nils-herrmann/clean_scirepeval huggingface dataset:
A hierarchical image segmentation algorithm

This paper presents a hierarchical image representation that preserves spatial relationships, and an image segmentation algorithm that efficiently constructs this representation. The overall structure of the hierarchical representation assumes a tree-like organization, in which a node at a higher level represents of a group of nodes from the next lower level. The root node of the representation is the full image, and the leaf nodes are K /spl times/ K blocks of pixels (usually 4 /spl times/ 4 or 8 /spl times/ 8), called blobs. The segmentation algorithm builds this hierarchical representation using an efficient clustering algorithm for grouping nodes progressively into larger objects. While performing the union of nodes at one level into a single parent node at the next higher level of the tree, nodes are grouped together according to similarities between their feature vectors, which include such features as color information, orientation, texture, size, energy, and neighbor information. The computation complexity of the hierarchical image segmentation algorithm is O(MN + MN/K/sup 2/ log(MN/K/sup 2/)), where M, N are the height and width of the original image. Experimental results show that this algorithm can segment images and extract objects effectively.
Breaking the memory bottleneck with an optical data path

We demonstrate the capability of optical buses in enabling orders of magnitude greater bandwidth between the processor and off-chip memory in a uniprocessor computer system. Through a simulation-based performance analysis of a 1 GHz processor model, we provide a preliminary evaluation of the benefits of an optical processor-to-memory bus in both eliminating the bandwidth bottleneck and in reducing the impact of the increasing processor-to-memory latency gap. The optical technology is constructed of two-dimensional arrays of lasers and detectors bonded to silicon that provide high-speed optical I/O on and off chip. These chip-to-chip light paths may be designed using either rigid free-space optics or flexible fiber image guides. Utilizing the optical data path between the processor and memory provides significantly greater bandwidth with no appreciable latency penalty. We assess the performance impact of this architecture enhancement on a number of media applications. Overall we found that the increased bandwidth nearly eliminates the transfer time between processor and memory, effectively reducing degradation from off-chip memory latency by 50% on average. Additionally, substantial extra bandwidth remains for more bandwidth-intensive architectural options like aggressive latency hiding techniques and single-chip multiprocessors.
An eight-issue tree-VLIW processor for dynamic binary translation

Presented is an 8-issue tree-VLIW processor designed for efficient support of dynamic binary translation. This processor confronts two primary problems faced by VLIW architectures: binary compatibility and branch performance. Binary compatibility with existing architectures is achieved through dynamic binary translation which translates and schedules PowerPC instructions to take advantage of the available instruction level parallelism. Efficient branch performance is achieved through tree instructions that support multi-way path and branch selection within a single VLIW instruction. The processor architecture is described, along with design details of the branch unit, pipeline, register file and memory hierarchy for a 0.25 micron standard-cell design. Performance simulations show that the simplicity of a VLIW architecture allows a wide-issue processor to operate at high frequencies.
The UltraSound ToolBox

We present the UltraSound ToolBox (USTB), a processing framework for ultrasound signals. USTB aims to facilitate the comparison of imaging techniques and the dissemination of research results. It fills the void of tools for algorithm sharing and verification, and enables a solid assessment of the correctness and relevance of new approaches. It also aims to boost research productivity by cutting down implementation time and code maintenance. USTB is a MATLAB toolbox for processing 2D and 3D ultrasound data, supporting both MATLAB and C++ implementations. Channel data from any origin, simulated and experimental, and using any kind of sequence, e.g. synthetic transmit aperture imaging (STAI) or coherent plane-wave compounding (CPWC), can be processed with USTB. Here we describe some of the elements of USTB such as: the ultrasound file format, the concept of the general beamformer, and the signal processing pipeline. We also show a minimal code example, and demonstrate that USTB can be used with the most used transmit sequences: STAI, CPWC, diverging wave imaging (DWI), focused imaging (FI), and retrospective transmit beamforming (RTB).
Compressive sensing ultrasound imaging using overcomplete dictionaries

The application of compressive sensing (CS) to medical ultrasound (US) imaging is a very recent field and the few existing studies mostly focus on fixed sparsifying transforms. In contrast to previous work, we propose a new approach based on the use of learned overcomplete dictionaries. Such dictionaries allow for much sparser representations of the signals since they are optimized for a particular class of images such as US images. In this study, the dictionary was learned using the K-SVD algorithm on patches extracted from the image to be reconstructed for an initial validation. Experiments were performed on experimental beamformed RF data acquired by imaging a general-purpose phantom. CS reconstruction was performed by removing 25% to 75% of the original samples according to a uniform law. Reconstructions using a K-SVD dictionary previously trained dictionary on experimental US images indicate minimal information loss, thus showing the potential of the overcomplete dictionaries.
A Pipeline for the Generation of Realistic 3D Synthetic Echocardiographic Sequences: Methodology and Open-Access Database

