nils-herrmann/clean_scirepeval huggingface dataset:
Rate-distortion modeling of binary shape using state partitioning

In this paper, the rate-distortion (R-D) characteristics of binary shapes are modeled. Specifically we are interested in predicting the rate and distortion that is produced by the shape coding techniques that have been adopted into the MPEG-4 standard. The shape coding algorithm is a context-based arithmetic encoder and operates on a per block basis. Currently, there is no efficient way of estimating the rate and distortion at various levels of resolution. Consequently, we propose a model that is based on a set of parameters that can be easily extracted from the binary blocks. The parameters represent states that arise from the possible binary patterns that can occur in a small neighborhood around the current pixel. Symmetry is exploited to keep the number of states to a minimum. It is shown that the proposed model is computationally efficient and provides accurate estimates of the R-D characteristics of a binary shape.
Reflection and refraction of SH plane waves at the interface between two inhomogeneous half-spaces

The reflection and refraction of SH plane wave at the interface between two inhomogeneous half-spaces are studied in this paper, where the distributions of shear modulus and mass density all obey the parabolic functions. Based on the fundamental solution of the wave equation, the general formulations of incident wave, reflected wave and transmitted wave are given. Substituting the formulations into the boundary condition at the interface, the unknown coefficients are obtained, where the absolute values of these coefficients represent the amplitudes of reflected wave and transmitted wave. A series of parametric discussion are made to exhibit the effect on the reflection and refraction by wave number, incident angle and material properties.
MPEG-4 rate control for multiple video objects

This paper describes an algorithm which can achieve a constant bit rate when coding multiple video objects. The implementation is a nontrivial extension of the MPEG-4 rate control algorithm for single video objects which employs a quadratic rate quantizer model. The algorithm is organized into two stages: a pre- and a post-encoding stage. In the pre-encoding stage, an initial target estimate is made for each object. Based on the buffer fullness, the total target is adjusted and then distributed proportional to the relative size, motion, and variance of each object. Based on the new individual targets and rate-quantizer relation for texture, appropriate quantization parameters are calculated. After each object is encoded, the model parameters for each object are updated, and if necessary, frames are skipped to ensure that the buffer does not overflow. A preframeskip control is exercised to avoid buffer overflow when the motion and shape information occupies a significant portion of the bit budget. The rate control algorithm switches between two operation modes so that the coder can reduce the spatial coding accuracy for an improved temporal resolution. A shape-coding control mechanism is also proposed, which provides a tradeoff between texture and shape coding accuracy. Overall, the algorithm is able to successfully achieve the target bit rate, effectively code arbitrarily shaped objects, and maintain a stable buffer level. These techniques have been adopted by the MPEG committee in July 1997 as part of the video verification model (VM8).
Study on the simulation system of NC horizontal milling

Based on the features of NC horizontal milling process, the 3D geometric model of virtual machine tool was established by Microsoft Visual C++6.0, MFC and OpenGL graphics library. The method of Construction of Solid Geometry (CSG) and Boundary Representation (B-Rep) were adopted, and some realistic effects treatments (including material, light, texture mapping, etc.) were applied. The established virtual machine tool can be zoomed, rotated and translated in the view area. The workpiece decomposition expression model was put forward, and Boolean subtraction was implemented in this procedure. At the same time, a tool solid model to simulate the real time process of cutting was established. Simulation example proves that this system runs reliably and avoids the inconvenience and material waste of other methods.
Deep hashing with mixed supervised losses for image search

Deep convolutional neural networks (DCNN) have revolutionized almost the whole computer vision fields, including learning to hash for image search. Recently, several supervised deep hashing methods are proposed to deal with large-scale image search, where most methods only consider one kind of supervised loss. In this paper, we show that image search performance can be further boosted by combining two kinds of supervised losses, by taking the combination of point-wise and triplet-wise losses as a study case. Two kinds of strategies are proposed to combine the strengths of them. One strategy is that the DCNN is first pre-trained with point-wise loss and then fine-tuned with triplet-wise loss. The other one is that the DCNN is trained jointly with point-wise and triplet-wise losses. We perform extensive experiments on two public benchmark datasets CIFAR-10 and NUS-WIDE. Experimental results demonstrate that the proposed methods outperform the compared methods with single supervised loss.
Accurate analytical expression for reflection-peak wavelengths of sampled Bragg grating

