nils-herrmann/clean_scirepeval huggingface dataset:
ECG classification and prognostic approach towards personalized healthcare

A very important aspect of personalized healthcare is to continuously monitor an individual’s health using wearable biomedical devices and essentially to analyse and if possible to predict potential health hazards that may prove fatal if not treated in time. The prediction aspect embedded in the system helps in avoiding delays in providing timely medical treatment, even before an individual reaches a critical condition. Despite of the availability of modern wearable health monitoring devices, the real-time analyses and prediction component seems to be missing with these devices. The research work illustrated in this paper, at an outset, focussed on constantly monitoring an individual's ECG readings using a wearable 3-lead ECG kit and more importantly focussed on performing real-time analyses to detect arrhythmia to be able to identify and predict heart risk. Also, current research shows extensive use of heart rate variability (HRV) analysis and machine learning for arrhythmia classification, which however depends on the morphology of the ECG waveforms and the sensitivity of the ECG equipment. Since a wearable 3-lead ECG kit was used, the accuracy of classification had to be dealt with at the machine learning phase, so a unique feature extraction method was developed to increase the accuracy of classification. As a case study a very widely used Arrhythmia database (MIT-BIH, Physionet) was used to develop learning, classification and prediction models. Neuralnet fitting models on the extracted features showed mean-squared error of as low as 0.0085 and regression value as high as 0.99. Current experiments show 99.4% accuracy using k-NN Classification models and show values of Cross-Entropy Error of 7.6 and misclassification error value of 1.2 on test data using scaled conjugate gradient pattern matching algorithms. Software components were developed for wearable devices that took ECG readings from a 3-Lead ECG data acquisition kit in real time, de-noised, filtered and relayed the sample readings to the tele health analytical server. The analytical server performed the classification and prediction tasks based on the trained classification models and could raise appropriate alarms if ECG abnormalities of V (Premature Ventricular Contraction: PVC), A ( APB), L (Left bundle branch block beat), R (Right bundle branch block beat) type annotations in MITDB were detected. The instruments were networked using IoT (Internet of Things) devices and abnormal ECG events related to arrhythmia, from analytical server could be logged using an FHIR web service implementation, according to a SNOMED coding system and could be accessed in Electronic Health Record by the concerned medic to take appropriate and timely decisions. The system focused on ‘preventive care rather than remedial cure’ which has become a major focus of all the health care and cure institutions across the globe.
QoE Prediction Model Based on Fuzzy Logic System for Different Video Contents

A model that can predict end user satisfaction or QoE (Quality of Experience) directly from the network QoS (Quality of Service) is still illusive in the field of image processing. This motivates the derivation of a meaningful QoS to QoE mapping function to allow one to be predicted in the absence of the other. This paper presents an affine fuzzy logic based model that can estimate the visual perceptual quality for different video content types using a combination of network level and application level QoS parameters. Video contents are classified based on their spatio-temporal feature extraction. The video QoE is predicted in terms of the Mean Opinion Score (MOS). From the results it is clear that the QoE is video content dependent. Also, the network level parameters have more impact on video quality than the application level parameters. Results show that the Fuzzy logic-based model provides high prediction accuracy. The performance of the model was evaluated using a public dataset with good prediction accuracy (~ 95%). The developed model has use in control methods for streaming standard encoded video.
Video coding for digital cinema

In this paper, we present several techniques to improve the efficiency of motion-compensated 3-D subband/wavelet video coding for digital cinema (DC). Since DC data coming from scanning photographic film contains film-grain noise, we introduce one level spatial subband decomposition to reduce the noise, and impose motion-compensated temporal filtering (MCTF) only on the spatial LL subbands. Also we make the GOP size adaptive to better handle motion complexity and editing effects. In order to realize approximately constant quality in a full length motion picture, we propose to interleave the subbands of the temporal frames, and encode all the GOPs to the same fractional bit plane using the EZBC coder.
Kalman filtering in two dimensions: Further results

The two-dimensional reduced update Kalman filter was recently introduced. The corresponding scalar filtering equations were derived for the case of estimating a Gaussian signal in white Gaussian noise and were shown to constitute a general nonsymmetric half-plane recursive filter. This paper extends the method to the deconvolution problem of image restoration. This paper also provides a more thorough treatment of the uniquely two-dimensional boundary condition problems. Numerical and subjective examples are presented.
Spatio-temporal adaptive 3-D Kalman filter for video

