Automatic Framework Research Articles

Automation of Data Consumption by Pluggable Module Software

Servers are required to monitor the health of the various I/O cards connected to it to alert the required personnel to service these cards. The Data Collection Unit (DCU) is responsible for detecting the I/O cards, sending their inventory as well as monitoring their health. Currently, the keys required to detect these I/O cards are manually coded into the source code. Such a task is highly laborious and time-consuming. To eliminate this manual work, a Software Pluggable Module was devised which would read the I/O card-related information from the I/O component list. This software design aims at using Data Science and OOPS concepts to automate certain tasks on server systems. The proposed methodology is implemented on a Linux system. The software design is modular in nature and extensible to accommodate future requirements. Such an automation framework can be used to track information maintained in Excel Spreadsheets and access them using an Application Programming Interface (API).

An automatic framework for evaluating the vascular permeability of bone metastases from prostate cancer

Objectives. Vascular permeability can reflect tumorigenesis and metastasis. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can assess microvascular permeability by pharmacokinetic parameter estimation. Most estimation methods require manually selected arterial input function (AIF) or reference regions. However, the result will be unstable due to the annotation, which relies on personal experience. In this study, we propose an automatic framework for evaluating vascular permeability of bone metastases from prostate cancer without selecting AIF. Materials and methods. This retrospective study comprised of 15 prostate cancer patients with bone metastases. Based on clinical consensus for three typical DCE-MRI curve patterns, three characteristic curves as regularization constraints were introduced to the extended Tofts model (ETM) using clustering strategy, and the clustering-based blind identification of multichannel (CBM) framework was then proposed for pharmacokinetic parameter estimation. With automatic segmentation of the whole bone area, we obtained the estimation of the pharmacokinetic parameters in the bone area and quantified for bone metastases. Two experienced radiologists compared the CBM estimations with the diagnostic results and we compared the estimations with those of the ETM in bone metastasis regions to evaluate the feasibility of the CBM framework. Results. The higher signal regions of K trans and K ep indicated the metastasis of prostate cancer, which is consistent with the cancer area marked by the radiologists. In addition, the K trans and K ep in bone metastasis regions were significantly higher than in normal bone regions (P < 0.001, P < 0.001). The consistency of estimation by using the CBM framework and conventional ETM method was confirmed by Bland–Altman analysis. Conclusion. The proposed CBM framework can provide a fully automatic and reliable quantitative estimation of vascular permeability for bone metastases in prostate cancer patients.

Measuring Vegetation Density in Marsh Grass Photographs Using Deep Neural Networks (Student Abstract)

To protect the world’s marshlands, it is of utmost importance to be able to monitor their vegetation composition and coverage. This currently is accomplished by large teams of researchers and volunteers manually looking at the marsh images and labeling randomly selected pixels by what species (or lack thereof) is present at the pixel. This task, however, is extremely labor intensive, limiting the amount of quality environmental monitoring that can be done in the field. If the task was automated, teams would be able to monitor larger swaths of land. In this paper, we propose a novel framework for such automation using deep neural networks. Then, we focus on the key component of this framework: a binary classifier to decide whether a pixel is vegetated or not. To this end, we create a dataset of labeled snippet images out of publicly available photoquadrats of the marshlands in Florida. Finally, we construct LeNet-5 and AlexNet, adjusted to our input snippets, faster training time, networks and experiment to learn them on our dataset for the binary classification task. Our results show that the AlexNet model achieves higher accuracy on the test set than the LeNet-5 model, with 92.41% for AlexNet and 91.34% for LeNet-5.

