Computer Engineering Commons^™

System Development Of An Unmanned Ground Vehicle And Implementation Of An Autonomous Navigation Module In A Mine Environment, Jonas Amoama Bredu Jnr

Graduate Theses, Dissertations, and Problem Reports (ETD)

There are numerous benefits to the insights gained from the exploration and exploitation of underground mines. There are also great risks and challenges involved, such as accidents that have claimed many lives. To avoid these accidents, inspections of the large mines were carried out by the miners, which is not always economically feasible and puts the safety of the inspectors at risk. Despite the progress in the development of robotic systems, autonomous navigation, localization and mapping algorithms, these environments remain particularly demanding for these systems. The successful implementation of the autonomous unmanned system will allow mine workers to autonomously determine …

Incentive Analysis Of Blockchain Technology, Rahul Reddy Annareddy

Graduate Theses, Dissertations, and Problem Reports (ETD)

Blockchain technology was invented in the Bitcoin whitepaper released in 2008. Since then, several decentralized cryptocurrencies and applications have become mainstream. There has been an immense amount of engineering effort put into developing blockchain networks. Relatively few projects backed by blockchain technology have succeeded and maintained a large community of developers, users, and customers, while many popular projects with billions of dollars in funding and market capitalizations have turned out to be complete scams.

This thesis discusses the technological innovations introduced in the Bitcoin whitepaper and the following work of the last fifteen years that has enabled blockchain technology. A …

Comparing Symbolic And Connectionist Algorithms For Correlating The Age Of Healthy Children With Sigma-Lognormal Neuromuscular Parameters, Zigeng Zhang, Christian O'Reilly, Rejean Plamondon

Publications

It is important to accurately evaluate the motor control maturity to help physicians diagnose delayed or abnormal motor development in children. Traditionally, it has been challenging to design assessment methods that are practical and accurate at the same time. This study aims to develop an effective algorithm to predict motor control maturity based on the Kinematic Theory of rapid human movements. We used handwritten pen strokes made on an electronic tablet by 513 children (5.5 to 13 years of age). We considered two types of movements: a single stroke and a triangle drawing test. For the analysis, Sigma-Lognormal parameters were …

A Graph-Based Approach To Boundary Estimation With Mobile Sensors, Sean Onufer Stalley, Dingyu Wang, Gautam Dasarathy, John Lipor

Electrical and Computer Engineering Faculty Publications and Presentations

We consider the problem of adaptive sampling for boundary estimation, where the goal is to identify the two dimensional spatial extent of a phenomenon of interest. Motivated by applications in estimating the spread of wildfires with a mobile sensor, we present a novel graph-based algorithm that is efficient in both the number of samples taken and the distance traveled. The key idea behind our approach is that by sampling locations close to known cut edges (edges whose vertices lie on opposite sides of the boundary), we can reliably find additional cut edges. Our approach repeats this process of using the …

Using Satellite Images Datasets For Road Intersection Detection In Route Planning, Fatmaelzahraa Eltaher, Susan Mckeever, Ayman Taha, Jane Courtney

Datasets

Understanding road networks plays an important role in navigation applications such as self-driving vehicles and route planning for individual journeys. Intersections of roads are essential components of road networks. Understanding the features of an intersection, from a simple T-junction to larger multi-road junctions is critical to decisions such as crossing roads or selecting safest routes. The identification and profiling of intersections from satellite images is a challenging task. While deep learning approaches offer state-of-the-art in image classification and detection, the availability of training datasets is a bottleneck in this approach. In this paper, a labelled satellite image dataset for the …

A Risk-Averse Mechanism For Suicidality Assessment On Social Media, Ramit Sawhney, Atula Tejaswi Neerkaje, Manas Gaur

