nils-herrmann/clean_scirepeval huggingface dataset:
Calibration of Catadioptric Sensors by Polarization Imaging

A new efficient method of calibration for catadioptric sensors is presented in this paper. It is based on an accurate reconstruction of the mirror by means of polarization imaging and it permits to relax most of the constraints related to the calibration of the catadioptric systems. No image processing and no calibration pattern are required so that the calibration can be performed in "one click". As a result, the coordinates of the projection rays and a mapping between projection rays and image pixels is obtained. Besides, two methods of triangulation have been adapted to catadioptric sensors and tested. Experiments prove the validity of the method with some preliminary results on 3D reconstruction.
Adapted processing of catadioptric images using polarization imaging

A non parametric method that defines a pixel neighborhood within catadioptric images is presented in this paper. It is based on an accurate modeling of the mirror shape by using polarization imaging. Unlike the most of current processing methods in the literature, this method is non-parametric and can deal with the deformation of catadioptric images. This paper demonstrates how an appropriate neighborhood can be derived from the polarization parameters by estimation of the degree of polarization and the angle of polarization which in return directly provide an adapted neighborhood of each pixel that can be used to perform image derivation, edge detection, interest point detection and namely image matching, all in the catadioptric image plane.
Towards health monitoring in visual surveillance

This paper presents a feasibility study on heart rate detection using a digital camera. The paper investigates the possibility of Heart rate detection for individuals far from the camera sensor. The study is done to exploit the feasibility for heart rate estimation using a digital camera to enables health monitoring in visual surveillance. An experiment was conducted using 14 healthy subjects of various skin tones. State of the art heart rate estimation methods from photoplethysmography and ballistocardiography was implemented. The method were experimented on videos of subjects that were standing away 5 meters from the camera. Results derived showed that the technology has many challenges are to be overcome. The effect of ambient light variation, involuntary artifact movement, and poor signal to noise ratio are some of the problems to be addressed.
Efficient Pruning LMI Conditions for Branch-and-Prune Rank and Chirality-Constrained Estimation of the Dual Absolute Quadric

We present a new globally optimal algorithm for self-calibrating a moving camera with constant parameters. Our method aims at estimating the Dual Absolute Quadric (DAQ) under the rank-3 and, optionally, camera centers chirality constraints. We employ the Branch-and-Prune paradigm and explore the space of only 5 parameters. Pruning in our method relies on solving Linear Matrix Inequality (LMI) feasibility and Generalized Eigenvalue (GEV) problems that solely depend upon the entries of the DAQ. These LMI and GEV problems are used to rule out branches in the search tree in which a quadric not satisfying the rank and chirality conditions on camera centers is guaranteed not to exist. The chirality LMI conditions are obtained by relying on the mild assumption that the camera undergoes a rotation of no more than 90 between consecutive views. Furthermore, our method does not rely on calculating bounds on any particular cost function and hence can virtually optimize any objective while achieving global optimality in a very competitive running-time.
A novel non-central catadioptric auto-calibration approach

Calibration is the determination of coordinates of all pixels' rays in some common coordinate system. It enables to compute a 3D ray, along which light travels, for every image pixel. In this paper we propose a novel non-central catadioptric system auto-calibration approach. It uses polarization imaging to know the specular surface shape in order to estimate the catacaustics of the catadioptric system which geometrically fully describe the imaging system. The catacaustic is a locus of viewpoints. Each pixel in the image maps to a point on the caustic surface on which every point maps to a unique light ray from the scene. Polarization imaging helps to widen the range of catadioptric systems to include all types of specular surfaces. Our novel approach helps to auto-calibrate any combination of specular surfaces and lenses.
Mirror-adapted matching of catadioptric images

Images produced with catadioptric sensors contain a significant amount of radial distortions and variation in inherent scale. Direct application of conventional methods of image processing without considering the mirror anamorphosis yields erroneous issues. Examples of such applications are those based on the pixel neighborhood: one can mention image derivation, image convolution or image matching. The inefficiency of classical algorithms in catadioptric images processing leads in general to biased results. In this paper, we propose a new method to match a pair of catadioptric images taking into account their distorted topology. This method is based on an adapted window resulting from a convenient parameterization of the mirror. Experiments have been performed on image sequences acquired with a parabolic catadioptric sensor.
Tracking moving objects with a catadioptric sensor using particle filter

Visual tracking in video sequences is a widely developed topic in computer vision applications. However, the emergence of panoramic vision using catadioptric sensors has created the need for new approaches in order to track an object in this type of images. Indeed the non-linear resolution and the geometric distortions due to the insertion of the mirror, make tracking in catadioptric images a very challenging task. This paper describes particle filter for tracking moving object over time using a catadioptric sensor. In this work different problems due to the specificities of the catadioptric systems such as geometry are considered. The obtained results demonstrate an important improvement of the tracking accuracy with our adapted method and a better robustness to clutter background and light changes.
A multi-phase sparse probability framework via entropy minimization for single sample face recognition

