Graduate Projects (Electrical and Computer Engineering)

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    Butterfly PUFS: Securing FPGA Intellectual Property
    (2024) Pandit, Sayali; Papadopoulos, Chris; Sima, Mihai
    This report delves into the creation, implementation, and assessment of the Butterfly Physical Unclonable Function (BPUF) in FPGA systems, with an aim to fortify hardware security. By exploiting natural circuit behavior variations, the BPUF generates unique, indeterminable cryptographic keys, offering solid protection against tampering and reverse engineering. The study initiates with an extensive review of current PUF technologies, emphasizing memory-based PUFs and their role in hardware security. During the implementation stage, the report outlines the configuration and results of both 1-bit and 8-bit BPUF settings. Experimental findings affirm the BPUF’s capability to produce distinctive, reproducible outputs, essential for dependable security implementations. Performance assessments employing Hamming distance metrics further evaluate the stability and uniqueness of BPUF outputs across different scenarios, highlighting their applicability in effective security systems. The report wraps up with considerations for future research, including the exploration of hybrid and more complex PUF designs to transcend existing barriers and augment security measures. The study makes significant contributions to hardware security, suggesting novel strategies to shield digital infrastructures from advanced threats.
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    Convolutional Neural Network Integration to a 3-D Ray-traced Biological Neural Network
    (2024) Chen, Xuan; Lu, Tao
    This study presents a machine learning model that integrates a Convolutional Neural Network (CNN) into a 3-D Ray-traced Biological Neural Network (RayBNN). RayBNN specializes in rearranging and adapting to various problems through transfer learning. CNN is renowned for extracting features from data efficiently, which may boost to RayBNN performance. In this report, two integration schemes are implemented and tested on the Modified National Institute of Standards and Technology(MNIST) and Wakefulness Test recordings (MWT) datasets respectively. Using MNIST dataset, we trained a CNN with an artificial neural network (ANN) and an auto encoder/decoder to extract features from datasets and used them as the input of RayBNN. Using the CNN with ANN approach, an accuracy of 0.9919 ± 0.0012, a precision of 0.9921 ± 0.0008, a recall of 0.9922 ± 0.0012 and an F1-score of 0.9920 ± 0.0008 were obtained. When using CNN with auto encoder/decoder for feature extraction, the accuracy, precision, recall, and 0.9905 ± 0.0035, 0.9901 ± 0.0050, 0.9881 ± 0.0068, and F1-score at 0.9908 ± 0.0010 respectively. For MWT dataset, Cohen’s Kappa values of 0.68 ± 0.05, 0.71 ± 0.04, 0.04 ± 0.02, and 0.06 ± 0.02 for Wakefulness, Microsleep Episode, Microsleep Episode Candidate, Episodes of Drowsiness classes were obtained using CNN with ANN to extract features for RayBNN.
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    Implementation of Frequency Divider by Ring Counter with Design Constraint based on FPGA
    (2024) Li, Wenpei; Sima, Mihai; McGuire, Michael
    Shift registers have been widely used in the field of integrated circuits, which can be used to transport data from a flip-flop to another. A ring counter is composed of a shift register by connecting the input and output of it to form a ring. In this project, a type of frequency divider is implemented with a ring counter to divide the frequency of the clock, and its optimization can be achieved by timing constraints, placement constraints, and routing constraints. In this simulation, Xilinx Vivado is utilized as an optimization tool to refine the timing results of frequency divider, whereas the setup slack time and hold slack time are used as important references. The timing results measure the quality of the design at different stages of adding constraints and other modifications and optimizations are made based on them. All the constraints are completed by adding constraint files in the source except that the routing design should be done by the router tool in Vivado to choose better net nodes for the critical path. After the modification of all constraints, it shows an improvement in the design timing summary.
