Vision-based Real-Time Aerial Object Localization and Tracking for UAV Sensing System

The paper focuses on the problem of vision-based obstacle detection and tracking for unmanned aerial vehicle navigation. A real-time object localization and tracking strategy from monocular image sequences is developed by effectively integrating the object detection and tracking into a dynamic Kalman model. At the detection stage, the object of interest is automatically detected and localized from a saliency map computed via the image background connectivity cue at each frame; at the tracking stage, a Kalman filter is employed to provide a coarse prediction of the object state, which is further refined via a local detector incorporating the saliency map and the temporal information between two consecutive frames. Compared to existing methods, the proposed approach does not require any manual initialization for tracking, runs much faster than the state-of-the-art trackers of its kind, and achieves competitive tracking performance on a large number of image sequences. Extensive experiments demonstrate the effectiveness and superior performance of the proposed approach.Comment: 8 pages, 7 figure

Correlations between the stress paths of a monotonic test and a cyclic test under the same initial conditions

In most experimental studies on liquefaction, cyclic loadings are applied on specimens with various initial conditions. However, few studies compared cyclic test results with monotonic results under the same initial conditions. The relation between monotonic tests and cyclic tests is crucial for understanding liquefaction mechanics and liquefaction resistance. This work compares the stress paths of a monotonic test with those of a cyclic test under the same initial conditions, and concluded that the stress path of monotonic tests envelops the stress path of cyclic tests under the same initial conditions. In addition, a new parameter, Level of Liquefaction Index (LI) is proposed to evaluate the liquefaction resistance of specimens under various initial conditions, and a linear relationship between LI and number of cycles at failure is found

Imaging through multimode fibres with physical prior

Imaging through perturbed multimode fibres based on deep learning has been widely researched. However, existing methods mainly use target-speckle pairs in different configurations. It is challenging to reconstruct targets without trained networks. In this paper, we propose a physics-assisted, unsupervised, learning-based fibre imaging scheme. The role of the physical prior is to simplify the mapping relationship between the speckle pattern and the target image, thereby reducing the computational complexity. The unsupervised network learns target features according to the optimized direction provided by the physical prior. Therefore, the reconstruction process of the online learning only requires a few speckle patterns and unpaired targets. The proposed scheme also increases the generalization ability of the learning-based method in perturbed multimode fibres. Our scheme has the potential to extend the application of multimode fibre imaging

Monotonic direct simple shear tests on sand under multidirectional loading

Stress–strain responses of Leighton Buzzard sand are investigated under bidirectional shear. The tests are conducted by using the variable direction dynamic cyclic simple shear (VDDCSS), which is manufactured by Global Digital Systems (GDS) Instruments Ltd., U.K. Soil samples are anisotropically consolidated under a vertical normal stress and horizontal shear stress and then sheared in undrained conditions by applying a horizontal shear stress acting along a different direction from the consolidation shear stress. The influence of the orientation and magnitude of the consolidation shear stress is investigated in this study. There are only a few previous studies on soil responses under bidirectional shear, of which most studies do not consider the impact of the magnitude of the consolidation shear stress. They are compared with current studies, indicating both similarities and differences. Generally, all test results indicate that a smaller angle between the first and second horizontal shear stress leads to more brittle responses with higher peak strengths, and a larger angle leads to more ductile responses. In addition, the consolidation shear tends to make soil samples denser, and both the magnitude of consolidation shear stress and its direction influence the following stress–strain responses of soil samples

Earning Extra Performance from Restrictive Feedbacks

Many machine learning applications encounter a situation where model providers are required to further refine the previously trained model so as to gratify the specific need of local users. This problem is reduced to the standard model tuning paradigm if the target data is permissibly fed to the model. However, it is rather difficult in a wide range of practical cases where target data is not shared with model providers but commonly some evaluations about the model are accessible. In this paper, we formally set up a challenge named \emph{Earning eXtra PerformancE from restriCTive feEDdbacks} (EXPECTED) to describe this form of model tuning problems. Concretely, EXPECTED admits a model provider to access the operational performance of the candidate model multiple times via feedback from a local user (or a group of users). The goal of the model provider is to eventually deliver a satisfactory model to the local user(s) by utilizing the feedbacks. Unlike existing model tuning methods where the target data is always ready for calculating model gradients, the model providers in EXPECTED only see some feedbacks which could be as simple as scalars, such as inference accuracy or usage rate. To enable tuning in this restrictive circumstance, we propose to characterize the geometry of the model performance with regard to model parameters through exploring the parameters' distribution. In particular, for the deep models whose parameters distribute across multiple layers, a more query-efficient algorithm is further tailor-designed that conducts layerwise tuning with more attention to those layers which pay off better. Our theoretical analyses justify the proposed algorithms from the aspects of both efficacy and efficiency. Extensive experiments on different applications demonstrate that our work forges a sound solution to the EXPECTED problem.Comment: Accepted by IEEE TPAMI in April 202

Theoretical study of Acousto-optical coherence tomography using random phase jumps on US and light

Acousto-Optical Coherence Tomography (AOCT) is variant of Acousto Optic Imaging (called also ultrasonic modulation imaging) that makes possible to get z resolution with acoustic and optic Continuous Wave (CW) beams. We describe here theoretically the AOCT e ect, and we show that the Acousto Optic tagged photons remains coherent if they are generated within a speci c z region of the sample. We quantify the z selectivity for both the tagged photon eld, and for the M. Lesa re et al. photorefractive signal