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This paper presents and experimentally demonstrates an extended state observer (ESO) -based nonlinear terminal sliding mode control strategy with feedforward compensation (ESO-F-NTSMC) for lower extremity exoskeleton. Since the lower extremity exoskeleton (LEE) is a coupled human-exoskeleton coordination system, the internal or external disturbances and uncertainties affect its performance. A nonlinear terminal sliding mode control with feedforward compensation (F-NTSMC) is proposed to drive the lower extremity to shadow the target human gait trajectory. An ESO is employed to estimate the total disturbances including these caused by the chattering phenomenon in F-NTSMC. ESO-F-NTSMC can assure that the human gait trajectory tracking can converge to a bounded region smoothly and robustly. The phase identification-based human gait generation approach is also presented. The derivation process of the ESO-F-NTSMC is shown the and the Lyapunov-based stability analysis is conducted. To illustrate the proposed method’s effectiveness, experiments are performed on three human subjects walking on the floor at a natural speed. The results demonstrate that the exoskeleton can actively collaborate with the user under the proposed method.

This work presents a technique for dielectric property retrieval through Debye parameter reconstruction from open-ended coaxial probe (OECP) response. Debye parameters were obtained with the application of a deep learning (DL) model to the reflection coefficient response of the OECP when terminated with a material under test. The OECP was modelled with the well-known admittance technique from 0.5 to 6 GHz with 20 MHz resolution. A dataset was generated using the admittance technique and obtained data was utilized to design the DL model. As part of the standard procedure, the dataset was separated to train, validate, and test parts by allocating the 80%, 10%, and 10% of the dataset to each section, respectively. Obtained percent relative error for Debye parameters were 1.86±3.01%, 3.33±9.52%, and 2.07±7.42% for $\epsilon _{s}$ , $\epsilon _\infty $ and $\tau $ , respectively. To further test the constructed DL model, OECP responses were measured at the same frequency band when it was terminated with five different standard liquids, four mixtures, and a gel-like material. Reconstructed Debye parameters from the DL model were used to retrieve the complex dielectric properties and obtained results were compared with the literature data. Obtained mean percent relative error was ranging from 1.21±0.06 to 10.89±0.08 within the frequency band of interest.

Automated social text annotation is the task of suggesting a set of tags for shared documents on social media platforms. The automated annotation process can reduce users’ cognitive overhead in tagging and improve tag management for better search, browsing, and recommendation of documents. It can be formulated as a multilabel classification problem. We propose a novel deep learning-based method for this problem and design an attention-based neural network with semantic-based regularization, which can mimic users’ reading and annotation behavior to formulate better document representation, leveraging the semantic relations among labels. The network separately models the title and the content of each document and injects an explicit, title-guided attention mechanism into each sentence. To exploit the correlation among labels, we propose two semantic-based loss regularizers, i.e., similarity and subsumption, which enforce the output of the network to conform to label semantics. The model with the semantic-based loss regularizers is referred to as the joint multilabel attention network (JMAN). We conducted a comprehensive evaluation study and compared JMAN to the state-of-the-art baseline models, using four large, real-world social media data sets. In terms of $F_{1}$ , JMAN significantly outperformed bidirectional gated recurrent unit (Bi-GRU) relatively by around 12.8%–78.6% and the hierarchical attention network (HAN) by around 3.9%–23.8%. The JMAN model demonstrates advantages in convergence and training speed. Further improvement of performance was observed against latent Dirichlet allocation (LDA) and support vector machine (SVM). When applying the semantic-based loss regularizers, the performance of HAN and Bi-GRU in terms of $F_{1}$ was also boosted. It is also found that dynamic update of the label semantic matrices (JMAN d ) has the potential to further improve the performance of JMAN but at the cost of substantial memory and warrants further study.

Image classification needs to consider image degradations in practice because an image classification network trained with clean images works poorly for degraded images while digital images usually include some degradations such as JPEG compression. To tackle this problem, a common approach is training classification networks on degraded images with various levels of degradation, e.g. various quality factors for JPEG compression. However, the classification networks do not usually have enough accuracy for clean images because the classification networks have been averagely trained for various levels of degradation. This paper aims to construct a classification network of degraded images without sacrificing the performance of clean images. This paper proposes a network to learn the classification of degraded images and degradation levels of degraded images as multi-task learning. Learning the classification of degraded images is the main task of multi-task learning. On the other hand, learning degradation levels is a sub-task of multi-task learning and reinforces the classification ability. In the proposed network, a feature extractor of the classification is trained with image features acquired from a classification network of clean images by using consistency regularization with the cosine similarity loss. The experimental results using different types of degradations, including JPEG compression, Gaussian blur, Gaussian noise, and salt-and-pepper noise, show that our proposed network has enough ability to classify degraded images without sacrificing the performance for clean images.

We propose a method to continuously frequency shift a target laser that is frequency stabilized by a reference laser, which is several hundreds of nanometers detuned. We demonstrate the technique using the $\mathrm {5S_{1/2}} \rightarrow \mathrm {5P_{3/2}} \rightarrow \mathrm {29D_{5/2}}$ Rydberg transition in 87Rb vapor and lock the 482 nm target laser to the 780 nm reference laser using the cascaded electromagnetically induced transparency signal. The stabilized frequency of the target laser can be shifted by about 1.6 GHz by phase modulating the reference laser using a waveguide-type electro-optical modulator. This simple method for stable frequency shifting can be used in atomic or molecular physics experiments that require a laser frequency scanning range on the order of several GHz.

COVID-19 is an extremely dangerous disease because of its highly infectious nature. In order to provide a quick and immediate identification of infection, a proper and immediate clinical support is needed. Researchers have proposed various Machine Learning and smart IoT based schemes for categorizing the COVID-19 patients. Artificial Neural Networks (ANN) that are inspired by the biological concept of neurons are generally used in various applications including healthcare systems. The ANN scheme provides a viable solution in the decision making process for managing the healthcare information. This manuscript endeavours to illustrate the applicability and suitability of ANN by categorizing the status of COVID-19 patients’ health into infected (IN), uninfected (UI), exposed (EP) and susceptible (ST). In order to do so, Bayesian and back propagation algorithms have been used to generate the results. Further, viterbi algorithm is used to improve the accuracy of the proposed system. The proposed mechanism is validated over various accuracy and classification parameters against conventional Random Tree (RT), Fuzzy C Means (FCM) and REPTree (RPT) methods.

