Department of Electrical Engineering
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Item Design Of Hemispherical Antenna For Breast Cancer(Publisher COMSATS University Islambad Lahore Campus, 2017) Iqra Asghar,; SP15-REE-005; Dr. Asim Ali Khan, Assistant Profesor [Supervisor]; LHR TP 6824Microwave imaging has proven to be successful for medical imaging applications including breast cancer, lungs cancer, brain stroke detection, cardiac imaging etc. Microwave imaging has the ability to detect malignancy present at depth of tissue, providing non-invasive imaging, measuring dielectric contrast between tissues. This imaging procedure provides an effective substitute of mammography, ultrasound or MRI as all these screenings provide unreliable and uncomfortable detection. In this thesis, microwave imaging system has been simulated for breast tumor detection. The designed microwave imaging system includes the use of holographic imaging. For the design of holographic imaging array, two-dimensional (2D) and three-dimensional (3D) breast models have been designed in MATLAB. Holographic array simulations have been carried out in MATLAB. To efficiently reconstruct an image, inverse scattering algorithm has been applied in holography imaging array. The building block of microwave imaging is to design a suitable antenna that must efficiently transmit and receive electromagnetic signals. An efficient compact double layer antenna has been designed in HFSS for the breast malignancy detection system. The antenna provides good impedance matching, wide bandwidth, stable and symmetric radiation pattern for near field imaging. To improve the performance of imaging system, the antenna was simulated in an array configuration. To overcome the drawbacks of array system, a novel array topology has been simulated in HFSS. The designed array system consists of separate transmitters and receivers for imaging, to provide low cost, reliable and effective detectiItem Mechine And Learning Based IoT Instruion Detection(Publisher COMSATS University Islambad Lahore Campus, 2018) Muhammad Saad Sarfraz Khan; , SP16-REE-006; Dr. Abbas Javed, Assistant Profesor [Supervisor]; LHR TP 6439In today’s world, technological advancements are gradually increasing, so small scale to large scale companies use the internet for business purposes. Other than that Internet of Things (IoT) is a current trend in the world. Most companies try to improve their companies by making them smart premises since this will give a lot of advantages; some are increasing customer satisfaction, saving time, control expenses, monitoring, and many more. So, with the rise of these technological advancements, diverse types of security issues like DDoS, malware, virus, worms, and many other issues occurs as well because of the software or device vulnerabilities. These are harmful to organizations’ sensitive data due to the violation of integrity, confidentiality, and availability. IoT devices have been extensively implemented in many different applications in recent years, such as smart homes, medical, heavy industry, agriculture, networks, security, transportation, etc. An adversary can capture the network traffic of IoT devices and analyze it to reveal user activities even if the traffic is encrypted. Due to the increasing number of cyber-attacks, we need IoT security solutions. The majority of the attacks are extended versions of previously known attacks and bypass the conventional firewall system. An Intrusion Detection System (IDS) performed an incredibly significant part to figure out the novel types of attacks by analyzing the network traffic. Machine Learning (ML) and Deep Learning (DL) based techniques such as Random Forest (RF), Support Vector Machine (SVM), J48, Naïve Bayes (NB), Logistic Regression (LR), etc. are used as a classifier to categorize the network traffic as normal and attack class. The objective of this research is to compare the impact of different Feature Selection (FS) techniques such as Information Gain (IG), Chi-Square, Correlation-based Feature Selection (CFS) subsets evaluator, Classifier based attributes, and other techniques on the accuracy, computational time and confusion matrix of ML & DL based techniques and identify the attack categories. FS methods and classifiers have been implemented on four different data sets NSL KDD, UNSW-15, CIC IDS2017, and TON_IoT and a comparative assessment of the outcome is presented. By applying our proposed comparative model, we will be able to trace out the malicious attacks i.e., DoS, DDoS, routing attacks, Man-in-The-Middle (MITM), etc. in the realm of IoT networks. This model detects the attacks with high accuracy and consumes less time as compared to the other methods.Item Performance Enhancement Of Maximum Power …(Publisher COMSATS University Islambad Lahore Campus, 2023) Muhammad Saqib Ashraf,; SP17-REE-022; Dr, Muhammad Yaqoob Javeed, Assistant Profesor [Supervisor]; LHR TP 6442The demand for electricity has been increased tremendously in the last decade due to the rapid increase in population and exponential growth. The main source of electricity is the conventional energy sources i.e., coal, petroleum and natural gas, etc. However, these non renewable energy sources are getting depleted quickly and putting adverse effects