A bi-objective task scheduling approach in fog computing using hybrid fireworks algorithm

The Journal of Supercomputing - With the rapid increase in the functionality of IoT applications, the services provided by edge/IoT devices have surged in the recent past. Fog computing is gaining...     

Performance Analysis of a Cognitive Radio Network Under Energy Harvesting from Primary User Emulation Attacker

Wireless Personal Communications - The paper reports the performance of an energy harvesting cognitive radio network under primary user emulation (PUE) attack. A secondary user (SU) can harvest...     

Deep learning approach for investigation of temporal radio frequency signatures of drones

The omnipresence of drones in the civilian air space has led to their malicious usage raising high alert security issues. In this paper, a deep learning approach to detect and identify drones and to determine their flight modes from the remotely sensed radio frequency (RF) signatures is presented. This work intends to detect the presence of drones using two-class classification, the presence along with identification of their make using four-class classification. And this is further extended to the determination of their flight modes using ten-class classification. It employs the proposed architectures of prominent deep learning classifiers, namely, autoencoder (AE), long short-term memory (LSTM), convolutional neural network (CNN), and CNN-LSTM hybrid model. To procure the relevant information from 227 RF signatures having 100 fragments each, the seven significant temporal statistical features, namely, maxima, minima, mean, variance, skewness, kurtosis, and root mean square, are extracted. In a two-class classification scenario, all considered classifiers perform near to idle, whereas in a four-class classification scenario, CNN performs best, followed by AE, CNN-LSTM, and LSTM, respectively. Moreover, in a ten-class classification scenario, AE far outperforms CNN, followed by LSTM and CNN-LSTM, respectively. The best performance in terms of accuracy and classification time confirms the feasibility of the proposed AE classifier for the three considered drone operations.     

Experimental investigations on silicon carbide mixed electric discharge machining

Silicon - In this study, silicon carbide mixed electrical discharge machining (SCMEDM) process has been developed and later on modelled also using an artificial neural network (ANN) based technique...     

Nanograins dependent dielectric constant, tunability, phase transition, impedance spectroscopy and leakage current of (Pb1 − xSrx)TiO3 thin films

Nanostructured (Pb_1 − xSr_x)TiO₃ (PST) (x = 0.1, 0.2 and 0.3) thin films have been prepared by chemical solution deposition process using spin coating technique. The solution as such was deposited on Pt/Ti/SiO₂/Si substrates and annealed at 650 °C/3h. Nanograins dependent dielectric properties of PST films show dielectric constant up to the higher frequency region, low losses, large tunability and phase transition at small temperature. The impedance data has been fitted by Cole-Cole model to study the effect of grain boundaries on the dielectric properties. The current-voltage characteristics have been measured to study leakage current in PST films and described by Poole-Frenkel emission model. It is suggested that the key carrier transport process in PST films is emission of electrons from a trap state near the metal-film interface into a continuum of states associated with each conductive dislocation. The activation energy value for carrier transport in PST films is obtained from temperature-dependent current-voltage characteristics.     

Facial expression recognition using modified Viola-John’s algorithm and KNN classifier

Multimedia Tools and Applications - In the way of communication, facial expression act as non-verbal communication and play an important role in social interaction by providing some contextual...     

Robust object tracking with crow search optimized multi-cue particle filter

Pattern Analysis and Applications - Particle filter is used extensively for estimation of target nonlinear and non-Gaussian state. However, its performance suffers due to its inherent problem of...     

Preparation,characterization and dielectric behaviour of some yttrium doped strontium stannates

Samples of Sr_{(1− 3x/2)}YxSnO₃ are prepared by usual solid state reaction route. X- ray diffraction studies confirm the formation of single cubic perovskite single phase. The dielectric constant and dielectric loss at 1 kHz were measured in the temperature range from room temperature up to -150†C. The dielectric constant decreases and losses increase with increased yttrium content in the samples. The percentage porosity and unit cell parameters are also calculated for the samples.     

Heat Transfer and Friction Characteristics of Perforated Fin with a Longitudinal Slot under Forced Convection

Extended surfaces are used in a variety of heat transfer applications owing to their ability in reducing the convection resistance by exposing a large surface area to the surrounding fluid. Surface modification in the form of perforations is a passive method of increasing the heat transfer rates with the additional benefit of weight reduction. This work deals with numerical investigation of heat transfer and friction from a perforated fin (with and without slot) subjected to forced convection. The perforated fin with slot has been found to have a maximum enhancement in heat transfer with the simultaneous increase in frictional losses versus that of a solid fin. Further, the perforated fin without slot has been able to transfer heat at a relatively higher rate with a considerable reduction in energy loss due to friction in comparison to a solid fin.     

High-pressure release and dispersion of hydrogen in a partially enclosed compartment: Effect of natural and forced ventilation

The study of compressed hydrogen releases from high-pressure storage systems has practical application for hydrogen and fuel cell technologies. Such releases may occur either due to accidental damage to a storage tank, connecting piping, or due to failure of a pressure release device (PRD). Understanding hydrogen behavior during and after the unintended release from a high-pressure storage device is important for development of appropriate hydrogen safety codes and standards and for the evaluation of risk mitigation requirements and technologies. In this paper, the natural and forced mixing and dispersion of hydrogen released from a high-pressure tank into a partially enclosed compartment is investigated using analytical models. Simple models are developed to estimate the volumetric flow rate through a choked nozzle of a high-pressure tank. The hydrogen released in the compartment is vented through buoyancy induced flow or through forced ventilation. The model is useful in understanding the important physical processes involved during the release and dispersion of hydrogen from a high-pressure tank into a compartment with vents at multiple levels. Parametric studies are presented to identify the relative importance of various parameters such as diameter of the release port and air changes per hour (ACH) characteristic of the enclosure. Compartment overpressure as a function of the size of the release port is predicted. Conditions that can lead to major damage of the compartment due to overpressure are identified. Results of the analytical model indicate that the fastest way to reduce flammable levels of hydrogen concentration in a compartment is by blowing through the vents. Model predictions for forced ventilation are presented which show that it is feasible to effectively and rapidly reduce the flammable concentration of hydrogen in the compartment following the release of hydrogen from a high-pressure tank.