Computer Engineering Commons^™

Reducing Attack Opportunities Through Decentralized Event-Triggered Control, Paul Griffioen, Raffaele Romagnoli, Bruce H. Krogh, Bruno Sinopoli

Faculty Work Comprehensive List

Cyber-physical systems are prevalent in many critical infrastructures and are vulnerable to a variety of attacks from the network. Decentralized control systems are particularly vulnerable since they rely heavily on network communication to achieve their goals. To address network vulnerabilities, we present an event-triggered network connection and communication protocol that minimizes the amount of time agents are connected to the network, reducing the window of opportunity for attacks. This mechanism is a function of only local information and ensures stability for the overall system in attack-free scenarios. Our approach distinguishes itself from current decentralized event-triggered control strategies by considering an …

2024 Summer Proceedings Teuscher Lab, Teuscher Group, Christof Teuscher, Chelsea Ogbede, Lauren Sanday, Sofia Vargas, Artem Arefev

altREU Projects

How will computation evolve in the coming years? What problems can be tackled using artificial intelligence, in a world increasingly driven by data? And how can that data be used to better inform our decisions as a society? In this unique collection of research projects, each chapter represents a distinct work undertaken by a single individual or a group of students as part of the altREU program led by Christof Teuscher. The projects, rooted in applications of artificial intelligence and innovative computation techniques, examine impactful solutions to numerous pressing challenges affecting communities around the world.

Enhancing Multi-Agent System Testing With Diversity-Guided Exploration And Adaptive Critical State Exploitation, Xuyan Ma, Yawen Wang, Junjie Wang, Xiaofei Xie

Research Collection School Of Computing and Information Systems

Multi-agent systems (MASs) have achieved remarkable success in multi-robot control, intelligent transportation, and multiplayer games, etc. Thorough testing for MAS is urgently needed to ensure its robustness in the face of constantly changing and unexpected scenarios. Existing methods mainly focus on single-agent system testing and cannot be directly applied to MAS testing due to the complexity of MAS. To our best knowledge, there are fewer studies on MAS testing. While several studies have focused on adversarial attacks on MASs, they primarily target failure detection from an attack perspective, i.e., discovering failure scenarios, while ignoring the diversity of scenarios. In this …

Paper-Recorded Ecg Digitization Method With Automatic Reference Voltage Selection For Telemonitoring And Diagnosis, Liang Hung Wang, Chao Xin Xie, Tao Yang, Hong Xin Tan, Ming Hui Fan, I. Chun Kuo, Zne Jung Lee, Tsung Yi Chen, Pao Cheng Huang, Shih Lun Chen, Patricia Angela R. Abu

Department of Information Systems & Computer Science Faculty Publications

In electrocardiograms (ECGs), multiple forms of encryption and preservation formats create difficulties for data sharing and retrospective disease analysis. Additionally, photography and storage using mobile devices are convenient, but the images acquired contain different noise interferences. To address this problem, a suite of novel methodologies was proposed for converting paper-recorded ECGs into digital data. Firstly, this study ingeniously removed gridlines by utilizing the Hue Saturation Value (HSV) spatial properties of ECGs. Moreover, this study introduced an innovative adaptive local thresholding method with high robustness for foreground–background separation. Subsequently, an algorithm for the automatic recognition of calibration square waves was proposed …

Dynamic Maze Puzzle Navigation Using Deep Reinforcement Learning, Luisa Shu Yi Chiu

Master's Theses

The implementation of deep reinforcement learning in mobile robotics offers a great solution for the development of autonomous mobile robots to efficiently complete tasks and transport objects. Reinforcement learning continues to show impressive potential in robotics applications through self-learning and biological plausibility. Despite its advancements, challenges remain in applying these machine learning techniques in dynamic environments. This thesis explores the performance of Deep Q-Networks (DQN), using images as an input, for mobile robot navigation in dynamic maze puzzles and aims to contribute to advancements in deep reinforcement learning applications for simulated and real-life robotic systems. This project is a step …

Investigating Hardware-Based Aes Countermeasures In The Sam4l Microcontroller For Side-Channel Attack Mitigation, Turki Mesbel Alamri

Theses and Dissertations

Side-channel attacks (SCAs) represent a sophisticated method by which attackers exploit indirect pathways, such as power leakage and electromagnetic emissions to glean sensitive information from microprocessors. These attacks analyze variation in power consumption during operations like data encryption to infer protected data, potentially compromising the security of the device. For example, observing the power draw differences when a device encrypts known data can also serve defensive purposes. Attacks often overlook emissions and power patterns, while focusing on avoiding network and host-based detection systems. This oversight presents an opportunity for security professionals to use SCAs to enhance system defenses by monitoring …

