nils-herrmann/clean_scirepeval huggingface dataset:
Application of Dempster-Shafer evidence theory to unsupervised classification in multisource remote sensing

The aim of this paper is to show that Dempster-Shafer evidence theory may be successfully applied to unsupervised classification in multisource remote sensing. Shafer formulation allows for consideration of unions of classes, and to represent both imprecision and uncertainty, through the definition of belief and plausibility functions. These two functions, derived from mass function, are generally chosen in a supervised way. In this paper, the authors describe an unsupervised method, based on the comparison of monosource classification results, to select the classes necessary for Dempster-Shafer evidence combination and to define their mass functions. Data fusion is then performed, discarding invalid clusters (e.g. corresponding to conflicting information) thank to an iterative process. Unsupervised multisource classification algorithm is applied to MAC-Europe'91 multisensor airborne campaign data collected over the Orgeval French site. Classification results using different combinations of sensors (TMS and AirSAR) or wavelengths (L- and C-bands) are compared. Performance of data fusion is evaluated in terms of identification of land cover types. The best results are obtained when all three data sets are used. Furthermore, some other combinations of data are tried, and their ability to discriminate between the different land cover types is quantified.
A chance-constrained programming level set method for longitudinal segmentation of lung tumors in CT

This paper presents a novel stochastic level set method for the longitudinal tracking of lung tumors in computed tomography (CT). The proposed model addresses the limitations of registration based and segmentation based methods for longitudinal tumor tracking. It combines the advantages of each approach using a new probabilistic framework, namely Chance-Constrained Programming (CCP). Lung tumors can shrink or grow over time, which can be reflected in large changes of shape, appearance and volume in CT images. Traditional level set methods with a priori knowledge about shape are not suitable since the tumors are undergoing random and large changes in shape. Our CCP level set model allows to introduce a flexible prior to track structures with a highly variable shape by permitting a constraint violation of the prior up to a specified probability level. The chance constraints are computed from two given points by the user or from segmented tumors from a reference image. The reference image can be one of the images studied or an external template. We present a numerical scheme to approximate the solution of the proposed model and apply it to track lung tumors in CT. Finally, we compare our approach with a Bayesian level set. The CCP level set model gives the best results: it is more coherent with the manual segmentation.
Information combination operators for data fusion: a comparative review with classification

In most data fusion systems, the information extracted from each sensor (either numerical or symbolic) is represented as a degree of belief in an event with real values, taking in this way into account the imprecise, uncertain, and incomplete nature of the information. The combination of such degrees of belief is performed through numerical fusion operators. A very large variety of such operators has been proposed in the literature. We propose in this paper a classification of these operators issued from the different data fusion theories with respect to their behavior. Three classes are thus defined. This classification provides a guide for choosing an operator in a given problem. This choice can then be refined from the desired properties of the operators, from their decisiveness, and by examining how they deal with conflictive situations.
Vessel geometry modeling and segmentation using convolution surfaces and an implicit medial axis

In the context of vessel tree structures segmentation with implicit deformable models, we propose to exploit convolution surfaces to introduce a novel variational formulation, robust to bifurcations, tangential vessels and aneurysms. Vessels are represented by an implicit function resulting from the convolution of the centerlines of the vessels, modeled as a second implicit function, with localized kernels of continuously-varying scales. The advantages of this coupled representation are twofold. First, it allows for a joint determination of the vessels centerlines and radii, with a single model relevant for segmentation and visualization tasks. Second, it allows us to define a new shape constraint on the implicit function representing the centerlines, to enforce the tubular shape of the segmented objects. The algorithm has been evaluated on the segmentation of the portal veins in 20 CT-scans of the liver from the 3D-IRCADb-01 database, achieving an average recovery of 73% of the trees with fast computational times.
Fuzzifying images using fuzzy wavelet denoising

Fuzzy connected filters were recently introduced as an extension of connected filters within the fuzzy set framework. They rely on the representation of the image gray levels by fuzzy quantities, which are suitable to represent imprecision usually contained in images. No robust construction method of these fuzzy images has been introduced so far. In this paper we propose a generic method to fuzzify a crisp image in order to explicitly take imprecision on grey levels into account. This method is based on the conversion of statistical noise present in an image, which cannot be directly represented by fuzzy sets, into a denoising imprecision. The detectability of constant gray level structures in these fuzzy images is also discussed.
Vectorial multi-phase mouse brain tumor segmentation in T1-T2 MRI