An analytical expression for calculating the reflection-peak wavelengths of sampled Bragg grating (SBG) is achieved for the first time. The index modulation distribution of SBG is expanded into Fourier series; then the equivalent index modulation and local Bragg period are obtained to derive the expression. Consequently, the introduction of duty cycle and sampling period ensures the accuracy of expression. The phase-matching condition is well explained by the constant phase shift technique. Under this condition, the calculated results based on expression are in good agreement with the numerical reflection spectra obtained by the piecewise uniform method
A multivariable adaptive reconfigurable control method applied to the wing damaged aircraft

Focusing on the abrupt parameter variation of the aircraft dynamic characteristics brought by the wing damage, a multivariable adaptive reconfigurable control method has been studied. First the nonlinear 6DOF model of structural damaged aircraft is presented. The linearization process and the coupling between the longitudinal and lateral dynamics are studied, and the damaged aircraft dynamic model is denoted as a linear form with uncertain variations. And then the Multivariable Model Reference Adaptive Control (M-MRAC) method is introduced to compensate the abrupt variations in the state matrix, control matrix and the constant uncertainty. Finally NASA Generic Transport Model (GTM) is taken as an example and a typical case of left wing tip with 15% damage is considered. Three-channel attitude simulations are presented through comparing with PID control method. The results illustrate that the impact due to the parameter variations are significantly reduced, and the output tracks the desired trajectories rapidly and stably under uncertain damage.
An automated 3D registration method for optical coherence tomography volumes

Optical coherence tomography (OCT) is able to provide high resolution volumetric data for biological tissues. However, the field of view (FOV) of OCT is sometimes smaller than the field of interest, which limits the clinical application of OCT. One way to overcome the drawback is to stitch multiple 3D volumes. In this paper, we propose a novel method to register multiple overlapped volumetric OCT data into a single volume. The relative positions of overlapped volumes were estimated on en face plane and at depth. On en face plane, scale invariant feature transform (SIFT) was implemented to extract the keypoints in each volume. Based on the invariant features, volumes were paired through keypoint matching. Then, we formulated the relationship between paired offsets and absolute positions as a linear model and estimated the centroid of each volume using least square method. Moreover, we calibrated the depth displacement in each paired volume and aligned the z coordinates of volumes globally. The algorithm was validated through stitching multiple volumetric OCT datasets of human cervix tissue and of swine heart. The experimental results demonstrated that our method is capable of visualizing biological samples over a wider FOV, which enhances the investigation of tissue structure such as fiber orientation.
Recognition and retrieval of sound events using sparse coding convolutional neural network

This paper proposes a novel deep convolutional neural network (CNN), called sparse coding convolutional neural network (SC-CNN), to address the problem of sound event recognition and retrieval task. Unlike the general framework of a CNN, in which feature learning process is performed hierarchically, the proposed framework models the whole memorizing procedures in the human brain, including encoding, storage, and recollection. Sound data from the RWCP sound scene dataset with added noise from NOISEX-92 noise dataset are used to compare the performance of the proposed system with the state-of-the-art baselines. The experimental results indicated that the proposed SC-CNN outperformed the state-of-the-art systems in sound event recognition and retrieval. In the sound event recognition task, the proposed system achieved an accuracy of 94.6%, 100% and 100% under 0db, 10db and clean noise conditions, respectively. In the retrieval task, the proposed system improves the mAP rate of the general CNN by approximately 6%.
Screen content image segmentation using sparse decomposition and total variation minimization

Sparse decomposition has been widely used for different applications, such as source separation, image classification, image denoising and more. This paper presents a new algorithm for segmentation of an image into background and foreground text and graphics using sparse decomposition and total variation minimization. The proposed method is designed based on the assumption that the background part of the image is smoothly varying and can be represented by a linear combination of a few smoothly varying basis functions, while the foreground text and graphics can be modeled with a sparse component overlaid on the smooth background. The background and foreground are separated using a sparse decomposition framework regularized with a few suitable regularization terms which promotes the sparsity and connectivity of foreground pixels. This algorithm has been tested on a dataset of images extracted from HEVC standard test sequences for screen content coding, and is shown to have superior performance over some prior methods, including least absolute deviation fitting, k-means clustering based segmentation in DjVu and shape primitive extraction and coding (SPEC) algorithm.
3-D Integration and Through-Silicon Vias in MEMS and Microsensors

