Radar Sub-surface Sensing for Mapping the Extent of Hydraulic Fractures and for Monitoring Lake Ice and Design of Some Novel Antennas.

Hydraulic fracturing, which is a fast-developing well-stimulation technique, has greatly expanded oil and natural gas production in the United States. As the use of hydraulic fracturing has grown, concerns about its environmental impacts have also increased. A sub-surface imaging radar that can detect the extent of hydraulic fractures is highly demanded, but existing radar designs cannot meet the requirement of penetration range on the order of kilometers due to the exorbitant propagation loss in the ground. In the thesis, a medium frequency (MF) band sub-surface radar sensing system is proposed to extend the detectable range to kilometers in rock layers. Algorithms for cross-hole and single-hole configurations are developed based on simulations using point targets and realistic fractured rock models. A super-miniaturized borehole antenna and its feeding network are also designed for this radar system. Also application of imaging radars for sub-surface sensing frozen lakes at Arctic regions is investigated. The scattering mechanism is the key point to understand the radar data and to extract useful information. To explore this topic, a full-wave simulation model to analyze lake ice scattering phenomenology that includes columnar air bubbles is presented. Based on this model, the scattering mechanism from the rough ice/water interface and columnar air bubbles in the ice at C band is addressed and concludes that the roughness at the interface between ice and water is the dominate contributor to backscatter and once the lake is completely frozen the backscatter diminishes significantly. Radar remote sensing systems often require high-performance antennas with special specifications. Besides the borehole antenna for MF band subsurface imaging system, several other antennas are also designed for potential radar systems. Surface-to-borehole setup is an alternative configuration for subsurface imaging system, which requires a miniaturized planar antenna placed on the surface. Such antenna is developed with using artificial electromagnetic materials for size reduction. Furthermore, circularly polarized (CP) waveform can be used for imaging system and omnidirectional CP antenna is needed. Thus, a low-profile planar azimuthal omnidirectional CP antenna with gain of 1dB and bandwidth of 40MHz is designed at 2.4GHz by combining a novel slot antenna and a PIFA antenna.PhDElectrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/120674/1/wujf_1.pd

Observation of the ground-state-geometric phase in a Heisenberg XY model

Geometric phases play a central role in a variety of quantum phenomena, especially in condensed matter physics. Recently, it was shown that this fundamental concept exhibits a connection to quantum phase transitions where the system undergoes a qualitative change in the ground state when a control parameter in its Hamiltonian is varied. Here we report the first experimental study using the geometric phase as a topological test of quantum transitions of the ground state in a Heisenberg XY spin model. Using NMR interferometry, we measure the geometric phase for different adiabatic circuits that do not pass through points of degeneracy.Comment: manuscript (4 pages, 3 figures) + supporting online material (6 pages + 7 figures), to be published in Phys. Rev. Lett. (2010

Research on the structure function recognition of PLOS

PurposeThe present study explores and investigates the efficiency of deep learning models in identifying discourse structure and functional features and explores the potential application of natural language processing (NLP) techniques in text mining, information measurement, and scientific communication.MethodThe PLOS literature series has been utilized to obtain full-text data, and four deep learning models, including BERT, RoBERTa, SciBERT, and SsciBERT, have been employed for structure-function recognition.ResultThe experimental findings reveal that the SciBERT model performs outstandingly, surpassing the other models, with an F1 score. Additionally, the performance of different paragraph structures has been analyzed, and it has been found that the model performs well in paragraphs such as method and result.ConclusionThe study's outcomes suggest that deep learning models can recognize the structure and functional elements at the discourse level, particularly for scientific literature, where the SciBERT model performs remarkably. Moreover, the NLP techniques have extensive prospects in various fields, including text mining, information measurement, and scientific communication. By automatically parsing and identifying structural and functional information in text, the efficiency of literature management and retrieval can be improved, thereby expediting scientific research progress. Therefore, deep learning and NLP technologies hold significant value in scientific research

Probe optimization for quantum metrology via closed-loop learning control

Experimentally achieving the precision that standard quantum metrology schemes promise is always challenging. Recently, additional controls were applied to design feasible quantum metrology schemes. However, these approaches generally does not consider ease of implementation, raising technological barriers impeding its realization. In this paper, we circumvent this problem by applying closed-loop learning control to propose a practical controlled sequential scheme for quantum metrology. Purity loss of the probe state, which relates to quantum Fisher information, is measured efficiently as the fitness to guide the learning loop. We confirm its feasibility and certain superiorities over standard quantum metrology schemes by numerical analysis and proof-of-principle experiments in a nuclear magnetic resonance (NMR) system