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    Clustering by Gaussian Mixture Model and Light Gradient Boosting Machine
    (2024) Yang, Feihan; Dong, Xiao-Dai
    This project studies clustering by Gaussian mixture model (GMM) and Bayesian Gaussian mixture model (BGMM) combined with light gradient boosting machine (LightGBM) respectively. One common unsupervised learning method for clustering, K-means, serves as the baseline for comparison. LightGBM is an ensemble supervised learning method that combines a number of weak learners to form a strong learner. In this project, LightGBM is combined with BGMM and GMM to improve the clustering performance. A Kaggle competition dataset is used to test these different learning algorithms. Performance evaluation is based on rand index that assesses the similarity between the ground truth clusters and predicted clusters. Moreover, intracluster distances and intercluster distances that indicate the aggregation of the clusters and the separation between different clusters respectively are calculated to generate other performance metrics. In particular, an intercluster distance named multi-cluster average centroid linkage distance is proposed to simplify the distance computation with high precision. The evaluation results reveal that LightGBM with BGMM consistently outperforms the other methods making it a preferred classification approach for the dataset.
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    SPICE Modeling Of Metal-Molecular Nanoelectronics Networks: An Exploration of Randomly Distributed Resistors & Diodes Modeling and Analysis
    (2024) Naik, Kenil Sandipkumar; Papadopoulos, Chris; Lu, Tao
    An innovative approach to the development of metal-molecular nanoelectronic networks through LTSpice modeling is represented in this project, focusing on the simulation and analysis of randomly distributed resistor blocks and diodes in the network. In the digital era of miniaturization and increasing demand for secure electronic devices, this study incorporates a unique methodology for enhancing hardware security primitives and their performances. The approach proposed a novel methodology using a distinct resistor network circuit resembling that of the gold nanoparticle-molecular network configuration and their internal resistance and defects used for previous investigations regarding the same. A unique Mesh Resistor Network (MRN) with structured randomness based on the resistor network building blocks offers new insights into the correlation and electrical behaviors of nanoscale networks when examined. This novel procedure provides new opportunities to enhance the understanding of electronic transport in nanoparticles and evolves our development of robust hardware encryption keys for increased hardware security. To insert an additional tunable parameter, resistors in the formed MRN are replaced by the proportion of diodes. This adjustment introduces directional current flow and non-linear responses, aligning with the theoretical insights from the previous research. The integration of diodes, particularly in varied orientations, showcases the potential for creating complex, tunable electronic systems that leverage components' resistive and rectifying properties. Furthermore, this opens a broad spectrum of methods to design customized electronic devices with tunable properties for better security and performance, addressing the limitations of current network simulations. By systematically changing the position of the building blocks in the resistor network, the proportion of diodes and metal-gold particles in the Mesh Network provides tunable physical unclonable functions for the design of electronic devices with tunable electrical properties, paving a road for future advancements in molecular electronics and secure communication technologies.
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    Breast Ultrasound Images Segmentation Using Deep Neural Networks
    (2024) Nguyen, Trong Khang; Dr. Rakhmatov, Daler
    Breast Ultrasound (US) imaging has emerged as an important diagnostic technique for detecting and characterizing breast tumors. Accurate segmentation of breast US images plays an essential role in enhancing the efficiency and precision of clinical assessments. This report explores the application of several well-known deep neural networks to the breast US image segmentation task. Specifically, we train and evaluate the following five models: SegNet, U Net, and DeepLab V3+ with three different bondnets (ResNet-18, ResNet-50, and Xception). The presented results are based on two labeled datasets. One is Breast US Images (BUSI) dataset, which was used for training, validation, and testing. The other is Breast US Lesions (BUL) dataset, which was used exclusively for testing. Data augmentation was applied to increase the number and diversity of the data samples by randomly varying the contrast, brightness, and gamma of US images. The performance of each model was evaluated based on Global Accuracy, Mean Accuracy, Mean Intersection-over-Union (IoU), Weighted IoU, Mean Boundary-F1 (BF) score, Average Dice score of Background, Average Dice score of Tumor, Mean Dice score of Tumor, and the model's cost. Overall, our results showed that Xception-based DeepLab V3+ and U-Net outperformed the other models under consideration when segmenting breast US images.