This paper studies the distribution network impacts of frequency containment services derived from domestic-scale devices. Risk metrics considering the likelihood and severity of violations are proposed, given uncertainty in the location of these devices. A novel linearization approach is proposed to enable detailed simulations of large-scale networks, capturing MV–LV coupling in European-style networks of over 100 000 nodes. The approach combines a novel factorization of the power flow Jacobian matrix and an efficient linearization update step. The first of these innovations improves the scalability and practicality of the linearization, reducing the memory and computational requirements by as much as twenty times, whilst the latter reduces the median linearization error by at least 50% in all networks studied. It is demonstrated that rapid voltage change (RVC) and overvoltage constraints could limit the uptake of these devices to just a fraction of one percent of customers if the response is not managed. Sensitivity analysis demonstrates that both the likelihood and severity of constraint violations increases rapidly with the size of the devices used for frequency response.

This letter presents a robust generalized correlation approach for monitoring of power system oscillatory modes. Due to the non-Gaussian nature of measurement noise, and presence of impulsive outliers, the wide-sense stationarity assumption of power system measurement data is violated even under ambient conditions thus limiting the effectiveness of conventional autocorrelation based approaches in mode metering. The fundamental definition of correlation function for power system measurements is extended to a generalized correlation function in order to incorporate higher order statistics into existing modal estimation technique. Simulations on synthetic and real-life test signals have been conducted to demonstrate the robustness of the proposed method.

With the integration of multiple types of loads and renewable generations, the number of system states significantly grows. As a result, running optimal power flow (OPF) to analyze a myriad of system states is challenging and this seriously restricts the efficiency of the state enumeration method. To address that, this paper proposes a Lagrange Multiplier based State Enumeration (LMSE) approach to accelerate the analysis without loss of accuracy. The core idea is to directly obtain the optimal load shedding of contingency states by Lagrange multiplier-based functions, rather than the time-consuming OPF algorithms. This approach can also be conveniently integrated with the impact-increment method and the clustering technique for further efficiency enhancement. Case studies are performed on the RTS-79 and IEEE 118-bus systems considering multiple types of loads, photovoltaics (PVs), and wind turbines (WTs). Results indicate that the proposed method can significantly reduce the computing time without compromising the calculation accuracy.

Low-bandgap semiconductor nanowires (NWs) attract considerable interest for mid-infrared (MIR) photonics and optoelectronics, where ideal candidate materials require surface-passivated core–shell systems with large tunability in band offset, lineup, and emission wavelength while maintaining close lattice-matching conditions. Here, we propose and demonstrate epitaxial InAs–AlAsSb core–shell NW arrays on silicon (Si) that offer exceptional control over both the internal strain close to lattice-matching as well as band lineups tunable between type-I and type-II, with almost no analogue in the III–V materials family. We develop direct monolithic growth of high-uniformity InAs–AlAsSb NWs with wide tunability in shell composition and employ correlated Raman scattering and micro-photoluminescence spectroscopy to elaborate the interplay among hydrostatic strain, band lineup, and emission energy of the NW core luminescence tuned from ∼0.4 to 0.55 eV. Electronic structure calculations further support the experimentally observed tunability between type-I and type-II band lineups. The Si-integrated InAs-AlAsSb NW materials system holds large prospects not only for on-chip MIR photonics but also for other applications including high-speed transistors and NW-based hot carrier solar cells.

The current trend in automated optical inspection (AOI) systems employs deep learning models to detect defects on a metal surface. The setback of deep learning models is that they are time-consuming because the images obtained after every lighting adjustment must be used to train the deep learning models again and confirm whether the detection results have improved. To save the time spent using datasets to train deep networks, we proposed a comprehensive assessment score that combines defect visibility, visibility distribution, and overexposure based on the operation principles of convolution neural networks. It can be used to assess whether the training image dataset can improve the defect detection rate of the deep learning model such as You Only Look Once (YOLO), Single Shot MultiBox Detector (SSD), and Faster Region-based Convolutional Neural Network (Faster R-CNN) without training defect image datasets. We collected all of the weight combinations with the right prediction results and used linear regression to obtain the optimal weight coefficients. We found that visibility and overexposure had a greater impact on the comprehensive assessment score. We compared the proposed approach with existing image quality assessment methods, including mean absolute error (MAE), peak signal-to-noise ratio (PSNR), universal image quality index (UIQI), natural image quality evaluator (NIQE), perception-based quality evaluator (PIQE), and blind/referenceless image spatial quality evaluator (BRISQUE). The experiment results indicated that our proposed comprehensive assessment score is more correlated to the F2-score of the detection models than the IQA methods by the verification methods of Spearman Rank Correlation Coefficient (SRCC), Pearson Correlation, and Kendall Correlation. Thus, referring to this index during the collection of image data and choosing datasets with the highest score to train the model will produce better detection accuracy.

To further enhance the nonlinear and cryptographic properties of chaotic S-boxes, a novel S-box design is proposed based on the discrete chaotic map and improved artificial bee colony algorithm. Firstly, the S-box generated by chaotic sequence is initialized by the opposition-based optimization. Then, the dual transposition artificial bee colony algorithm is used to optimize the population, where the Gaussian swap is engaged to strengthen the optimum efficiency, and make the population acquire a better quality with each other. With the Gaussian mutation operator, the opposition-based optimization performs self-learning to obtain better population. Comparing with some recent S-boxes based on chaos. The S-boxes generated by the proposed scheme can achieve better performance in bijection, nonlinearity, strict avalanche criterion(SAC), bit independence criterion(BIC), I / O XOR distribution and linear approximation probability(LP). It provides a new and effective method for designing S-boxes with good cryptographic properties.

In this paper, an integrated design scheme of fault estimation (FE) and fault-tolerant control (FTC) is presented for a closed-loop system with uncertain short delay. Firstly, a closed-loop system with uncertain short delay is modeled as a system with uncertain parameters, and a bi-directional robustness interaction of uncertainty between the observer and the control system is analyzed. Then a fault estimation observer and a fault-tolerant controller are constructed, and the integrated design problem is converted into a robust control problem for the augmented system under H∞ performance index. Furthermore, the parameters of the fault estimation observer and fault-tolerant controller are solved by Lyapunov function and relaxation methods. Finally, the effectiveness and superiority of the proposed method are verified by an aircraft simulation.

Accurate and timely prediction of crop yield based on remote sensing data is important for food security. However, crop growth is a complex process, which makes it quite difficult to achieve better performance. To address this problem, a novel 3-D convolutional neural multikernel network is proposed to capture hierarchical features for predicting crop yield. First, a full 3-D convolutional neural network is constructed to maximally explore deep spatial–spectral features from multispectral images. Then, a multikernel learning (MKL) approach is proposed for fusion of intraimage deep spatial–spectral features and intersample spatial consistency features. Specifically, we assign a group of nonlinear kernels for each feature in the MKL framework, which provides a robust way to fit features extracted from different domains. Finally, the probability distribution of prediction results is obtained by a kernel-based method. We evaluate the performance of the proposed method on China wheat yield prediction and offer detailed and systematic analyses of the performance of the proposed method. In addition, our method is compared with several competing methods. Experimental results demonstrate that the proposed method has certain advantages and can provide better prediction performance than the competitive methods.

