International Journal of Innovations in Science & Technology
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Compact Frequency Selective Surface (FSS) for X-Band Shielding
Get PDFWith the increase in the usage of electromagnetic devices, electromagnetic interference increased many folds. Frequency Selective Surface (FSS) provide effective shielding from unwanted frequency ranges. A thin, conformal band-stop FSS is presented in this research that provides effective electromagnetic shielding properties in X-band. The FSS acts as a band stop filter at 10 GHz. The proposed FSS has 54.7% fractional bandwidth. The design is of the dimensions 6.79 x 6.79 x 0.127 milimeter cube, employing Rogers RT 5880 substrate with 0.0009 dielectric constant. It has an attenuation of at least -57.97 dB. The proposed FSS shows oblique incidence angle independence for both TE and TM modes, up to 60o scan angle. The incidence angle independence makes the FSS response stable for both normal and varying angles of the incident waves. The design has a copper cladding of 0.018 mm, making the overall FSS thickness of 0.145 mm. The thin substrate makes the design flexible and easily bendable for curved surfaces. Its thin structure makes it easily applicable on buildings, vehicles and military aircrafts for electromagnetic shielding purposes. The conformability and shielding properties make the design suitable for various other applications.
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Health Implications of Arsenic and Qualitative Deterioration of drinking Water from Underground Water Supply Lines of Lahore, Pakistan
Get PDFThe study is a comparative analysis of water quality among two variant areas of Lahore. There are several problems regarding drinking water facilities. Drinkable water can be contaminated due to various reasons. Thus, the study highlights infrastructural causes (material of pipes and outdated pipes) of water contamination. Wall City and Gulberg are the study areas of this research. Gulberg area is far much better in various terms as compared to the wall city. Under this study, four parameters were selected for water quality pH, Total dissolved solids, E.coli and Arsenic. There were 13 water samples collected from each study area by random sampling. Samples were tested on the latest footing in this field. All results validate the problematic statement and highlight severe health effects. The results of these four parameters were far above the water quality standards declared by World Health Organization. Causes of these severe results include the outdated water pipes that are being laid down for the past several decades, for example Wall City area, etc. Results also depict low values in the Gulberg area which is recently developed as compared to the wall city. The comparative study also attests problem statement of the study.
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Visualizing Impact of Weather on Traffic Congestion Prediction: A Quantitative Study
Get PDFA substantial amount of research has been done to develop improved Intelligent Transportation Systems (ITS) to alleviate traffic congestion problems. These include methods that incorporate the indirect impact on traffic flow such as weather. In this paper, we studied the impact of weather conditions on traffic congestion along with more spatial and temporal factors, such as weekdays/time and location, which is a different approach to this problem. The proposed solution uses all these indicators to estimate the flow of traffic. We evaluate the level of congestion (LOC) based on the traffic volume grouped in certain regions of the city. The index for the defined LOC indicates the traffic flow from “free -flowing” to “traffic jam”. The data for the traffic volume count is collected from the Department of Transportation (DOT) for NYMTC. Weather conditions along with special and temporal information have an essential role in predicting the congestion level. We used supervised machine learning for this purpose. The prediction models are based on certain factors such as the volume count of the traffic at the entry and exit point of each street pair, particular days of the week, timestamp, geographical location, and weather parameters. The study is done on the major roadways of each of the four prominent boroughs in New York. The results of the traffic prediction model were established by using the Gradient Boosting Regression Tree (GBRT) which showed an accuracy of 97.12%. Moreover, the calculation speed was relatively fast, and it has stronger applicability to the prediction of congestion conditions.
