Cross-Layer MAC Protocol for Semantic Wireless Sensor Network

Wireless Personal Communications - Wireless sensor networks (WSN) are ad-hoc wireless networks used in many domains involving the analysis of various properties. Along with the growth in the number...     

Analysing the power of deep learning techniques over the traditional methods using medicare utilisation and provider data

Deep Learning Technique (DLT) is the sub-branch of Machine Learning (ML) which assists to learn the data in multiple levels of representation and abstraction and shows impressive performance on many Artificial Intelligence (AI) tasks. This paper presents a new method to analyse the healthcare data using DLT algorithms and associated mathematical formulations. In this study, we have first developed a DLT to programme two types of deep learning neural networks, namely: (a) a two-hidden layer network, and (b) a three-hidden layer network. The data was analysed for predictability in both of these networks. Additionally, a comparison was also made with simple and multiple Linear Regression (LR). The demonstration of successful application of this method is carried out using the dataset that was constructed based on 2014 Medicare Provider Utilization and Payment Data. The results indicate a stronger case to use DLTs compared to traditional techniques like LR. Furthermore, it was identified that adding more hidden layers to neural network constructed for performing deep learning analysis did not have much impact on predictability for the dataset considered in this study. Therefore, the experimentation described in this article sets up a case for using DLTs over the traditional predictive analytics. The investigators assume that the algorithms described for deep learning is repeatable and can be applied for other types of predictive analysis on healthcare data. The observed results indicate, the accuracy obtained by DLT was 40% more accurate than the traditional multivariate LR analysis.     

Galerkin finite element solution for Benjamin–Bona–Mahony–Burgers equation with cubic B-splines

In this article, we study solitary-wave solutions of the nonlinear Benjamin–Bona–Mahony–Burgers(BBM–Burgers) equation based on a lumped Galerkin technique using cubic B- spline finite elements for the spatial approximation. The existence and uniqueness of solutions of the Galerkin version of the solutions have been established. An accuracy analysis of the Galerkin finite element scheme for the spatial approximation has been well studied. The proposed scheme is carried out for four test problems including dispersion of single solitary wave, interaction of two, three solitary waves and development of an undular bore. Then we propose a full discrete scheme for the resulting IVP. Von Neumann theory is used to establish stability analysis of the full discrete numerical algorithm. To display applicability and durableness of the new scheme, error norms $L_2$, $L_{\infty }$ and three invariants $I_1,I_2$ and $I_3$ are computed and the acquired results are demonstrated both numerically and graphically. The obtained results specify that our new scheme ensures an apparent and an operative mathematical instrument for solving nonlinear evolution equation.     

Failure analysis of a geomembrane lined reservoir embankment

This paper presents case study and failure analysis of an embankment enclosing a raw water reservoir at a coal-based thermal power plant. The embankments and the base of the reservoir were all lined with geomembrane. Major breaches occurred in the embankment separating two compartments of the reservoir (i.e., the partition embankment) approximately one year after the filling of one of the reservoirs. Seepage and slope stability analyses were carried out to detect the causes of failure. The post–failure field observations and results of stability analyses indicated that the use of a single layer geomembrane as the sole component of barrier layer was inadequate. Pipe drains provided at the base of the reservoir to intercept rising groundwater level acted as a flow pathway for water seeping from tears and punctures in geomembrane liner at the base of the reservoir. The design of internal drainage system for both the partition embankment and peripheral embankment (i.e., the embankments other than the partition embankment surrounding the reservoir) was insufficient. The remedial measures which could be adopted for geosynthetic lined reservoir and embankment were evaluated and presented in the paper. The study highlights the need to provide a secondary liner in form of clay or geosynthetic clay liner whenever a geomembrane is used as a barrier layer. In cases where use of single layer of geomembrane is unavoidable, seepage and safety analysis should be carried out with the assumption that it may leak. This is important when an adequate quality control in laying the geomembrane is lacking or the embankment facilities would continue to be operated at full head even after the design life of the geomembrane is exceeded.     

Comparative studies of solvent bonding and adhesive bonding for fabrication of transparent polymers

This investigation highlights rationale of solvent bonding and adhesive bonding for fabrication of a transparent polymer such as polycarbonate with a high-throughput process. Studies under ultra violet spectra and visible spectra reveal that in comparison with adhesive bonding of a polymer, solvent diffusion bonding is more transparent. Polycarbonate is hydrophilic in nature resulting in a low contact angle of water as well as the presence of polar functional groups on the polymer surface. It is observed that a lap shear tensile strength of a solvent bonding polymer is significantly higher than that of an acrylic adhesive bonded polycarbonate, and fabrication of polycarbonate by solvent bonding merely takes few seconds. Solvent bonding of a polymer results in a cohesive failure from polymer as analyzed under the scanning electron microscopy, this is why solvent bonding shows a significantly higher bond strength.     