For better performance of hyperspectral target detectors, the prior target spectrum is expected to be accurate and consistent with the target spectrum in the hyperspectral image to be detected. The existing hyperspectral target detection algorithms usually assume that the prior target spectrum is highly reliable. However, the label obtained is not always precise in practice, and pixels of the same object may have quite different spectra. Since it is hard to acquire a highly reliable prior target spectrum in some application scenarios, we propose a Bayesian constrained energy minimization (B-CEM) method for hyperspectral target detection. Instead of the point estimation of the target spectrum, we infer the posterior distribution of the true target spectrum based on the prior target spectrum. Specifically, considering the characteristics of hyperspectral image and target detection task, we adopt the Dirichlet distribution to approximate the true target spectrum. Experimental results on three datasets demonstrate the effectiveness of the proposed B-CEM when the known target spectrum is noisy or inconsistent with the true target spectrum. The necessity to approximate the true target spectrum is also proved. Generally, the distributional estimate achieves better performance than using the known target spectrum directly.

Reliability is a crucial consideration in the expansion of generation or transmission in a bulk power system, especially in a power grid with a high penetration of renewables. Reliability indices, such as loss of load probability (LOLP), are generally evaluated to determine the adequacy of a bulk power system in the future. When a Monte-Carlo simulation is conducted to evaluate the LOLP, the computational time is long because chronological time-series data are involved. This work proposes a novel scenario-based method for studying the LOLP in a bulk power system with a high penetration of renewables. Scenarios are generated by aggregating Markov states of hourly loads, photovoltaic power generations and wind power generations. The power flow result in each scenario is examined to ensure power balance among demand, supply and losses, so the LOLP can be obtained. This novel scenario-based method is more efficient than the traditional chronological time-series approach because the number of considered scenarios is much smaller than the number of considered time-series cases. A set of realistic data regarding Taiwan power system, consisting of 2078 buses associated with a peak load of 39.178 GW, wind power of 6.938GW and photovoltaic power of 20 GW in 2025 is used to validate the proposed method.

In this paper, the non-fragile containment control problem for linear multi-agent systems (MASs) with exogenous disturbance is investigated. The communication links among agents are constructed by a set of arbitrarily fast switching and directed topologies. First, there always exist uncertainties in the controller and observer gain matrices, a new class of distributed non-fragile disturbance observer-based controller is proposed to address such a problem with disturbance rejection under switching topologies. By making use of matrix transformation and Lyapunov theory, the containment control problem of MASs is converted into the asymptotic stability analysis problem of some containment error dynamics. Second, the corresponding non-fragile containment control problem of inherent nonlinear case with exogenous disturbance under switching topologies is further studied. Finally, two simulation examples are given to investigate the effectiveness of the proposed control strategy.

Spectral clustering makes use of the spectrum of an input affinity matrix to segment data into disjoint clusters. The performance of spectral clustering depends heavily on the quality of the affinity matrix. Commonly used affinity matrices are constructed by either the Gaussian kernel or the self-expressive model with sparse or low-rank constraints. A technique called diffusion which acts as a post-process has recently shown to improve the quality of the affinity matrix significantly, by taking advantage of the contextual information. In this paper, we propose a variant of the diffusion process, named Self-Supervised Diffusion, which incorporates clustering result as feedback to provide supervisory signals for the diffusion process. The proposed method contains two stages, namely affinity learning with diffusion and spectral clustering. It works in an iterative fashion, where in each iteration the clustering result is utilized to calculate a pseudo-label similarity so that it can aid the affinity learning stage in the next iteration. Extensive experiments on both synthetic and real-world data have demonstrated that the proposed method can learn accurate and robust affinity, and thus achieves superior clustering performance.

Discrete complex exponentials are almost periodic signals, not always periodic; when periodic, the frequency determines the period, but not viceversa, the period being a chaotic function of the frequency, expressible in terms of Thomae's function. The absolute value of the frequency is an increasing function of the subadditive functional of average variation. For discrete signals that are either sums or series of periodic complex exponentials, the decomposition into their periodic, additive components allows for their filtering according to period. Likewise, their period-frequency spectrum makes predictable the effects on period of convolution filtering. Ramanujan-Fourier series are a particular case of the signal class of series of periodic complex exponentials, a broad class of signals on which a transform, discrete both in time and in frequency, called the DFDT Transform, is defined.

In the modern world of technology, the cascaded DC-DC converters with multiple output configurations are contributing a dominant part in the DC distribution systems and DC micro-grids. An individual DC-DC converter of any configuration exhibits complex non-linear dynamic behavior resulting in instability. This paper presents a cascaded system with one source boost converter and three load converters including buck, Cuk, and Single-Ended Primary Inductance Converter (SEPIC) that are analyzed for the complex non-linear bifurcation phenomena. An outer voltage feedback loop along with an inner current feedback loop control strategy is used for all the sub-converters in the cascaded system. To explain the complex non-linear dynamic behavior, a discrete mapping model is developed for the proposed cascaded system and the Jacobian matrix's eigenvalues are evaluated. For the simplification of the analysis, every load converter is regarded as a fixed power load (FPL) under reasonable assumptions such as fixed frequency and input voltage. The eigenvalues of period-1 and period-2 reveal that the source boost converter undergoes period-2 orbit and chaos whereas all the load converters operate in a stable period-1 orbit. The proposed configuration eliminates the period-2 and chaotic behavior from all the load converters and is also validated using simulation in MATLAB/Simulink and experimental results.

Crane operations might be very dangerous in rough sea conditions due to unexpected payload swing induced by ship excitations. In this paper, a novel Cable-Driven Inverted Tetrahedron Mechanism (CDITM) is presented to suppress the payload swing for sake of the workers’ safety. The CDITM retrofitting on an offshore crane is simplified as a constrained pendulum with a moving base, and its equations of motion are obtained by Newton Euler Method. Next, three-dimensional dynamic analysis is performed using Matlab/Simulink, and the influence law of ship excitations, tagline tension, crane pose and CDITM configuration on the payload swing are investigated. Finally, through comparative experimental verification, it is found that the simulation tendencies of in-plane swing follows the experiment curves quite well, and the variations between the simulation and experiment results are acceptable. Thus the dynamic modeling and analysis of CDITM are verified. The theoretical and experimental results are fundamental and valuable for the engineering application of CDITM in the offshore industries.