on the environment by the emission of greenhouse gases. Moreover, the current conventional energy system is also facing difficulties in fulfilling the electricity demands, due to increasing power outages, coal prices, and the amount of electricity wastage during transmission through the grids. Therefore, renewable energy sources i.e., solar energy, wind power, and hydroelectricity have been getting immense popularity in the world and they have very little effect on the environment. However, solar energy has numerous advantages over others. Therefore, researchers have been investigating various techniques to convert solar energy to electricity with maximum efficiency. One of the critical problems is the complex partial shading, this occurs when the clouds or any other object stops the light from hitting the panels and this results in two or more peaks that globalize the MPP and it becomes a nonlinear problem. To solve this problem, in this research work a hybrid technique based on Perturb and Observe (P&O) and Dragonfly Algorithm (DA) has been implemented to track maximum power point under both partial shading and complex partial shading scenarios. The simulations are done on Simulink MATLAB and different techniques i.e., P&O, DA, Particle Swarm Optimization (PSO), and Cuckoo Search Algorithm (CSA) are compared with the purposed hybrid technique under four different irradiances i.e., uniform irradiance, partial shading 1, partial shading 2 and complex partial shading. In all cases, the results have been compared and it shows that the developed technique is superior to the compared techniques in terms of transient and power loss. Similarly, GMPP is tracked faster than any method compared with, because the P&O which works before DA reduces the search space and time to achieve GMPP as well.Item A Research On Feasibility Of Intra Vehicle(Publisher COMSATS University Islambad Lahore Campus, 2018) Naeem Mirza,; FA13-MSEE-016; Dr. Ali Nawaz Khan, Assistant Profesor [Supervisor]In Intra Vehicular Wireless Sensor Network (IVWSN), Engine Control Units (ECUs) gathers information about the vehicle from the sensors over wireless channels. This information is then transferred to On-Board Unit (OBU) through Controller Area Network (CAN) in order to monitor and maintain vehicular operations. CAN bus is widely used wired media for communication between ECU and sensors by several car manufacturers but it requires very careful sensor deployment and wiring. In this research thesis, we have achieved low cost and energy efficient communication between (1) sensor nodes and ECU using CAN bus and (2) OBU and ECU within Vehicular Ad Hoc Network (VANET) using Bluetooth low energy (BLE) CC2540/CC2541 modules that can be readily employed in densely urban environment for wireless inter vehicular (up to 50m range) as well as wireless intra vehicular (up to 10m range) communication. The BLE system was invented for the purpose of transmitting small packets of data at once, while consuming less power than Basic Rate & Enhanced Data Rate (BR & EDR) and IEEE 802.15.4 standard based ZigBee devices. A typical communication scenario involves either a Peripheral/ Central or Broadcaster/ Observer device pair. For our tests, we have chosen the Peripheral/ Central modes as they provide greater flexibility to configure application parameters and allow bidirectional communication. We have included comparison between BLE and CAN bus for communication between (1) ECUs and sensors and (2) ECU and OBU, in terms of energy efficiency, throughput, latency and coverage area. Furthermore, wireless communication between ECUs and sensors had been aItem Wavelet Transform Based Sparse Code Multiple(Publisher COMSATS University Islambad Lahore Campus, 2020) Muhammad Sajid Sarwar,; SP17-REE-010; Dr. Amjad Hussain, Assistant Profesor; LHR TP 5633Fifth Generation (5G) of wireless communications that incorporates very high data rates, massive connectivity and very low latency can be fundamental part of 4th Industrial revolution. As far as, physical layer is concerned, Multiple Access (MA) techniques are significant in accommodating users on frequency and time resources for an efficient spectrum utilization, least Multi-Access Interference (MAI) and greater throughput. 5G is transforming user access techniques from orthogonal to non-orthogonal resource sharing methods. Non-Orthogonal Multiple Access (NOMA) technique is the transmission of superimposed signal of multi-users on shared frequency band simultaneously instead of utilizing separate frequency & time slot for each user. NOMA is categoried into Power Domain (PD) NOMA & Code Domain (CD) NOMA. Sparse Code Multiple Access (SCMA), a type of CD-NOMA that facilitates greater user connectivity for 5G. SCMA utilizes sparse resource allocation and non-orthogonal three dimensional codebooks to increase spectrum efficiency. Superimposed encoded data of all users employs a complex receiver such as Message Passing Algorithm (MPA) to extract user specific signal. In this research work, MAI for overloaded SCMA will be examined and different signal processing techniques will be employed to reduce it. Multi-Carrier (MC) communication techniques such as Fast Fourier Transformed Orthogonal Frequency Division Multiplexing (FFT-OFDM) combined with SCMA