Simple Floating Voltage-Controlled Memductor Emulator for Analog Applications

The topic of memristive circuits is a novel topic in circuit theory that has become of great importance due to its unique behavior which is useful in different applications. But since there is a lack of memristor samples, a memristor emulator is used instead of a solid state memristor. In this paper, a new simple floating voltage-controlled memductor emulator is introduced which is implemented using commercial off the shelf (COTS) realization. The mathematical modeling of the proposed circuit is derived to match the theoretical model. The proposed circuit is tested experimentally using different excitation signals such as sinusoidal, square, and triangular waves showing an excellent matching with previously reported simulations

Experimental and computational study of multiphase flow in dry powder inhalers

Dry Powder Inhalers (DPIs) have great potential in pulmonary drug delivery; the granular powder, used as active ingredient in DPIs, is ozone friendly and the operation of DPIs ensures coordination between dose release and patient inhalation. However, the powder fluidisation mechanisms are poorly understood which leads to low efficiency of DPIs with 10-35 % of the dose reaching the site of action. The main aim of this thesis is to study the hydrodynamics of powder fluidisation in DPIs, using experimental and computational approaches. An experimental test rig was developed to replicate the process of transient powder fluidisation in an impinging air jet configuration. The powder fluidisation chamber was scaled up resulting in a two dimensional particle flow prototype, which encloses 3.85 mm glass beads. Using optical image processing techniques, individual particles were detected and tracked throughout the experimental time and domain. By varying the air flow rate to the test section, two particle fluidisation regimes were studied. In the first fluidisation regime, the particle bed was fully fluidised in less than 0.25 s due to the strong air jet. Particle velocity vectors showed strong convective flow with no evidence of diffusive motion triggered by inter-particle collisions. In the second fluidisation regime, the particle flow experienced two stages. The first stage showed strong convective flow similar to the first fluidisation regime, while the second stage showed more complex particle flow with collisional and convective flow taking place on the same time and length scales. The continuum Two Fluid Model (TFM) was used to solve the governing equations of the coupled granular and gas phases for the same experimental conditions. Sub-models for particle-gas and particle-particle interactions were used to complete the model description. Inter-particle interactions were resolved using models based on the kinetic theory of granular flow for the rapid flow regime and models based on soil mechanics for the frictional regime. Numerical predictions of the first fluidisation regime showed that the model should incorporate particle-wall friction and minimise diffusion, simultaneously. Ignoring friction resulted in fluidisation timing mismatch, while increasing the diffusion resulted in homogenous particle fluidisation in contrast to the aggregative convective fluidisation noticed in the experiments. Numerical predictions of the second fluidisation regime agreed well with the experiments for the convection dominated first stage of flow up to 0.3 s. However, later stages of complex particle flow showed qualitative discrepancies between the experimental and the computational approaches suggesting that current continuum granular models need further development. The findings of the present thesis have contributed towards better understanding of the mechanics of particle fluidisation and dense multiphase flow in DPI in particular, and particle bed fluidisation using impinging air jet in general. The use of TFM for predicting high speed convective granular flows, such as those in DPIs, is promising. Further studies are needed to investigate the form of particle-particle interactions within continuum granular flow models

Power Dissipation of Memristor-Based Relaxation Oscillators

Recently, many reactance-less memristive relaxation oscillators were introduced, where the charging and discharging processes depend on memristors. In this paper, we investigate the power dissipation in different memristor based relaxation oscillators. General expressions for these memristive circuits as well as the power dissipation formulas for three different topologies are derived analytically. In addition, general expressions for the maximum and minimum power dissipation are calculated. Finally, the calculated expressions are verified using PSPICE simulations showing very good matching

Detection of Lying Electrical Vehicles in Charging Coordination Application Using Deep Learning

The simultaneous charging of many electric vehicles (EVs) stresses the distribution system and may cause grid instability in severe cases. The best way to avoid this problem is by charging coordination. The idea is that the EVs should report data (such as state-of-charge (SoC) of the battery) to run a mechanism to prioritize the charging requests and select the EVs that should charge during this time slot and defer other requests to future time slots. However, EVs may lie and send false data to receive high charging priority illegally. In this paper, we first study this attack to evaluate the gains of the lying EVs and how their behavior impacts the honest EVs and the performance of charging coordination mechanism. Our evaluations indicate that lying EVs have a greater chance to get charged comparing to honest EVs and they degrade the performance of the charging coordination mechanism. Then, an anomaly based detector that is using deep neural networks (DNN) is devised to identify the lying EVs. To do that, we first create an honest dataset for charging coordination application using real driving traces and information revealed by EV manufacturers, and then we also propose a number of attacks to create malicious data. We trained and evaluated two models, which are the multi-layer perceptron (MLP) and the gated recurrent unit (GRU) using this dataset and the GRU detector gives better results. Our evaluations indicate that our detector can detect lying EVs with high accuracy and low false positive rate

Biochemical and mineral compositions of six brown seaweeds collected from Red Sea at Hurghada Coast

484-491The biochemical and mineral compositions of six brown seaweeds collected during spring season from Red Sea at Hurghada coast were determined to evaluate their significance in animal nutrition. The results suggested that the highest ash and protein contents were recorded in Padina gymnospora (45.48%) and Sargassum muticum (5.31%), respectively. The carbohydrates content was > 24.0% in the studied seaweeds, while the fat content ranged from 0.11 to 0.27%, and the essential amino acids formed 38.13-42.34% of the total amino acids. Both Padina gymnospora and Turbinaria sp. had the majority of measured phenolic compounds. Also, the studied seaweed species were rich in vitamin B2 (> 105.0 ppm), except Sargassum aspirofolium. The highest/lowest vitamin C content existed in Sargassum muticum (11.76 ppm) and Sargassum aspirofolium (3.96 ppm), respectively. Moreover, the tested seaweeds exhibited high amounts of essential minerals. Therefore, Red Sea brown seaweeds are good food sources for animal nutrition