An automated, level-set based, segmentation framework is proposed in this work for computation of tumoral volumes on mice brain bearing gliomal tumors. Tl and T2 weighted MRI images were acquired to monitor tumor growth, at different time points. We developed an original multi-phase and multi-channel segmentation method, based on the level set framework of Chan and Vese, to facilitate the estimation of tumoral volumes. A clinical study comparing manual and segmented volumes on 18 mice demonstrate the adequacy of the multi-channel segmentation and its superiority over single-T1 channel automated segmentation in terms of measurement accuracy and correlation.
On the ternary spatial relation "Between"

The spatial relation "between" is a notion which is intrinsically both fuzzy and contextual, and depends, in particular, on the shape of the objects. The literature is quite poor on this and the few existing definitions do not take into account these aspects. In particular, an object B that is in a concavity of an object A/sub 1/ not visible from an object A/sub 2/ is considered between A/sub 1/ and A/sub 2/ for most definitions, which is counter intuitive. Also, none of the definitions deal with cases where one object is much more elongated than the other. Here, we propose definitions which are based on convexity, morphological operators, and separation tools, and a fuzzy notion of visibility. They correspond to the main intuitive exceptions of the relation. We distinguish between cases where objects have similar spatial extensions and cases where one object is much more extended than the other. Extensions to cases where objects, themselves, are fuzzy and to three-dimensional space are proposed as well. The original work proposed in this paper covers the main classes of situations and overcomes the limits of existing approaches, particularly concerning nonvisible concavities and extended objects. Moreover, the definitions capture the intrinsic imprecision attached to this relation. The main proposed definitions are illustrated on real data from medical images.
A primal sketch of the cortex mean curvature: a morphogenesis based approach to study the variability of the folding patterns

In this paper, we propose a new representation of the cortical surface that may be used to study the cortex folding process and to recover some putative stable anatomical landmarks called sulcal roots usually buried in the depth of adult brains. This representation is a primal sketch derived from a scale space computed for the mean curvature of the cortical surface. This scale-space stems from a diffusion equation geodesic to the cortical surface. The primal sketch is made up of objects defined from mean curvature minima and saddle points. The resulting sketch aims first at highlighting significant elementary cortical folds, second at representing the fold merging process during brain growth. The relevance of the framework is illustrated by the study of central sulcus sulcal roots from antenatal to adult age. Some results are proposed for ten different brains. Some preliminary results are also provided for superior temporal sulcus.
Shape-based averaging for craniofacial anthropometry

We present in this work original methods for defining and obtaining robust, shape-based averages of any feature of the human head anatomy from a normal population. Our goal is the construction of computerized atlases with representative facial features and craniofacial anthropometry for specific human populations. A method for true-morphological averaging in three dimensions is presented, consisting of a suitable blend of shape-related information for N objects to obtain a progressive average. It is made robust by penalizing, in a morphological sense, the contributions of features less similar to the current average. The morphological error and similarity, as well as penalization, are based on the same paradigm as the morphological averaging. We present current results for the shape of the ear in 24 subjects.
Comparison of fusion techniques for 3D+T echocardiography acquisitions from different acoustic windows

We propose a new method to combine real-time 3D echocardiography acquisitions from different apical windows. It consists in registering the volumes using only the image data, without external positioning sensors, and combining their intensity values, without requiring a feature extraction method. Registration shows reasonable quantitative results on phantom data and visually acceptable results on in vivo data. We present two new methods for the fusion process: generalized averaging and multiview deconvolution. The latter, which performed best, improved the myocardium contrast by +49.67% and the signal-to-noise ratio by +4.61 dB. These improvements lead to more accurate quantification of cardiac function
Realistic models of children heads from 3D-MRI segmentation and tetrahedral mesh construction

In order to analyze the sensitivity of children to RF fields and mobile phones in particular, the SAR (specific absorption ratio) defined as the power absorbed by a unit of mass of tissues (W/kg) should be computed based on a numerical model of the head. We propose to build realistic models from 3D-MRI of children heads. The method is composed of two steps. The first one consists in segmenting the main tissues in these images (skin, fat, muscles, cortical and marrow bones, cerebrospinal fluid, grey and white matter, blood, etc.). The segmentation is based on mathematical morphology methods which are well adapted to this aim and provide a robust and automatic method requiring minimum user intervention. Using simplified segmented images, the second step concerns the tetrahedral mesh generation. Our method uses almost regular meshes and topological tools to preserve the topological arrangement of the head tissues. A method to guarantee a good geometrical quality is also provided.
Denoising based on non local means for ultrasound images with simultaneous multiple noise distributions

