Open-source MATLAB Toolbox Research Articles

Computing Dynamic User Equilibrium on Large-Scale Networks Without Knowing Global Parameters

Dynamic user equilibrium (DUE) is a Nash-like solution concept describing an equilibrium in dynamic traffic systems over a fixed planning period. DUE is a challenging class of equilibrium problems, connecting network loading models and notions of system equilibrium in one concise mathematical framework. Recently, Friesz and Han introduced an integrated framework for DUE computation on large-scale networks, featuring a basic fixed-point algorithm for the effective computation of DUE. In the same work, they present an open-source MATLAB toolbox which allows researchers to test and validate new numerical solvers. This paper builds on this seminal contribution, and extends it in several important ways. At a conceptual level, we provide new strongly convergent algorithms designed to compute a DUE directly in the infinite-dimensional space of path flows. An important feature of our algorithms is that they give provable convergence guarantees without knowledge of global parameters. In fact, the algorithms we propose are adaptive, in the sense that they do not need a priori knowledge of global parameters of the delay operator, and which are provable convergent even for delay operators which are non-monotone. We implement our numerical schemes on standard test instances, and compare them with the numerical solution strategy employed by Friesz and Han.

Inertial Parameter Identification in Robotics: A Survey

This work aims at reviewing, analyzing and comparing a range of state-of-the-art approaches to inertial parameter identification in the context of robotics. We introduce “BIRDy (Benchmark for Identification of Robot Dynamics)”, an open-source Matlab toolbox, allowing a systematic and formal performance assessment of the considered identification algorithms on either simulated or real serial robot manipulators. Seventeen of the most widely used approaches found in the scientific literature are implemented and compared to each other, namely: the Inverse Dynamic Identification Model with Ordinary, Weighted, Iteratively Reweighted and Total Least-Squares (IDIM-OLS, -WLS, -IRLS, -TLS); the Instrumental Variables method (IDIM-IV), the Maximum Likelihood (ML) method; the Direct and Inverse Dynamic Identification Model approach (DIDIM); the Closed-Loop Output Error (CLOE) method; the Closed-Loop Input Error (CLIE) method; the Direct Dynamic Identification Model with Nonlinear Kalman Filtering (DDIM-NKF), the Adaline Neural Network (AdaNN), the Hopfield-Tank Recurrent Neural Network (HTRNN) and eventually a set of Physically Consistent (PC-) methods allowing the enforcement of parameter physicality using Semi-Definite Programming, namely the PC-IDIM-OLS, -WLS, -IRLS, PC-IDIM-IV, and PC-DIDIM. BIRDy is robot-agnostic and features a complete inertial parameter identification pipeline, from the generation of symbolic kinematic and dynamic models to the identification process itself. This includes functionalities for excitation trajectory computation as well as the collection and pre-processing of experiment data. In this work, the proposed methods are first evaluated in simulation, following a Monte Carlo scheme on models of the 6-DoF TX40 and RV2SQ industrial manipulators, before being tested on the real robot platforms. The robustness, precision, computational efficiency and context of application the different methods are investigated and discussed.

Estimation of Tissue Attenuation from Ultrasonic B-Mode Images-Spectral-Log-Difference and Method-of-Moments Algorithms Compared.

We report on results from the comparison of two algorithms designed to estimate the attenuation coefficient from ultrasonic B-mode scans obtained from a numerical phantom simulating an ultrasound breast scan. It is well documented that this parameter significantly diverges between normal tissue and malignant lesions. To improve the diagnostic accuracy it is of great importance to devise and test algorithms that facilitate the accurate, low variance and spatially resolved estimation of the tissue’s attenuation properties. A numerical phantom is realized using k-Wave, which is an open source Matlab toolbox for the time-domain simulation of acoustic wave fields that facilitates both linear and nonlinear wave propagation in homogeneous and heterogeneous tissue, as compared to strictly linear ultrasound simulation tools like Field II. k-Wave allows to simulate arbitrary distributions, resolved down to single voxel sizes, of parameters including the speed of sound, mass density, scattering strength and to include power law acoustic absorption necessary for simulation tasks in medical diagnostic ultrasound. We analyze the properties and the attainable accuracy of both the spectral-log-difference technique, and a statistical moments based approach and compare the results to known reference values from the sound field simulation.

