Development of a distributed optical fiber sensor for geological applications

The purpose of the study was to develop a distributed optical fiber acoustic sensor for monitoring ground subsidence before collapse sinkholes form causing costly damage on infrastructure. Costs in excess of R1.3 billion have been incurred while dealing with sinkhole related measures in South Africa. Monitoring sinkholes and the presence of an early warning alert system can drastically reduce the impact, risk and cost caused by sudden ground collapse. A related goal was to construct a reliable collapse alert early warning system to facilitate disaster preparedness and avoid further damage from accidents. This was achieved by developing a spectroscopic shift monitoring algorithm which analysed changes in the subsurface vibration modes using ambient noise signals. For the first time to our knowledge, an optic fiber sensor with an early warning alarm, using ambient noise vibrations to detect and monitor sinkholes was developed at NMU. A polarisation-based, interferometric optical fiber seismic sensor was developed and compared to a commercial geophone. The fiber sensor exhibited superior performance in sensitivity, bandwidth, signal response and recovery times. The sensitivity of the optical fiber sensor was 0.47 rad/Pa surpassing the geophone sensitivity by 9.32%, and the bandwidth of 3.349kHz was 20 times greater for the optical fiber sensor. The fiber sensor was used to measure millisecond events as the impact duration of a bouncing ball was successfully obtained. It was used to detect sinkhole formation in the simulator model, designed. Ground collapse precursors were identified, and early warning alert was achieved using the spectral analysis algorithm, developed. The collapse precursor condition was identified as a functional combination of variations in the peak frequency, bandwidth and peak intensity. A distributed acoustic sensor was built to detect ambient noise induced subsurface signals. Vibrations were located along the 28km length of optical fiber with a relative error of 9.6%. The sensor demonstrated a frequency response range of 212.25Hz, an event distance precision of 224m with time resolution of 1.12µs, and a spatial resolution of 1km. The position of disturbance was measured within 300m of its actual point of 3.21km along the optical fiber. The results showed that distributed optical fiber sensing allows real-time monitoring of the subsurface over extended distances, using ambient noise signals.Thesis (PhD) -- Faculty of Science, School of Computer Science, Mathematics, Physics and Statistics, 202

Development of a distributed optical fiber sensor for geological applications

The purpose of the study was to develop a distributed optical fiber acoustic sensor for monitoring ground subsidence before collapse sinkholes form causing costly damage on infrastructure. Costs in excess of R1.3 billion have been incurred while dealing with sinkhole related measures in South Africa. Monitoring sinkholes and the presence of an early warning alert system can drastically reduce the impact, risk and cost caused by sudden ground collapse. A related goal was to construct a reliable collapse alert early warning system to facilitate disaster preparedness and avoid further damage from accidents. This was achieved by developing a spectroscopic shift monitoring algorithm which analysed changes in the subsurface vibration modes using ambient noise signals. For the first time to our knowledge, an optic fiber sensor with an early warning alarm, using ambient noise vibrations to detect and monitor sinkholes was developed at NMU. A polarisation-based, interferometric optical fiber seismic sensor was developed and compared to a commercial geophone. The fiber sensor exhibited superior performance in sensitivity, bandwidth, signal response and recovery times. The sensitivity of the optical fiber sensor was 0.47 rad/Pa surpassing the geophone sensitivity by 9.32%, and the bandwidth of 3.349kHz was 20 times greater for the optical fiber sensor. The fiber sensor was used to measure millisecond events as the impact duration of a bouncing ball was successfully obtained. It was used to detect sinkhole formation in the simulator model, designed. Ground collapse precursors were identified, and early warning alert was achieved using the spectral analysis algorithm, developed. The collapse precursor condition was identified as a functional combination of variations in the peak frequency, bandwidth and peak intensity. A distributed acoustic sensor was built to detect ambient noise induced subsurface signals. Vibrations were located along the 28km length of optical fiber with a relative error of 9.6%. The sensor demonstrated a frequency response range of 212.25Hz, an event distance precision of 224m with time resolution of 1.12µs, and a spatial resolution of 1km. The position of disturbance was measured within 300m of its actual point of 3.21km along the optical fiber. The results showed that distributed optical fiber sensing allows real-time monitoring of the subsurface over extended distances, using ambient noise signals.Thesis (PhD) -- Faculty of Science, School of Computer Science, Mathematics, Physics and Statistics, 202

Magnetotransport properties of a polarization-doped three-dimensional electron slab

We present evidence of strong Shubnikov-de-Haas magnetoresistance oscillations in a polarization-doped degenerate three-dimensional electron slab in an Al$_{x}$Ga$_{1-x}$N semiconductor system. The degenerate free carriers are generated by a novel technique by grading a polar alloy semiconductor with spatially changing polarization. Analysis of the magnetotransport data enables us to extract an effective mass of $m^{\star}=0.19 m_{0}$ and a quantum scattering time of $\tau_{q}= 0.3 ps$. Analysis of scattering processes helps us extract an alloy scattering parameter for the Al$_{x}$Ga$_{1-x}$N material system to be $V_{0}=1.8eV$

