Engineering Commons^™

Human-Machine Communication: Complete Volume. Volume 7 Special Issue: Mediatization

Human-Machine Communication

This is the complete volume of HMC Volume 7. Special Issue on Mediatization

Artificial Sociality, Simone Natale, Iliana Depounti

Human-Machine Communication

This article proposes the notion of Artificial Sociality to describe communicative AI technologies that create the impression of social behavior. Existing tools that activate Artificial Sociality include, among others, Large Language Models (LLMs) such as ChatGPT, voice assistants, virtual influencers, socialbots and companion chatbots such as Replika. The article highlights three key issues that are likely to shape present and future debates about these technologies, as well as design practices and regulation efforts: the modelling of human sociality that foregrounds it, the problem of deception and the issue of control from the part of the users. Ethical, social and cultural …

Mediatization And Human-Machine Communication: Trajectories, Discussions, Perspectives, Andreas Hepp, Göran Bolin, Andrea L. Guzman, Wiebke Loosen

Human-Machine Communication

As research fields, mediatization and Human-Machine Communication (HMC) have distinct historical trajectories. While mediatization research is concerned with the fundamental interrelation between the transformation of media and communications and cultural and societal changes, the much younger field of HMC delves into human meaning-making in interactions with machines. However, the recent wave of “deep mediatization,” characterized by an increasing emphasis on general communicative automation and the rise of communicative AI, highlights a shared interest in technology’s role within human interaction. This introductory article examines the trajectories of both fields, demonstrating how mediatization research “zooms out” from overarching questions of societal and …

The Development And Testing Of A Gyroscope-Based Neck Strengthening Rehabilitation Device, Nicole D. Devos

Electronic Thesis and Dissertation Repository

Neck pain can be debilitating, and is experienced by the majority of people at some point over the course of their life. Resistance training has been shown to have significant improvement in pain or disability for patients. There are few options available for telerehabilitation, and the use of gyroscope stabilizers is proposed for this use. A biomechanics model of a head--neck--gyroscope system was created. In order to also model the dynamics of such a system, this work proposes a blended method using the Denavit--Hartenberg (DH) convention, popular in the field of robotics, with the Lagrangian mechanics approach to analyze an …

Love Machina, John C. Lyden

Journal of Religion & Film

This is a film review of Love Machina (2024), directed by Peter Sillen.

Securing Edge Computing: A Hierarchical Iot Service Framework, Sajan Poudel, Nishar Miya, Rasib Khan

Posters-at-the-Capitol

Title: Securing Edge Computing: A Hierarchical IoT Service Framework

Authors: Nishar Miya, Sajan Poudel, Faculty Advisor: Rasib Khan, Ph.D.

Department: School of Computing and Analytics, College of Informatics, Northern Kentucky University

Abstract:

Edge computing, a paradigm shift in data processing, faces a critical challenge: ensuring security in a landscape marked by decentralization, distributed nodes, and a myriad of devices. These factors make traditional security measures inadequate, as they cannot effectively address the unique vulnerabilities of edge environments. Our research introduces a hierarchical framework that excels in securing IoT-based edge services against these inherent risks.

Our secure by design approach prioritizes …

Control Of Fully-Actuated Aerial Manipulators And Omni-Directional Multirotors, Riley M. Mccarthy

Mechanical Engineering ETDs

This thesis details the system modeling, design, control, simulation, construction, and
testing of both a fully-actuated and omni-directional multirotor aerial system created
for the primary purpose of performing active tasks with their environment. This work
verifies the capabilities of both systems through empirical testing, and demonstrates
how through the use of new control methods and physical designs multirotors can
expand their purpose from passive inspection based tasks to active contact based
tasks. These systems take advantage of newly implemented control allocation features present in the PX4 flight control software, version 1.14. The use of which makes designing controllers for such …

Brain-Inspired Spatio-Temporal Learning With Application To Robotics, Thiago André Ferreira Medeiros

USF Tampa Graduate Theses and Dissertations

The human brain still has many mysteries and one of them is how it encodes information. The following study intends to unravel at least one such mechanism. For this it will be demonstrated how a set of specialized neurons may use spatial and temporal information to encode information. These neurons, called Place Cells, become active when the animal enters a place in the environment, allowing it to build a cognitive map of the environment. In a recent paper by Scleidorovich et al. in 2022, it was demonstrated that it was possible to differentiate between two sequences of activations of a …

