In this paper, we propose an improved three-dimensional underwater electric field-based target localization method. This method combines the subspace scanning algorithm and the meta evolutionary programming (meta-EP) particle swarm optimization (PSO) algorithm. The subspace scanning algorithm is applied as the evaluation function of the electric field-based underwater target locating problem. The meta-EP PSO method is used to select M elite particles by the q-tournament selection method, which could effectively reduce the computational complexity of the three-dimensional underwater target localization. Moreover, the proposed meta-EP PSO optimization algorithm can avoid subspace scanning trapping into local minima. We also analyze the positioning performance of the uniform circular and cross-shaped electrodes arrays by using the subspace scanning algorithm combined with meta–EP PSO. According to the simulation, the calculation amount of the proposed algorithm is greatly reduced. Moreover, the positioning accuracy is effectively improved without changing the positioning accuracy and search speed.
Aiming at the digital quadrature modulation system, a mathematical Pan-function model of the optimized baseband symbol signals with a symbol length of4Twas established in accordance with the minimum out-band energy radiation criterion. The intersymbol interference (ISI), symbol-correlated characteristics, and attenuation factor were introduced to establish the mathematical Pan-function model. The Pan-function was added to the constraints of boundary conditions, energy of a single baseband symbol signal, and constant-envelope conditions. Baseband symbol signals with the optimum efficient spectrum were obtained by introducing Fourier series and minimizing the Pan-function. The characteristics of the spectrum and peak-to-average power ratio (PAPR) of the obtained signals were analyzed and compared with the minimum shift keying (MSK) and quadrature phase-shift keying (QPSK) signals. The obtained signals have the characteristics of a higher spectral roll-off rate, less out-band radiation, and quasi-constant envelope. We simulated the performance of the obtained signals, and the simulation results demonstrate that the method is feasible.
In this paper, a novel inversion method is proposed to recover the sharp boundary of blocky targets buried beneath the seabed with conductivities different from that of the background environment. This method is implemented by combining the Laplacian-of-Gaussian (LoG) function with minimum gradient support (MGS) regularization. A two-stage inversion strategy is introduced to obtain stable and sharp boundary inversion results. We first use the LoG operator in 3D space to obtain the profile of the target and then switch to LoG-MGS coupled regularized inversion to obtain the sharp boundary of the target. It is crucial to choose an appropriate regularization parameter adjustment strategy. We use a bounded function to adjust the regularization parameters during the inversion, which can balance the observation information and the a priori information in a reasonable interval. Theoretical analysis and numerical simulations are conducted and the recovered results demonstrate that the proposed inversion method has a better performance in recovering blocky targets than canonical regularization terms. INDEX TERMS Block targets, inversion, sharp boundary, LoG-MGS coupled regularization.
Background and purposeAs one common feature of cerebral small vascular disease (cSVD), white matter lesions (WMLs) could lead to reduction in brain function. Using a convenient, cheap, and non-intrusive method to detect WMLs could substantially benefit to patient management in the community screening, especially in the settings of availability or contraindication of magnetic resonance imaging (MRI). Therefore, this study aimed to develop a useful model to incorporate clinical laboratory data and retinal images using deep learning models to predict the severity of WMLs.MethodsTwo hundred fifty-nine patients with any kind of neurological diseases were enrolled in our study. Demographic data, retinal images, MRI, and laboratory data were collected for the patients. The patients were assigned to the absent/mild and moderate–severe WMLs groups according to Fazekas scoring system. Retinal images were acquired by fundus photography. A ResNet deep learning framework was used to analyze the retinal images. A clinical-laboratory signature was generated from laboratory data. Two prediction models, a combined model including demographic data, the clinical-laboratory signature, and the retinal images and a clinical model including only demographic data and the clinical-laboratory signature, were developed to predict the severity of WMLs.ResultsApproximately one-quarter of the patients (25.6%) had moderate–severe WMLs. The left and right retinal images predicted moderate–severe WMLs with area under the curves (AUCs) of 0.73 and 0.94. The clinical-laboratory signature predicted moderate–severe WMLs with an AUC of 0.73. The combined model showed good performance in predicting moderate–severe WMLs with an AUC of 0.95, while the clinical model predicted moderate–severe WMLs with an AUC of 0.78.ConclusionCombined with retinal images from conventional fundus photography and clinical laboratory data are reliable and convenient approach to predict the severity of WMLs and are helpful for the management and follow-up of WMLs patients.
The continuous phase modulation (CPM) signal has the characteristics of continuous phase, excellent spectral properties, less out-band radiation, and constant envelope. Thus, CPM technology is widely used in communication systems. The shape of frequency pulse can influence the bandwidth occupancy of CPM, and smoother phase trajectories are obtained by using smoother frequency pulses. In this paper, a mathematical Pan-function model of the optimized frequency pulse is established and solved by introducing Fourier series, which can provide smooth phase trajectories of CPM signal. The simulations of the CPM signal quadrature modulation and coherent demodulation are performed using the MATLAB software. Moreover, the spectral characteristics of the obtained optimized CPM signal were analyzed and compared with the minimum shift keying (MSK) signal and other CPM signals with smooth frequency pulses. The simulation results indicate that the proposed method provides an excellent bandwidth efficiency compared to other existing methods discussed in this paper. The whole system has been successfully downloaded to field programmable gate array (FPGA) devices. The operating results are consistent with expected results, verifying the correctness of this method.
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