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    Simulating NoC Mesh and Torus Topologies
    (2017) Khan, Muhammad Ahsan; Gebali, Fayez
    An interconnection network is a programmable system that transports the data between the terminals. The interconnection is important because of the limiting factor in the performance of many systems. Network on chip (NoC) plays a vital role in the memory latency or memory bandwidth, which are the two key performances in computer systems. Apart from them the topologies are also one of the most important performance factors. In this project the two most signi cant topologies, mesh topology and torus topology are studied. A study is conducted on the above two mentioned topologies by injecting various it rates with di erent combinations of virtual channels. The main objective of this project is to explain how virtual channels are e ective on throughput and latency on di erent topologies. The comparative evaluation of topologies will help to explore more features in detail which will be helping in future developing in NoC.
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    Breast Cancer Prediction Using Machine Learning Algorithms
    (2024) Shahzad, Zeeshan Ali; Gulliver, T. Aaron
    Breast cancer has become a pressing global health issue with its prevalence increasing worldwide. The rise in breast cancer cases is a cause for concern as it not only affects the physical and emotional well-being of individuals but also places a significant burden on the healthcare system. Early detection and timely intervention are critical factors in effectively combatting this disease. The ability to predict and diagnose breast cancer at its earliest stages can have a profound difference in patient outcomes, potentially saving countless lives. In recent years, the importance of Machine Learning (ML) in the field of healthcare has become paramount. This study considers the utility of supervised ML models to address the challenges posed by breast cancer using the publicly available Breast Cancer Wisconsin (Diagnostic) dataset from the University of California Irvine (UCI) ML repository. The Logistic Regression, Decision Tree, Random Forest, Support Vector Machine (SVM), Naive Bayes and K-Nearest Neighbors (KNN) classifiers are implemented using Jupyter Notebook with Python programming. The goal of the proposed methodology is accurate breast cancer prediction. First, data preprocessing is employed to clean the dataset by removing null values and duplicates, and handling missing data. In order to balance the target labels of the dataset, Synthetic Minority Oversampling Technique (SMOTE) is employed. Then, Principal Component Analysis (PCA) is used to reduce the dimensions of the dataset. The number of components is varied (n=2, 5, 10, 15). For training and testing the ML models, five data splits, namely 80/20, 70/30, 50/50, 30/70, and 20/80 are employed to assess the impact on model performance. The performance of the models is evaluated using the metrics accuracy, precision, recall, F1-score, and execution time. The results obtained show that SVM and Logistic Regression outperform the other models with SVM having an accuracy of 98.2% and an execution time of 9.99 ms with an 80/20 split using 10 features and Logistic Regression having an accuracy of 97.9% and an execution time of 8.42 ms with a 50/50 split using 15 features.
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    PUF Evaluation Metrics on 7 Series FPGA: Comparative Analysis of Arbiter, XOR Arbiter, and Double Arbiter PUFs for Uniqueness, Randomness, and Stability
    (2024-01-23) Lunagariya, janviben; Sima, Mihai; Papadopoulos, Chris
    Hardware security modules play a crucial role in protecting and preserving technologically integrated systems that are used in daily life. They employ cryptographic protocols to secure a system against adversaries. Generally, cryptographic algorithms and security keys are essential for maintaining the security of a system. Cryptography uses a secret key to encipher and decipher the data. The confidential keys are stored in a non-volatile memory, making it easily accessible to potential attackers.The hardware security primitive, Physical Unclonable Function (PUF) is a promising alternative for enhancing the security of interconnected devices. Physical Unclonable Functions are specialized circuit components that exploit the subtle variations inherent in microchip fabrication. These variances enable the creation of unique "fingerprint" output sequences, or responses, in reaction to specific inputs or challenges. The random, device-specific nature of these variations and their replication difficulty - even by the original manufacturer using identical methods, tools, and parameters - make PUFs an excellent choice for cryptographic key generation. Moreover, these characteristics are designed to remain unchanged, reinforcing their suitability for this application. The Arbiter-based Physically Unclonable Function (PUF) is a type of delay-based PUF that utilizes signal delay-line time differences. However, previous studies indicated that Arbiter PUF, when implemented on Xilinx Virtex-5 FPGAs, produced nearly identical responses by exhibiting low uniqueness. Other variants of Arbiter PUF, such as XOR Arbiter PUF and the Double Arbiter PUF, were introduced to address this issue. This novel technique generates highly unique responses from duplicated Arbiter PUFs on FPGAs at a comparable cost to the 2-XOR Arbiter PUF. The Double Arbiter PUF differs from the 2-XOR version in the mode of operation, particularly regarding wire assignment between the arbiter and the final selector output signals. This study evaluates these PUFs for uniqueness, randomness, and stability on Xilinx 7-series FPGA Devices and seeks to identify a new Arbiter PUF operation mode that is feasible for FPGA implementation. We propose the 3-1 Double Arbiter PUF, which includes an extra duplicated Arbiter PUF, yielding three Arbiter PUFs that produce a 1-bit response. When compared with the 3-XOR Arbiter PUF, the 3-1 Double Arbiter PUF shows better response uniqueness and randomness estimated at 50%, indicating that the evaluation metrices of the PUF can be improved by using a new Arbiter PUF operation mode. We show that we can improve uniqueness and randomness using the new mode of operation for the Arbiter PUF performance characteristics for 16, 32, and 64-bit selector pairs for 65,536 responses.