Performance Evaluation And Integration Of Distortion Mitigation Methods For Fisheye Video Object Detection, John Benedict Du, Gian Paolo Mayuga, Maria Leonora Guico

Electronics, Computer, and Communications Engineering Faculty Publications

The distortion observed in fisheye cameras has proven to be a persistent challenge for numerous state-of-the-art object detection algorithms, instigating the development of various techniques aimed at mitigating this issue. This study aims to evaluate various methods for mitigating distortion in fisheye camera footage and their impact on video object detection accuracy and speed. Using Python, OpenCV, and third-party libraries, the researchers modified and optimized said methods for video input and created a framework for running and testing different distortion correction methods and object detection algorithm configurations. Through experimentation with different datasets, the study found that undistorting the image using …

Bugs In Pods: Understanding Bugs In Container Runtime Systems, Jiongchi Yu, Xiaofei Xie, Ceng Zhang, Sen Chen

Research Collection School Of Computing and Information Systems

Container Runtime Systems (CRSs), which form the foundational infrastructure of container clouds, are critically important due to their impact on the quality of container cloud implementations. However, a comprehensive understanding of the quality issues present in CRS implementations remains lacking. To bridge this gap, we conduct the first comprehensive empirical study of CRS bugs. Specifically, we gather 429 bugs from 8,271 commits across dominant CRS projects, including runc, gvisor, containerd, and cri-o. Through manual analysis, we develop taxonomies of CRS bug symptoms and root causes, comprising 16 and 13 categories, respectively. Furthermore, we evaluate the capability of popular testing approaches, …

Precision Medicine For Apical Lesions And Peri-Endo Combined Lesions Based On Transfer Learning Using Periapical Radiographs, Pei Yi Wu, Yi Cheng Mao, Yuan Jin Lin, Xin Hua Li, Li Tzu Ku, Kuo Chen Li, Chiung An Chen, Tsung Yi Chen, Shih Lun Chen, Wei Chen Tu, Patricia Angela R. Abu

Ateneo Laboratory for Intelligent Visual Environments

An apical lesion is caused by bacteria invading the tooth apex through caries. Periodontal disease is caused by plaque accumulation. Peri-endo combined lesions include both diseases and significantly affect dental prognosis. The lack of clear symptoms in the early stages of onset makes diagnosis challenging, and delayed treatment can lead to the spread of symptoms. Early infection detection is crucial for preventing complications. PAs used as the database were provided by Chang Gung Memorial Medical Center, Taoyuan, Taiwan, with permission from the Institutional Review Board (IRB): 02002030B0. The tooth apex image enhancement method is a new technology in PA detection. …

Federated Learning Systems For Mobile Sensing Data, Xiaopeng Jiang

Dissertations

Federated Learning (FL) has emerged as a new distributed Deep Learning (DL) paradigm that enables privacy-aware training and inference on mobile devices with help from the cloud. This dissertation presents a comprehensive exploration of FL with mobile sensing data, covering systems, applications, and optimizations.

First, a mobile-cloud FL system, FLSys, is designed to balance model performance with resource consumption, tolerate communication failures, and achieve scalability. In FLSys, different DL models with different FL aggregation methods can be trained and accessed concurrently by different apps. In addition, FLSys provides advanced privacy-preserving mechanisms and a common API for third-party app developers to …

Enhancing Security And Privacy For Smarter Environment Through A Robust Cyber-Physical System Framework, Ramya S

Theses and Dissertations

As digital computing paradigm and practices have emerged in disciplines, devices with processors and sensors were rudimentary, performing independent tasks with limited power. The first computer processors were slow, bulky and consuming high energy, as sensors in thermometers and pressure gauges provide original, independent measurements without effective communication, 1999. It often required powerful, energy-efficient processors and advanced sensors to enable seamless communication and sophisticated data processing.

These devices, since smart home systems to industrial automation tools, which continuously collect, analyse and share data via the internet, facilitating if real-time management, predictive maintenance, and improved seamless experience are used, transforming everyday …

Deep Learning Technique For The Classification Of Stress Among The Students Using Physiological Biomarkers With A Hybrid Feature Approach, Rajendran Vg

Theses and Dissertations

Adolescence is a crucial part in life, and the presence of stress, anxiety, depression, and health issues during this stage is a great concern. This research aims to analyze and predict the cognitive stress in students during the examination period using EEG biomarkers. In this study, raw EEG data is acquired under two different experimental conditions, before and after examination, from 14 subjects with an eight-channel Enobio device. After preprocessing of the EEG signal, the brain rhythms such as theta, alpha, and beta sub-band energies and EEG band ratios such as neural activity, heart rate, arousal index, vigilance index and …

Ai In Healthcare: Early Diagnosis Of Skin Cancer Using Medical Image Processing And Deep Neural Networks, Nirmala V

Theses and Dissertations

Several cancer types are commonly prevalent, and skin cancer is one among them, becoming even more widespread worldwide in the last few decades. To diagnose skin cancer at an early stage and obtain appropriate therapy to treat it, there is a demand to know more about the disease’s characteristics or severity. Skin cancer is caused mainly by various reasons, including damage of the sun or tanning beds by ultraviolet light exposure.

