Physical Sciences and Mathematics Commons^™

Towards Machine Learning-Based Control Of Autonomous Vehicles In Solar Panel Cleaning Systems, Farima Hajiahmadi

Theses and Dissertations

This thesis presents a machine learning (ML)-based approach for the intelligent control of Autonomous Vehicles (AVs) utilized in solar panel cleaning systems, aiming to mitigate challenges arising from uncertainties, disturbances, and dynamic environments. Solar panels, predominantly situated in dedicated lands for solar energy production (e.g., agricultural solar farms), are susceptible to dust and debris accumulation, leading to diminished energy absorption. Instead of labor-intensive manual cleaning, robotic cleaners offer a viable solution. AVs equipped to transport and precisely position these cleaning robots are indispensable for efficient navigation among solar panel arrays. However, environmental obstacles (e.g., rough terrain), variations in solar panel …

Towards Energy-Efficient Edge Computing For Tiny Ai Applications, Vamsi Krishna Bhagavathula

Theses and Dissertations

As artificial intelligence (AI) applications become more common on the edge of networks, like Raspberry Pi servers, it is crucial to optimize their energy use. This research project investigates how AI algorithms affect energy efficiency and resource usage on Raspberry Pi servers. Two models were created: one predicts resource usage, and the other predicts power consumption of AI algorithms on Raspberry Pi. Several factors are considered like CPU and memory use, algorithm speed, dataset size, and types of algorithms and datasets. Using regression-based methods, we model how these factors affect energy use. By converting categorical factors into numerical ones, we …

Blockchain For Computational Integrity And Privacy, Rahul Raj

Theses and Dissertations

This study proposes a blockchain based system that utilizes fully homomorphic encryption to provide security of data in use as well as computational integrity. This is achieved by leveraging the attributes of blockchain which provides availability and data integrity combined with homomorphic encryption that provides confidentiality. The proposed system is designed to perform statistical operations, including mean, median and variance, on encrypted data, thus providing confidentiality of data while in use. The computations are performed on the smart contract, residing on the blockchain which provides computational integrity. The results indicate that it is possible to perform fully homomorphic computations on …

Advancing Explainability In Multi-Label Classification For Tomato Disease Detection Using Machine Learning Interpretability Techniques, Md. Nurullah

Theses and Dissertations

Plant diseases pose a significant threat to global food security, affecting crop yield, quality, and overall agricultural productivity. Traditionally, diagnosing plant diseases has relied on timeconsuming visual inspections by experts, which can often lead to errors. With the rapid growth of technology, machine learning (ML) and artificial intelligence (AI) have opened new possibilities for automating this process. One of the most promising technologies for plant disease diagnosis is Convolutional Neural Networks (CNNs), which have proven effective in image classification tasks. Plant leaves, often exhibiting symptoms such as discoloration and irregular textures, serve as key indicators for disease detection. By processing …

Determining Hydraulic Characteristics In Close Proximity Of Hymenocallis Coronaria Within The Flint River Basin, Georgia, Sarah K. Tash

Theses and Dissertations

Emergent macrophytes in river systems occupy a narrow habitat for optimal survival. Each plant species adapts to a set of conditions that meets the physiological requirements to grow and reproduce. The influence of hydraulic characteristics on plant morphology is significant within fluvial systems. Macrophytes must be able to withstand swift flowing water and changes in water surface levels in order to compete with other plant species for limited habitat availability. In addition, emergent macrophytes require water levels to be low enough to allow the reproductive portion of plants to remain above water during certain times of the year. Hymenocallis coronaria …

Physics-Informed Neural Networks (Pinns) And Inverse Pinns For The Modeling And Parameter Estimation Of Electric Pumps In Liquid-Propellant Rocket Engines, Logan Mcfarland

Theses and Dissertations

This thesis presents a machine learning (ML) based approach to both model and estimate the parameters of an electric pump system that is used in liquid-propellent rocket engines. This is accomplished by utilizing Physics-Informed Neural Networks (PINNs) and inverse Physics-Informed Neural Networks (iPINNs). In an extreme system such as a liquidpropellent rocket engine, accurate control and efciency is imperative for successful performance. The electric pump system is inherently nonlinear and its dynamics are governed by ordinary diferential equations (ODEs). While entirely approachable, these nonlinear dynamics create a challenge in achieving precise control and accurate parameter estimations. Utilization of a PINN …

Assessing And Understanding Soil Characteristics​, Amy Papineau

UNH Cooperative Extension

No abstract provided.

