Graphical User Interface for Robust Control Education Based on 3DOF Helicopter System

Robustness is an important characteristic of feedback controllers. Many systems with non-linearities and parameter uncertainties usually require sucient robustness to stay stable. Robust control education is therefore very important for graduate students. However, many educational programs in robust control are exclusively theoretical. This thesis work aims at designing and developing a user-friendly Graphical User Interface (GUI) based on MATLAB for robust-control education based on a nonlinear and highly-coupled helicopter system. Although the control design method is limited to the S/KS mixed-sensitivity method, it oers a convenient and ready-to-use GUI for robust control design, dynamic response simulation, robust stability/robust performance assessment, and control implementation. Users can generate the controller by inputting parameters of two weighting functions. Linear and nonlinear simulations based on a discrete-time model are used to assess performance. Structured singular values are used to assess robust stability and robust performance conditions with three typical types of uncertainty. In the end, the designed controller can be loaded into Simulink to control the actual helicopter device. This robust-control educational experiment oers an easy way to test in practice fundamentals of robust control theory

Facial Video-based Remote Physiological Measurement via Self-supervised Learning

Facial video-based remote physiological measurement aims to estimate remote photoplethysmography (rPPG) signals from human face videos and then measure multiple vital signs (e.g. heart rate, respiration frequency) from rPPG signals. Recent approaches achieve it by training deep neural networks, which normally require abundant facial videos and synchronously recorded photoplethysmography (PPG) signals for supervision. However, the collection of these annotated corpora is not easy in practice. In this paper, we introduce a novel frequency-inspired self-supervised framework that learns to estimate rPPG signals from facial videos without the need of ground truth PPG signals. Given a video sample, we first augment it into multiple positive/negative samples which contain similar/dissimilar signal frequencies to the original one. Specifically, positive samples are generated using spatial augmentation. Negative samples are generated via a learnable frequency augmentation module, which performs non-linear signal frequency transformation on the input without excessively changing its visual appearance. Next, we introduce a local rPPG expert aggregation module to estimate rPPG signals from augmented samples. It encodes complementary pulsation information from different face regions and aggregate them into one rPPG prediction. Finally, we propose a series of frequency-inspired losses, i.e. frequency contrastive loss, frequency ratio consistency loss, and cross-video frequency agreement loss, for the optimization of estimated rPPG signals from multiple augmented video samples and across temporally neighboring video samples. We conduct rPPG-based heart rate, heart rate variability and respiration frequency estimation on four standard benchmarks. The experimental results demonstrate that our method improves the state of the art by a large margin.Comment: IEEE Transactions on Pattern Analysis and Machine Intelligenc

Multimodal-Transport Collaborative Evacuation Strategies for Urban Serious Emergency Incidents Based on Multi-Sources Spatiotemporal Data (Short Paper)

When serious emergency events happen in metropolitan cities where pedestrians and vehicles are in high-density, single modal-transport cannot meet the requirements of quick evacuations. Existing mixed modes of transportation lacks spatiotemporal collaborative ability, which cannot work together to accomplish evacuation tasks in a safe and efficient way. It is of great scientific significance and application value for emergency response to adopt multimodal-transport evacuations and improve their spatial-temporal collaboration ability. However, multimodal-transport evacuation strategies for urban serious emergency event are great challenge to be solved. The reasons lie in that: (1) large-scale urban emergency environment are extremely complicated involving many geographical elements (e.g., road, buildings, over-pass, square, hydrographic net, etc.); (2) Evacuated objects are dynamic and hard to be predicted. (3) the distributions of pedestrians and vehicles are unknown. To such issues, this paper reveals both collaborative and competitive mechanisms of multimodal-transport, and further makes global optimal evacuation strategies from the macro-optimization perspective. Considering detailed geographical environment, pedestrian, vehicle and urban rail transit, a multi-objective multi-dynamic-constraints optimization model for multimodal-transport collaborative emergency evacuation is constructed. Take crowd incidents in Shenzhen as example, empirical experiments with real-world data are conducted to evaluate the evacuation strategies and path planning. It is expected to obtain innovative research achievements on theory and method of urban emergency evacuation in serious emergency events. Moreover, this research results provide spatial-temporal decision support for urban emergency response, which is benefit to constructing smart and safe cities