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    Improving Large Graph Visualization Using a Paging Mechanism
    (2023-11-15) Jafarrangchi, Fatemeh; Traore, Issa; Woungang, Isaac
    The activity and event network (AEN) model captures the network activities and events using a large random dynamic graph that is continuously maintained and updated as new information and data arrive. The AEN engine leverages extensive graph database technology in creating, maintaining, and visualizing the produced graph. Because the graph can become very large (e.g., have millions of nodes) over time, a visual analysis by a security analyst can be unwieldy, overwhelming, and thus counterproductive. This thesis presents an extension of the AEN graph engine visualization module, which consists on developing a timeline feature that improves the visualization process by allowing the analyst to access and work on segments or portions of the graph as needed. A graph paging mechanism was developed to implement the timeline feature, where a graph is structured into multiple pages that enable navigating back and forth and other related functionality. To reduce memory/storage usage, the proposed graph paging mechanism supports consolidating fine-grain changes into coarser-grain ones without losing the timeline integrity and altering the order in which the changes occurred. An experimental evaluation using the CIC 2017 IDS evaluation dataset yielded improved results in visualizing and handling large graphs while achieving low performance overhead in terms of response time, CPU time, and memory utilization.
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    Optimizing the Movement Path in a Network of Ground and Aerial Mobile Robots in the Field of Communications and Transportation
    (2023-10-12) Doostniae, Reza; Baniasadi, Amirali
    In digital systems and industries related to the IoT, especially in mechatronic systems and communication industries, as well as land and air transportation, motion sensors and routing systems play a crucial and indispensable role. The primary goal of this research is to find the optimal movement path while effectively avoiding obstacles . In other words, the path should be chosen in a way that ensures the robot does not collide with any obstacles, whether they are stationary or moving objects. To achieve this, the shapes of obstacles are extracted, and the need to avoid them is determined. By implementing obstacle avoidance algorithms , we can guarantee safe and reliable navigation for the robot. This paper describes an object-oriented software system for continuous optimization by a new metaheuristic method, the Bat Algorithm, based on the echolocation behavior of bats. Bat algorithm was successfully used for many optimization problems and there is also a corresponding program in MATLAB
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    Assessing the Effectiveness of Snort in Detecting Malicious URLs
    (2023-08-29) Zuva, Simbarashe; Traore, Issa; Wougang, Isaac
    Web attacks have been on the rise in recent years, and organisations are constantly searching for new and better ways to detect and block the corresponding attack vectors. Some of the prominent attributes of web attack vectors are malicious domains used to trigger or sustain these attacks, for instance, through launching phishing attacks or by hosting command and control (C&C) infrastructures. Detecting accurately and blocking the malicious domains has become increasingly difficult due to the evasive techniques used by the attackers to mask their activities by emulating legitimate network traffic to an accurately high degree and through tactics such as domain generation algorithms (DGA) and fast flux DNS. Snort, an open-source intrusion detection system, has traditionally been utilized to detect network intrusions through network traffic signature analysis. However, while snort has subsequently been upgraded to enable the detection of web attacks, its effectiveness in detecting malicious domains is questionable because of the coarse-grained nature of web attack signatures. At the same time, it is a reasonable proposition to assume that there would be an implicit relation between granular attacks and the usage/occurrence of malicious domains. In this project, a platform is developed to explore and assess experimentally the ability of snort in detecting malicious domains. The proposed approach extracts some useful indicators of compromise (IoC) from the granular Snort alerts triggered by web visits and leverage such information to establish whether the corresponding URLs are benign or malicious. The platform was built around a headless chrome browser and the pfSense open-source firewall which has a built-in snort engine. The experimental evaluation, conducted using a public dataset of benign and malicious domains, yielded important insights into the strengths and limitations of snort in detecting malicious domains, and helped identify directions for future improvements.