2024 Directory Of Landscape Professionals Trained In Ecological Landscaping For Water Quality Protection, Julia Peterson

UNH Cooperative Extension

No abstract provided.

Blackout (2011) By John Rocco, Peter Shea

Picture Books

In this Caldecott Honor Book, Blackout, a hot urban night takes an interesting turn when the power goes out and an urban family -- each busy with something that requires electricity -- discover new things about themselves and their community.

Effective Land Use Classification Through Hybrid Transformer Using Remote Sensing Imagery, Muhammad Zia Ur Rehman, Syed Mohammed Shamsul Islam, Anwaar Ulhaq, David Blake, Naeem Janjua

Research outputs 2022 to 2026

Recent advances in deep learning for hyperspectral image (HSI) classification have shown exceptional performance in resource management and environmental planning through land use classification. Despite these successes, challenges continue to persist in land use classification due to the complex topology of natural and man-made structures. The uneven distribution of land cover introduces spectral-spatial variability, causing inter- and intra-class similarity. To address this issue, this study adopts a hybrid approach that combines convolutional neural networks (CNNs) and a transformer model. The technique comprises three key components: a spectral-spatial convolutional module (SSCM), a spatial attention module (SAM), and a transformer module. Each …

Attitudes And Perceptions Towards Privacy And Surveillance In Australia, Aleatha J. Shanley

Theses: Doctorates and Masters

Understanding attitudes towards privacy and surveillance technologies used to enhance security objectives is a complex, but crucial aspect for policy makers to consider. Historically, terrorism-related incidents justified the uptake of surveillance practices. More recently however, biosecurity concerns have motivated nation-states to adopt more intrusive surveillance measures. There is a growing body of literature that supports the public’s desire to maintain privacy despite fears of biological or physical threats.

This research set out to explore attitudes towards privacy and surveillance in an Australian context. Throughout the course of this endeavour, the COVID-19 pandemic emerged bringing with it a variety of track …

From Grain To Malt: Tracking Changes Of Ultra-Low-Gluten Barley Storage Proteins After Malting, Mahya Bahmani, Angéla Juhász, Utpal Bos, Mitchell G. Nye-Wood, Malcolm Blundell, Crispin A. Howitt, Michelle L. Colgrave

Research outputs 2022 to 2026

Barley (Hordeum vulgare L.) is a major cereal crop produced globally. Hordeins, the major storage proteins in barley, can trigger immune responses leading to celiac disease or symptoms associated with food allergy. Here, proteomics approaches were employed to investigate the proteome level changes of grain and malt from the malting barley cultivar, Sloop, and single-, double- and triple hordein-reduced lines. The triple hordein-reduced line is an ultra-low gluten barley cultivar, Kebari®. Using discovery proteomics, 2,688 and 3,034 proteins in the barley and malt samples were detected respectively. Through the application of targeted proteomics, a significant reduction in the quantity …

Adaptive Resilient Control For A Class Of Nonlinear Distributed Parameter Systems With Actuator Faults, Hasan Ferdowsi, Jia Cai, Sarangapani Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

This paper presents a new model-based fault resilient control scheme for a class of nonlinear distributed parameter systems (DPS) represented by parabolic partial differential equations (PDE) in the presence of actuator faults. A Luenberger-like observer on the basis of nonlinear PDE representation of DPS is developed with boundary measurements. A detection residual is generated by taking the difference between the measured output of the DPS and the estimated one given by the observer. Once a fault is detected, an unknown actuator fault parameter vector together with a known basis function is utilized to adaptively estimate the fault dynamics. A novel …

Meta-Icvi: Ensemble Validity Metrics For Concise Labeling Of Correct, Under- Or Over-Partitioning In Streaming Clustering, Niklas M. Melton, Sasha Petrenko, Donald C. Wunsch

Electrical and Computer Engineering Faculty Research & Creative Works

Understanding the performance and validity of clustering algorithms is both challenging and crucial, particularly when clustering must be done online. Until recently, most validation methods have relied on batch calculation and have required considerable human expertise in their interpretation. Improving real-time performance and interpretability of cluster validation, therefore, continues to be an important theme in unsupervised learning. Building upon previous work on incremental cluster validity indices (iCVIs), this paper introduces the Meta- iCVI as a tool for explainable and concise labeling of partition quality in online clustering. Leveraging a time-series classifier and data-fusion techniques, the Meta- iCVI combines the outputs …