A Modular Framework For Surface-Embedded Actuation And Optical Sensing In Soft Robots., Paul Bupe Jr

Electronic Theses and Dissertations

This dissertation explores the development and integration of modular technologies in soft robotics, with a focus on the OptiGap sensor system. OptiGap serves as a simple, flexible, cost-effective solution for real-time sensing of bending and deformation, validated through simulation and experimentation. Working as part of an emerging category of soft robotics called Soft, Curved, Reconfigurable, Anisotropic Mechanisms, or SCRAMs, this research also introduces the Thermally-Activated SCRAM Limb (TASL) technology, which employs shape-memory alloy (SMA) wire embedded in curved sheets for surface actuation and served as the initial inspiration for OptiGap. In addition, the EneGate system is presented as a complementary …

Accelerating Machine Learning Inference For Satellite Component Feature Extraction Using Fpgas., Andrew Ekblad

Theses and Dissertations

Running computer vision algorithms requires complex devices with lots of computing power, these types of devices are not well suited for space deployment. The harsh radiation environment and limited power budgets have hindered the ability of running advanced computer vision algorithms in space. This problem makes running an on-orbit servicing detection algorithm very difficult. This work proposes using a low powered FPGA to accelerate the computer vision algorithms that enable satellite component feature extraction. This work uses AMD/Xilinx’s Zynq SoC and DPU IP to run model inference. Experiments in this work centered around improving model post processing by creating implementations …

Developing A Flexible System For A Friendly Robot To Ease Dementia (Fred) Using Cloud Technologies And Software Design Patterns, Robert James Bray

Masters Theses

In this work, we designed two prototypes for a friendly robot to ease dementia (FRED). This affordable social robot is designed to provide company to older adults with cognitive decline, create reminders for important events and tasks, like taking medication, and providing cognitive stimulus through games. This project combines several cloud technologies including speech-to-text, cloud data storage, and chat generation in order to provide high level interactions with a social robot. Software design patterns were employed in the creation of the software to produce flexible code base that can sustain platform changes easily, including the framework used for the graphical …

An In-Depth Analysis Of Domain Adaptation In Computer And Robotic Vision, Muhammad Hassan Tanveer, Zainab Fatima, Shehnila Zardari, David A. Guerra-Zubiaga

Faculty and Research Publications

This review article comprehensively delves into the rapidly evolving field of domain adaptation in computer and robotic vision. It offers a detailed technical analysis of the opportunities and challenges associated with this topic. Domain adaptation methods play a pivotal role in facilitating seamless knowledge transfer and enhancing the generalization capabilities of computer and robotic vision systems. Our methodology involves systematic data collection and preparation, followed by the application of diverse assessment metrics to evaluate the efficacy of domain adaptation strategies. This study assesses the effectiveness and versatility of conventional, deep learning-based, and hybrid domain adaptation techniques within the domains of …

Six-Degree-Of-Freedom Optimal Feedback Control Of Pinpoint Landing Using Deep Neural Networks, Omkar S. Mulekar, Hancheol Cho, Riccardo Bevilacqua

Student Works

Machine learning regression techniques have shown success at feedback control to perform near-optimal pinpoint landings for low fidelity formulations (e.g. 3 degree-of-freedom). Trajectories from these low-fidelity landing formulations have been used in imitation learning techniques to train deep neural network policies to replicate these optimal landings in closed loop. This study details the development of a near-optimal, neural network feedback controller for a 6 degree-of-freedom pinpoint landing system. To model disturbances, the problem is cast as either a multi-phase optimal control problem or a triple single-phase optimal control problem to generate examples of optimal control through the presence of disturbances. …

Examining The Externalities Of Highway Capacity Expansions In California: An Analysis Of Land Use And Land Cover (Lulc) Using Remote Sensing Technology, Serena E. Alexander, Bo Yang, Owen Hussey, Derek Hicks