Generative Adversarial Networks (GANs) are a powerful subclass of generative models. Yet, how to effectively train them to reach Nash equilibrium is a challenge. A number of experiments have indicated that one possible solution is to bound the function space of the discriminator. In practice, when optimizing the standard loss function without limiting the discriminator’s output, the discriminator may suffer from lack of convergence. To be able to reach the Nash equilibrium in a faster way during training and obtain better generative data, we propose constrained generative adversarial networks, GAN-C, where a constraint on the discriminator’s output is introduced. We theoretically prove that our proposed loss function shares the same Nash equilibrium as the standard one, and our experiments on mixture of Gaussians, MNIST, CIFAR-10, STL-10, FFHQ, and CAT datasets show that our loss function can better stabilize training and yield even better high-quality images.

Multi-organ segmentation from whole-body computed tomography (CT) scans has gained increasing research interest over recent years. While the learning-based segmentation algorithm has lately achieved tremendous success, the need for detailed annotation of multiple organs further increases the manual burden. With a limited number of annotated volumetric datasets, it would be beneficial to apply the trained model from such a set to CT images acquired from other sites with different scanners. Nevertheless, the discrepancy among training and testing images significantly deteriorates segmentation performance. While there are many domain adaptation efforts, in this work we proposed a filtered back-projection based algorithm for performing domain adaptation for CT imagery. An optimal CT reconstruction kernel was obtained by minimizing the disparity between two images. Furthermore, since the Gaussian kernel is an eigen-function of the Fourier transformation, the adaptation computation was proven to be simple linear filtering. The proposed method was tested and compared with multiple methods to demonstrate improvement by employing such a model/theory-based adaptation approach. The proposed method, used in conjunction with a common convolutional neural network, such as the U-Net or V-Net, with or without the domain adaptation, achieves high accuracy in a multiple-organ segmentation task. Approximately 30% of data was used for training, 70% was used for testing, and an average dice of 0.88 was achieved in 8 organs.

In this paper, a novel adaptive fault-tolerant controller is proposed for a typical electro-hydraulic rotary actuator in the presence of disturbances, internal leakage fault, and sensor fault simultaneously. To construct the suggested controller, a nonlinear unknown input observer is developed to effectively identify the sensor fault, which is unaffected by not only internal leakage fault but also mismatched disturbances/uncertainties. Furthermore, a radial basis function neural network is designed to compensate for the mismatched disturbances/uncertainties caused by payload variation and unknown friction nonlinearities. Besides, an adaptive law based on the projection mapping function is applied to tackle the effect of the internal leakage fault. The integration of the above-mentioned techniques into the adaptive backstepping terminal sliding mode is investigated to obtain high tracking performance, robustness as well as fast convergence. The stability of the closed-loop system is proven by the Lyapunov theory. Finally, the capability and effectiveness of the proposed approach are validated via simulation results under various faulty scenarios.

This paper proposes a simple $1^{\mathrm {st}}$ -order disturbance observer (DO)-based optimal control (DOBOC) strategy for surface-mounted permanent magnet synchronous motor (SPMSM) drives. A detailed comparative analysis of three reduced-order (i.e., $1^{\mathrm {st}}$ , $2^{\mathrm {nd}}$ , and $3^{\mathrm {rd}}$ ) DOs has been presented in terms of structural complexity along with transient and steady-state performances. A simple $1^{\mathrm {st}}$ -order DOBOC is proposed with a considerably simple and easy gain tuning procedure without any complex equation and time-consuming weighting matrices selection while ensuring a competitive control performance. Also, the stability of three DOBOCs is proven using the Lyapunov function, which has not been carried out in the previous studies. Furthermore, this paper provides a detailed procedure to systematically select the controller and observer gains of three reduced-order (i.e., $1^{\mathrm {st}}$ , $2^{\mathrm {nd}}$ , and $3^{\mathrm {rd}}$ ) DOBOC by experiment. Comparative experiments are performed to confirm the feasibility of the proposed $1^{\mathrm {st}}$ -order DOBOC using a prototype SPMSM test-bed with a TI TMS320F28335 DSP. Finally, verification results and comparative table are presented to compare the speed tracking performance (e.g., transient response, undershoot (US), steady-state error (SSE), and robustness to load torque disturbances) of three reduced-order DOBOC methods under speed and load torque step-changes.

The modeling of low earth orbit (LEO) satellite channel depends on its Doppler power spectrum. Due to satellite during transit, diversity and dynamic channel scene, the modeling of Doppler power spectrum has two serious problems: one is that the shape of the Doppler power spectrum will vary with the change of scenes and time, but the use of the existing traditional Doppler power spectrum models is difficult to accurately describe them. The other one is that the amount of measured data used for modeling is too large to handle, and data aliasing is easy to occur between different scenes, which will make it difficult to ensure the accuracy of model parameter fitting. In this paper, a two-side truncated asymmetric Doppler power spectrum model is proposed to universally describe the Doppler power spectrum during satellite transit. In addition, the atlas fingerprint method clustering is adopted to realize the classification of the measured data samples of Doppler power spectrum in multiple scenes, and the data with the strongest representation ability in each scene is selected to fit the model parameters. The simulation results show that the proposed model is in good agreement with the measured data. Therefore, parameter fitting using the proposed method can improve the accuracy of the model, so as to better describe the characteristics of LEO satellite channel fading in the frequency domain.

Dempster-Shafer(D-S) evidence theory is one of the most widely used methods in the field of data fusion. The key step in applying D-S evidence theory is to determine the basic probability assignment(BPA), but how to determine it appropriately is still a problem to be solved. In this paper, a novel method to determine the BPA is proposed. Firstly, a method to obtain the initial BPA by combining weighted grey correlation degrees and Gaussian membership degrees is proposed. The initial BPA is obtained by constructing the integration form between the weighted grey correlation degrees and the Gaussian membership degrees, and applying the Bayesian optimization algorithm to obtain the better adjusting parameter. Secondly, a BPA reconstruction process is introduced. The BPA is reconstructed by using the matching degrees between the initial BPA and the BPA benchmarks. Finally, the reconstructed BPA is combined by applying Dempster’s combination rule. The conflicts between the BPA of different attributes can be mitigated during the process of restructuring the initial BPA, which can prevent from getting a counterintuitive result. By applying this method to the classification experiments on UCI datasets, the results confirmed that this method can effectively improve the classification performance.