An automatic framework for fusing information from differently stained consecutive digital whole slide images: A case study in renal histology

Objective: This article presents an automatic image processing framework to extract quantitative high-level information describing the micro-environment of glomeruli in consecutive whole slide images (WSIs) processed with different staining modalities of patients with chronic kidney rejection after kidney transplantation.Methods: This four-step framework consists of: 1) approximate rigid registration, 2) cell and anatomical structure segmentation 3) fusion of information from different stainings using a newly developed registration algorithm 4) feature extraction.Results: Each step of the framework is validated independently both quantitatively and qualitatively by pathologists. An illustration of the different types of features that can be extracted is presented.Conclusion: The proposed generic framework allows for the analysis of the micro-environment surrounding large structures that can be segmented (either manually or automatically). It is independent of the segmentation approach and is therefore applicable to a variety of biomedical research questions.Significance: Chronic tissue remodelling processes after kidney transplantation can result in interstitial fibrosis and tubular atrophy (IFTA) and glomerulosclerosis. This pipeline provides tools to quantitatively analyse, in the same spatial context, information from different consecutive WSIs and help researchers understand the complex underlying mechanisms leading to IFTA and glomerulosclerosis.

Characterizing a standard cell library for large scale design of memristive based signal processing

In recent years, the use of memristors in circuits design has rapidly increased and attracted research interest. Advances have been made to both the size and the complexity of memristor designs. Therefore, computer aided design tools are required to handle memristor-based large-scale designs. A comprehensive automatic framework for the design and synthesis of large-scale memristor-complementary metal-oxide-semiconductor (CMOS) circuits is described herein. This framework provides a synthesis approach that can be applied to all memristor-based digital logic designs. In particular, it is a proposal for a characterization methodology of memristor-based logic cells to generate a standard cell library file for large-scale simulation. The proposed architecture is based on RRAM and ReRAM redox-based devices and the memristor ratioed logic design approach. The proposed framework is implemented in the Cadence Virtuoso schematic-level environment and was verified with Verilog-XL, MATLAB, and the electronic design automation synopses compiler after being translated to the behavioral level. The proposed method can be applied to implement any digital logic design. Nevertheless, it is perfectly suitable for signal processing applications that require MATLAB functions to produce text files with hex values in order to overcome the limitations of the simulation environment. A framework is deployed herein for design of the memristor-based parallel 8-bit adder/subtractor and a 2D memristive-based median filter. Both proposed designs memristor-based adder/subtractor and memristive median filter have significant power reductions of 66% and 16% respectively, when compared to the same designs using CMOS technology.

Exploration of Word Width and Cluster Size Effects on Tree-Based Embedded FPGA Using an Automation Framework

This paper introduces a novel framework that automates and accelerates the development of embedded Field Programmable Gate Arrays (eFPGAs). The proposed solution is considered as the first environment for tree-based eFPGA implementation including software, hardware and loader. The developed framework allows users to generate eFPGA architecture in the form of hardware description language using Physical Design Flow (PDF) tool. It is a powerful tool that can produce a wide variety of designs ranging from small eFPGA to complex eFPGA. The bit file description of practical application is done in parallel, simultaneously and rapidly by the suggested Computer Aided Design (CAD) tools. The Loader, called Multi-Level Loader (MLL), is also provided to inject the bits into the corresponding SRAMs. Our framework is widely explored by modifying the data width. This research proves that data width equal to 17 has the best trade-off between performance, area and static power. However, it is penalized for buses having data length greater than 32. The experimentation demonstrates that a data width equal to 12 is the best for a 32-bit bus. Automation and significant acceleration of the eFPGA development cycle are also achieved in this study. A set of bench-marking applications with various multi-use purposes is mapped. The experimental results show the efficiency and flexibility of the proposed framework.

TEST COVERAGE ENHANCEMENT USING HYBRID MODULAR APPROACH FOR WEB BASED APPLICATION

Automation testing may be a process that compares the particular results to the specified outcome within the software application. Automation testing is one of the foremost effective ways to check web-based applications since it allows you to hide more scenarios in less time. Selenium is one every of the foremost popular and effective automation frameworks that have been widely used for automating the testing of applications. The modular testing framework is one of the techniques used for automated testing, in which test scripts are built by breaking down the entire application into smaller pieces. Multiple data sets cannot be used thanks to hardcoded data within the test scripts. In this paper, a hybrid approach of keyword-data driven strategy is presented, in which the test data and script are separated to allow for various datasets and greater coverage. Furthermore, for improved test coverage, this split procedure is used across many modules.

