There is no easy way to convert Photovoltaic (PV) energy with high efficiency due to dynamic changes in solar irradiance and temperature. This paper illustrates a control strategy to design and implementation of Maximum Power Point Tracking (MPPT) in a photovoltaic system using Perturb and Observe (P&O) algorithm. The PSIM simulation results confirm proper functioning of the proposed MPPT sub-circuit to achieve a constant 48V DC output from fluctuating voltage of solar panel by varying duty cycle of the MOSFET in the 24V-48V boost converter. The filtered output waveform of the SPWM driven H-bridge inverter via the L-C low pass filter is found to be a pure sine-wave of 48V peak which is then stepped-up 312V peak (220V rms) by using a step up transformer. The frequency of output voltage is found to be 50Hz with a total harmonic distortion (THD) of 0.001 which is much lower than the IEEE 519 standard.

This paper presents an interdisciplinary studies of electronic systems: engineering, psychology and neuro-cognition. It evaluates the neurophysiological activities of human emotion using electroencephalography (EEG). This study is aimed to classify a comparison of Electroencephalogram (EEG) signal to observe human reflection towards relaxation state of mind during divine Quran recitation and listening to music. The objectives of this study is to measure the changes in alpha band and prove that the brain is less active when the subject is listening to Quran compared to music. Six healthy subjects were recruited to measure their behaviors of the mind for a total duration of three minutes. We have highlighted the observation in Topographic Map of the brain through ERP Analysis to observe whether the brain experience any changes. The results showed that the brain activity is less active and the Alpha Power is higher when the subject is listening to Quran Recitation. We conclude that listening to Quran Recitation is a useful tool for a healthy and happy mind which can help people recognize the need of Islamic practice in human life.

No abstract

Alzheimer’s disease (AD) is a progressive and irreversible neurodegenerative disease that requires attentive medical evaluation. Therefore, diagnosing of AD accurately is crucial to provide the patients with appropriate treatment to slow down the progression of AD as well to facilitate the treatment interventions. To date, deep learning by means of convolutional neural networks (CNNs) has been widely used in diagnosing of AD. There are several well-established CNNs architectures that have been used in the image classification domain for magnetic resonance imaging (MRI) images analysis such as LeNet-5, Inception-V4, VGG-16 and Residual Network. However, these existing deep learning-based methods have lack of ability to be spatial invariance to the input data, due to overlooking some salient local features of the region of interest (ROI) (i.e., hippocampal). In medical image analysis, local features of MRI images are hard to exploit due to the small pixel size of ROI. On the other hand, CNNs requires large dataset sample to perform well, but we have limited number of MRI images to train, thus, leading to overfitting. Therefore, we propose a novel deep learning-based model without pre-processing techniques by incorporating attention mechanism and global average pooling (GAP) layer to VGG-16 architecture to capture the salient features of the MRI image for subtle discriminating of AD and normal control (NC). Also, we utilize transfer learning to surpass the overfitting issue. Experiment is performed on data collected from Open Access Series of Imaging Studies (OASIS) database. The accuracy performance of binary classification (AD vs NC) using proposed method significantly outperforms the existing methods, 12-layered CNNs (trained from scratch) and Inception-V4 (transfer learning) by increasing 1.93% and 3.43% of the accuracy. In conclusion, Attention-GAP model capable of improving and achieving notable classification accuracy in diagnosing AD.

Protozoa detection and identification play important roles in many practical domains such as parasitology, scientific research, biological treatment processes, and environmental quality evaluation. Traditional laboratory methods for protozoan identification are time-consuming and require expert knowledge and expensive equipment. Another approach is using micrographs to identify the species of protozoans that can save a lot of time and reduce the cost. However, the existing methods in this approach only identify the species when the protozoan are already segmented. These methods study features of shapes and sizes. In this work, we detect and identify the images of cysts and oocysts of various species such as: Giardia lamblia, Iodamoeba butschilii, Toxoplasma gondi, Cyclospora cayetanensis, Balantidium coli, Sarcocystis, Cystoisospora belli and Acanthamoeba, which have round shapes in common and affect human and animal health seriously. We propose Segmentation-driven Hierarchical RetinaNet to automatically detect, segment, and identify protozoans in their micrographs. By applying multiple techniques such as transfer learning, and data augmentation techniques, and dividing training samples into life-cycle stages of protozoans, we successfully overcome the lack of data issue in applying deep learning for this problem. Even though there are at most 5 samples per life-cycle category in the training data, our proposed method still achieves promising results and outperforms the original RetinaNet on our protozoa dataset.