-encoded frame structure for a streaming server to enable a new functionality—interactive multiview switching, where a streaming client can send requests periodically to a server to switch to different views while continuing uninterrupted temporal playback of streaming video. We observe that providing bandwidth-efficient interactive view switching usually comes at the price of additional overall storage. Thus, our goal is to find a frame structure that minimizes the expected transmission rate during interactive multiview streaming, subject to a storage constraint. Noting that standard tools for random access (i.e., I-frame insertion) can be bandwidth-inefficient for this functionality, we propose to automatically generate a structure, combining I-frames, redundant P-frames and Distributed Source Coded (DSC) frames, in a near-optimal fashion to facilitate view switching. We present three new DSC techniques for view switching and discuss how these techniques can be integrated into an optimization framework. We show experimentally that near-optimal coding structures using DSC frames, in addition to I- and P-frames, reduce transmission cost over structures using I-frames only for view switching by up to 28%, and over structures using I- and P-frames only by up to 20% for the same storage cost.
Rate-distortion model and analytical bit allocation for wavelet-based region of interest coding

We introduce a novel rate-distortion (RD) model for images coded with a progressive wavelet coder, such as SPIHT (set partitioning in hierarchical trees) or JPEG2000, and especially designed to capture RD behavior when different parts of an image are refined at different speeds. Our model is an extension of Mallat's model (Mallat and Falzon 1998), which takes into account that the rates used are not necessarily the same throughout the image. Because of the different rates, certain modeling approximations (e.g., those for coarse quantization) can not be used uniformly throughout the image. Allocating different numbers of bits to different regions in an image is a problem that appears in applications such as region of interest (ROI) coding or multiple description coding (MDC), where the wavelet coefficients are divided by different factors before coding to enable different bit allocations to different regions. We show that the proposed RD model provides a more accurate estimate than Mallat's model.
Distributed source coding techniques for interactive multiview video streaming

We investigate coding tools for interactive multiview streaming (IMVS), where clients interactively request desired views for successive video frames, and in response the server sends the appropriate pre-compressed video data to the clients. Solution based on using only I-frames to support view switching would incur high transmission cost, while for that based on using only P-frames to encode every possible traversal, although it can minimize transmission cost, prohibitive server's storage may be required. Therefore, efficient solutions for IMVS need to consider the trade-off between transmission and storage cost. In this paper, we study the potential use of distributed source coding (DSC) in IMVS. Specifically, we propose two DSC constructions that could achieve good transmission-storage trade-offs. Central to these constructions is a method that can efficiently encode the least significant bits (LSB) of a frame to be decoded, leading to competitive storage and transmission requirements. Experiment results demonstrate these constructions compare favorably to existing tools, and could be valuable for interactive multiview streaming.
Diagnosis method based on topology codification and neural network applied to an industrial camshaft

Since the last years, there is an increasing interest from the industrial sector to provide the electromechanical systems with diagnosis capabilities. In this context, this work presents a novel monitoring scheme applied to diagnose faults in the main rotatory element of an industrial packaging machine, the camshaft. The developed diagnosis method considers a coherent procedure to process the acquired measurement. First, the current signals acquired from the main motor are processed in a normalized time-frequency map. Next, the characteristics fault patterns are identified and numerically characterized. A double self-organized map structure is proposed to manage the information till compress it to just two features by means of a topology codification of the data space. Finally, a neural network based classification algorithm is used to classify the condition of the camshaft. The effectiveness of this condition monitoring scheme has been verified by experimental results obtained from industrial machinery.
Perfect Reconstruction Two-Channel Wavelet Filter Banks for Graph Structured Data