Mineta Transportation Institute Publications

There are over 590,000 bridges dispersed across the roadway network that stretches across the United States alone. Each bridge with a length of 20 feet or greater must be inspected at least once every 24 months, according to the Federal Highway Act (FHWA) of 1968. This research developed an artificial intelligence (AI)-based framework for bridge and road inspection using drones with multiple sensors collecting capabilities. It is not sufficient to conduct inspections of bridges and roads using cameras alone, so the research team utilized an infrared (IR) camera along with a high-resolution optical camera. In many instances, the IR camera …

Feasibility And Outcomes Of Supplemental Gait Training By Robotic And Conventional Means In Acute Stroke Rehabilitation, Mukul Talaty, Alberto Esquenazi

Moss-Magee Rehabilitation Papers

INTRODUCTION: Practicality of implementation and dosing of supplemental gait training in an acute stroke inpatient rehabilitation setting are not well studied but can have positive impact on outcomes.

OBJECTIVES: To determine the feasibility of early, intense supplemental gait training in inpatient stroke rehabilitation, compare functional outcomes and the specific mode of delivery.

DESIGN AND SETTING: Assessor blinded, randomized controlled trial in a tertiary Inpatient Rehabilitation Facility.

PARTICIPANTS: Thirty acute post-stroke patients with unilateral hemiparesis (≥ 18 years of age with a lower limb MAS ≤ 3).

INTERVENTION: Lokomat® or conventional gait training (CGT) in addition to standard mandated therapy time. …

Stability Of Deep Neural Networks For Feedback-Optimal Pinpoint Landings, Omkar S. Mulekar, Hancheol Cho, Riccardo Bevilacqua

Student Works

The ability to certify systems driven by neural networks is crucial for future rollouts of machine learning technologies in aerospace applications. In this study, the neural networks are used to represent a fuel-optimal feedback controller for two different 3-degree-of-freedom pinpoint landing problems. It is shown that the standard sum-ofsquares Lyapunov candidate is too restrictive to assess the stability of systems with fuel-optimal control profiles. Instead, a parametric Lyapunov candidate (i.e. a neural network) can be trained to sufficiently evaluate the closed-loop stability of fuel-optimal control profiles. Then, a stability-constrained imitation learning method is applied, which simultaneously trains a neural network …

Exploring Cognition And Affect During Human-Cobot Interaction, Angelika T. Canete, Javier Gonzalez-Sanchez, Rafael Guerra Silva

College of Engineering Summer Undergraduate Research Program

Collaborative robots (Cobots) have recently gained popularity due to their capability to work collaboratively with human operators. This collaborative relationship has been named under the robotics discipline of Human-Robot Collaboration (HRC), in which humans and robots work together to accomplish a common task while also being in the same physical space. An important part of collaboration is the human's decision-making, which is largely affected by their affective and cognitive state. A cobot lacks this fundamental understanding of the human operator. In this research, we utilize a server-client program to communicate the affective states of a human user to a Raspberry …

Modeling And Control Strategies For A Two-Wheel Balancing Mobile Robot, John Alan Moritz

Graduate Theses and Dissertations

The problem of balancing and autonomously navigating a two-wheel mobile robot is an increasingly active area of research, due to its potential applications in last-mile delivery, pedestrian transportation, warehouse automation, parts supply, agriculture, surveillance, and monitoring. This thesis investigates the design and control of a two-wheel balancing mobile robot using three different control strategies: Proportional Integral Derivative (PID) controllers, Sliding Mode Control, and Deep Q-Learning methodology. The mobile robot is modeled using a dynamic and kinematic model, and its motion is simulated in a custom MATLAB/Simulink environment. The first part of the thesis focuses on developing a dynamic and kinematic …

Drones For Marine Science And Agriculture, David Caldera, Sai Murthy

College of Engineering Summer Undergraduate Research Program

Our research project was launched at Cal Poly in 2019 with the goal of assisting researchers at the CSULB Shark Lab in detecting sharks from aerial images. Under the guidance of Dr. Franz J. Kurfess, students trained an object detection algorithm using shark images and were able to achieve high rate of detection. Following this success, the team has constructed multiple drones and expanded their research to include applications in the fields of agriculture and ecology. This summer the goal is to use a iPhone 14 Pro in lieu of a traditional camera system for real-time object recognition. Object detection …

