Anomalous behavior detection-based approach for authenticating smart home system users

International Journal of Information Security - This paper presents Duenna, an authentication framework for smart home systems (SHSs). When using controlling apps (e.g., a smartphone app), Duenna...     

Molecular weight influence on shape memory effect of shape memory polymer blend (poly(caprolactone)/styrene-butadiene-styrene)

The shape memory effect (SME) does not only concern the macroscopic structure. It concerns also the polymer structure at morphological, macromolecular, and molecular scales. This effect may depend on different physicochemical properties like morphology heterogeneity, chain rigidity, steric hindrance, chain polarity, free volume, cross-linking or entanglement density, molecular shape and weight, and so on. Hence, finding the relationship between the SME and these properties is very important. This can help to obtain the knowledge about the phenomenon origin and mechanism. One of the basic polymer properties, which can have direct SME, may be the molecular weight (M_w ). The question here is: If the M_w of a shape memory polymer (SMP) changes, for different reasons like degradation, what will be the effect of this change on its SME. In order to answer to this question, the investigation is focused on an SMP blend of 40% poly(ɛ-caprolactone) (PCL) and 60% styrene-butadiene-styrene (SBS). Then, enzymatic hydrolysis is performed on this blend to change its M_w . It is shown that this change is only related to the variation in the M_w of PCL. After that, different samples with a distinct average M_w are prepared and characterized by various experimental methods. Shape memory tests are performed on these blends, and the recovery rate (R_r ) for each of them is determined. It is found that when M_w of PCL decreases, its degree of crystallinity, its glass transition, and its melting temperatures, corresponding to the PCL phase, increase. However, the elongation at break of the blend declines with the reduction in M_w . The tests show that the alteration in the blend's M_w influences its SME. Indeed, R_r of the (PCL/SBS) mixture drops with the decrease in M_w of PCL.     

ECRP: an energy-aware cluster-based routing protocol for wireless sensor networks

Energy conservation is the main issue in wireless sensor networks. Many existing clustering protocols have been proposed to balance the energy consumption and maximize the battery lifetime of sensor nodes. However, these protocols suffer from the excessive overhead due to repetitive clustering resulting in high-energy consumption. In this paper, we propose energy-aware cluster-based routing protocol (ECRP) in which not only the cluster head (CH) role rotates based on energy around all cluster members until the end of network functioning to avoid frequent re-clustering, but also it can adapt the network topology change. Further, ECRP introduces a multi-hop routing algorithm so that the energy consumption is minimized and balanced. As well, a fault-tolerant mechanism is proposed to cope up with the failure of CHs and relay nodes. We perform extensive simulations on the proposed protocol using different network scenarios. The simulation results demonstrate the superiority of ECRP compared with recent and relevant existing protocols in terms of main performance metrics.

     

Feature selection and fault‐severity classification–based machine health assessment methodology for point machine sliding‐chair degradation

In this paper, we propose an offline and online machine health assessment (MHA) methodology composed of feature extraction and selection, segmentation‐based fault severity evaluation, and classification steps. In the offline phase, the best representative feature of degradation is selected by a new filter‐based feature selection approach. The selected feature is further segmented by utilizing the bottom‐up time series segmentation to discriminate machine health states, ie, degradation levels. Then, the health state fault severity is extracted by a proposed segment evaluation approach based on within segment rate‐of‐change (RoC) and coefficient of variation (CV) statistics. To train supervised classifiers, a priori knowledge about the availability of the labeled data set is needed. To overcome this limitation, the health state fault‐severity information is used to label (eg, healthy, minor, medium, and severe) unlabeled raw condition monitoring (CM) data. In the online phase, the fault‐severity classification is carried out by kernel‐based support vector machine (SVM) classifier. Next to SVM, the k‐nearest neighbor (KNN) is also used in comparative analysis on the fault severity classification problem. Supervised classifiers are trained in the offline phase and tested in the online phase. Unlike to traditional supervised approaches, this proposed method does not require any a priori knowledge about the availability of the labeled data set. The proposed methodology is validated on infield point machine sliding‐chair degradation data to illustrate its effectiveness and applicability. The results show that the time series segmentation‐based failure severity detection and SVM‐based classification are promising.     