Publications

Recent studies have shown that social media has increasingly become a platform for users to express suicidal thoughts outside traditional clinical settings. With advances in Natural Language Processing strategies, it is now possible to design automated systems to assess suicide risk. However, such systems may generate uncertain predictions, leading to severe consequences. We hence reformulate suicide risk assessment as a selective prioritized prediction problem over the Columbia Suicide Severity Risk Scale (C-SSRS). We propose SASI, a risk-averse and self-aware transformer-based hierarchical attention classifier, augmented to refrain from making uncertain predictions. We show that SASI is able to refrain from 83% …

Process Knowledge-Infused Learning For Suicidality Assessment On Social Media, Kaushik Roy, Manas Gaur, Qi Zhang, Amit Sheth

Publications

Improving the performance and natural language explanations of deep learning algorithms is a priority for adoption by humans in the real world. In several domains, such as healthcare, such technology has significant potential to reduce the burden on humans by providing quality assistance at scale. However, current methods rely on the traditional pipeline of predicting labels from data, thus completely ignoring the process and guidelines used to obtain the labels. Furthermore, post hoc explanations on the data to label prediction using explainable AI (XAI) models, while satisfactory to computer scientists, leave much to be desired to the end users due …

Wise Causal Models: Wisdom Infused Semantics Enhanced Causal Models - A Study In Suicidality Diagnosis, Kaushik Roy, Yuxin Zi, Vignesh Narayanan, Manas Gaur, Sanjay Chandrasekar, Amit Sheth

Publications

The COVID-19 Pandemic has highlighted the gap between the number of mental health care seekers and care providers. Netizens have taken to internet-based platforms such as Reddit to express their experiences. Mental illness diagnosis processes have clinically accepted causal interpretations and semantics. Curiously, mental illness diagnosis accuracy is low relative to similar well-studied illnesses. Motivated by this discrepancy, we propose Wisdom Infused Semantics Enhanced (WISE) causal models, inspired by the wisdom of the crowd idea that learns from a collective agreement among causal models and their semantics for mental illness diagnoses. We use suicidality diagnosis task descriptions, datasets, and baseline …

Knowledge-Infused Reinforcement Learning, Kaushik Roy, Manas Gaur, Qi Zhang, Amit Sheth

Publications

Virtual health agents (VHAs) have received considerable attention, but the early focus has been on collecting data, helping patients follow generic health guidelines, and providing reminders for clinical appointments. While presenting the collected data and frequency of visits to the clinician is useful, further context and personalization are needed for a VHA to interpret and understand what the data means in clinical terms. This has made their use in managing health limited. Such understanding enables patient empowerment and self-appraisal – i.e., aiding the patient in interpreting the data to understand the changes in the patient’s health conditions, and self-management – …

Effect Of Connection State & Transport/Application Protocol On The Machine Learning Outlier Detection Of Network Intrusions, George Yuchi, Torrey J. Wagner, Paul Auclair, Brent T. Langhals

Faculty Publications

The majority of cyber infiltration & exfiltration intrusions leave a network footprint, and due to the multi-faceted nature of detecting network intrusions, it is often difficult to detect. In this work a Zeek-processed PCAP dataset containing the metadata of 36,667 network packets was modeled with several machine learning algorithms to classify normal vs. anomalous network activity. Principal component analysis with a 10% contamination factor was used to identify anomalous behavior. Models were created using recursive feature elimination on logistic regression and XGBClassifier algorithms, and also using Bayesian and bandit optimization of neural network hyperparameters. These models were trained on a …

การพัฒนาเครื่องมืออัตโนมัติสำหรับสร้างแบบจำลองความสัมพันธ์ของส่วนต่อประสานโปรแกรมประยุกต์เว็บเซอร์วิสแบบเรสต์ฟูล, วิภาดา กลึงเทศ

Chulalongkorn University Theses and Dissertations (Chula ETD)

