nils-herrmann/clean_scirepeval huggingface dataset:
HoIP: Haptics over Internet Protocol

Applications such as telesurgery need low latency communication of haptic data between remote nodes. For the stability of the control loop, a typical round trip delay target is 5 ms or lower. In this paper, we specify an application layer protocol - Haptics over Internet Protocol (HoIP) - which is designed to allow such low latency haptic data transmission without sacrificing on the quality of haptic perception. The key ingredients of HoIP include adaptive sampling strategies for the digitization of the haptic signal, use of a multithreaded architecture at the transmitter and receiver to minimize processing delay, and use of an existing UDP implementation. In this paper, we describe in detail the frame structure used in HoIP and the rationale behind our design choices, the implementation details of HoIP at the transmitter and receiver, and report processing delay measurements done using real haptic devices and HAPI (an open source software used to interface with the haptic device). The HoIP software is entirely written in C++ and our results show that in the worst case transmitter and receiver side processing delays are less than 0.6 ms.
Mixed H2/ H∞ algorithm for exponentially windowed adaptive filtering

The RLS or its equivalent H<inf>2</inf> algorithms achieve the best average performance. But these suffer from poor worst case performance. Stochastic gradient based algorithms (like LMS) achieve best worst case performance, but a poor average performance. In this paper we propose switching criteria for acquiring advantages of both of these algorithms. The proposed algorithm uses a nonlinear combination of H<inf>2</inf> optimal and H<inf>∞</inf> optimal estimation. We present a mixed H<inf>2</inf>/H<inf>∞</inf> algorithm employing an exponential window and the achievable bound for this estimation strategy.
Automatic capsule preparation for lecture video

In this paper, we propose a visual quality and content based approach for creating a lecture video capsule which essentially contains the highlights of the original lecture video. We first segment and recognize the activities in the instructional video using a hidden Markov model(HMM). The activities are classified into three categories : talking head, writing hand and slide show. Then a no-reference objective quality assessment of the non-content frames, i.e., talking head frames, are performed to detect a few high quality frames for highlight creation. A new method of defining the visual quality of content frames, i.e., writing hand and slide show frames which are very crucial in a lecture video, is proposed in this paper. Statistical parameters of the intensity histogram along with the horizontal projection profile(HPP) of the the content frames are used to derive an objective quality measure of these frames. This allows us to extract the high quality content frames, which are ‘well-written’. Finally on media re-creation to produce the video capsule, we select the video segments of a few seconds duration around these good quality frames along with the audio and merge with suitable proportion of the detected classes of instructional activity. This technique accounts for a very compact representation of the whole lecture video which finds a very potential application of video previews.
A Novel Graph-Matching-Based Approach for Domain Adaptation in Classification of Remote Sensing Image Pair

This paper addresses the problem of land-cover classification of remotely sensed image pairs in the context of domain adaptation. The primary assumption of the proposed method is that the training data are available only for one of the images (source domain), whereas for the other image (target domain), no labeled data are available. No assumption is made here on the number and the statistical properties of the land-cover classes that, in turn, may vary from one domain to the other. The only constraint is that at least one land-cover class is shared by the two domains. Under these assumptions, a novel graph theoretic cross-domain cluster mapping algorithm is proposed to detect efficiently the set of land-cover classes which are common to both domains as well as the additional or missing classes in the target domain image. An interdomain graph is introduced, which contains all of the class information of both images, and subsequently, an efficient subgraph-matching algorithm is proposed to highlight the changes between them. The proposed cluster mapping algorithm initially clusters the target domain data into an optimal number of groups given the available source domain training samples. To this end, a method based on information theory and a kernel-based clustering algorithm is proposed. Considering the fact that the spectral signature of land-cover classes may overlap significantly, a postprocessing step is applied to refine the classification map produced by the clustering algorithm. Two multispectral data sets with medium and very high geometrical resolution and one hyperspectral data set are considered to evaluate the robustness of the proposed technique. Two of the data sets consist of multitemporal image pairs, while the remaining one contains images of spatially disjoint geographical areas. The experiments confirm the effectiveness of the proposed framework in different complex scenarios.
Visualization of Hyperspectral Images Using Bilateral Filtering

This paper presents a new approach for hyperspectral image visualization. A bilateral filtering-based approach is presented for hyperspectral image fusion to generate an appropriate resultant image. The proposed approach retains even the minor details that exist in individual image bands, by exploiting the edge-preserving characteristics of a bilateral filter. It does not introduce visible artifacts in the fused image. A hierarchical fusion scheme has also been proposed for implementation purposes to accommodate a large number of hyperspectral image bands. The proposed scheme provides computational and storage efficiency without affecting the quality and performance of the fusion. It also facilitates the midband visualization of a subset of the hyperspectral image cube. Quantitative performance results are presented to indicate the effectiveness of the proposed method.
On line sensor fault detection, isolation and accommodation in tactical aerospace vehicle