This paper presents three-dimensional (spatio-temporal) Kalman filters for video as the extension of the two-dimensional (2-D) reduced update Kalman filter (RUKF) approach for images. We start out with three-dimensional (3-D) RUKF, a shift-invariant recursive estimator with efficiency advantages over the 3-D Wiener filter. Then, we turn to the motion-compensated extension MC-RUKF, which gives improved performance when coupled with a motion estimator. Since motion compensation sometimes fails, causing severe fluctuations in temporal correlation, we then present multimodel MC-RUKF, to adapt to variation in temporal and spatial correlation, by detecting the local image model out of a class, and using it in MC-RUKF. Finally, we introduce a novel multiscale model detection algorithm for use in high noise environments.
Two-dimensional recursive estimation for ARMA signal models

We present a recursive estimation algorithm for autoregressive moving average (ARMA) random field models. The estimator is an extension to ARMA models of the efficient reduced update Kalman filter (RUKF). We also discuss the identification of the ARMA model parameters from noise-free image data. The ARMA estimator is run on several sets of random field data as well as on real images. The experimental results are compared to those achievable using an AR model and RUKF on the same data fields. We find no significant improvement for comparable model orders.
Active Discarding Packet Mechanisms for Video Transmission

This paper presents a survey of active discarding packet mechanisms for video transmissions. Conventional queue management algorithms based their packet dropping decision on fairness, latency, etc. However, they don't take into account packet content and its importance in relation to the transmitted media type. Active mechanisms are a solution to overcome this limitation. Three key factors are identified: (i) the packet discarding algorithm; (ii) active solution approach; and (iii) the packetisation scheme. Our review is focused on these elements. For (i) this includes the packet discarding policy considering frame type, dependency and size, and loss pattern. In (ii) it involves when, where and how the active network solution is implemented. For (Hi) we consider packet content, size and transmission time. We present some of the main active solutions along with a discussion of what we consider opened research questions in this area.
Multi-gigahertz low-power low-skew rotary clock scheme

On-chip clock frequencies in the gigaHertz range require generators with low skew and low jitter to avoid timing problems. This rotary clock distribution architecture provides low-skew low-jitter, gigaHertz-rate clocking with high edge rates and low power consumption, works over a wide power supply range and is completely scalable. The frequency is limited only by f/sub T/ of the integrated circuit technology used; an f/sub T/ of approximately 30 GHz produces square waves with 20 ps transition times. In addition, there is no limit to the size of the chip that can be clocked, and both multiphase and non-overlapping noise-immune differential clocking are supported.
Multiscale modeling and estimation of motion fields for video coding

We present a systematic approach to forward-motion-compensated predictive video coding. The first step is the definition of a flexible model that compactly represents motion fields. The inhomogeneity and spatial coherence properties of motion fields are captured using linear multiscale models. One possible design is based on linear finite elements and yields a multiscale extension of the triangle motion compensation (TMC) method. The second step is the choice of a computational technique that identifies the coefficients of the linear model. We study a modified optical flow technique and minimize a cost function closely related to Horn and Schunck's (1981) criterion. The cost function balances accuracy and complexity of the motion compensated predictor and is viewed as a measure of goodness of the motion field. It determines not only the coefficients of the model, but also the quantization method. We formulate the estimation and quantization problems jointly as a discrete optimization problem and solve it using a fast multiscale relaxation algorithm. A hierarchical extension of the algorithm allows proper handling of large displacements. Simulations on a variety of video sequences have produced improvements over TMC and over the half-pel-accuracy, full-search block matching algorithm, in excess of 0.5 dB in average. The results are visually superior as well. In particular, the reconstructed video is entirely free of blocking artifacts.
Image identification and restoration in the subband domain

When faced with a large support point spread function (PSF), the iterative expectation-maximization (EM) algorithm, which is often used for PSF identification, is very sensitive to the initial PSF estimate. To deal with this problem, the authors propose to do EM image identification and restoration in the subband domain. After the image is first divided into subbands, the EM algorithm is applied to each subband separately. Since the PSF can be taken to have smaller support in each subband, these subbands should be less of a problem with the EM model identification. They also introduce an adaptive subband EM method for use in the upper frequency subbands.
Multi-vehicle testbed for decentralized environmental sensing

In this paper we present our multi-vehicle testbed that was designed for verification and validation of cooperative control algorithms involving environmental sensing. Two cooperative control algorithms: prioritized multi-sensing behavior, and a distributed adaptive algorithm for nonholonomic sensor networks are qualitatively verified using our multi-vehicle testbed. The multi-vehicle testbed allows for a straightforward transition from simulation to experimenting on actual hardware and has the flexibility to interface various types of sensors, vehicles, as well as enable indoor and outdoor experiments.
Time-frequency analysis of skeletal muscle and cardiac vibrations

