Maryland Shared Open Access Repository

MD-SOAR is a shared digital repository platform for twelve colleges and universities in Maryland. It is currently funded by the University System of Maryland and Affiliated Institutions (USMAI) Library Consortium (usmai.org) and other participating partner institutions. MD-SOAR is jointly governed by all participating libraries, who have agreed to share policies and practices that are necessary and appropriate for the shared platform. Within this broad framework, each library provides customized repository services and collections that meet local institutional needs. Please follow the links below to learn more about each library's repository services and collections.

 

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The Mancala Effect
(2024) Boyce-Gaither, Treesa "Poesis"; Evan Hughes; Michelle Orange; Porscha Burke; Leslie Rubinkowski; MFA in Creative Nonfiction
This is a collection of essays consisting of how I move through life through preparation and what I can and cannot control.
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Pretest Estimation for the Common Mean of Several Normal Distributions: In Meta-Analysis Context
(MDPI, 2024-9-22) Mphekgwana, Peter M.; Kifle, Yehenew Getachew; Marange, Chioneso S.
The estimation of unknown quantities from multiple independent yet non-homogeneous samples has garnered increasing attention in various fields over the past decade. This interest is evidenced by the wide range of applications discussed in recent literature. In this study, we propose a preliminary test estimator for the common mean (𝜇) with unknown and unequal variances. When there exists prior information regarding the population mean with consideration that 𝜇 might be equal to the reference value for the population mean, a hypothesis test can be conducted: H₀ : 𝜇 = 𝜇₀ versus H₁ : 𝜇 ≠ 𝜇₀. The initial sample is used to test H₀, and if H₀ is not rejected, we become more confident in using our prior information (after the test) to estimate 𝜇. However, if H₀ is rejected, the prior information is discarded. Our simulations indicate that the proposed preliminary test estimator significantly decreases the mean squared error (MSE) values compared to unbiased estimators such as the Garybill-Deal (GD) estimator, particularly when 𝜇 closely aligns with the hypothesized mean (𝜇₀). Furthermore, our analysis indicates that the proposed test estimator outperforms the existing method, particularly in cases with minimal sample sizes. We advocate for its adoption to improve the accuracy of common mean estimation. Our findings suggest that through careful application to real meta-analyses, the proposed test estimator shows promising potential.
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Singularity-free Backstepping-based Adaptive Control of a Bicopter with Unknown Mass and Inertia
(2024-09-19) Delgado, Jhon Manuel Portella; Goel, Ankit
The paper develops a singularity-free backstepping-based adaptive control for stabilizing and tracking the trajectory of a bicopter system. In the bicopter system, the inertial parameters parameterize the input map. Since the classical adaptive backstepping technique requires the inversion of the input map, which contains the estimate of parameter estimates, the stability of the closed-loop system cannot be guaranteed due to the inversion of parameter estimates. This paper proposes a novel technique to circumvent the inversion of parameter estimates in the control law. The resulting controller requires only the sign of the unknown parameters. The proposed controller is validated in simulation for a smooth and nonsmooth trajectory-tracking problem.
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An SDN-Based Framework for E2E QoS Guarantee in Internet-of-Things Devices
(IEEE, 2024-09-23) Ali, Jehad; Song, Houbing; Roh, Byeong-hee
In 5G and Beyond-based Internet-of-Things (IoT) sensor networks, the end-to-end (E2E) route traverses via multiple heterogeneous network domains, necessitating inter-domain interaction to guarantee and confirm quality-of-service (QoS) for low power IoT devices applications. Moreover, in heterogeneous IoT sensor networks, the E2E path often encompasses domains with diverse QoS parameters or classes. The unique E2E requirements for delay, packet loss ratio (PLR), and other factors present further challenges. However, existing legacy network architectures and typical software-defined networking (SDN) models lack effective strategies for QoS provisioning tailored to the service requests of IoT low power sensor devices. To address these issues, this study proposes a novel multi-objective SDN-based framework for IoT sensors, ensuring E2E QoS across multiple domains with heterogeneous traffic service classes (TSC). A two-layer software-defined networking (SDN) framework is presented to provision QoS for IoT sensors based on their specific service demands at the E2E network level. Central to the framework is the deployment of an optimal additive weighting module (OAWM), facilitating TSC ranking according to their weights and incorporating a priority mechanism for specific service parameters such as delay, PLR, and jitter. Additionally, the global controller statistics enable the provisioning of E2E QoS by mapping the service requests from IoT sensors. Experimental evaluations are conducted to compare the proposed approach with existing schemes. The results validate the effectiveness of our proposed method, demonstrating improved E2E QoS provisioning and meeting the specific requirements of IoT sensors in precision agriculture with low-power IoT devices.
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Neural filtering for Neural Network-based Models of Dynamic Systems
(2024-09-20) Oveissi, Parham; Rozario, Turibius; Goel, Ankit
The application of neural networks in modeling dynamic systems has become prominent due to their ability to estimate complex nonlinear functions. Despite their effectiveness, neural networks face challenges in long-term predictions, where the prediction error diverges over time, thus degrading their accuracy. This paper presents a neural filter to enhance the accuracy of long-term state predictions of neural network-based models of dynamic systems. Motivated by the extended Kalman filter, the neural filter combines the neural network state predictions with the measurements from the physical system to improve the estimated state's accuracy. The neural filter's improvements in prediction accuracy are demonstrated through applications to four nonlinear dynamical systems. Numerical experiments show that the neural filter significantly improves prediction accuracy and bounds the state estimate covariance, outperforming the neural network predictions.