Automatic Framework for Detecting Obstacles Restricting 3D Highway Sight Distance Using Mobile Laser Scanning Data

AbstractPeriodic measurements of sight distance on as-built roads and subsequent removal of sight obstructions are important for guaranteeing highway safety. In this paper, an accurate and efficien...

Vegetation Coverage in Marsh Grass Photography Using Convolutional Neural Networks

Vegetation monitoring is one of the major cornerstones of environmental protection today, giving scientists a look into changing ecosystems. One important task in vegetation monitoring is to estimate the coverage of vegetation in an area of marsh. This task often calls for extensive human labor carefully examining pixels in photos of marsh sites, a very time-consuming process. In this paper, aiming to automate this process, we propose a novel framework for such automation using deep neural networks. Then, we focus on the utmost component to build convolutional neural networks (CNNs) to identify the presence or absence of vegetation. To this end, we collect a new dataset with the help of Guana Tolomato Matanzas National Estuarine Research Reserve (GTMNERR) to be used to train and test the effectiveness of our selected CNN models, including LeNet-5 and two variants of AlexNet. Our experiments show that the AlexNet variants achieves higher accuracy scores on the test set than LeNet-5, with 92.41\% for a AlexNet variant ondistinguishing between vegetation and the lack thereof. These promising results suggest us to confidently move forward with not only expanding our dataset, but also developing models to determine multiple species in addition to the presence of live vegetation.

Identification of the probability of the park effect in a wave-to-power system using the analytical hierarchical process and a polynomial neural network model

The park effect occurs for various reasons in field applications of wave energy conversion. Not all possible causal factors are equally responsible for causing the park effect. The varying significances of the causes were approximated in an objective manner using the analytical hierarchical process multi-criteria decision-making technique and were assessed with an index function where the reasons were used as indicators and the function was the aggregated weighted contribution of each individual reason. A real-time monitoring system was also developed to monitor peak effects in a wave-to-power system in a continuous manner, such that loss due to the park effect could be prevented in real time, ensuring minimum loss of production capacity. Such a monitoring system would use the same aggregated weighted function to monitor the effects, and a network of sensors fixed at strategic points would be used to retrieve the magnitudes of the causal factors. An automatic framework to estimate the park effect was developed using a polynomial neural network architecture, and it was tested for three different locations. An experimental validation of the index values for these three locations was performed. The results indicated that offshore locations of wave-to-power systems were the most vulnerable to the park effect. It was also found that, both offshore and near shore, the distance between converters has the highest significance, whereas for onshore converters, the position of the WEC in relation to the incoming wave was found to be the most important indicator among the six investigated in the present study. Significant uncertainties appeared due to the lack of field-tested results for use in calculating the park effect on devices. However, the use of a physical model reduced these uncertainties, and a reliable framework was developed. Further real-time testing of the indicators will help develop practical implementations of the index for the formulation of policy.

An Automatic Framework to Create Patient-specific Eye Models From 3D Magnetic Resonance Images for Treatment Selection in Patients With Uveal Melanoma

