nils-herrmann/clean_scirepeval huggingface dataset:
Efficient inter-band prediction and wavelet based compression for hyperspectral imagery: a distributed source coding approach

Hyperspectral images have correlation at the level of pixels; moreover, images from neighboring frequency bands are also closely correlated. In this paper, we propose to use distributed source coding to exploit this correlation with an eye to a more efficient hardware implementation. Slepian-Wolf and Wyner-Ziv based correlated coding theorems have quantified how much additional rate reduction can be obtained. In order to better exploit these correlations, we first propose a prediction model to align images. This model is based on linear prediction techniques and it is simple and shown to be effective for hyperspectral images. We then propose a coding scheme to exploit these correlations. A set-partitioning approach is used on wavelet transformed data to extract bitplanes. Under our correlation model, bitplanes from neighboring bands are correlated and we then use a low-density parity-check based Slepian-Wolf code to exploit this bitplane level correlation. This scheme is appealing for hardware implementation as it is easy to parallelize and it has modest memory requirements. As for coding performance, our preliminary results for high correlation spectral bands from the NASA AVIRIS dataset show, at medium to high reconstructed qualities, gains of about a factor of 3 in compression efficiency as compared to encoding the spectral bands independently using SPIHT.
Filter optimization and complexity reduction for video coding using graph-based transforms

The basis functions of lifting transform on graphs are completely determined by finding a bipartition of the graph and defining the prediction and update filters to be used. In this work we consider the design of prediction filters that minimize the quadratic prediction error and therefore the energy of the detail coefficients, which will give rise to higher energy compaction. Then, to determine the graph bipartition, we propose a distributed maximum-cut algorithm that significantly reduces the computational cost with respect to the centralized version used in our previous work. The proposed techniques show improvements in coding performance and computational cost as compared to our previous work.
Community Streaming With Interactive Visual Overlays: System and Optimization

Community streaming is an enhanced form of joint content viewing where a sense of community is reinforced by the addition of interactive visual overlays, controlled in real-time by viewers, on top of a shared video stream. As a concrete example, we describe a community video system called ECHO, where personalized avatars are overlaid on top of a real-time encoded video stream of an Internet game for multicast consumption. Recognizing that only the visual overlays are generated live, we propose schemes that encode and schedule the live and non-live portions of the overlaid video separately in order to exploit the difference in delay sensitivity of the two, leading to video streams that contain two sub-streams with different delay constraints. We show that, in the known channel case, a low complexity ldquoearliest deadline firstrdquo packet scheduling algorithm minimizes receiver buffer delay. We also analyze the case where multiple streams are multiplexed, which allows us to quantify the potential gains of allowing different delay constraints for different sub-streams. We show that a ldquowater fillingrdquo strategy maximizes the total number of streams that can be supported. Simulation results show that the bandwidth necessary to maintain low-latency for visual overlays is reduced by about 40% when our proposed sub-stream approach is used. For multiplexing of multiple streams, our approach can increase the number of supported streams (e.g., a 30% increase when around ten streams are multiplexed).
Challenges in multiview video — The 3 D'S

For the purpose of this paper we group, under the generic term multiview video, different systems for which multiple standard video cameras and, possibly, additional depth-capturing cameras, are used. Video is then presented to the user using special glasses or displays. Research work in this area has focused on topics ranging from designing compression techniques to developing new 3D displays. In this paper we primarily consider the challenges involved in developing efficient compression tools. Our primary observation is that the “right” coding tools could depend heavily on choices made for content capture, display and communication. This is of course true for conventional video coding as well. But we will argue that it is even more important to address these issues for multiview video because there are much greater differences between different application scenarios (as compared to conventional video). The risk is that coding tools that are too narrowly focused on a specific application scenario may not be at all suitable for others. We focus specifically on three factors for which there exists significant uncertainty, namely, displays, depth estimation and content delivery. Our goal is not to discuss in detail current and future approaches (e.g., emerging alternative display technologies), but rather to show how these various approaches may have an impact on compression system design.
Edge-adaptive depth map coding with lifting transform on graphs

We present a novel edge adaptive depth map coding based on lifting on graphs. The transform is localized, of low complexity, and guarantees perfect reconstruction as long as a proper predict-update split is defined. During the transform process, data in the prediction set are predicted by data in the update set; the prediction errors are then stored for encoding. In order to reduce the energy of the prediction residue, we propose to use optimized sampling on graphs to select the update set. Experiments show that the optimized sampling approach achieves better results than the conventional maximum cut based splitting in terms of transform efficiency and reconstruction quality. In addition, performance using the lifting transform is comparable to the state-of-the-art graph based depth map encoder using graph Fourier transform (GFT), which requires high complexity for signal projection.
Graph-based approach for motion capture data representation and analysis