Quantification of cardiac deformation and strain with 3D ultrasound takes considerable research efforts. Nevertheless, a widespread use of these techniques in clinical practice is still held back due to the lack of a solid verification process to quantify and compare performance. In this context, the use of fully synthetic sequences has become an established tool for initial in silico evaluation. Nevertheless, the realism of existing simulation techniques is still too limited to represent reliable benchmarking data. Moreover, the fact that different centers typically make use of in-house developed simulation pipelines makes a fair comparison difficult. In this context, this paper introduces a novel pipeline for the generation of synthetic 3D cardiac ultrasound image sequences. State-of-the art solutions in the fields of electromechanical modeling and ultrasound simulation are combined within an original framework that exploits a real ultrasound recording to learn and simulate realistic speckle textures. The simulated images show typical artifacts that make motion tracking in ultrasound challenging. The ground-truth displacement field is available voxelwise and is fully controlled by the electromechanical model. By progressively modifying mechanical and ultrasound parameters, the sensitivity of 3D strain algorithms to pathology and image properties can be evaluated. The proposed pipeline is used to generate an initial library of 8 sequences including healthy and pathological cases, which is made freely accessible to the research community via our project web-page.
Plane-Wave Imaging Challenge in Medical Ultrasound

Plane-Wave imaging enables very high frame rates, up to several thousand frames per second. Unfortunately the lack of transmit focusing leads to reduced image quality, both in terms of resolution and contrast. Recently, numerous beamforming techniques have been proposed to compensate for this effect, but comparing the different methods is difficult due to the lack of appropriate tools. PICMUS, the Plane-Wave Imaging Challenge in Medical Ultrasound aims to provide these tools. This paper describes the PICMUS challenge, its motivation, implementation, and metrics.
Continuous-domain ARMA modeling for ultrasound tissue characterization

In this study, we investigate the possibility of applying a continuous-time ARMA (CARMA) model to radio-frequency ultrasound signals. We consider the effect of the discretization process on the parameters of the continuous system, and we take into account the exponential nature of the autocorrelation function of the model to derive continuous-domain information from the parameters of the discrete ARMA process. We validate the effectiveness of the CARMA model parameters for the characterization of ultrasound tissues on a sequence of phantom images that represent various concentrations of scatterers. We also compare the proposed CARMA coefficients and the traditional ARMA parameters on the basis of their performance in discriminating between phantom tissues. We show that working in the continuous domain brings additional useful information to characterize the imaged materials.
Compressed sensing reconstruction of line-wise sub-sampled 3D echographic images based on dictionary learning: an experimental study

This paper proposes a compressed sensing method based on overcomplete learned dictionaries for experimental 3D ultrasound (US) imaging. Two undersampling patterns suited for 3D US imaging are investigated in ex vivo 3D US volume acquisitions: a spatially uniform random acquisition and a line-wise random acquisition. The overcomplete dictionary was learned using the K-SVD algorithm on patches extracted from 3D US datasets. The CS reconstruction problem was solved through the l1 minimization using the spectral projected gradient algorithm and was performed by removing 20% to 80% of the original samples according to the two undersampling patterns. Besides the K-SVD dictionary we include the Fourier basis and the discrete cosine transform to our experiments for comparison. The approach is evaluated on 3D US experimental data acquired from ex vivo pig brains and sheep hearts. Reconstructions from 50% of the samples of the original 3D volume show little information loss in terms of normalized root mean squared errors.
Optimization of a photobioreactor biomass production using natural light

We address the question of optimization of the biomass long term productivity in the framework of microalgal biomass production in photobioreactors under the influence of day/night cycles. For that, we propose a simple bioreactor model accounting for light attenuation in the reactor due to biomass density and obtain the control law that optimizes productivity over a single day through the application of Pontryagin's maximum principle, with the dilution rate being the control. An important constraint on the obtained solution is that the biomass in the reactor should be at the same level at the beginning and at the end of the day so that the same control can be applied everyday and optimizes the long term productivity. Several scenarios are possible depending on the microalgae's strain parameters and the maximal admissible value of the dilution rate: bang-bang or bang-singular-bang control or, if the growth rate of the algae is very strong in the presence of light, constant maximal dilution. A bifurcation diagram is presented to illustrate for which values of the parameters these different behaviors occur.
P2F-5 Statistical Modeling of the Radio-Frequency Signal in Echocardiographic Images Using Modified K-Distribution and Generalized Generalized Gaussian Distribution