Multiple Satellites Collaboration for Joint Code-aided CFOs and CPOs Estimation

Low Earth Orbit (LEO) satellites are being extensively researched in the development of secure Internet of Remote Things (IoRT). In scenarios with miniaturized terminals, the limited transmission power and long transmission distance often lead to low Signal-to-Noise Ratio (SNR) at the satellite receiver, which degrades communication performance. A solution to address this issue is the utilization of cooperative satellites, which can combine signals received from multiple satellites, thereby significantly improve SNR. However, in order to maximize the combination gain, the signal coherent combining is necessary, which requires the carrier frequency and phase of each receiving signal to be aligned. Under low SNR circumstances, carrier parameter estimation can be a significant challenge, especially for short burst transmission with no training sequence. In order to tackle it, we propose an iterative code-aided estimation algorithm for joint Carrier Frequency Offset (CFO) and Carrier Phase Offset (CPO). The Cram\'er-Rao Lower Bound (CRLB) is suggested as the limit on the parameter estimation performance. Simulation results demonstrate that the proposed algorithm can approach Bit Error Rate (BER) performance bound within 0.4 dB with regards to four-satellite collaboration

Enhancing Space-time Video Super-resolution via Spatial-temporal Feature Interaction

The target of space-time video super-resolution (STVSR) is to increase both the frame rate (also referred to as the temporal resolution) and the spatial resolution of a given video. Recent approaches solve STVSR with end-to-end deep neural networks. A popular solution is to first increase the frame rate of the video; then perform feature refinement among different frame features; and last increase the spatial resolutions of these features. The temporal correlation among features of different frames is carefully exploited in this process. The spatial correlation among features of different (spatial) resolutions, despite being also very important, is however not emphasized. In this paper, we propose a spatial-temporal feature interaction network to enhance STVSR by exploiting both spatial and temporal correlations among features of different frames and spatial resolutions. Specifically, the spatial-temporal frame interpolation module is introduced to interpolate low- and high-resolution intermediate frame features simultaneously and interactively. The spatial-temporal local and global refinement modules are respectively deployed afterwards to exploit the spatial-temporal correlation among different features for their refinement. Finally, a novel motion consistency loss is employed to enhance the motion continuity among reconstructed frames. We conduct experiments on three standard benchmarks, Vid4, Vimeo-90K and Adobe240, and the results demonstrate that our method improves the state of the art methods by a considerable margin. Our codes will be available at https://github.com/yuezijie/STINet-Space-time-Video-Super-resolution

Chirality-enabled unidirectional light emission and nanoparticle detection in parity-time-symmetric microcavity

Achieving unidirectional emission and manipulating waves in a microcavity are crucial for information processing and data transmission in next-generation photonic circuits (PCs). Here we show how to impose twin microcavities with opposite chirality by incorporating parity-time (PT) symmetry to realize unidirectional emission. Our numerical calculation results show that the opposite chirality in microcavities stems from the asymmetric coupling of the clockwise (CW) and counterclockwise (CCW) components carried by the attached waveguide to the left- or right-sided microcavities, respectively. Notably, by engineering PT symmetry in the coupled system via the gain-loss control, the clockwise component of the lossy cavity could be selectively suppressed, which leads to the unidirectional emission with an extinction ratio of up to -52 dB. Furthermore, the chirality and PT-symmetry breaking enabled unidirectional emission is extremely sensitive to external scatters, allowing the detection of nanoparticles with an ultrasmall radius of 5-50 nm by recording the extinction ratio change. The proposed system provides a simple yet general way to manipulate the standing waves in a microcavity and will be essential for advancing the potentials of the microcavity in PCs.This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Setting the Width of Emergency Exit in Pedestrian Walking Facilities

AbstractTo improve the safety of pedestrian evacuation and the utilization efficiency of emergency exits in pedestrian walking facilities, the method of computing the width of emergency exits was presented in this paper. These factors of influencing the setup of exit width were analyzed from the process of pedestrian evacuation, the capacity of pedestrian passing exit and the strategy of pedestrian selecting exit. It is shown that the setup of exit width is dependent on the capacity of passing exit, the strategy of exit selection and the total sum, initial site, and aggregation degree of pedestrians in walking facilities. It is also found that the total capacity of passing exits will be low with the number of exits rising under the condition with a fixed total width of exits. The procedure of setting exit was presented to compute the number, site and width of every exit in pedestrian facilities through an example