This paper presents on line sensor fault detection, isolation (FDI) and the associated fault tolerant control (FTC) algorithm for a tactical aerospace vehicle. A study on the analytical redundancy and a sensor fault detection scheme (FDI ) into a flight control system has been performed for a tactical aerospace vehicle using the longitudinal model. There are various methods available in the academic literature to apply FDI and FTC schemes to control systems and some have already been applied to real applications. Among these, observer-based approaches have arisen as one of the most widespread. The basic ideas behind observer-based FDI schemes are the generation of residuals, and the use of an optimal threshold function to differentiate faults from disturbances. Generally, the residuals, also known as diagnostic signals, are generated from estimates of the system's measurements obtained by a Luenberger observer or a Kalman filter. The threshold function is then used to 'detect' the fault by separating the residuals from false faults and disturbances. The change in residual signal is used to detect and isolate the fault and corresponding fault tolerant control action is taken to arrest the failure of the aerospace vehicle. A closed-loop simulation with nonlinear 6-degree of freedom (6-DoF) model shows that the above FDI and FTC scheme will be able to reduce the probability of mission failure due to the fault in one of the sensors.
Fault detection, diagnosis and control in a tactical aerospace vehicle

In this paper we propose a fault-tolerant control ( FTC ) scheme using multiple controller switching. The performance of this scheme is studied on a tactical aerospace vehicle. A parity space (PS) based residual generation approach is used to detect the fault. Once a fault is detected the diagnosis scheme identifies the faulty actuator. Using this information on-line reconfiguration of the controller is done based on the configuration of the existing healthy actuator. To implement this scheme no modification were done in hardware (H/W) configuration and only existing redundancies were utilised. Simulation with nonlinear 6-degree of freedom (6-DoF) model shows that the above fault tolerant control approach is able to reduce the probability of failure due to actuators.
Qualitative Visual Environment Retrieval

A system for retrieval of an unstructured environment under static and dynamic scenarios is proposed. The use of cylindrical mosaics or omnidirectional images is exploited for providing a rich description about the surrounding environment spanning 360 deg. The environment description is based on defining the attributes of the nodes of a graph derived from the angular partitions of the captured images. Content-based image retrieval for each of these partitions is performed on an exemplar image database to annotate the nodes of the graph. The complete environment description is recovered by collating the retrieval results over all the partitions based on a simple voting scheme. This offers a qualitative description of the location in a totally natural and unstructured surrounding. The experiments yield quite promising results
A Variational Approach to Recovering Depth From Defocused Images

In this paper, we propose a regularized solution to the depth from defocus (DFD) problem using the space-frequency representation (SFR) framework. A smoothness constraint is imposed on the estimates of the blur parameter, and a variational approach to the DFD problem is developed. Among the numerous SFRs, we study the applicability of the complex spectrogram and the Wigner distribution, in particular, for depth recovery. The performance of the proposed variational method is tested on both synthetic and real images. The method yields good results, and the quality of the estimates is significantly better than that obtained without the smoothness constraint on the blur parameter.
Signum function as a compensating technique for minimizing the effects of dead band of actuators used for control of aerospace vehicles

The nonlinearities in the actuators of flight control system for the high speed aerospace vehicles like missiles will affect the vehicle's performance in terms of its body rates and accelerations. The high body rates in turn affect the precise tracking of the target. In the proposed work, Signum function technique was implemented to see its effectiveness in reducing the effect of dead band of the actuator on the vehicle. The magnitude of the signum function is found to be critically affecting the usage of the function. Comparative studies are conducted and the results are presented.
A Fourier Domain Directional Filterng Method for Analysis of Collagen Alignment in Ligaments

Collagen fibers and their component fibrils make up the protenaceous " backbone" of most tissues and provide the majority of their resistance to tensile loading. Spatial orientation of collagen fibrils is an important factor in determining tissue properties. This is particularly true in ligament tissue, since ligaments must be loose enough to allow joints to move but tight enough to prevent joint surfaces from separating. A method is presented here to reproducibly quantify this collagen arrangement, which should be useful in studies on ligament healing and growth.
Crowd Motion Analysis Using Linear Cyclic Pursuit

