Unified Software Framework Research Articles

Comparison of CT and Dixon MR Abdominal Adipose Tissue Quantification Using a Unified Computer-Assisted Software Framework.

Reliable and objective measures of abdominal fat distribution across imaging modalities are essential for various clinical and research scenarios, such as assessing cardiometabolic disease risk due to obesity. We aimed to compare quantitative measures of subcutaneous (SAT) and visceral (VAT) adipose tissues in the abdomen between computed tomography (CT) and Dixon-based magnetic resonance (MR) images using a unified computer-assisted software framework. This study included 21 subjects who underwent abdominal CT and Dixon MR imaging on the same day. For each subject, two matched axial CT and fat-only MR images at the L2-L3 and the L4-L5 intervertebral levels were selected for fat quantification. For each image, an outer and an inner abdominal wall regions as well as SAT and VAT pixel masks were automatically generated by our software. The computer-generated results were then inspected and corrected by an expert reader. There were excellent agreements for both abdominal wall segmentation and adipose tissue quantification between matched CT and MR images. Pearson coefficients were 0.97 for both outer and inner region segmentation, 0.99 for SAT, and 0.97 for VAT quantification. Bland-Altman analyses indicated minimum biases in all comparisons. We showed that abdominal adipose tissue can be reliably quantified from both CT and Dixon MR images using a unified computer-assisted software framework. This flexible framework has a simple-to-use workflow to measure SAT and VAT from both modalities to support various clinical research applications.

EOdal: An open-source Python package for large-scale agroecological research using Earth Observation and gridded environmental data

Earth Observation by means of remote sensing imagery and gridded environmental data opens tremendous opportunities for systematic capture, quantification and interpretation of plant–environment interactions through space and time. The acquisition, maintenance and processing of these data sources, however, requires a unified software framework for efficient and scalable integrated spatio-temporal analysis taking away the burden of data and file handling from the user. Existing software products either cover only parts of these requirements, exhibit a high degree of complexity, or are closed-source, which limits reproducibility of research. With the open-source Python library EOdal (Earth Observation Data Analysis Library) we propose a novel software that enables the development of fully reproducible spatial data science chains through the strict use of open-source developments. Thanks to its modular design, EOdal enables advanced data warehousing especially for remote sensing data, sophisticated spatio-temporal analysis and intersection of different data sources, as well as nearly unlimited expandability through application programming interfaces (APIs).

A novel software framework for magnetic particle imaging reconstruction

AbstractMagnetic particle imaging is a novel tomographic imaging technique that enables noninvasive and highly sensitive imaging of superparamagnetic iron oxide nanoparticles distributed in living subjects. Several studies have reported on the development of reconstruction algorithms; however, a unified software framework for magnetic particle imaging reconstruction has yet to be developed. Herein, we propose a high‐performance, flexible, and easy‐to‐use magnetic particle imaging reconstruction framework using the Python programming language. The magnetic particle imaging reconstruction framework consists of the data access, preprocessing, image reconstruction, and postprocessing phases. We used the proposed framework to simulate the x‐space and system matrix‐based reconstruction methods with Cartesian and Lissajous scan trajectories. The reconstruction results of an open magnetic particle imaging dataset and a numerically simulated phantom demonstrated that the magnetic particle imaging reconstruction framework provides a reliable and accessible environment for magnetic particle imaging reconstruction, which can be extended and customized.

Subgroup Identification Using the personalized Package

A plethora of disparate statistical methods have been proposed for subgroup identification to help tailor treatment decisions for patients. However a majority of them do not have corresponding R packages and the few that do pertain to particular statistical methods or provide little means of evaluating whether meaningful subgroups have been found. Recently, the work of Chen, Tian, Cai, and Yu (2017) unified many of these subgroup identification methods into one general, consistent framework. The goal of the personalized package is to provide a corresponding unified software framework for subgroup identification analyses that provides not only estimation of subgroups, but evaluation of treatment effects within estimated subgroups. The personalized package allows for a variety of subgroup identification methods for many types of outcomes commonly encountered in medical settings. The package is built to incorporate the entire subgroup identification analysis pipeline including propensity score diagnostics, subgroup estimation, analysis of the treatment effects within subgroups, and evaluation of identified subgroups. In this framework, different methods can be accessed with little change in the analysis code. Similarly, new methods can easily be incorporated into the package. Besides familiar statistical models, the package also allows flexible machine learning tools to be leveraged in subgroup identification. Further estimation improvements can be obtained via efficiency augmentation.