are analysed in literature and offers satisfactory performance for wireless communications. Recently proposed MC modulation schemes such as Wavelet Transformed OFDM (WT-OFDM) remained relatively unexplored with reference to SCMA. WT provides better decomposition & reconstruction as compared to FFT which is helpful for interference mitigation in a signal. Therefore, Wavelet Transform (WT) is expected to improve Signal to Interference and Noise Ratio (SINR) for SCMA users. Simulation models of SCMA and MC-SCMA schemes are presented that utilize MPA receiver to decode data of all superimposed users and show that WT-SCMA is superior to FFT-SCMA in the context of BER performance. Analytical models are derived for SINR employing Maximal Ratio Combining (MRC) to examine MAI for a particular user in the SCMA/MC-SCMA systems. SINR comparison shows that WT-SCMA outperforms FFT-SCMA.Item Photovoltaic Cell Health Assessment Using Image(Publisher COMSATS University Islambad Lahore Campus, 2021) Abdur Rahman,; FA17-REE-004; Dr. Ikramullah Khosa, Assistant ProfesorWith the beginning of 21st century, stimulation of improving energy efficient policies increased the public interest towards renewable energy especially solar energy through Photovoltaic Systems. It is because solar energy is noiseless and pollution free. This interest opened the research gate of achieving high optimal performance of these solar systems. In this work, classification of the PV cells with respect to health i.e. healthy or faulty is proposed. A dataset of 2624 images of PV modules (containing both healthy and faulty images) is used in this proposed work. Faulty PV cells contained shadow effect, cracked PV cells and dust contamination. The results of this work are obtained using three approaches. First approach includes extraction of hand crafted features from the original and augmented dataset and then training artificial neural network for binary and multi classification based on the extracted features of healthy and faulty solar panels. Second approach includes the transfer learning using pre-trained convolutional neural networks for the classification problem. Third approach includes the designing of customized convolutional neural network architectures to classify the PV cell dataset with respect to their health status. 94.12% accuracy for binary classification, 89.20% accuracy for binary classification with 0.5 threshold and 83.29% accuracy for multi classification is achieved as best results using the third approach.Item Financial Risk Assessment Based On Disaster Induce(Publisher COMSATS University Islambad Lahore Campus, 2021) Rafal Ali Sheikh,; FA17-REE-002; Dr. Mujtaba Jaffery, Assistant Profesor [Supervisor]The security, authenticity and protection of electric power framework is a major dilemma now-a days because it is not a single system many other systems are dependent on it. Severe weather conditions are badly effecting our power infrastructures causing billions of dollar of financial losses. Apart from the economic losses, power outages disrupt the lives of millions of people including industrial and commercial customers. United States has a huge power infrastructure and face many problems due to these disasters. The aim of this thesis is to assess the financial risk associated with disaster-induced power outages. These disasters include mostly severe weather events including hurricanes, thunder storm, wildfire, snowfall, heavy wind and winter storms. It is necessary to take precautionary measures to avoid huge losses and also save the humanity from disasters. The prediction of financial losses and the factors being involved for power outage events is important. To accomplish this task a detailed exploratory and statistical analysis is required to see the correlation between different key parameters. For this purpose a machine learning algorithm Random Forest is used on publicly available data set of the United States from 2000 2016, containing data of about 49 States and comprising of 51 different variables. Exploratory analysis is carried out to form a base for this research work. Random Forest is a useful classifier used both for classification and regression. In this algorithm predicted results are compared with actual output in order to find the error and accuracy. In this research random forest is used for the purpose of predicting the financial losses against each disaster category for the top 8 financially affected states of US. It has one more advantage of predicting the importance of the features being involved for the losses. For the evaluation of results three types of error are calculated including MAE, MAPE and RMSE. It was found that outage duration and customers affected have the highest importance for each disaster in parameters ranking, and the major losses are due to Hurricanes, thunders storm and winter storms.Item Health Assessment Of Human Knee Using Acoustic(Publisher COMSATS University Islambad Lahore Campus, 2023) Zeeshan Arif; SP19-REE-011; Dr, Khurram Ali, Assistant Profesor [Supervisor]Based on the analysis of 116 healthy and 116 osteoarthritic knees, this thesis details the discovery of knee Acoustic Emission (AE) as a helpful tool for the health assessment of the human knee. Knee Osteoarthritis (KOA) is a widely spread disease