The Rapidly-exploring Random Tree (RRT) algorithm faces issues in path planning, including low search efficiency, high randomness, and suboptimal quality. To overcome these issues, this paper proposes an improved RRT planning based on vehicle lane change trajectory data. This dynamically adjusts the sampling area road environment laws, allowing random tree to sample within effective area, thereby improving algorithm’s efficiency. After determining a point optimization strategy is used...

Low-light images are a common phenomenon when taking photos in low-light environments with inappropriate camera equipment, leading to shortcomings such as low contrast, color distortion, uneven brightness, and high loss of detail. These not only subjectively annoying but also affect the performance many computer vision systems. Enhanced can be better applied image recognition, object detection segmentation. This paper proposes novel RetinexDIP method enhance images. Noise is considered...

Remote sensing images often have limited resolution, which can hinder their effectiveness in various applications. Super-resolution techniques enhance the resolution of remote images, and arbitrary super-resolution provide additional flexibility choosing appropriate image resolutions for different tasks. However, subsequent processing, such as detection classification, input may vary greatly methods. In this paper, we propose a method continuous using feature-enhanced implicit neural...

Due to problems such as the shooting light, viewing angle, and camera equipment, low-light images with low contrast, color distortion, high noise, unclear details can be seen regularly in real scenes. These will not only affect our observation but also greatly performance of computer vision processing algorithms. Low-light image enhancement technology help improve quality make them more applicable fields vision, machine learning, artificial intelligence. In this paper, we propose a novel...

Random matrix series are a significant component of random theory, offering rich theoretical content and broad application prospects. In this paper, we propose modified versions tail bounds for series, including Gaussian (or Rademacher) sub-Gaussian infinitely divisible (i.d.) series. Unlike present studies, our results depend on the intrinsic dimension instead ambient dimension. some cases, is much smaller than dimension, which makes suitable high-dimensional or infinite-dimensional setting...

We establish $L^{p_1}(\mathbb R^d) \times \cdots L^{p_n}(\mathbb \rightarrow L^r(\mathbb R^d)$ bounds for spherical averaging operators $\mathcal A^n$ in dimensions $d \geq 2$ indices $1\le p_1,\dots , p_n\le \infty$ and $\frac{1}{p_1}+\cdots +\frac{1}{p_n}=\frac{1}{r}$. obtain this result by first showing that maps $L^1 L^1 L^1$. Our extends \cite{IPS2021} the case of $n = 2$. also similar estimates lacunary maximal averages largest possible open region indices.

The matrix Gaussian series refers to a sum of fixed matrices weighted by independent standard normal variables and plays an important in various fields related probability theory. In this paper, we present the dimension-free tail bounds expectation for largest singular value (LSV) series, respectively. By using resulting bounds, compute LSVs Wigner Toeplitz matrix,

In this paper, we study tail inequalities of the largest eigenvalue a matrix infinitely divisible (i.d.) series, which is finite sum fixed matrices weighted by i.d. random variables. We obtain several types inequalities, including Bennett-type and Bernstein-type inequalities. This allows us to further bound expectation spectral norm series. Moreover, developing new lower-bound function for $Q(s)=(s+1)\log(s+1)-s$ that appears in inequality, derive tighter inequality series than when...

Random matrices have played an important role in many fields including machine learning, quantum information theory, and optimization. One of the main research focuses is on deviation inequalities for eigenvalues random matrices. Although there are intensive studies large-deviation matrices, only a few works discuss small-deviation behavior In this paper, we present largest sums Since resulting independent matrix dimension, they applicable to high-dimensional even infinite-dimensional cases.

As major components of the random matrix theory, Gaussian matrices have been playing an important role in many fields, because they are both unitary invariant and independent entries can be used as models for multivariate data or phenomena. Tail bounds eigenvalues one hot study problems. In this paper, we present tail expectation ℓ 1 norm matrices, respectively. Moreover, Wigner calculated based on resulting bounds. Compared with existing results, our results more suitable high‐dimensional...

Because of the fast advance rate and improved personnel safety, tunnel boring machines (TBMs) have been widely used in a variety construction projects. The dynamic modeling TBM load parameters (including torque, thrust) plays an essential part design, safe operation fault prognostics this complex engineering system. In paper, based on in-situ operational data, we use machine-learning (ML) methods to build real-time forecast models for parameters, which can instantaneously provide future...

Random matrices have played an important role in many fields including machine learning, quantum information theory and optimization. One of the main research focuses is on deviation inequalities for eigenvalues random matrices. Although there are intensive studies large-deviation matrices, only a few works discuss small-deviation behavior In this paper, we present largest sums Since resulting independent matrix dimension, they applicable to high-dimensional even infinite-dimensional cases.

In this paper, we obtain a refined non-asymptotic tail bound for the largest singular value (the soft edge) of sub-Gaussian matrix. As an application, use obtained theorem to compute Gaussian Toeplitz