SupraCells: Living Mammalian Cells Protected within Functional Modular Nanoparticle‐Based Exoskeletons

Creating a synthetic exoskeleton from abiotic materials to protect delicate mammalian cells and impart them with new functionalities could revolutionize fields like cell‐based sensing and create diverse new cellular phenotypes. Herein, the concept of “SupraCells,” which are living mammalian cells encapsulated and protected within functional modular nanoparticle‐based exoskeletons, is introduced. Exoskeletons are generated within seconds through immediate interparticle and cell/particle complexation that abolishes the macropinocytotic and endocytotic nanoparticle internalization pathways that occur without complexation. SupraCell formation is shown to be generalizable to wide classes of nanoparticles and various types of cells. It induces a spore‐like state, wherein cells do not replicate or spread on surfaces but are endowed with extremophile properties, for example, resistance to osmotic stress, reactive oxygen species, pH, and UV exposure, along with abiotic properties like magnetism, conductivity, and multifluorescence. Upon decomplexation cells return to their normal replicative states. SupraCells represent a new class of living hybrid materials with a broad range of functionalities.     

Fast and low complexity frequency domain analysis of nonuniform substrate integrated waveguide‐based structures

A novel, low complexity approach for the analysis of nonuniform lossy substrate‐integrated waveguide transmission lines based on the method of moments is proposed. The approach uses frequency‐dependent basis functions derived from the structure's propagation characteristics. Two tapered structures are analyzed, fabricated, and measured to validate the proposed approach. The analytical results of the proposed approach for both structures are compared to those obtained by measurement and by three‐dimensional field simulation. Excellent agreement is observed between the three sets of results with simulation time savings on more than 98% and memory requirement reduction of more than 97%.     

GPU fuzzy c-means algorithm implementations: performance analysis on medical image segmentation

Multimedia Tools and Applications - Image segmentation in the medical imagery such as MRI, is an essential step to the sensitive analysis of human tissues lesions with the objective to improve the...     

Modular Assembly of Red Blood Cell Superstructures from Metal–Organic Framework Nanoparticle-Based Building Blocks

Bio/artificial hybrid nanosystems based on biological matter and synthetic nanoparticles (NPs) remain a holy grail of materials science. Herein, inspired by the well-defined metal–organic framework (MOF) with diverse chemical diversities, the concept of “armored red blood cells” (armored RBCs) is introduced, which are native RBCs assembled within and protected by a functional exoskeleton of interlinked MOF NPs. Exoskeletons are generated within seconds through MOF NP interlocking based on metal-phenolic coordination and RBC membrane/NP complexation via hydrogen-bonding interactions at the cellular interface. Armored RBC formation is shown to be generalizable to many classes of MOF NPs or any NPs that can be coated by MOF. Moreover, it is found that armored RBCs preserve the original properties of RBCs (such as oxygen carrier capability and good ex ovo/in vivo circulation property) and show enhanced resistance against external stressors (like osmotic pressure, detergent, toxic NPs, and freezing conditions). By modifying the physicochemical properties of MOF NPs, armored RBCs provide the capability for blood nitric oxide sensing or multimodal imaging. The synthesis of armored RBCs is straightforward, reliable, and reversible and hence, represent a new class of hybrid biomaterials with a broad range of functionalities.     

IARP: An Intelligent ACO-Based Routing Protocol with Multiple Mobile Sinks Support for Wireless Sensor Networks

Wireless Personal Communications - Using a single fixed sink in wireless sensor networks (WSNs) creates the hotspot problem. Recently, the mobile sink technique is considered as a good solution to...