After two decades of intensive development, 3-D integration has proven invaluable for allowing integrated circuits to adhere to Moore's Law without needing to continuously shrink feature sizes. The 3-D integration is also an enabling technology for hetero-integration of microelectromechanical systems (MEMS)/microsensors with different technologies, such as CMOS and optoelectronics. This 3-D hetero-integration allows for the development of highly integrated multifunctional microsystems with small footprints, low cost, and high performance demanded by emerging applications. This paper reviews the following aspects of the MEMS/microsensor-centered 3-D integration: fabrication technologies and processes, processing considerations and strategies for 3-D integration, integrated device configurations and wafer-level packaging, and applications and commercial MEMS/microsensor products using 3-D integration technologies. Of particular interest throughout this paper is the hetero-integration of the MEMS and CMOS technologies.
Numerical Analysis and Optimization of Insulator-based Dielectrophoresis Devices for Cell Sorter Applications

Insulator-based dielectrophoresis (iDEP) is an innovative approach in which the nonuniform electric field needed to drive dielectrophoresis (DEP) is produced by insulators, avoiding problems associated with the use of electrodes. The aim of this thesis is to analyse the iDEP devices, demonstrate the function of it by numerical calculation, and optimize the parameters of iDEP microdevices. In this study, the electrical field generated by iDEP microdevice is calculated by Matlab software using finite element analysis (FEA) method. Based on this, the dielectrophoretic force experienced by cells in the microdevice are calculated. Furthermore, we optimize the parameters of iDEP microdevice such as dielectric shape, dielectric size, dielectric intervals, the size of microchannel and etc. At last, we give a set of optimum parameters.
End-to-end modeling and simulation of MPEG-2 transport streams over ATM networks with jitter

The operation of MPEG-2 systems is modeled and simulated when an MPEG-2 transport stream is delivered through an ATM network with jitter. End-to-end packet-based analysis is performed for delivery of MPEG-2 transport streams over ATM networks. A novel approach to analyzing the decoder buffer behaviour in the presence of network jitter is presented. The probability density function of the interarrival time of the ATM adaptation layer 5 (AAL5) protocol data unit (PDU) is derived from an MPEG-2 video source model and an ATM network jitter model. Based on a real-time decoding requirement of the MPEG-2 transport stream (TS) system target decoder (T-STD), the decoder buffer behaviour is simulated. In this simulation, the packets' arrivals follow the derived probability density function of the AAL5 PDU interarrival time. The modeling and simulation results show the interactions among packet loss ratio, decoder buffer size, and network jitter level. We found that jitter affects decoder buffer size and packet loss ratio in a significant way.
An investigation of 3D dual-tree wavelet transform for video coding

This paper examines the properties of a recently introduced 3D dual-tree discrete wavelet transform (DDWT) for video coding. The 3D DDWT is an attractive video representation because it isolates motion along different directions in separate subbands. However, it is an overcomplete transform with 8:1 redundancy. We examine the effectiveness of the iterative projection-based noise shaping scheme proposed by Kingsbury et al., (2002) on reducing the number of coefficients. We also investigate the correlation between subbands at the same spatial/temporal location, both in the significance map and in actual coefficient values.
A Multi-View Pedestrian Tracking Framework Based on Graph Matching

In the applications of video monitoring over large public or private spaces, multiple cameras are required to cover the entire space and resolve the problems of occlusion, object intersection and so on. In this work, a novel multi-view pedestrian tracking framework is proposed to simultaneously detect and associate human objects across views using graph matching techniques to fully exploit the object features and the spatial/temporal relationships among the objects. Experimental results are provided to demonstrate the accuracy of our proposed framework.
Hemodynamic alterations of a patient-specific intracranial aneurysm induced by virtual deployment of stents in various axial orientation

The aim of this study is to investigate the influence of stent axial orientation on saccular aneurysm hemodynamics. Two commercial stents, Neuroform stent (stent 1) and Zilver stent (stent 2) are modeled in this study. Both stents are virtually deployed with four different axial orientations to fit into the luminal surface of a patient-specific internal carotid artery (ICA) aneurysm model. Computational hemodynamic analyses are carried out in unstented and stented aneurysm models. The intra-aneurismal flows of stented aneurysm models show disturbed and complex flow patterns while the flow activities and the forces acting on the aneurysm wall are generally alleviated by stenting. The influence of the axial orientation of the stent on the aneurysm hemodynamics is more significant for stent 2 which has a larger strut size compared to stent 1. Interestingly, the flow activity in the aneurysm is rather increased when the intra-luminal scaffolding of stent is not sufficient.
Propagation of measurement noise through backprojection reconstruction in electrical impedance tomography