ในอุตสาหกรรมซอฟต์แวร์ นิยมนำซอฟต์แวร์กลับมาใช้ใหม่เป็นจำนวนมาก เนื่องจากเป็นการลดต้นทุนในการพัฒนาซอฟต์แวร์ เอกสารต่าง ๆ ในการพัฒนาระบบจึงมีความสำคัญในการอ้างอิง งานวิจัยนี้ ให้ความสนใจกับแผนภาพยูเอ็มแอลที่เป็นส่วนหนึ่งของเอกสารส่วนต่อประสานโปรแกรมประยุกต์ จึงได้นำเสนอการออกแบบ และพัฒนาเครื่องมืออัตโนมัติสำหรับการสร้างแบบจำลองความสัมพันธ์ของส่วนต่อประสานโปรแกรมประยุกต์เว็บเซอร์วิสแบบเรสต์ฟูลโดยใช้เครื่องมือเสริม PlantUML ที่เป็นเครื่องมือเสริมสำหรับการสร้างแผนภาพ ซึ่งงานวิจัยนี้ได้นำเครื่องมือเสริม PlantUML มาสร้างแผนภาพแบบจำลองความสัมพันธ์ระหว่างคอนโทรลเลอร์ เมธอด และคุณลักษณะภายในของพารามิเตอร์ ที่นำเสนอในรูปแบบของแผนภาพยูเอ็มแอล ดังนั้น เมื่อนำเครื่องมือที่พัฒนาขึ้นไปประยุกต์ใช้ จะช่วยให้ปรับปรุงเอกสารส่วนต่อประสานโปรแกรมประยุกต์ได้ง่ายยิ่งขึ้น และยังช่วยให้เอกสารตรงกับรหัสต้นฉบับ จากการประยุกต์ใช้เครื่องมือกับโครงการ ทำให้ได้ผลลัพธ์ของความถูกต้องเป็น 100%

Improving Feature Learning Capability And Interpretability Of Unsupervised Neural Networks, Chathurika S. Wickramasinghe Brahmana

Theses and Dissertations

The motivation for this dissertation is two-prong. Firstly, the current state of machine learning imposes the need for unsupervised Machine Learning (ML). Secondly, once such models are developed, a deeper understanding of ML models is necessary for humans to adapt and use such models.

Real-world systems generate massive amounts of unlabeled data at rapid speed, limiting the usability of state-of-the-art supervised machine learning approaches. Further, the manual labeling process is expensive, time-consuming, and requires the expertise of the data. Therefore, the existing supervised learning algorithms are unable to take advantage of the abundance of real-world unlabeled data. Thus, relying on …

Continual Learning From Stationary And Non-Stationary Data, Lukasz Korycki

Theses and Dissertations

Continual learning aims at developing models that are capable of working on constantly evolving problems over a long-time horizon. In such environments, we can distinguish three essential aspects of training and maintaining machine learning models - incorporating new knowledge, retaining it and reacting to changes. Each of them poses its own challenges, constituting a compound problem with multiple goals.

Remembering previously incorporated concepts is the main property of a model that is required when dealing with stationary distributions. In non-stationary environments, models should be capable of selectively forgetting outdated decision boundaries and adapting to new concepts. Finally, a significant difficulty …

Machine Learning (Ml) - Assisted Tools For Enhancing Security And Privacy Of Edge Devices, Santosh Kumar Nukavarapu

Theses and Dissertations

The rapid growth of edge-based IoT devices, their use cases, and autonomous communication has created new challenges with privacy and security. Side-channel attacks are one of the examples of security and privacy vulnerabilities that can cause inference at Internet-Service Provider (ISP) and local Wi-Fi networks. Such an attack would leak user’s sensitive information such as home occupancy, medical activity, and daily routines. Another example is that these devices have weak authentication and low encryption standards, making them an easy target for malware-based attacks such as denial of service or launching other network attacks using these infected devices. This thesis dissertation …

Learning Robot Motion From Creative Human Demonstration, Charles C. Dietzel

Theses and Dissertations

This thesis presents a learning from demonstration framework that enables a robot to learn and perform creative motions from human demonstrations in real-time. In order to satisfy all of the functional requirements for the framework, the developed technique is comprised of two modular components, which integrate together to provide the desired functionality. The first component, called Dancing from Demonstration (DfD), is a kinesthetic learning from demonstration technique. This technique is capable of playing back newly learned motions in real-time, as well as combining multiple learned motions together in a configurable way, either to reduce trajectory error or to generate entirely …

