A Model for Low Power,High Speed and Energy Efficient Early Landslide Detection System Using IoT

A landslide, also called as landslip appear to be the most threatening disaster of all time especially in the hillside regions. It involves enormous surface movements including debris flows, slopes failure, rock fall etc. It mainly occurs when the land slopes become unstable. Other measures that also lead to landslides are Ground water changes, Earthquakes, Floods, Volcano eruptions and Heavy rainfalls. People among these hill areas doesn’t know about the disaster that’s about to happen massively. The perfect way to avoid such hazards is by predicting it at initial phase with maximum accuracy. There are many wired and wireless supervising systems available to detect landslides which require higher cost and maintenance. But we have a suitable solution for this with Internet of Things (IoT) based approach. It improves objects control and detection remotely between various networks thereby creating possibilities for direct communication between physical and computer-based world. By this approach Landslides can be predicted at the initial phase. If there is a higher chance of Landslide then an alert will be sent to disaster management sector immediately to take necessary precautions so that enormous precious lives can be protected. This paper proposes a model for IoT based Landslide detection mechanisms in detail.

     

Pattern Analysis and Regressive Linear Measure for Botnet Detection

Capturing the distributed platform with remotely controlled compromised machines using botnet is extensively analyzed by various researchers. However, certain limitations need to be addressed efficiently. The provisioning of detection mechanism with learning approaches provides a better solution more broadly by saluting multi-objective constraints. The bots’ patterns or features over the network have to be analyzed in both linear and non-linear manner. The linear and non-linear features are composed of high-level and low-level features. The collected features are maintained over the Bag of Features (BoF) where the most influencing features are collected and provided into the classifier model. Here, the linearity and non-linearity of the threat are evaluated with Support Vector Machine (SVM). Next, with the collected BoF, the redundant features are eliminated as it triggers overhead towards the predictor model. Finally, a novel Incoming data Redundancy Elimination-based learning model (RedE-L) is built to classify the network features to provide robustness towards BotNets detection. The simulation is carried out in MATLAB environment, and the evaluation of proposed RedE-L model is performed with various online accessible network traffic dataset (benchmark dataset). The proposed model intends to show better trade-off compared to the existing approaches like conventional SVM, C4.5, RepTree and so on. Here, various metrics like Accuracy, detection rate, Mathews Correlation Coefficient (MCC), and some other statistical analysis are performed to show the proposed RedE-L model's reliability. The F1-measure is 99.98%, precision is 99.93%, Accuracy is 99.84%, TPR is 99.92%, TNR is 99.94%, FNR is 0.06 and FPR is 0.06 respectively.     

Enhanced dielectric properties and energy storage density of surface engineered BCZT/PVDF-HFP nanodielectrics

Polymer nanocomposites have proved to be promising energy storage devices for modern power electronic systems. In this work we have studied the dielectric properties and dielectric energy storage densities of 0–3 type BCZT/PVDF-HFP polymer nanocomposites with different filler volume concentrations. BCZT nanopowder was synthesized by solgel method through citrate precursor method. The structural and morphological features of the BCZT nanopowder were examined by X-ray diffraction and transmission electron microscopy. For better polymer ceramic interface coupling, BCZT was surface functionalized with extended aromatic ligand, naphthyl phosphate (NPh). The surface functionalization was validated and quantified by thermogravimetric analysis and X-ray photoelectron spectroscopy. The dielectric constant of surface passivated BCZT nanoparticles was estimated to be ~?155 using slurry technique, while the dielectric permittivity of pristine BCZT nanopowder could not be assessed due to high innate surface conductivity. BCZT/PVDF-HFP polymer nanocomposite thin films were fabricated using solution casting technique. The dispersion quality of the ceramic fillers in the polymer matrix was examined by scanning electron microscopy. Due to better polymer ceramic interface, At 5 vol% filler concentration, NPh modified nanoBCZT/PVDF-HFP films showed enhanced dielectric breakdown strength and energy storage density than untreated nanoBCZT/PVDF-HFP and even pure polymer films. Maximum energy storage density of 8.5 J cm^?3 was obtained at an optimum filler concentration of 10 vol% for surface functionalized BCZT/PVDF-HFP composite films of 10 μm thickness.     

A novel cascade encryption algorithm for digital images based on anti-synchronized fractional order dynamical systems

In this paper, an active control technique is employed for anti-synchronization between two identical fractional order reverse butterfly-shaped hyperchaotic systems. We have shown that the convergence rate of anti-synchronization error is very faster by increasing the value of an active controller gain. A new algorithm for image encryption and decryption is introduced and established by anti-synchronized fractional order dynamical systems. Experimental results show that the proposed encryption algorithm has high level security against various attacks. Further, it confirms that the new algorithm is more efficient compared to other existing algorithms.     