RaPId - A Parameter Estimation Toolbox for Modelica/FMI-Based Models Exploiting Global Optimization Methods

Abstract This paper describes new additions to the Rapid Parameter Identification Toolbox (RaPId), which is an open-source MATLAB toolbox for parameter estimation using models developed with the Modelica language and exported with the functional mock-up interface (FMI) Standard. These additions include an updated graphical user interface (GUI), an optimization method utilizing multiple starting points for a gradient descent optimization, and examples for different cyber-physical system applications such as the Duffing-Holmes equation modeling in a form of electrical circuit and a hydroelectric power plant modeling.

RFWD2 induces cellular proliferation and selective proteasome inhibitor resistance by mediating P27 ubiquitination in multiple myeloma

RFWD2 induces cellular proliferation and selective proteasome inhibitor resistance by mediating P27 ubiquitination in multiple myeloma

Algorithmic localization of high-density EEG electrode positions using motion capture

BackgroundAccurate source localization from electroencephalography (EEG) requires electrode co-registration to brain anatomy, a process that depends on precise measurement of 3D scalp locations. Stylus digitizers and camera-based scanners for such measurements require the subject to remain still and therefore are not ideal for young children or those with movement disorders. New methodMotion capture accurately measures electrode position in one frame but marker placement adds significant setup time, particularly in high-density EEG. We developed an algorithm, named MoLo and implemented as an open-source MATLAB toolbox, to compute 3D electrode coordinates from a subset of positions measured in motion capture using spline interpolation. Algorithm accuracy was evaluated across 5 different-sized head models. ResultsMoLo interpolation reduced setup time by approximately 10 min for 64-channel EEG. Mean electrode interpolation error was 2.95 ± 1.3 mm (range: 0.38–7.98 mm). Source localization errors with interpolated compared to true electrode locations were below 1 mm and 0.1 mm in 75 % and 35 % of dipoles, respectively. Comparison with existing methodsMoLo location accuracy is comparable to stylus digitizers and camera-scanners, common in clinical research. The MoLo algorithm could be deployed with other tools beyond motion capture, e.g., a stylus, to extract high-density EEG electrode locations from a subset of measured positions. The algorithm is particularly useful for research involving young children and others who cannot remain still for extended time periods. ConclusionsElectrode position and source localization errors with MoLo are similar to other modalities supporting its use to measure high-density EEG electrode positions in research and clinical settings.

An open-source toolbox for measuring dynamic video framerates and synchronizing video stimuli with neural and behavioral responses

BackgroundResearchers rely on the specified capabilities of their hardware and software even though, in reality, these capabilities are often not achieved. Considering that the number of experiments examining neural oscillations has increased steadily, easy-to-implement tools for testing the capabilities of hardware and software are necessary. New methodWe present an open-source MATLAB toolbox, the Schultz Cigarette Burn Toolbox (SCiBuT) that allows users to benchmark the capabilities of their visual display devices and align neural and behavioral responses with veridical timing of visual stimuli. Specifically, the toolbox marks the corners of the display with black or white squares to indicate the timing of the onset of static images and the timing of frame changes within videos. Using basic hardware (i.e., a photodiode, an Arduino microcontroller, and an analogue input box), the light changes in the corner of the screen can be captured and synchronized with EEG recordings and/or behavioral responses. ResultsWe demonstrate that the SCiBuT is sensitive to framerate inconsistencies and provide examples of hardware setups that are suboptimal for measuring fine timing. Finally, we show that inconsistencies in framerate during video presentation can affect EEG oscillations. ConclusionsThe SCiBuT provides tools to benchmark framerates and frame changes and to synchronize frame changes with neural and behavioral signals. This is the first open-source toolbox that can perform these functions. The SCiBuT can be freely downloaded (www.band-lab.com/scibut) and be used during experimental trials to improve the accuracy and precision of timestamps to ensure videos are presented at the intended framerate.

Multi-robot Path Planning with Boolean Specifications and Collision Avoidance

Abstract In this paper we consider the path planning problem for a team of identical mobile robots that should fulfill a given global specification. This specification is given as a Boolean formula over some regions of interest and should be satisfied on the final state (when the robots stop) and on the trajectories. The main novelty of this paper is the automatic computation of collision-free trajectories. The approach is based on a Petri net model and on solving two Mixed Integer Linear Programming problems. Based on the solutions of these problems, intermediate synchronization points are introduced in order to avoid possible collisions. Additionally, the algorithm in this paper is implemented in an open-source Matlab toolbox, called RMTool.

