seyed mohammadjalal rastegar fatemi

In this research, we deal with the detailed simulation of CIGS solar cells using band-gap grading. Our main goal is to improve the electrical and optical properties of these cells. In order to check the performance, we also evaluate the change in the thickness of the absorbent layer as a key factor. In particular, we analyze the effect of these changes on the efficiency and filling factor of the cells. In conventional CIGS solar cells, the efficiency is about 20.8% that we have achieved so far. But by taking advantage of the bandgap grading on the absorber layer and by using engineering techniques in the absorber and buffer layers, we have achieved the most impressive efficiency ever of 33.7%. This increase is the result of successful achievements in optimizing the structure and properties of cells. In summary, the values of important output parameters for these advanced cells include 33.7% efficiency, 79% filling factor, 1mV open circuit voltage and 40.96mA/cm2 short circuit current.

With the expansion and success of convolutional networks, the topic of deep learning has attracted increasing attention in recent years; Since convolutional networks include many layers, optimal learning of network layers is of great importance. In this paper, a new model, called the 4-stream model, is presented with the aim of helping to linearize the data space using representational dissimilarity transformation, and the effects of this transformation on standard classifications for artificial data and Cifar10 images are investigated. Then, two models based on data preprocessing with dissimilarity transform representation and Sobel and Edge Detector filters are analyzed. The 4-stream model increased the accuracy by 3.2% due to the increase in the number of model parameters, and hence the capacity of the network. Besides, adding the dissimilarity representation wherever the classifier cannot perform a high-resolution classification by merely using the main features, can help to increase the discriminability of classes by adding linear features.

Environmental monitoring via vehicle detecting using unmanned aerial vehicle (UAV) images is a challenging task, due to small-size, low-resolution, and large-scale variation of the objects. In this paper, a two-level ensemble deep learning (named 2EDL) based on Faster R-CNN (regional-based convolutional neural network) is introduced for multiple vehicle detection in UAV images. We use three CNN models (VGG16, ResNet50, and GoogLeNet) that have already pre-trained on huge auxiliary data as feature extraction tools, combined with five learning models (KNN, SVM, MLP, C4.5 Decision Tree, and Naïve Bayes), resulting 15 different base learners in two levels. The final class is obtained via a majority vote rule ensemble of these 15 models into five vehicle classes (car, van, truck, bus, trailer) or “no-vehicle”. Simulation results on the AU-AIR dataset of UAV images show the superiority of the proposed 2EDL technique against existing methods, in terms of the total accuracy, and FPR-FNR trade-off.

Recently, attention to polymeric nanocomposites has gained a great extent as they present new opportunities to provide superior properties in microwave absorbing materials. In this study polystyrene (PS) nanocomposites containing various nano-fillers were successfully synthesized and employed as microwave absorbing materials. The mentioned materials are usually designed to solve protection against electromagnetic interference in wireless communication systems and high frequency circuit mechanisms. In this study the performance of three various polystyrene (PS) nanocomposites containing: semi-conductor zinc oxide, non-metallic conductive graphene oxide and magnetic Fe3O4 were compared. The fillers type was selected as variable parameter and its influence on the electromagnetic wave absorption and reflection loss (RL) amount was investigated. The scanning electron microscopy (SEM) was used in morphological and particle size study of the nanocomposites. The electromagnetic wave absorption properties of nanocomposites were studied and compared using a vector network analyzer (frequency range of 5-8 GHz). The results indicate that at the same preparation conditions the polystyrene/graphene oxide nanocomposites have higher absorption compared with others.

Microwave absorbing materials are usually designed to solve protection against electromagnetic interference in wireless communication systems and high frequency circuit mechanisms. In this research polystyrene (PS) nanocomposites containing various nano-fillers were successfully synthesized. The novelty of this work is comparing of three various nanostructures: non-metallic conductive graphene oxide, magnetic Fe3O4 and semi-conductor zinc oxide were used as additive. The effect of different fillers loading and homogenizer speed on the reflection loss (RL) amount and electromagnetic wave absorption was investigated. In order to investigate particle size and morphology of the nanostructures the scanning electron microscopy (SEM) was used. The frequency range of 5-8 GHz was employed in the investigation of electromagnetic wave absorption properties of nanocomposites using a vector network analyzer and eventually their absorption properties were analyzed and compared. The results indicate that graphene oxide has substantial effect on absorption in compare with the other nanocomposite samples. Increase of homogenizer speed led to a dispersion improvement of nanostructures and absorption. Therefore, the broadening of the microwave absorption band-width is attributed to the suitable dispersion of nanostructures in polymeric matrix.