A hybrid shape-based image clustering using time-series analysis

Clustering of different shapes of the same object has an inordinate impact on various domains, including biometrics, medical science, biomedical signal analysis, and forecasting, for the analysis of huge volume of data into different groups. In this work, we present a novel shape-based image clustering approach using time-series analysis, to guarantee the robustness over the conventional clustering techniques. To evaluate the performance of the proposed procedure, we employed a dataset consists of various real-world irregular shaped objects. The shapes of different objects are first extracted from the entire dataset based on similar pattern using mean structural similarity index. Furthermore, we performed radical scan on the extracted shapes for converting them to one-dimensional (1D) time-series data. Finally, the time series are clustered to form subgroups using hierarchical divisive clustering approach with average linkage, and Pearson as distance metrics. A comparative study with other conventional distance metrices was also conducted. The results established the superiority of using Pearson correlation measure, which provided the maximum F1-score with exact number of shapes under a sub-cluster, while the corresponding outcomes of other approaches results in a poor and inappropriate clustering.

     

Customer on-boarding strategies for cloud computing services with dynamic service-level agreements

A multi-tenant software as a service (SaaS) provider has to meet the needs of several tenants which adopt its services with diverse business requirements. The tenant needs vary widely with time, and the provider has to account for such fluctuations by suitable provisioning at its end. Handling this elasticity arising out of the tenant base is one of the key challenges for the SaaS provider. In this paper, we study the problem specifically in the SaaS context with the idea built around license provisioning in a tenant–provider perspective. For a given set of tenants with diverse license requirements, it is important to analyze whether there is any way to on-board them such that all constraints laid out as part of the service-level agreement can be honored. The total number of licenses available with the provider plays a crucial role in answering this question. We propose an intuitive model of elasticity that can capture anticipated license need variations at the tenant end. We propose an ILP-based approach for solving this schedulability problem for a collection of tenants. We also propose a simple-minded greedy heuristic to solve the on-boarding problem with elasticity constraints. Results show that our approach gives acceptable performance.     

Synthesis,Characterization, Surface Properties and Micellization Behaviour of Imidazolium‐based Ionic Liquids

Ionic liquids (IL) have emerged as a prospective new material for a wide range of industrial applications, owing to their unique structures and properties. Hence, understanding the structure–property relationships of IL is very important for both fundamental and industrial applications. In this study, a series of imidazolium‐based IL with different chain lengths, namely C₈mimBF₄, C₁₀mimBF₄ and C₁₂mimBF₄, were synthesized. Their molecular structures were confirmed by proton nuclear magnetic resonance (¹H NMR) and Fourier transform infrared (FTIR) analysis. Thermal gravimetric analysis (TGA) revealed that the IL synthesized were thermally stable in the desired temperature range. Dynamic light scattering (DLS) results of IL was analyzed in order to understand the effect of alkyl length on micellar size. Conductivity and surface tension measurements were carried out to determine the adsorption and aggregation characteristics in aqueous solutions. The influences of temperature as well as alkyl chain length on viscosity were also investigated. Surface adsorption parameters such as surface excess, minimum area, effectiveness and efficiency were also determined from equilibrium surface tension data. A careful analysis of the thermodynamic aspects of air‐ionic liquid aqueous systems reveals that free energies of adsorption are favoured over micellization for all systems.     

Physiology-based affect recognition for computer-assisted intervention of children with Autism Spectrum Disorder

Generally, an experienced therapist continuously monitors the affective cues of the children with Autism Spectrum Disorders (ASD) and adjusts the course of the intervention accordingly. In this work, we address the problem of how to make the computer-based ASD intervention tools affect-sensitive by designing therapist-like affective models of the children with ASD based on their physiological responses. Two computer-based cognitive tasks are designed to elicit the affective states of liking, anxiety, and engagement that are considered important in autism intervention. A large set of physiological indices are investigated that may correlate with the above affective states of children with ASD. In order to have reliable reference points to link the physiological data to the affective states, the subjective reports of the affective states from a therapist, a parent, and the child himself/herself were collected and analyzed. A support vector machines (SVM)-based affective model yields reliable prediction with approximately 82.9% success when using the therapist's reports. This is the first time, to our knowledge, that the affective states of children with ASD have been experimentally detected via physiology-based affect recognition technique.     