ПРОГРАММНО-АППАРАТНЫЕ СРЕДСТВА ДЛЯ БОРТОВЫХ СИСТЕМ ИНФОРМАЦИОННОГО ОБЕСПЕЧЕНИЯ ПОДВИЖНЫХ СРЕДСТВ С ИСПОЛЬЗОВАНИЕМ СТЗ

Research objective is increase of autonomous mobile means information support efficiency with use of visual data and technologies of its development due to rational use of nonconventional calculators and special preparation of algorithmic providing. Use of nonconventional, heteroge-neous computing means allows to expand significantly a circle of visual data processing problems on the real time scale and, thereby, to increase situational awareness of autonomous robots and efficiency of its receiving. Rational use of nonconventional calculators demands essential altera-tion of algorithmic providing. The majority of visual data algorithms were developed counting on realization on traditional, von Neumann architecture of calculators and demand essential efforts for realization on parallel structures and developments of special programming tools. In work the emphasis on researches in the area use FPGA is placed and a number of approaches in special preparation of necessary algorithmic is considered. As model, demonstration examples of use of nonconventional calculators realization of such algorithms of visual data processing which areactively used in a wide range of information support problems of purposeful movements of mobile robot is considered. Preparation and realization on FPGA of such algorithms as creation of histo-grams, an optical flow calculation, segmentation of images is described. Results of experiments are given in the operating models of airborne computers. As basic data the visual data collected at the movement of mobile means in the conditions of habitat are used. All used software is executed on the basis of the unified software framework of the real time vision systems of domestic devel-opment. In the conclusion further steps in the specified direction, taking into account aspiration to use of domestic software and hardware are discussed.

Toward a real-time system for temporal enhanced ultrasound-guided prostate biopsy.

We have previously proposed temporal enhanced ultrasound (TeUS) as a new paradigm for tissue characterization. TeUS is based on analyzing a sequence of ultrasound data with deep learning and has been demonstrated to be successful for detection of cancer in ultrasound-guided prostate biopsy. Our aim is to enable the dissemination of this technology to the community for large-scale clinical validation. In this paper, we present a unified software framework demonstrating near-real-time analysis of ultrasound data stream using a deep learning solution. The system integrates ultrasound imaging hardware, visualization and a deep learning back-end to build an accessible, flexible and robust platform. A client-server approach is used in order to run computationally expensive algorithms in parallel. We demonstrate the efficacy of the framework using two applications as case studies. First, we show that prostate cancer detection using near-real-time analysis of RF and B-mode TeUS data and deep learning is feasible. Second, we present real-time segmentation of ultrasound prostate data using an integrated deep learning solution. The system is evaluated for cancer detection accuracy on ultrasound data obtained from a large clinical study with 255 biopsy cores from 157 subjects. It is further assessed with an independent dataset with 21 biopsy targets from six subjects. In the first study, we achieve area under the curve, sensitivity, specificity and accuracy of 0.94, 0.77, 0.94 and 0.92, respectively, for the detection of prostate cancer. In the second study, we achieve an AUC of 0.85. Our results suggest that TeUS-guided biopsy can be potentially effective for the detection of prostate cancer.

