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Through phase-amplitude analysis, this study investigated how low-frequency postural fluctuations interact with high-frequency scalp electroencephalography (EEG) amplitudes, shedding light on age-related mechanic differences in balance control during uneven surface navigation. Twenty young ( $24.1~\pm ~1.9$ years) and twenty older adults ( $66.2~\pm ~2.7$ years) stood on a training stabilometer with visual guidance, while their scalp EEG and stabilometer plate movements were monitored. In addition to analyzing the dynamics of the postural fluctuation phase, phase-amplitude coupling (PAC) for postural fluctuations below 2 Hz and within EEG sub-bands (theta: 4-7 Hz, alpha: 8-12 Hz, beta: 13-35 Hz) was calculated. The results indicated that older adults exhibited significantly larger postural fluctuation amplitudes(p ${p} =0.005$ ) than young adults. The PAC between postural fluctuation and theta EEG (FCz and bilateral temporal-parietal-occipital area), as well as that between postural fluctuation and alpha EEG oscillation, was lower in older adults than in young adults (p ${p}=0.006$ ), was higher in older adults than in young adults. In summary, the postural fluctuation phase and phase-amplitude coupling between postural fluctuation and EEG are sensitive indicators of the age-related decline in postural adjustments, reflecting less flexible motor state transitions and adaptive changes in error monitoring and visuospatial attention.

Optimization problems are ubiquitous, and obtaining ideal solutions to optimization problems is a challenging task. In terms of denoising the electrocardiogram (ECG) signal, the weight parameters of the adaptive filtering algorithm determine the quality of the output ECG signal to a large extent. However, adaptive filters need to adjust too many parameters, which is a challenging problem. Heuristic algorithm is a powerful tool for solving various optimization problems, and it is very suitable for solving such complex problems. In this paper, a novel ECG denoising method is proposed, which combines a heuristic algorithm with an adaptive filtering algorithm to adjust the weight parameters of the filter. In addition, a new heuristic algorithm, Chaotic Adaptive Fish Migration Optimization (CAFMO), is proposed to introduce the chaotic strategy into the Adaptive Fish Migration Optimization (AFMO) algorithm. The efficiency of a novel denoising method is validated through the use of synthetic data generated by the FECGSYN toolbox. The CAFMO algorithm exhibits superior performance in noise mitigation in ECG data, outperforming other algorithms such as PSO, ABC, BH, GWO, SO and AFMO. The combination of CAFMO algorithm and adaptive filter produces a significant 28% improvement over traditional LMS adaptive filter, with another 20% improvement over other heuristic algorithms combined with adaptive filter.

This paper concentrates on analyzing the stability problems of aperiodic sampled-data systems with time delay. Based on Lyapunov theory, a new time-square-dependent two-side looped-functional (TTLF) is proposed, which can take full advantage of the second order terms with respect to time. And by using the intrinsic relationships of state vectors, a new zero equality is obtained. Then, a less conservative stability condition is gained. In addition, the method proposed is applied to an electric power market (EPM) to study the influence of market clearing time (MCT) and communication delay on system stability. Finally, the effectiveness of the proposed stability criterion is verified based on numerical experiments.

Deep neural network is a promising method to recognize motor imagery electroencephalography (MI-EEG), which is often used as the source signal of a rehabilitation system; and the core issues are the data representation and the matched neural networks. MI-EEG images is one of the main expressions, however, all the measured data of a trial are usually integrated into one image, causing information loss, especially in the time dimension; and the neural network architecture might not fully extract the features over the $\alpha $ and $\beta $ frequency bands, which are closely related to MI. In this paper, we propose a key band imaging method (KBIM). A short time Fourier transform is applied to each electrode of the MI-EEG signal to generate a time-frequency image, and the parts corresponding to the $\alpha $ and $\beta $ bands are intercepted, fused, and further arranged into the EEG electrode map by the nearest neighbor interpolation method, forming two key band image sequences. In addition, a hybrid deep neural network named the parallel multimodule convolutional neural network and long short-term memory network (PMMCL) is designed for the extraction and fusion of the spatial-spectral and temporal features of two key band image sequences to realize automatic classification of MI-EEG signals. Extensive experiments are conducted on two public datasets, and the accuracies after 10-fold cross-validation are 97.42% and 77.33%, respectively. Statistical analysis shows the superb discrimination ability for multiclass MI-EEG too. The results demonstrate that KBIM can preserve the integrity of the feature information, and they well match with PMMCL.

