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...     

Uplift capacity of horizontal anchor plate in geocell reinforced sand

The paper investigates the uplift performance of horizontal anchor plate in geocell reinforced sand through a series of model tests. It is noted that the unreinforced anchor plate undergoes a clear failure at a displacement of about 3% of its width, whereas with the provision of geocell and a layer of geotextile right below the geocell mattress significantly increases the uplift capacity by about 4.5 times higher than that of unreinforced sand and could sustain anchor displacement of more than 60%. Results indicates that the geocell mattress by virtue of its rigidity distributes the uplift load in the lateral directions to a larger area, thereby reducing the stress in the overlying soil mass and hence increases the performance of anchor plate system. The provision of the additional geotextile layer right below the geocell mattress is found to be very effective in increasing the stiffness as well as load carrying capacity of anchor plate system. The optimum size (i.e., width and length) of geocell mattress giving adequate load carrying capacity of anchor plate is found to be 5.4 times of anchor width (5.4B). The comparison of model tests results with 3D numerical analysis shows good agreement, indicating that the proposed model is able to capture the uplift load-displacement behaviour of geocell reinforced anchor plate system.     

Proton Nuclear Magnetic Resonance-Based Method for the Quantification of Epoxidized Methyl Oleate

Epoxidized methyl esters (EMO) with their high oxirane ring reactivity, acts as a raw material in the synthesis of various industrial chemicals including polymers, stabilizers, plasticizers, glycols, polyols, carbonyl compounds, biolubricants etc. EMO has been generally quantified by the gas chromatography (GC) and high-performance liquid chromatography (HPLC) techniques. Taking into the account of the limitations of these techniques, two qHNMR-based equations have been proposed for the quantification of EMO in the mixture of EMO and methylesters (MO). The validity of the proposed method was determined using standard mixtures of MO and EMO having different molar concentrations. The developed equations have been applied on the samples of EMO prepared from oleic acid in two-step process viz., esterification followed by epoxidation. The qHNMR-based EMO quantification showed acceptable agreement with the results obtained from HPLC analysis.     

Depicting the role of gas-solid interactions on the hydrodynamics of converging pneumatic riser

The understanding of the flow characteristics and effect of gas-solid interactions in pneumatic risers is fundamental to investigate to ensure effective design cost-effective operation. Thus, to understand the effect of gas-solid interactions on the hydrodynamics of newly proposed conversing risers, this study mainly focused on predicting pressure drop in the dilute phase pneumatic conveying system. The experiments were conducted in a converging riser having a convergence angle of 0.2693°. Various solid particles such as sago, black mustard, and alumina have been considered to study the effect of particle sizes and density on the pressure drop. The experimental outcomes indicate that the total pressure drop increases with an increase in the solid density and gas mass flow rate. Moreover, smaller particle sizes are also increased the pressure drop. An empirical correlation is developed for the prediction of total pressure drop ΔP_T in converging pneumatic riser via dimensional analysis. All dependent variables such as particle and air density, drag force, acceleration due to gravity, the mass flow rate of air and particle, the diameter of particle and converging riser, the height of converging riser were considered to develop the empirical correlation. The established relationship is tested, and experimental data have been fitted for its validation. The estimated relative error of less than 0.05 proved the significance of the developed correlation. Hence, it can be stated that the established relationship is useful in studying the effects of various parameters on the pressure drop across the length of the conversing riser.     

Performance evaluation of breast lesion detection systems with expert delineations: a comparative investigation on mammographic images

Multimedia Tools and Applications - Performance of computerized diagnostic systems yearning to be approved by medical regulatory bodies must meet the expectations of human experts. Highly accurate...     

Contrast enhancement of MRI images using morphological transforms and PSO

Medical imaging plays a crucial role in correct extraction of the significant information for monitoring the patient’s health and providing the quality treatment. A deluge of medical images requires initial interpretation for the presence of any abnormality, however, the correct diagnosis requires the images to be of good quality. To cope with the problem of poor contrast in medical images, this paper presents a method based on morphological transforms to improve the quality of the images. The proposed method incorporates Particle Swarm Optimization to find an optimum value of a parameter which controls the enhancement of the resulting image. The proposed algorithm is executed on a set of MRI images for testing its efficacy. The experimental results are compared in terms of both qualitative and quantitative parameters. The mean opinion score is obtained with the help of experts, which clearly shows the better performance of the proposed method. Furthermore, the parameters like Contrast Improvement Ratio, signal-to-noise ratio, peak signal-to-noise ratio, PL, and Structural Similarity Index are evident of better performance of proposed method when compared with the state-of-the-art methods and few recent methods. The comparison shows that the performance of the proposed method based on morphological transforms incorporating Particle Swarm Optimization is better not only visually but also in terms of other evaluation parameters.