We study in this work the statistics of the radio frequency (RF) signal for both fully and partially developed speckle in echocardiographic images in the context of image segmentation and classification. From physical image formation model, we first derive the probability density function (pdf) of the RF signal using the K distribution framework. We then show that this pdf may be reliably approximated through a generalized Gaussian distribution. The ability of the proposed distribution to model RF echographic signals from cardiac tissue and blood regions is demonstrated on data acquired in vivo
A New Technique for the Estimation of Cardiac Motion in Echocardiography Based on Transverse Oscillations: A Preliminary Evaluation In Silico and a Feasibility Demonstration In Vivo

Quantification of regional myocardial motion and deformation from cardiac ultrasound is fostering considerable research efforts. Despite the tremendous improvements done in the field, all existing approaches still face a common limitation which is intrinsically connected with the formation of the ultrasound images. Specifically, the reduced lateral resolution and the absence of phase information in the lateral direction highly limit the accuracy in the computation of lateral displacements. In this context, this paper introduces a novel setup for the estimation of cardiac motion with ultrasound. The framework includes an unconventional beamforming technique and a dedicated motion estimation algorithm. The beamformer aims at introducing phase information in the lateral direction by producing transverse oscillations. The estimator directly exploits the phase information in the two directions by decomposing the image into two 2-D single-orthant analytic signals. An in silico evaluation of the proposed framework is presented on five ultra-realistic simulated echocardiographic sequences, where the proposed motion estimator is contrasted against other two phase-based solutions exploiting the presence of transverse oscillations and against block-matching on standard images. An implementation of the new beamforming strategy on a research ultrasound platform is also shown along with a preliminary in vivo evaluation on one healthy subject.
Detailed Evaluation of Five 3D Speckle Tracking Algorithms Using Synthetic Echocardiographic Recordings

A plethora of techniques for cardiac deformation imaging with 3D ultrasound, typically referred to as 3D speckle tracking techniques, are available from academia and industry. Although the benefits of single methods over alternative ones have been reported in separate publications, the intrinsic differences in the data and definitions used makes it hard to compare the relative performance of different solutions. To address this issue, we have recently proposed a framework to simulate realistic 3D echocardiographic recordings and used it to generate a common set of ground-truth data for 3D speckle tracking algorithms, which was made available online. The aim of this study was therefore to use the newly developed database to contrast non-commercial speckle tracking solutions from research groups with leading expertise in the field. The five techniques involved cover the most representative families of existing approaches, namely block-matching, radio-frequency tracking, optical flow and elastic image registration. The techniques were contrasted in terms of tracking and strain accuracy. The feasibility of the obtained strain measurements to diagnose pathology was also tested for ischemia and dyssynchrony.
Fast medical image segmentation through an approximation of narrow-band B-spline level-set and multiresolution

We have recently proposed a new level-set formulation, where the level-set is modelled as a continuous parametric function expressed in a B-spline basis. We propose in this paper to adapt this formalism to the class of narrow-band level-set methods, where the implicit function evolves only around its zero-level. For this purpose, we propose to model the interface by two lists of boundary points and we express the level-set evolution into the B-spline framework. We show that the flexibility of the method makes the algorithm well suited to the segmentation of 2-D and 3-D medical images. In particular, we introduce a multiresolution implementation of the method, yielding an efficient algorithm in term of computational time. The behavior of this approach is illustrated on medical images from various fields.
Robust Interval Observers for Uncertain Chaotic Systems

In this paper we develop and apply a robust interval observer to estimate the unknown variables of uncertain chaotic systems. Considering that bounds of the uncertainties are known, we propose an observer that computes guaranteed upper and lower bounds for the unknown variables. We show that practical estimation (observer based synchronization) is asymptotically reached with bounded interval estimates. The method is applied to the synchronization of Chua's chaotic system
Fourier-based ultrafast ultrasound imaging based on in-phase quadrature (IQ) data

Ultrafast imaging based on plane wave (PW) / diverging wave (DW) is an active area of research in ultrasound acquisitions because of its capacity of reaching high frame rate. Beamforming of received echoes in Fourier domain provides comparable image quality but lower computational complexity with respect to the conventional DAS approach [Zhang et al., UFFC, 2016]. However, the current state-of-the-art Fourier-based techniques only perform the reconstruction on radio frequency (RF) data. To process less data without loss of information, we adapted in this study the Fourier-based formalism of Lu [Lu et al., UFFC, 1997] to IQ data reconstruction for both PW and DW imaging.
Construction of ISS interval observers for triangular systems

We consider classes of nonlinear block triangular systems, for which we design interval observers. They possess the well known robustness property of Input to State Stability with respect to the bounds of the time varying disturbances. The systems under study are in general not cooperative and not globally Lipschitz. We illustrate the constructions by two bioreactors models, the first one dealing with competition of two bacterial species and the second one representing a cascade of anaerobic bioreactions.
Adaptative interval observer with application to the estimation of biofuel production by microalgae