Selectivity-based QSAR approach for screening and evaluation of TRH analogs for TRH-R1 and TRH-R2 receptors subtypes

Design and development of therapeutically useful CNS selective thyrotropin-releasing hormone (TRH) analogs acting on TRH-R2 receptor subtype, exerting weak or no TRH-R1-mediated TSH-releasing side effects has gained imagination of researchers in the recent past. The present study reports the development and implementation of a selectivity-based QSAR approach for screening selective agonists of TRH-R2 receptor subtype. The statistically significant predictive models were thoroughly validated using an external validation set whose activity was previously unknown. The model was able to predict preference for either of the receptor subtypes successfully.     

On the estimation of the consensus rate of convergence in graphs with persistent interconnections

The aim of the current article is to establish myriad convergence rate estimates to consensus for time-varying graphs with persistent interaction. Several novel analysis methodologies for consensus protocols employing the notions of persistence of excitation and Lyapunov functions are provided. The estimates are compared with each other and existing literature. Numerical simulations on test examples are illustrated to support the theoretical findings.     

Design and evaluation of an actuated exoskeleton for examining motor control in stroke thumb

Chronic hand impairment is common following stroke. This paper presents an actuated thumb exoskeleton (ATX) to facilitate research in examining motor control and hand rehabilitation. The ATX presented in this work aims to provide independent bi-directional actuation in each of the 5 degrees of freedom (DOF) of the thumb using a novel flexible shaft-based mechanism that has 5 active DOF and 3 passive DOF. A prototype has been built and experiments have been conducted to measure the allowable workspace at the thumb and evaluate the kinematic and kinetic performance of the ATX. The experimental results show that the ATX is able to provide individual actuation at all five thumb joints with high joint velocity and torque capacities. Further improvement and future work are discussed.     

Energy Efficient Relay Placement in Dual Hop 802.15.4 Networks

IEEE 802.15.4 has emerged as a popular standard for short range wireless sensor networks used in industrial, military, health, and environmental sectors. The limited lifetime of such networks is one of the critical design challenges. This paper examines the how relaying through intermediate sensor nodes can enhance the lifetime of an 802.15.4 network. In particular, novel energy consumption models for both AF and DF relays have been developed. Different relay gain scaling mechanisms and forwarding strategies under each of the relay categories were also considered and their energy efficiencies were compared. For every relaying protocol, it was found that there exists an optimum location where energy saving is maximum and this location is not necessarily different for different modes. In summary, it has been observed that the optimum location for AF relays is the equidistant point from source and destination. In contrast, the optimal location for DF relays is closer to source. The effect of different PHY level (outage probability, path loss) and MAC level parameters (frame length) on the energy efficiency are also studied.     

Nonparametric de‐noising filter optimization using structure‐based microscopic image classification

The Local Polynomial Approximation (LPA) is a nonparametric filter that performs pixel‐wise polynomial fit on a certain neighborhood. This filter can be supported by the Intersection of Confidence Interval rule (ICI) as an adaptation algorithm to identify the most suited neighborhood at which the polynomial assumptions provide superior fit for the observations. However, the LPA‐ICI is considered to be a near‐optimal de‐noising filter. Moreover, the ICI rule has several parameters that affect its performance. The current study applied an optimization algorithm, namely the Particle swarm optimization (PSO) to determine the optimal ICI parameter values for microscopic images de‐noising. As the ICI parameters are image's structure based, bag‐of‐features classifier is used to classify the images based on their structure into different classes. Afterward, a generated optimal ICI parameters' table was created using the LPA‐ICI‐PSO for further direct use without optimization. This table included the optimal ICI parameters based on the image structure. Based on the image category, the generated table can be used to attain the suitable ICI optimal parameters without using PSO. This guarantees less computational time along with the optimal de‐noising compared to the LPA‐ICI as established by the performance metrics. The experimental results established the superiority of the proposed LPA‐ICI‐PSO over the classical LPA‐ICI filter.     

MODELING OF TURBULENT TRANSPORT IN LASER SURFACE ALLOYING

A three-dimensional, transient model is developed in order to address the turbulent transport in a typical laser surface alloying process. Turbulent melt-pool convection is taken into account by using a suitably modified high-Reynolds-number k–? model in the presence of a continuously evolving phase-change interface. The phase-change aspects of the problem are addressed using a modified enthalpy-porosity technique. This newly developed mathematical model is subsequently utilized to simulate a typical high-power laser surface alloying process, where effects of turbulent transport can actually be realized. In order to investigate the effects of turbulence on laser molten pool convection, simulations with laminar and turbulent transport are carried out for some problem parameters. Significant differences in the molten pool morphology are observed on comparing the laminar and turbulent simulation results. It is also revealed that turbulent simulations yield a much better match with the experimentally obtained species concentration distribution within the alloyed layer, compared to that in corresponding laminar simulations.