In this work, we propose the construction of two-channel wavelet filter banks for analyzing functions defined on the vertices of any arbitrary finite weighted undirected graph. These graph based functions are referred to as graph-signals as we build a framework in which many concepts from the classical signal processing domain, such as Fourier decomposition, signal filtering and downsampling can be extended to graph domain. Especially, we observe a spectral folding phenomenon in bipartite graphs which occurs during downsampling of these graphs and produces aliasing in graph signals. This property of bipartite graphs, allows us to design critically sampled two-channel filter banks, and we propose quadrature mirror filters (referred to as graph-QMF) for bipartite graph which cancel aliasing and lead to perfect reconstruction. For arbitrary graphs we present a bipartite subgraph decomposition which produces an edge-disjoint collection of bipartite subgraphs. Graph-QMFs are then constructed on each bipartite subgraph leading to “multi-dimensional” separable wavelet filter banks on graphs. Our proposed filter banks are critically sampled and we state necessary and sufficient conditions for orthogonality, aliasing cancellation and perfect reconstruction. The filter banks are realized by Chebychev polynomial approximations.
Bearing fault diagnosis by EXIN CCA

EXIN CCA is an extension of the Curvilinear Component Analysis (CCA), which solves for the noninvariant CCA projection and allows representing data drawn under different operating conditions. It can be applied to data visualization, interpretation (as a kind of sensor of the underlying physical phenomenon) and classification for real time industrial applications. Here an example is given for bearing fault diagnostics in an electromechanical device.
Sparse recovery for discrete tomography

Discrete tomography (DT) focuses on the reconstruction of a discrete valued image from few projection angles. Prior knowledge about the image can greatly increase the quality of the reconstructed image, especially when a small number of projections are available. In this paper, we show that DT can be formulated as a sparse signal recovery problem. By using a well designed dictionary, it is possible to represent a binary image with very few coefficients. Starting from this concept, we modify the reweighed l1 algorithm to achieve a sparse solution and preserve the binary property of image. Preliminary simulation results show that our algorithm can outperform conventional continuous reconstruction methods in cases when very limited data is available.
A Note on the Bias in SVMs for Multiclassification

During the usual SVM biclassification learning process, the bias is chosen a posteriori as the value halfway between separating hyperplanes. A note on different approaches on the calculation of the bias when SVM is used for multiclassification is provided and empirical experimentation is carried out which shows that the accuracy rate can be improved by using bias formulations, although no single formulation stands out as providing better performance.
Efficient discrete wavelet transform architectures based on filterbank factorizations

In this paper, a boundary postprocessing technique is proposed to compute the discrete wavelet transform (DWT) near block boundaries. The basic idea is to take advantage of available lifting filterbank factorizations to model the DWT as a Finite State Machine (FSM). The proposed technique can reduce the size of auxiliary buffers in block-based DWT implementations and reduce the communication overhead between adjacent blocks. Two new DWT system architectures, Overlap-State sequential and Split-and-Merge parallel, are presented using this technique. Experimental results show that, for the popular (9, 7) filters, the size of auxiliary buffers can be reduced by 42% and that the parallel algorithm is 30% faster than existing approaches.
Study on the Permanent Magnet Demagnetization Fault in Permanent Magnet Synchronous Machines

This article studies the permanent magnet demagnetization fault in permanent magnet synchronous machines (PMSM). The study is carried out by analyzing the harmonics obtained using the fast Fourier transform (FFT) of stator and zero sequence currents at nominal torque for the whole range of machine operation speeds. The appropriate combination of analyses on the different currents makes it possible to determine the faults for any speed range
An efficient face identification method in a transformed domain

In this paper we propose a low-complexity face recognition system based on the Walsh-Hadamard transform. This system can be easily implemented on a fixed point processor and offers a good compromise between computational burden and identification rates. We have evaluated that with 169 integer coefficients per face we achieve better results (92%) than the classical eigenfaces approach (86.5%), and close to the DCT (92.5%) with a reduced computational cost. computational burden and memory requirements are still important. In order to alleviate this problem we have resized the original images from 112*92 to 56*46 for the KLT results. Geometry-feature-based methods try to identify the position and relationship between face parts, such as eyes, nose, mouth, etc., and the extracted parameters are measures of textures, shapes, sizes, etc. of these regions.