Software toolkit for modeling, simulation, and control of soft robots

The technological differences between traditional robotics and soft robotics have an impact on all of the modeling tools generally in use, including direct kinematics and inverse models, Jacobians, and dynamics. Due to the lack of precise modeling and control methods for soft robots, the promising concepts of using such design for complex applications (medicine, assistance, domestic robotics, etc.) cannot be practically implemented. This paper presents a first unified software framework dedicated to modeling, simulation, and control of soft robots. The framework relies on continuum mechanics for modeling the robotic parts and boundary conditions like actuators and contacts using a unified representation based on Lagrange multipliers. It enables the digital robot to be simulated in its environment using a direct model. The model can also be inverted online using an optimization-based method which allows to control the physical robots in the task space. To demonstrate the effectiveness of the approach, we present various soft robots scenarios including ones where the robot is interacting with its environment. The software has been built on top of SOFA, an open-source framework for deformable online simulation and is available at https://project.inria.fr/softrobot/.

ELSI: A unified software interface for Kohn–Sham electronic structure solvers

Solving the electronic structure from a generalized or standard eigenproblem is often the bottleneck in large scale calculations based on Kohn–Sham density-functional theory. This problem must be addressed by essentially all current electronic structure codes, based on similar matrix expressions, and by high-performance computation. We here present a unified software interface, ELSI, to access different strategies that address the Kohn–Sham eigenvalue problem. Currently supported algorithms include the dense generalized eigensolver library ELPA, the orbital minimization method implemented in libOMM, and the pole expansion and selected inversion (PEXSI) approach with lower computational complexity for semilocal density functionals. The ELSI interface aims to simplify the implementation and optimal use of the different strategies, by offering (a) a unified software framework designed for the electronic structure solvers in Kohn–Sham density-functional theory; (b) reasonable default parameters for a chosen solver; (c) automatic conversion between input and internal working matrix formats, and in the future (d) recommendation of the optimal solver depending on the specific problem. Comparative benchmarks are shown for system sizes up to 11,520 atoms (172,800 basis functions) on distributed memory supercomputing architectures. Program summaryProgram title: ELSI InterfaceProgram Files doi:http://dx.doi.org/10.17632/y8vzhzdm62.1Licensing provisions: BSD 3-clauseProgramming language: Fortran 2003, with interface to C/C++External routines/libraries: MPI, BLAS, LAPACK, ScaLAPACK, ELPA, libOMM, PEXSI, ParMETIS, SuperLU_DISTNature of problem: Solving the electronic structure from a generalized or standard eigenvalue problem in calculations based on Kohn–Sham density functional theory (KS-DFT).Solution method: To connect the KS-DFT codes and the KS electronic structure solvers, ELSI provides a unified software interface with reasonable default parameters, hierarchical control over the interface and the solvers, and automatic conversions between input and internal working matrix formats. Supported solvers are: ELPA (dense generalized eigensolver), libOMM (orbital minimization method), and PEXSI (pole expansion and selected inversion method).Restrictions: The ELSI interface requires complete information of the Hamiltonian matrix.

Rapid process synthesis supported by a unified modular software framework.

Although known to be very powerful, the widespread application of model-based techniques is still significantly hampered in the area of bio-processes. Reasons for this situation can be found along the whole chain to set up and implement such approaches. In a time-consuming step, models are typically hand-crafted. Whether alternatives of better models exist to actually fulfill the final goals is undocumented, most often even unknown. In a next step, model-based process control methods are hand-coded in an error-prone procedure. For many of these methods given in the literature, only simulation studies are shown, leaving the interested reader with the unanswered question whether the implementation of a specific method in a real process is viable. As the potentially time-consuming implementation of such a method presents a risk for a rapid process development, promising candidates may be overlooked. To remediate this unsatisfactory situation, a combination of theoretical methods and information technology is proposed here. By an exemplarily realized software tool, it is shown how such an environment helps to promote model-based optimization, supervision, and control of bio-processes and allows for an inexpensive test of new ideas as well in real-life experiments. The contribution concentrates on an overview of a possible software architecture with respect to necessary methods and a meaningful information strategy, highlighting some of the more crucial building blocks. Experimental results exploiting parts of the proposed methods are given for a yeast strain synthesizing a product of industrial interest.

