Community reporting system: road violation

In the era of internet and wireless, an online community reporting system that is easy to use and hustle free is much needed to allow the user to place a misconduct report to the relevant authority. The available system is inefficient and time-consuming since mostly are using web-based which makes the user unwilling to make a report to the authority. The objective of this project is to design a system using android application that is cost-effective and easy to use. The scope of this project is on the road violation reporting system. The outcome of the system will provide a user with an easy reporting system and also the authority can manage the report easily. The development of the system is expected to enhance the reporting system and betterment for the community as well as the authority as a whole

The effect of haze attenuation on Free Space Optics Communication (FSO) at two wavelengths under Malaysia weather

Free Space Optical FSO is a promising optical technology that has a great chance of complementing the traditional wireless communication. It offers unlicensed, higher speed, broader, unlimited bandwidth and excellent security. However, the quality of FSO links is greatly affected by weather conditions and link distance. In the tropical regions, the quality of the FSO links is affected mainly by rain attenuation while the air quality is presumed to have little or no impact. However, a state of emergency has consecutively been declared in some part of Malaysia during the past three years due to high air pollution index (API). Since the range of FSO link is limited by air pollution, haze attenuation must be considered as one of the important factors in FSO link design. The aim of this paper is to provide an analysis and simulation of the FSO link with real data from Meteorological Malaysia department (MMD) on haze weather under two different wavelengths 850nm and 1550nm. This paper will discuss the different rate of attenuation operating in the medium between transmitter and receiver and their impact on the link margin calculation. In addition, it will evaluate the maximum distance link for wavelengths and consider the different visibility under the attenuated weather

Implementation of green technology system in a classroom

Green technology a technology to mitigate the effects of human activity on the environment and power efficiency. The scope of this project is only for office or classroom application. The issue of the system in the classroom is the high consumption of energy due to the nonstop operation of the system even though there is no one in the classroom. The main objective of this project is to design. The main objective of this project is to design an autonomous lighting system that can minimize the electricity usage and improve its productivity on human life. The methodology of the system is by using sensors to detect the presence of human and control the intensity of the lamp. Three sensors which are the Infrared (IR), the passive infrared sensor (PIR) and light sensor work separately, but it operates simultaneously under a few conditions. This system can measure and analyze the brightness of light according to the surrounding exposure. The system is designed for easy installation. The outcome of the system is showing the potential application in real time

Availability assessment of free-space-optics links with rain data from tropical climates

Rainfall in tropical environments acts as a dominant parameter for estimating the availability of free-space optics (FSO). Long fades are caused by precipitation in particular heavy rains, and this precipitation increases atmospheric attenuation due to rain and reduces the availability of optical power from FSO links. In this paper, the link availability of an FSO system is estimated under the impact of rain in a tropical climate. The influence of rain on the propagation of FSO signal is investigated based on experimental and statistical analyses of rain-rate measurements under tropical conditions. Rain-rate data are measured in Malaysia for three years and used to estimate the availability of FSO links. Models developed in Malaysia are used to predict atmospheric attenuation due to rain. Long-term statistical measurements of rain data can enable good estimation of link availability. Based on the prediction model and measured rain-rate data, the FSO link budget is analyzed, and the link availability as a function of distance is predicted. Carrier class availability limits the FSO link to a few hundred meters only, whereas enterprise class availability can exceed FSO links over a few kilometers long

Analysis of free space optics link availability with real data measurement in tropical weather

Free Space Optical communications (FSO) have attracted a lot of attention for a variety of applications in telecommunications field. The unlicensed, higher speed, broader and unlimited bandwidth, low cost solutions, and shortest deployment period are some of the drives to deploy FSO. However, weather attenuation has a big impact on the transmission line of FSO. In temperate region a thorough studies are available on the link availability due to weather effect. In tropical region however most FSO links install especially in Malaysia are as a backup system. There is no analysis on the FSO link availability under tropical weather condition. Therefore, the objective of this paper is to provide an analysis on the FSO link availability with real data measured in tropical weather condition. Based on one year measurement of rain attenuation and rain intensity, link availability of 99.99% can be achieved for a link distance of 1.25km and fade margin of 25dB. The outcome of this research is expected to give a foundation for the design and development of a long range FSO link under tropical weather condition

Atmospheric effects on free space earth-to-satellite optical link in tropical climate

Free Space Optic (FSO) vulnerability towards atmospheric phenomena is becoming a main issue towards its implementation. A lot of studies have been conducted to estimate the climatological effects on FSO links but most of them are based on terrestrial link with short path lengths and centred in temperate regions.Hence, this paper aims to analyse the performance of FSO links from earth-to-LEO satellite in tropical regions where rain and haze are the main concerns. This analysis is based on available methods and data to predict earth-to-satellite FSO links with the scaled up data from the measurement on terrestrial FSO link installed at International Islamic University Malaysia (IIUM) Kuala Lumpur campus. Data collected from terrestrial link are FSO received signal levels, corresponding rain intensity and visibility variations. The measured data are scaled up to the effective distance of slant path lengths and then compared with the predicted values. These findings will be very useful for FSO earth-space communication link designers and can be as a benchmark of FSO system design

Performance Analysis of Free Space Optics Link Under the Effect of Rain Attenuation