Optimized Cancer Detection On Various Magnified Histopathological Colon Imagesbased On Dwt Features And Fcm Clustering, Tina Babu, Tripty Singh, Deepa Gupta, Shahin Hameed

Turkish Journal of Electrical Engineering and Computer Sciences

Due to the morphological characteristics and other biological aspects in histopathological images, the computerized diagnosis of colon cancer in histopathology images has gained popularity. The images acquired using the histopathology microscope may differ for greater visibility by magnifications. This causes a change in morphological traits leading to intra and inter-observer variability. An automatic colon cancer diagnosis system for various magnification is therefore crucial. This work proposes a magnification independent segmentation approach based on the connected component area and double density dual tree DWT (discrete wavelet transform) coefficients are derived from the segmented region. The derived features are reduced further shortened …

Cnn Based Sensor Fusion Method For Real-Time Autonomous Robotics Systems, Berat Yildiz, Aki̇f Durdu, Ahmet Kayabaşi, Mehmet Duramaz

Turkish Journal of Electrical Engineering and Computer Sciences

Autonomous robotic systems (ARS) serve in many areas of daily life. The sensors have critical importance for these systems. The sensor data obtained from the environment should be as accurate and reliable as possible and correctly interpreted by the autonomous robot. Since sensors have advantages and disadvantages over each other they should be used together to reduce errors. In this study, Convolutional Neural Network (CNN) based sensor fusion was applied to ARS to contribute the autonomous driving. In a real-time application, a camera and LIDAR sensor were tested with these networks. The novelty of this work is that the uniquely …

Fft Enabled Ecc For Wsn Nodes Without Hardware Multiplier Support, Utku Gülen, Selçuk Baktir

Turkish Journal of Electrical Engineering and Computer Sciences

ECC is a popular cryptographic algorithm for key distribution in wireless sensor networks where power efficiency is desirable. A power efficient implementation of ECC without using hardware multiplier support was proposed earlier for wireless sensor nodes. The proposed implementation utilized the number theoretic transform to carry operands to the frequency domain, and conducted Montgomery multiplication, in addition to other finite field operations, in that domain. With this work, we perform in the frequency domain only polynomial multiplication and use the fast Fourier transform to carry operands between the time and frequency domains. Our ECC implementation over $GF((2^{13}-1)^{13})$ on the MSP430 …

Using Vertical Areas In Finite Set Model Predictive Control Of A Three-Level Inverter Aimed At Computation Reduction, Alireza Jaafari, Alireza Davari, Cristian Garcia, Jose Rodriguez

Turkish Journal of Electrical Engineering and Computer Sciences

In power electronics applications, finite set model predictive control (FS-MPC) has proven to be a viable strategy. However, due to the high processing power required, using this technology in multilevel converters is difficult. This strategy, which is based on predicting the behavior of the system for all conceivable states, has an issue with a numerous of possible switching states. A recent and useful strategy for dealing with the problem is the limiting of calculations based on triangle regions. Despite its success, this method has several limitations, including the computation required to locate the right triangle and the boundary modes. In …

Application Of Long Short-Term Memory (Lstm) Neural Network Based On Deeplearning For Electricity Energy Consumption Forecasting, Mehmet Bi̇lgi̇li̇, Ni̇yazi̇ Arslan, Ali̇i̇hsan Şekerteki̇n, Abdulkadi̇r Yaşar

Turkish Journal of Electrical Engineering and Computer Sciences

Electricity is the most substantial energy form that significantly affects the development of modern life, work efficiency, quality of life, production, and competitiveness of the society in the ever-growing global world. In this respect, forecasting accurate electricity energy consumption (EEC) is fairly essential for any country?s energy consumption planning and management regarding its growth. In this study, four time-series methods; long short-term memory (LSTM) neural network, adaptive neuro-fuzzy inference system (ANFIS) with subtractive clustering (SC), ANFIS with fuzzy cmeans (FCM), and ANFIS with grid partition (GP) were implemented for the short-term one-day ahead EEC prediction. Root mean square error (RMSE), …