A unified software framework for deriving, visualizing, and exploring abstraction networks for ontologies

Software tools play a critical role in the development and maintenance of biomedical ontologies. One important task that is difficult without software tools is ontology quality assurance. In previous work, we have introduced different kinds of abstraction networks to provide a theoretical foundation for ontology quality assurance tools. Abstraction networks summarize the structure and content of ontologies. One kind of abstraction network that we have used repeatedly to support ontology quality assurance is the partial-area taxonomy. It summarizes structurally and semantically similar concepts within an ontology. However, the use of partial-area taxonomies was ad hoc and not generalizable. In this paper, we describe the Ontology Abstraction Framework (OAF), a unified framework and software system for deriving, visualizing, and exploring partial-area taxonomy abstraction networks. The OAF includes support for various ontology representations (e.g., OWL and SNOMED CT’s relational format). A Protégé plugin for deriving “live partial-area taxonomies” is demonstrated.

A Unified Software Framework for Automatic Precise Georeferencing of Large Remote Sensing Image Archives

Though georeferencing satellite images is a solved industry problem long back, every satellite sensor data is unique to process due to its proprietary information and expected geometric accuracies usually demands use of reference data. Industry is moving towards unifying frameworks, sensor models where the users have to just plug-in details. The growing archive is inevitable and therefore (re) processing requirements have to handle huge image archives with state of art georeferencing standards automatically. Use of reference data is necessary to georeference the data sets to better accuracies beyond systematic corrections. This paper presents an approach for such tedious task of automatically reliably georeference huge image archives involving reference image/control points use in georeferencing Indian Remote Sensing satellite images.

OpenSees Software Architecture for the Analysis of Structures in Fire

Computational modeling of structures subjected to extreme static and dynamic loads (such as snow, wind, impact, and earthquake) using finite-element software are part of mainstream structural engineering curricula in universities (at least at graduate level), and many experts can be found in industry who routinely undertake such analyses. However, only a handful or institutions around the world teach structural response to fire (at any level) and only a few of the top consulting engineers in the world truly specialize in this niche area. Among the reasons for this are the lack of cheap and easily accessible software to carry out such analyses and the highly tedious nature of modeling the full (often coupled) sequence of a realistic fire scenario, heat transfer to structure and structural response (currently impossible using a single software). The authors in this paper describe how finite-element software can be extended to include the modeling of structures under fire load. The added advantage of extending existing finite-element codes, as opposed to creating fire-specific applications, is due to ability to perform multihazard type analysis, e.g., fire following earthquake. Due to its open source nature and object-oriented design, the OpenSees software framework is used for this purpose. In this work, the OpenSees framework, which was initially designed for the earthquake analysis of structures, is extended by the addition of new concrete classes for thermal loads, temperature distributions across element cross sections, and material laws based on Eurocodes. Through class and sequence diagrams, this paper shows the interaction of these classes with the existing classes in the OpenSees framework. The performance of this development is tested using benchmark solutions of a single beam with finite stiffness boundary conditions and a steel frame test. The results from OpenSees agree well with analytical solutions for the benchmark problem chosen and provide reasonable agreement with the test. The experience with OpenSees so far suggests that it has excellent potential to be the basis of a unified software framework for enabling computational modeling of realistic fires, and further work is continuing towards the achievement of this goal. The extensions made to OpenSees described in this work, in keeping with the open source ideals of the framework, have been included in the current OpenSees code and are available for researchers and practicing engineers to test, develop, and use for their own purposes.

A Unified Software Framework for Empirical Gramians

A common approach in model reduction is balanced truncation, which is based on Gramian matrices classifying certain attributes of states or parameters of a given dynamic system. Initially restricted to linear systems, the empirical Gramians not only extended this concept to nonlinear systems but also provided a uniform computational method. This work introduces a unified software framework supplying routines for six types of empirical Gramians. The Gramian types will be discussed and applied in a model reduction framework for multiple-input multiple-output systems.

