Classification of interstitial lung disease patterns using local DCT features and random forest.

Over the last decade, a plethora of computer-aided diagnosis (CAD) systems have been proposed aiming to improve the accuracy of the physicians in the diagnosis of interstitial lung diseases (ILD). In this study, we propose a scheme for the classification of HRCT image patches with ILD abnormalities as a basic component towards the quantification of the various ILD patterns in the lung. The feature extraction method relies on local spectral analysis using a DCT-based filter bank. After convolving the image with the filter bank, q-quantiles are computed for describing the distribution of local frequencies that characterize image texture. Then, the gray-level histogram values of the original image are added forming the final feature vector. The classification of the already described patches is done by a random forest (RF) classifier. The experimental results prove the superior performance and efficiency of the proposed approach compared against the state-of-the-art.

Fully automatic segmentation of AP pelvis X-rays via random forest regression with efficient feature selection and hierarchical sparse shape composition

In clinical practice, traditional X-ray radiography is widely used, and knowledge of landmarks and contours in anteroposterior (AP) pelvis X-rays is invaluable for computer aided diagnosis, hip surgery planning and image-guided interventions. This paper presents a fully automatic approach for landmark detection and shape segmentation of both pelvis and femur in conventional AP X-ray images. Our approach is based on the framework of landmark detection via Random Forest (RF) regression and shape regularization via hierarchical sparse shape composition. We propose a visual feature FL-HoG (Flexible- Level Histogram of Oriented Gradients) and a feature selection algorithm based on trace radio optimization to improve the robustness and the efficacy of RF-based landmark detection. The landmark detection result is then used in a hierarchical sparse shape composition framework for shape regularization. Finally, the extracted shape contour is fine-tuned by a post-processing step based on low level image features. The experimental results demonstrate that our feature selection algorithm reduces the feature dimension in a factor of 40 and improves both training and test efficiency. Further experiments conducted on 436 clinical AP pelvis X-rays show that our approach achieves an average point-to-curve error around 1.2 mm for femur and 1.9 mm for pelvis.

Lung Pattern Classification for Interstitial Lung Diseases Using a Deep Convolutional Neural Network

Automated tissue characterization is one of the most crucial components of a computer aided diagnosis (CAD) system for interstitial lung diseases (ILDs). Although much research has been conducted in this field, the problem remains challenging. Deep learning techniques have recently achieved impressive results in a variety of computer vision problems, raising expectations that they might be applied in other domains, such as medical image analysis. In this paper, we propose and evaluate a convolutional neural network (CNN), designed for the classification of ILD patterns. The proposed network consists of 5 convolutional layers with 2×2 kernels and LeakyReLU activations, followed by average pooling with size equal to the size of the final feature maps and three dense layers. The last dense layer has 7 outputs, equivalent to the classes considered: healthy, ground glass opacity (GGO), micronodules, consolidation, reticulation, honeycombing and a combination of GGO/reticulation. To train and evaluate the CNN, we used a dataset of 14696 image patches, derived by 120 CT scans from different scanners and hospitals. To the best of our knowledge, this is the first deep CNN designed for the specific problem. A comparative analysis proved the effectiveness of the proposed CNN against previous methods in a challenging dataset. The classification performance (~85.5%) demonstrated the potential of CNNs in analyzing lung patterns. Future work includes, extending the CNN to three-dimensional data provided by CT volume scans and integrating the proposed method into a CAD system that aims to provide differential diagnosis for ILDs as a supportive tool for radiologists.

The impact of a computer based adult literacy program on literacy and numeracy : evidence from India

With over 700 million illiterate adults in the world, many governments have implemented adult literacy programs across the world, although typically with low rates of success partly because the quality of teaching is low. One solution may lie in the standardization of teaching provided by computer-aided instruction. We present the first rigorous evidence of the effectiveness of a computer-based adult literacy program. A randomized control trial study of TARA Akshar Plus, an Indian adult literacy program, was implemented in the state of Uttar Pradesh in India. We find large, significant impacts of this computer-aided program on literacy and numeracy outcomes. We compare the improvement in learning to that of other traditional adult literacy programs and conclude that TARA Akshar Plus is effective in increasing literacy and numeracy for illiterate adult women.

Self-learning of fault diagnosis identification

A good and early fault detection and isolation system along with efficient alarm management and fine sensor validation systems are very important in today¿s complex process plants, specially in terms of safety enhancement and costs reduction. This paper presents a methodology for fault characterization. This is a self-learning approach developed in two phases. An initial, learning phase, where the simulation of process units, without and with different faults, will let the system (in an automated way) to detect the key variables that characterize the faults. This will be used in a second (on line) phase, where these key variables will be monitored in order to diagnose possible faults. Using this scheme the faults will be diagnosed and isolated in an early stage where the fault still has not turned into a failure.

Integration of design tools and knowledge capture into a CAD system: a case study

onceptual design phase is partially supported by product lifecycle management/computer-aided design (PLM/CAD) systems causing discontinuity of the design information flow: customer needs — functional requirements — key characteristics — design parameters (DPs) — geometric DPs. Aiming to address this issue, it is proposed a knowledge-based approach is proposed to integrate quality function deployment, failure mode and effects analysis, and axiomatic design into a commercial PLM/CAD system. A case study, main subject of this article, was carried out to validate the proposed process, to evaluate, by a pilot development, how the commercial PLM/CAD modules and application programming interface could support the information flow, and based on the pilot scheme results to propose a full development framework.