PurposeThe optimal treatment strategy for uveal melanoma (UM) relies on many factors, the most important being tumor size and location. Building on recent developments in high-resolution 3D ocular magnetic resonance imaging (MRI), we developed an automatic image-processing framework to create patient-specific eye models and to subsequently determine the full 3D tumor shape and size automatically. Methods and MaterialsFrom 15 patients with UM, 3D inversion-recovery gradient-echo (T1-weighted) and 3D fat-suppressed spin-echo (T2-weighted) images were acquired with a 7T MRI scanner. First, the sclera and cornea were segmented from the T2-weighted image by mesh-fitting. The T1- and T2-weighted images were then coregistered. From the registered T1-weighted image, the lens, vitreous body, retinal detachment, and tumor were segmented. Fuzzy C-means clustering was used to differentiate the tumor from retinal detachments. The tumor model was verified and (if needed) edited by an ophthalmic MRI specialist. Subsequently, the prominence and largest basal diameter of the tumor were measured automatically based on the verified contours. These results were compared with manual assessments on the original images and with ultrasound measurements to show the errors in manual analysis. ResultsThe framework successfully created an eye model fully automatically in 12 cases. In these cases, a Dice similarity coefficient (mean surface distance) of 97.7%±0.84% (0.17±0.11 mm) was achieved for the sclera, 96.8%±1.05% (0.20±0.06 mm) for the vitreous body, 91.6%±4.83% (0.15±0.06 mm) for the lens, and 86.0%±7.4% (0.35±0.27 mm) for the tumor. The manual assessments deviated, on average, 0.39±0.31 mm in prominence and 1.7±1.22 mm in basal diameter from the automatic measurements. ConclusionsThe described framework combined information from T1- and T2-weighted images to accurately determine tumor boundaries in 3D. The proposed process may have a direct effect on clinical workflow, as it enables an accurate 3D assessment of tumor dimensions, which directly influences therapy selection.

Technical Note: A standardized automation framework for monitoring institutional radiotherapy protocol compliance.

To establish a framework for the standardization of monitoring radiotherapy protocol compliance. An automated protocol compliance tool was developed using best practice in software design and a flexible framework to easily adapt to changing institutional standards. The Eclipse scripting environment was used to develop the application with the scripting application programing interface (API) and direct data extraction from ARIA. For each institutional protocol, external validation was specified in a JavaScript Object Notation (JSON) file that stores protocol specific constraints and evaluates compliance of the data from Eclipse and Aria. This tool was applied prospectively to a cohort of prostate cancer patients undergoing radiotherapy with a prescription regimen of 60Gy in 20 fractions. The prospective evaluation was performed on 58 prostate cancer patients. For this cohort, the mean (standard deviation) pass rate is 92.3% (6.1%). The overall fail rate is 6.0% (5.8%); the percentage of these failures is in 2.6% in Patient Assessment, 0% in Simulation, and 97.4% in Treatment Planning. A protocol compliance application is developed and implemented in a standard radiotherapy information system. The application functionality is demonstrated on a cohort of 58 patients undergoing prostate radiotherapy, which highlights the utility of assessing adherence to institutional protocols. A unified method must be available for the community to ensure consistency in compliance reporting.

A novel approach to the classification of terrestrial drainage networks based on deep learning and preliminary results on solar system bodies

Several approaches were proposed to describe the geomorphology of drainage networks and the abiotic/biotic factors determining their morphology. There is an intrinsic complexity of the explicit qualification of the morphological variations in response to various types of control factors and the difficulty of expressing the cause-effect links. Traditional methods of drainage network classification are based on the manual extraction of key characteristics, then applied as pattern recognition schemes. These approaches, however, have low predictive and uniform ability. We present a different approach, based on the data-driven supervised learning by images, extended also to extraterrestrial cases. With deep learning models, the extraction and classification phase is integrated within a more objective, analytical, and automatic framework. Despite the initial difficulties, due to the small number of training images available, and the similarity between the different shapes of the drainage samples, we obtained successful results, concluding that deep learning is a valid way for data exploration in geomorphology and related fields.