all over the world which arises because of damage to the joint cartilage and subchondral bone. The main factors, which increase KOA, are unhealthy lifestyle, injuries, and aging. Currently, diagnosis mainly depends on symptoms reported by patients and medical tests like X-rays and Magnetic resonance imaging (MRI). Symptoms start to appear over a years. Current diagnosis methods have some limitations and approachability issues because of the heavy machinery required to perform these tests and the cost of the test is also the main factor that this disease is not identified on time. One of the major limitations is that these tests are carried out when the body is in a static position and doctors get no visualization of the knee performance in normal functional movement. In this research, we are purposing a cost-effective method to get the status of knee health in its normal functional movement by studying the AE produced during the movement of the knee. We tried to identify the presence of OA in the knee and also tried to figure out how we can suggest the severity of the disease by using AEs. Hardware was developed with the collaboration of LUMS hardware was capable to listen to the acoustic sounds from the knee. The hardware setup includes the piezoelectric sensor, piezo film lab amplifier, and NI-DAQ USB6009. The signal is acquired from the knee joint by attaching the piezoelectric sensor with the help of double-sided tape.Item Voltage And Frequency Regulation For Distribution(Publisher COMSATS University Islambad Lahore Campus, 2021) Muhammad Zubair; , SP17-REE-007; : Dr. Sobia Baig, Assistant Profesor [Supervisor]; LHR TP 5632Microgrids comprising the renewable energy resources are the new face of modern elec tricity infrastructure. These grids work autonomously, have their own control architecture and give support to the utility grid in case of any failure. Most of the energy resources in microgrids are renewable like solar, wind etc. These resources have an intermittent nature making the control architecture an essential tool for its better performance. The induction of excessive renewable resources in the utility grid causes serious stability problems as these resources have intermittent nature. Therefore, an efficient control system is the main priority in these types of grids. The control system that is designed for microgrids requires to control some important parameters like voltage and frequency, active/reactive power bal ancing, power flow etc. Regulation of voltage and frequency within its nominal values is the most important parameter that ensures the stability of microgrid. The disturbance in voltage and frequency occurs due to variation in both generation and load. A centralized control approach is adopted in this research work using classical PID con troller to tackle this problem of voltage and frequency regulation. An isolated microgrid model is integrated in a Simulink interface tool using solar, wind and battery as resources. The centralized PID controller is implemented on load bus bar of 0.4 KV. This PID con troller is then tuned with closed loop PID autotuner block in real time as load and gen eration of the isolated microgrid are not constant. A detailed comparison of simple PID and PID with autotuner block is done using the frequency and voltage response of both techniques in constant and varying load scenarios. Results and calculation of various pa rameters shows that the PID working with closed loop autotuner has better performance compared to the simple PID whose gains are calculated at the start using conventional technique like Ziegler-Nichols method. This is because the PID used with autotuner block has its gains updated by observing the real time response of the system whereas simple PID tries to tackle the constantly varying load conditions based on its initial gain values. The performance of control strategy is also observed using real site data of solar and wind for Nooriabad, a city of Sindh Province in PakistanItem Active Power Management Of Standalone DC Micro And(Publisher COMSATS University Islambad Lahore Campus, 2020) Tahir Riaz Sindhu; , FA16-REE-004; Dr. Muhammad Yaqoob Javed, Assistant Profesor; LHR TP 5629Standalone DC microgrids are emerging as an efficient solution for integrating renewable energy sources in remote and off-grid areas. Effective active power management is essential to ensure system stability, reliability, and optimal utilization of available resources. This paper presents a comprehensive approach to active power management in a standalone DC microgrid incorporating renewable energy sources such as solar photovoltaic systems, energy storage units, and varying load demands. The proposed control strategy dynamically balances power generation, storage, and consumption by prioritizing critical loads and maintaining DC bus voltage within acceptable limits. Advanced control techniques, including droop control and energy management algorithms, are implemented to enhance system performance under fluctuating conditions. Simulation results demonstrate improved power sharing, reduced energy losses, and enhanced system stability. The study highlights the importance of intelligent power management strategies in achieving sustainable and reliable standalone DC microgrid operation.