Skeletal muscle and the heart vibrate during contraction producing nonstationary signals whose time-varying frequency reflects dynamic changes in physiological properties. Consequently, pathological changes in the mechanical integrity or loading of skeletal muscle or the heart can be expected to alter their vibrations. Classic frequency analysis techniques have been inadequate to characterize these subtle changes because of rapidly varying frequency components. A poor understanding of heart and muscle sound generation has also limited investigations. This paper demonstrates how time-frequency (TF) techniques have illuminated the relationships between muscle/heart material properties and loading and frequency dynamics of heart and muscle vibrations. Studies of evoked twitches from frog skeletal muscle reveal that muscle vibrations occur as transverse oscillations at the muscle's resonant frequency. Using a classic Rayleigh-Ritz model and crude estimates of the muscle geometry, muscle force can be accurately predicted from the muscle sound TF profile. First heart sound vibrations, in contrast, are shown to be a nonresonant phenomena, consisting of propagating transients superimposed upon bulk acceleration of myocardial contraction. Consequently, first heart sound frequency dynamics depend upon cardiac electrical excitation and hemodynamic loading in addition to intrinsic material properties and geometry, necessitating further work to characterize pathophysiologic correlations.
A hybrid prediction model for video quality by QoS/QoE mapping in wireless streaming

In the video streaming arena, and especially within the wireless transmission domain, measuring users' quality of experience (QoE) has become a pressing issue for it offers several benefits to both the service provider and the end user. However, available measurement techniques that adopt a full reference model (FR) are impractical in real-time transmission scenarios since they require the original video sequence available at the receiver's end. Hence, no-reference (NR) models fill this gap by providing less accurate measurement but sufficiently reliable for real-time video streaming. In this paper, we propose and evaluate a hybrid no-reference prediction model for the perceptual quality of video in the wireless domain. The model is based on fuzzy inference systems (FIS), and exploits several key parameters from both the application layer and physical layer. Hence the model is realized by means of mapping quality of service to quality of experience (QoS/QoE). The model is evaluated in contrast to random neural networks (RNN), and simulation results show considerable prediction accuracy of the model with a correlation coefficient of 92.17% and 0.1098 root mean square error.
Laser tagging: An approach for rapid robot trajectory definition

This paper describes directed laser tagging as an approach to define robot trajectories for low volume applications. The method employs a free-standing laser that is used to define a goal point, a 3D point on an object of interest. Biclops, a directed stereo vision system, uses triangulation to compute the coordinates of the 3D point. A trajectory is defined by collecting a sequence of quasi-static points designated by the free-standing laser. This trajectory can be executed by the robot in 3D space either on the fly or stored for subsequent execution. While a single robot is needed to capture the laser-tagged points, we use a second robot to verify that the 3D point was computed correctly. The first robot carries Biclops and computes the coordinates of the points designated by the free-standing laser. The second robot, which carries a laser, receives the 3D point information from the first robot. Then, the second robot aims at the free-standing laser point. Ideally, the two laser points will overlap. Directed laser-tagging is a rapid way to obtain 3D points compared to other traditional methods including teach-pendants and off-line programming. Experiments show that the error between the laser designated points and the vision acquired points is typically less than 3 mm.
Scalable Video Streaming With Fine-Grain Adaptive Forward Error Correction

In this paper, we investigate a fine-grain adaptive forward error correction (FGA-FEC) coding scheme for scalable video bitstreams. In our work, both the embedded source bitstream and the error-control codes are granularly adapted at block level in intermediate overlay nodes to satisfy heterogeneous users with both different video frame-rate/spatial resolution/quality preferences and different network connections. The proposed FGA-FEC scheme encodes and adapts the embedded source-coded bitstream in such a way that if part of the video source data is actively dropped, parity bits protecting that piece of data are also removed, yielding an efficient result without any transcoding.
Orchestral fully convolutional networks for small lesion segmentation in brain MRI

White matter (WM) lesion identification and segmentation has proved of clinical importance for diagnosis, treatment and neurological outcomes. Convolutional neural networks (CNN) have demonstrated their success for large lesion load segmentation, but are not sensitive to small deep WM and sub-cortical lesion segmentation. We propose to use multi-scale and supervised fully convolutional networks (FCN) to segment small WM lesions in 22 anemic patients. The multiple scales enable us to identify the small lesions while reducing many false alarms, and the multi-supervised scheme allows a better management of the unbalanced data. Compared to a single FCN (Dice score ∼ 0.31), the performance on the testing dataset of our proposed networks achieved a Dice score of 0.78.
Image estimation by stochastic relaxation in the compound Gaussian case