The structure and electrical characteristics of a dielectric resonator (abbreviated as DR hereafter) loaded plasmonic waveguide with controllable bandwidth are investigated in the present research. By loading the high permittivity dielectric medium on a spoof surface plasmonic waveguide, the working bandwidth of the filtering structure can be regulated flexibly due to the resonant effect of the loaded cylindrical DR. The DR is home-made and possesses the permittivity of 25.66, tanδ of 5.3 × 10-5 and stable temperature coefficient of -6.3 ppm/°C in microwave region. After the DR is loaded, the filtering structure can form the transmission zero on the edge of the pass band, regulating the bandwidth as well as increasing the out-of-band rejection. By controlling the height between the loaded DR and the substrate, or adjusting the inter-distance between the DRs, the relative bandwidth of the filtering structure can be effectively tailored.

This paper presents a non-orthogonal multiplexing scheme using the concept of rateless coding for a downlink broadcast scheme with two users. The proposed approach does not require the inclusion of successive interference cancellation (SIC) techniques for signal detection when compared to the power-domain non-orthogonal multiple access (PD-NOMA) schemes. In various user pair scenarios, it is shown that the proposed scheme not only enhances the user transmission rate but also reduces the overhead required for reliable transmissions when compared to PD-NOMA-based schemes. To further enhance the error performance when considering both unconstrained and constrained inputs, a code design algorithm is proposed based on the transmission rate analysis. It is shown that the designed rateless multiplexing schemes achieve error performances that accurately approach the predicted performance limits.

The accurate prediction and research about the dynamic characteristic of machine tool during the design process are essential to develop high-performance machines. In order to consider the effect of mechanical modal characteristics on the whole dynamic performance and servo controller of ball screw feed drive fully, the modal characteristics of mechanical transmission are extracted and solved based on reduced state space matrix of ball screw feed system, which is also easy for integration with servo control model. On this basis, the step-by-step optimization method from the global to the local is proposed to optimize the servo controller and overall dynamic performance of ball screw feed drive. This step-by-step optimization method is as follows: first, the global initial optimal tuning of servo controller parameters under the influence of structure modal characteristics is originally realized through the analytic derivation of servo controller based on the extraction of state space modal characteristics. Second, the integrated model of the feed drive system is established. Finally, based on the above global optimization results, the overall dynamic performance, and the servo parameters of the feed drive system under the influence of the modal characteristics are further studied and optimized locally. Taking the ball screw feed drive studied in this paper like the example, its rise time and the settling time of step response are 0.089 and 0.104s before optimization, while these reduce to 0.038 and 0.092s after optimization. Its dynamic response performance after optimization is much better. The optimization result is also validated by the test: the rise time and the settling time by the test are 0.042 and 0.094s, which are close to that from the simulation. Also, the consistency of step response curves from experiment and simulation as well. All these show that: this prediction and optimization method of overall dynamic performance considering the modal characteristic influence of mechanical transmission fully is correct and reliable.

A microcavity laser is reported using dye-doped cholesteric liquid crystals (CLCs) filled in a glass capillary with photo-alignment polyimide films. This laser runs based on the properties that a CLC has a refractive index much higher than that of glass, so whispering-gallery-mode (WGM) laser and distributed-feedback-mode lasers are included in the emission spectrum. We obtain a low threshold energy of 4.5 μJ · pulse-1 · mm-2, establish the shift of 3.04 nm of the WGMs in the temperature range 43-48 °C, and investigate electric field tunability.

In this paper, we propose a weighted Dempster-Shafer (DS) evidence theory-based fusion algorithm to take advantages of state-of-the-art salient object detection methods. First, we define the mass function value for each saliency detection method to be fused at the pixel level, based on which we further calculate the similarity coefficient and similarity matrix. The credibility of each saliency detection method will be computed by considering to what degree it is supported by other saliency detection methods. Second, using the credibility of each image saliency detection method as the weight, we compute the weighted mass function value of each method and get a saliency map. Third, we use the synthetic rules of DS evidence theory to fuse the weighted mass function values and get the other saliency map. The final saliency map will be obtained by fusing the aforementioned two saliency maps. Extensive experiments on three publicly available benchmark datasets demonstrate the superiority of the proposed weighted DS evidence theory-based fusion model against each individual saliency detection algorithm in terms of three evaluation metrics of precision-recall rate, F-measure, and average absolute error. The saliency map after fusion utilizing weighted DS evidence theory is closer to the ground-truth map.