In this paper, an extension of the framework proposed by Deledalle et al.  for Non Local Means (NLM) method is proposed. This extension is a general adaptive method to denoise images containing multiple noises. It takes into account a segmentation stage that indicates the noise type of a given pixel in order to select the similarity measure and suitable parameters to perform the denoising task, considering a certain patch on the image. For instance, it has been experimentally observed that fetal 3D ultrasound images are corrupted by different types of noise, depending on the tissue. Finally, the proposed method is applied to denoise these images, showing very good results.
Constructing a fuzzy mathematical morphology: alternative ways

The authors propose two definitions for fuzzy mathematical morphology. They extend the set of possible operations on fuzzy sets by adding morphological operations, taking into account a fuzzy neighborhood. They are compatible with classical mathematical morphology on binary sets or gray-level functions with binary structuring elements. Their properties are presented and comparisons are made between the two approaches. Fuzzy mathematical morphology provides new operations on fuzzy sets and can be applied in particular for introducing spatial uncertain information in a decision process for data fusion.<<ETX>>
Fuzzy relative position between objects in images: a morphological approach

In order to cope with the ambiguity of spatial relative position concepts, we propose new definitions of the relative position between two objects in the fuzzy set framework, which are based on fuzzy pattern matching approaches. They have good properties, are flexible, fit the intuition, and they can be used for structural pattern recognition under imprecision. Moreover, they apply also in 3D, and for fuzzy image objects.
Representation of structural information in images using fuzzy set theory

In this paper, we show how fuzzy set theory can be used to represent structural information in images, in particular, relationships between imprecise objects, defined as spatial fuzzy sets. We distinguish two types of relationships: on the one hand, relationships that are well defined in the case of crisp objects (like adjacency or distance), and on the other hand, relationships that do not find any consensual definition even in the binary case (typically relative position between objects). We propose several ways to generalize relationships of the first class in order to incorporate imprecision attached to the objects. For the second class, we argue that fuzzy definitions are appropriate even when dealing with crisp objects, and we propose original definitions.
ADAPTIVE SEGMENTATION OF INTERNAL BRAIN STRUCTURES IN PATHOLOGICAL MR IMAGES DEPENDING ON TUMOR TYPES

This paper introduces a novel methodology for the segmentation of internal brain structures in MRI volumes in the presence of a tumor. The proposed method relies on an initial segmentation of the tumor. Based on the tumor's type, a set of spatial relations between internal structures, remaining stable even in presence of the pathology, is established. Segmentation and recognition of surrounding anatomical structures are based on prior knowledge about their spatial arrangement. Segmentation results on tumors inducing small or large deformations are provided to illustrate the potential of the approach.
Fuzzy spatial relations for high resolution remote sensing image analysis: The case of “to go across”

High resolution remote sensing (HR RS) images allow discriminating between different objects in a scene. Spatial reasoning techniques can be used to interpret and describe the scene. One component of spatial reasoning deals with the modeling and assesment of spatial relations among objects. In this work we propose three models that seize the semantics of the spatial relations “to go across” and “to go through” between a linear object and a region. To develop these three models we considered the usual perception of these natural language expressions which leads to the development of the three fuzzy models. They have been implemented and tested in scenes of HR RS images. Results are in good agreement with intuition.
Improvements to the Constant Area Quantization Bandwidth Compression Scheme

Two modifications to the constant area quantization (CAQ) video bandwidth compression scheme are described which together reduce the mean square error (MSE) in the reconstructed image by up to 70 percent. The first modification is merely a change in the CAQ algorithm requiring no additional hardware, whereas the second modification exploits line-to-line redundancies and necessitates the inclusion of one line of memory.
Linearly Tapered Loaded Transmission Lines

Working formulas for the calculation of input impedances and attenuation are obtained for a transmission line in which the inductance and resistance per unit length are linear functions of distance, and in which the capacitance and leakance are constant. Generalized functions used in the formulas, which were developed in an earlier paper, are here expressed in terms of Bessel's functions of the initial constants and the rate of taper, a form more readily applied in some cases. For the tapered loaded submarine cable the formulas are further simplified through the use of the hyperbolic functions applicable to the smooth uniform line, with appropriate correction factors.
Toward collaborative software processes

"Integration" takes on many meanings in a software engineering context. Much progress has been made in the integration of tools available to a software engineer. Now the integration also encompasses the integration of software engineering teams. This paper presents an evolutionary view of software integration that includes the tool integration and the collaboration aspects of a fully integrated software engineering team. An example of a tool set integrated with a collaborative software process is presented. This example can serve as a starting point in the discussion of how process-centered software development environments can best be integrated with the collaborative tools that are emerging from computer-supported cooperative work research.
An all-optical switch employing the cascaded second-order nonlinear effect