Modeling And Compensating Of Noise In Time-Of-Flight Sensors, Bryan Rodriguez

Electrical Engineering Theses and Dissertations

Three-dimensional (3D) sensors provide the ability to perform contactless measurements of objects and distances that are within their field of view. Unlike traditional two-dimensional (2D) cameras, which only provide RGB data about objects within a scene, 3D sensors are able to directly provide depth information for objects within a scene. Of these 3D sensing technologies, Time-of-Flight (ToF) sensors are becoming more compact which allows them to be more easily integrated with other devices and to find use in more applications. ToF sensors also provide several benefits over other 3D sensing technologies that increase the types of applications where ToF sensors …

Objectfusion: Multi-Modal 3d Object Detection With Object-Centric Fusion, Q. Cai, Y. Pan, T. Yao, Chong-Wah Ngo, T. Mei

Research Collection School Of Computing and Information Systems

Recent progress on multi-modal 3D object detection has featured BEV (Bird-Eye-View) based fusion, which effectively unifies both LiDAR point clouds and camera images in a shared BEV space. Nevertheless, it is not trivial to perform camera-to-BEV transformation due to the inherently ambiguous depth estimation of each pixel, resulting in spatial misalignment between these two multi-modal features. Moreover, such transformation also inevitably leads to projection distortion of camera image features in BEV space. In this paper, we propose a novel Object-centric Fusion (ObjectFusion) paradigm, which completely gets rid of camera-to-BEV transformation during fusion to align object-centric features across different modalities for …

Advanced Traffic Video Analytics For Robust Traffic Accident Detection, Hadi Ghahremannezhad

Dissertations

Automatic traffic accident detection is an important task in traffic video analysis due to its key applications in developing intelligent transportation systems. Reducing the time delay between the occurrence of an accident and the dispatch of the first responders to the scene may help lower the mortality rate and save lives. Since 1980, many approaches have been presented for the automatic detection of incidents in traffic videos. In this dissertation, some challenging problems for accident detection in traffic videos are discussed and a new framework is presented in order to automatically detect single-vehicle and intersection traffic accidents in real-time.

First, …

Multi-Agent Deep Reinforcement Learning For Radiation Localization, Benjamin Scott Totten

Dissertations and Theses

For the safety of both equipment and human life, it is important to identify the location of orphaned radioactive material as quickly and accurately as possible. There are many factors that make radiation localization a challenging task, such as low gamma radiation signal strength and the need to search in unknown environments without prior information. The inverse-square relationship between the intensity of radiation and the source location, the probabilistic nature of nuclear decay and gamma ray detection, and the pervasive presence of naturally occurring environmental radiation complicates localization tasks. The presence of obstructions in complex environments can further attenuate the …

Evaluating Eeg–Emg Fusion-Based Classification As A Method For Improving Control Of Wearable Robotic Devices For Upper-Limb Rehabilitation, Jacob G. Tryon

Electronic Thesis and Dissertation Repository

Musculoskeletal disorders are the biggest cause of disability worldwide, and wearable mechatronic rehabilitation devices have been proposed for treatment. However, before widespread adoption, improvements in user control and system adaptability are required. User intention should be detected intuitively, and user-induced changes in system dynamics should be unobtrusively identified and corrected. Developments often focus on model-dependent nonlinear control theory, which is challenging to implement for wearable devices.

One alternative is to incorporate bioelectrical signal-based machine learning into the system, allowing for simpler controller designs to be augmented by supplemental brain (electroencephalography/EEG) and muscle (electromyography/EMG) information. To extract user intention better, sensor …

Autonomous Shipwreck Detection & Mapping, William Ard

LSU Master's Theses

This thesis presents the development and testing of Bruce, a low-cost hybrid Remote Operated Vehicle (ROV) / Autonomous Underwater Vehicle (AUV) system for the optical survey of marine archaeological sites, as well as a novel sonar image augmentation strategy for semantic segmentation of shipwrecks. This approach takes side-scan sonar and bathymetry data collected using an EdgeTech 2205 AUV sensor integrated with an Harris Iver3, and generates augmented image data to be used for the semantic segmentation of shipwrecks. It is shown that, due to the feature enhancement capabilities of the proposed shipwreck detection strategy, correctly identified areas have a 15% …

