Nanogrooved carbon microtubes for wet three‐dimensional printing of conductive composite structures

Recent advances in three‐dimensional (3D) printing have enabled the fabrication of interesting structures which are not achievable using traditional fabrication approaches. The 3D printing of carbon microtube composite inks allows fabrication of conductive structures for practical applications in soft robotics and tissue engineering. However, it is challenging to achieve 3D printed structures from solution‐based composite inks, which requires an additional process to solidify the ink. Here, we introduce a wet 3D printing technique which uses a coagulation bath to fabricate carbon microtube composite structures. We show that through a facile nanogrooving approach which introduces cavitation and channels on carbon microtubes, enhanced interfacial interactions with a chitosan polymer matrix are achieved. Consequently, the mechanical properties of the 3D printed composites improve when nanogrooved carbon microtubes are used, compared to untreated microtubes. We show that by carefully controlling the coagulation bath, extrusion pressure, printing distance and printed line distance, we can 3D print composite lattices which are composed of well‐defined and separated printed lines. The conductive composite 3D structures with highly customised design presented in this work provide a suitable platform for applications ranging from soft robotics to smart tissue engineering scaffolds. © 2019 Society of Chemical Industry     

Flexible Distribution-Based Regression Models for Count Data: Application to Medical Diagnosis

Abstract

Data mining techniques have been successfully utilized in different applications of significant fields, including medical research. With the wealth of data available within the health-care systems, there is a lack of practical analysis tools to discover hidden relationships and trends in data. The complexity of medical data that is unfavorable for most models is a considerable challenge in prediction. The ability of a model to perform accurately and efficiently in disease diagnosis is extremely significant. Thus, the model must be selected to fit the data better, such that the learning from previous data is most efficient, and the diagnosis of the disease is highly accurate. This work is motivated by the limited number of regression analysis tools for multivariate counts in the literature. We propose two regression models for count data based on flexible distributions, namely, the multinomial Beta-Liouville and multinomial scaled Dirichlet, and evaluated the proposed models in the problem of disease diagnosis. The performance is evaluated based on the accuracy of the prediction which depends on the nature and complexity of the dataset. Our results show the efficiency of the two proposed regression models where the prediction performance of both models is competitive to other previously used regression models for count data and to the best results in the literature.     

Mobility management support and performance analysis for wireless MPLS networks

Multi‐Protocol Label Switching (MPLS) is deployed in the Internet backbone to support service differentiation and traffic engineering. In recent years, there has been interest in extending the MPLS capability to wireless access networks for mobility management support. In this paper, we present analysis of Micro Mobile MPLS, a new micro‐mobility management scheme which integrates the Mobile IP and MPLS protocols by using two‐level hierarchy architecture. Our proposal supports two protocol variants. First, the fast handoff process, which anticipates the LSP procedure set‐up with neighboring locations where a mobile node (MN) may move to, is provided to reduce service disruption. Second, a new mechanism based on the forwarding chain concept is proposed to track efficiently the host mobility within a domain. This concept can significantly reduce registration update costs and provide low handoff latency. Analytical models are developed and simulations are conducted to justify the benefits of our proposed mechanisms. Copyright © 2006 John Wiley & Sons, Ltd.     

Precautious-RM: a predictable non-preemptive scheduling algorithm for harmonic tasks

A major requirement of many real-time embedded systems is to have time-predictable interaction with the environment. More specifically, they need fixed or small sampling and I/O delays, and they cannot cope with large delay jitters. Non-preemptive execution is a known method to reduce the latter delay; however, the corresponding scheduling problem is NP-Hard for periodic tasks. In this paper, we present Precautious-RM as a predictable linear-time online non-preemptive scheduling algorithm for harmonic tasks which can also deal with the former delay, namely sampling delay. We derive conditions of optimality of Precautious-RM and show that satisfying those conditions, tight bounds for best- and worst-case response times of the tasks can be calculated in polynomial-time. More importantly, response time jitter of the tasks is analyzed and it is proven that under specific conditions, each task has either one or two values for response time, which leads to improving the predictability of the system interaction with the environment. Simulation results demonstrate efficiency of Precautious-RM in increasing accuracy of control applications.     