Structural And Magnetic Properties Of Molecular Beam Epitaxy (Mnsb2te4)X(Sb2te3)1−X Topological Materials With Exceedingly High Curie Temperature, Candice R. Forrester, Christophe Testelin, Kaushini Wickramasinghe, Ido Levy, Dominique Demaille, David Hrabovsky, Xiaxin Ding, Lisa Krusin-Elbaum, Gustavo Lopez, Maria C. Tamargo

Publications and Research

Tuning the properties of magnetic topological materials is of interest to realize exotic physical phenomena, new quantum phases and quasiparticles, and topological spintronic devices. However, current topological materials exhibit Curie temperature (T_C) values far below those needed for practical applications. In recent years, significant progress has been made to control and optimize T_C, particularly through defect-engineering of these structures. Most recently, we reported T_C values up to 80 K for (MnSb₂Te₄)_x(Sb₂Te₃)_1−x when 0.7 ≤ x ≤ 0.85 by controlling the composition x and the …

It Is Normal: The Probability Distribution Of Temperature Extremes, Nir Y. Krakauer

Publications and Research

The probability of heat extremes is often estimated using the non-stationary generalized extreme value distribution (GEVD) applied to time series of annual maximum temperature. Here, this practice was assessed using a global sample of temperature time series, from reanalysis (both at the grid point and the region scale) as well as station observations. This assessment used forecast negative log-likelihood as the main performance measure, which is particularly sensitive to the most extreme heat waves. It was found that the computationally simpler normal distribution outperforms the GEVD in providing probabilistic year-ahead forecasts of temperature extremes. Given these findings, it is suggested …

Quotients Of The Curve Complex, Joseph Maher, Hidetoshi Masai, Saul Schleimer

Publications and Research

We consider three kinds of quotients of the curve complex, which are obtained by coning off uniformly quasiconvex subspaces: symmetric curve sets, non-maximal train track sets, and compression body disc sets. We show that the actions of the mapping class group on those quotients are strongly weakly properly discontinuously (WPD), which implies that the actions are non-elementary and those quotients are of infinite diameter.

Time-Efficient Filtering Of Imaging Polarimetric Data By Checking Physical Realizability Of Experimental Mueller Matrices, Tatiana Novikova, Alexey Ovchinnikov, Gleb Pogudin, Jessica C. Ramella-Roman

Publications and Research

Motivation: Imaging Mueller polarimetry has already proved its potential for biomedicine, remote sensing, and metrology. The real-time applications of this modality require both video rate image acquisition and fast data post-processing algorithms. First, one must check the physical realizability of the experimental Mueller matrices in order to filter out non-physical data, i.e. to test the positive semi-definiteness of the 4 × 4 Hermitian coherency matrix calculated from the elements of corresponding Mueller matrix pixel-wise. For this purpose, we compared the execution time for the calculations of (i) eigenvalues, (ii) Cholesky decomposition, (iii) Sylvester’s criterion, and (iv) coefficients of the characteristic …

Effective Size Of A Parity-Time-Symmetric Dimer, Li Ge

Publications and Research

Parity-time (PT)-symmetric dimers were introduced to highlight the unusual properties of non-Hermitian systems that are invariant after a combined parity and time reversal operation. They are also the building blocks of a variety of symmetry and topologically protected structures, especially on integrated photonic platforms. As the name suggests, it consists of two coupled oscillators, which can be optical, mechanical, electronic, and so on in nature. In this article, we show that its effective size, defined by the number of lattice sites inversely proportional to the lattice momentum, is surprisingly three instead of two from the perspective of energy quantization. More …

Deep Adaptive Graph Clustering Via Von Mises-Fisher Distributions, Pengfei Wang, Daqing Wu, Chong Chen, Kunpeng Liu, Yanjie Fu, Jianqiang Huang, Yuanchun Zhou, Jianfeng Zhan, Xiansheng Hua

Computer Science Faculty Publications and Presentations

Graph clustering has been a hot research topic and is widely used in many fields, such as community detection in social networks. Lots of works combining auto-encoder and graph neural networks have been applied to clustering tasks by utilizing node attributes and graph structure. These works usually assumed the inherent parameters (i.e., size and variance) of different clusters in the latent embedding space are homogeneous, and hence the assigned probability is monotonous over the Euclidean distance between node embeddings and centroids. Unfortunately, this assumption usually does not hold since the size and concentration of different clusters can be quite different, …

