Optimization energy consumption with multiple mobile sinks using fuzzy logic in wireless sensor networks

Wireless Networks - Each sensor in WSNs receives data from the limited area under its coverage. The received data is processed by the sensor; then, it is wirelessly transmitted to the sink....     

Carbon Nanotubes and Graphene Powder based Multifunctional Pressure,Displacement and Gradient of Temperature Sensors

Semiconductors - This work presents the fabrication and investigation of the multiwalled carbon nanotubes (MWCNTs) and graphene pristine powders based multifunctional pressure, displacement and...     

Energy provisioning in wireless rechargeable sensor networks with limited knowledge

Internet of Things in many applications depends on Wireless Sensor Networks where the sensors are battery powered. Recent advances in wireless energy transfer and rechargeable batteries provide a new chance for Wireless Rechargeable Sensor Networks when the mobile chargers (MCs) patrol the network field and replenish the power of sensors. We consider multiple MCs and a few charging stations (CSs) in the network. The MCs lose their power too, so they move toward CSs to replenish the energy of themselves. We propose an approach named Limited Knowledge Charging (LKC) where each CS makes a virtual area by using grid cells. Based on the cell’s information, CSs coordinate among themselves to direct MCs in the network. The main design goal of LKC is to prolong the network lifetime, by using many techniques such as balancing the energy of network areas. LKC reduces movements of MCs too as a second goal. LKC is an online approach that adapts itself with situation changes of the network. Many related studies use global knowledge, which is not always satisfied in practice. Instead, LKC is a local knowledge approach. Using exhaustive simulation, the satisfaction of the design goals of LKC is demonstrated.

     

Voxel selection framework based on meta-heuristic search and mutual information for brain decoding

Visual stimulus decoding is an increasingly important challenge in neuroscience. The goal is to classify the activity patterns from the human brain; during the sighting of visual objects. One of the crucial problems in the brain decoder is the selecting informative voxels. We propose a meta-heuristic voxel selection framework for brain decoding. It is composed of four phases: preprocessing of fMRI data; filtering insignificant voxels; postprocessing; and meta-heuristics selection. The main contribution is benefiting a meta-heuristics search algorithm to guide a wrapper voxel selection. The main criterion to nominate a voxel is based on its mutual information with the provided stimulus label. The results show impressive accuracy rates which are 90.66 ± 3.66 and 91.61 ± 8.24 for DS105 and DS107, respectively. This outperforms the most of existing brain decoders in similar validation conditions. The experimental results are very encouraging which can be successfully used in the brain-computer interface.     

Smart Security Framework for Educational Institutions Using Internet of Things (IoT)

Educational institutions are soft targets for the terrorist with massive and defenseless people. In the recent past, numbers of such attacks have been executed around the world. Conducting research, in order to provide a secure environment to the educational institutions is a challenging task. This effort is motivated by recent assaults, made at Army Public School Peshawar, following another attack at Charsada University, Khyber Pukhtun Khwa, Pakistan and also the Santa Fe High School Texas, USA massacre. This study uses the basic technologies of edge computing, cloud computing and IoT to design a smart emergency alarm system framework. IoT is engaged in developing this world smarter, can contribute significantly to design the Smart Security Framework (SSF) for educational institutions. In the emergency situation, all the command and control centres must be informed within seconds to halt or minimize the loss. In this article, the SSF is proposed. This framework works on three layers. The first layer is the sensors and smart devices layer. All these sensors and smart devices are connected to the Emergency Control Room (ECR), which is the second layer of the proposed framework. The second layer uses edge computing technologies to process massive data and information locally. The third layer uses cloud computing techniques to transmit and process data and information to different command and control centres. The proposed system was tested on Cisco Packet Tracer 7. The result shows that this approach can play an efficient role in security alert, not only in the educational institutions but also in other organizations too.     

Structural,Electrical, and Magnetic Properties of Ferrite–Polymer Composites

Six samples consisting of polypyrrole (PPy), pure NiFe₂O₄ ferrite, and their composites at ratio of 1:4 (PF1), 1:2 (PF2), 3:4 (PF3), and 1:1 (PF4) have been prepared by a coprecipitation method and their structural, electrical, dielectric, and magnetic properties measured experimentally. The direct-current (DC) resistivity was enhanced with increasing ferrite content, while the dielectric constant and loss decreased. The magnetic properties of all the composites and the ferrite revealed a narrow loop, confirming their soft nature. The saturation magnetization, remanence, and coercivity values increased with increasing ferrite content. The obtained parameter values suggest that such materials might be suitable for use in high-frequency applications.     

Robust impedance control of robot manipulators using differential equations as universal approximator

This paper presents a robust impedance controller for robot manipulators using function approximation techniques (FATs). Recently, some FAT-based robust impedance control approaches have been presented using Fourier series expansion or Legendre polynomials for uncertainty estimation. However, the dimensions of regressor matrices in these approaches are relatively large. This problem becomes hypersensitive especially for higher degree of freedom robot manipulators. In this paper, a simpler and less computational FAT-based robust controller is presented without considering discontinuous nonlinearities. It is assumed that the lumped uncertainty can be modelled by a linear differential equation with unknown coefficients. Then, using the Stone–Weierstrass theorem, it is verified that these differential equations are universal approximators. The advantage of the proposed controller in comparison with previous related works is reducing the dimensions of regressor matrices. Simulation results on a Puma560 robot manipulator indicate the efficiency of the proposed method.     

MHD flow of radiative micropolar nanofluid in a porous channel: optimal and numerical solutions

The flow of a radiative and electrically conducting micropolar nanofluid inside a porous channel is investigated. After implementing the similarity transformations, the partial differential equations representing the radiative flow are reduced to a system of ordinary differential equations. The subsequent equations are solved by making use of a well-known analytical method called homotopy analysis method (HAM). The expressions concerning the velocity, microrotation, temperature, and nanoparticle concentration profiles are obtained. The radiation tends to drop the temperature profile for the fluid. The formulation for local Nusselt and Sherwood numbers is also presented. Tabular and graphical results highlighting the effects of different physical parameters are presented. Rate of heat transfer at the lower wall is seen to be increasing with higher values of the radiation parameter while a drop in heat transfer rate at the upper wall is observed. Same problem has been solved by implementing the numerical procedure called the Runge–Kutta method. A comparison between the HAM, numerical and already existing results has also been made.

     

Quality assessment of used-products under uncertain age and usage conditions

Quality of a used-product is often highly uncertain, impacts pricing decisions, and is influenced by many factors like age and usage. However, analysis of usage and its uncertain nature is not well understood. In this paper, joint effects of age and usage on quality of used/end-of-life products are analysed. To conduct this research, Product State Transformation Diagram is proposed and used to analyse quality of used-products under uncertain age and usage conditions. Subsequently, governing multivariate stochastic partial differential equation is derived and paired with operational conditions to develop Quality Degradation Model (QDM). Then, QDM is numerically analysed and compared with common age-based model. Results indicate that age-based models overestimate quality. Using one-factor-at-time approach, sensitivity of QDM to its parameters and inputs is analysed. In a real-life case study, QDM is applied to evaluate prices of used-cars. To validate QDM and to generate insights towards its accuracy, a pool of popular statistical and machine learning models are trained and compared with QDM. Results show that performance of QDM is comparable to known models like SVM, LR, ANN-MLP for this application.     

Moving from Resource Development to Resource Management: Problems,Prospects and Policy Recommendations for Sustainable Groundwater Management in Bangladesh

Water Resources Management - Increased groundwater accessibility resulting from the expansion of deep and shallow tube wells helped Bangladesh attain near self-sufficiency in rice, with national...