Adaptive deep feature representation learning for cross-subject EEG decoding.

Background: The collection of substantial amounts of electroencephalogram (EEG) data is typically time-consuming and labor-intensive, which adversely impacts the development of decoding models with strong generalizability, particularly when the available data is limited. Utilizing sufficient EEG data from other subjects to aid in modeling the target subject presents a potential solution, commonly referred to as domain adaptation. Most current domain adaptation techniques for EEG decoding primarily focus on learning shared feature representations through domain alignment strategies.

Intraoperative interaction modeling between surgical instruments and soft tissues in neurosurgery based on energy functions.

A physical model of soft tissue that provides realistic and real-time haptic and visual feedback is crucial for neurosurgical procedures. This paper investigates the interaction between surgical instruments and soft brain tissue, proposing a soft tissue deformation simulation method based on the principle of energy minimization and constrained energy function. The model includes a permanent deformation energy function induced by friction and a volume preservation energy function to more accurately depict tissue response during procedures such as resection of convex meningiomas and evacuation of intracerebral hematomas.

Assessing the causal effects of and on constipation: a Mendelian randomized study.

Background: Constipation is affected by a number of risk variables, including cardiovascular disease and growth factors. However, the impacts of gut flora on constipation incidence has not been shown. This work, Single-Variable Mendelian Randomization (SVMR) was utilized to estimate the causal relationship between the or , and constipation.

Multilevel thresholding with divergence measure and improved particle swarm optimization algorithm for crack image segmentation.

Crack formation is a common phenomenon in engineering structures, which can cause serious damage to the safety and health of these structures. An important method of ensuring the safety and health of engineered structures is the prompt detection of cracks. Image threshold segmentation based on machine vision is a crucial technology for crack detection.

Microscopic image recognition of diatoms based on deep learning.

Diatoms are a crucial component in the study of aquatic ecosystems and ancient environmental records. However, traditional methods for identifying diatoms, such as morphological taxonomy and molecular detection, are costly, are time consuming, and have limitations. To address these issues, we developed an extensive collection of diatom images, consisting of 7983 images from 160 genera and 1042 species, which we expanded to 49,843 through preprocessing, segmentation, and data augmentation.

Dynamic Lane Reversal Strategy in Intelligent Transportation Systems in Smart Cities.

Route guidance strategies are an important part of advanced traveler information systems, which are a subsystem of intelligent transportation systems (ITSs). In previous research, many scholars have proposed a variety of route guidance strategies to guide vehicles in order to relieve traffic congestion, but few scholars have considered a strategy to control transportation infrastructure. In this paper, to cope with tidal traffic, we propose a dynamic lane reversal strategy (DLRS) based on the density of congestion clusters over the total road region.

Study on Identification Method of Pulmonary Nodules: Improved Random Walk Pulmonary Parenchyma Segmentation and Fusion Multi-Feature VGG16 Nodule Classification.

Purpose: The purpose of this study was to realize automatic segmentation of lung parenchyma based on random walk algorithm to ensure the accuracy of lung parenchyma segmentation. The explicable features of pulmonary nodules were added into VGG16 neural network to improve the classification accuracy of pulmonary nodules.

Materials And Methods: LIDC-IDRI, a public dataset containing lung Computed Tomography images/pulmonary nodules, was used as experimental data.

An Electrically Controlled Wavelength-Tunable Nanoribbon Laser.

Nanoscale laser sources with downscaled device footprint, high energy efficiency, and high operation speed are pivotal for a wide array of optoelectronic and nanophotonic applications ranging from on-chip interconnects, nanospectroscopy, and sensing to optical communication. The capability of on-demand lasing output with reversible and continuous wavelength tunability over a broad spectral range enables key functionalities in wavelength-division multiplexing and finely controlled light-matter interaction, which remains an important subject under intense research. In this study, we demonstrate an electrically controlled wavelength-tunable laser based on a CdS nanoribbon (NR) structure.

Research about the Characteristics of Chaotic Systems Based on Multi-Scale Entropy.

The logistic chaotic system, as a classical complex phenomenon of nonlinear dynamic systems, has received extensive attention in the field of secure communication. It is generally believed that the characteristics of chaos are suitable for the needs of encryption systems. In this paper, a multi-scale entropy theory analysis and statistical analysis are carried out on the chaotic sequences produced by different parameters and different initial values of logistic systems.

Phonon-Assisted Electro-Optical Switches and Logic Gates Based on Semiconductor Nanostructures.

High-performance nanostructured electro-optical switches and logic gates are highly desirable as essential building blocks in integrated photonics. In contrast to silicon-based optoelectronic devices, with their inherent indirect optical bandgap, weak light-modulation mechanism, and sophisticated device configuration, direct-bandgap-semiconductor nanostructures with attractive electro-optical properties are promising candidates for the construction of nanoscale optical switches for on-chip photonic integrations. However, previously reported semiconductor-nanostructure optical switches suffer from serious drawbacks such as high drive voltage, limited operation spectral range, and low modulation depth.

Enhanced monocular visual odometry integrated with laser distance meter for astronaut navigation.

Visual odometry provides astronauts with accurate knowledge of their position and orientation. Wearable astronaut navigation systems should be simple and compact. Therefore, monocular vision methods are preferred over stereo vision systems, commonly used in mobile robots.

Robust adaptive control for a class of uncertain nonlinear systems with time-varying delay.

We present adaptive neural control design for a class of perturbed nonlinear MIMO time-varying delay systems in a block-triangular form. Based on a neural controller, it is obtained by constructing a quadratic-type Lyapunov-Krasovskii functional, which efficiently avoids the controller singularity. The proposed control guarantees that all closed-loop signals remain bounded, while the output tracking error dynamics converge to a neighborhood of the desired trajectories.