ScholarWorks@UMassAmherst
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Recent Submissions
Publication Phase Transitions and Self-Assembly of Charged Polymer Solutions(2024-09)Charged polymers ubiquitously play a crucial role in numerous biological and synthetic systems, exhibiting diverse phases and self-assembly behaviors due to the entanglement of polymer connectivity and long-range electrostatic interactions. Charged polymer physics delves into understanding their highly coupled and non-linear response, shedding light on comprehending biological systems composed of charged biopolymers. Inspired by biomolecular condensates, membrane-less organelles assembled by intrinsically disordered proteins within cells, we aim to explore the phases and self-assembly in complex charged polymer solutions, where complexities stem from the chemical sequences and physical associations of polymers. These complexities cause difficulties in responding to the growth interest of biomolecular condensates. Employing the field theory formalism and statistical properties of conformations, we address the connectivity and interaction into the spatial correlation of monomer concentration, leading to the Landau free energy predicting the formations of phases and self-assembly. This thesis comprises two works. The first work focuses on predicting the stability, size, and morphology of microphase separation for sequence-specified charged polymers, resulting in machinery for microphases for general sequences. In the second work, utilizing polyzwitterions as a simplified model for associative charged polymers, we explore the thermoreversible behaviors of electric-dipole-driven macrophase separation and gelation. Furthermore, our use of the renormalization group reveals that concentration fluctuations near critical points deviate from the Ising universality class due to the presence of associations. These results can facilitate future investigations on more complex systems in biological and synthetic realms with suitable modifications.Publication Advancing Acoustic Sensing from the Laboratory to Real World: Theories, Applications, and Practical Considerations(2024-09)With the proliferation of voice assistants, speakers and microphones are essential components in billions of smart devices that people interact with on a daily basis, such as smartphones, smart watches, smart speakers, home appliances, etc. This dissertation explores the transformation of these devices from simple audio tools into sophisticated acoustic radars, expanding their applications beyond basic audio playback and voice interactions to include gesture tracking, vital sign monitoring, and eye blink detection. We address fundamental technical challenges and practical considerations, which not only resolve existing system limitations but also facilitate the creation of new applications. One major challenge in acoustic sensing is tracking multiple targets simultaneously due to the inherent nature of contact-free tracking. Signals reflected from multiple targets are mixed at the microphone, and thus, it is difficult to separate them to obtain the context information of each individual target. FM-Track pioneers in enabling contact-free multi-target tracking using acoustic signals. A signal model is introduced to characterize the location and motion status of targets by fusing the information from multiple dimensions (i.e., range, velocity, and angle of targets). Then a series of techniques are developed to separate signals reflected from multiple targets and accurately track each individual target. FM-Track can successfully differentiate two targets with a spacing as small as 1 cm. Another significant challenge for acoustic sensing is the extremely limited sensing range, particularly for fine-grained activities due to weak signal reflections. LASense dramatically increases the sensing range for fine-grained human activities by introducing a virtual transceiver idea that purely leverages delicate signal processing techniques in software. LASense can significantly increase the sensing range of respiration monitoring from the state-of-the-art 2 m to 6 m, and enhance the sensing range for finger tapping and eye blink detection by 150% and 80%, respectively. Additionally, this dissertation demonstrates how to apply acoustic sensing techniques to enable new applications, i.e., “listening” to your hand gestures using smart speakers. In SpeakerGesture, we develop a series of novel signal processing techniques and implement our system on two commodity smart speaker prototypes. SpeakerGesture can achieve over 90% accuracy in gesture recognition even when the user is 4 m away from the smart speaker and there is strong interference. At last, this dissertation shares the experience and findings when transitioning acoustic sensing systems from laboratory settings to real-world environments. We identify multiple practical considerations that were not paid attention to in the research community and propose the corresponding solutions. The challenges include: (i) there exists annoying audible sound leakage caused by acoustic sensing; (ii) acoustic sensing actually affects music play and voice calls; (iii) acoustic sensing consumes a significant amount of power, degrading the battery life; (iv) real-world device mobility can fail acoustic sensing.Publication Microwave Metasurfaces Based on Field Synthesis for Radiation and Scattering Applications(2024-09)This thesis aims to explore electromagnetic metasurfaces, focusing on their numerical and physical design in the microwave regime. The metasurfaces meticulously manipulate electromagnetic fields by synthesizing a set of auxiliary surface waves in the invisible region. Initially, a large plane-wave-to-surface-wave coupler is numerically and physically designed