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Title: Performance Analysis of Energy Harvesting- Non-Orthogonal Multiple Access IoT Network
Creator: Ni, Zhou
Date: 2019
Description: Internet of Things (IoT) systems in general consist of a lot of devices with massive connectivity. Those devices are usually constrained with...
Show moreInternet of Things (IoT) systems in general consist of a lot of devices with massive connectivity. Those devices are usually constrained with limited energy supply and can only operate at low power and low rate. One solution to limited energy is to use energy harvesting to provide sustainable energy. The set of technologies adopted in next-generation wireless communication systems, such as massive MIMO and Non-Orthogonal Multiple Access (NOMA), can provide solutions to increase the throughput of IoT systems. In this thesis, we investigate a cellular-based IoT system combined with energy harvesting and NOMA. We consider all base stations (BS) and IoT devices follow the Poisson Point Process (PPP) distribution in a given area. The unit time slot is divided into two phases, energy harvesting phase in downlink (DL) and data transmission phase in uplink (UL). That is, IoT devices will first harvest energy from all BS transmissions and then use the harvested energy to do the NOMA information transmission. We define an energy harvesting circle within which all IoT devices can harvest enough energy for NOMA transmission. The design objective is to maximize the total throughput in UL within the circle by varying the duration T of energy harvesting phase. In our work, we also consider the inter-cell interference in the throughput calculation. The analysis of Probability Mass Function (PMF) for IoT devices in the energy harvesting circle is also compared with simulation results. It is shown that the BS density needs to be carefully set so that the IoT devices in the energy harvesting circle receive relatively smaller interference and energy circles overlap only with small probability. Our simulations show that there exists an optimal T to achieve the maximum throughput. When the BSs are densely deployed consequently the total throughput will decrease because of the interference.
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Title: Information Security Analysis of Modern Wireless Printers
Creator: Mehta, Keval Samirbhai
Date: 2019
Description: In today’s world, everything is becoming wireless. User-friendliness and security have always been on two opposite sides of the old-fashioned...
Show moreIn today’s world, everything is becoming wireless. User-friendliness and security have always been on two opposite sides of the old-fashioned scale when you give priority to one the second will get hit on somewhat level. This paper concentrates on the same thing in the case of printers. As wireless technology has been made available in the printers and they have got cheaper in recent time, the numbers of households owning the printers have increased dramatically in recent years. New printers use Wi-Fi direct or Wi-Fi AP technology to give wireless access to the user. Wi-Fi P2P also uses the same 802.11 protocol as Wi-Fi AP to help the user to print wirelessly; by directly connecting to or by directly sending commands and documents to the printers. We use a printer to print and scan important documents, which makes it a necessity, that the whole thing is secured. In this paper, I have tried to do analysis on possible security issues with wireless printers with the only wireless connection. The tests include the case where the bad guy will try to prevent the user to use the printer (DoS), from a distance and the case where the bad guy will try to sniff the packets or say important documents that the user is trying to print. I have tried to include different printers like HP, Brother, Canon to do testing, to get the overall idea of security in wireless printers. The first part includes the way of authentication available and the protocols used by the printers and the second part includes the possible ways to get bypass the security and recreate the printing materials that were printed by the user.
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Title: Leakage Power Attack-Resilient Designs of A SRAM Cell in 7nm FinFET Technology
Creator: Chen, Kangqi
Date: 2019
Description: Recently, the classic metal-oxide-semiconductor field-effect-transistor (MOS- FET) has reached its limit for scaling. Another transistor...
Show moreRecently, the classic metal-oxide-semiconductor field-effect-transistor (MOS- FET) has reached its limit for scaling. Another transistor structure, FinFET, gradually has become the alternative choice for next generation of integrated circuits. Excellent features like reduced short channel effects, low threshold-voltage variability, less random dopant fluctuation, etc, offer this transistor model more stability, less leakage and faster performance. In particular, scaling trends force SRAM cells to be more vulnerable while using conventional MOSFET. The application of FinFET helps SRAM cell designs to overcome stability issues and achieve less power and faster speed. Another critical feature of an SRAM cell that needs to be considered is the correlation between data stored in cell and leakage of this cell. Side-Channel Attacks (SCA) like Leakage Power Analysis (LPA) would exploit this correlation to decrypt the secret key inside the memory. SCA has been proved to be a non-invasive but dangerous threat. Therefore, LPA would be the main focus of this thesis research.In this thesis, firstly, threshold voltage of various models are investigated using fundamental logic circuits including full-adders built with pass transistors, CLRCL and SERF. Secondly, conventional 6T SRAM cell design and single-ended 9T SRAM cell design targeting high stability and low power, are implemented and compared. Thirdly, the leakage balance method is applied to 9T cell design. Two novel solutions for LPA prevention of 9T design are proposed, implemented and compared against the original 9T design and conventional 6T design. The results confirm improved leakage balance and attack resilience while maintaining the stability and low-power features of the original 9T SRAM cell.
