CHROMATOGRAPHIC SEPARATION AND CHARACTERIZATION OF MONO-, DI- AND TRIAROMATIC HYDROCARBONS IN GAS OIL

A simple procedure is described for separation of aromatic hydrocarbons into mono-, di- and trinuclear types in Iraqi gas oil. This is accomplished by elution through an alumina adsorption column under standardized conditions. Characterization is performed by UV-absorption and ratio matching method. The method can be used also for investigating aromatic hydrocarbon structures of other petroleum fractions.     

Receive Space-Time Filter and Transmit Sequence Design in MIMO Radar Systems

Wireless Personal Communications - In this paper, we are going to joint design of the continuous/discrete phase transmit waveform and receive space-time filter to maximize the Signal to...     

Multi-carrier Visible Light Communication System Using Enhanced Sub-carrier Index Modulation and Discrete Wavelet Transform

Wireless Personal Communications -     

A residual network-based framework for COVID-19 detection from CXR images

Neural Computing and Applications - In late 2019, a new Coronavirus disease (COVID-19) appeared in Wuhan, Hubei Province, China. The virus began to spread throughout many countries, affecting a...     

Automatic Real-Time Medical Mask Detection Using Deep Learning to Fight COVID-19

The COVID-19 pandemic is a virus that has disastrous effects on human lives globally; still spreading like wildfire causing huge losses to humanity and economies. There is a need to follow few constraints like social distancing norms, personal hygiene, and masking up to effectively control the virus spread. The proposal is to detect the face frame and confirm the faces are properly covered with masks. By applying the concepts of Deep learning, the results obtained for mask detection are found to be effective. The system is trained using 4500 images to accurately judge and justify its accuracy. The aim is to develop an algorithm to automatically detect a mask, but the approach does not facilitate the percentage of improper usage. Accuracy levels are as low as 50% if the mask is improperly covered and an alert is raised for improper placement. It can be used at traffic places and social gatherings for the prevention of virus transmission. It works by first locating the region of interest by creating a frame boundary, then facial points are picked up to detect and concentrate on specific features. The training on the input images is performed using different epochs until the artificial face mask detection dataset is created. The system is implemented using TensorFlow with OpenCV and Python using a Jupyter Notebook simulation environment. The training dataset used is collected from a set of diverse open-source datasets with filtered images available at Kaggle Medical Mask Dataset by Mikolaj Witkowski, Kera, and Prajna Bhandary. To simulate MobilNetV2 classifier is used to load and pre-process the image dataset for building a fully connected head. The objective is to assess the accuracy of the identification, measuring the efficiency and effectiveness of algorithms for precision, recall, and F1 score.     

Dressings for burn wound: a review

Journal of Materials Science - Burn injuries are the most severe and physically debilitating injuries affecting almost every organ system resulting in high mortality and morbidity. Excision of...     

A novel Harris hawks’ optimization and k-fold cross-validation predicting slope stability

Engineering with Computers - Stability of the soil slopes is one of the most challenging issues in civil engineering projects. Due to the complexity and non-linearity of this threat, utilizing...     

KDB: a fast update and high speed packet classifier in SDN

The Journal of Supercomputing - Packet classification is a fundamental function to support several services of software defined networking (SDN). Increasing complexity of the flow tables in SDN...