Providing better representation methods for motion capture data can lead to improved performance in terms of classification, recognition, synthesis and dimensionality reduction. In this paper, we propose a novel representation method inspired by algebraic and spectral graph theoretic concepts. Our proposed method represents motion data in a space constructed with bases for skeleton-like graphs. We introduce two criteria and as well as its influences on the generated bases. With experiments on CMU MoCap database, we will also discuss how this method may act as a good preprocessing tool in order to enhance the further analysis steps on MoCap data.
A novel condition monitoring scheme for bearing faults based on Curvilinear Component Analysis and hierarchical neural networks

Mostly the faults in electrical machines are related with the bearings. Thus, a reliable bearing condition monitoring scheme able to detect either local or distributed defects are mandatory to avoid a breakdown in the machine. So far, the research has been carried out mainly in the detection of local faults, such as balls and raceways faults, but surface roughness is not so reported. This paper deals with a novel and reliable scheme capable to detect any fault that may occur in a bearing, based on EXIN Curvilinear Component Analysis, CCA, and Neural Network. The EXIN CCA, which is an improvement of the Curvilinear Component Analysis, has been conceived for data visualization, interpretation and classification for real time industrial applications. The effectiveness of this condition monitoring scheme has been verified by experimental results obtained from different operation conditions.
A Framework for the Analysis and Optimization of Encoding Latency for Multiview Video

We present a novel framework for the analysis and optimization of encoding latency for multiview video. First, we characterize the elements that have an influence in the encoding latency performance: 1) the multiview prediction structure and 2) the hardware encoder model. Then, we provide algorithms to find the encoding latency of any arbitrary multiview prediction structure. The proposed framework relies on the directed acyclic graph encoder latency (DAGEL) model, which provides an abstraction of the processing capacity of the encoder by considering an unbounded number of processors. Using graph theoretic algorithms, the DAGEL model allows us to compute the encoding latency of a given prediction structure, and determine the contribution of the prediction dependencies to it. As an example of DAGEL application, we propose an algorithm to reduce the encoding latency of a given multiview prediction structure up to a target value. In our approach, a minimum number of frame dependencies are pruned, until the latency target value is achieved, thus minimizing the degradation of the rate-distortion performance due to the removal of the prediction dependencies. Finally, we analyze the latency performance of the DAGEL derived prediction structures in multiview encoders with limited processing capacity.
Induction Motor Fault Detection by using Wavelet decomposition on dq0 components

Early detection of interturn shorts during motor operation would eliminate consequential damage to adjacent coils and the stator core, then reducing repair costs and motor outage time. In addition to the benefits gained from early detection of turn insulation breakdown, significant advantages would accrue by locating the faulted coil within the stator winding. Fault location would not only increase the speed of the repair, but would also permit more optimal scheduling of the repair outage. Motor current signature analysis (MCSA) method is widely used as a diagnose tool for industrial applications. On the other hand, Park's transform is the most popular transformation used in vector control algorithms. By analyzing the current spectra of dqO Park components with MCSA method it is possible to improve earlier fault detection. Moreover, using wavelet transform as signal analysis method it is possible to reduce signal noise effects. Experimental results clearly corroborate the main aim of the paper
Low-cost and minimal conditioning interface for a PbSe photoconductor array [for pollutant detection]

A low-cost conditioning structure for PbSe photoconductor arrays with few components is described. We use a PbSe array of 64 pixels in a simultaneous atmospheric pollutant detector of several gases. The sensor system is based on infrared optical absorption spectroscopy. We present a second version of the sensor. We deduced: (i) it is necessary work at high chopper frequencies to reduce the effect of 1/f noise, (ii) a conditioning circuit is necessary for each channel, (iii) the conditioning interface must be near the array. Therefore, we propose to amplify and mix the incoming signal to produce a signal at a fixed intermediate frequency (IF), in our case 1 Hz. The circuit has few components which makes possible to place 64 conditioners in a small volume with SMT technology, 10/spl times/4.9/spl times/7.6 cm. The conditioner is divided into 8 boards. Each board has eight channels and an analog multiplexor. This permits to approach the circuitry to the array. Each channel conditioner has up to 100 dB voltage gain. Low IF technique makes possible to have high-performance circuits with low-cost components.
A probabilistic interpretation of sampling theory of graph signals

