Effect of sodium doping on graded Cu(In1−xGax)Se2 thin films prepared by chemical spray pyrolysis

In the present work we have studied the effect of Na on the properties of graded Cu(In_1−xGa_x)Se₂ (CIGS) layer. Graded CIGS structures were prepared by chemical spray pyrolysis at a substrate temperature of 350 °C on soda lime glass. Sodium chloride is used as a dopant along with metal (Cu/In/Ga) chlorides and n, n-dimethyl selenourea precursors. The addition of Na exhibited better crystallinity with chalcopyrite phase and an improvement in preferential orientation along the (112) plane. Energy dispersive analysis of X-rays (line/point mapping) revealed a graded nature of the film and percentage incorporation of Na (0.86 at%). Raman studies showed that the film without sodium doping consists of mixed phase of chalcopyrite and CuAu ordering. Influence of sodium showed a remarkable decrease in electrical resistivity (0.49–0.087 Ω cm) as well as an increase in carrier concentration (3.0×10¹⁸–2.5×10¹⁹ cm⁻³) compared to the un-doped films. As carrier concentration increased after sodium doping, the band gap shifted from 1.32 eV to 1.20 eV. Activation energies for un-doped and Na doped films from modified Arrhenius plot were calculated to be 0.49 eV and 0.20 eV, respectively. Extremely short carrier lifetimes in the CIGS thin films were measured by a novel, non-destructive, noncontact method (transmission modulated photoconductive decay). Minority carrier lifetimes of graded CIGS layers without and with external Na doping are found to be 3.0 and 5.6 ns, respectively.     

燃煤锅炉除尘专用滤料的开发

介绍机械制造厂燃煤锅炉的烟尘特点,分析滤料失效的原因,提出一套针对该工况的滤料解决方案。介绍针对复杂工况条件所选用的纤维种类以及复合面层原料成分配比的确定,最终选用针刺工艺加工并对该新产品的基本性能进行了测试分析。     

Crystallization and spectroscopic properties in Er3+ doped oxyfluorogermanate glass ceramics containing Na

The Er³⁺ doped oxyfluorogermanate glasses, with a composition containing Na element, were synthesized by the conventional melting–quenching technique. When Na element was introduced into the composition of oxyfluorogermanate glass, the crystals behavior was investigated in details. Depending on the annealing procedure supplied, thermal annealing of precursor glasses in the system GeO₂/BaF₂/AlF₃/Na₂O/NaF/ZnO/GdF₃/ErF₃ led to the precipitation of different crystal phase nanocrystals. It was confirmed the nanocrystals in GC600 is orthorhombic NaBaAlF₆ which led to enhance obviously in the UC luminescence of Er³⁺. However, the nanocrystals in G585 led to decrease in the UC luminescence, which indicated few Er ions enter into the lattice of this nanocrystal phase. The reason of the decrease in UC emission intensity of GC585 was analyzed.     

Superior foetal electrocardiogram signal elicitation using a novel artificial intelligent Bayesian methodology

Innumerable casualties due to intrauterine hypoxia are a major worry during prenatal phase besides advanced patient monitoring with latest science and technology. Hence, the analysis of foetal electrocardiogram (fECG) signals is very vital in order to evaluate the foetal heart status for timely recognition of cardiac abnormalities. Regrettably, the latest technology in the cutting edge field of biomedical signal processing does not seem to yield the desired quality of fECG signals required by physicians, which is the major cause for the pathetic condition. The focus of this work is to extort non-invasive fECG signal with highest possible quality with a motive to support physicians in utilizing the methodology for the latest intrapartum monitoring technique called STAN (ST analysis) for forecasting intrapartum foetal hypoxia. However, the critical quandary is that the non-invasive fECG signals recorded from the maternal abdomen are affected by several interferences like power line interference, baseline drift interference, electrode motion interference, muscle movement interference and the maternal electrocardiogram (mECG) being the dominant interference. A novel hybrid methodology called BANFIS (Bayesian adaptive neuro fuzzy inference system) is proposed. The BANFIS includes a Bayesian filter and an adaptive neuro fuzzy filter for mECG elimination and non-linear artefacts removal to yield high quality fECG signal. Kalman filtering frame work has been utilized to estimate the nonlinear transformed mECG component in the abdominal electrocardiogram (aECG). The adaptive neuro fuzzy filter is employed to discover the nonlinearity of the nonlinear transformed version of mECG and to align the estimated mECG signal with the maternal component in the aECG signal for annulment. The outcomes of the investigation by the proposed BANFIS system proved valuable for STAN system for efficient prediction of foetal hypoxia.     

Systematic convergence of nonlinear stochastic estimators on the Special Orthogonal Group SO(3)

This paper introduces two novel nonlinear stochastic attitude estimators developed on the Special Orthogonal Group with the tracking error of the normalized Euclidean distance meeting predefined transient and steady‐state characteristics. The tracking error is confined to initially start within a predetermined large set such that the transient performance is guaranteed to obey dynamically reducing boundaries and decrease smoothly and asymptotically to the origin in probability from almost any initial condition. The proposed estimators produce accurate attitude estimates with remarkable convergence properties using measurements obtained from low‐cost inertial measurement units. The estimators proposed in continuous form are complemented by their discrete versions for the implementation purposes. The simulation results illustrate the effectiveness and robustness of the proposed estimators against uncertain measurements and large initialization error, whether in continuous or discrete form.     

Food recognition using neural network classifier and multiple hypotheses image segmentation

ABSTRACT

This paper proposes the multiple-hypotheses image segmentation and feed-forward neural network classifier for food recognition to improve the performance. Initially, the food or meal image is given as input. Then, the segmentation is applied to identify the regions, where a particular food item is located using salient region detection, multi-scale segmentation, and fast rejection. Then, the features of every food item are extracted by the global feature and local feature extraction. After the features are obtained, the classification is performed for each segmented region using a feed-forward neural network model. Finally, the calorie value is computed with the aid of (i) food volume and (ii) calorie and nutrition measure based on mass value. The experimental results and performance evaluation are validated. The outcome of the proposed method attains 0.947 for Macro Average Accuracy (MAA) and 0.959 for Standard Accuracy (SA), which provides better classification performance.