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Title: DAMAGE ASSESSMENT OF CIVIL STRUCTURES AFTER NATURAL DISASTERS USING DEEP LEARNING AND SATELLITE IMAGERY
Creator: Jones, Scott F
Date: 2019
Description: Since 1980, millions of people have been harmed by natural disasters that have cost communities across the world over three trillion dollars....
Show moreSince 1980, millions of people have been harmed by natural disasters that have cost communities across the world over three trillion dollars. After a natural disaster has occurred, the creation of maps that identify the damage to buildings and infrastructure is imperative. Currently, many organizations perform this task manually, using pre- and post-disaster images and well-trained professionals to determine the degree and extent of damage. This manual task can take days to complete. I propose to do this task automatically using post-disaster satellite imagery. I use a pre-trained neural network, SegNet, and replaced its last layer with a simple damage classification scheme. This final layer of the network is re-trained using cropped segments of the satellite image of the disaster. The data were obtained from a publicly accessible source, the Copernicus EMS system. They provided three channel (RGB) reference and damage grading maps that were used to create the images of the ground truth and the damaged terrain. I then retrained the final layer of the network to identify civil structures that had been damaged. The resulting network was 85% accurate at labelling the pixels in an image of the disaster from typhoon Haiyan. The test results show that it is possible to create these maps quickly and efficiently.
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Title: A Complete Machine Learning Approach for Predicting Lithium-Ion Cell Combustion
Creator: Almagro Yravedra, Fernando
Date: 2020
Description: The object of the herein thesis work document is to develop a functional predictive model, able to predict the combustion of a US18650 Sony...
Show moreThe object of the herein thesis work document is to develop a functional predictive model, able to predict the combustion of a US18650 Sony Lithium-Ion cell given its current and previous states. In order to build the model, a realistic electro-thermal model of the cell under study is developed in Matlab Simulink, being used to recreate the cell's behavior under a set of real operating conditions. The data generated by the electro-thermal model is used to train a recurrent neural network, which returns the chance of future combustion of the US18650 Sony Lithium-Ion cell. Independently obtained data is used to test and validate the developed recurrent neural network using advanced metrics.
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Title: Rapidly Deployable PV-Based Smart Irrigation System
Creator: Usta, Salih
Date: 2019
Description: There are many agricultural fields in developing countries such as Turkey which do not have electricity on site. In order to water these...
Show moreThere are many agricultural fields in developing countries such as Turkey which do not have electricity on site. In order to water these fields, there is usually a need to store water in a water reservoir nearby. This purpose is achieved by manpower or by using diesel-operated water pumps which are often inefficient and require a high degree of maintenance over time. Furthermore, extending the power supply grid to the field is not considered an option by governors, due to the high cost for a relatively small-scale application. Along with this, watering the field is done by farmers, which frequently leads to waste of water, or leads to watering one particular area of the field less than the others, which causes a drop in crop efficiency. Preventing water waste is considered an important issue in the 21st century. Also, increasing crop efficiency in a developing country is an important consideration. To prevent water waste and to enhance crop efficiency, an automated irrigation system is needed. The objective of this study is to develop a photovoltaic-based irrigation system for an agricultural field that is not tied to an existing conventional electric grid. Firstly, a stand-alone PV system is designed according to the field requirements. Secondly, a soil moisture sensor-based smart irrigation system is developed for an automated irrigation process compatible with drip irrigation systems. This system also enables users to monitor and analyze soil moisture data. By developing this type of complete irrigation mechanism, a long-term lower cost, efficient, and environmental-friendly system is designed.
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Title: Wireless Body Sensor Network for Tracking Human Mobility using Long Short-Term Memory Neural Network for Classification
Creator: Gupta, Saumya
Date: 2019
Description: A large number of sensors are used without justification of the number chosen or placement choice. Many papers about body sensor networks...
