nils-herrmann/clean_scirepeval huggingface dataset:
Compression of dynamic 3D point clouds using subdivisional meshes and graph wavelet transforms

The advent of advanced acquisition techniques in 3D media applications has led to an increasing trend of capturing dynamic objects and scenes via 3D point cloud sequences. This form of data is composed of time-indexed frames, each consisting of a collection of points with position and color attributes. Compression of such datasets is challenging because of the lack of efficient techniques for exploiting spatial and temporal correlations between the attributes. In our approach, we create an intermediate high-resolution representation of the point clouds, using consistent subdivisional triangular meshes, that captures all the features of the underlying object or scene. This representation is easy to obtain, significantly simplifies motion compensation and allows us to design efficient wavelet transforms using the recently developed framework of Biorthogonal Graph Wavelet Filterbanks. Preliminary experiments show that our approach can be an effective compression technique for 3D point cloud sequences.
Optimization of Overlapped Tiling for Efficient 3D Image Retrieval

Remote visualization of an arbitrary 2-D planar "cut" from a large volumetric dataset with random access has both gained importance and posed significant challenges over the past few years in industrial and medical applications. In this paper, a prediction model is presented that relates transmission efficiency to voxel coverage statistics for a fast random 2-D image retrieval system. This model can be for parameter selection and also provides insights that lead us to propose a new 3D rectangular tiling scheme, which achieves an additional 10% - 30% reduction in average transmission rate as compared to our previously proposed technique, e.g.,a nearly 30% / 45% reduction in the average transmission rate at the cost of a factor of ten / fifteen in storage overhead compared to traditional cubic tiling. Furthermore, this approach leads to improved random access, with less storage and run-time memory required at the client.
Signal processing techniques for interpolation in graph structured data

In this paper, we propose a novel algorithm to interpolate data defined on graphs, using signal processing concepts. The interpolation of missing values from known samples appears in various applications, such as matrix/vector completion, sampling of high-dimensional data, semi-supervised learning etc. In this paper, we formulate the data interpolation problem as a signal reconstruction problem on a graph, where a graph signal is defined as the information attached to each node (scalar or vector values mapped to the set of vertices/edges of the graph). We use recent results for sampling in graphs to find classes of bandlimited (BL) graph signals that can be reconstructed from their partially observed samples. The interpolated signal is obtained by projecting the input signal into the appropriate BL graph signal space. Additionally, we impose a `bilateral' weighting scheme on the links between known samples, which further improves accuracy. We use our proposed method for collaborative filtering in recommendation systems. Preliminary results show a very favorable trade-off between accuracy and complexity, compared to state of the art algorithms.
Rate-Distortion Optimized Scheduling for Redundant Video Representations

This paper extends rate-distortion optimized streaming techniques to operate on a general class of coding formats that explicitly support redundancy in their coding structure. Examples include multiple description layered coding (MDLC) and multiple independently encoded versions of a video source. Such source codecs usually produce multiple decoding paths, while previous work on video streaming has mostly focused on those encoding techniques that only generate a single decoding path. A new source model called directed acyclic hypergraph is introduced to describe the dependency and redundancy relationship between different video data units with multiple decoding paths. Based on this model, we then propose two rate-distortion based packet scheduling algorithms, i.e., Lagrangian optimization and a greedy algorithm, to dynamically adjust the system's real-time redundancy to match the channel behavior. The proposed streaming system introduces two types of redundancies, namely, source redundancy and transport redundancy. This paper presents a detailed performance analysis of the individual benefits for error robustness provided by these redundancies and their interplay. Experimental results show that our proposed system with both redundancies achieves the best end-to-end performance on real-time video communication over a wide range of network scenarios.
Closed-Loop Controller for Eliminating the Contact Bounce 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 been 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 contactor's magnetizing coil. 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, and the dynamic behavior of the contactor is discussed.
Interactive Streaming of Stored Multiview Video Using Redundant Frame Structures

