Lindgrenklemmensen8861

Accurate segmentation of nasopharyngeal carcinoma is essential to its treatment effect. However, there are several challenges in existing deep learning-based segmentation methods. First, the acquisition of labeled data are challenging. Second, the nasopharyngeal carcinoma is similar to the surrounding tissues. Third, the shape of nasopharyngeal carcinoma is complex. These challenges make the segmentation of nasopharyngeal carcinoma difficult. This paper proposes a novel semi-supervised method named CAFS for automatic segmentation of nasopharyngeal carcinoma. CAFS addresses the above challenges through three mechanisms the teacher-student cooperative segmentation mechanism, the attention mechanism, and the feedback mechanism. CAFS can use only a small amount of labeled nasopharyngeal carcinoma data to segment the cancer region accurately. The average DSC value of CAFS is 0.8723 on the nasopharyngeal carcinoma segmentation task. Moreover, CAFS has outperformed the state-of-the-art nasopharyngeal carcinoma segmentation methods in the comparison experiment. Among the compared state-of-the-art methods, CAFS achieved the highest values of DSC, Jaccard, and precision. In particular, the DSC value of CAFS is 7.42% higher than the highest DSC value in the state-of-the-art methods.Taking representative Tamarix chinensis forest in the national-level special protection zone for ocean ecology of Changyi city in Shandong province of China as the objective, this research studied how to use remote sensing technology to evaluate natural eco-environment and analyze spatiotemporal variation. In the process of constructing the index system of ecological environment effect evaluation based on RSEI (Remote Sensing Ecological Index) model, AOD (Aerosol Optical Depth), Salinity, Greenness, Wetness, Heat and Dryness, which can represent the ecological environment of the reserve, were selected as the corresponding indexes. In order to accurately obtain the value of the RSEI of the study area and to retain the information of the original indexes to the greatest extent, the SPCA (spatial principal components analysis) method was applied in this research. Finally, the RSEI was applied to evaluate the ecological and environmental effects and to analyze the spatial characteristics and spatiotemporal evolution of the study area. The results not only provide scientific evidence and technical guidance for the protection, transformation and management of the Tamarix chinensis forest in the protection zone but also push the development of the universal model of the ecological environment quality with a remote sensing evaluation index system at a regional scale.With the continuous development and improvement in Internet-of-Things (IoT) technology, indoor localization has received considerable attention. Particularly, owing to its unique advantages, the Wi-Fi fingerprint-based indoor-localization method has been widely investigated. However, achieving high-accuracy localization remains a challenge. This study proposes an application of the standard particle swarm optimization algorithm to Wi-Fi fingerprint-based indoor localization, wherein a new two-panel fingerprint homogeneity model is adopted to characterize fingerprint similarity to achieve better performance. In addition, the performance of the localization method is experimentally verified. The proposed localization method outperforms conventional algorithms, with improvements in the localization accuracy of 15.32%, 15.91%, 32.38%, and 36.64%, compared to those of KNN, SVM, LR, and RF, respectively.The leaf area index (LAI) is a key parameter in the context of monitoring the development of tree crowns and plants in general. As parameters such as carbon assimilation, environmental stress on carbon, and the water fluxes within tree canopies are correlated to the leaves surface, this parameter is essential for understanding and modeling ecological processes. However, its continuous monitoring using manual state-of-the-art measurement instruments is still challenging. To address this challenge, we present an innovative sensor concept to obtain the LAI based on the cheap and easy to integrate multi-channel spectral sensor AS7341. click here Additionally, we present a method for processing and filtering the gathered data, which enables very high accuracy measurements with an nRMSE of only 0.098, compared to the manually-operated state-of-the-art instrument LAI-2200C (LiCor). The sensor that is embedded on a sensor node has been tested in long-term experiments, proving its suitability for continuous deployment over an entire season. It permits the estimation of both the plant area index (PAI) and leaf area index (LAI) and provides the first wireless system that obtains the LAI solely powered by solar cells. Its energy autonomy and wireless connectivity make it suitable for a massive deployment over large areas and at different levels of the tree crown. It may be upgraded to allow the parallel measurement of photosynthetic active radiation (PAR) and light quality, relevant parameters for monitoring processes within tree canopies.Recently, piezoelectric materials have received remarkable attention in marine applications for energy harvesting from the ocean, which is a harsh environment with powerful and impactful waves and currents. However, to the best of the authors' knowledge, although there are various designs of piezoelectric energy harvesters for marine applications, piezoelectric materials have not been employed for sensory and measurement applications in marine environment. In the present research, a drifter-based piezoelectric sensor is proposed to measure ocean waves' height and period. To analyze the motion principle and the working performance