Causal schema induction for knowledge discovery

Making sense of familiar yet new situations typically involves making generalizations about causal schemas, stories that help humans reason about event sequences. Reasoning about events includes identifying cause and effect relations shared across event instances, a process we refer to as causal schema induction. Statistical schema induction systems may leverage structural knowledge encoded in discourse or the causal graphs associated with event meaning, however resources to study such causal structure are few in number and limited in size. In this work, we investigate how to apply schema induction models to the task of knowledge discovery for enhanced search of English-language news texts. To tackle the problem of data scarcity, we present Torquestra, a manually curated dataset of text-graph-schema units integrating temporal, event, and causal structures. We benchmark our dataset on three knowledge discovery tasks, building and evaluating models for each. Results show that systems that harness causal structure are effective at identifying texts sharing similar causal meaning components rather than relying on lexical cues alone. We make our dataset and models available for research purposes.Comment: 8 pages, appendi

Search-Related Suppression of Hippocampus and Default Network Activity during Associative Memory Retrieval

Episodic memory retrieval involves the coordinated interaction of several cognitive processing stages such as mental search, access to a memory store, associative re-encoding, and post-retrieval monitoring. The neural response during memory retrieval is an integration of signals from multiple regions that may subserve supportive cognitive control, attention, sensory association, encoding, or working memory functions. It is particularly challenging to dissociate contributions of these distinct components to brain responses in regions such as the hippocampus, which lies at the interface between overlapping memory encoding and retrieval, and “default” networks. In the present study, event-related functional magnetic resonance imaging (fMRI) and measures of memory performance were used to differentiate brain responses to memory search from subcomponents of episodic memory retrieval associated with successful recall. During the attempted retrieval of both poorly and strongly remembered word pair associates, the hemodynamic response was negatively deflected below baseline in anterior hippocampus and regions of the default network. Activations in anterior hippocampus were functionally distinct from those in posterior hippocampus and negatively correlated with response times. Thus, relative to the pre-stimulus period, the hippocampus shows reduced activity during intensive engagement in episodic memory search. Such deactivation was most salient during trials that engaged only pre-retrieval search processes in the absence of successful recollection or post-retrieval processing. Implications for interpretation of hippocampal fMRI responses during retrieval are discussed. A model is presented to interpret such activations as representing modulation of encoding-related activity, rather than retrieval-related activity. Engagement in intensive mental search may reduce neural and attentional resources that are otherwise tonically devoted to encoding an individual’s stream of experience into episodic memory

SplatArmor: Articulated Gaussian splatting for animatable humans from monocular RGB videos

We propose SplatArmor, a novel approach for recovering detailed and animatable human models by `armoring' a parameterized body model with 3D Gaussians. Our approach represents the human as a set of 3D Gaussians within a canonical space, whose articulation is defined by extending the skinning of the underlying SMPL geometry to arbitrary locations in the canonical space. To account for pose-dependent effects, we introduce a SE(3) field, which allows us to capture both the location and anisotropy of the Gaussians. Furthermore, we propose the use of a neural color field to provide color regularization and 3D supervision for the precise positioning of these Gaussians. We show that Gaussian splatting provides an interesting alternative to neural rendering based methods by leverging a rasterization primitive without facing any of the non-differentiability and optimization challenges typically faced in such approaches. The rasterization paradigms allows us to leverage forward skinning, and does not suffer from the ambiguities associated with inverse skinning and warping. We show compelling results on the ZJU MoCap and People Snapshot datasets, which underscore the effectiveness of our method for controllable human synthesis

Instruction and Jump-Landing Kinematics in College-Aged Female Athletes Over Time

Context: Instruction can be used to alter the biomechanical movement patterns associated with anterior cruciate ligament (ACL) injuries. Objective: To determine the effects of instruction through combination (self and expert) feedback or self-feedback on lower extremity kinematics during the box–drop-jump task, running–stop-jump task, and sidestep-cutting maneuver over time in college-aged female athletes. Design: Randomized controlled clinical trial. Setting: Laboratory. Patients or Other Participants: Forty-three physically active women (age = 21.47 ± 1.55 years, height = 1.65 ± 0.08 m, mass = 63.78 ± 12.00 kg) with no history of ACL or lower extremity injuries or surgery in the 2 months before the study were assigned randomly to 3 groups: self-feedback (SE), combination feedback (CB), or control (CT). Intervention(s): Participants performed a box–drop-jump task for the pretest and then received feedback about their landing mechanics. After the intervention, they performed an immediate posttest of the box–drop-jump task and a running–stop-jump transfer test. Participants returned 1 month later for a retention test of each task and a sidestep-cutting maneuver. Kinematic data were collected with an 8-camera system sampled at 500 Hz. Main Outcome Measure(s): The independent variables were feedback group (3), test time (3), and task (3). The dependent variables were knee- and hip-flexion, knee-valgus, and hip- abduction kinematics at initial contact and at peak knee flexion. Results: For the box–drop-jump task, knee- and hip-flexion angles at initial contact were greater at the posttest than at the retention test (P \u3c .001). At peak knee flexion, hip flexion was greater at the posttest than at the pretest (P = .003) and was greater at the retention test than at the pretest (P = .04); knee valgus was greater at the retention test than at the pretest (P = .03) and posttest (P = .02). Peak knee flexion was greater for the CB than the SE group (P = .03) during the box–drop-jump task at posttest. For the running–stop-jump task at the posttest, the CB group had greater peak knee flexion than the SE and CT (P ≤ .05). Conclusions: Our results suggest that feedback involving a combination of self-feedback and expert video feedback with oral instruction effectively improved lower extremity kinematics during jump-landing tasks