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    Design and Implementation of a new Visualization Aided Anomaly Detection Framework
    (2023-08-21) Farag, Ahmed; Traore, Issa; Yousef, Waleed
    In today's data-driven world, the identification of unusual patterns or anomalies in data sets has become increasingly vital, especially in the realm of security data where the detection of these atypical patterns can preempt security threats. This is the juncture where our work, as an extension to UNAVOIDS (Unsupervised and Nonparametric Approach for Visualizing Outliers and Invariant Detection Scoring), becomes instrumental. UNAVOIDS is a distinctive model that integrates specialized techniques for both detection algorithms and visualization methods, operating within a unique space known as the Neighborhood Cumulative Distribution Function (NCDF) space. In this two-dimensional space, each data point is transformed into a unique 2D curve, facilitating visual identification and examination. A salient feature of UNAVOIDS is its fully unsupervised nature, which requires neither prior training nor specific data inputs, eliminating the need for parameter selection or tuning. Another feature is its assignment of a deviation score to each unusual data point, offering a clear gauge of its abnormality. In this study, we successfully deployed UNAVOIDS across four platforms: the Python Package Index (PyPI), a Restful API, a software named VAAD—which integrates UNAVOIDS with the Data Visualization Platform (DVP)—, and a custom Microsoft PowerBivisual. Two main challenges were tackled in this implementation. First, handling large datasets within the RESTful API posed an ongoing challenge. To address this, we adopted compression over file streaming, enabling the efficient transmission of data within the API constraints. Second, creating an interactive visual representation presented a significant challenge due to the unique nature of the data, where each observation is mapped to a 2D curve. We overcame this challenge by mapping curve indices and implementing a reflection mechanism for interactivity between selected curves and other visuals. Our study contributes to the practical implementation and effectiveness of UNAVOIDS, and all these implementations along with their documentations are accessible from the official repository of the ISOT lab. These implementations, catering to users from various sectors including research and development, provide the versatility and effectiveness of UNAVOIDS in diverse environments.
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    A Long-Range Transmission Network for Animal Sighting in the Wilderness
    (2023-08-16) Zhang, Yan; Li, Kin Fun
    When wild animals are monitored in the vast wilderness of Canada, data transmission is considerably challenging due to the lack of effective network service provided by telecom operators or carriers, especially in sparsely populated areas. A Long-Range Transmission Network for a wildlife detection system using low-power and low-cost embedded software and hardware is designed and implemented. The objective of the system is to transmit the results of wildlife identification with environmental data through independent long-range networking. The system consists of a Camera-embedded System for wildlife image capturing and environmental data logging, a user system for scanning images and notifications, and a LoRaWAN networking for Long-Range Transmission. Once a targeted animal is detected and identified, the system issues an alarm in the monitored area and sends a LoRa data frame to an application server for further analysis and user notification. The transmission distance of data is effectively extended through the relay between nodes. The system can process up to nine frames per second from the camera and identify the designated wildlife with high accuracy by asynchronous multi-threading in a low-cost embedded system. The application could be beneficial for a variety of purposes in the vast and diverse wilderness areas, such as traffic alarms for large wild animals’ crossing, monitoring wildlife migrations by biologists, or a warning system in urban areas when there is a potential threat to the public such as approaching dangerous animals.