A new, ultrafast, low loss, all-optical switch based on the cascaded second-order nonlinearity is proposed. The device is based on an integrated Mach-Zehnder interferometer where quasi-phase matching produces a nonlinear phase shift with a different sign in each arm of the interferometer. A full treatment of the nonlinear phase shift indicates that the proposed device, if fabricated in AlGaAs, could switch with powers around 1.3 W in a length of approximately 1 cm.
Tunable InGaAs/AlGaInAs (de)multiplexer based on integrated asymmetric Mach-Zehnder interferometer using quantum well intermixing

Monolithic asymmetric Mach-Zehnder interferometers have been fabricated in InGaAs/AlGaInAs using quantum well intermixing. The devices combine low-loss waveguides, MMI couplers and phase shifters. Extinction ratio of more than 22 dB is achieved. Efficient wavelength tunability is demonstrated over a full wavelength period with injection current as low as 15 mA.
Stacked nanosheet gate-all-around transistor to enable scaling beyond FinFET

In this paper, for the first time we demonstrate that horizontally stacked gate-all-around (GAA) Nanosheet structure is a good candidate for the replacement of FinFET at the 5nm technology node and beyond. It offers increased Weff per active footprint and better performance compared to FinFET, and with a less complex patterning strategy, leveraging EUV lithography. Good electrostatics are reported at Lg=12nm and aggressive 44/48nm CPP (Contacted Poly Pitch) ground rules. We demonstrate work function metal (WFM) replacement and multiple threshold voltages, compatible with aggressive sheet to sheet spacing for wide stacked sheets. Stiction of sheets in long-channel devices is eliminated. Dielectric isolation is shown on standard bulk substrate for sub-sheet leakage control. Wrap-around contact (WAC) is evaluated for extrinsic resistance reduction.
56nm-pitch low-k/Cu dual-damascene interconnects integration with sidewall image transfer (SIT) patterning scheme

Three metal level 56nm-pitch Cu dual damascene interconnects in k2.7 low-k ILD have been demonstrated by using sidewall-image-transfer (SIT) patterning scheme to investigate the feasibility of the SIT process for sub 50nm-pitch technology node. 45nm-pitch line resistance (R) and capacitance (C) simulation are performed to estimate the R-C variation for double patterning schemes. The photoresist mandrel SIT process for 56nm-pitch Cu line is investigated to develop the constant line pitch and less line edge roughness (LER). The basic electrical properties such as line open/short and via chain open/short yields are ~100%. The 56nm-pitch R-C variation is comparable to simulated 80nm-pitch R-C variation. The SIT patterning process is a strong candidate to improve the R-C variation for sub50nm-pitch technology nodes.
Wide range 40-GHz Passive Mode-Locking operation of an AlGaInAs 1.55-µm Strained Quantum Well laser

We develop a comprehensive theoretical description of passive mode-locked semiconductor lasers based on a coarse grained time-domain approach. Under the approximation of intraband quasi equilibrium, our model accounts for the dispersion of gain, absorption and refractive index, nonlinear gain saturation from ultrafast processes, self-phase modulation, and spontaneous emission noise.We compare the predictions of our model with the performances of a Quasi-40-GHz Passively Mode-Locked AlGaInAs 1.55-m Strained Quantum Well laser.
Analysis of monolithic parallel-compound-cavity semiconductor lasers for high brightness, single-frequency, and short pulse operation

Recent advances in monolithic integration technology have led to a number of new monolithic active and passive optical devices based on interferometric waveguide constructions, such as monolithic TDM and WDM multiplexers and demultiplexers. Here, we propose to apply the interferometric principle to design of semiconductor laser structures, resulting in parallel compound cavities (PCC). Whilst the idea of such a cavity allows numerous permutations, here we concentrate on the fractal-dimensional geometry in which light from two or more parallel waveguides is combined by means of a coupler (of either directional, Y-type, or multimode interference (MMI) type) into a single output waveguide. We theoretically analyse these structures using a model of their threshold, spectral and dynamical. Properties based on decomposition into modes of the active compound cavity and analysis of complex eigenfrequencies of these modes.
64 nm pitch Cu dual-damascene interconnects using pitch split double exposure patterning scheme

This work demonstrates the building of 64 nm pitch copper single and dual damascene interconnects using pitch split double patterning scheme to enable sub 80nm pitch patterning. A self-aligned-via (SAV) litho/RIE scheme was used to create vias confined by line trenches such that via to line spacing is maximized for better reliability. An undercut free post RIE trench profile enabled the good metal fill. Initial reliability test result and the possibility of using the same scheme for 56 nm pitch interconnects are also discussed.