Note On The Spectra Of Steiner Distance Hypermatrices, Joshua Cooper, Zhibin Du

Faculty Publications

The Steiner distance of a set of vertices in a graph is the fewest number of edges in any connected subgraph containing those vertices. The order-k Steiner distance hypermatrix of a graph is the n-dimensional array indexed by vertices, whose entries are the Steiner distances of their corresponding indices. In the case of k = 2, this reduces to the classical distance matrix of a graph. Graham and Pollak showed in 1971 that the determinant of the distance matrix of a tree only depends on its number n of vertices. Here, we show that the hyperdeterminant of the Steiner distance …

Global Meta-Analysis Of Demersal Fishing Impacts On Organic Carbon And Associated Biogeochemistry, Justin Tiano, Emil De Borger, Sarah Paradis, Clare Bradshaw, Claudia Morys, Antonio Pusceddu, Claudia Ennas, Karline Soetaert, Pere Puig, Pere Masqué, Marija Sciberras

Research outputs 2022 to 2026

The potential threat of fisheries on seabed carbon is a topic of growing concern, yet existing literature presents inconsistencies leaving experts divided on the topic. We conducted a global meta-analysis to synthesize the current knowledge and quantify how demersal fishing impacts various biogeochemical properties. Direct impact studies revealed overall reductions in chlorophyll-a (Chl-a, 17%), phaeopigments (24%), and proteins (32%). Effects on these reactive compounds were more pronounced on surface sediment (0–2 cm), where the impact on total organic carbon (TOC) also became significant, demonstrating the effect of gear penetration, and highlighting that sampling strategies combining sediment layers can mask observed …

Human-Empathy Accessibility Learning (Heal) Intervention Model Towards Critical Soft Skills Development For Career Readiness Among Computing Undergraduate Students, Jami Cotler, Eszter Kiss, Dmitry Burshteyn, Eben Afrifa-Yamoah

Research outputs 2022 to 2026

This pilot mixed methods study explores the impact of Human-Empathy Accessibility Learning (HEAL) interventions on soft skills development in undergraduate computing students, emphasizing their role in job readiness and perceived employability. HEAL interventions aimed to enhance accessibility awareness, focusing on motivation, empathy, and emotional intelligence. Participants were assigned to control and experiment groups, with qualitative findings showing empathetic growth in the experiment group. Quantitative results partially supported qualitative findings, indicating statistically significant changes across measures. Despite quantitative limitations, short-term empathy interventions showed potential benefits for job readiness. The study discusses implications of mixed findings and recommends future research directions.

Irs-Enabled Noma Communication Systems: A Network Architecture Primer With Future Trends And Challenges, Haleema Sadia, Ahmad Kamal Hassan, Ziaul Haq Abbas, Ghulam Abbas, Muhammad Waqas, Zhu Han

Research outputs 2022 to 2026

Non-Orthogonal Multiple Access (NOMA) has already proven to be an effective multiple access scheme for 5th Generation (5G) wireless networks. It provides improved performance in terms of system throughput, spectral efficiency, fairness, and energy efficiency (EE). However, in conventional NOMA networks, performance degradation still exists because of the stochastic behavior of wireless channels. To combat this challenge, the concept of Intelligent Reflecting Surface (IRS) has risen to prominence as a low-cost intelligent solution for Beyond 5G (B5G) networks. In this paper, a modeling primer based on the integration of these two cutting-edge technologies, i.e., IRS and NOMA, for B5G wireless …

Analysis Of Element Yield, Bacterial Community Structure And The Impact Of Carbon Sources For Bioleaching Rare Earth Elements From High Grade Monazite, Melissa K. Corbett, April Gifford, Nick Fimognari, Elizabeth L. J. Watkin

Research outputs 2022 to 2026

Rare earth element (REE) recovery from waste streams, mine tailings or recyclable components using bioleaching is gaining traction due to the shortage and security of REE supply as well as the environmental problems that occur from processing and refining. Four heterotrophic microbial species with known phosphate solubilizing capabilities were evaluated for their ability to leach REE from a high-grade monazite when provided with either galactose, fructose or maltose. Supplying fructose resulted in the greatest amount of REE leached from the ore due to the largest amount of organic acid produced. Gluconic acid was the dominant organic acid identified produced by …