Crowd motion analysis, where there is interdependence amongst the constituent elements, is a relatively unexplored application area in computer vision. In this work, we propose a fast method for short-term crowd motion prediction using a sparse set of particles. We study the dynamics of a crowd motion model and linear cyclic pursuit. We show that linear cyclic pursuit naturally captures the repulsive and attractive forces acting on the individual crowd member. The pursuit parameters are estimated from videos in an online manner using a feature tracker. Short term trajectory prediction is done by numerical solution of estimated cyclic pursuit equation. We demonstrate the suitability of the proposed technique through extensive experimentations.
Photometric stereo under blurred observations

In this paper, we address the problem of simultaneous estimation of structure and restoration of images from blurred photometric measurements. Given the blurred observations of a static scene captured with a stationary camera, under different illuminant directions, we obtain the structure represented by the surface gradients and the albedo and also perform blind image restoration. The surface gradients and the albedo are modeled as separate Markov random fields (MRF) and a suitable regularization scheme is used to estimate the different fields as well as the blur parameter.
A learning-based method for image super-resolution from zoomed observations

We propose a technique for super-resolution imaging of a scene from observations at different camera zooms. Given a sequence of images with different zoom factors of a static scene, we obtain a picture of the entire scene at a resolution corresponding to the most zoomed observation. The high-resolution image is modeled through appropriate parameterization, and the parameters are learned from the most zoomed observation. Assuming a homogeneity of the high-resolution field, the learned model is used as a prior while super-resolving the scene. We suggest the use of either a Markov random field (MRF) or an simultaneous autoregressive (SAR) model to parameterize the field based on the computation one can afford. We substantiate the suitability of the proposed method through a large number of experimentations on both simulated and real data.
Modeling of PSF for refractive index variation in fluorescence microscopy

In fluorescence microscopy, thickness of the specimen is significant compared to the depth of focus of a high numerical aperture (Na) lens of the imaging system. An aberration is introduced when there is a variation in refractive index in the specimen. Aberrations decrease the resolution and contrast of the image. Deconvolution is a method used to restore the image computationally, and for a successful decon-volution an accurate knowledge of the imaging system's point spread function (PSF) is a must. Here, we derive an appropriate model to obtain shift varying PSF for a thick specimen. Deconvolution results obtained for such a specimen are significantly better compared to those obtained using the standard Gibson and Lanni PSF model.
An Optimization-Based Approach to Fusion of Hyperspectral Images

In this paper we propose a new approach for visualization-oriented fusion of hyperspectral image bands. The proposed technique has been devised to generate the fused image with a certain set of desired properties for a better visualization. The fusion technique should provide a resultant image with a high local contrast without driving individual pixels into over- or under-saturation. We focus on these desired properties of the resultant image, and formulate a multi-objective cost function for the same. We have shown how we can incorporate the constraint of spatial smoothness of the weight vectors, as opposed to the smoothness of the fused image. The solution of this optimization problem has been provided using the Euler-Lagrange technique. By using an appropriate auxiliary variable, we show how the constrained optimization problem can be converted into a computationally efficient unconstrained one. The effectiveness of the proposed technique is substantiated from the visual and quantitative results provided.
On Stabilisation of Parametric Active Contours

Parametric active contours have been used extensively in computer vision for different tasks like segmentation and tracking. However, all parametric contours are known to suffer from the problem of frequent bunching and spacing out of curve points locally during the curve evolution. In a spline based implementation of active contours, this leads to occasional formation of loops locally, and subsequently the curve blows up due to instabilities. It has been shown earlier that in addition to usual evolution along the normal direction, the curve should also be evolved in the tangential direction for stability purposes. In this paper, we provide a mathematical basis for selecting such a suitable tangential component for stabilisation. We prove the boundedness of the evolved curve in this paper, and provide the physical significance. We demonstrate the usefulness of the proposed method with a number of experiments.
Exploring the Effect of the Core Boundary Curvature in Hollow Antiresonant Fibers

Through numerical simulations, we systematically study the leakage loss properties of a simplified novel hollow antiresonant fiber in which the core is surrounded by semi-elliptical elements. These studies lead to new insight into the effect of the curvature of the core boundary in antiresonant fibers. We observe in particular that in our design, there exists an optimum curvature of the elements—which we quantify simply through the aspect ratio of the ellipses—for which the fiber’s leakage loss is minimized. Furthermore, it is shown that elliptical elements can lead to orders of magnitude loss reduction as compared with similar fibers with circular ones.
Novel Speed-Up Strategies for Non-Local Means Denoising With Patch and Edge Patch Based Dictionaries