The azimuth multichannel synthetic aperture radar (SAR) system can meet the requirements of high resolution and wide swath (HRWS) simultaneously, which overcomes the constraint of the traditional single-channel SAR. However, for a moving target illuminated by the azimuth multichannel SAR system, its radial velocity will lead to ambiguous components and mislocation in the image. Therefore, the radial velocity estimation plays an important role in improving the image quality and moving target relocation, especially for large ships on the open sea. However, as the pulse repetition frequency of a single channel is less than the Doppler spectrum, the traditional velocity estimation methods working in the image domain are out of action. This article suggests an idea that the issue of velocity estimation is converted into that of the linear phase errors estimation combining the linear fitting method, and it is assumed that the target has been already detected before applying the velocity estimation algorithms. To estimate the linear phase errors, two algorithms operating in the Doppler domain are introduced and compared, namely the subspace-based method and the modified frequency correlation method. The advantages of the proposed approaches are free from iteration operation and high accuracy. Besides, the effectiveness of the methods is demonstrated via simulation data and GaoFen-3 real data from ultra-fine stripmap mode. Finally, this article analyzes the velocity estimation accuracy of the two methods and the influence of channel imbalance through substantial experiments.

Treatments for epilepsy include pharmacotherapy or surgery. Recently, focused ultrasound stimulation has been investigated as a promising non-invasive neuromodulation tool for neurological disorders, including epilepsy. To investigate the neuronal dynamics in epilepsy, we acutely stimulated 29 Sprague–Dawley rats to low-intensity focused ultrasound stimulation (LIFUS) three times for 3 minutes. Pentylenetetrazol (PTZ) was injected into the abdominal cavity of the anesthetized rats to induce epilepsy. During the anesthesia, the electroencephalography (EEG) signal was measured and analyzed for 1 h in groups of animals untreated (sham) and treated with LIFUS. The EEG signal was quantitatively processed to show the different characteristics of the frequency change over time and of the band power between sham and treated groups. Histological analyses (Nissl, Iba1, c-Fos, and GAD65) measured the degree of staining of expression factors to confirm the effect of the stimulation on seizure suppression. These results suggest that repetitive LIFUS can effectively reduce epileptic seizure activity by attenuating theta and beta-band oscillation in a PTZ-induced rat model. LIFUS can potentially facilitate hippocampal and cortical cellular recovery by augmenting GABAergic inhibitory neurons via its anti-seizure effect.

This paper proposes an approach to predicting fatigue crack growth under mixed-mode loading based on improved Gaussian process. In terms of analyzing the theoretical background for fatigue crack growth, a corresponding finite element model is built to generate sufficient simulation data, which is utilized to obtain the key parameters (e.g., stress intensity factor) for the fatigue crack growth process. And then, a Gaussian process model is achieved to meet the condition that the stress intensity factor is a nonlinear continuous change in crack growth, especially for mixed-mode loading. Following, an idea of local sample densification method is implemented to improve Gaussian sample generation process according to the simplified model of the crack growth path. Based on the above investigation, a fatigue crack growth prediction model using the improved Gaussian process is finished, which is subsequently verified through the test data of lower bainite steel (SCM435) material. The results show that the proposed approach has better computational accuracy and efficiency than the traditional finite element method in predicting fatigue crack growth under mixed-mode loading.

Since the introduction of Dynamic Bayesian Networks (DBNs), their efficiency and effectiveness have increased through the development of three significant aspects: (i) modeling, (ii) learning and (iii) inference. However, no reviews of the literature have been found that chronicle their importance and development over time. The aim of this study is to provide a systematic review of the literature that details the evolution and advancement of DBNs, focusing in the period 1997–2019 that emphasize the aspects of modeling, learning and inference. While the literature presents temporal event networks, knowledge encapsulation, relational and time varying representations as the four predominant DBN modeling approaches, this work groups them as essential techniques within DBNs and help practitioners by associating each to various challenge that arise in pattern discovery and prediction in dynamic processes. Regarding learning, the predominant methods mainly focus on scoring with greedy search. Finally, our study suggests that the main methods used in DBN inference extend or adapt those used in static BNs, and are oriented to either optimize processing time or error rate.

With the development of smart substations and the promotion of 61850 standards, sampling values based on IEC61850-9-2 have become an important part of smart substation construction. With the popularization and application of the sample value (SV), the interpolation algorithm has been increasingly used in protection, measurement, control, wave recorder and power quality applications. However, the error in the interpolation algorithm poses a challenge to its use. This paper describes the basic methods of linear Lagrange and parabolic Lagrange interpolation and presents maximum theoretical values for the interpolation error when Lagrange linear and second-order approximations of sinusoidal signals are performed. The single-point error of each sampling point is analyzed using the remainder equation, and the harmonic error of the Fourier transform after interpolation is strictly mathematically derived. Finally, the accuracy of the theory is verified by real measurement data, and suggestions for the application of the interpolation method are introduced.

Surface deformation of mining area caused by mining subsidence can be divided into three stages: the initial stage, the active stage and the decline stage. At the active stage, D-InSAR technology could be easily affected by the incoherence of large-gradient deformation, thus leading to the failure of conventional D-InSAR technology in monitoring mining subsidence.In order to solve the problem, this paper developed a 3D surface deformation prediction method of mining subsidence by integrating dynamic Boltzmann prediction model with D-InSAR. It was firstly to monitor the surface movement and deformation by D-InSAR technology during the decline stage and obtain the surface LOS deformation field data. Then, on the basis of Boltzmann model, the geometric equation between LOS deformation by D-InSAR monitored and 3D deformation was established. Next, the geometric equation was solved based on the GA theory, and the parameters of Boltzmann prior model were obtained. On the basis, the 3D deformation of mining area were obtained, proving that D-InSAR technology was effective to get 3D deformation of mining subsidence. Through simulation experiments, the effectiveness of the proposed method is verified. The surface deformation of 1613 working face in Guqiaonan coal mine was monitored by D-InSAR during the decline stage, and the LOS deformation values were obtained. The 3D deformation was obtained from the initial stage to the decline stage by using the method of DB-InSAR. The monitoring results showed that the fitting errors in the LOS deformation were mostly within 3 mm, the RMSES was ±2.37 mm, shown that the fitting accuracy was high. The RMSE of subsidence were ±120 mm. The results showed that the method of DB-InSAR has engineering application values.

