Investigations on mode-division multiplexed free-space optical transmission for inter-satellite communication link

Wireless Networks - Inter-satellite data transmission links are very crucial for providing global inter-connectivity. We report designing and investigations on high date rate inter-satellite...     

Investigating the effect of varying block size on power and energy consumption of GPU kernels

The Journal of Supercomputing - Power consumption is likely to remain a significant concern for exascale performance in the foreseeable future. In addition, graphics processing units (GPUs) have...     

An automatic query expansion based on hybrid CMO-COOT algorithm for optimized information retrieval

The World Wide Web(WWW) comprises a wide range of information, and it is mainly operated on the principles of keyword matching which often reduces accurate information retrieval. Automatic query expansion is one of the primary methods for information retrieval, and it handles the vocabulary mismatch problem often faced by the information retrieval systems to retrieve an appropriate document using the keywords. This paper proposed a novel approach of hybrid COOT-based Cat and Mouse Optimization (CMO) algorithm named as hybrid COOT-CMO for the appropriate selection of optimal candidate terms in the automatic query expansion process. To improve the accuracy of the Cat and Mouse Optimization (CMO) algorithm, the parameters are tuned with the help of the Coot algorithm. The best suitable expanded query is identified from the available expanded query sets also known as candidate query pools. All feasible combinations in this candidate query pool should be obtained from the top retrieved documents. Benchmark datasets such as the GOV2 Test Collection, the Cranfield Collections, and the NTCIR Test Collection are utilized to assess the performance of the proposed hybrid COOT-CMO method for automatic query expansion. This proposed method surpasses the existing state-of-the-art techniques using many performance measures such as F-score, precision, and mean average precision (MAP).

     

Transfer Learning on Deep Neural Networks to Detect Pornography

While the internet has a lot of positive impact on society, there are negative components. Accessible to everyone through online platforms, pornography is, inducing psychological and health related issues among people of all ages. While a difficult task, detecting pornography can be the important step in determining the porn and adult content in a video. In this paper, an architecture is proposed which yielded high scores for both training and testing. This dataset was produced from 190 videos, yielding more than 19 h of videos. The main sources for the content were from YouTube, movies, torrent, and websites that hosts both pornographic and non-pornographic contents. The videos were from different ethnicities and skin color which ensures the models can detect any kind of video. A VGG16, Inception V3 and Resnet 50 models were initially trained to detect these pornographic images but failed to achieve a high testing accuracy with accuracies of 0.49, 0.49 and 0.78 respectively. Finally, utilizing transfer learning, a convolutional neural network was designed and yielded an accuracy of 0.98.     

Defect Prediction Using Akaike and Bayesian Information Criterion

Data available in software engineering for many applications contains variability and it is not possible to say which variable helps in the process of the prediction. Most of the work present in software defect prediction is focused on the selection of best prediction techniques. For this purpose, deep learning and ensemble models have shown promising results. In contrast, there are very few researches that deals with cleaning the training data and selection of best parameter values from the data. Sometimes data available for training the models have high variability and this variability may cause a decrease in model accuracy. To deal with this problem we used the Akaike information criterion (AIC) and the Bayesian information criterion (BIC) for selection of the best variables to train the model. A simple ANN model with one input, one output and two hidden layers was used for the training instead of a very deep and complex model. AIC and BIC values are calculated and combination for minimum AIC and BIC values to be selected for the best model. At first, variables were narrowed down to a smaller number using correlation values. Then subsets for all the possible variable combinations were formed. In the end, an artificial neural network (ANN) model was trained for each subset and the best model was selected on the basis of the smallest AIC and BIC value. It was found that combination of only two variables’ ns and entropy are best for software defect prediction as it gives minimum AIC and BIC values. While, nm and npt is the worst combination and gives maximum AIC and BIC values.     

Effect of Initial Annealing Temperature on Microstructural Development and Microhardness in High‐Purity Copper Processed by High‐Pressure Torsion

The effect of the initial annealing temperature on the evolution of microstructure and microhardness in high purity OFHC Cu is investigated after processing by HPT. Disks of Cu are annealed for 1 h at two different annealing temperatures, 400 and 800 °C, and then processed by HPT at room temperature under a pressure of 6.0 GPa for 1/4, 1/2, 1, 5, and 10 turns. Samples are stored for 6 months after HPT processing to examine the self‐annealing effects. Electron backscattered diffraction (EBSD) measurements are recorded for each disk at three positions: center, mid‐radius, and near edge. Microhardness measurements are also recorded along the diameters of each disk. Both alloys show rapid hardening and then strain softening in the very early stages of straining due to self‐annealing with a clear delay in the onset of softening in the alloy initially annealed at 800 °C. This delay is due to the relatively larger initial grain size compared to the alloy initially annealed at 400 °C. The final microstructures consist of homogeneous fine grains having average sizes of ≈0.28 and ≈0.34 µm for the alloys initially annealed at 400 and 800 °C, respectively. A new model is proposed to describe the behavior of the hardness evolution by HPT in high purity OFHC Cu.     

A note on the stability of fractional order systems

In this paper, a new approach is suggested to investigate stability in a family of fractional order linear time invariant systems with order between 1 and 2. The proposed method relies on finding a linear ordinary system that possesses the same stability property as the fractional order system. In this way, instead of performing the stability analysis on the fractional order systems, the analysis is converted into the domain of ordinary systems which is well established and well understood. As a useful consequence, we have extended two general tests for robust stability check of ordinary systems to fractional order systems.     

Potential of near-field microwave imaging in breast cancer detection utilizing tapered rectangular waveguide probes

Microwave imaging for medical applications has been of interest for many years. A novel near-field microwave non-invasive testing and evaluation (NIT&E) technique utilizing tapered rectangular waveguide probes is presented for breast cancer detection. Near-field microwave NIT&E techniques can be a successful candidate for the detection of breast cancer because of their potential in dealing with materials of low conductivity (i.e. lossy dielectrics like the breast tissue). The physical basis for breast tumor detection with microwave imaging is the contrast in dielectric properties of normal and malignant breast tissues. A method adopting Fourier transform matching (FTM) technique and utilizing the reflection coefficient at the aperture of a tapered rectangular waveguide sensor radiating into a breast is described resulting in microwave images that indicate the presence of a tumor. These images demonstrate the feasibility of detecting breast tumors using this approach.     

Isolation, purification and ultrastructure of bacteriophages from natural mosquito breeding places in Egypt

Two bacteriophages were isolated from field collected samples representing two different mosquito breeding places. The phage AB-1 (isolated from Abheit village, Faiyoum Governorate "seepage water") and the phage GA-2 (isolated from El-Gabal El-Asfer Qualyobia Governorate "sewage drain water") were purified. Both bacteriophages were ultrastructurally described with respect to their morphology, dimensions, phases of bacterial attack and lysogeny. No major differences were observed between both isolated phages in relation to specificity, however; they were isolated from two different types of breeding places and two different geographic areas as well. This study may assume a wide host range of the isolated phages and reflect how bacterial insecticides used for mosquito larval control could be inhibited by such bacteriophage.