While much of multiview video coding focuses on the rate-distortion performance of compressing all frames of all views for storage or non-interactive video delivery over networks, we address the problem of designing a frame structure to enable interactive multiview streaming, where clients can interactively switch views during video playback. Thus, as a client is playing back successive frames (in time) for a given view, it can send a request to the server to switch to a different view while continuing uninterrupted temporal playback. Noting that standard tools for random access (i.e., I-frame insertion) can be bandwidth-inefficient for this application, we propose a redundant representation of I-, P-, and “merge” frames, where each original picture can be encoded into multiple versions, appropriately trading off expected transmission rate with storage, to facilitate view switching. We first present ad hoc frame structures with good performance when the view-switching probabilities are either very large or very small. We then present optimization algorithms that generate more general frame structures with better overall performance for the general case. We show in our experiments that we can generate redundant frame structures offering a range of tradeoff points between transmission and storage, e.g., outperforming simple I-frame insertion structures by up to 45% in terms of bandwidth efficiency at twice the storage cost.
Self-Powered Wireless Sensor Applied to Gear Diagnosis Based on Acoustic Emission

Advanced sensing strategies in the industrial sector are becoming a valued technological answer to increase the performance and competitiveness. The development of enhanced sensing solutions considering both technology and monitoring requirements is, nowadays, subject of concern in the industrial maintenance field. In this context, this paper presents a novel self-powered wireless sensor applied to condition monitoring of gears. The proposed sensor is based on a modular architecture, offering multipoint sensing, local wireless communication, multisource energy harvesting, and embedded diagnosis algorithm for mechanical fractures detection based on acoustic emission analysis. The developments are complemented by means of a remote management interface, from which the user can configure the functionalities of the sensors, visualize the network status as well as analyze the diagnosis evolution. The sensor performance, in terms of power consumption and fault detection, has been analyzed by means of experimental results.
Exploration of linear discriminant analysis for transform coding in distributed image classification

In this paper, we address the issue of feature compression for distributed image classification systems. Such systems often extract a set of features, such as color, texture and shape, from the raw image data and store them as content descriptors. Users on the network search and exchange information with each other using feature data that contains sufficient information for image classification/retrieval. A query is formed at the client by extracting the features from the sample image, having them compressed and transmitted to the remote server. The corresponding image is classified through comparing the query feature vector with all the entries in the database and finding the most similar ones. We pose this problem as a statistical classification problem and consider the design of a transform coding scheme minimizing the detrimental effect of compression. Different from traditional transform coding which was designed to provide the best reconstruction from compressed data, here the goal is to preserve the separation between the feature vectors generated by different classes. Linear discriminant analysis is used due to the property that discrimination information is preserved in lowest dimensional space in transform domain, so that efficient scalar quantization and entropy coding can be applied to the transform coefficients. We propose a greedy algorithm to perform bit allocation in transform domain such that classification information from all dimensions can be employed in quantizer design. We show that our proposed transform coding scheme achieves much better classification performance than traditional KLT transform coding, based on both real data (Brodatz textures) and synthesized random source.
A novel distributed fiber optic strain sensor

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

We propose a pixel prediction algorithm, which learns a regression function corresponding to each context. A context refers to a group of pixels, that have similar correlations with its neighboring pixels. We propose to form a pixel's feature vector by its neighboring pixels' ratios, so that they better capture the pixel properties described by the regression weights. Then we use K-means clustering to classify the feature vectors of all pixels into several contexts. Clustering reduces pixel randomness within each context, thus reducing prediction error. We apply three regression algorithms, the least square, quantile and lasso regression, which assume different loss function and regularization. Experimental results demonstrate that all context based regression methods have outperformed conventional pixel predictors. Among them, quantile regression, which assumes l1-norm loss function has the best result. It has 3.1% less bits per pixel (bpp) than least square prediction with 12 neighboring pixels.
Adaptive compressed sensing for depthmap compression using graph-based transform

In this paper we present an adaptive compressed sensing (CS) framework for depth map compression using a family of graph-based transforms (GBT). To improve overall performance, we propose a greedy algorithm that selects for each block a GBT minimizing a metric, based on average mutual coherence, that takes into consideration both the edge structure of the block and the characteristics of the CS measurement matrix. This algorithm uses a low-complexity estimate of the mutual coherence, so that explicit construction of the GBT at the encoder is not required in the iterative process. As compared to coding using H.264/AVC, the proposed approach applied to intra-frames shows an average of 39 % bitrate savings or 3.8 dB PSNR gain for views rendered using a depth image based rendering (DIBR) technique.
Efficient context-based entropy coding for lossy wavelet image compression

