Resource allocation algorithm for LTE networks using fuzzy based adaptive priority and effective bandwidth estimation

In this paper, we propose a scheme to allocate resource blocks for the Long Term Evolution (LTE) downlink based on the estimation of the effective bandwidths of traffic flows, where users’ priorities are adaptively computed using fuzzy logic. The effective bandwidth of each user traffic flow that is estimated through the parameters of the adaptive β-Multifractal Wavelet Mode modeling, is used to attain their quality of service (QoS) parameters. The proposed allocation scheme aims to guarantee the QoS parameters of users respecting the constraints of modulation and code schemes (modulation and coding scheme) of the LTE downlink transmission. The proposed algorithm considers the average channel quality and the adaptive estimation of effective bandwidth to decide about the scheduling of available radio resources. The efficiency of the proposed scheme is verified through simulations and compared to other algorithms in the literature in terms of parameters such as: system throughput, required data rate not provided, fairness index, data loss rate and network delay.     

Dynamic intermittent Q‐learning–based model‐free suboptimal co‐design of ‐stabilization

This paper proposes an intermittent model‐free learning algorithm for linear time‐invariant systems, where the control policy and transmission decisions are co‐designed simultaneously while also being subjected to worst‐case disturbances. The control policy is designed by introducing an internal dynamical system to further reduce the transmission rate and provide bandwidth flexibility in cyber‐physical systems. Moreover, a Q‐learning algorithm with two actors and a single critic structure is developed to learn the optimal parameters of a Q‐function. It is shown by using an impulsive system approach that the closed‐loop system has an asymptotically stable equilibrium and that no Zeno behavior occurs. Furthermore, a qualitative performance analysis of the model‐free dynamic intermittent framework is given and shows the degree of suboptimality concerning the optimal continuous updated controller. Finally, a numerical simulation of an unknown system is carried out to highlight the efficacy of the proposed framework.     

Backstepping sliding mode control with functional tuning based on an instantaneous power approach applied to an underwater vehicle

In this paper, we present a modified backstepping sliding mode control to deal with Euler–Lagrange systems. The controller is applied in an underwater vehicle in order to show the effectiveness of the approach proposed. Instantaneous power data provided by the propulsion system are used to tune the controller in order to guarantee robust performance and energy saving. Thanks to the combination of an internal Proportional Integral and Derivative (PID) controller, it is possible implement high gains to deal with the influence of disturbances and uncertainties. A comparative study among this backstepping sliding mode controller and standard sliding mode controls is presented.     

Multi‐criteria IoT resource discovery: a comparative analysis

The growth of real‐world objects with embedded and globally networked sensors allows to consolidate the Internet of things paradigm and increase the number of applications in the domains of ubiquitous and context‐aware computing. The merging between cloud computing and Internet of things named cloud of things will be the key to handle thousands of sensors and their data. One of the main challenges in the cloud of things is context‐aware sensor search and selection. Typically, sensors require to be searched using two or more conflicting context properties. Most of the existing work uses some kind of multi‐criteria decision analysis to perform the sensor search and selection, but does not show any concern for the quality of the selection presented by these methods. In this paper, we analyse the behaviour of the SAW, TOPSIS and VIKOR multi‐objective decision methods and their quality of selection comparing them with the Pareto‐optimality solutions. The gathered results allow to analyse and compare these algorithms regarding their behaviour, the number of optimal solutions and redundancy. Copyright © 2016 John Wiley & Sons, Ltd.     

DropCircuit : A Modular Regularizer for Parallel Circuit Networks

How to design and train increasingly large neural network models is a topic that has been actively researched for several years. However, while there exists a large number of studies on training deeper and/or wider models, there is relatively little systematic research particularly on the effective usage of wide modular neural networks. Addressing this gap, and in an attempt to solve the problem of lengthy training times, we proposed Parallel Circuits (PCs), a biologically inspired architecture based on the design of the retina. In previous work we showed that this approach fails to maintain generalization performance in spite of achieving sharp speed gains. To address this issue, and motivated by the way dropout prevents node co-adaptation, in this paper, we suggest an improvement by extending dropout to the parallel-circuit architecture. The paper provides empirical proof and multiple insights into this combination. Experiments show promising results in which improved error rates are achieved in most cases, whilst maintaining the speed advantage of the PC approach.     

Adaptive envelope process and minimum service curve applied to resource block allocation in the LTE downlink

In this work, we propose a resource allocation algorithm for the LTE downlink that makes use of an adaptive multifractal envelope process and a minimum service curve. The proposed scheduling algorithm aims to improve some network parameters while guaranteeing a maximum delay to the user by considering the following information: backlog, channel condition and user traffic behavior. In order to estimate the maximum network delay, we propose an adaptive minimum service curve for the LTE network that can be used for admission control purposes in the resource allocation algorithm. The performance of the proposed scheduling algorithm is compared to those of several scheduling schemes known in the literature through computational simulations of the LTE downlink. In order to develop a new adaptive envelope process and to precisely describe network flows, we propose an adaptive algorithm to estimate the parameters of the Multifractal Wavelet Model (MWM). The proposed envelope process is compared to the main traffic model based envelope processes known in the literature. Simulations of the LTE downlink considering AMC (Adaptive Modulation and Coding) are carried out showing the efficiency of the proposed resource allocation approach that considers adaptive estimation of network traffic parameters.     