Deep Learning-Aided Automated Personal Data Discovery And Profiling, Apdullah Yayik, Vedat Aybar, Hasan Hüseyi̇n Apik, Sevcan İçöz, Beki̇r Bakar, Tunga Güngör

Turkish Journal of Electrical Engineering and Computer Sciences

In Turkey, Turkish Personal Data Protection Rule (PDPR) No. 6698, in force since 2016, provides protection to citizens for the legal existence of their personal data. Although the law provides excellent guidance, companies currently face challenges in complying with its regulations in terms of storing, sharing, or monitoring personal data. Since any specially designed software with wide industrial usage is not on the market, almost all of the companies have no other choice but to take expensive and error-prone operations manually to ensure their compliance. In this paper, we present an automated solution to facilitate and accelerate PDPR compliance. In …

Evaluating The English-Turkish Parallel Treebank For Machine Translation, Onur Görgün, Olcay Taner Yildiz

Turkish Journal of Electrical Engineering and Computer Sciences

This study extends our initial efforts in building an English-Turkish parallel treebank corpus for statistical machine translation tasks. We manually generated parallel trees for about 17K sentences selected from the Penn Treebank corpus. English sentences vary in length: 15 to 50 tokens including punctuation. We constrained the translation of trees by (i) reordering of leaf nodes based on suffixation rules in Turkish, and (ii) gloss replacement. We aim to mimic human annotator?s behavior in real translation task. In order to fill the morphological and syntactic gap between languages, we do morphological annotation and disambiguation. We also apply our heuristics by …

A Novel Energy Consumption Model For Autonomous Mobile Robot, Gürkan Gürgöze, İbrahi̇m Türkoğlu

Turkish Journal of Electrical Engineering and Computer Sciences

In this study, a novel predictive energy consumption model has been developed to facilitate the development of tasks based on efficient energy consumption strategies in mobile robot systems. For the proposed energy consumption model, an advanced mathematical system model that takes into account all parameters during the motion of the mobile robot is created. The parameters of inclination, load, dynamic friction, wheel slip and speed-torque saturation limit, which are often neglected in existing models, are especially used in our model. Thus, the effects of unexpected disruptors on energy consumption in the real world environment are also taken into account. As …

Automatically Classifying Familiar Web Users From Eye-Tracking Data:A Machine Learning Approach, Meli̇h Öder, Şükrü Eraslan, Yeli̇z Yesi̇lada

Turkish Journal of Electrical Engineering and Computer Sciences

Eye-tracking studies typically collect enormous amount of data encoding rich information about user behaviours and characteristics on the web. Eye-tracking data has been proved to be useful for usability and accessibility testing and for developing adaptive systems. The main objective of our work is to mine eye-tracking data with machine learning algorithms to automatically detect users' characteristics. In this paper, we focus on exploring different machine learning algorithms to automatically classify whether users are familiar or not with a web page. We present our work with an eye-tracking data of 81 participants on six web pages. Our results show that …

Shape Investigations Of Structures Formed By The Self-Assembly Of Aromaticamino Acids Using The Density-Based Spatial Clustering Of Applications With Noise Algorithm, Mehmet Gökhan Habi̇boğlu, Helen W. Hernandez, Şahi̇n Uyaver

Turkish Journal of Electrical Engineering and Computer Sciences

Tyrosine, tryptophan, and phenylalanine are important aromatic amino acids for human health. If they are not properly metabolized, severe rare mental or metabolic diseases can emerge, many of which are not researched enough due to economic priorities. In our previous simulations, all three of these amino acids are discovered to be self-organizing and to have complex aggregations at different temperatures. Two of these essential stable formations are observed during our simulations: tubular-like and spherical-like structures. In this study, we develop and implement a clustering analyzing algorithm using density-based spatial clustering of applications with noise (DBSCAN) to measure the shapes of …

