2,501 research outputs found
Correction: Umer et al. High Order Split Operators for the Time-Dependent Wavepacket method of Triatomic Reactive Scattering in Hyperspherical Coordinates. Entropy 2019, 21, 979
No full textThe authors would like to update the affiliations for Umair Umer and Syed Kazim Usman in the published publication [...
Biologically inspired attitude control of robotic systems using center of gravity reallocation
No full textNatural species often rely on inertial forces for their orientation control. Lizards, geckos and arboreal animals effectively use their inertial appendages to control their attitude dynamics. On the other hand, flying species such as biological bats employ their relatively heavier wings to produce inertial forces during their flight. Bats, while performing highly agile maneuvers such as upside-down perching (performed in order to approach roosting position), employ these inertial forces to reallocate the center of gravity of their bodies. The study of these natural species, motivates us to consider the effectiveness of center of gravity reallocation as a mechanism for the attitude control of robotic systems.
This thesis explores the use of center of gravity reallocation for the control of robotic systems. In particular we attempt to use the mechanism employed by biological bats in their landing maneuvers with a micro aerial vehicle (MAV) called Allice. Allice is capable of adjusting the position of its center of gravity (CG) with respect to the center of pressure (CP) using nonlinear closed-loop feedback. In the case of flying machines, CoM reallocation leads to the change in CG-CP distance of the system. In the case of robots with no aerodynamic surfaces, CoM reallocation leads to manipulating the torques produced by numerous forces acting in the system. For the control of robotic systems, we employ nonlinear control techniques. This nonlinear control law, which is based on the method of input-output feedback linearization, enables attitude regulations through CoM reallocation in the system. To design the model-based nonlinear controller, the Lagrangian dynamics of the system are considered, in which the aerodynamic coefficients of lift and drag are obtained experimentally. This work covers the design, system identification and nonlinear controller design. The performance of the proposed control architecture is validated by conducting several experiments.Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2021-05-01The student, Usman Syed, accepted the attached license on 2019-04-05 at 10:10.The student, Usman Syed, submitted this Thesis for approval on 2019-04-05 at 10:30.This Thesis was approved for publication on 2019-04-05 at 16:21.DSpace SAF Submission Ingestion Package generated from Vireo submission #13492 on 2019-08-22 at 15:05:21Made available in DSpace on 2019-08-23T20:35:45Z (GMT). No. of bitstreams: 2
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Previous issue date: 2019-04-05Embargo set by: Seth Robbins for item 112108
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Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD systemU of I Only Restriction Lifted for Item 112108 on 2021-08-24T09:15:20Z
Comparing a Hybrid Testing Process with Scripted and Exploratory Testing: An Experimental Study with Practitioners
No full textThis study presents an experimental study comparing the testing quality of a Hybrid Testing (HT) process with the commonly used approaches in industry: Scripted Testing (ST) and Exploratory Testing (ET). The study was conducted in an international IT service company in Sweden with the involvement of six experienced testers. Two measures were used for comparison: 1) defect detection eectiveness (DDE) and 2) functionality coverage (FC). The results indicated that HT performed better in terms of DDE than ST and worse than ET. In terms of FC, HT performed better than ET, while no signicant dierences were observed between the HT and ST. Furthermore, HT performed best for experienced testers, but worse with less experienced testers
Mediapipe based Preprocessed VGGFace2 Dataset
No full textVGGFace2 Dataset and Face Mesh PreprocessingIntroductionThe VGGFace2 dataset is a large-scale face recognition dataset containing over 3.31 million images of 9,131 identities, with an average of 362 images per identity. The dataset is designed to include extensive variations in pose, age, illumination, ethnicity, and profession, making it one of the most diverse and challenging face recognition datasets available. For more details, please refer to the original publication:VGGFace2: A dataset for recognizing faces across pose and age - DOI: 10.48550/arXiv.1710.08092
Preprocessing Using MediaPipe 3D Face MeshOn this dataset, we applied the MediaPipe-based 3D face mesh algorithm to accurately detect faces while removing all background elements, including hair. Our preprocessing strictly retained facial landmarks, ensuring that only the essential facial features were preserved. This approach significantly enhanced the accuracy and generalization of our model, as the model was trained exclusively on landmark-based facial data.
Training and PerformanceThe preprocessed data was utilized to train Xception model, which resulted in remarkably accurate outcomes due to the strictly landmark-based facial representation. The model demonstrated robust performance including explainable-AI, proving that eliminating unnecessary background elements contributed positively to its efficiency and reliability.