Performance Evaluation Of A Dual-Chamber Plant Microbial Fuel Cell Developed For Electricity Generation And Wastewater Treatment, Mahmood Golzarian, M. Ghiasvand, S. Shokri, M. Bahreini, Fatemeh Kazemi

Research outputs 2022 to 2026

Plant microbial fuel cells (PMFC) have attracted great scholarly attention as a renewable energy source. These cells have three main components: anode, cathode chambers, and a proton exchange membrane. In this study, a dual-chamber plant microbial fuel cell system was designed using Cyperus papyrus and Shewanella oneidensis. The effects of various factors, including the size of the electrodes, the distance between the electrodes, and the inoculation volume of Shewanella oneidensis, on the ability of electricity generation, were scrutinized. The results indicated that increasing the size area of the electrodes from 2 × 2 to 4 × 4 and 6 × …

Region-Specific Drivers Cause Low Organic Carbon Stocks And Sequestration Rates In The Saltmarsh Soils Of Southern Scandinavia, Carmen Leiva-Dueñas, Anna E. L. Graversen, Gary T. Banta, Jeppe N. Hansen, Marie L. K. Schrøter, Pere Masqué, Marianne Holmer, Dorte Krause-Jensen

Research outputs 2022 to 2026

Saltmarshes are known for their ability to act as effective sinks of organic carbon (OC) and their protection and restoration could potentially slow down the pace of global warming. However, regional estimates of saltmarsh OC storage are often missing, including for the Nordic region. To address this knowledge gap, we assessed OC storage and accumulation rates in 17 saltmarshes distributed along the Danish coasts and investigated the main drivers of soil OC storage. Danish saltmarshes store a median of 10 kg OC m−2 (interquartile range, IQR: 13.5–7.6) in the top meter and sequester 31.5 g OC m−2 yr−1 (IQR: 41.6–15.7). …

Malware Detection With Artificial Intelligence: A Systematic Literature Review, Matthew G. Gaber, Mohiuddin Ahmed, Helge Janicke

Research outputs 2022 to 2026

In this survey, we review the key developments in the field of malware detection using AI and analyze core challenges. We systematically survey state-of-the-art methods across five critical aspects of building an accurate and robust AI-powered malware-detection model: malware sophistication, analysis techniques, malware repositories, feature selection, and machine learning vs. deep learning. The effectiveness of an AI model is dependent on the quality of the features it is trained with. In turn, the quality and authenticity of these features is dependent on the quality of the dataset and the suitability of the analysis tool. Static analysis is fast but is …

Pdf Malware Detection: Toward Machine Learning Modeling With Explainability Analysis, G. M.Sakhawat Hossain, Kaushik Deb, Helge Janicke, Iqbal H. Sarker

Research outputs 2022 to 2026

The Portable Document Format (PDF) is one of the most widely used file types, thus fraudsters insert harmful code into victims' PDF documents to compromise their equipment. Conventional solutions and identification techniques are often insufficient and may only partially prevent PDF malware because of their versatile character and excessive dependence on a certain typical feature set. The primary goal of this work is to detect PDF malware efficiently in order to alleviate the current difficulties. To accomplish the goal, we first develop a comprehensive dataset of 15958 PDF samples taking into account the non-malevolent, malicious, and evasive behaviors of the …

Opportunities And Challenges Posed By Disruptive And Converging Information Technologies For Australia's Future Defence Capabilities: A Horizon Scan, Pi-Shen Seet, Anton Klarin, Janice Jones, Mike Johnstone, Helen Cripps, Jalleh Sharafizad, Violetta Wilk, David Suter, Tony Marceddo

Research outputs 2022 to 2026

Introduction: The research project's objective was to conduct a comprehensive horizon scan of Network Centric Warfare (NCW) technologies-specifically, Cyber, IoT/IoBT, AI, and Autonomous Systems. Recognised as pivotal force multipliers, these technologies are critical to reshaping the mission, design, structure, and operations of the Australian Defence Force (ADF), aligning with the Department of Defence (Defence)’s offset strategies and ensuring technological advantage, especially in the Indo-Pacific's competitive landscape.

Research process: Employing a two-pronged research approach, the study first leveraged scientometric analysis, utilising informetric mapping software (VOSviewer) to evaluate emerging trends and their implications on defence capabilities. This approach facilitated a broader understanding …