Free space optical (FSO) networking has emerged as a promising technology for bridging the last mile gap in current high-rate fibre networks as well as for high-rate next-generation broadband wireless communication networks. This paper theoretically investigates the performance of the Free Space Optic system under rainy weather conditions using Optisystem software. Quality factor and eye-diagram analysis are used to analyse the FSO performance for several rain intensity and precipitation, following the Carbonneau model for rain attenuation. The results show that the link is completely lost when the distances are at 1.5 km, 1.2 km, and 0.8 km for light rain, moderate rain, and heavy rain, respectively. An optical amplifier is then introduced to improve the system performances and increase the signal intensity, which helps to mitigate the atmospheric effects easily. As a result, data transmission is sustained without interruptions, and overall connectivity is enhanced. Results show that the transmission range improves up to several meters, specifically under moderate rain weather condition when using the optical amplifier gain is applied

Optimizing U-Net Architecture with Feed-Forward Neural Networks for Precise Cobb Angle Prediction in Scoliosis Diagnosis

In the burgeoning field of Artificial Intelligence (AI) and its notable subsets, such as Deep Learning (DL), there is evidence of its transformative impact in assisting clinicians, particularly in diagnosing scoliosis. AI is unrivaled for its speed and precision in analyzing medical images, including X-rays and computed tomography (CT) scans. However, the path does not lack obstacles. Biases, unanticipated outcomes, and false positive and negative predictions present significant challenges. Our research employed three complex experimental sets, each focusing on adapting the U-Net architecture. Through a nuanced combination of feed-forward neural network (FFNN) configurations and hyperparameters, we endeavored to determine the most effective nonlinear regression model configuration for predicting the Cobb angle. This was done with the dual purpose of reducing AI training time without sacrificing predictive accuracy. Utilizing the capabilities of the PyTorch framework, we meticulously crafted and refined the deep learning models for each of the three experiments, focusing on an FFFN dropout rate of p=0.45. The Root Mean Square Error (RMSE), the number of epochs, and the number of nodes spanning three hidden layers in each FFFN were utilized as crucial performance metrics while a base learning rate of 0.001 was maintained. Notably, during the optimization phase, one of the experiments incorporated a learning rate scheduler to protect against potential pitfalls such as local minima and saddle points. A judiciously incorporated Early Stopping technique, triggered between the patience range of 5-10 epochs, ensured model stability as the Mean Squared Error (MSE) plateau loss approached approximately 1. Consequently, the model converged between 50 and 82 epochs. We hypothesize that our proposed architecture holds promise for future refinements, conditioned on assiduous experimentation with an array of medical deep learning paradigms

Availability analysis of terrestrial free space optical link under the impact of rain condition

Availability is one of the main factors to measure the QoS of any telecommunication networks. An accurate availability prediction for Free Space Optical link of carrier class grade is needed. In tropical areas, rain is the most dominant factor affecting the FSO link availability. In this paper, the effect of rain attenuation on the availability of FSO links is analyzed by examining the impact distance of different FSO systems. As the rain attenuation of FSO is independent of wavelength, the analysis of rain attenuation will be based on rain intensity. ITU-R (Carbonneau and Japan) models have been used for the analysis. From the results; Carrier class availability with good enough resolution for estimated availability can be predicted. This paper provides recommendations to FSO researchers in general and local telecom service provider in particular about possible availability figures that can be useful for deployment of FSO link as a last mile solution, back-up for fiber optic and other applications

Systematic review on ai-blockchain based e-healthcare records management systems

Electronic health records (EHRs) are digitally saved health records that provide information about a person's health. EHRs are generally shared among healthcare stakeholders, and thus are susceptible to power failures, data misuse, a lack of privacy, security, and an audit trail, among other problems. Blockchain, on the other hand, is a groundbreaking technology that provides a distributed and decentralized environment in which nodes in a list of networks can connect to each other without the need for a central authority. It has the potential to overcome the limits of EHR management and create a more secure, decentralized, and safer environment for exchanging EHR data. Further, blockchain is a distributed ledger on which data can be stored and shared in a cryptographically secure, validated, and mutually agreed-upon manner across all mining nodes. The blockchain stores data with a high level of integrity and robustness, and it cannot be altered. When smart contracts are used to make decisions and conduct analytics with machine-learning algorithms, the results may be trusted and unquestioned. However, Blockchain is not always indestructible and suffers from scalability and complexity issues that might render it inefficient. Combining AI and blockchain technology can handled some of the drawbacks of these two technical ecosystems effectively. AI algorithms rely on data or information to learn, analyze, and reach conclusions. The performance of AI algorithms is enhanced through the data obtained from a data repository or a reliable, secure, trustworthy, and credible platform. Researchers have identified three categories of blockchain-based potential solutions for the management of electronic health records: conceptual, prototype, and implemented. The purpose of this research work is to conduct a Systematic Literature Review (SLR) to identify and assess research articles that were either conceptual or implemented to manage EHRs using blockchain technology. The study conducts a comprehensive evaluation of the literature on blockchain technology and enhanced health record management systems utilizing artificial intelligence technologies. The study examined 189 research papers collected from various publication categories. The in-depth analysis focuses on the privacy, security, accessibility, and scalability of publications. The SLR has illustrated that blockchain technology has the potential to deliver decentralization, security, and privacy that are frequently lacking in traditional EHRs. Additionally, the outcomes of the extensive analysis inform future researchers about the type of blockchain to use in their research. Additionally, methods used in healthcare are summarized per application area while their pros and cons are highlighted. Finally, the emphasized taxonomy combines blockchain and artificial intelligence, which enables us to analyze possible blockchain and artificial intelligence applications in health records management systems. The article ends with a discussion on open issues for research and future directions