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    Stock Market Prediction using LSTM and Markov Chain Models: A Case Study of Royal Bank of Canada Stock
    (2023-08-09) Kumar, Amer; Gebali, Fayez; El-Kharashi, Mohamed Watheq
    Stock price prediction is one of the most important aspects of financial investment. This research aims to provide insights into the dynamics of stock prices, enabling more informed decision-making in financial investments by combining these two modeling approaches. Using a four-layer long short-term memory (LSTM) architecture and the Root Mean Square Error (RMSE) as the loss function, we aim to capture temporal dependencies and patterns to predict closing prices. Furthermore, we employ a threestate Markov chain to estimate the transition matrix, and metrics like steady-state distribution and mean hitting times have been used to calculate the matrix. The preliminary results indicate that this approach shows promising results for stock market prediction as LSTM has predictive power that caters more to long-term temporal trends while Markov Chain provides probabilistic values for staying and transitioning to states. The findings of the study highlight the effectiveness of combining LSTM and Markov Chain in capturing the intricate dynamics of the stock market data and predicting stock market prices.
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    Stock Price Prediction Using Natural Language Processing and Machine Learning
    (2023-08-09) Amer, Ahmed; Gebali, Fayez; El-Kharashi, Mohamed
    Predicting the stock market is an infamous problem that many people have tried to solve. Can real time textual data in the form of tweets be used to predict stock movements? In this project, the use of different natural language processing methods are used to process twitter data to try to find out their sentiment. Furthermore, based on the sentiment, further analysis is done using machine learning techniques to try and predict next day returns for individual stocks. Two and Three different features were used to try and predict the next day's percentage change. The metrics used to assess the methodology were accuracy, precision and cumulative percentage gain or loss using a specific strategy or method. The results of this project suggest that using tweets as input for natural language processing and machine learning can achieve average accuracies and result in strategies that have consistently beaten the market in terms of cumulative returns.
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    Optimizing Demand Response in Deregulated Electricity Markets: A Customer-Centric Game Theory Approach
    (2023-08-01) Goudarzi, Arman; Traore, Issa
    In the era of IoT-enabled smart grid technologies and the ever-increasing integration of renewable energy sources, the need for efficient and customer-oriented demand response programs is becoming crucial for the stability and flexibility of power systems. In this regard, this report presents an innovative customercentric game theory-based demand response (CC-GTDR) for managing electricity consumption during periods of high demand in a deregulated electricity market. The proposed CC-GTDR method exceptionally combines both incentive and price-based demand response programs while emphasizing customer benefits and flexibility of choice. A fuzzy analytic hierarchy process based on non-linear programming (FAHPNLP) is employed to determine the optimum weightings of the designed multi-criteria objective function of the study. To solve the proposed model, a hybrid optimization algorithm is implemented, which merges enthusiasm-assisted teaching and learning-based optimization (EaTLBO) with an enhanced variant of particle swarm optimization (EPSO). The study investigates various dynamic pricing mechanisms, such as time-of-use pricing, real-time pricing, and their combinations, in deregulated electricity markets. The proposed approach demonstrates significant improvements in overall load and peak load reductions, as well as utility profit gains. Additionally, the integration of renewable energy sources (RESs) within the CCGTDR and profit-based dynamic cost environmental economic dispatch (DCEED) model results in substantial reductions in NOx emissions. The developed CC-GTDR model contributes to a more resilient and efficient electrical system by prioritizing customer engagement and empowerment, ultimately enhancing grid reliability and facilitating the integration of renewable resources.