Patient-specific virtual spine straightening and vertebra inpainting: An automatic framework for osteoplasty planning

Symptomatic spinal vertebral compression fractures (VCFs) often require osteoplasty treatment. A cement-like material is injected into the bone to stabilize the fracture, restore the vertebral body height and alleviate pain. Leakage is a common complication and may occur due to too much cement being injected. In this work, we propose an automated patient-specific framework that can allow physicians to calculate an upper bound of cement for the injection and estimate the optimal outcome of osteoplasty. The framework uses the patient CT scan and the fractured vertebra label to build a virtual healthy spine using a high-level approach. Firstly, the fractured spine is segmented with a three-step Convolution Neural Network (CNN) architecture. Next, a per-vertebra rigid registration to a healthy spine atlas restores its curvature. Finally, a GAN-based inpainting approach replaces the fractured vertebra with an estimation of its original shape. Based on this outcome, we then estimate the maximum amount of bone cement for injection. We evaluate our framework by comparing the virtual vertebrae volumes of ten patients to their healthy equivalent and report an average error of 3.88$\pm$7.63\%. The presented pipeline offers a first approach to a personalized automatic high-level framework for planning osteoplasty procedures.

Social Signal-Driven Knowledge Automation: A Focus on Social Transportation

Urban transportation systems are shaped by factors that include people, vehicles, roads, and the environment, forming a complex and giant system with dynamics, diversity, and uncertainty. Physical signal-driven intelligent transportation systems (ITSs) typically lack the ability to capture social behaviors or crowd willingness, and they achieve only information automation for transportation decision support. The crowdsourcing social signals consist of timely, extensive, comprehensive, and rich intelligence that concern urban dynamics, social behaviors, and traffic environments. Such social signals provide a new paradigm for operating ITS with unstructured semantic data, making knowledge automation for decision intelligence a possibility. This article reviews the knowledge automation paradigms for cyber-physical-social systems (CPSSs) compared with traditional information automation paradigms for cyber-physical systems (CPSs) in ITS, from the perspective of data-driven, modeling space, analytical methodologies, and decision support services. To investigate the key methodology in social spaces that enhance information automation into knowledge automation, we summarize the current research into a multisource heterogeneous social signal-based traffic decision knowledge automation framework and further exploit the computational paradigm and applications scenarios of this framework. Finally, we discuss future challenges for designing and realizing knowledge automation on CPSS in transportation.

Automatic Generation of ROP Through Static Instructions Assignment and Dynamic Memory Analysis

W⊕X is a protection mechanism against control-flow hijacking attacks. Return-oriented programming (ROP) can perform a specific function by searching for appropriate assembly instruction fragments (gadgets) in a code segment and bypass the W⊕X. However, manual search for gadgets that match the conditions is inefficient, with high error and missing rates. In order to improve the efficiency of ROP generation, the authors propose an automatic generation method based on a fragmented layout called automatic generation of ROP. This method designs new intermediate instruction construction rules based on an automatic ROP generation framework Q, uses symbolic execution to analyze program memory states and construct data constraints for multi-modules ROP, and solves ROP data constraints to generate test cases of an ROP chain. Experiments show that this method can effectively improve the space efficiency of the ROP chain and lower the requirements of the ROP layout on memory conditions.

Renal Cortex, Medulla and Pelvicaliceal System Segmentation on Arterial Phase CT Images with Random Patch-based Networks.

Renal segmentation on contrast-enhanced computed tomography (CT) provides distinct spatial context and morphology. Current studies for renal segmentations are highly dependent on manual efforts, which are time-consuming and tedious. Hence, developing an automatic framework for the segmentation of renal cortex, medulla and pelvicalyceal system is an important quantitative assessment of renal morphometry. Recent innovations in deep methods have driven performance toward levels for which clinical translation is appealing. However, the segmentation of renal structures can be challenging due to the limited field-of-view (FOV) and variability among patients. In this paper, we propose a method to automatically label the renal cortex, the medulla and pelvicalyceal system. First, we retrieved 45 clinically-acquired deidentified arterial phase CT scans (45 patients, 90 kidneys) without diagnosis codes (ICD-9) involving kidney abnormalities. Second, an interpreter performed manual segmentation to pelvis, medulla and cortex slice-by-slice on all retrieved subjects under expert supervision. Finally, we proposed a patch-based deep neural networks to automatically segment renal structures. Compared to the automatic baseline algorithm (3D U-Net) and conventional hierarchical method (3D U-Net Hierarchy), our proposed method achieves improvement of 0.7968 to 0.6749 (3D U-Net), 0.7482 (3D U-Net Hierarchy) in terms of mean Dice scores across three classes (p-value < 0.001, paired t-tests between our method and 3D U-Net Hierarchy). In summary, the proposed algorithm provides a precise and efficient method for labeling renal structures.