In this paper, we propose a robust probability based sparse method to solve single sample face recognition, which harvests the advantages of both local and global representation. Different from previous sparse representation methods that generate sparse coefficients by l1, we produce sparse class probability distribution by proposing a multi-phase sparse probability (MSP) framework. To create class probability distribution, we divide each face image into many local blocks and vote based on the classification results of all blocks. For classifying each block, we propose local similarity assumption that makes many conventional methods feasible to SSPP problem. Moreover, we also propose a heuristic multiphase class selection scheme to solve the entropy minimization problem, which finally provides a higher classification confidence from the global perspective. Experimental results on three popular databases show that our approach not only generalizes well to SSPP problem but also has strong robustness to expression, illumination, occlusion and time variation.
Performance analysis of front rubber bushing stiffness of lower control arm on McPherson Suspension

McPherson Suspension is researched with multi-body dynamics and Suspension Kinematics theory. When the front rubber bushing stiffness of lower control arm is changed, the suspension performance is analyzed and discussed in the vertical bounce, roll bounce, brake force and drive force. The conclusion is drawn that stiffness of front rubber bushing in all directions has no effect on the suspension performance in the vertical bounce and roll bounce. In the brake force and drive force radical stiffness of rubber bushing has heavy effect on the suspension performance and axial and torsion stiffness of rubber bushing has a little effect on the suspension performance.
Local structure based sparse representation for face recognition with single sample per person

In this paper, we propose local structure based sparse representation classification (LS SRC) to solve single sample per person (SSPP) problem. By adopting the “divide-conquer-aggregate” strategy, we successfully alleviate the dilemma of high data dimensionality and small samples, where we first divide the face into local blocks, and classify each local block, and then integrate all the classification results by voting. For each block, we further divide it into overlapped patches and assume that these patches lie in a linear subspace. This subspace assumption reflects local structure relationship of the overlapped patches and makes SRC feasible for SSPP problem. To lighten the computing burden, we further propose local structure based collaborative representation classification (LS CRC). Experimental results on three public face databases show that our methods not only generalize well to SSPP problem but also have strong robustness to expression, illumination, little pose variation, occlusion and time variation.
A novel technique to achieve strong colliding-pulse effect in CPM QW LD

We introduce in this paper a novel structure to achieve ultrashort optical pulses in colliding-pulse mode-locking (CPM) quantum well laser diodes. Two modulation segments have been introduced symmetrically around the center saturable absorber and the 5-segment CPM-LD is thus fabricated. By changing the injected current on the modulation segments separately, the optical FWHM has been been shortened from 1.6 ps, giving a narrowest pulse width without modulation segments of up to 0.96 ps.
An applicable short-term traffic flow forecasting method based on chaotic theory

Short-term traffic flow forecasting plays a very important role in urban traffic management and control. In this paper, According to the chaotic property of urban traffic flow, we compute the parameters of phrase space reconstruction for traffic flow system. Meanwhile, a local-forecasting method is introduced to predict urban road short-term traffic flow based on the theory of phrase space reconstruction. Self-organizing Map (SOM) network is introduced to seek the near neighbor. Case study using real traffic flow data from UTC-SCOOT system proves the validity of the method. The research in this paper is a significant attempt to forecast traffic flow from the viewpoint of non-linear time series.
Finite-horizon Robust H∞ filter design for discrete systems with convex polytopic uncertainty

The paper addresses the finite-horizon robust H∞ filtering for discrete-time systems with convex polytopic uncertainty. A sufficient condition for the existence of finite-horizon robust H∞ filtering is presented by parameter-dependent Lyapunov function method with LMI approach. Two slack variable matrixs with special structure are introduced to design the finite-horizon robust H∞ filter. The resulting filter can satisfy the given performance index for all admissible uncertainties. Finally, a numeric simulation shows the filter obtained achieves the specified design goal.
Two new DOA estimation methods based on orthogonal joint diagonalization

Two new DOA (Direction Of Arrival) estimation methods based on orthogonal joint diagonalization using Jacobi rotation are proposed. Through processing spatio-temporal correlation matrices and high-order cumulant matrices by the new technique, the spatial spectrum can be redefined by the joint diagonalization matrix and a set of diagonal matrices. At the same time, these methods are also justified to be effective to process the coherent sources. Finally, the simulated and experimental results justify the proposed methods to be useful methods to DOA estimation and show that they have higher angle resolution, lower RMSE (Root Mean Squared Error), and better robustness than the conventional DOA estimation methods.
cDNA, Genomic Sequence Cloning and Sequence Analysis of Heat Shock Protein Beta-1 Gene (HSPB1) from the Giant Panda (Ailuropoda melanoleuca)