We give a probabilistic interpretation of sampling theory of graph signals. To do this, we first define a generative model for the data using a pairwise Gaussian random field (GRF) which depends on the graph. We show that, under certain conditions, reconstructing a graph signal from a subset of its samples by least squares is equivalent to performing MAP inference on an approximation of this GRF which has a low rank covariance matrix. We then show that a sampling set of given size with the largest associated cut-off frequency, which is optimal from a sampling theoretic point of view, minimizes the worst case predictive covariance of the MAP estimate on the GRF. This interpretation also gives an intuitive explanation for the superior performance of the sampling theoretic approach to active semi-supervised classification.
New Techniques for Fault Detection Analysis by Injecting Additional Frequency Test

Double frequency tests are used for evaluating stator windings analyzing the temperature. Likewise, signal injection on induction machines is used on sensorless motor control fields to find out the rotor position. Motor current signature analysis (MCSA) is the most widely used method for identifying faults in induction motors. MCSA focuses its efforts on the spectral analysis of stator current. Motor faults such as broken rotor bars, bearing damage and eccentricity of the rotor axis can be detected. However, the method presents some problems at low speed and low torque, mainly due to the proximity between the frequencies to be detected and the small amplitude of the resulting harmonics. This paper proposes to inject an additional voltage into the machine under test at a frequency different from the fundamental one, and then to study the resulting harmonics around the new frequencies appearing due to the composition between injected and main frequencies
Lookahead search for lossy context-based adaptive entropy coding

We motivate the need for lookahead search in a context-based entropy coder. An efficient algorithm based on modeling of the context coder as a finite state machine is presented. A key contribution of this paper is the use of the per survivor processing (PSP) principle to enable a lookahead search in scenarios where adaptive entropy coding is used. Our results show that lookahead searches based on PSP result in performance improvements over traditional schemes.
Tree-structured filter banks for M-block cyclic graphs

In this paper, we study the design of graph wavelet filter banks over M-block cyclic graphs. These graphs are natural directed extensions of bipartite graphs and their special structure is particularly suitable for the design of M-channel filter banks. Obtaining polynomial filter designs in this case that satisfy perfect reconstruction conditions is challenging since the Fourier domain of these graphs encompasses the entire complex-unit disc unlike just the complex unit-circle in the classical domain. Therefore, in this work, we consider a simpler setting where M is a power of 2 and propose a perfect reconstruction tree-structured biorthogonal filter bank solution comprised of a hierarchical 2-channel design. This approach significantly simplifies the design process by requiring the design of only one 2-channel filter bank for a directed bipartite graph, and repeating it across the hierarchy.
Depth map coding using graph based transform and transform domain sparsification

Depth map compression is important for compact “texture-plus-depth” representation of a 3D scene, where texture and depth maps captured from multiple camera viewpoints are coded into the same format. Having received such format, the decoder can synthesize any novel intermediate view using texture and depth maps of two neighboring captured views via depth-image-based rendering (DIBR). In this paper, we combine two previously proposed depth map compression techniques that promote sparsity in the transform domain for coding gain-graph-based transform (GBT) and transform domain sparsification (TDS) — together under one unified optimization framework. The key to combining GBT and TDS is to adaptively select the simplest transform per block that leads to a sparse representation. For blocks without detected prominent edges, the synthesized view's distortion sensitivity to depth map errors is low, and TDS can effectively identify a sparse depth signal in fixed DCT domain within a large search space of good signals with small synthesized view distortion. For blocks with detected prominent edges, the synthesized view's distortion sensitivity to depth map errors is high, and the search space of good depth signals for TDS to find sparse representations in DCT domain is small. In this case, GBT is first performed on a graph defining all detected edges, so that filtering across edges is avoided, resulting in a sparsity count ρ in GBT. We then incrementally add the most important edge to an initial no-edge graph, each time performing TDS in the resulting GBT domain, until the same sparsity count ρ is achieved. Experimentation on two sets of multiview images showed gain of up to 0.7dB in PSNR in synthesized view quality compared to previous techniques that employ either GBT or TDS alone.
Smart Environment Software Reference Architecture

Nowadays ubiquitous computing is spreading to all scopes of our lives. Smart environments are present at every location such us homes with automation and control devices, offices full of control networks to assist workers, or hotels with even more control devices in order to save energy and satisfy guests preferences.This paper focus on the proposal of a Reference Architecture for developing Smart Applications and deploy them in Smart Environments. The proposal consider three main process in the Software Architecture of these applications:a) perception, b) reasoning and c) acting.
Hardware testing for error tolerant multimedia compression based on linear transforms