CitationIf you use this dataset or the preprocessed version in your work, please cite both of the following:
VGGFace2 Dataset:
@article{Cao2018VGGFace2, title={VGGFace2: A dataset for recognizing faces across pose and age}, author={Cao, Qiong and Shen, Li and Xie, Weidi and Parkhi, Omkar M and Zisserman, Andrew}, journal={arXiv preprint arXiv:1710.08092}, year={2018}}
DOI: [10.48550/arXiv.1710.08092](https://doi.org/10.48550/arXiv.1710.08092) Preprocessed Dataset using MediaPipe:@dataset{Shah2025_MediaPipe_FaceMesh, title={MediaPipe-based 3D Face Mesh Preprocessed VGGFace2 Dataset}, author={Shah, Syed Taimoor Hussain and Shah, Syed Adil Hussain and Zamir, Ammara and Qayyum, Kainat and Shah, Syed Baqir Hussain and Fatima, Syeda Maryam and Deriu, Marco Agostino}, year={2025}, doi={10.5281/zenodo.15078557}} DOI: [10.5281/zenodo.15078557](https://doi.org/10.5281/zenodo.15078557)
ContactFor any questions or further details, please feel free to contact us.Syed Taimoor Hussain ShahPolitoBIOMed Lab, Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Turin, ItalyEmail: [email protected]: 0000-0002-6010-677
Demand forecasting accuracy in airline revenue management : analysis of practical issues with forecast error reduction
No full textThesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2003.Includes bibliographical references (leaves 107-109).by Adeem Syed Usman.S.M
Bridging control theory and learning systems: advances in adaptation, robustness, and automation
No full textSubmission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo termsThe student, Usman Syed, accepted the attached license on 2025-04-09 at 23:25.The student, Usman Syed, submitted this Dissertation for approval on 2025-04-09 at 23:27.This Dissertation was approved for publication on 2025-04-24 at 15:06.DSpace SAF Submission Ingestion Package generated from Vireo submission #21727 on 2025-10-19 at 18:17:57The integration of control theory and learning based systems has become increasingly vital for developing intelligent and autonomous systems that can adapt, ensure robustness, and minimize human intervention. This dissertation explores three key aspects of this integration: Adaptation, Robustness, and Automation, providing novel theoretical insights and practical advancements in each area. In the first part of the dissertation, we investigate adaptive control in the presence of unknown disturbances, drawing connections between Online Nonstochastic Control and Retrospective Cost Adaptive Control (RCAC). While Online Nonstochastic Control ensures provable near-optimal regret bounds given a stabilizing policy, RCAC is capable of stabilizing unknown unstable plants through the use of a target model. We propose a unified framework that combines the strengths of both approaches, providing a foundation for more powerful adaptive control algorithms. The second part of this dissertation addresses robustness in deep neural networks by focusing on the problem of computing tight Lipschitz bounds, which are crucial for analyzing stability, generalization, and adversarial robustness. Given that computing the exact Lipschitz constant is NP-hard, existing approaches either suffer from scalability issues due to semidefinite programming (SDP) formulations or provide overly conservative estimates. Building upon ECLipsE-Fast, a state-of-the-art scalable method, we introduce a new family of Lipschitz bounds that significantly reduces conservatism while maintaining computational efficiency. Our approach generalizes the feasible points of LipSDP at each recursive step, strictly encompassing ECLipsE-Fast as a special case, and demonstrates improved scalability and precision in empirical evaluations. In the final part of the dissertation, we explore LLM-powered automation in control design, aiming to minimize human oversight while maintaining stability and robustness guarantees. We develop a framework that automates controller synthesis for both linear and nonlinear systems, ensuring its applicability across diverse control strategies. To validate real-world feasibility, we implement a fully automated control design pipeline, employing Simulation-in-the-Loop and Hardware-in-the-Loop methodologies. Our results highlight the potential of LLM-driven automation in streamlining control design, reducing manual effort, and ensuring reliable controller deployment. Together, these contributions bridge the gap between control theory and learning systems, advancing the fields of adaptive control, neural network robustness, and automated control design. The findings presented in this dissertation pave the way for more intelligent, robust, and autonomous systems, with applications spanning robotics, autonomous vehicles, aerospace, and intelligent infrastructure
Towards a hybrid testing process unifying exploratory testing and scripted testing
No full textCONTEXT Given the current state of the art in research, practitioners are faced with the challenge of choosing scripted testing (ST) or exploratory testing (ET). OBJECTIVE This study aims at systematically incorporating strengths of ET and ST in a hybrid testing process to overcome the weaknesses of each. METHOD We utilized systematic review and practitioner interviews to identify strengths and weaknesses of ET and ST. Strengths of ET were mapped to weaknesses of ST and vice versa. Noblit and Hare's lines-ofargument method was used for data analysis. The results of the mapping were used as input to codesign a hybrid process with experienced practitioners. RESULTS We found a clear need to create a hybrid process as follows: (i) both ST and ET provide strengths and weaknesses, and these depend on some particular conditions, which prevents preference of one approach to another; and (ii) the mapping showed that it is possible to address the weaknesses in one process by the strengths of the other in a hybrid form. With the input from literature and industry experts, a flexible and iterative hybrid process was designed. CONCLUSIONS Practitioners can clearly benefit from using a hybrid process given the mapping of advantages and disadvantage
Hydraulic simulations to evaluate and predict design and operation of the Chashma Right Bank Canal
No full textIrrigation systems / Irrigation canals / Flow control / Velocity / Canal regulation techniques / Hydraulics / Simulation models / Design / Operations / Crop-based irrigation / Distributary canals / Water delivery / Policy / Protective irrigation / Water allocation / Water requirements / Sedimentation / Water distribution / Equity / Water conveyance / Pakistan / Chashma Right Bank Canal
Ion oxide silicon capacitor based pH sensing
No full textSeveral biological applications involve measuring intracellular and extracellular pH values. This thesis reports a novel technique that uses the basic principles of a MOSCAP. Based on its functionality, the device is termed as iOSCAP (Ion Oxide Silicon Capacitor). As the name suggests, the device consists of an ionic layer on top of an oxidized Si wafer. The strength of
the ionic solution determines the capacitance of the device, as the ionic strength modifies the work function of the ionic layer, thereby changing the surface potential of the device. The sensitivity of the device based on the results obtained is ~ 100pF/80 L/pH which is sufficient
for the target applications, and the changes can be recorded using an LCR meter. In addition, the device can be used for tracking the intracellular and extracellular changes as its response
time is ~ 5 ns. Light addressability has been tested but it will find more use when specific cell measurements are done. The device has been tested in the lab and can be made commercial by
proper packaging and modeling.Item withdrawn by Mark Zulauf ([email protected]) on 2012-07-19T13:06:42Z
Item was in collections:
University of Illinois Theses & Dissertations (ID: 1)
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