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    Maximizing Energy Efficiency in Energy Management System using Optimization Algorithm in Microgrids
    (2023-07-04) Shah, Sarthak Umeshkumar; Baniasadi, Dr. Amirali
    Due to technological advancements, population growth, and urbanization, the demand for electricity is increasing day by day. Meeting the global electricity demand is a challenge considering its socio-economic and environmental impacts. Energy Management Systems (EMS) are becoming a vital topic of discussion, as renewable energy sources such as solar, wind, hydro, and energy storage systems are being considered. EMS is becoming an essential component of a microgrid, as the system works when connected with the grid and also in islanded mode, connected with renewable sources. However, the increasing use of renewable energy resources is causing operational efficiency and reliability issues. Additionally, meeting demands during high energy consumption and reducing costs during high demand for electricity are challenging. Therefore, optimization techniques are being implemented to solve issues related to demand response and cost reduction. The proposed approach focuses on minimizing the total cost of energy consumption, taking into account demand, load control, energy storage systems, and PV systems using the novel algorithm Ant Colony Optimization. The results demonstrate that the Ant Colony Optimization algorithm is effective in reducing costs and can be used to address increasing demands and constraints related to energy management in microgrids. Future work may include fault detection, power quality improvement through optimization algorithms in the real-world grid model, and automating it to prevent losses, power outages, and asset failures.
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    Deep Learning-Based Automatic Modulation Classification for Telecommunication Systems
    (2023-05-31) Sanatimehrizi, Sara; Baniasadi, Amirali
    Modulation schemes play a crucial role in various communication systems, as they enable the transmission of information through electromagnetic signals. Accurately identifying the modulation scheme employed in a signal is essential for efficient signal processing, interference mitigation, and overall system performance. However, predicting modulation schemes based solely on their features remains a challenging task due to the complexity and variability of modern communication signals. This thesis addresses the problem of modulation scheme prediction by developing and evaluating a model and algorithm that capable to analyze the distinctive features of different modulation schemes. The dataset used in this study is a real-time series dataset obtained from MCI, consisting of 36,000 signals with features such as Modulation, In-phase Signal, Quadrature Signal, and Signal-to-Interference-plus-Noise Ratio. The goal is to train a fully connected neural network to accurately classify and predict the modulation used in unknown signals. Experimental results demonstrate the effectiveness of the proposed algorithm, with a validation accuracy of 83.33% and an overall accuracy of 93.90%. While these results indicate the algorithm's capability to predict modulation types and classify instances accurately, it is important to acknowledge that there is room for improvement. In comparison to real-world scenarios, further enhancements can be made to achieve even better results. It is essential to recognize that the proposed model and algorithm provide a solid foundation for enhancing signal processing and system performance in communication systems. By accurately identifying modulation schemes, this research contributes to the advancement of efficient communication techniques. Future work in this area has the potential to build upon these findings and further refine the algorithm, potentially yielding improved accuracy and robustness when applied to real-world scenarios.
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    Authentication Algorithms modelling and Simulations of an Arbiter PUF
    (2023-05-08) Khan, Vaseem; Gebali, Fayez
    Physical attacks represent a threat to intellectual property, confidential data, and service security because they typically involve reading and modifying data. Attackers frequently have access to tools and resources that can be utilised, either invasively or non-invasively, to read or corrupt memory. Secret keys for cryptographic techniques are often kept in memory. Physical Unclonable Functions (PUFs), which dynamically construct keys only when necessary and do not need to be retained on a powered-off chip, appear to be a potential remedy for such issues. PUFs are circuit primitives that use inherent differences of microchips made during the manufacturing process to produce distinctive "fingerprint" output sequences (response) to a particular input (challenge). The PUF is a fantastic choice for creating cryptographic keys since these modifications are stochastic, device-specific, hard to duplicate even by the same manufacturer using similar procedures, tools, and settings, and are intended to be static. The delay based PUF, an arbiter PUF, is the subject of our study. It benefits from the differences in propagation delays that are present between two symmetrical channels. Without the need for helper data or secure sketch techniques, we created some of the most modern algorithms that may be used to enable solid authentication and secret key generation. Finally, we present data that demonstrates how these devices behave and how their functionality is influenced by the chosen authentication mechanism and key system variables.
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