Development Of A Soft Robotic Approach For An Intra-Abdominal Wireless Laparoscopic Camera, Hui Liu

Doctoral Dissertations

In Single-Incision Laparoscopic Surgery (SILS), the Magnetic Anchoring and Guidance System (MAGS) arises as a promising technique to provide larger workspaces and field of vision for the laparoscopes, relief space for other instruments, and require fewer incisions. Inspired by MAGS, many concept designs related to fully insertable magnetically driven laparoscopes are developed and tested on the transabdominal operation. However, ignoring the tissue interaction and insertion procedure, most of the designs adopt rigid structures, which not only damage the patients' tissue with excess stress concentration and sliding motion but also require complicated operation for the insertion. Meanwhile, lacking state tracking of …

The Heterogeneous Vehicle Routing Problem With Multiple Time Windows For The E-Waste Collection Problem, Aldy Gunawan, Minh P.K Nguyen, Vincent F. Yu, Dang Viet Anh Nguyen

Research Collection School Of Computing and Information Systems

Waste from electrical and electronic equipment (WEEE) or e-waste describes end-of-life electronic products that are discarded. Due to their toxic and negative impacts to humans' health, many publications have been proposed to handle, however, studies related to e-waste collection and transportation to waste disposal sites are not widely studied so far. This study proposes a mixed integer linear programming (MILP) model to solve the e-waste collecting problem by formulating it as the heterogeneous vehicle routing problem with multiple time windows (HVRPMTW). The model is validated with newly developed benchmark instances that are solved by commercial software, CPLEX. The model is …

Data-Driven Predictive Modeling To Enhance Search Efficiency Of Glowworm-Inspired Robotic Swarms In Multiple Emission Source Localization Tasks, Payal Nandi

Mechanical & Aerospace Engineering Theses & Dissertations

In time-sensitive search and rescue applications, a team of multiple mobile robots broadens the scope of operational capabilities. Scaling multi-robot systems (< 10 agents) to larger robot teams (10 – 100 agents) using centralized coordination schemes becomes computationally intractable during runtime. One solution to this problem is inspired by swarm intelligence principles found in nature, offering the benefits of decentralized control, fault tolerance to individual failures, and self-organizing adaptability. Glowworm swarm optimization (GSO) is unique among swarm-based algorithms as it simultaneously focuses on searching for multiple targets. This thesis presents GPR-GSO—a modification to the GSO algorithm that incorporates Gaussian Process Regression (GPR) based data-driven predictive modeling—to improve the search efficiency of robotic swarms in multiple emission source localization tasks. The problem formulation and methods are presented, followed by numerical simulations to illustrate the working of the algorithm. Results from a comparative analysis show that the GPR-GSO algorithm exceeds the performance of the benchmark GSO algorithm on evaluation metrics of swarm size, search completion time, and travel distance.

An Enhanced Adaptive Learning System Based On Microservice Architecture, Abdelsalam Helmy Ibrahim, Mohamed Eliemy, Aliaa Abdelhalim Youssif

Future Computing and Informatics Journal

This study aims to enhance Adaptive Learning Systems (ALS) in Petroleum Sector in Egypt by using the Microservice Architecture and measure the impact of enhancing ALS by participating ALS users through a statistical study and questionnaire directed to them if they accept to apply the Cloud Computing Service “Microservices” to enhance the ALS performance, quality and cost value or not. The study also aims to confirm that there is a statistically significant relationship between ALS and Cloud Computing Service “Microservices” and prove the impact of enhancing the ALS by using Microservices in the cloud in Adaptive Learning in the Egyptian …

Visual Question Answering: A Survey, Gehad Assem El-Naggar

Future Computing and Informatics Journal

Visual Question Answering (VQA) has been an emerging field in computer vision and natural language processing that aims to enable machines to understand the content of images and answer natural language questions about them. Recently, there has been increasing interest in integrating Semantic Web technologies into VQA systems to enhance their performance and scalability. In this context, knowledge graphs, which represent structured knowledge in the form of entities and their relationships, have shown great potential in providing rich semantic information for VQA. This paper provides an abstract overview of the state-of-the-art research on VQA using Semantic Web technologies, including knowledge …