in three types of physical structures under an assumption of impenetrable surface. Moving beyond the assumption, this thesis presents a detailed theoretical analysis and design methodology for a penetrable metasurface. The scalar and tensorial penetrable metasurfaces have been developed for two applications: invisibility cloaking and leaky-wave antennas. First, thin and passive single-layer metasurfaces for invisibility cloaking are presented. The metasurfaces convert an incident plane wave into surface waves on the lit side and continuously leak the power as a plane wave with the aligned wavefront to the incident wave on the shadow side. The 2-D cylindrical cloaking techniques are demonstrated and experimentally validated for single- and dual-polarization. Extending the ideas, a 3-D spherical cloaking strategy has been developed with a physical design consisting of a crossed dipole array on a conducting dielectric shell. For leaky-wave antennas, this thesis introduces planar and conformal apertures. In the planar leaky-wave antenna designs, the design methodology achieves an accurate radiating performance by taking into account the constituent material losses which significantly have an impact on radiating performances in practice. Based on the design method, a completely flush planar leaky-wave antenna has been introduced as well. Designing leaky-wave antennas on a conformal surface is accomplished via forward field projection and total field synthesis method. Furthermore, a circularly-polarized leaky-wave antenna on a conformal surface is designed, demonstrating the scalability of the design approach.Publication Machine Learning for Chaotic Dynamical Systems(2024-09)This dissertation is on the usage of machine learning for the study of dynamical systems, particularly chaotic dynamical systems. Chapter 1 provides a brief intro- duction to the fields of chaotic dynamical systems and machine learning as well as a small overview of chaptes 2-4 In chapter 2 we study the usage of machine learning methods to forecast the spread of COVID-19. We consider the Susceptible-Infected-Confirmed-Recovered- Deceased (SICRD) compartmental model, with the goal of estimating the unknown infected compartment I, and several unknown parameters. We apply a variation of a “Physics Informed Neural Network” (PINN), which uses knowledge of the system to aid learning. First, we ensure estimation is possible by verifying the model’s identifiability. Then, we propose a wavelet transform to process data for the network training. Finally, our central result is a novel modification of the PINN’s loss function to reduce the number of simultaneously considered unknowns. We find that our modified network is capable of stable, efficient, and accurate estimation, while the unmodified network consistently yields incorrect values. The modified network is also shown to be efficient enough to be applied to a model with time-varying parameters. We present an application of our model results in ranking states by estimated relative testing efficiency. Our findings suggest the effectiveness of our modified PINN network, especially in this case of multiple unknown variables. In chapter 3 we introduce the Discrete-Temporal Sobolev Network (DTSN), a neural network loss function that assists dynamical system forecasting by minimiz- ing variational differences between the network output and the training data via a temporal Sobolev norm. This approach is entirely data-driven, architecture agnos- tic, and does not require derivative information from the estimated system. The DTSN is particularly well suited to chaotic dynamical systems as it minimizes noise in the network output which is crucial for such sensitive systems. For our test cases we consider discrete approximations of the Lorenz-63 system and the Chua circuit. For the network architectures we use the Long Short-Term Memory (LSTM) and the Transformer. The performance of the DTSN is compared with the standard MSE loss for both architectures, as well as with the Physics Informed Neural Net- work (PINN) loss for the LSTM. The DTSN loss is shown to substantially improve accuracy for both architectures, while requiring less information than the PINN and without noticeably increasing computational time, thereby demonstrating its potential to improve neural network forecasting of dynamical systems. In chapter 4 we present a new method of performing extended dynamic mode decomposition (EDMD) for systems which admit a symbolic representation. EDMD generates an estimate Km of the Koopman operator K for a system by defining a dictionary of observables on the space and estimating K restricted to be invariant on the span of this dictionary. One of the most important questions of the EDMD is what should be chosen for the choice of dictionary? We consider a class of chaotic dynamical systems with a known or estimable generating partition. For these systems we construct an effective dictionary from indicators of the ”cylinder sets” which have great significance in defining the ”symbolic system” which uses the generating partition. We prove strong operator topology convergence for both the projection onto the span of our dictionary and for Km. We also prove practical finite step estimation bounds for the projection and Km as well. Finally we demonstrate some numerical applications of the algorithm to two example systems, the dyadic map and the logistic map. Finally chapter 5 briefly recaps the results of chapters 2-4 and discusses directions for potential future researchPublication L'Oeuvre d'Alfred Delvau (1825-1867)(1974-01)Cette thèse est une étude d'ensemble sur Alfred Delvau et son œuvre. Elle est la première étude de ce genre sur cet auteur. Elle se divise en six chapitres.
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