High-speed and accurate meat composition imaging method has been proposed based on mechanically-flexible electrical impedance tomography ( mech-f- EIT) with $k$ -nearest neighbor and fuzzy $k$ -means machine learning approaches. This proposed method has four stages which are 1) estimation of meat boundary shape $\partial \Omega $ by mech-f- EIT for base data, 2) approximation of Jacobian matrix J* by $k$ -nearest neighbor ( $k$ -NN) algorithm under $\partial \Omega $ for high speed, 3) clustering of meat composition $^{k} \boldsymbol {\sigma }$ (fat $k=1$ , lean $k=2$ , bone $k=3$ ) by fuzzy $k$ -means algorithm based on the reconstructed meat conductivity distribution $\sigma $ for high accuracy, and 4) edge detection of meat composition $^{k}\Omega $ by Canny algorithm for sharp edge. This method is qualitatively evaluated by using two agar phantoms, a cow’s lower leg and three lamb’s lower legs. As the results, mech-f- EIT estimates $\partial \Omega $ with total mean boundary error $\langle \widetilde {e_{b}} \rangle =4.81$ %. This method achieves high-speed approximation of J* with total mean speed-up performance $\langle \widetilde {sp} \rangle =4.51$ times as compared with the computation time of standard J; nonetheless, total mean cross correlation between J* and J is accurate $\langle \widetilde {cc} \rangle =0.92$ . Moreover, this method clusters the $^{k} \boldsymbol {\sigma } $ with total mean area error $\langle \widetilde {e_{a}} \rangle =4.49$ %. Furthermore, this imaging method detects sharply the meat composition edges $^{k}\Omega $ between fat and lean ( $k=1-2$ ) and between lean and bone ( $k=2-3$ ) with total mean edge error $\langle \widetilde {e_{e}} \rangle =6.90$ %.

We present a simple and general theory of compressive sensing (CS) that relies on elements of the sensing matrix rather than on the number of measurements. We prove the exact recovery using a dual certificate by showing that the sensing matrix satisfies an incoherence property and isotropy property if the sparsity level is kept lower than the reciprocal of the largest element of a matrix created from the sensing matrix. Unlike the CS literature, this unconventional approach does not require a linear relationship between the sparsity and the number of measurements and at the same time, can easily be evaluated. This adaptability captures anisotropic measurements appropriately as with anisotropic measurements, adding more measurements does not really imply that a signal with more nonzero elements will be recovered exactly. As an illustration, we demonstrate the theory’s ability to accurately handle the anisotropic (Green’s function-based sensing matrix) measurements and also its similarity to the existing CS literature for isotropic (Fourier) measurements. Further, we show the usefulness of the theory in comparing different sensing matrices and in generating dielectric images. The dielectric images are perfectly recovered even when there is only a single transmitter.

Sparse Bayesian Extreme Learning Machine (SBELM) constructs an extremely sparse and probabilistic model with low computational cost and high generalization. However, the update rule of hyperparameters (ARD prior) in SBELM involves using the diagonal elements from the inversion of the covariance matrix with the full training dataset, which raises the following two issues. Firstly, inverting the Hessian matrix may suffer ill-conditioning issues in some cases, which hinders SBELM from converging. Secondly, it may result in the memory-overflow issue with computational memory $O(L^{3})$ ( $L$ : number of hidden nodes) to invert the big covariance matrix for updating the ARD priors. To address these issues, an inverse-free SBELM called QN-SBELM is proposed in this paper, which integrates the gradient-based Quasi-Newton (QN) method into SBELM to approximate the inverse covariance matrix. It takes $O(L^{2})$ computational complexity and is simultaneously scalable to large problems. QN-SBELM was evaluated on benchmark datasets of different sizes. Experimental results verify that QN-SBELM achieves more accurate results than SBELM with a sparser model, and also provides more stable solutions and a great extension to large-scale problems.

This paper presents a new approach to neuro-fuzzy model identification based on a filtered recursive least squares method combined with an incrementally evolving Gaussian clustering method. The proposed identification algorithm generates the model on the fly and requires few user-defined parameters, which is one of the main advantages compared to other methods. The partitioning of the input-output data space depends on the chosen criteria and thresholds and depends only on the operating point of the model. As an example, the Wiener-Hammerstein type of a dynamic process was identified to show the potential of the proposed method in identifying nonlinear dynamic models. The Wiener-Hammerstein structure was used because a variety of processes can be modeled with this type of structure. Moreover, we tested the same identification concept on a real heat exchanger plant with strong nonlinear behavior. In addition, the limitations of the real sensors and actuators represent a serious challenge to the identification procedure. Both experiments, on a simulated Wiener-Hammerstein model and on a real plant, have shown that the proposed new neuro-fuzzy model identification with the new merging concept is very easy to implement, can perform all necessary calculations online, and can generate meaningful models.

This paper focuses on the disturbance attenuation control of the quadrotor suspended payload system with unknown payload mass. Since the quadrotor transportation system is an eight-degree-of-freedom system with only four actuated degrees, it is difficult to achieve rapid stabilization of the quadrotor under varying payload and external disturbances. In this paper, a nonlinear controller based on rotation matrix is designed to rapid stabilization of attitude. By analyzing the effect of load mass on the trajectory tracking control, a parameter adaptive controller is proposed for position control. A mass estimation algorithm is established to estimate the payload mass in the air, which improves the robustness of the system. This approach can maximize load-bearing capacity within the thrust range of the quadrotor. The Lyapunov-based analysis proves the exponential convergence of the attitude error and the stability of the whole system. The contrast simulations demonstrate that the controller is superior to the sliding-mode controller incorporating input shaping on mass estimation, trajectory tracking, and disturbance resistance.

The realization of molecular logic circuit is inseparable from the design and analysis of catalytic reaction chain, and the DNA catalytic gate plays an important role in it. Discuss the nature of the solution to DNA catalytic reaction system, using Khasminskii's periodicity and Lyapunov analysis methods to obtain the existence of non-trivial positive periodic solutions of the system, and the solution is globally attractive. The existence of the solution indicates that according to the mathematical model established by the DNA catalytic reaction system, the system may reach the expected concentration value of an ideal state and obtain better reaction data, which provides a theoretical basis for the realization of the DNA catalytic gate function. Numerical simulation results show that under the influence of random disturbance and periodic parameters, the solution to the random DNA catalytic reaction system exists and is globally attractive, which also reflects that the DNA catalytic reaction system can reach an ideal reaction state. The solution to the DNA catalytic system with random disturbance will converge on a certain value and oscillate periodically between the solution to the deterministic system.

