Deep-Learning Based DOA Estimation in the Presence of Multiplicative Noise

Wireless Personal Communications - The paper addresses the narrowband direction of arrival estimation problem in the presence of multiplicative noise, namely, the local scatterers affect the...     

Modeling of Synergetic Effect of LIX 984N and D2EHPA on Separation of Iron and Zinc Using Artificial Neural Network

Artificial neural network was used to predict the synergetic effect of LIX 984N and D2EHPA on separation of iron from zinc solution. The aim was to predict iron and zinc extraction as a function of pH, temperature, and various organic compositions. Optimum number of hidden layers and nodes in each layer were determined. A multilayer network, with two hidden layers (4:9:5:1) was applied to predict zinc and iron extraction. Effect of pH, temperature, extractant composition, and interaction of them on extraction percent was also investigated using 3D plots. The modeling results showed that there is an excellent agreement between the experimental data and the predicted values.     

Recovery of zinc from an industrial zinc leach residue by solvent extraction using D2EHPA

A hydrometallurgical treatment involving solvent extraction of zinc using di-2-ethylhexyl phosphoric acid (D2EHPA) has been investigated to recover zinc from an industrial leach residue. The residue was leached with sulfuric acid producing leach liquor which was subjected to solvent extraction for enrichment of zinc and removal of impurities. Operating variables, such as pH, D2EHPA concentration, temperature, aqueous/organic (A/O) phase ratio, tri-butyl phosphate (TBP) concentration and sodium sulfate (Na₂SO₄) concentration in aqueous phase were studied. Practically, all zinc was extracted from the aqueous solution at pH 2.5 with 20% w/w D2EHPA in kerosene. Increasing either TBP concentration up to 5%, or Na₂SO₄ concentration up to 0.2 M, increased the zinc extraction. Zinc could be extracted at one theoretical stage at A/O of 1/1, as calculated by McCabe–Thiele method.     

A New Image Mining Approach for Detecting Micro-Calcification in Digital Mammograms

Although mammography is typically the best method to detect breast cancer, it does not recognize 3–20% of the cancer cases. Mammography has established itself as the most efficient technique for detecting tiny cancerous tumor and micro-calcifications are the most difficult to detect since they are very small (0.1–1.0 mm) and they are almost contrasted against the images background. The main purpose of this paper is to provide a new method for the automatic diagnosis of micro-calcification in digital mammograms. It is based on image mining, and the results show 97.35% accuracy, which is improved than the previous works. Tests are based on the standard images data corpus, MIAS. The practical result of this research is registered as an invention in the Patents and Industrial Property Registration Organization numbered as 83119.     

Optimized Reliable Data Combining Cooperative Spectrum Sensing Method in Cognitive Radio Networks

Spectrum sensing is one of the most important tasks of each cognitive radio network. Cooperation among secondary users, by increasing the sensing accuracy can improve the network throughput, but also increases the energy consumption of cognitive radio network. In this paper, we propose the reliable data combining method for cooperative spectrum sensing, according to which the fusion center by using two threshold values, makes the final decision only if it is confident enough in validity of received local data. Otherwise, an additional sensing will be performed. Throughput maximization problems under interference constraints are formulated for both soft and hard fusion schemes and the optimal sensing time and threshold values are obtained. Simulations show that for all SNRs, higher absolute throughput and also higher throughput per energy consumption are accessible, rather than conventional cooperative sensing. Moreover, for a large range of SNRs the less energy is consumed.     

Mechanical properties of SiC-C-B4C composites with different carbon additives produced by pressureless sintering

In this research, carbon sources (Resin Phenolic, Carbon Black and Graphite) and Boron Carbide (B₄C) were used to improve the mechanical properties of Silicon Carbide (SiC) composite. For this purpose, carbon sources of 0.5, 0.75, 1, 1.5, 2, 2.5, 3, and 5 wt% as well as 0.5 wt% B₄C were added to SiC powder, respectively. The sample containing SiC—2.5 wt% Resin Phenolic—0.5 wt% B₄C had the best properties with relative density, hardness, and fracture toughness values of 98.5%, 2820 GPa, and 3.9 MPa.√m, respectively. Examination of SEM images showed that by increasing carbon from 0.5 to 2.5 wt%, the fracture changes from intergranular to transgranular.     

Permeation and modeling studies on Ge(IV) facilitated transport using trioctylamine through supported liquid membrane

Germanium transport from a solution containing tartaric acid by a flat sheet supported liquid membrane (FSSLM) using trioctylamine (TOA) as a carrier and polytetrafluoroethylene (PTFE) as a membrane was investigated. A mass transfer model was developed to monitor the transport process based on experimental results. The effect of parameters such as feed solution pH, TOA concentration, initial germanium concentration, and strip hydrochloric acid concentration on the germanium flux and the transport percentage were studied. A high permeation was observed at a feed solution pH of 3.00, 40%v/v TOA and 5 mg/dm³ Ge⁴⁺. At HCl concentrations of 1–3 mol/dm³, the germanium transport was complete. Finally, based on the mass transfer model, the aqueous and organic resistance values were 11,802 and 860.85 h/cm, respectively. The validity of the model was investigated by fitting the model and experimental data. The correlation coefficient of 0.99 showed the validity of the model.     

Determination of the optimum conditions for the cementation of cadmium with zinc powder in sulfate medium

The orthogonal array design has been used to determine the optimum conditions for the cementation of cadmium from a synthetic zinc sulfate solution and hence to achieve the highest cementation efficiency and the best robustness of the quantitation from the least number of trials in a laboratory scale. Cementation was performed using zinc powder. The orthogonal array L₉ (3⁴) that comprises four parameters at three levels was chosen. The parameters and their levels were as: zinc powder quantity (Z): 1, 1.2 and 1.5 times of stoichiometric quantity of cadmium; time (t): 4, 8 and 12 min; temperature (T): 25, 35 and 45 °C and zinc powder particle size distribution (R): nos. 1–3. The ultimate optimum cementation conditions were found to be Z₁ (equal to stoichiometric quantity of cadmium), T₁ (25 °C), R₃ (no. 3) and t₁ (4 min). Under these conditions, cementation percentages for cadmium and nickel were 95.83% and 6.63%, respectively.