Engineering Commons^™

Improving Fused Filament Fabrication Additive Manufacturing Through Computer Vision Analysis And Fabrication Optimization, Aliaksei Petsiuk

Electronic Thesis and Dissertation Repository

Additive manufacturing (AM), also known as 3-D printing, is one of the fundamental elements of Industry 4.0. According to ASTM standards, AM can be classified by production principles, types of raw materials, energy sources, and fabrication volumes. Fused filament fabrication (FFF) is one of the most accessible technologies that offers independent manufacturers great opportunities due to its simplicity, scalability, and low cost.

Modern 3-D printing is moving from single-material prototyping to complex multi-material product creation. It is firmly established in a wide range of applications, significantly expanding manufacturing horizons, providing innovative design capabilities, and improving product quality through the optimal …

Star-Based Reachability Analysis Of Binary Neural Networks On Continuous Input, Mykhailo Ivashchenko

Department of Computer Science and Engineering: Dissertations, Theses, and Student Research

Deep Neural Networks (DNNs) have become a popular instrument for solving various real-world problems. DNNs’ sophisticated structure allows them to learn complex representations and features. However, architecture specifics and floating-point number usage result in increased computational operations complexity. For this reason, a more lightweight type of neural networks is widely used when it comes to edge devices, such as microcomputers or microcontrollers – Binary Neural Networks (BNNs). Like other DNNs, BNNs are vulnerable to adversarial attacks; even a small perturbation to the input set may lead to an errant output. Unfortunately, only a few approaches have been proposed for verifying …

Uncovering And Mitigating Spurious Features In Domain Generalization, Saeed Karimi, Hamdi̇ Di̇bekli̇oğlu

Turkish Journal of Electrical Engineering and Computer Sciences

Domain generalization (DG) techniques strive to attain the ability to generalize to an unfamiliar target domain solely based on training data originating from the source domains. Despite the increasing attention given to learning from multiple training domains through the application of various forms of invariance across those domains, the enhancements observed in comparison to ERM are nearly insignificant under specified evaluation rules. In this paper, we demonstrate that the disentanglement of spurious and invariant features is a challenging task in conventional training since ERM simply minimizes the loss and does not exploit invariance among domains. To address this issue, we …

Motion Magnification-Inspired Feature Manipulation For Deepfake Detection, Aydamir Mirzayev, Hamdi Di̇bekli̇oğlu

Turkish Journal of Electrical Engineering and Computer Sciences

Recent advances in deep learning, increased availability of large-scale datasets, and improvement of accelerated graphics processing units facilitated creation of an unprecedented amount of synthetically generated media content with impressive visual quality. Although such technology is used predominantly for entertainment, there is widespread practice of using deepfake technology for malevolent ends. This potential for malicious use necessitates the creation of detection methods capable of reliably distinguishing manipulated video content. In this work we aim to create a learning-based detection method for synthetically generated videos. To this end, we attempt to detect spatiotemporal inconsistencies by leveraging a learning-based magnification-inspired feature manipulation …

Ai And 6g Into The Metaverse: Fundamentals, Challenges And Future Research Trends, Muhammad Zawish, Fayaz Ali Dharejo, Sunder Ali Khowaja, Saleem Raza, Steven Davy, Kapal Dev, Paolo Bellavista

Articles

Since Facebook was renamed Meta, a lot of attention, debate, and exploration have intensified about what the Metaverse is, how it works, and the possible ways to exploit it. It is anticipated that Metaverse will be a continuum of rapidly emerging technologies, usecases, capabilities, and experiences that will make it up for the next evolution of the Internet. Several researchers have already surveyed the literature on artificial intelligence (AI) and wireless communications in realizing the Metaverse. However, due to the rapid emergence and continuous evolution of technologies, there is a need for a comprehensive and in-depth survey of the role …

A Survey On Few-Shot Class-Incremental Learning, Songsong Tian, Lusi Li, Weijun Li, Hang Ran, Xin Ning, Prayag Tiwari

Computer Science Faculty Publications

Large deep learning models are impressive, but they struggle when real-time data is not available. Few-shot class-incremental learning (FSCIL) poses a significant challenge for deep neural networks to learn new tasks from just a few labeled samples without forgetting the previously learned ones. This setup can easily leads to catastrophic forgetting and overfitting problems, severely affecting model performance. Studying FSCIL helps overcome deep learning model limitations on data volume and acquisition time, while improving practicality and adaptability of machine learning models. This paper provides a comprehensive survey on FSCIL. Unlike previous surveys, we aim to synthesize few-shot learning and incremental …

Indian Sign Language Classification (Isl) Using Machine Learning, Subhalaxmi Chakraborty, Nanak Bandyopadhyay, Piyal Chakraverty, Swatilekha Banerjee, Zinnia Sarkar, Sweta Ghosh

American Journal of Electronics & Communication (AJEC)

Communication is a crucial for humans, it is most vital. People with hearing or speaking disabilities need a way to communicate with other people of the society and vice versa. This paper presents a novel methodology in classifying the English Alphabets shown via various hand gestures in The Indian Sign Language (ISL) using Mediapipe Hands API, launched by Google. The objective of using this API is to detect 21 landmarks in each hand along with their x, y and z coordinates in 3D space. Due to the scarcity of proper dataset available on the internet for ISL, at the very …

Implementing A Self Driven Edge Avoiding Robot ------------ Using Arduino, Dr.Sudipta Basu Pal, Amit Kumar Maji, Rohit Hazra

American Journal of Electronics & Communication (AJEC)

Robot navigation requires the guidance of a mobile robot through the desired path to the desired goal avoiding obstacles and hazards encountered in an unknown environment. Detection and avoidance of obstacles, collisions and hazardous situations are in the first place. However, path planning and arrival at the desired goal is also an essential part of the reliable and secure navigation of mobile robots. Planning the optimal path requires optimization of specific navigation performance, such as the minimum time until the robot reaches desired goal with a minimum of control, but also requires to comply with certain restrictions in robot motion, …