Performance analysis of rain attenuation at Ku-band in Malaysia

Due to the lack of reliable analysis in Malaysia as tropical region, this study presents an analysis of experimental data compared against existing rain attenuation prediction models, namely the Dissanayake Allnut Haidara (DAH), and International Telecommunication Union rain ITU-R models, which have been used previously in satellite communication systems. Experimental data was measured at Universiti Putra Malaysia, Selangor, by retrieving signals from the MEASAT-3 satellite, which is geostationary at 91.5°E; at 11.096 GHz. Serdang Selangor is the southern-most state of the Kuala Lumpur region of Malaysia. Data analysis was conducted in two ways. Firstly, by performing statistical analysis on rain attenuation prediction models; and secondly, by making a comparison between measured data and the existing rain attenuation prediction model. Of all, the ITU-R model gave the lowest real mean square value of (2.2) for the three chosen states in Malaysia

Brain Tumor Segmentation in Fluid-Attenuated Inversion Recovery Brain MRI using Residual Network Deep Learning Architectures

Early and accurate detection of brain tumors is very important to save the patient's life. Brain tumors are generally diagnosed manually by a radiologist by analyzing the patient’s brain MRI scans which is a time-consuming process. This led to our study of this research area for finding out a solution to automate the diagnosis to increase its speed and accuracy. In this study, we investigate the use of Residual Network deep learning architecture to diagnose and segment brain tumors. We proposed a two-step method involving a tumor detection stage, using ResNet50 architecture, and a tumor area segmentation stage using ResU-Net architecture. We adopt transfer learning on pre-trained models to help get the best performance out of the approach, as well as data augmentation to lessen the effect of data population imbalance and hyperparameter optimization to get the best set of training parameter values. Using a publicly available dataset as a testbed we show that our approach achieves 84.3% performance outperforming the state-of-the-art using U-Net by 2% using the Dice Coefficient metric

Data Augmentation Using Generative Adversarial Networks to Reduce Data Imbalance with Application in Car Damage Detection

Automatic car damage detection and assessment are very useful in alleviating the burden of manual inspection associated with car insurance claims. This will help filter out any frivolous claims that can take up time and money to process. This problem falls into the image classification category and there has been significant progress in this field using deep learning. However, deep learning models require a large number of images for training and oftentimes this is hampered because of the lack of datasets of suitable images. This research investigates data augmentation techniques using Generative Adversarial Networks to increase the size and improve the class balance of a dataset used for training deep learning models for car damage detection and classification. We compare the performance of such an approach with one that uses a conventional data augmentation technique and with another that does not use any data augmentation. Our experiment shows that this approach has a significant improvement compared to another that does not use data augmentation and has a slight improvement compared to one that uses conventional data augmentation

Semantic Segmentation and Depth Estimation of Urban Road Scene Images Using Multi-Task Networks

In autonomous driving, environment perception is an important step in understanding the driving scene. Objects in images captured through a vehicle camera can be detected and classified using semantic segmentation and depth estimation methods. Both these tasks are closely related to each other and this association helps in building a multi-task neural network where a single network is used to generate both views from a given monocular image. This approach gives the flexibility to include multiple related tasks in a single network. It helps reduce multiple independent networks and improve the performance of all related tasks. The main aim of our research presented in this paper is to build a multi-task deep learning network for simultaneous semantic segmentation and depth estimation from monocular images. Two decoder-focused U- Net-based multi-task networks that use a pre-trained Resnet-50 and DenseNet-121 which shared encoder and task-specific decoder networks with Attention Mechanisms are considered. We also employed multi-task optimization strategies such as equal weighting and dynamic weight averaging during the training of the models. The corresponding models’ performance is evaluated using mean IoU for semantic segmentation and Root Mean Square Error for depth estimation. From our experiments, we found that the performance of these multi-task networks is on par with the corresponding single-task networks

The correlation of blue shift of photoluminescence and morphology of silicon nanoporous

Porous silicon with diameters ranging from 6.41 to 7.12 nm were synthesized via electrochemical etching by varied anodization current density in ethanoic solutions containing aqueous hydrofluoric acid up to 65mA/cm2.The luminescence properties of the nanoporous at room temperature were analyzed via photoluminescence spectroscopy. Photoluminescence PL spectra exhibit a broad emission band in the range of 360-700 nm photon energy. The PL spectrum has a blue shift in varied anodization current density; the blue shift incremented as the existing of anodization although the intensity decreased. The current blue shift is owning to alteration of silicon nanocrystal structure at the superficies. The superficial morphology of the PS layers consists of unified and orderly distribution of nanocrystalline Si structures, have high porosity around (93.75%) and high thickness 39.52 µm