Variational learning for Dirichlet process mixtures of Dirichlet distributions and applications

In this paper, we propose a Bayesian nonparametric approach for modeling and selection based on a mixture of Dirichlet processes with Dirichlet distributions, which can also be seen as an infinite Dirichlet mixture model. The proposed model uses a stick-breaking representation and is learned by a variational inference method. Due to the nature of Bayesian nonparametric approach, the problems of overfitting and underfitting are prevented. Moreover, the obstacle of estimating the correct number of clusters is sidestepped by assuming an infinite number of clusters. Compared to other approximation techniques, such as Markov chain Monte Carlo (MCMC), which require high computational cost and whose convergence is difficult to diagnose, the whole inference process in the proposed variational learning framework is analytically tractable with closed-form solutions. Additionally, the proposed infinite Dirichlet mixture model with variational learning requires only a modest amount of computational power which makes it suitable to large applications. The effectiveness of our model is experimentally investigated through both synthetic data sets and challenging real-life multimedia applications namely image spam filtering and human action videos categorization.     

Rheological,thermal, and morphological properties of blends based on poly(propylene), ethylene propylene rubber,and ethylene-1-octene copolymer that could result from end of life vehicles: Effect of maleic anhydride grafted poly(propylene)

The objective of this work is to study the properties of blends that could result from the recycling of end-of-life vehicles (ELV). While ethylene propylene rubber (EPR) and ethylene propylene diene monomer (EPDM) have been used extensively as elastomeric additives in poly(propylene) (PP), they can be substituted by ethylene-1-octene copolymer (EOC). As a consequence, the matter resulting from the sorting of ELV might be more complex and made of PP, EPR, and EOC. The effect of incorporating EOC [that is a polyethylene elastomer (PEE)] and maleic anhydride grafted polypropylene (PP-g-MAH) on the rheological, thermal, and morphological properties of PP/EPR blends has been investigated. Blends of various compositions (with and without compatibilizer) were prepared using a corotating twin-screw extruder. The results were compared to the ones presented by a commercial (PP/EPR) blend. The EPR phase is dispersed in the form of spherical particles in (PP/EPR). The EOC phase is dispersed in the form of aggregated particles. Dynamic viscoelastic and differential scanning calorimetry properties of (PP/EPR)/EOC blends shows the incompatibility of the components even in presence of PP-g-MAH copolymer. POLYM. ENG. SCI., 47:1009–1015, 2007. © 2007 Society of Plastics Engineers     

A statistical framework for few-shot action recognition

Along with the exponential growth of online video creation platforms such as Tik Tok and Instagram, state of the art research involving quick and effective action/gesture recognition remains crucial. This work addresses the challenge of classifying short video clips, using a domain-specific feature design approach, capable of performing significantly well using as little as one training example per action. The method is based on Gunner Farneback’s dense optical flow (GF-OF) estimation strategy, Gaussian mixture models, and information divergence. We first aim to obtain accurate representations of the human movements/actions by clustering the results given by GF-OF using K-means method of vector quantization. We then proceed by representing the result of one instance of each action by a Gaussian mixture model. Furthermore, using Kullback-Leibler divergence (KL-divergence), we attempt to find similarities between the trained actions and the ones in the test videos. Classification is done by matching each test video to the trained action with the highest similarity (a.k.a lowest KL-divergence). We have performed experiments on the KTH and Weizmann Human Action datasets using One-Shot and K-Shot learning approaches, and the results reveal the discriminative nature of our proposed methodology in comparison with state-of-the-art techniques.

     

Color object segmentation and tracking using flexible statistical model and level-set

Multimedia Tools and Applications - This study presents an unsupervised novel algorithm for color image segmentation, object detection and tracking based on unsupervised learning step followed with...     

A process model for robotic disc grinding

Automation of deburring and cleaning of castings is desirable for many reasons. The major reasons are dangerous working conditions, difficulties in finding workers for cleaning sections, and improved profitability. A suitable robot cell capable of using different tools, such as cup grinders, disc grinders and rotary files, is the solution. This robot should be completed with sensors in order to keep the quality of the cleaned surface at an acceptable level. Although using sensors simplifies both the programming and quality control there are still other problems that need to be solved. These involve selection of machining data, e.g. feeding rate and grinding force in a force controlled operation based on parameters such as tool type, disc grinder and geometry. In order to decrease the programming time, a process model for disc grinders has been developed. This article investigates this process model and pays attention to problems such as wavy or burned surfaces and the effect of a robot's repetition accuracy in the results obtained. Many aspects treated in this article are quite general, and can be applied in other types of grinding operations.