Show moreA large number of sensors are used without justification of the number chosen or placement choice. Many papers about body sensor networks explore how to capture a type or types of motion, but all their sensors are placed in different locations; making their algorithms very specific to that movement. In this research, we explore the enhancement of human activity classification algorithm using long short-term memory (LSTM) neural network and wearable sensor network. There are five identical nodes used in the body sensor network to collect data. Each node incorporates an ESP8266 Microcontroller with Wi-Fi which is connected to an inertial measurement unit consisting of triple axis accelerometer and gyroscope sensor board. An analysis on the accuracy that each sensor node provides separately and in different combinations has been conducted to allow future research to focus their positioning in optimal positions. A Robot Operating System (ROS) central node is used to illustrate the in-built multi-threading capability. For demonstration, the positions chosen are waist, ankles and wrists. The raw sensor data can be observed on screen while it is being labelled live to create fitting dataset for developing an artificial neural network. Expectation is that increasing the number of sensors should raise the overall accuracy of the output but that isn’t the case observed, positioning of the sensor is pertinent to improvement. These platforms can be further extended to understand different motions and different sensor positions, also expanded to include other sensors.
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Title: Assistant to Dr. Daniel Graupe demonstrating a computerized arm prothesis, Illinois Institute of Technology, Chicago, Illinois, 1980s
Date: 1980-1989
Description: Photograph of an unknown assistant to Dr. Daniel Graupe demonstrating a computerized prosthetic arm. Photographer unknown. Date of photograph...
Show morePhotograph of an unknown assistant to Dr. Daniel Graupe demonstrating a computerized prosthetic arm. Photographer unknown. Date of photograph unknown. Date range listed is approximate.
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Collection: Office of Communications and Marketing photographs, 1905-1999

Title: Dr. Daniel Graupe and an assistant using a computerized arm prothesis, Illinois Institute of Technology, Chicago, Illinois, 1980s
Date: 1980-1989
Description: Photograph of Electrical Engineering professor Dr. Daniel Graupe and an assistant demonstrating a computerized prosthetic arm. Photographer...
Show morePhotograph of Electrical Engineering professor Dr. Daniel Graupe and an assistant demonstrating a computerized prosthetic arm. Photographer unknown. Date of photograph unknown. Date range listed is approximate.
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Collection: Office of Communications and Marketing photographs, 1905-1999

Title: Electrical Engineering handheld scanning device, Illinois Institute of Technology, Chicago, Illinois, 1980s
Date: 1980-1989
Description: Photograph of a handheld scanning device used (and possibly created) by the Illinois Institute of Technology's Department of Electrical...
Show morePhotograph of a handheld scanning device used (and possibly created) by the Illinois Institute of Technology's Department of Electrical Engineering. Photographer unknown.
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Collection: Office of Communications and Marketing photographs, 1905-1999

Title: Video Object Detection using CenterNet
Creator: Mondal, Madhusree
Date: 2021
Description: This thesis investigates the options of video object detection with key-point-based approaches. The problem of recognizing, locating, and...
Show moreThis thesis investigates the options of video object detection with key-point-based approaches. The problem of recognizing, locating, and tracking objects in videos has been a challenging task in the computer vision area. There are few applications on key-point-based object detectors like CornerNet and CenterNet. At the first stage, this work involves the use of the previously proposed CenterNet module as a baseline detector on each frame of the Imagenet Video dataset. Then we apply an RNN module to exploit the temporal information from the past frames for better results.There are challenges in video object detection compared to still image-based object detection. It is not efficient to apply a still-image-based detector on each frame independently because we cannot exploit the temporal contextual information in videos since neighboring frames in a video are highly correlated. Object detection from videos suffers from motion blur, video focus, rare poses, etc. To overcome these issues one way of improving CenterNet for video object detection is to propagate the previous reliable detection results to boost the detection performance.
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Title: ANALYTICAL APPROACH TO ESTIMATE ROTOR TEMPERATURE IN SWITCHED RELUCTANCE MOTOR
Creator: Koujalagi, Shweta Manohar
Date: 2022
Description: Motors contribute most of the loads. Motors find major applications in automobile industries, household appliances, industrial equipment, and...
Show moreMotors contribute most of the loads. Motors find major applications in automobile industries, household appliances, industrial equipment, and other areas. With the time, engineers and industries found some of the drawbacks or disadvantages of using induction motors in certain applications. They started developing other types of motors that are more efficient than existing ones. Among those, switched reluctance motor, referred as SRM is the one. SRMs are simple in construction, rugged and highly efficient motors.Even though SRM has higher efficiency, it still contribute some losses in the form of heat which will increase the temperature of SRM. If the temperature increases beyond certain limit, cable insulation fails, degrades rotor capability of aligning characteristics, damages bearings, etc. Therefore, it is important to understand the flow of heat in SRM. This thesis focuses on heat transfer analysis from stator coil to rotor of SRM using analytical method and numerical method such as finite element analysis from available coil temperature without using any kind of sensors. Analytical and FEA models are built separately to obtained rotor temperatures at various coil temperatures and rotor speeds. Finally, analytical results are validated with FEA model results. Therefore, once the rotor temperature is estimated accurately, model can be implemented in automotive and other industrial applications to continuously monitor the rotor temperature. It is important to monitor temperature to avoid damage of SRM by thermal effects.