Due to the continuous increase of fuel prices and pollutions, the use of renewable energy especially wind has increased. In developing countries including Egypt, squirrel cage induction generator wind turbine (SCIG- WT) represents a considerable proportion of the total capacity of installed wind farm due to its qualities such as low cost and easy availability. However, its operation has a substantial effect on system stability. In contrast, doubly fed induction generator wind turbine (DFIG-WT) is broadly penetrated the electrical grid as it keeps the system stable. In this work, the ability of WT generators to continue operating rather than tripping at the time of faults is analyzed for proper stability investigation. The detailed control and stability of a grid-connected large scale SCIG and DFIG of Zafarana, Suez Gulf area, Egypt are discussed whereas the parameters of fault ride through (FRT) curve of Egypt grid code is utilized. Moreover, a precise analytical stability argument using a proposed integrated nonlinear dynamical model is presented. Conditions for global asymptotic stability of the SCIG in the sense of Lyapunov function (LF) are given and tested by time domain simulation. The eigenvalues of the matrices of LF and its derivative are determined by which the stability boundaries are determined depending on the positivity of these matrices. The dynamic behavior of the whole system is simulated in MATLAB/Simulink interface programming while the practical data are collected from an experimental model consisting of DFIG-WT to demonstrate the efficacy of the FRT control system.

Hamiltonian Monte Carlo is a Markov Chain Monte Carlo method that has been widely applied to numerous posterior inference problems within the machine learning literature. Markov Chain Monte Carlo estimators have higher variance than classical Monte Carlo estimators due to autocorrelations present between the generated samples. In this work we present three new methods for tackling the high variance problem in Hamiltonian Monte Carlo based estimators: 1) We combine antithetic and importance sampling techniques where the importance sampler is based on sampling from a modified or shadow Hamiltonian using Separable Shadow Hamiltonian Hybrid Monte Carlo, 2) We present the antithetic Magnetic Hamiltonian Monte Carlo algorithm that is based on performing antithetic sampling on the Magnetic Hamiltonian Monte Carlo algorithm and 3) We propose the antithetic Magnetic Momentum Hamiltonian Monte Carlo algorithm based on performing antithetic sampling on the Magnetic Momentum Hamiltonian Monte Carlo method. We find that the antithetic Separable Shadow Hamiltonian Hybrid Monte Carlo and antithetic Magnetic Momentum Hamiltonian Monte Carlo algorithms produce effective sample sizes that are higher than antithetic Hamiltonian Monte Carlo on all the benchmark datasets. We further find that antithetic Separable Shadow Hamiltonian Hybrid Monte Carlo and antithetic Magnetic Hamiltonian Monte Carlo produce higher effective sample sizes normalised by execution time in higher dimensions than antithetic Hamiltonian Monte Carlo. In addition, the antithetic versions of all the algorithms have higher effective sample sizes than their non-antithetic counterparts, indicating the usefulness of adding antithetic sampling to Markov Chain Monte Carlo algorithms. The methods are assessed on benchmark datasets using Bayesian logistic regression and Bayesian neural network models.

This article analyses the effects of system distortions (crosstalk and channel imbalance), Faraday rotation and system noise on estimates of the cross-polarized backscattering coefficient, ${\boldsymbol{\sigma }}_{{{\bf hv}}}^0$, by a spaceborne synthetic aperture radar. Modeling the unknown system errors and noise by a joint complex Gaussian distribution allows analytic first-order approximations to the mean and variance of the error in ${\boldsymbol{\sigma }}_{{{\bf hv}}}^0$ to be derived that do not depend on the SAR operating frequency. Simulation shows these approximations to be very accurate, given the statistical model and the expected magnitudes of system errors and noise for the P-band instrument to be carried by the European Space Agency BIOMASS mission. Simulation further shows that the ${\boldsymbol{\sigma }}_{{{\bf hv}}}^0$ errors are Gaussian distributed, so their exceedance probabilities can be calculated from just the analytic expressions for the mean and variance of the errors. Exceedance probabilities for above-ground biomass (AGB) can then be calculated under a power law relation between ${\boldsymbol{\sigma }}_{{{\bf hv}}}^0{{\rm{\sigma }}_{{\rm{hv}}}}$ and AGB that is consistent with P-band observations. This allows tradeoff curves between crosstalk and channel imbalance (shown to be segments of hyperbolas) to be calculated, along which the relative error in AGB is within a given percentage of its true value, from which limits on the permissible size of the errors can be determined if BIOMASS mission requirements are to be met.

Markov chains (MC) are statistical models used to predict very short to short-term wind speed accurately. Such models are generally trained with a single moving window. However, wind speed time series do not possess an equal length of behavior for all horizons. Therefore, a single moving window can provide reasonable estimates but is not an optimal choice. In this study, a forecasting model is proposed that integrates MCs with an adjusting dynamic moving window. The model selects the optimal size of the window based on a similar approach to the leave-one-out method. The traditional model is further optimized by introducing a self-adaptive state categorization algorithm. Instead of synthetically generating time series, the modified model directly predicts one-step ahead wind speed. Initial results indicate that adjusting the moving window MC prediction model improved the forecasting performance of a single moving window approach by 50%. Based on preliminary findings, a novel hybrid model is proposed integrating maximal overlap discrete wavelet transform (MODWT) with auto-regressive integrated moving average (ARIMA) and adjusting moving window MC. It is evident from the literature that MC models are suitable for predicting residual sequences. However, MCs were not considered as a primary forecasting model for the decomposition-based hybrid approach in any wind forecasting studies. The improvement of the novel model is, on average, 55% for single deep learning models and 30% for decomposition-based hybrid models.

This paper is concerned with stability of interconnected systems with time delays. We develop a self-contained approach to stability analysis for linear and nonlinear systems in a unified framework. New lemmas are established on matrix properties and used as the key to make negativity of the derivative of the Lyapunov function. The scalar and simple analytical stability conditions are given. Unlike the majority of the literature on stability of delay systems, no matrix equations/inequalities are involved in our conditions, which is true even for large-scale systems and nonlinear subsystems with delayed interconnections. They are applicable to the more general nonlinear, time-varying, and/or interconnected systems than the relevant results reported in the literature. The examples are presented for illustration of the new results.

For antenna modeling and optimization design, high-fidelity full-wave electromagnetic simulation software can generally be used to obtain training samples. However, this process takes a long time. To solve this problem, a two-stage Gaussian process (GP) considering electromagnetic sensitivity information is proposed. Based on the coarse grid and fine grid of electromagnetic simulation software, the first stage learns the mapping relationships of antenna performance and sensitivity of each parameter, simultaneously. In the second stage, the accurate surrogates are established based on the mapping relationships above obtained. Because the method in this study takes sensitivity information into consideration during the modeling process, it can better reflect the mapping relationship between antenna input and output, which can develop a more accurate surrogate model. The proposed two-stage GP surrogate model considering sensitivity information can significantly reduce the demand of high-fidelity training samples for modeling, and greatly save the simulation time of calling electromagnetic simulation software. Therefore, this method is more suitable for the problems of insufficient electromagnetic simulation samples. The proposed approach is evaluated by modeling and optimization of an inverted F antenna and an ultra-wideband planar monopole antenna. The results show that the method has high modeling accuracy with limited training samples given by high-fidelity electromagnetic simulation, which further verify its effectiveness and efficiency.