     

Energy management system for smart grid: An overview and key issues

Energy crisis and the global impetus to “go green” have encouraged the integration of renewable energy resources, plug-in electric vehicles, and energy storage systems to the grid. The presence of more than one energy source in the grid necessitates the need for an efficient energy management system to guide the flow of energy. Moreover, the variability and volatile nature of renewable energy sources, uncertainties associated with plug-in electric vehicles, the electricity price, and the time-varying load bring new challenges to the power engineers to achieve demand-supply balance for stable operation of the power system. The energy management system can effectively coordinate the energy sharing/trading among all available energy resources, and supply loads economically in all the conditions for the reliable, secure, and efficient operation of the power system. This paper reviews the framework, objectives, architecture, benefits, and challenges of the energy management system with a comprehensive analysis of different stakeholders and participants involved in it. The review paper gives a critical analysis of the distributed energy resources behavior and different programs such as demand response, demand-side management, and power quality management implemented in the energy management system. Different uncertainty quantification methods are also summarized. This review paper also presents a comparative and critical analysis of the main optimization techniques used to achieve different energy management system objectives while satisfying multiple constraints. Thus, the review offers numerous recommendations for research and development of the cutting-edge optimized energy management system applicable for homes, buildings, industries, electric vehicles, and the whole community.     

Modeling and simulation of temperature drift for ISFET-based pH sensor and its compensation through machine learning techniques

The paper presents modeling and simulation of ion-sensitive field-effect transistor (ISFET)-based pH sensor with temperature-dependent behavioral macromodel and proposes to compensate the temperature drift in the sensor using intelligent machine learning (ML) models. The macromodel is built using SPICE by introducing electrochemical parameters in a metal-oxide-semiconductor field-effect transistor (MOSFET) model to simulate ISFET characteristics. We account for the temperature dependence of electrochemical and semiconductor parameters in our macromodel to increase its robustness. The macromodel is then exported as a subcircuit element, which is used to design the readout interface circuit. A simple constant-voltage, constant-current (CVCC) topology is utilized to generate the data for temperature drift in ISFET pH sensor, which is used to train and test state-of-the-art ML-based regression models in order to compensate the drift behavior. The experimental results demonstrate that the random forest (RF) technique achieves the best performance with very high correlation and low error rate. Corresponding curves for output signal using the trained models show highly temperature-independent characteristics when tested for pH 2, 4, 7, 10, and 12, and we obtained a root mean squared error (RMS) variation of ΔpH ≤ 0.024 over a temperature range of 15°C to 55°C in comparison with ΔpH ≤ 1.346 for uncompensated output signal. This work establishes the framework for integration of ML techniques for drift compensation of ISFET chemical sensor to improve its performance.     

Functionalization of Graphene Oxide Films with Au and MoOx Nanoparticles as Efficient p‐Contact Electrodes for Inverted Planar Perovskite Solar Cells

A graphene oxide (GO) film is functionalized with metal (Au) and metal‐oxide (MoO_x) nanoparticles (NPs) as a hole‐extraction layer for high‐performance inverted planar‐heterojunction perovskite solar cells (PSCs). These NPs can increase the work function of GO, which is confirmed with X‐ray photoelectron spectra, Kelvin probe force microscopy, and ultraviolet photoelectron spectra measurements. The down‐shifts of work functions lead to a decreased level of potential energy and hence increased V_oc of the PSC devices. Although the GO‐AuNP film shows rapid hole extraction and increased V_oc, a J_sc improvement is not observed because of localization of the extracted holes inside the AuNP that leads to rapid charge recombination, which is confirmed with transient photoelectric measurements. The power conversion efficiency (PCE) of the GO‐AuNP device attains 14.6%, which is comparable with that of the GO‐based device (14.4%). In contrast, the rapid hole extraction from perovskite to the GO‐MoO_x layer does not cause trapping of holes and delocalization of holes in the GO film accelerates rapid charge transfer to the indium tin oxide substrate; charge recombination in the perovskite/GO‐MoO_x interface is hence significantly retarded. The GO‐MoO_x device consequently shows significantly enhanced V_oc and J_sc, for which its device performance attains PCE of 16.7% with great reproducibility and enduring stability.