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Title: Large Language Model Based Machine Learning Techniques for Fake News Detection
Creator: Chen, Pin-Chien
Date: 2024
Description: With advanced technology, it’s widely recognized that everyone owns one or more personal devices. Consequently, people are evolving into...
Show moreWith advanced technology, it’s widely recognized that everyone owns one or more personal devices. Consequently, people are evolving into content creators on social media or the streaming platforms sharing their personal ideas regardless of their education or expertise level. Distinguishing fake news is becoming increasingly crucial. However, the recent research only presents comparisons of detecting fake news between one or more models across different datasets. In this work, we applied Natural Language Processing (NLP) techniques with Naïve Bayes and DistilBERT machine learning method combing and augmenting four datasets. The results show that the balanced accuracy is higher than the average in the recent studies. This suggests that our approach holds for improving fake news detection in the era of widespread content creation.
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Title: Improving self-supervised monocular depth estimation from videos using forward and backward consistency
Creator: Shen, Hui
Date: 2020
Description: Recently, there has been a rapid development in monocular depth estimation based on self-supervised learning. However, these existing self...
Show moreRecently, there has been a rapid development in monocular depth estimation based on self-supervised learning. However, these existing self-supervised learning methods are insufficient for estimating motion objects, occlusions, and large static areas. Uncertainty or vanishing easily occurs during depth inferencing. To address this problem, the model proposed in this thesis further explores the consistency in video and builds a multi-frame model for depth estimation; secondly, by taking advantage of the optical flow, a motion mask is generated, with additional photometric loss applied for those masked regions. Experiments are carried out on the KITTI dataset. The proposed model performs better than the baseline model in quantitative results, and as seen from the depth map, the scale uncertainty and depth incomplete situations are improved in motion objects and occlusions explicitly.
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Title: Adaptive Learning Approach of a Domain-Aware CNN-Based Model Observer
Creator: Bogdanovic, Nebojsa
Date: 2023
Description: Application of convolutional neural networks (CNNs) for performing defect detection tasks and their use as model observers (MO) has become...
Show moreApplication of convolutional neural networks (CNNs) for performing defect detection tasks and their use as model observers (MO) has become increasingly popular in the medical imaging field. Building upon this use of CNN MOs, we have trained the CNNs to discern between the data it was trained on, and the previously unseen images. We termed this ability domain awareness. To achieve domain awareness, we are simultaneously training a new variation of U-Net CNN to perform defect detection task, as well as to reconstruct a noisy input image. We have shown that the values of the reconstruction mean squared error can be used as a good indicator of how well the algorithm performs in the defect localization task, making a big step towards developing a domain aware CNN MO. Additionally, we have proposed an adaptive learning approach for training these algorithms, and compared them to the non-adaptive learning approach. The main results that we achieved were for the ideal observers, but we also extended these results to human observer data. We have compared different architectures of CNNs with different numbers and sizes of layers, as well as introduced data augmentation to further improve upon our results. Finally, our results show that the proposed adaptive learning approach with introduced data augmentation drastically improves upon the results of a non-adaptive approach in both human and ideal observer cases.
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Title: Voxel Transformer with Density-Aware Deformable Attention for 3D Object Detection
Creator: Kim, Taeho
Date: 2023
Description: The Voxel Transformer (VoTr) is a prominent model in the field of 3D object detection, employing a transformer-based architecture to...
Show moreThe Voxel Transformer (VoTr) is a prominent model in the field of 3D object detection, employing a transformer-based architecture to comprehend long-range voxel relationships through self-attention. However, despite its expanded receptive field, VoTr’s flexibility is constrained by its predefined receptive field. In this paper, we present a Voxel Transformer with Density-Aware Deformable Attention (VoTr-DADA), a novel approach to 3D object detection. VoTr-DADA leverages density-guided deformable attention for a more adaptable receptive field. It efficiently identifies key areas in the input using density features, combining the strengths of both VoTr and Deformable Attention. We introduce the Density-Aware Deformable Attention (DADA) module, which is specifically designed to focus on these crucial areas while adaptively extracting more informative features. Experimental results on the KITTI dataset and the Waymo Open dataset show that our proposed method outperforms the baseline VoTr model in 3D object detection while maintaining a fast inference speed.
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