The authentication of the navigation signals can be considered as the contribution of the system to the robustness against spoofing attacks and it is becoming an important requirement for a growing number of user communities. GPS and Galileo systems are proposing evolutions of their civil signals to embed features of authentication. For Galileo, the Open Service Navigation Message Authentication (OSNMA) is integrated in the Galileo E1 OS signal. For the GPS, the Chips-Message Robust Authentication (Chimera) solution, designed for the GPS L1C signal, is foreseen to be tested soon. On the other hand, suitable signal processing techniques can be implemented inside the receiver to monitor the quality of the received signals and protect against spoofing attacks. Such techniques shall work as a complement to the authentication strategies, to further increase the signals’ robustness. Within this context, the paper presents the Joint Chimera/OSNMA scheme, designed to be adopted by a multi-constellation receiver that already exploits both OSNMA and Chimera enhancements. The idea is to further strengthen the robustness with respect to the individual use of the two solutions, to tackle sophisticated spoofing attacks, which are able to avoid detection from navigation message authentication (NMA) techniques. The manuscript proves the high performance of the joint scheme, presenting the results of a wide bench of tests, under different scenarios of spoofing, and user conditions.

The belief-rule-base (BRB) inference methodology using the evidential reasoning (ER) approach is widely used in different fields, such as fault diagnosis, system identification, and decision analysis. However, the calculation characteristic of the conventional rule activation weight makes the inference system have the rule zero activation problem. The difficulty of constructing partial derivatives restricts the optimization of parameters using the gradient method. Hence, this paper proposes a new belief rule structure and its gradient training method to solve the rule zero activation problem during the inference process and improve inference accuracy. The Gaussian function is applied to calculate the activation weight of the rule with the new structure. Its characteristics avoid the zero activation problem caused by the attribute reference value set in the original method. Based on the newly proposed method, the corresponding distance-sensitive parameter is set for each attribute, and the weight parameter of each rule is discarded. It simplifies the calculation of rule activation weights in the inference process and enables the partial derivatives of the parameters of the inference system to be easily constructed. In the parameter optimization, the momentum optimization gradient stochastic descent method is used to train the new BRB system, which improves the training speed and accuracy compared with the conventional methods. Experiments with nonlinear function fitting, oil pipeline leak detection, and classification of several public datasets are carried out to verify whether the new BRB system trained with momentum stochastic gradient descent (SGDM-BRB) has better performance than other conventional methods. The experimental results show that in the case of complete data, SGDM-BRB has higher accuracy and faster training speed than the conventional methods.

POCI-01-0145-FEDER-030433 LISBOA-01-0145-FEDER-0307095 This work presents an innovative methodology to predict the future trajectories of vehicles when its current and previous locations are known. We propose an algorithm to adapt the vehicles trajectories' data based on consecutive GPS locations and to construct a statistical inference module that can be used online for mobility prediction. The inference module is based on a hidden Markov model (HMM), where each trajectory is modeled as a subset of consecutive locations. The prediction stage uses the statistical information inferred so far and is based on the Viterbi algorithm, which identifies the subset of consecutive locations (hidden information) with the maximum likelihood when a prior subset of locations are known (observations). By analyzing the disadvantages of using the Viterbi algorithm (TDVIT) when the number of hidden states increases, we propose an enhanced algorithm (OPTVIT), which decreases the prediction computation time. Offline analysis of vehicle mobility is conducted through the evaluation of a dataset containing real traces of 442 taxis running in the city of Porto, Portugal, during a full year. Experimental results obtained with the dataset show that the prediction process is improved when more information about prior vehicle mobility is available. Moreover, the computation time of the prediction process is significantly improved when OPTVIT is adopted and approximately 90% of prediction performance can be achieved, showing the effectiveness of the proposed method for vehicle trajectory prediction. publishersversion published

In order to pursue rapid development of the new generation of wireless communication systems and elevate their security and efficiency, this paper proposes a novel scheme for automatic dual determination of modulation types and signal to noise ratios (SNR) for next generations of wireless communication systems, fifth-generation (5G) and beyond. The proposed scheme adopts unique signatures depicted in two-dimensional asynchronously sampled in-phase-quadrature amplitudes’ histograms (2D-ASIQHs)-based images and applies the support vector machines (SVMs) tool. Along with the estimation of the instantaneous SNR values over 0–35 dB range, the determination of nine modulation types that belong to different modulation categories i.e., phase-shift keying (Binary-PSK, Quadrature-PSK, and 8-PSK), amplitude-shift keying (2-ASK and 4-ASK) and quadrature-amplitude modulation (4-QAM, 16-QAM, 32-QAM, and 64-QAM) could be achieved by this scheme. The application of this scheme has been simulated using a channel model that is impaired by additive white Gaussian noise (AWGN) and Rayleigh fading, covering a broad range of SNRs of 0–35 dB. The performance of this dual-determination scheme shows high modulation recognition accuracy and low mean SNR estimation error. Therefore, it can be a better alternative for designers of next generation wireless communication systems.

In this paper, an Ag nanoparticles (AgNPs) modified multimode fiber (MMF) SERS probe prepared based on sol self-assembly method is proposed and fabricated. Different fiber SERS probes (AgNPs-MMF-x, x is the self-assembly time) are obtained by adjusting the self-assembly time. Using rhodamine 6G (R6G) solution as the probe molecule, the far-end performances of the prepared fiber SERS probe are studied, and the AgNPs-MMF-30 probe with obvious advantages in SERS performance is obtained. The limit of detection (LOD) for R6G is as low as 10−9 M, demonstrating the high sensitivity of AgNPs-MMF-30 probe. The enhancement factor (EF) of AgNPs-MMF-30 probe to 10−7 M R6G is about 1.36 × 108. In addition, the AgNPs-MMF-30 probe has an outstanding reproducibility across the entire area with the maximum value of relative standard deviation (RSD) of less than 10.94%. The AgNPs-MMF-30 probe exhibit a long-term stability regarding Raman enhancement of up to 35 days. Importantly, the high-performance AgNPs-MMF-30 probe is further applied as a highly sensitive SERS platform for the trace detection of antibiotic in milk. The fiber SERS probe has the characteristics of fast, effective, remote real-time detection and so on, so it has great potential application in biochemical sensing and food security.