Concerns developing algorithms for obtaining the maximum a posteriori probability (MAP) estimate from blurred and noisy images modeled as compound Gauss-Markov random fields. These models consist of several image submodels having different characteristics along with a structure model, a 2D Markov chain, which governs transitions between these image submodels. Compound random field models are attractive for image estimation because the resulting estimates do not suffer the over-smoothing of edges that occurs when one employs linear shift-invariant (LSI) models.<<ETX>>
Sources of acoustic scattering in normal kidneys

Properties derived from acoustic backscatter measurements are examined to disclose sources of acoustic scattering in normal kidneys. The backscatter intensity from kidney parenchyma may be modeled using a two-component Gaussian correlation function. These two components yield effective scatterer size estimates of 220+or-15 mu m at low frequencies and 55+or-10 mu m at high frequencies, which correspond to the average glomerular and renal tubule diameters, respectively. A separate study using test media verifies the ability of the method to distinguish between mixtures of two different scattering structures from backscatter measurements at different frequencies.<<ETX>>
Question answer system for online feedable new born Chatbot

Designing a new born Chatbot and feeding it from the web with specific areas of interest is a new research field. Few researchers have investigated empty database Chatbots and populating it from web pages or plain text corpora. Extracting data from web pages needs considerable processing before the response sentences are ready for the Chatbot. Feature extraction is also needed in order to filter and quantify the extracted plain text. In addition, ranking and classification are also required. This paper presents a new method that employs multiple feature extraction methods to quantify text responses for a new born (uneducated) Chatbot. Multiple measurement metrics are examined simultaneously in order to find the nearest match to a query. The nearest matches with the highest score have been obtained by re-ranking the scores of extracted features for text responses. The results show that the highest scored sentences have subjectively a good match to the query. The evaluation results indicate that the performance of the system increases significantly by using cosine similarity to find lexical match between the query and the response sentence rather than Jaccard's coefficient.
The modeling of electrostatic forces in small electrostatic actuators

The basic elements of electrostatic devices (conductors, dielectrics, insulators, electrets, and ferroelectrics) are reviewed and it is shown that the modeling of electrostatic forces is a very different, and in many ways a more difficult, undertaking than the modeling of magnetic interactions; the difference between the real-world behavior of electrostatic configurations and textbook models is much greater than for magnetic configurations. Models must take into account the realities of materials, in particular the fact that resistivity is never infinite, and that any structural part of an actuator will affect fields through its dielectric properties. It is seen that in this sense (the behavior of materials), as well as in the most basic conceptual viewpoint (fields determined by potentials rather than by direct sources), electrostatics and magnetostatics are nearly opposite studies.<<ETX>>
Optical flow techniques applied to video coding

Motion estimation is an important part of most video coding schemes because it enables us to exploit the high degree of temporal redundancy present. Though block matching algorithms (BMA) yield coarse and piecewise-constant fields, they are very popular due to their simplicity and low bit overhead. In this paper, we propose to use a more advanced gradient-based technique to overcome the disadvantages of BMA. A dense motion field is estimated and compressed using a hierarchical finite element (HFE) representation, leading to an efficient, highly parallel, iterative, multiresolution optimization algorithm. The scheme also uses multiresolution measurements and a coarse-to-fine strategy to estimate large displacements. At comparable bit rates, the motion fields are much smoother and more natural than those produced by BMA. Coding gains of about 0.6 dB were obtained on Claire. More importantly, substantial visual improvements were obtained, mainly due to improved performance near the edges.
Simplifying and interpreting two-tone measurements

We develop a mathematical description of the response of weakly nonlinear systems to second-order memory mechanisms. Our description is based on a time-varying gain-modulation function. We show that intermodulation (IM) products arising from interactions at baseband have phase symmetries different from both interactions at second harmonic frequencies and gain compression and, thus, may be readily distinguished through measurement. We also demonstrate a technique for measuring and aligning the phase of IM products outside the measurement bandwidth of our instrumentation to identify contributions to memory with broad frequency response.
Multiscale motion estimation for scalable video coding

Motion estimation is an important component of video coding systems because it enables us to exploit the temporal redundancy in the sequence. The popular block-matching algorithms (BMAs) produce unnatural, piecewise constant motion fields that do not correspond to "true" motion. In contrast, our focus here is on high-quality motion estimates that produce a video representation that is less dependent on the specific frame-rate or resolution. To this end, we present an iterated registration algorithm that extends previous work on multiscale motion models and gradient-based estimation for coding applications. We obtain improved motion estimates and higher overall coding performance. Promising applications are found in temporally-scalable video coding with motion-compensated frame interpolation at the decoder. We obtain excellent interpolation performance and video quality; in contrast, BMA leads to annoying artifacts near moving image edges.