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A Deep Learning Framework for Multi Drug Side Effects Prediction with Drug Chemical Substructure
Get PDFNowadays, side effects and adverse reactions of drugs are considered the major concern regarding public health. In the process of drug development, it is also considered the main cause of drug failure. Due to the major side effects, drugs are withdrawan from the market immediately. Therefore, in the drug discovery process, the prediction of side effects is a basic need to control the drug development cost and time as well as launching of an effective drug in the market in terms of patient health recovery. In this study, we have proposed a deep learning model named “DLMSE” for the prediction of multiple side effects of drugs with the chemical structure of drugs. As it is a common experience that a single drug can cause multiple side effects, that’s why we have proposed a deep learning model that can predict multiple side effects for a single drug. We have considered three side effects (Dizziness, Allergy, Headache) in this study. We have collected the drug side effects information from the SIDER database. We have achieved an accuracy of ‘0.9494’ with our multi-label classification based proposed model. The proposed model can be used in different stages of the drug development process.
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Non-invasive EEG based Feature Extraction framework for Major Depressive Disorder analysis
Get PDFDepression and several other behavioral health disorders are serious public health concerns worldwide. Persistent behavioral health issues have a wide range of consequences that affect people personally, culturally and socially. Major depressive disorder (MDD) is a psychiatric ailment that affects people of all ages worldwide. It has grown into a major global health issue as well as an economic burden. Clinicians are using several medications to limit the growth of this disease at an early stage in young people. The goal of this research is to improve the depression diagnosis by altering Electroencephalogram (EEG) signals and extracting the Differential Entropy (DE) and Power Spectral Density (PSD), using machine learning and deep learning techniques. This study analyzed the EEG signals of 30 healthy people and 34 people with Major Depressive Disorder (MDD). K-nearest neighbors (KNN) had the highest accuracy among machine learning algorithms of 99.7%, while Support vector machine (SVM) had acquired 95.7% accuracy. The developed Deep Learning approach, convolution neural network (CNN), achieved 99.6% accuracy. With these promising results, this study establishes the viability of an Electroencephalogram based diagnosis of MDD.
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Prominence of Filtering Techniques for Harmonics Mitigation in Advanced Power Electronics Systems
Get PDFWith the advancement in technology, non-linear loads, continue to increase, and the enigma of harmonics is getting more and more serious. The huge addition of electronic loads in power systems has formed the problem of harmonic generation that has resulted in many associated drawbacks. This paper aims to analyze the harmonics in advanced power electronics-based systems and describes a solution to mitigate them through two distinguished filtering techniques. A rigorous analysis is done concerning the generation of harmonic distortion through different types of loads and harmonic mitigation by employing passive and active power filters. The active power (adaptive) filtering method mitigates all sorts of undesirable frequency components using artificial intelligence (AI) based algorithms by calculating the weight of the fundamental component and generating a harmonics replica to subtract it from the original periodic wave. The simulations that were done show that the indicated techniques can mitigate undesirable harmonics and can lower the total harmonic distortion (THD) effectively according to the statutory limit of the IEEE 519-2014 standard, thus lowering the associated drawbacks of harmonic generation in advanced power electronics systems
How to Combat Against Upcoming Varients of Covid-19
Get PDFNew variant of Covid 19 poses a threat to re-imagine and re-design our cities, which will result in to reduced transportation and a brighter sky. The key objective of the study intends to enlighten how the new variant of COVID-19 may effect cities and their residents socially, economically, psychologically and to suggest measures to combat the effects of a pandemic. Cities are growth engines, and policymakers can help them become more sustainable by creating jobs, reducing poverty, and assisting in the resilience of cities. This is especially relevant for developing countries, which, in comparison to developed ones, are rapidly urbanizing. The researcher carried out a detailed survey in the case study area (Lahore) to gather the facts regarding the impacts of COVID-19. The reliability analysis technique was used to analyze the results. The variables/factors were reliable at the value of 0.8, and 0.7. The analysis shows that residents faced problems in mobility, daily commute, and unavailability of hospitals and health care units. Residents were affected psychologically as well. The most significant impact of the lockdown which proved itself a blessing was the improvement in air quality and the environment of Lahore. The researcher concluded that the epidemic will have a significant impact on Pakistani city administration and governance. Future decisions will determine if post-COVID cities are more environmentally friendly to construct and manage. However, in addition to economic growth, it is vital to address the social and environmental aspects of long-term sustainability