Failing to treat skin cancer might substantially impair an individual’s quality of life as the victim. They likely to experience physical issues linked with the deformities caused by psychological …

Motion Based Analysis Of Ultrasound Imaging For The Study Of Musculoskeletal Tissue Bio Mechanics, Ananth Hari R

Theses and Dissertations

Ultrasound image analysis plays an important role in diagnosing musculoskeletal injuries and monitoring rehabilitation exercises. The first and foremost step in this analysis involves segmentation of region of interest from the ultrasound images. The segmentation of the musculoskeletal tissues from the ultrasound images is challenging due to the inherent drawback present in ultrasound like : (1) Poor image quality due to image corruption by speckle noise, shadows, and attenuation. (2) Dis-continuous boundaries due to orientation dependence during the acquisition of image. (3) Low contrast between nearby anatomical structures. Hence in order to overcome these drawbacks, there is a need for …

Abnormal Event Detection Using Hypergraph Based Multiple Objects Tracking Techniques In Surveillance Videos, Palanivel S

Theses and Dissertations

Abnormal event detection aims to identify the events that deviate from expected normal patterns. This work primarily focuses on detection of rare events in public places. The existing research challenges in a video-based surveillance systems for the vehicle have been analysed and presence of abnormal objects in traffic-oriented videos have been detected. A novel approach for event summarization and rare event detection has been proposed in this work. The key ingredient in this work is the incorporation of Hypergraph (HG) matching.

Despite the reasonable amount of success achieved by a large number of researchers over the globe, distinguishing important videos …

An Adaptive Hybrid Deep Learning Architecture For Providing Guaranteed Qos In 5g Cellular Networks, Rajilal Mv Ms

Theses and Dissertations

Wireless network systems must have effective resource allocation, particularly in the context of 5G networks when flexibility is needed to meet a range of network requirements. Resource allocation is essential in cellular network contexts to guarantee equitable access to customers, partners, and cellular service users. Since resource distribution determines network performance, it offers significant advantages when executed well. One of the biggest issues with 5G technology is resource allocation, particularly when it comes to the Quality of Service (QoS) for various applications. Resources in wireless networks include items like channels, power, and spectrum; these must all be apportioned according to …

Review Of Fuzzy Models And Fuzzy Methods For Analysis Of Information In Conditions Of Emotional Decision Making, Latafat Gardashova, Royal Shirinov, Diana Boqdanova

Chemical Technology, Control and Management

In the modern world, decision-making often takes place in an environment of uncertainty and under the significant influence of emotional factors, which requires the use of special methods for analyzing information. This study is devoted to an overview of fuzzy models and methods that allow such factors to be taken into account when making decisions. In particular, the approaches based on fuzzy logic, fuzzy cognitive maps and fuzzy clustering methods that provide flexibility and adaptability in conditions of uncertainty are considered. The study analyzes examples of the application of these methods in various fields, including risk management, medical diagnostics and …

Scla 521 Ai In Society, Bert Chapman

Libraries Faculty and Staff Presentations

Provides access to information resources on societal impacts of artificial intelligence from multiple libraries databases covering multiple disciplines including government information resources.

Real Time Pii Scanning, John David

Electronic Theses and Dissertations

The increased amount of web applications and internet software solutions utilizing cloud frameworks has contributed to large data sets of system log messages being generated constantly. These messages may contain sensitive data, creating an additional security risk for the systems and contributing to the need for analysis of such large volumes of data in real time. Large commercial data monitoring systems can solve for these analysis requirements, but they can be costly. We present a solution to analyzing web application log data which ingests it, processes it and visualizes sensitive data found within in real time. Our solution utilizes an …

Heterogeneous Multi-Robot Person-Following In Constrained Environments, Ori A. Miller

Electronic Theses and Dissertations

Maintaining visibility of a person requires effective systems. Security cameras or ground robots might be ideal, but they often fail in uncontrolled or unknown environments. A single ground robot struggles to navigate and track an agent at the same time. This work addresses the challenge by developing a multi-robot system with a slow ground robot and an agile aerial robot. Three methods are evaluated: FORWARD-PF, RL-Person Following (RL), and a baseline closed-loop method. FORWARD-PF proved the most reliable, completing all nine paths and reaching targets nearly twice as fast as RL. Despite completing seven paths, RL faltered on complex tasks. …