The HSPB1'office full name is heat shock protein beta-1, which is well-defined as a stress-inducible protein. It is a significant and interesting work to study it . The cDNA and the genomic sequence of HSPB1 was cloned successfully from the Giant Panda (Ailuropoda melanoleuca) using RT-PCR technology and touchdown-PCR, respectively, Both sequences were sequenced and analyzed preliminarily. The cDNA of the HSPB1 cloned from Giant Panda is 633bp in size, containing an open reading frame (ORF) of 624bp encoding 207 amino acids. The length of the genomic sequence is 1193 bp, which was found to possess three exons and two introns. Alignment analysis indicates that the nucleotide sequence of the coding sequence shows a high homology to those of Homo sapiens, Mus musculus, Bos Taurus, Canis lupus familiaris, Rattus norvegicus and Equus caballus are 89%, 85%, 88%,93%,83% and 91%, respectively . The homologies for deduced amino acid sequence are 89%, 90%,90%, 92%,88% and 92%, respectively. Topology prediction shows there are two N-myristoylation sites, four Protein kinase C phosphorylation sites, one Tyrosine kinase phosphorylation site, one cAMP- and cGMP-dependent protein kinase phosphorylation site and a ATP/GTP-binding site motif A (P-loop) in the HSPB1 protein of the Giant Panda. Futher analysis indicates that the expression sequence of HSPB1and the protein encoded are highly homologous to those of human and other mammals reported.
A Model-Aided Segmentation in Urethra Identification Based on an Atlas Human Autopsy Image for Intensity Modulated Radiation Therapy

In order to protect urethra in radiation therapy of prostate cancer, the urethra must be identified and localized as an organ at risk (OAR) for the inverse treatment planning in intensity modulated radiation therapy (IMRT). Because the prostatic urethra and its surrounding prostate tissue have similar physical characteristics, such as linear attenuation coefficient and density, it is difficult to distinct the OAR from the target in CT images. To localize the urethra without using contrast agent or additional imaging modalities other than planning CT images, a different approach was developed using a standard atlas of human anatomy image. This paper reports an investigation, in which an adult urethra was modeled based on a human anatomic image. An elastic model was build to account for a uniform tissue deformation of the prostate. This model was then applied to patients to localize their urethras and preliminary results are presented.
The new research on harmonic assessment model of power system

With the establishment of the power market and great increase in demands of electric energy in China, the importance of the power quality has became more and more evident in electric industry. In recent years, plenty of nonlinear impact loads have been connected to electrical network which caused voltage distortion and the decline of power quality. So, harmonic pollution has become a prominent problem in modern power grid. We must assess the harmonic current which through common connection point and caused by electrical equipments before they are connected with the electrical network. In this paper the methods of consumers' harmonic measurement and assessment are expounded, and the computational conditions and meanings of parameters of the formula for consumer harmonics index distribution are introduced. Followed, we will analyze the actual timing characteristics of harmonics and finally I will put forward the harmonic voltage and current assessment model which including four elements of the power quality assessment including process, time combinations, phase combinations, and index definition.
Design and Implementation of Tele-Calibration for Thermocouple and Resistance Thermometer Based on Virtual Instrument

With the development of network technology, due to the deficiencies of traditional calibration technology, a tele-calibration plan of thermocouple and resistance is presented. Combing the Data Socket with the Remote panel technology and Virtual Instrument technology, this system realizes remote temperature control, multi-channel data acquisition, video monitoring, network data transmission and remotely completes the calibration process. Experimental results show that the tele-calibration improves the efficiency of the calibration, it allows the calibration anytime and anywhere possible, transmission of data are accurate. The system provides a friendly user interface. Meanwhile, it ensures the reliability and real-time of network communication.
Empirical analysis of the influence of the reserve requirement ratio adjustment on the Chinese stock index

This paper uses the event study to research the influence of the reserve requirement ratio adjustment on the Chinese stock index. The paper indicates that the reserve requirement ratio adjustment although dose not influence the stock index directly, but still have some indirect effects. Firstly, when the stock market rises or falls. The stock index will not be influenced by the reserve requirement ratio adjustment in a short term. Secondly, when the stock market is in a period of adjustment. Raising the reserve requirement ratio will make the stock index fall, but it will not last for a long time. Thirdly, when the stock market falls obviously. After the policy which is lowering the reserve requirement ratio achieves a certain result. Some investors will invest in stock, but it will not last for a long time yet. Investors' viewpoint on the stock market bearish expectations do not change.
Research and Implementation of Clustering Algorithm for Arbitrary Clusters