Designing Guest Flow and Operations Logistics for the Dolphin Tales

To prepare for its new Dolphin Tales exhibit, Georgia Aquarium partnered with Georgia Institute of Technology to design the exhibit's guest movement with the goal of enhancing the guest experience while limiting congestion. The result was the development and application of the RealOpt-ABM© simulation decision support system for complex modeling and optimization. This work advanced the knowledge frontier in analytical methods in three ways. First, we developed a powerful heuristic algorithm for finding good feasible solutions for intractable nonlinear mixed-integer program resource allocation instances. Second, we integrated simulation and optimization technology into a unified software framework for realistic modeling of large-scale applications with human cognitive and behavioral elements; the software assists in optimizing overall operational dynamics. Third, we developed software for automatically interpreting computer-rendered drawings of processes and tasks into system process maps for simulation and optimization; as advanced visualization becomes more common, automatic translation from mouse clicks and keystrokes to an analytic workflow model serves as an important facilitator in human-computer interfaces. The system provided recommendations that were implemented for strategic and operational planning of the exhibit. These recommendations resulted in reduced congestion, enhanced guest experience, lower operating costs, improved wellness of the dolphins, and better performance in operational logistics, efficiency, and show schedules. The total dollar value of improvements from using the decision support system in reducing the number of shows by one show per week was estimated to be at least $1.5 million per year.

Unified reconstruction framework for multi-modal medical imaging

Various types of advanced imaging technologies have significantly improved the quality of medical care available to patients. Corresponding medical image reconstruction algorithms, especially 3D reconstruction, play an important role in disease diagnosis and treatment assessment. However, these increasing reconstruction methods are not implemented in a unified software framework, which brings along lots of disadvantages such as breaking connection of different modalities, lack of module reuse and inconvenience to method comparison. This paper discusses reconstruction process from the viewpoint of data flow and implements a free, accelerated, extensible Unified Reconstruction Software Framework (URSF). The software framework is an abstract solution that supports multi-modal image reconstruction. The goal of this framework is to capture the common processing work flow for different modalities and different methods, make the development of reconstruction for new devices much easier, and implement a set of popular reconstruction algorithms, so that it is convenient for researchers to compare against. The overall design and certain key technologies are introduced in detail. Presented experiment examples and practical applications commendably demonstrate the validity of this framework.

Studies for a common selection software environment in ATLAS: from the level-2 trigger to the offline reconstruction

The ATLAS High Level Trigger's primary function of event selection will be accomplished with a Level-2 trigger farm and an Event Filter farm, both running software components developed in the ATLAS offline reconstruction framework. While this approach provides a unified software framework for event selection, it poses strict requirements on offline components critical for the Level-2 trigger. A Level-2 decision in ATLAS must typically he accomplished within 10 ms and with multiple event processing in concurrent threads. In order to address these constraints, prototypes have been developed that incorporate elements of the ATLAS Data Flow -, High Level Trigger -, and offline framework software. To realize a homogeneous software environment for offline components in the High Level Trigger, the Level-2 Steering Controller was developed. With electron/gamma- and muon-selection slices it has been shown that the required performance can he reached, if the offline components used are carefully designed and optimized for the application in the High Level Trigger.

The Unified Software Development Process and Framework Development

Uygulama iskelet yapilari cok umut vaadeden bir yeniden kullanim yazilim teknolojisidir. Uygulama iskelet yapilarinin gelistirilmesi karmasik bir sureci gerektirir. Bu karmasik sureci minimize etmek amaciyla bircok yontem ve yaklasim onerilmistir. Birlesik Yazilim Gelistirme Sureci bugunku yazilim uygulamalarindaki karmasiklik engelini direkt olarak hedefleyen bir yontemdir. Bu yazida uygulama iskelet yapilarindaki karmasikligin yonetimi konusunda Birlesik Yazilim Gelistirme Sureci'nin rolu yaygin olarak kullanilan bir CASE (Bilgisayar Destekli Yazilim Muhendisligi) araci olan Rational Rose ile birlikte arastirilmaya calisilmistir.