A novel artificial intelligence automatic detection framework to increase reliability of PLT gas bubble sensing

Production logging tools (PLTs) and formation testing, even in logging while drilling (LWD) conditions during underbalanced drilling, are key technologies for assessing the productivity potential of a gas well and therefore to maximize recovery. Gas bubble detection sensors are key components in determining the fluid phases in the reservoir and accurately quantify recoverable reserves, optimize well placement, geosteering and to qualify the production ability of the well. We present here a new nonlinear autoregressive - breakdown artificial intelligence (AI) detection framework for PLT gas bubble detection sensors that categorize in real-time whether and which sensors become unreliable or have broken down during the logging measurements. AI tools allow the automatization of this method that is critical during data quality control of post-drilling PLT, but it is essential when the measurements are performed in LWD as data assessment and processing need to occur in real-time. This AI framework was validated on both a training and testing dataset, and exhibited strong classification performance. This method enables accurate real-time breakdown detection for gas bubble detection sensors.

Convolutional neural networks applied to semantic segmentation of landslide scars

Landslides are considered to be among the most alarming natural hazards. Therefore, there is a growing demand for databases and inventories of these events worldwide, since they are a vital resource for landslide risk assessment applications. Given the recent advances in the field of image processing, the objective of this study is to evaluate the performance of a deep convolutional neural network architecture called U-Net for the mapping of landslide scars from satellite imagery. The question that drives the study is: can fully convolutional neural networks be successfully applied as the backbone of automatic frameworks for building landslide inventories, keeping or improving the identification accuracy and agility when compared to other methods? To seek for an answer to it, scenes from the Landsat-8 satellite of a region of Nepal were obtained and processed in order to compose a landslide image database that served as the basis for the training, validation and test of deep convolutional neural networks. The U-Net architecture was applied and the results indicate that it has the potential to identify landslide scars, improving over previously published research on the topic for the same study region. The validation process resulted in recall, precision and F1-score values of 0.74, 0.61 and 0.67, respectively, thus higher than those from previous studies using different methodologies. The results indicate the potential of the method to be applied in dynamic mapping systems for landslide scar identification, which paves the way to the composition and updating of landslide scar databases. These, in turn, can support a great deal of quantitative landslide susceptibility mapping methods that heavily rely on data to provide accurate results.

DeepHaul: a deep learning and reinforcement learning-based smart automation framework for dump trucks

In surface mining operations, the haul truck contributes the largest share to the overall operating cost, along with being a significant contributor to the injuries and fatalities to the operators; therefore, measures need to be taken for improving truck haulage safety and efficiency. In the absence of any major advancement in the automation technology for the mining sector, this study attempted to eliminate the existing technology lull by developing a novel DeepHaul framework for inducing smartness and intelligence within any dump truck, using the advanced algorithmic knowledge of artificial intelligence and machine learning. The DeepHaul framework consisted of two major components: first, inducing an object recognition ability by using deep learning methodology, for any dump truck. Experiments were conducted with different deep learning architectures, different training batch size ranges, and various image sizes for developing an optimum deep learning model with state-of-the-art performance for the haul truck. The second component consisted of the steering action decision making ability for the dump truck based on the given state of the haulage route. A reinforcement learning-based algorithm was designed, implemented, and tested for achieving the aforementioned objective. The algorithm exhibited an accuracy of 100% regarding safety and an average accuracy score of more than 97% regarding haulage efficiency. With the implementation of this DeepHaul framework, the existing autonomous haulage truck control technology can be greatly enhanced by inducing intelligence and smartness within the haul trucks, which would result in improved safety, efficiency, and the effectiveness of mining operations.