For applications of clustering algorithms, a key technique is to handle complicatedly distributed clusters effectively and efficiently. On the basis of analysis and research of traditional clustering algorithms, a clustering algorithm based on density and adaptive density-reachable is presented in this paper. Experimental results show that the algorithm can handle clusters of arbitrary shapes, sizes and densities. At the same time, the algorithm can evidently reduce time and space complexity as compared with other density-based algorithms.
A novel method of non-stationary sEMG signal analysis and decomposition using a latent process model

To solve the problems of conventional signal analysis methods about non-stationary and frequency characteristics of surface electromyogrphy (sEMG) is of great significance to rehabilitation robot control with EMG-based human-computer interfaces (HCI). In this paper, the latent process models of sEMG signals were developed based on the combination of time-varying auto-regression (TVAR) model and dynamic linear model (DLM), which decomposed the signals into several components, and each component represents different time-frequency behavior of sEMG signals. On the basis of the latent process model, time-varying parameters, modulus and wavelength features were extracted. The fusing features of sEMG signals in two elbow movement conditions (elbow flexion and elbow extension) were adopted for clustering analysis and classification of data was visualized by using self-organizing map (SOM). An experiment with 9 healthy participants was carried out to verify the validity of this algorithm. The result implied that latent process model is a meaningful and valuable non-stationary sEMG signal analysis method which may be promising in rehabilitation robot control.
The Relationship between Change of Organizational Structure and Implementation of Advanced Manufacturing Technology: An Empirical Study

Since advanced manufacturing technology (AMT) was applied widely, how to properly accommodate organizational structure with the implementation of technology has become the focus of both academy and enterprise. Based on the literature review and cases study, this paper conducts a stepwise regression analysis with a large-scale sample of 307 machine tool firms. The results indicate that changes of organizational structure are significantly correlated with the implementation effects of AMT. This paper also discusses the diversity influences of structural changes on the implementation of AMT. Results illuminate that decentralization and integration have positive influences on the implementation effects of AMT; complexity is positively correlated with operational performance and organizational and managerial performance, which is inconsistent with the findings of foreign researches
Compact convolutional neural network transfer learning for small-scale image classification

Transfer learning methods have demonstrated state-of-the-art performance on various small-scale image classification tasks. This is generally achieved by exploiting the information from an ImageNet convolution neural network (ImageNet CNN). However, the transferred CNN model is generally with high computational complexity and storage requirement. It raises the issue for real-world applications, especially for some portable devices like phones and tablets without high-performance GPUs. Several approximation methods have been proposed to reduce the complexity by reconstructing the linear or non-linear filters (responses) in convolutional layers with a series of small ones., In this paper, we present a compact CNN transfer learning method for small-scale image classification. Specifically, it can be decomposed into fine-tuning and joint learning stages. In fine-tuning stage, a high-performance target CNN is trained by transferring information from the ImageNet CNN. In joint learning stage, a compact target CNN is optimized based on ground-truth labels, jointly with the predictions of the high-performance target CNN. The experimental results on CIFAR-10 and MIT Indoor Scene demonstrate the effectiveness and efficiency of our proposed method.
Dynamic vehicle routing problem using hybrid ant system

The dynamic vehicle routing problem, DVRP, consists in optimally routing a fleet occur at random. The optimization consists in finding the solution that minimizes the total length of tours and satisfying the customer delivery time window. In this paper, we present a hybrid ant system to handle the dynamism by means of modifying the pheromone matrix and candidate sets strategy. In addition, we make further improvements on vehicle routes with the local search heuristics. The results show that the hybrid ant system works effectively to find competitive solutions.
Time-frequency algorithm for octave analysis of nonstationary interior vehicle noises

Based on the discrete wavelet transform (DWT), a new time-frequency algorithm for octave analysis (OA) of nonstationary sounds, so-called DWT-OA method, is proposed in this paper. A DWT-based multirate filter bank combined by a set of low-pass and high-pass filters was developed for octave analysis of some measured nonstationary interior vehicle noises. To establish the multirate filter bank, the Mallat algorithm was applied, and some wavelet functions in common use, such as the Daubechies, Coiflets, Symlets, etc., were studied and discussed. The Daubechies wavelet with the filter length of seventy was determined and applied to the vehicle noises. Verifications suggest that the newly presented DWT-OA algorithm is accurate and feasible for octave analysis of the nonstationary vehicle noises. It may be extended to dealing with other types of sound signals in engineering.