Propagation models for train environment over geo satellite networks in Malaysia

Recent advances in satellite communication technologies in the tropical regions have led to significant increase in the demand for services and applications that require high channel quality for stationary and mobile satellite terminals. The lack in reliable, accurate analysis and assessment for the stationary and mobile scenarios regarding the attenuation due to rain and power arch supply PAs. These is a need to determine and quantify these risk factors which, in its turn, leads to optimize service quality particularly in Malaysian region. Moreover, the current satellite propagation models are done at temperate regions which exhibit different environmental characteristics than seen in Malaysia. That makes their propagation models inaccurate and irrelevant to the tropical regions in general. The propagation models for the stationary and mobile scenario of high speed train to produce a reliable analysis on the attenuation, due to rain and power arch supply, in tropical region, represents an interesting area to study for propagation impairment in Malaysia. The rainfall characteristics in the tropical region differ significantly from those in temperate regions, the rain effects problem is more crucial for tropical regions such as Malaysia because of their high intensity rainfall. This study presents for stationary scenario (Malaysia-PMSS) an analysis of experimental data compared against six existing rain attenuation prediction models namely, the ITU-R-618-11, ITU-R-618-5, DAH, Crane, Brazil, and SAM models. The data are analyzed in two ways. First, rain attenuation prediction models are statistically analyzed. Second, the measured data and existing prediction model are compared. A communication system design can estimate the exact rain attenuation for three locations Selangor, Penang, and Johor regions of Malaysia and can produce a suitable design for better communication service. Additionally, new method for developing measured data is suggested: the Exponential Moving Average (EMA). Throughout the literature, the location Selangor and elevation angle 77.5◦ are not considered. Therefore, our new model takes into account the location and elevation angle to make it more applicable. Hence an extension for improving the performance assessment and analysis of satellite/Earth stations is achieved. Of all studied models, the Brazil, ITU-R-618-11, and DAH models gave the lowest root mean square (RMS) error for the three chosen states in Malaysia for stationary scenario. For the mobile scenario (Malaysia-PMMS), enables a first-hand coarse estimation of and an analysis attenuation because it is much simpler to obtain attenuation due to rain and power arch supply PAs. The attenuation rustled from rain either rain or power arch supply were measured independently. The obtained output were statistically analyzed to calculate the total attenuation composite (PAs with rain) time series synthesizer. A parallel to that, attenuation resulted from power arch supply were compared with noise floor level. This comparison is useful to validate attenuation due to power arch supply measurement. Incorporating both phenomena to enable a more comprehensive study of relevant fade mitigation techniques (FMTs). The underlying analytical tool represents a first effort (to be validated by measurements) to dynamically model mobile satellite links operating higher than 10 GHz. In order to increase the quantitative and qualitative information database of the satellite signal performance under link impairments in tropical regions

Propagation measurement on earth-sky signal effects for high speed train satellite channel in tropical region at Ku-band

Recent advances in satellite communication technologies in the tropical regions have led to significant increase in the demand for services and applications that require high channel quality for mobile satellite terminals. Determination and quantification of these requirements are important to optimize service quality, particularly in the Malaysian region. Moreover, the tests on current satellite propagation models were carried out at temperate regions whose environmental characteristics are much different from those in Malaysia. This difference renders these propagation models inapplicable and irrelevant to tropical regions in general. This paper presents the link characteristics observations and performance analysis with propagation measurements done in tropical region to provide an accurate database regarding rain and power arches supply (PAs) attenuations in the tropics for mobile scenarios. Hence, an extension for improving the performance assessment and analysis of satellite/transmission has been achieved. The Malaysia propagation measurement for mobile scenario (Malaysia-PMMS) enables first-hand coarse estimation and attenuation analysis, because the attenuation resulting from rain and PAs becomes easily amenable for measurement. Parallel to that, the measured attenuation has been compared with that of the simulated output at noise floor level. The underlying analytical tool is validated by measurements specific at tropical region, for dynamic model of mobile satellite links operating at higher than 10 GHz