A new approach based on a discrete hidden Markov model using the Rocchio algorithm for the diagnosis of heart valve diseases

Abstract: Application of the Doppler ultrasound technique in the diagnosis of heart diseases has been increasing in the last decade since it is non‐invasive, practicable and reliable. In this study, a new approach based on the discrete hidden Markov model (DHMM) is proposed for the diagnosis of heart valve disorders. For the calculation of hidden Markov model (HMM) parameters according to the maximum likelihood approach, HMM parameters belonging to each class are calculated by using training samples that only belong to their own classes. In order to calculate the parameters of DHMMs, not only training samples of the related class but also training samples of other classes are included in the calculation. Therefore HMM parameters that reflect a class's characteristics are more represented than other class parameters. For this aim, the approach was to use a hybrid method by adapting the Rocchio algorithm. The proposed system was used in the classification of the Doppler signals obtained from aortic and mitral heart valves of 215 subjects. The performance of this classification approach was compared with the classification performances in previous studies which used the same data set and the efficiency of the new approach was tested. The total classification accuracy of the proposed approach (95.12%) is higher than the total accuracy rate of standard DHMM (94.31%), continuous HMM (93.5%) and support vector machine (92.67%) classifiers employed in our previous studies and comparable with the performance levels of classifications using artificial neural networks (95.12%) and fuzzy‐C‐means/CHMM (95.12%).     

Prosthetic Hand Finger Control Using Fuzzy Sliding Modes

In order to improve the life quality of amputees, providing approximate manipulation ability of a human hand to that of a prosthetic hand is considered by many researchers. In this study, a biomechanical model of the index finger of the human hand is developed based on the human anatomy. Since the activation of finger bones are carried out by tendons, a tendon configuration of the index finger is introduced and used in the model to imitate the human hand characteristics and functionality. Then, fuzzy sliding mode control where the slope of the sliding surface is tuned by a fuzzy logic unit is proposed and applied to have the finger model to follow a certain trajectory. The trajectory of the finger model, which mimics the motion characteristics of the human hand, is pre-determined from the camera images of a real hand during closing and opening motion. Also, in order to check the robust behaviour of the controller, an unexpected joint friction is induced on the prosthetic finger on its way. Finally, the resultant prosthetic finger motion and the tendon forces produced are given and results are discussed.     

The Lattice-Boltzmann Method on Optimal Sampling Lattices

In this paper, we extend the single relaxation time Lattice-Boltzmann Method (LBM) to the 3D body-centered cubic (BCC) lattice. We show that the D3bQ15 lattice defined by a 15 neighborhood connectivity of the BCC lattice is not only capable of more accurately discretizing the velocity space of the continuous Boltzmann equation as compared to the D3Q15 Cartesian lattice, it also achieves a comparable spatial discretization with 30 percent less samples. We validate the accuracy of our proposed lattice by investigating its performance on the 3D lid-driven cavity flow problem and show that the D3bQ15 lattice offers significant cost savings while maintaining a comparable accuracy. We demonstrate the efficiency of our method and the impact on graphics and visualization techniques via the application of line-integral convolution on 2D slices as well as the extraction of streamlines of the 3D flow. We further study the benefits of our proposed lattice by applying it to the problem of simulating smoke and show that the D3bQ15 lattice yields more detail and turbulence at a reduced computational cost.     

TEM characterization of Ge precipitates in an Al-1.6at% Ge alloy

The growth mechanism and morphology of Ge precipitates in an Al-Ge alloy was characterized by a combination of in-situ transmission electron microscopy, high-resolution transmission electron microscopy and three-dimensional electron tomography. Anisotropic growth of rod-shaped Ge precipitates was observed by in-situ transmission electron microscopy over different time periods, and faceting of the precipitates was clearly seen using high-resolution transmission electron microscopy and three-dimensional electron tomography. This anisotropic growth of rod-shaped Ge precipitates was enhanced by vacancy concentration as proposed previously, but also by surface diffusion as observed during the in-situ experiment. Furthermore, a variety of precipitate morphologies was identified by three-dimensional electron tomography.     

A Framework of Deep Learning and Selection-Based Breast Cancer Detection from Histopathology Images

Breast cancer (BC) is a most spreading and deadly cancerous malady which is mostly diagnosed in middle-aged women worldwide and effecting beyond a half-million people every year. The BC positive newly diagnosed cases in 2018 reached 2.1 million around the world with a death rate of 11.6% of total cases. Early diagnosis and detection of breast cancer disease with proper treatment may reduce the number of deaths. The gold standard for BC detection is biopsy analysis which needs an expert for correct diagnosis. Manual diagnosis of BC is a complex and challenging task. This work proposed a deep learning-based (DL) solution for the early detection of this deadly disease from histopathology images. To evaluate the robustness of the proposed method a large publically available breast histopathology image database containing a total of 277524 histopathology images is utilized. The proposed automatic diagnosis of BC detection and classification mainly involves three steps. Initially, a DL model is proposed for feature extraction. Secondly, the extracted feature vector (FV) is passed to the proposed novel feature selection (FS) framework for the best FS. Finally, for the classification of BC into invasive ductal carcinoma (IDC) and normal class different machine learning (ML) algorithms are used. Experimental outcomes of the proposed methodology achieved the highest accuracy of 92.7% which shows that the proposed technique can successfully be implemented for BC detection to aid the pathologists in the early and accurate diagnosis of BC.     