A framework to analyze the propagation of measurement noise through backprojection reconstruction algorithms in electrical impedance tomography (EIT) is presented. Two measurement noise sources were considered: noise in the current drivers and in the voltage detectors. The influence of the acquisition system architecture (serial/semi-parallel) is also discussed. Three variants of backprojection reconstruction are studied: basic (unweighted), weighted and exponential backprojection. The results of error propagation theory have been compared with those obtained from simulated and experimental data. This comparison shows that the approach provides a good estimate of the reconstruction error variance. It is argued that the reconstruction error in EIT images obtained via backprojection can be approximately modeled as a spatially nonstationary Gaussian distribution. This methodology allows us to develop a spatial characterization of the reconstruction error in EIT images.
Color object recognition via cross-domain learning on RGB-D images

This paper addresses the object recognition problem using multiple-domain inputs. We present a novel approach that utilizes labeled RGB-D data in the training stage, where depth features are extracted for enhancing the discriminative capability of the original learning system that only relies on RGB images. The highly dissimilar source and target domain data are mapped into a unified feature space through transfer at both feature and classifier levels. In order to alleviate cross-domain discrepancy, we employ a state-of-the-art domain-adaptive dictionary learning algorithm that updates image representations in both domains and the classifier parameters simultaneously. The proposed method is trained on a RGB-D Object dataset and evaluated on the Caltech-256 dataset. Experimental results suggest that our approach can lead to significant performance gain over the state-of-the-art methods.
Simulation of late gadolinium enhancement cardiac magnetic resonance studies

In this study we propose a pipeline for simulation of late gadolinium enhancement images. We used a modified version of the XCAT phantom to improve simulation realism. Modifications included the modeling of trabeculae and papillary muscles, and the increase of sublabels to resemble tissue intensity variability. Magnetic properties for each body tissue were sampled in three settings: from Gaussian distributions, combining Rayleigh-Gaussian distributions, and from Rayleigh distributions. Thirty-two simulated datasets were compared with 32 clinical datasets from infarcted patients. Histograms were obtained for five tissues: lung, pericardium, myocardium, blood and hyper-enhanced area. Real and simulated histograms were compared with the Chi-square dissimilarity metric (χ2) and Kullback-Leibler divergence (KL). The generated simulated images look similar to real images according to both metrics. Rayleigh and the Rayleigh-Gaussian models obtained comparable average results (respectively: χ2= 0.16±0.12 and 0.18±0.11; KL=0.15±0.17 and 0.16±0.18).
Statistical Interspace Models (SIMs): Application to Robust 3D Spine Segmentation

Statistical shape models (SSM) are used to introduce shape priors in the segmentation of medical images. However, such models require large training datasets in the case of multi-object structures, since it is required to obtain not only the individual shape variations but also the relative position and orientation among objects. A solution to overcome this limitation is to model each individual shape independently. However, this approach does not take into account the relative position, orientations and shapes among the parts of an articulated object, which may result in unrealistic geometries, such as with object overlaps. In this article, we propose a new Statistical Model, the Statistical Interspace Model (SIM), which provides information about the interaction of all the individual structures by modeling the interspace between them. The SIM is described using relative position vectors between pair of points that belong to different objects that are facing each other. These vectors are divided into their magnitude and direction, each of these groups modeled as independent manifolds. The SIM was included in a segmentation framework that contains an SSM per individual object. This framework was tested using three distinct types of datasets of CT images of the spine. Results show that the SIM completely eliminated the inter-process overlap while improving the segmentation accuracy.
A Predictive Model of Vertebral Trabecular Anisotropy From Ex Vivo Micro-CT

Spine-related disorders are amongst the most frequently encountered problems in clinical medicine. For several applications such as 1) to improve the assessment of the strength of the spine, as well as 2) to optimize the personalization of spinal interventions, image-based biomechanical modeling of the vertebrae is expected to play an important predictive role. However, this requires the construction of computational models that are subject-specific and comprehensive. In particular, they need to incorporate information about the vertebral anisotropic micro-architecture, which plays a central role in the biomechanical function of the vertebrae. In practice, however, accurate personalization of the vertebral trabeculae has proven to be difficult as its imaging in vivo is currently infeasible. Consequently, this paper presents a statistical approach for accurate prediction of the vertebral fabric tensors based on a training sample of ex vivo micro-CT images. To the best of our knowledge, this is the first predictive model proposed and validated for vertebral datasets. The method combines features selection and partial least squares regression in order to derive optimal latent variables for the prediction of the fabric tensors based on the more easily extracted shape and density information. Detailed validation with 20 ex vivo T12 vertebrae demonstrates the accuracy and consistency of the approach for the personalization of trabecular anisotropy.