The marvelous optical performance of AlGaN-based deep ultraviolet light-emitting diodes with AlInGaN-based last quantum barrier and step electron blocking layer.

The optoelectronic characteristics of AlGaN-based deep ultraviolet light-emitting diodes (DUV LEDs) with quaternary last quantum barrier (QLQB) and step-graded electron blocking layer (EBL) are investigated numerically. The results show that the internal quantum efficiency (IQE) and radiative recombination rate are remarkably improved with AlInGaN step-graded EBL and QLQB as compared to conventional or ternary AlGaN EBL and last quantum barrier (LQB). This significant improvement is assigned to the optimal recombination of electron-hole pairs in the multiple quantum wells (MQWs). It is due to the decrease in strain and lattice mismatch between the epi-layers which alleviates the effective potential barrier height of the conduction band and suppressed the electron leakage without affecting the holes transportation to the active region. Moreover, to figure out quantitatively, the electron and hole quantity increased by ~ 25% and ~ 15%, respectively. Additionally, the IQE and radiative recombination rate are enhanced by 48% and 55%, respectively, as compared to conventional LED. So, we believe that our proposed structure is not only a feasible approach for achieving highly efficient DUV LEDs, but the device physics presented in this study establishes a fruitful understanding of III nitride-based optoelectronic devices.

Transfer Learning Assisted Classification and Detection of Alzheimer's Disease Stages Using 3D MRI Scans.

Alzheimer's disease effects human brain cells and results in dementia. The gradual deterioration of the brain cells results in disability of performing daily routine tasks. The treatment for this disease is still not mature enough. However, its early diagnosis may allow restraining the spread of disease. For early detection of Alzheimer's through brain Magnetic Resonance Imaging (MRI), an automated detection and classification system needs to be developed that can detect and classify the subject having dementia. These systems also need not only to classify dementia patients but to also identify the four progressing stages of dementia. The proposed system works on an efficient technique of utilizing transfer learning to classify the images by fine-tuning a pre-trained convolutional network, AlexNet. The architecture is trained and tested over the pre-processed segmented (Grey Matter, White Matter, and Cerebral Spinal Fluid) and un-segmented images for both binary and multi-class classification. The performance of the proposed system is evaluated over Open Access Series of Imaging Studies (OASIS) dataset. The algorithm showed promising results by giving the best overall accuracy of 92.85% for multi-class classification of un-segmented images.