Defect engineering during the contact co-firing step in an industrial belt furnace

This work implements an optimization of the phosphorus gettering effect during the contact co-firing step by means of both simulations and experiments in an industrial belt furnace. An optimized temperature profile, named ‘extended co-firing step’, is presented. Simulations show that the effect of the short annealing on the final interstitial iron concentration depends strongly on the initial contamination level of the material and that the ‘extended co-firing’ temperature profile can enhance the gettering effect within a small additional time. Experimental results using sister wafers from the same multicrystalline silicon ingot confirm these trends and show the potential of this new defect engineering tool to improve the solar cell efficiency.

Dispersive optical bistability in quantum wells with logarithmic gain

We present an analytical model for studying optical bistability in semiconductor lasers that exhibit a logarithmic dependence of the optical gain on carrier concentration. Model results are shown for a Fabry–Pérot quantum-well laser and compared with the predictions of a commercial computer-aided design (CAD) software tool.

Information integration: generating functional models from structural ones

Information integration is a very important topic. Reusing the knowledge and having common representations have been (and it is) an active research topic in the process systems community. Conventional (structural) But only structural models have been dealt with so far. In this paper the issue of integration is related with two types of different knowledge, functional and structural. Functional representation and analysis have proved very useful, but still it is developed and presented in a completely isolated way from the classic structural description of the process. This paper presents an architecture to integrate both representations.

Simulation of impulse response for indoor visible light communications using 3D CAD models

n this article, a tool for simulating the channel impulse response for indoor visible light communications using 3D computer-aided design (CAD) models is presented. The simulation tool is based on a previous Monte Carlo ray-tracing algorithm for indoor infrared channel estimation, but including wavelength response evaluation. The 3D scene, or the simulation environment, can be defined using any CAD software in which the user specifies, in addition to the setting geometry, the reflection characteristics of the surface materials as well as the structures of the emitters and receivers involved in the simulation. Also, in an effort to improve the computational efficiency, two optimizations are proposed. The first one consists of dividing the setting into cubic regions of equal size, which offers a calculation improvement of approximately 50% compared to not dividing the 3D scene into sub-regions. The second one involves the parallelization of the simulation algorithm, which provides a computational speed-up proportional to the number of processors used.

Geometric documentation of the flour factory "El Puente Colgante" in Aranjuez (Madrid).

The goal of this communication is to offer, through computer-aided design tools, a methodology to recover and virtually reconstruct disappeared buildings of our industrial historical heritage. It will be applied to the case of the flour factory "El Puente Colgante" (The Suspended Bridge) in Aranjuez, which was demolished in 2001. The process is as follows: After a historical analysis of the evolution in time of the flour factory, a field work provides data allowing an info graphic reconstruction of the factory. Once this information has been processed, a lifting of the current state is made with AutoCAD, and a three-dimensional model is built with the Rhinoceros application. Then images of the ensemble are obtained with the applications Rhinoceros and V-Ray, ending with a postproduction with Photoshop. The proposed methodology has permitted to obtain a three-dimensional model of the flour factory ?El Puente Colgante? in Aranjuez, with an accurate virtual reconstruction of its original state prior to demolition. The procedure exposed is susceptible to be generalized for any other example of industrial architecture.

The p-Adaptive Biem Approach for Two-Dimensional Elasticity Analysis

This paper presents the development and application of the p-adaptive BIEM version in elastostatics. The basic concepts underlying the p-adaptive technique are summarized and discussed. Some Pascal pseudocodes which show the way how such a technique can be implemented easily in microcomputers are also provided. Both the applicability and the accuracy of the method proposed here are illustrated through a numerical example.

Toward mass production of microtextured microdevices: linking rapid prototyping with microinjection molding

The possibility of manufacturing textured materials and devices, with surface properties controlled from the design stage, instead of being the result of machining processes or chemical attacks, is a key factor for the incorporation of advanced functionalities to a wide set of micro and nanosystems. Recently developed high-precision additive manufacturing technologies, together with the use of fractal models linked to computer-aided design tools, allow for a precise definition and control of final surface properties for a wide set of applications, although the production of larger series based on these resources is still an unsolved challenge. However, rapid prototypes, with controlled surface topography, can be used as original masters for obtaining micromold inserts for final large-scale series manufacture of replicas using microinjection molding. In this study, an original procedure is presented, aimed at connecting rapid prototyping with microinjection molding, for the mass production of two different microtextured microsystems, linked to tissue engineering tasks, using different thermoplastics as ultimate materials.

Interfaz gráfica de usuario para la detección de microcalcificaciones mediante análisis de mamografía digitalizada

The graphical user interface (GUI) are all graphic elements that help to communicate with a system. The design of a GUI allow to land the central idea of a draft information technology. Today technology has become one of the largest and most useful tools to automate and facilitate processes for that reason fit into any kind of productive sectors, for example, in the health sector. The CAD systems (Systems Computer Aided Diagnosis) are the type of technology used in the health sector, in order to automate online modular learning environment with a fast placed in service. In the present paper the use of a Learning Management Systems (LMS) as continuous education tool is proposed.

Optimization methods for the operating room management under uncertainty: stochastic programming vs. decomposition approach

The operating theatres are the engine of the hospitals; proper management of the operating rooms and its staff represents a great challenge for managers and its results impact directly in the budget of the hospital. This work presents a MILP model for the efficient schedule of multiple surgeries in Operating Rooms (ORs) during a working day. This model considers multiple surgeons and ORs and different types of surgeries. Stochastic strategies are also implemented for taking into account the uncertain in surgery durations (pre-incision, incision, post-incision times). In addition, a heuristic-based methods and a MILP decomposition approach is proposed for solving large-scale ORs scheduling problems in computational efficient way. All these computer-aided strategies has been implemented in AIMMS, as an advanced modeling and optimization software, developing a user friendly solution tool for the operating room management under uncertainty.