Morphological analysis of colon goblet cells and submucosa in type I diabetic rats submitted to physical training

Colon layers, especially the submucosa, as well as the secretion of goblet cells are extremely important for the functioning and transit of substances in this organ. However, the damages arising from type I diabetes and the effects of physical training, which plays crucial role in the treatment of this disease, are not yet known in these regions. To analyze the changes in colon submucosa and goblet cells of diabetic rats, as well as the effects of physical training, Wistar rats were divided into four groups: sedentary control, trained control, sedentary diabetic (SD), and trained diabetic (TD). The training protocol consisted of swimming for 60 min a day, 5 days per week, during 8 weeks. Colon samples were collected, processed, and evaluated by histochemical and ultrastructural techniques. Goblet and submucosa cells did not show alterations in shape, size, protein and carbohydrate content, in all treatment groups. Decreased amount of collagen fibers, however, was observed in the submucosa and lamina propria of SD rats, but this alteration was recovered in TDs. The ultrastructural analysis, in turn, revealed greater quantity of Golgi apparatus cisterns in SDs, distinctly than TDs, which showed improvement in this diabetic condition. Thus, physical training was responsible for the recovery of some important diabetic alterations, possibly improving the motility of substances in the large intestine. Nevertheless, it cannot be considered alone in the treatment of this disease, requiring the combined practice of other methods.     

Development and characterization of printable PLA/β-TCP bioactive composites for bone tissue applications

In this study, poly(lactic acid) (PLA) and PLA/β-tricalcium phosphate (TCP) biocomposites were developed by melt compounding using an internal melt mixer with three different TCP contents (5, 10, and 25 wt%). A comprehensive analysis of the thermal, rheological, and mechanical properties of these biocomposites was performed. TCP presented proper distribution in the PLA/TCP biocomposites: PLA5TCP and PLA10TCP exhibited rheological behavior similar to that of neat PLA. However, PLA25TCP presented significant agglomeration and reduction in thermal stability. Addition of TCP to the biocomposites enhanced their bioactivity and biocompatibility. The bioactivity assay was conducted by immersing the samples in SBF solution for 7 and 21 days, and the SEM and XRD surface analyses of the PLA/TCP biocomposites presented evidence of carbonated hydroxyapatite formation. The biocompatibility assay was performed using the extract method until 7 days, and PLA10TCP presented improved relative cell viability compared with the control. Finally, since the materials presented suitable thermal and rheological properties, filaments for additive manufacturing (AM) were developed, and they were used to produce screw models for bone-ligament fixation. The 3D printed screws exhibited excellent printability and accuracy. Therefore, the PLA/TCP biocomposites developed can be used in further biomedical applications using AM, namely, guided bone tissue engineering.     

An intelligent system for prognosis of noncommunicable diseases’ risk factors

Noncommunicable diseases are the main reason to the rise of diseases incidence in the developed world. The management and prevention of these diseases can be done by controlling the behavioral and biological risk factors which are related to them. ChronicPrediction is an intelligent system for noncommunicable diseases care which determines in real time the impact on risk factors due to actions taken by users. Based on impact information, the system presents on users’ smartphones strategic messages to help in their treatment. ChronicPrediction applies Bayesian Networks (BNs) which use risk factors for mapping the causes of noncommunicable diseases worsening. The support to multiple chronic diseases and the integrated use of multiple BNs based on risk factors are the main contributions of this work and differentiate the proposed system from related work. We have built a functional prototype that allowed us to conduct two experiments. The first one successfully tested the main functionalities provided by ChronicPrediction to support BNs based on risk factors and the sending of messages to users’ smartphones. The evaluation involved the building of a BN for predicting coronary artery disease made with real world data obtained in a prospective cohort study. The study involved 302 patients from a hospital localized in southern Brazil. The second experiment assessed the ChronicPrediction support to multiple BNs at same time. The test involved the previous BN and another from a thirty part research work to map risk factors of diabetes. The results were encouraging and show potential for implementing ChronicPrediction in real-life situations.     

Evaluation of the etching and chrome plating on the ABS,PVC, and PVC/ABS blends surface

As replacement for acrylonitrile‐butadiene‐styrene (ABS), which is commonly used in the manufacture of chrome parts, polyvinyl chloride (PVC) and PVC/ABS blend parts were produced by injection process, etched in sulfochromic solution in different concentrations, immersion time, and temperature, and they were subsequently chrome‐plated by conventional method. After etching, the sample surfaces were analyzed by Scanning Electron Microscopy (SEM), Atomic Force Microscopy (AFM), roughness, and contact angle, and compared with the ABS surface. The metal deposition was assessed by visual inspection and adhesion test. The roughness influenced the adhesion of the metal layer directly. The chemical etching increased the surface wettability. To achieve a good metal layer adhesion, higher temperatures, immersion time, and etching solution concentration were necessary. A concentration of 350 g/L chromic acid and 400 g/L sulfuric acid, a 70 °C temperature and a 15 min immersion time resulted in good adhesion in PVC and PVC/ABS blends. © 2016 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2017 , 134, 44571.