Scientific Workflow Makespan Minimization in Edge Multiple Service Providers Environment

The edge computing model offers an ultimate platform to support scientific and real-time workflow-based applications over the edge of the network. However, scientific workflow scheduling and execution still facing challenges such as response time management and latency time. This leads to deal with the acquisition delay of servers, deployed at the edge of a network and reduces the overall completion time of workflow. Previous studies show that existing scheduling methods consider the static performance of the server and ignore the impact of resource acquisition delay when scheduling workflow tasks. Our proposed method presented a meta-heuristic algorithm to schedule the scientific workflow and minimize the overall completion time by properly managing the acquisition and transmission delays. We carry out extensive experiments and evaluations based on commercial clouds and various scientific workflow templates. The proposed method has approximately 7.7% better performance than the baseline algorithms, particularly in overall deadline constraint that gives a success rate.

     

Piezoelectric properties in two-dimensional materials: Simulations and experiments

The piezoelectric effect, discovered in 1880 by Jacques and Pierre Curie, effectively allows to transduce signals from the mechanical domain to the electrical domain and vice versa. For this reason, piezoelectric devices are already ubiquitous, including, for instance, quartz oscillators, mechanical actuators with sub-atomic resolution and microbalances. However, the ability to synthesize two-dimensional (2D) materials may enable the fabrication of innovative devices with unprecedented performance. For instance, many materials which are not piezoelectric in their bulk form become piezoelectric when reduced to a single atomic layer; moreover, since all the atoms belong to the surface, piezoelectricity can be effectively engineered by proper surface modifications. As additional advantages, 2D materials are strong, flexible, easy to be co-integrated with conventional integrated circuits or micro-electromechanical systems and, in comparison with bulk or quasi-1D materials, easier to be simulated at the atomistic level. Here, we review the state of the art on 2D piezoelectricity, with reference to both computational predictions and experimental characterization. Because of their unique advantages, we believe 2D piezoelectric materials will substantially expand the applications of piezoelectricity.     

A Novel Contour Tracing Algorithm for Object Shape Reconstruction Using Parametric Curves

Parametric curves such as Bézier and B-splines, originally developed for the design of automobile bodies, are now also used in image processing and computer vision. For example, reconstructing an object shape in an image, including different translations, scales, and orientations, can be performed using these parametric curves. For this, Bézier and B-spline curves can be generated using a point set that belongs to the outer boundary of the object. The resulting object shape can be used in computer vision fields, such as searching and segmentation methods and training machine learning algorithms. The prerequisite for reconstructing the shape with parametric curves is to obtain sequentially the points in the point set. In this study, a novel algorithm has been developed that sequentially obtains the pixel locations constituting the outer boundary of the object. The proposed algorithm, unlike the methods in the literature, is implemented using a filter containing weights and an outer circle surrounding the object. In a binary format image, the starting point of the tracing is determined using the outer circle, and the next tracing movement and the pixel to be labeled as the boundary point is found by the filter weights. Then, control points that define the curve shape are selected by reducing the number of sequential points. Thus, the Bézier and B-spline curve equations describing the shape are obtained using these points. In addition, different translations, scales, and rotations of the object shape are easily provided by changing the positions of the control points. It has also been shown that the missing part of the object can be completed thanks to the parametric curves.     

A Framework of Deep Optimal Features Selection for Apple Leaf Diseases Recognition

Identifying fruit disease manually is time-consuming, expert-required, and expensive; thus, a computer-based automated system is widely required. Fruit diseases affect not only the quality but also the quantity. As a result, it is possible to detect the disease early on and cure the fruits using computer-based techniques. However, computer-based methods face several challenges, including low contrast, a lack of dataset for training a model, and inappropriate feature extraction for final classification. In this paper, we proposed an automated framework for detecting apple fruit leaf diseases using CNN and a hybrid optimization algorithm. Data augmentation is performed initially to balance the selected apple dataset. After that, two pre-trained deep models are fine-tuning and trained using transfer learning. Then, a fusion technique is proposed named Parallel Correlation Threshold (PCT). The fused feature vector is optimized in the next step using a hybrid optimization algorithm. The selected features are finally classified using machine learning algorithms. Four different experiments have been carried out on the augmented Plant Village dataset and yielded the best accuracy of 99.8%. The accuracy of the proposed framework is also compared to that of several neural nets, and it outperforms them all.     

Cyclic crack growth behaviour of two nickel base turbine disc alloys

The high temperature fatigue crack growth behaviour of the nickel base superalloys Alloy 718 and Rene 95 (specimen thickness=4.1 mm) were investigated and compared with each other. Fatigue crack propagation (FCP) tests were carried out in laboratory air at room temperature and 600°C by using C-T (compact tension) type specimen that were fatigue precracked at room temperature.Alloy 718 was found to provide the higher resistance to crack propagation under the present testing conditions.At 600°C, in Alloy 718, the fracture path was of mixed type at low and transgranular at high K (stress intensity factor range) values, while it remained intergranular in Rene 95 throughout the whole K range tested. The difference in the crack growth rates of Alloy 718 with different thicknesses (4.1 mm and 13.0 mm) was related to their different fracture modes.The striation spacings, both at room temperature and 600°C, of Alloy 718 were found to be proportional to the empirical equation proposed by Bates and Clark [2] but with a constant of 9.5 instead of 6. However, although the correlation between the microscopic FCP rate obtained from fatigue striation measurements – and hence the empirical equation – and the macroscopic FCP rate was pretty good at room temperature, it was found to be poor at 600°C, indicating that, at 600°C, striation formation alone did not control the fatigue resistance of Alloy 718 which is thought to account for the insufficiency of the COD (crack opening displacement) approach to correctly correlate the macroscopic FCP rates of Alloy 718 at these two test temperatures. © 1998 Chapman & Hall