Stability Regions In Time Delayed Two-Area Lfc System Enhanced By Evs, Ausnain Naveed, Şahi̇n Sönmez, Saffet Ayasun

Turkish Journal of Electrical Engineering and Computer Sciences

With the extensive usage of open communication networks, time delays have become a great concern in load frequency control (LFC) systems since such inevitable large delays weaken the controller performance and even may lead to instabilities. Electric vehicles (EVs) have a potential tool in the frequency regulation. The integration of a large number of EVs via an aggregator amplifies the adverse effects of time delays on the stability and controller design of LFC systems. This paper investigates the impacts of the EVs aggregator with communication time delay on the stability. Primarily, a graphical method characterizing stability boundary locus is implemented. …

An Active Contour Model Using Matched Filter And Hessian Matrix For Retinalvessels Segmentation, Mahtab Shabani, Hossein Pourghassem

Turkish Journal of Electrical Engineering and Computer Sciences

Medical image analysis, especially of the retina, plays an important role in diagnostic decision support tools. The properties of retinal blood vessels are used for disease diagnoses such as diabetes, glaucoma, and hypertension. There are some challenges in the utilization of retinal blood vessel patterns such as low contrast and intensity inhomogeneities. Thus, an automatic algorithm for vessel extraction is required. Active contour is a strong method for edge extraction. However, it cannot extract thin vessels and ridges very well. In this research, we propose an improved active contour method that uses discrete wavelet transform for energy minimization to solve …

Stressed Or Just Running? Differentiation Of Mental Stress And Physical Activityby Using Machine Learning, Yekta Sai̇d Can

Turkish Journal of Electrical Engineering and Computer Sciences

Recently, modern people have excessive stress in their daily lives. With the advances in physiological sensors and wearable technology, people?s physiological status can be tracked, and stress levels can be recognized for providing beneficial services. Smartwatches and smartbands constitute the majority of wearable devices. Although they have an excellent potential for physiological stress recognition, some crucial issues need to be addressed, such as the resemblance of physiological reaction to stress and physical activity, artifacts caused by movements and low data quality. This paper focused on examining and differentiating physiological responses to both stressors and physical activity. Physiological data are collected …

Spectrum Sensing With Energy Detection In Multiple Alternating Time Slots, Călin Vlădeanu, Alexandru Marţian, Dimitrie C. Popescu

Electrical & Computer Engineering Faculty Publications

Energy detection (ED) represents a low complexity approach used by secondary users (SU) to sense spectrum occupancy by primary users (PU) in cognitive radio (CR) systems. In this paper, we present a new algorithm that senses the spectrum occupancy by performing ED in K consecutive sensing time slots starting from the current slot and continuing by alternating before and after the current slot. We consider a PU traffic model specified in terms of an average duty cycle value, and derive analytical expressions for the false alarm probability (FAP) and correct detection probability (CDP) for any value of K . Our …

Collective Action On Behalf Of Women: Testing The Conceptual Distinction Between Traditional Collective Action And Small Acts In College Women, Anca M. Miron, Thomas C. Ball, Nyla R. Branscombe, Monica Fieck, Cristinel Ababei, Serena Raymer, Baylee Tkaczuk, Megan M. Meives

Electrical and Computer Engineering Faculty Research and Publications

The current study examines the nature of actions that U.S. college women (N = 267) engage in to promote, protect, or enhance the welfare of other women. The study had two goals: 1) to distinguish between traditional forms of action (traditional collective action) and more informal, interpersonal, forms of action (small acts) among college women; and 2) to test whether the classic antecedents of collective action (gender identity, feminist identity, women’s activist identity, efficacy, appraisals of gender inequality, and injustice standards) are differentially predictive of these two types of participation. A confirmatory factor analysis provided strong support for these two …