In this paper, we propose a system-level error tolerance scheme for systems where a linear transform is combined with quantization. These are key components in multimedia compression systems, e.g., video and image codecs. Using the concept of acceptable degradation, our scheme classifies hardware faults into acceptable and unacceptable faults. We propose analysis techniques that allow us to estimate the faults' impact on compression performance, and in particular on the quality of decoded images/video. We consider as an example the discrete cosine transform (DCT), which is part of a large number of existing image and video compression systems. We propose methods to establish thresholds of acceptable degradation and corresponding testing algorithms for DCT-based systems. Our results for a JPEG encoder using a typical DCT architecture show that over 50% of single stuck-at interconnection faults in one of its 1D DCT modules lead to imperceptible quality degradation in the decoded images, over the complete range of compression rates at which JPEG can operate.
Creating adaptive learning paths using Ant Colony Optimization and Bayesian Networks

This paper presents a new way to combine two different approaches of artificial intelligence looking for the best path in a graph, ant colony optimization and Bayesian networks. The main objective is to develop a learning management system which will have the capability of adapting the learning path to the learnerpsilas needs in execution time, taking into account the pedagogical weight of each learning unit and the systempsilas social behavior.
Analysis of cache efficiency in 2D wavelet transform

In software implementations of image processing algorithms, efficient cache utilization is one of the most important factors to accomplish high performance microprocessor computing. In this paper, we propose a cache efficient block-based wavelet decomposition procedure and provide an analysis for the 2D wavelet transform. The wavelet transform is the core algorithm for the JPEG2000 compression standard, which can achieve higher compression ratio than the original JPEG standard while not suffering from the block boundary artifacts that appear at low rates when a DCT based algorithm such as JPEG is used. On the other hand, wavelet transform is a memory consuming algorithm and it turns out that many cache misses occur in the microprocessor computation process. This paper introduces a theoretical analysis to predict the most cache effective block-size to be used, given the cache size and image sizes. The simulation results achieved with a microprocessor architecture simulator confirm the cache efficiency predictions obtained with our theoretical analysis.
Motor Fault Detection Using a Rogowski Sensor Without an Integrator

This paper presents a new approach for the current acquisition system in motor fault detection applications. This paper includes the study, design, and implementation of a Rogowski-coil current sensor without the integrator circuit that is typically used. The circuit includes an autotuning block able to adjust to different motor speeds. Equalizing the amplitudes of the fundamental and fault harmonics leads to higher precision current measurements. The resulting compact sensor is used as a current probe for fault detection in induction motors through motor current signal analysis. The use of a Rogowski coil without an integrator allows a better discrimination of the fault harmonics around the third and fifth main harmonics. Finally, the adaptive conditioning circuit is tested over an induction machine drive. Results are presented, and quantitative comparisons are carried out.
Graph-based lifting transform for intra-predicted video coding

In this paper, we propose a graph-based lifting transform for intra-predicted video sequences. The transform can approximate the performance of a Graph Fourier Transform (GFT) for a given graph, but does not require computing eigenvectors. A predict-update bipartition is designed based on a Gaussian Markov Random Field (GMRF) model with the goal to minimize the energy in the prediction set. Additionally, a novel re-connection method is applied for multi-level graphs, leading to significant gain for the proposed bipartition method and for the conventional MaxCut based bipartition. Experiments on intra-predicted video sequences show that the proposed method, even considering the extra overhead for edge information, outperforms the Discrete Cosine Transform (DCT) and approximates the performance of the higher complexity GFT.
Moving towards a more electric aircraft

The latest advances in electric and electronic aircraft technologies from the point of view of an "all-electric" aircraft are presented herein. Specifically, we describe the concept of a "more electric aircraft" (MEA), which involves removing the need for on-engine hydraulic power generation and bleed air off-takes, and the increasing use of power electronics in the starter/generation system of the main engine. Removal of the engine hydraulic pumps requires fully-operative electrical power actuators and mastery of the flight control architecture. The paper presents a general overview of the electrical power generation system and electric drives for the MEA, with special regard to the flight controls. Some discussion regarding the interconnection of nodes and safety of buses and protocols in distributed systems is also presented
Reliable electro-mechanical actuators in aircraft

There is a general trend in the aerospace industry toward increasing the use of electrically-powered equipment. This trend is usually referred to as "Power-by-Wire" in the "More Electric Aircraft" (MEA) concept and it leads, among others, to the substitution of hydraulic actuators by electro-mechanical actuators (EMA). Its benefits are a decrease in maintenance effort and weight, and an increase in efficiency. However, jamming case obstacles stop the massive use of EMAs in flight-control actuators. The aim herein is to introduce the safety needs of electro-mechanical actuators in aircraft applications, and Its major goal is to present the suitability and reliability of such actuator systems, including electric motors and power converters.