Selective MOCVD growth of ZnO nanotips

ZnO is a wide bandgap semiconductor with a direct bandgap of 3.32eV at room temperature. It is a candidate material for ultraviolet LED and laser. ZnO has an exciton binding energy of 60 meV, much higher than that of GaN. It is found to be significantly more radiation hard than Si, GaAs, and GaN, which is critical against wearing out during field emission. Furthermore, ZnO can also be made as transparent and highly conductive, or piezoelectric. ZnO nanotips can be grown at relatively low temperatures, giving ZnO a unique advantage over the other nanostructures of wide bandgap semiconductors, such as GaN and SiC. In the present work, we report the selective growth of ZnO nanotips on various substrates using metalorganic chemical vapor deposition. ZnO nanotips grown on various substrates are single crystalline, n-type conductive and show good optical properties. The average size of the base of the nanotips is 40 nm. The room temperature photoluminescence peak is very intense and sharp with a full-width-half-maximum of 120 meV. These nanotips have potential applications in field emission devices, near-field microscopy, and UV photonics.     

Biomimetic mineralization of novel silane crosslinked collagen

Studies on the mechanisms of mineralization of connective tissues, have gained momentum in the recent past. In the present study, Biomimetic mineralization of modified fish scale collagen in vitro is reported. The fish scale collagen was crosslinked with 3-Aminopropyl triethoxysilane and the crosslinked collagen (FCSi) was characterized using conventional methods. The biomimetic mineralization capacity of FCSi was evaluated in SBF solution for 7 days. Formation of hydroxyapatite crystals on the matrix in vitro has been confirmed by FTIR, XRD and SEM-EDAX techniques. The FCSi may be used as a scaffold in bone tissue engineering and as an osteoinductive material in experimental animal models before applying clinically.     

Performance investigations of a single-stage metal hydride heat pump

This paper discusses the performance investigations of a single-stage metal hydride heat pump (SS-MHHP) working with five different alloy pairs, namely, MmNi_4.6Al_0.4/MmNi_4.6Fe_0.4, LaNi_4.61Mn_0.26Al_0.13/La_0.6Y_0.4Ni_4.8Mn_0.2, LmNi_4.91Sn_0.15/Ti_0.99Zr_0.01V_0.43Fe_0.09Cr_0.05Mn_1.5, LaNi_4.6Al_0.4/MmNi_4.15Fe_0.85 and Zr_0.9Ti_0.1Cr_0.9Fe_1.1/Zr_0.9Ti_0.1Cr_0.6Fe_1.4. The performance of the system is predicted by solving the unsteady, two-dimensional coupled heat and mass transfer processes in metal hydride bed of cylindrical configuration using a fully implicit finite volume method. The influences of operating temperatures such as heat source (T_H), heat sink (T_M) and refrigeration (T_C) temperatures on the coefficient of performance (COP) and specific cooling power (SCP) of the system are presented. The predicted hydride bed temperature profiles are compared with the experimental data reported in the literature and a reasonably good agreement is observed between them. The optimum operating temperature ranges of each pair of alloys are suggested. For the selected operating temperatures, a maximum COP of 0.66 is predicted for Zr_0.9Ti_0.1Cr_0.9Fe_1.1/Zr_0.9Ti_0.1Cr_0.6Fe_1.4 hydride pair, while LmNi_4.91Sn_0.15/Ti_0.99Zr_0.01V_0.43Fe_0.09Cr_0.05Mn_1.5 hydride pair produces the highest SCP of 53.25 W/kg of total mass of the system.     

Optimal Cluster Based Routing Technique for Wireless Sensor Networks using Hybrid Optimization Algorithm for Maximizing Life of Sensors

Wireless Sensor Network (WSN) has many sensor nodes that connect with sync nodes. The sensor node's power is a limitation. The expense and difficulty of battery charging and replacement affect sensor node life and network length. Clustering reduces the cost of internal cluster communication, thereby conserving energy. Generally, researchers seek for low energy usage via providing data to monitor the cluster's energy use. Many of them are tied to network length. The Ant Group (TAS) technique is the first notion for establishing a cluster using the OC algorithm that saves electricity. Next, we use improved myopia (IM) to find the cluster head (CH). This minimises the number of clusters and the expense of internal communications. The proposed OC-TAS-IM algorithm attempts to enhance energy efficiency. In the network. The route is also conducted using a special algorithm in the low energy adaptive cluster range (reach). It contains Network Simulator implementation and simulation experiments to test specific OC-TAS-IM algorithms (NS2). Because of optimum clustering, the OC-TAS-IM method is stable in terms of energy clustering and grid lifespan.