In this paper, the design of an interval observer with an adaptive dynamical gain is presented. The observer is formulated in the framework of robust state estimation of uncertain dynamical systems, where an interval that encloses the unknown state variables is provided. Here the observer is based on a change of coordinate that involves a time varying gain. We introduce a dynamics for the gain, whose trajectory converges toward a predefined optimal value (which maximizes the convergence rate of the observer). The observer performance is illustrated with the estimation of microalgal oil in the framework of biofuel production. The proposed observer design, when applied to experimental data of Isochrysis affinis galbana, appears to be a suitable robust estimation technique.
Blood velocity estimation using compressed sensing

Duplex ultrasonography is a mode of medical ultrasonography that allows to visualize at the same time the inner structure of the body (B-mode), and the blood flow at a particular point in the body (Doppler mode). This mode requires a strategy for alternating B-mode and flow emissions. Traditional strategies either halve the maximum measurable velocity or introduce gaps in in the flow data. Our approach is based on randomly alternating both types of emission. Randomly sampled data is very favorable for compressed sensing (CS), which we use to reconstruct the Doppler signal segment by segment. This paper investigates how to design the cutting of the Doppler data into segments to ensure optimal quality, real-time reconstruction. By spending 60% of the time on velocity data acquisition at a pulse repetition frequency of 5 kHz, our method yields an average SNR of 18 dB and readable spectrograms.
Asymptotically Stable Interval Observers for Planar Systems With Complex Poles

In some parametric domains, the problem of designing an exponentially stable interval observer for an exponentially stable two dimensional time-invariant linear system is open. We show that, in some cases, no linear time-invariant change of coordinates can help to determine an exponentially stable interval observer. Next, we solve the problem by constructing interval observers of a new type, which have as key feature the property of being time-varying. This new design is applied to the chaotic Chua's system.
Driving competition in a complex ecosystem: Application to anaerobic digestion

Anaerobic digestion is a wastewater treatment process where bacteria degrade an organic substrate and produce methane, which can be used as a biofuel. The first task when starting up an anaerobic digester is the increase of its microbial population. It is a delicate phase, which is still not well understood, and its influence on the digester's future performance is not well known. During this phase, we show that a competition between the various species occurs and finally some species become dominant. In this paper, extending the competitive exclusion principle, we propose to drive the competition during this start-up phase, by regulating the volatile fatty acids concentration, with the aim of selecting species with good performance in the standard operating mode of the process. This new “selective” start-up strategy should lead to more efficient ecosystems.
Statistical Modeling of the Radio-Frequency Signal for Partially- and Fully-Developed Speckle Based on a Generalized Gaussian Model with Application to Echocardiography

We have shown previously that the statistics of the radio-frequency (RF) signals may be faithfully modeled through the so-called K RF distribution, in situations ranging from fully to partially-developed speckle. We demonstrate in this paper that the generalized Gaussian provides a reliable and computationally convenient approximation of the K RF. The performance of the parameters estimators for the two distributions is evaluated and compared in terms of their bias and variance through numerical simulations. This framework is applied to the modeling of echocardiographic data. The ability of the generalized Gaussian to model RF signals from cardiac tissues (myocardium) and blood regions is demonstrated on data acquired in vivo.
A Sparse Reconstruction Framework for Fourier-Based Plane-Wave Imaging

Ultrafast imaging based on plane-wave (PW) insonification is an active area of research due to its capability of reaching high frame rates. Among PW imaging methods, Fourier-based approaches have demonstrated to be competitive compared with traditional delay and sum methods. Motivated by the success of compressed sensing techniques in other Fourier imaging modalities, like magnetic resonance imaging, we propose a new sparse regularization framework to reconstruct high-quality ultrasound (US) images. The framework takes advantage of both the ability to formulate the imaging inverse problem in the Fourier domain and the sparsity of US images in a sparsifying domain. We show, by means of simulations, in vitro and in vivo data, that the proposed framework significantly reduces image artifacts, i.e., measurement noise and sidelobes, compared with classical methods, leading to an increase of the image quality.
Fourier-based 3D ultrafast ultrasound imaging with diverging waves: In vitro experiment validation

Ultrafast imaging based on diverging wave (DW) is an active area of research in ultrasound sectorial acquisition because of its capacity of reaching high frame rate. Recently, we have introduced a 3D Fourier-based formalism for the reconstruction of 3D sectorial images with DW insonifications and validated with numerical simulations [Zhang et al. IUS 2016]. The proposed method can provide comparable image quality as DAS but with a much lower computational complexity. This study aims to experimentally verify these theoretical conclusions in vitro.