Parameter optimization on compressive strength of steel fiber reinforced high strength concrete

This paper illustrates parameter optimization of compressive strength of steel fiber reinforced high strength concrete (SFRHSC) by statistical design and analysis of experiments. Among several factors affecting the compressive strength, five parameters that maximize all of the responses have been chosen as the most important ones as age of testing, binder type, binder amount, curing type and steel fiber volume fraction. Taguchi analysis techniques have been used to evaluate L₂₇ (3¹³) Taguchi’s orthogonal array experimental design results. Signal to noise ratio transformation and ANOVA have been applied to the results of experiments in Taguchi analysis. The confirmation runs were conducted for the optimal parameter level combination, which is obtained from the results of the above methodologies. The maximum compressive strength has been observed as around 124 MPa. By using the optimal parameter level combination, the direct tensile strength and flexural strength tests have been conducted. The mean values at the age of 28 days are obtained as 7.5 MPa and 13 MPa respectively. In this study, it is clearly demonstrated that all main factors except steel fiber significantly contribute to the compressive strength of steel fiber reinforced high strength concrete, yet age and binder type are the most significant contributors.     

Built-In Fault Localization Circuitry for High Performance FPGA Based Implementations

In order to meet superior performance metrics along with denser logic integration and device miniaturization, FPGAs have become more susceptible to transistor related aging, coupled with manufacturing defects owing to increased complexity in photolithographic techniques, thereby reducing the reliability and lifetime. In this paper, we propose certain built-in circuit techniques that are integrated with the original design, to localize the source of any hard or soft errors, if any, with tolerable penalty in performance, against acceptable time and/or hardware redundancy. Circuit realization on FPGA has been achieved through primitive instantiation and constrained placement, such that the exact location from which the fault has emanated can be traced, and bypassed for mapping any subsequent logic on the same FPGA. The adopted design paradigm which had earlier proved its potential for high performance FPGA based designs, has now been adopted to facilitate fault localization.     

Development of β-cyclodextrin-based sustained release microparticles for oral insulin delivery

Polymeric microparticles have been previously demonstrated to deliver various therapeutic agents efficiently to targeted regions by protecting the drug from harsh gastric milieu of the gastrointestinal tract. In this study, we investigated the hypoglycemic effect of β-cyclodextrin polymeric insulin microparticles in diabetic rats via the oral route of administration. β-cyclodextrin microparticles were prepared by a unique one-step spray-drying technique and stabilized by incorporating enteric retardant polymers in the formulation. The insulin-loaded microparticles had a mean size of 0.8?±?0.25?μm with a zeta potential of 3.57?+?0.62?mV. As seen with the chromatographic analysis, the drug content in the microparticles was determined to be 94.9?±?2.77%. RAW macrophage cells showed greater than 80% viability after 24?h of incubation with the insulin and blank microparticles. For the in vitro release study, the microparticles were able to protect the insulin in gastric fluid where no significant release was detected, followed by only 50% release in intestinal fluid for the first 8?h of the study. This was seen to correlate with the in vivo data where 50% glucose inhibition was seen after 8?h of oral administration in diabetic rats. This data suggest that the oral insulin microparticles were able to reduce glucose levels in disease conditions and would be a favorable route of administration to patients as an alternative to daily subcutaneous injections.     

Akin-based Orthogonal Space (AOS): a subspace learning method for face recognition

A projection learning space is an approach to mapping a high-dimensional vector space to a lower dimensional vector space. In this paper, we proposed an algorithm, namely, AOS: Akin based Orthogonal Space. The algorithm is driven with two major targets - (i) to choose most representative image(s) from a group of face images of an individual, (ii) finally to produce a learning space which follows a Gaussian distribution to reduce the influence of grosses like non-Gaussianly distributed data noises, variations in facial expression and illumination. To improve the recognition performance, we proposed another approach i.e. fusion between AOS features and a custom VGG features. We justify the effectiveness of the proposed approaches over five benchmark face datasets using two classifiers. Experimental results show that the proposed learning algorithm has obtained maximum of 92.22% recognition rate, as well deep learning based fusion approch greatly improves the recognition accuracy. The comparative performances demonstrate that the proposed method could significantly outperform other relevant subspace learning methods.