Double Auction used Artificial Neural Network in Cloud Computing
Get PDFDouble auction (DA) algorithm is widely used for trading systems in cloud computing. Distinct buyers request different attributes for virtual machines. On the other hand, different sellers offer several types of virtual machines according to their correspondence bids. In DA, getting multiple equilibrium prices from distinct cloud providers is a difficult task, and one of the major problems is bidding prices for virtual machines, so we cannot make decisions with inconsistent data. To solve this problem, we need to find the best machine learning algorithm that anticipates the bid cost for virtual machines. Analyzing the performance of DA algorithm with machine learning algorithms is to predict the bidding price for both buyers and sellers. Therefore, we have implemented several machine learning algorithms and observed their performance on the bases of accuracy, such as linear regression (83%), decision tree regressor (77%), random forest (82%), gradient boosting (81%), and support vector regressor (90%). In the end, we observed that the Artificial Neural Network (ANN) provided an astonishing result. ANN has provided 97% accuracy in predicting bidding prices in DA compared to all other learning algorithms. It reduced the wastage of resources (VMs attributes) and soared both users\u27 profits (buyers & sellers). Different types of models were analyzed on the bases of individual parameters such as accuracy. In the end, we found that ANN is effective and valuable for bidding prices for both users
Sun Tracking and Control Design for PV Solar energy system
Get PDFIn this modern era of the rapid increase in population, a high rise in technology, and a large number of machinery installed, fuel demand has increased significantly. Non-renewable energies contribute a lot to producing energy worldwide, and that\u27s why they are decreasing at an alarming rate. As an alternative, renewable energies have a high potential to solve this upcoming issue. In this paper, sunlight is utilized for the location of Islamabad, and an active solar tracker is designed. The objective is to develop a cost-effective system with low maintenance requirements. The tracking mechanism is modeled by two sensors, LDR and PV sensor. LDR sensor generates high resistance when light is incident on them, thus reducing the voltage production. PV sensors produce a voltage when sunlight is incident on them, and a voltage drop occurs if a shadow occurs. A thin plate between two LDR sensors or two PV sensors will cast a shadow according to the sun\u27s position. It will create a voltage difference between the two sides, thus causing the system to track the sun. For smooth movement, a servomotor is an effective choice. The system is integrated with a microcontroller for a feedback system of output; Arduino Uno will regulate the uniform and accurate movement of the system. The research on azimuth and elevation angles for the location of an installment is also included in this paper. Different tests are performed for comparative study for both sensors to have performance analysis
Quantum Key Distribution Protocols for Secure Communication
No full textData protection and information security have been the essence of communication in today\u27s digital era. Authentication and secrecy of secure communication are achieved using key-based cryptographic primitives; the security of which significantly relies upon the underlying computationally complex mathematics. Moreover, these existing cryptographic primitives are considered to be non-deterministic on the basis of the existing computational capabilities. However, the considerable advancements in the development of quantum computers have significantly enhanced parallel computations; thereby, posing a great threat to these existing encryption primitives. Thus, in the future, the physical manifestation of a large successful quantum computer is likely to break all the existing public-key encryption algorithms in no time. This has led to a remarkable surge of interest in propelling quantum mechanics into existence; subsequently, leading cryptographers to research various viable domains to offer quantum-resistant secure communications. Resultantly, quantum cryptography/quantum key distribution has emerged as a futuristic replacement for classical cryptography as it offers unconditionally secure communication along with the inherent detection of any unintended user. Thus, keeping in view the significance of this relatively newer domain of cryptography this research focuses on presenting a consolidated review of the various Quantum Key Distribution (QKD) protocols. A comparative analysis of the working mechanism of the prominent QKD protocols is presented along with an overview of the various emerging trends that have been proposed to optimize the implementational efficiency of the BB84 protocol