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In practical exploration, the complex and diverse topographic effects are important for the processing and interpretation of EM exploration data. The interpretation of the electromagnetic data results in large errors when the influence of topographical factors is simply ignored. At present, the study of TEM is mainly based on the ideal situation of flat terrain, and the study of topographic effect is less. However, topographic relief is inevitable in practical exploration. In this article, the correctness of the time-domain 3-D forward evolution based on the finite-element method (FEM) is verified by comparing the numerical results with those of the homogeneous full-space and half-space analytical solutions and the lumped high conductor model. In this article, 3-D numerical simulations of time-domain transient electromagnetic methods are calculated based on the FEM. Several exploration scenarios for complex terrain were constructed under the transmitting loop source, and the effect of terrain size and filling medium changes on the electromagnetic response was analyzed. The parameters of the topographic effect, earth resistivity, surface resistivity, and target conductivity are analyzed from the z-component of the magnetic field, apparent resistivity, and time-domain numerical solution, and the relative error distribution results in the whole observation period are shown. The results show that the influence of topographic effects on the electromagnetic response is concentrated in the early part of the time domain, with puddles of the same size having a higher influence on the electromagnetic response than raised soils and hollows. In practical exploration, puddles should be avoided where possible, whereas small-sized topographic reliefs and hollows have a negligible effect on the electromagnetic response.

In this research work, we proposed a novel ChildGAN, a pair of GAN networks for generating synthetic boys and girls facial data derived from StyleGAN2. ChildGAN is built by performing smooth domain transfer using transfer learning. It provides photo-realistic, high-quality data samples. A large-scale dataset is rendered with a variety of smart facial transformations: facial expressions, age progression, eye blink effects, head pose, skin and hair color variations, and variable lighting conditions. The dataset comprises more than 300k distinct data samples. Further, the uniqueness and characteristics of the rendered facial features are validated by running different computer vision application tests which include CNN-based child gender classifier, face localization and facial landmarks detection test, identity similarity evaluation using ArcFace, and lastly running eye detection and eye aspect ratio tests. The results demonstrate that synthetic child facial data of high quality offers an alternative to the cost and complexity of collecting a large-scale dataset from real children. The complete dataset along with the trained model are open-sourced on our GitHub website and GitHub page: https://github.com/MAli-Farooq/ChildGAN.

This work presents a study on the effects of aging on the performance and reliability of facial authentication methods. First, a brief review of the literature on the effect of age on face recognition algorithms is presented, followed by a detailed description of the face aging datasets. In contrast with some recent studies, we demonstrate significant variations in authentication robustness between age groups. The second part of this paper focuses on a comprehensive comparative assessment on the effects across age groups. Four different face recognition algorithms are studied of which three are state-of-the-art neural network based models and the fourth one is a conventional machine learning model. Two different age range threshold settings (±3 in Experiment Category A and ±5 in Experiment Category B) of the age groups are adopted in the experimental analysis to get a proper comparison. Moreover, a synthetic aging method has been incorporated to augment the age data. Experimental result shows that the older adults groups are easier to identify with higher levels of accuracy and robustness compared to other age groups, while younger adults are the most challenging and false authentications are more likely to occur.

Robust authentication for low-power consumer devices without a keyboard remains a challenge. The recent availability of low-power neural accelerator hardware, combined with improvements in neural facial recognition algorithms provides enabling technology for low-power, on-device facial authentication. The present research work explores a number of approaches to test the robustness of a state-of-the-art facial recognition (FR) technique, Arcface for such end-to-end applications. As extreme lighting conditions and facial pose are the two more challenging scenarios for FR we focus on these. Due to the general lack of large-scale multiple-identity datasets, GAN-based re-lighting and pose techniques are used to explore the effects on FR performance. These results are further validated on the best available multi-identity datasets - MultiPIE and BIWI. The results show that FR is quite robust to pose variations up to 45–55 degrees, but the outcomes are not definitive for the tested lighting scenarios. For lighting, the tested GAN-based relighting augmentations show significant effects on FR robustness. However, the lighting scenarios from MultiPIE dataset - the best available public dataset - show some conflicting results. It is unclear if this is due to an incorrectly learned GAN relighting transformation or, alternatively, to mixed ambient/directional lighting scenes in the dataset. However, it is shown that the GAN-induced FR errors for extreme lighting conditions can be corrected by fine-tuning the FR network layers. The conclusions support the feasibility of implementing a robust authentication method for low-power consumer devices.

In an environment with multiple static obstacles, UAVs usually communicate with each other to avoid collisions during trajectory planning. However, such communication may become infeasible or unreliable due to interference or jam in practice. This paper introduces a neighbors trajectory prediction algorithm based on model predictive control (MPC), which enables each UAV to predict the motion behavior of its neighbors without communication. By solving the MPC model of its neighbors, an UAV can predict their trajectories and then avoid collision with them in the future. To prove the practicability, we integrate the proposed algorithm into distributed model predictive control (DMPC) framework to realize multi-UAV trajectory planning without communication and with static obstacles. The performance of our method is verified by simulation experiments in two scenes.