We present an adaptive image coding algorithm based on novel backward-adaptive quantization/classification techniques. We use a simple uniform scalar quantizer to quantize the image subbands. Our algorithm puts the coefficient into one of several classes depending on the values of neighboring previously quantized coefficients. These previously quantized coefficients form contexts which are used to characterize the subband data. To each context type corresponds a different probability model and thus each subband coefficient is compressed with an arithmetic coder having the appropriate model depending on that coefficient's neighborhood. We show how the context selection can be driven by rate-distortion criteria, by choosing the contexts in a way that the total distortion for a given bit rate is minimized. Moreover the probability models for each context are initialized/updated in a very efficient way so that practically no overhead information has to be sent to the decoder. Our results are comparable or in some cases better than the recent state of the art, with our algorithm being simpler than most of the published algorithms of comparable performance.
Systemic Diagnosis and strategy-based Performance Indicators—Bamboo panel company case

This paper addresses the situation of a Bamboo Panel Company as a part of the Program for Research Stimulation, Technological Development and Innovation of the NATIONAL COUNCIL OF SCIENCE AND TECHNOLOGY. Specifically, on the Strategic Planning: Systemic Diagnosis and Performance Indicators through the Balanced Scorecard. The identification of the productive chain is presented. The vision and the mission are revised and the general strategy and the policies are determined. Another of the deliverables was the “Performance Indicators”, but they were not intended to establish solely the relation to the productive process; they were indicators that really aligned the organization with its strategy to lead it to achieve its vision. The results of this research are the basis of the actions undertaken to make the process more efficient: semiautomate the process with a machine specially designed to assemble bamboo panels, determine a layout, invest on training of employees, among others.
Adaptive reference filtering for bidirectional disparity compensation with focus mismatches

In this paper, we consider compensation of focus mismatches for frames that are encoded with inter-view bi-prediction (B-frames) in multiview coding (MVC). We start with an analysis of a multiview system with focus mismatches, to demonstrate that a B-frame may suffer from different types of mismatches with respect to the frames from different views used as references. As compared to our previous work for inter-view P-frames, filter estimation for B-frames has to consider not only the depth-dependency of focus mismatches, but also i) the possibility that the two predictors, from different directions, exhibit different types of prediction mismatches, and ii) the effect of bi-predictive search on the generation of filtered references. We show that, designing filters only for the averaged bi- predictors could lead to a suboptimal solution when combined with conventional bi-predictive search schemes. Instead, we propose a filter design approach that estimates two sets of depth-related filters, each set compensating for the focus mismatches exhibit in one of the two references used for bi-directional prediction. Simulation results shows that for views coded with inter-view bi-prediction, the proposed method provides up to 0.8 dB gain over current H.264/AVC in the sequences we tested.
Intelligent sensor based on acoustic emission analysis applied to gear fault diagnosis

The development of intelligent and autonomous monitoring systems applied to rotating machinery, represents the evolution towards the automatic industrial plants supervision. In this regard, an acoustic emission based intelligent sensor is presented in this work. The proposed sensor records regularly the acoustic emission signal generated by gearboxes. A time domain statistical analysis is applied in order to characterize the acquired data. Afterwards, a neural network based algorithm is applied to detect gear fault patterns. Finally, the diagnosis result is sent through a wireless transceiver to the central control unit. Moreover, in order to reach a real autonomous operation, the sensor power is approached by different energy harvesting solutions.
Evaluation of feature calculation methods for electromechanical system diagnosis

The use of intelligent machine health monitoring schemes is increasing in critical applications as traction tasks in the transport sector. The high diagnosis capability and reliability required in these systems are being supported by intelligent classification algorithms. These classifiers use calculated features from the system to perform the diagnosis. In this context, different features calculation methods can be applied to characterize the system condition obtaining different classification results. The aim of this work is based on diagnosis capabilities evaluation of the main features calculation methods: statistical features from time, statistical features from frequency, time-frequency distributions and signal decomposition techniques. The features capabilities are quantitatively evaluated by two parameters: the classification accuracy and the discriminant coefficient. Experimental results are obtained from an electromechanical actuator under different diagnosis requirements: from single fault to combined faults detection under stationary and non-stationary speed and torque conditions.
Optimal blockwise dependent quantization for stereo image coding

