Unsupervised Monocular Depth Estimation in Highly Complex Environments

With the development of computational intelligence algorithms, unsupervised monocular depth and pose estimation framework, which is driven by warped photometric consistency, has shown great performance in the daytime scenario. While in some challenging environments, like night and rainy night, the essential photometric consistency hypothesis is untenable because of the complex lighting and reflection, so that the above unsupervised framework cannot be directly applied to these complex scenarios. In this paper, we investigate the problem of unsupervised monocular depth estimation in highly complex scenarios and address this challenging problem by adopting an image transfer-based domain adaptation framework. We adapt the depth model trained on day-time scenarios to be applicable to night-time scenarios, and constraints on both feature space and output space promote the framework to learn the key features for depth decoding. Meanwhile, we further tackle the effects of unstable image transfer quality on domain adaptation, and an image adaptation approach is proposed to evaluate the quality of transferred images and re-weight the corresponding losses, so as to improve the performance of the adapted depth model. Extensive experiments show the effectiveness of the proposed unsupervised framework in estimating the dense depth map from highly complex images.Comment: Accepted by IEEE Transactions on Emerging Topics in Computational Intelligenc

GasMono: Geometry-Aided Self-Supervised Monocular Depth Estimation for Indoor Scenes

This paper tackles the challenges of self-supervised monocular depth estimation in indoor scenes caused by large rotation between frames and low texture. We ease the learning process by obtaining coarse camera poses from monocular sequences through multi-view geometry to deal with the former. However, we found that limited by the scale ambiguity across different scenes in the training dataset, a na\"ive introduction of geometric coarse poses cannot play a positive role in performance improvement, which is counter-intuitive. To address this problem, we propose to refine those poses during training through rotation and translation/scale optimization. To soften the effect of the low texture, we combine the global reasoning of vision transformers with an overfitting-aware, iterative self-distillation mechanism, providing more accurate depth guidance coming from the network itself. Experiments on NYUv2, ScanNet, 7scenes, and KITTI datasets support the effectiveness of each component in our framework, which sets a new state-of-the-art for indoor self-supervised monocular depth estimation, as well as outstanding generalization ability. Code and models are available at https://github.com/zxcqlf/GasMonoComment: ICCV 2023. Code: https://github.com/zxcqlf/GasMon

Quadrilateral Current Mode (QCM) Paralleling of Power MOSFETs for Zero-Voltage Switching (ZVS)

This paper proposes a generic zero-voltage switching (ZVS) scheme for parallel power MOSFETs. Uncoupled or inversely-coupled differential-mode (DM) commutation inductors are added to the midpoints (AC terminals) of parallel MOSFET half-bridges (HBs), and a time-delay-based control scheme is applied, generating a circulating current flowing through these commutation inductors. Thus, the inductor currents are reshaped as quadrilaterals, which enable all the parallel transistors to achieve ZVS. The mode of operation of the proposed paralleling technique is entitled quadrilateral current mode (QCM) due to the quadrilateral-shaped commutation inductor currents. The operating principle of the QCM-paralleling technique is detailed mathematically, yielding accurate closed-form analytical expressions for modulation parameters. Finally, simulations and experimental results of a QCM-enabled synchronous Buck dc-dc converter are presented to validate the theoretical considerations

A Mendelian randomization study of testosterone and cognition in men

Testosterone replacement for older men is increasingly common, with some observations suggesting a protective effect on cognitive function. We examined the association of endogenous testosterone with cognitive function among older men in a Mendelian randomization study using a separate-sample instrumental variable (SSIV) analysis estimator to minimize confounding and reverse causality. A genetic score predicting testosterone was developed in 289 young Chinese men from Hong Kong, based on selected testosterone-related single nucleotide polymorphisms (rs10046, rs1008805 and rs1256031). The association of genetically predicted testosterone with delayed 10-word recall score and Mini-Mental State Examination (MMSE) score was assessed at baseline and follow-up using generalized estimating equation among 4,212 older Chinese men from the Guangzhou Biobank Cohort Study. Predicted testosterone was not associated with delayed 10-word recall score (−0.02 per nmol/L testosterone, 95% confidence interval (CI) −0.06–0.02) or MMSE score (0.06, 95% CI −0.002–0.12). These estimates were similar after additional adjustment for age, education, smoking, use of alcohol, body mass index and the Framingham score. Our findings do not corroborate observed protective effects of testosterone on cognitive function among older men

Estimation of mechanics parameters of rock in consideration of confining pressure using monitoring while drilling data

During the drilling process, high-strength rock can lead to various issues such as drilling suppression, bit wear, and increased operational costs. To ensure safe and efficient drilling operations, it is crucial to accurately predict the strength parameters of the rock and recommend modifications to operational procedures. This paper proposes a low-cost and fast measurement method for predicting the strength parameters of rock in the field. To evaluate the effectiveness of this method, a drilling process monitoring experiment was conducted on sandstone, limestone, and granite. The experiment studied the effect of confining pressure on the response of cutting with an impregnated diamond bit. By analyzing the relationship between the thrust force, torque force, and penetration depth under different confining pressures, the researchers developed an analytical model for drilling that considers confining pressure, compressed crushed zone, and bit geometry. The results show that the confining pressure has a significant effect on the cutting response. As the confining pressure increases, the thrust force, torque force, and penetration depth at the cutting point also increase. Furthermore, a new measurement method was proposed to determine the strength parameters, such as cohesion, internal friction angle, and unconfined compressive strength. The estimated strength parameters for the three rock types using the drilling method were in good agreement with those of the standard laboratory test, with an error range of 10%. This method of estimating rock strength parameters is a practical tool for engineers. It can continuously and quickly obtain the drilling parameters of in-situ rocks

Perception and Navigation in Autonomous Systems in the Era of Learning: A Survey

Autonomous systems possess the features of inferring their own state, understanding their surroundings, and performing autonomous navigation. With the applications of learning systems, like deep learning and reinforcement learning, the visual-based self-state estimation, environment perception and navigation capabilities of autonomous systems have been efficiently addressed, and many new learning-based algorithms have surfaced with respect to autonomous visual perception and navigation. In this review, we focus on the applications of learning-based monocular approaches in ego-motion perception, environment perception and navigation in autonomous systems, which is different from previous reviews that discussed traditional methods. First, we delineate the shortcomings of existing classical visual simultaneous localization and mapping (vSLAM) solutions, which demonstrate the necessity to integrate deep learning techniques. Second, we review the visual-based environmental perception and understanding methods based on deep learning, including deep learning-based monocular depth estimation, monocular ego-motion prediction, image enhancement, object detection, semantic segmentation, and their combinations with traditional vSLAM frameworks. Then, we focus on the visual navigation based on learning systems, mainly including reinforcement learning and deep reinforcement learning. Finally, we examine several challenges and promising directions discussed and concluded in related research of learning systems in the era of computer science and robotics.Comment: This paper has been accepted by IEEE TNNL