of the proposed drifter-based piezoelectric sensor, a dynamic model was developed. The developed dynamic model investigated the system's response to an input of ocean waves and provides design insights into the geometrical and material parameters. Next, finite element analysis (FEA) simulations using the commercial software COMSOL-Multiphysics were carried out with the help of a coupled physics analysis of Solid Mechanics and Electrostatics Modules to achieve the output voltages. An experimental prototype was fabricated and tested to validate the results of the dynamic model and the FEA simulation. A slider-crank mechanism was used to mimic ocean waves throughout the experiment, and the results showed a close match between the proposed dynamic modeling, FEA simulations, and experimental testing. In the end, a short discussion is devoted to interpreting the output results, comparing the results of the simulations with those of the experimental testing, sensor's resolution, and the self-powering functionality of the proposed drifter-based piezoelectric sensor.The sensitive detection and degradation of synthetic dyes are pivotal to maintain safety owing to the adverse side effects they impart on living beings. In this work, we developed a sensitive electrochemical sensor for the nanomolar-level detection of rhodamine B (RhB) using a dual-functional, silver-decorated zinc oxide (Ag/ZnO) composite-modified, screen-printed carbon electrode. The plasmon-enhanced photocatalytic degradation of organic pollutant RhB was also performed using this nanocomposite prepared by embedding different weight percentages (1, 3, and 5 wt%) of Ag nanoparticles on the surface of a three-dimensional (3D), hierarchical ZnO nanostructure based on the photoreduction approach. The structure and morphology of an Ag/ZnO nanocomposite were characterized by scanning electron microscopy (SEM), transmission electron microscopy (TEM), elemental mapping, ultraviolet-visible (UV-vis) spectroscopy, and X-ray diffraction (XRD). The electrochemical sensor exhibited a very high sensitivity of 151.44 µAµM-1cm-2 and low detection limit of 0.8 nM towards RhB detection. The selectivity, stability, repeatability, reproducibility, and practical feasibility were also analyzed to prove their reliability. Furthermore, the photocatalysis results revealed that 3 wt% of the Ag/ZnO hybrid nanostructure acquired immense photostability, reusability, and 90.5% degradation efficiency under visible light. Additionally, the pseudo-first-order rate constant of Ag-3/ZnO is 2.186 min-1 suggested promising activity in visible light photocatalysis.Soft sensing technologies offer promising prospects in the fields of soft robots, wearable devices, and biomedical instruments. However, the structural design, fabrication process, and sensing algorithm design of the soft devices confront great difficulties. In this paper, a soft tactile actuator (STA) with both the actuation function and sensing function is presented. The tactile physiotherapy finger of the STA was fabricated by a fluid silica gel material. Before pulse detection, the tactile physiotherapy finger was actuated to the detection position by injecting compressed air into its chamber. The pulse detecting algorithm, which realized the pulse detection function of the STA, is presented. Finally, in actual pulse detection experiments, the pulse values of the volunteers detected by using the STA and by employing a professional pulse meter were close, which illustrates the effectiveness of the pulse detecting algorithm of the STA.The ultrasonic Lamb wave detection principle can realize the noncontact measurement of liquid level in closed containers. When designing an ultrasonic Lamb wave sensor, it is vital to thoroughly study and select the optimal wedge size at the front of the sensor. In this paper, firstly, we select the best working mode of Lamb waves according to their propagation dispersion curve in aluminum alloy, and we obtain the best angle of wedge through experiments. Secondly, we study the impact of the size of the wedge block on the results, and we obtain the selection method of wedge block parameters. The evaluations show that, when the frequency-thickness product is 3 MHz·mm, the Lamb waves work in the A1 mode, and the experimental effect is the best. At this time, the incident angle of the ultrasonic wave is 27.39°. The wedge thickness should be designed to avoid the near-field area of the ultrasonic field, and we should choose the length as odd multiples of 1/4 wavelength. The rules obtained from the experiment can effectively select the best working mode for ultrasonic Lamb waves, while also providing a basis for the design of the wedge block size in a Lamb wave sensor.In the field of high accuracy dual-axis rotational inertial navigation system (RINS), the calibration accuracy of the gyroscopes and accelerometers is of great importance. Although rotation modulation can suppress the navigation error caused by scale factor error and bias error in a static condition, it cannot suppress the scale factor errors thoroughly during the maneuvering process of the vehicle due to the two degrees of rotation freedom. The self-calibration method has been studied by many researchers. However, traditional calibration methods need several hours to converge, which is unable to meet the demand for quick response to positioning and orientation. To solve the above problems, we do the following work in this study (1) we propose a 39-dimensional online calibration Kalman filtering (KF) model to estimate all calibration parameters; (2) Error relationship between calibration parameters error and navigation error are derived; (3) A backtracking filtering scheme is proposed to shorten the calibration process.