IEEGview: an open-source multifunction GUI-based Matlab toolbox for localization and visualization of human intracranial electrodes

Objective. The precise localization of intracranial electrodes is a fundamental step relevant to the analysis of intracranial electroencephalography (iEEG) recordings in various fields. With the increasing development of iEEG studies in human neuroscience, higher requirements have been posed on the localization process, resulting in urgent demand for more integrated, easy-operation and versatile tools for electrode localization and visualization. With the aim of addressing this need, we develop an easy-to-use and multifunction toolbox called iEEGview, which can be used for the localization and visualization of human intracranial electrodes. Approach. iEEGview is written in Matlab scripts and implemented with a GUI. From the GUI, by taking only pre-implant MRI and post-implant CT images as input, users can directly run the full localization pipeline including brain segmentation, image co-registration, electrode reconstruction, anatomical information identification, activation map generation and electrode projection from native brain space into common brain space for group analysis. Additionally, iEEGview implements methods for brain shift correction, visual location inspection on MRI slices and computation of certainty index in anatomical label assignment. Main results. All the introduced functions of iEEGview work reliably and successfully, and are tested by images from 28 human subjects implanted with depth and/or subdural electrodes. Significance. iEEGview is the first public Matlab GUI-based software for intracranial electrode localization and visualization that holds integrated capabilities together within one pipeline. iEEGview promotes convenience and efficiency for the localization process, provides rich localization information for further analysis and offers solutions for addressing raised technical challenges. Therefore, it can serve as a useful tool in facilitating iEEG studies.

Algorithm 1001

IPscatt is a free, open-source MATLAB toolbox facilitating the solution for time-independent scattering (also known as time-harmonic scattering) in two- and three-dimensional settings. The toolbox has three main application cases: simulation of the scattered field for a given transmitter-receiver geometry; the generation of simulated data as well as the handling of the real-world data from Institute Fresnel; and the reconstruction of the contrast from several measured, scattered fields. In each case, a variety of options tailored to the needs of practitioners is provided. For example, the toolbox allows the simulation of the scattered near field as well as of the far field. Also, it provides methods for the modeling of the incident field as point sources as well as plane waves. Finally, many common geometries of transmitters and receivers are included out of the box. Regarding the reconstruction, the provided functions implement the regularization scheme that relies on a primal-dual algorithm and was introduced by F. Bürgel, K. S. Kazimierski, and A. Lechleiter [ Journal of Computational Physics 339 (2017), 1–30]. This article provides a survey of the mathematical concepts in scattering, connects them with the provided implementation, gives an overview of the software framework as well as its application areas, and compares it with existing software packages solving the same problem.

SRS-GDA: A spatial random sampling toolbox for grid-based hydro-climatic data analysis in environmental change studies

We present in this paper the development of a new, open-source MATLAB toolbox SRS-GDA that aims to provide random spatial sampling of grid-based hydro-climatic datasets for environmental change studies. This toolbox addresses the needs of quantifying how hydro-climatic responses, which are often driven by grid-based forcing datasets such as climate model projections, vary with location and scale. The toolbox can be used to carry out random spatial sampling of grid-based quantities with various constraints: shape, size, location, dominant orientation and resolution. A case study of a large dataset, the GEAR rainfall dataset is supplied to demonstrate the typical uses case of this toolbox. The provision of the toolbox for downloading together with the sample data is also presented.

Automated design process for hybrid regression modeling with a one-class SVM

Abstract The accuracy of many regression models suffers from inhomogeneous data coverage. Models loose accuracy because they are unable to locally adapt the model complexity. This article develops and evaluates an automated design process for the generation of hybrid regression models from arbitrary submodels. For the first time, these submodels are weighted by a One-Class Support Vector Machine, taking local data coverage into account. Compared to reference regression models, the newly developed hybrid models achieve significant better results in nine out of ten benchmark datasets. To enable straightforward usage in data science, an implementation is integrated in the open source MATLAB toolbox SciXMiner.