In this paper, a novel technique to speed-up a non-local means (NLM) filter is proposed. In the original NLM filter, most of its computational time is spent on finding distances for all the patches in the search window. Here, we build a dictionary in which patches with similar photometric structures are clustered together. Dictionary is built only once with high resolution images belonging to different scenes. Since the dictionary is well organized in terms of indexing its entries, it is used to search similar patches very quickly for efficient NLM denoising. We achieve a substantial reduction in computational cost compared with the original NLM method, especially when the search window of NLM is large, without much affecting the PSNR. Second, we show that by building a dictionary for edge patches as opposed to intensity patches, it is possible to reduce the dictionary size; thus, further improving the computational speed and memory requirement. The proposed method preclassifies similar patches with the same distance measure as used by NLM method. The proposed algorithm is shown to outperform other prefiltering based fast NLM algorithms computationally as well as qualitatively.
Deadzone analysis of 2D kinesthetic perception

In this paper, we perform a data driven analysis of human perception of 2 D haptic data, and study possible structures of the perceptual deadzone. We describe an experimental set up where a user is subjected to a 2 D piecewise constant haptic force, and is asked to respond with a click whenever he/she feels any change in the stimuli. The jumps of the force vector are distributed in the first quadrant of a circle of radius 2 N. The timespacing between the jumps is a uniform random variable over a range of [2,3] seconds. A jump is labelled as perceived or nonperceived based on the clicks. After recording the haptic response of a user, we identify signal features and apply several classifiers to predict the user response. Our thesis is that a classifier which predicts better, gives a good structure of the deadzone. We start with the Weber and level crossings classifiers to predict the perceivability of a jump, and find that level crossings performs significantly better than the Weber classifier over a range of [0,2] N. The level crossings classifier gives the best fit circular deadzone. Then, we apply a general conic section based classifier which points towards an elliptical deadzone. The elliptical deadzone is studied in detail to study the directional behaviour. It is found that the accuracy of both the circular and elliptical deadzone are nearly identical for all the users. Additionally, eccentricity and orientation of the elliptical deadzone match well with that of the distribution of the input data. It signifies that a user does not have direction preference while perceiving the change in 2 D haptic force. Importance plot of the features used in a random forest classifier also confirms our observation.
High-resolution slow-motion sequencing: how to generate a slow-motion sequence from a bit stream

Video surveillance is one of the applications where high-resolution (HR) images are crucial. The frames need to be super resolved to obtain an HR description of the scene and must be replayed at a relatively slow speed. HR slow-motion sequencing refers to the task of creating an HR slow-motion video from a given video bit stream. A better visual quality of slow video sequence can be obtained by applying the HR slow-motion sequencing algorithm to any given low-resolution (LR) video sequence. This article presents a tutorial concept demonstration type. It describes the image acquisition model utilized by the super resolution (SR) technique. The article also explains the general methodology used by the existing video compression standards to see how the existing SR techniques exploit the bit stream information to obtain the HR sequence. The authors also describe the method employed in obtaining the HR slow-motion sequences and demonstrate the results of the developed algorithm for surveillance videos.
Relevant Lifelong Nutrition Information for the Prevention and Treatment of Childhood Obesity - Design and Creation of New openEHR Archetype Set

The worldwide prevalence of obesity had a drastic increase in childhood/ adolescents. In 2014, it was estimated that 41 million children in all world, under the age of 5 were overweight or obese. Despite all the efforts, the number of overweight and obese children/adolescents is rising in almost all countries. Meanwhile, some points impaired the healthcare professionals to follow obesity guideline clinical practice (CPG). Information systems have an important role to play in the maintaining of this reality.  To propose a set of archetypes for the integration of CPG recommendations for treatment and prevention of obesity in children and adolescents in an openEHR based Electronic Health Record (EHR).  We assessed obesity guidelines as National Heart, Lung and Blood Institute (NHLBI), American Academic of Paediatrics (AAP), Brazilian Association for the Study of Obesity and Metabolic Syndrome (ABESO), World Health Organization (WHO) and Dietary Reference Intake (DRI) and selected the most representative variables in nutrition parameters (anthropometry, body composition, nutrients intake, etc.) in order to create/ find archetypes in Clinical Knowledge Manager (CKM) repository, and, finally, compose a template in openEHR.  38 variables were identified in guidelines. However, only 22 archetypes were necessary to represent all of them. Of these 22 archetypes, 8 are already available in CKM, while 14 clinical statements had no archetype representation and they had to be created.  Based on the created archetypes, an openEHR template can be created for the representation of clinical statements regarding the most important clinical guidelines in obesity. This template can be used by nutritionists and others health professionals to manage data relevant to obesity care.