Research in coding of stereo images has focused mostly on the issue of disparity estimation to exploit the redundancy between the two images in a stereo pair, with less attention being devoted to the equally important problem of allocating bits between the two images. This bit allocation problem is complicated by the dependencies arising from using a prediction based on the quantized reference images. We address the problem of blockwise bit allocation for coding of stereo images and show how, given the special characteristics of the disparity field, one can achieve an optimal solution with reasonable complexity, whereas in similar problems in motion compensated video only approximate solutions are feasible. We present algorithms based on dynamic programming that provide the optimal blockwise bit allocation. Our experiments based on a modified JPEG coder show that the proposed scheme achieves higher mean peak signal-to-noise ratio over the two frames (0.2-0.5 dB improvements) as compared with blockwise independent quantization. We also propose a fast algorithm that provides most of the gain at a fraction of the complexity.
Microarray classification using block diagonal linear discriminant analysis with embedded feature selection

In this paper, block diagonal linear discriminant analysis (BDLDA) is improved and applied to gene expression data. BDLDA is a classification tool with embedded feature selection, that has demonstrated good performance on simulated data. However, by using cross validation in training, BDLDA is time consuming, thus not an appropriate algorithm for gene expression data, which has a large number of features and relatively small number of samples. In our algorithm, estimated error rate is used as a measure to choose the best model. The algorithm is optimized by repeating the model construction procedure with previously selected features removed, which leads to increased classification robustness. Our algorithm is tested using 10 fold cross validation. In most simulated and real data, our method outperforms the state-of-the-art techniques, showing promise for its use in microarray classification problems. The resulting block structure allows to identify discriminating correlated genes, which is potentially useful in cancer research.
Quantizer Design for Energy-Based Source Localization in Sensor Networks

We consider energy-based source localization applications, where distributed sensors quantize acoustic signal energy readings and transmit quantized data to a fusion node, which then produces an estimate of the source location. We propose an iterative quantizer design algorithm that allows us to take into account localization accuracy for quantizer design in the framework of the generalized Lloyd algorithm. Since source coding methodologies based on the Lloyd algorithm suffer from the presence of numerous poor local optima depending on initialization of quantizers, we introduce an efficient initialization, the equally distance-divided quantizer (EDQ), designed so that quantizer partitions correspond to a uniform partitioning in terms of distance. Our simulations demonstrate that improved performance over traditional quantizer designs can be achieved by using our proposed application specific strategy.
Distance Quantization Method for Fast Nearest Neighbor Search Computations with applications to Motion Estimation

The problem of given a query vector finding its nearest neighbor within a large set of vectors in high dimensional space arises in many applications. It often poses serious computational challenges due to the size of data point set (database), dimensionality of the search space, and the metric complexity. There exists a wealth of results in the literature that reduce complexity primarily based on altering the data set while still computing the chosen distance metric to full precision. However further significant simplification is attainable with our proposed approach which reduces the search metric computation resolution by applying non-uniform quantization within the metric computation process, in such a way that the minimum distance ranking is most likely to be preserved. This paper provides analytical and experimental studies of our proposed approach. We present an analytical formulation of the search performance measure that gives insight into understanding this approach. Based on this formulation we present a quantizer optimized to minimize the impact of quantization on identifying the nearest neighbor. The main advantages of this approach are that: i) it can reduce the number and complexity of required arithmetic operations significantly, ii) complexity does not increase with the order of lp norm or input bit size, and increases only slowly with dimensionality, and most importantly iii) the penalty to be paid in performance for the complexity reduction is very small if designed optimally. Motion estimation and compensation for video coding is chosen as an example application. Without requiring any filtering, transform, or sorting process, using a simple hardware oriented mapping, our experimental results show on average 0.05 dB loss using only 1 bit per pixel distance (0.01 dB when 2 bits are used) instead of typical 8 or 16 bits metrics.