GFLIB: an Open Source Library for Genetic Folding Solving Optimization Problems

This paper aims at presenting GFLIB, a Genetic Folding MATLAB toolbox for supervised learning problems. In essence, the goal of GFLIB is to build a concise model of supervised learning, and a free open source MATLAB toolbox for performing classification and regression. The GFLIB is specifically designed for most of the traditionally used features, to evolve in applications of mathematical models. The toolbox suits all kinds of users; from the users who implemented GFLIB as “black box”, to advanced researchers who want to generate and test new functionalities and parameters of GF algorithm. The toolbox and its documentation are freely available for download at: https://github.com/mohabedalgani/gflib.git

Automagic: Standardized preprocessing of big EEG data

Electroencephalography (EEG) recordings have been rarely included in large-scale studies. This is arguably not due to a lack of information that lies in EEG recordings but mainly on account of methodological issues. In many cases, particularly in clinical, pediatric and aging populations, the EEG has a high degree of artifact contamination and the quality of EEG recordings often substantially differs between subjects. Although there exists a variety of standardized preprocessing methods to clean EEG from artifacts, currently there is no method to objectively quantify the quality of preprocessed EEG. This makes the commonly accepted procedure of excluding subjects from analyses due to exceeding contamination of artifacts highly subjective. As a consequence, P-hacking is fostered, the replicability of results is decreased, and it is difficult to pool data from different study sites. In addition, in large-scale studies, data are collected over years or even decades, requiring software that controls and manages the preprocessing of ongoing and dynamically growing studies. To address these challenges, we developed Automagic, an open-source MATLAB toolbox that acts as a wrapper to run currently available preprocessing methods and offers objective standardized quality assessment for growing studies. The software is compatible with the Brain Imaging Data Structure (BIDS) standard and hence facilitates data sharing. In the present paper we outline the functionality of Automagic and examine the effect of applying combinations of methods on a sample of resting and task-based EEG data. This examination suggests that applying a pipeline of algorithms to detect artifactual channels in combination with Multiple Artifact Rejection Algorithm (MARA), an independent component analysis (ICA)-based artifact correction method, is sufficient to reduce a large extent of artifacts.

RaaMLab: A MATLAB toolbox that generates amino acid groups and reduced amino acid modes

Amino acid (AA) classification and its different biophysical and chemical characteristics have been widely applied to analyze and predict the structural, functional, expression and interaction profiles of proteins and peptides. We present RaaMLab, a free and open-source MATLAB toolbox, to facilitate studies on proteins and peptides, to generate AA groups and to extract the structural and physicochemical features of reduced AAs (RedAA). This toolbox offers 4 kinds of databases, including the physicochemical properties of AAs and their groupings, 49 AA classification methods and 5 types of biophysicochemical features of RedAAs. These factors can be easily computed based on user-defined alphabet size and AA properties of AA groupings. RaaMLab is an open source freely available at https://github.com/bioinfo0706/RaaMLab. This website also contains a tutorial, extensive documentation and examples.

Brain annotation toolbox: exploring the functional and genetic associations of neuroimaging results.

Advances in neuroimaging and sequencing techniques provide an unprecedented opportunity to map the function of brain regions and identify the roots of psychiatric diseases. However, the results from most neuroimaging studies, i.e. activated clusters/regions or functional connectivities between brain regions, frequently cannot be conveniently and systematically interpreted, rendering the biological meaning unclear. We describe a brain annotation toolbox that generates functional and genetic annotations for neuroimaging results. The voxel-level functional description from the Neurosynth database and gene expression profile from the Allen Human Brain Atlas are used to generate functional/genetic information for region-level neuroimaging results. The validity of the approach is demonstrated by showing that the functional and genetic annotations for specific brain regions are consistent with each other; and further the region by region functional similarity network and genetic similarity network are highly correlated for major brain atlases. One application of brain annotation toolbox is to help provide functional/genetic annotations for newly discovered regions with unknown functions, e.g. the 97 new regions identified in the Human Connectome Project. Importantly, this toolbox can help understand differences between psychiatric patients and controls, and this is demonstrated using schizophrenia and autism data, for which the functional and genetic annotations for the neuroimaging changes in patients are consistent with each other and help interpret the results. BAT is implemented as a free and open-source MATLAB toolbox and is publicly available at http://123.56.224.61:1313/post/bat. Supplementary data are available at Bioinformatics online.

TDNetGen: An Open-Source, Parametrizable, Large-Scale, Transmission, and Distribution Test System

In this paper, an open-source MATLAB toolbox is presented that is able to generate synthetic, combined transmission and distribution network models. These can be used to analyse the interactions between transmission and multiple distribution systems, such as the provision of ancillary services by active distribution grids, the co-optimization of planning and operation, the development of emergency control and protection schemes spanning over different voltage levels, the analysis of combined market aspects, etc. The generated test-system models are highly customizable, providing the user with the flexibility to easily choose the desired characteristics, such as the level of renewable energy penetration, the size of the final system, etc.

Spindler: a framework for parametric analysis and detection of spindles in EEG with application to sleep spindles

Objective. EEG spindles, narrow-band oscillatory signal bursts, are widely-studied biomarkers of subject state and neurological function. Most existing methods for spindle detection select algorithm parameters by optimizing agreement with expert labels. We propose a new framework for selecting algorithm parameters based on stability of spindle properties and elucidate the dependence of these properties on parameter selection for several algorithms. Approach. To demonstrate this approach we developed a new algorithm (Spindler) that decomposes the signal using matching pursuit with Gabor atoms and computes the spindles for each point in a fine grid of parameter values. After computing characteristic surfaces as a function of parameters, Spindler selects algorithm parameters based on the stability of characteristic surface geometry. Main results. Spindler performs well relative to several common supervised and unsupervised EEG sleep spindle detection methods. Spindler is available as an open-source MATLAB toolbox (https://github.com/VisLab/EEG-Spindles). In addition to Spindler, the toolbox provides implementations of several other spindle detection algorithms as well as standardized methods for matching ground truth to predictions and a framework for understanding algorithm parameter surfaces. Significance. This work demonstrates that parameter selection based on physical constraints rather than labelled data can provide effective, fully-automated, unsupervised spindle detection. This work also exposes the dangers of applying cross-validation without considering the dependence of spindle properties on parameters. Parameters selected to optimize one performance metric or matching method are not optimized for others. Furthermore, elucidation of the stability of predicted indicators with respect to algorithm parameter selection is critical to practical application of these algorithms.

“Ficedula”: an open-source MATLAB toolbox for cutting, segmenting and computer-aided clustering of bird song

Qualitative and quantitative assessments of bird song repertoires are important in studies related to song learning, sexual selection and cultural evolution. Despite methods for automatic analysis, it is still necessary to engage in manual cutting, segmenting and clustering of bird song elements in many cases. Here, we describe a program, the Ficedula Toolbox, which has been made available for free to the bird song research community and has recently come into extensive use. The main advantages of this package are the opportunity to conduct all processing steps in one framework and the option of carrying out computer-aided manual clustering. Output files are ready for further analyses, such as estimation of repertoire size, sequential analysis and repertoire overlap calculation. With this program, findings based on empirical data from the Collared Flycatcher (Ficedula albicollis) song show high inter-observer similarity, and thus, reproducible results. The toolbox may be especially applicable to the analysis of song in species with moderately high repertoires.

Patient Specific Simulation of HIFU Kidney Tumour Ablation.

High Intensity Focussed Ultrasound (HIFU) is emerging as a non-invasive treatment for localised renal tumours. However, challenges remain in the delivery of the treatment to tumours at depth, with clinical results showing a variation in the ablation efficacy. One clinical trial conducted at the Churchill hospital, Oxford, to investigate the applicability of HIFU for renal tumour ablation found that in 4/10 patients less than 5% of the tumour volume was ablated successfully. The current study looks at the role tissue geometry has on the resulting focal pressure and focal heating. CT scans from 4 patients within the trial were selected, who experienced 70%, < 5%, < 5% and 95% ablation of the target tumour. The CT scans were segmented into bone, fat, kidney, and generic tissue. Full three-dimensional ultrasound simulations were carried out using k-Wave (an open source Matlab toolbox) and for three patients a tight focus was achieved in the kidney but peak pressures varied by 20%. While in the fourth patient there was significant fragmentation of the -6 dB focal volume due to the intervening ribcage. Thermal simulations were used to compare the temperature rise induced across the different patient models. For the three patients with a tight focus, the predicted 47°C iso-volume of the patient with 70% ablation was 2-3 times larger than the two patients with < 5% ablation. For the patient in which the ribcage resulted in focal fragmentation the thermal simulation predicted just a 1°C temperature rise.