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Background: Amnestic mild cognitive impairment (aMCI) is an important window of opportunity for early intervention and rehabilitation in dementia. The aim of this study was to investigate the feasibility and effect of delivering transcutaneous electrical acupuncture stimulation (TEAS) intervention to elders with aMCI. Methods: A total of 61 aMCI patients were randomly allocated into the intervention group (receiving a 12-week TEAS) and control group (receiving health education). The feasibility outcomes included recruitment rate, retention rate, adherence rate, and an exploration of patients' views and suggestions on the research. The effective outcomes included cognitive function, sleep quality, and life quality, which were measured by the Montreal cognitive assessment scale (MoCA), auditory verbal learning test—Huashan version (AVLT-H), Pittsburgh sleep quality index (PSQI), and quality of life short-term-12 (QoL SF-12). Results: The recruitment rate, retention rate, and adherence rate were 67.35%, 92.42%, and 85.29%, respectively. Most aspects of the research design and administration of the TEAS intervention were acceptable. The quantitative analysis suggests that compared with the control group, the scores of MoCA, AVLT-H, and SF-12 (mental component summary) were significantly better (p < 0.05); however, the differences were not statistically significant in PSQI and SF-12 (physical component summary) (p > 0.05). Conclusions: The findings demonstrated that the study was feasible. TEAS awas possible for enhancing cognitive function and mental health in people with aMCI. [ABSTRACT FROM AUTHOR]

With the development of the smart grid, the traditional defect detection methods in transmission lines are gradually shifted to the combination of robots or drones and deep learning technology to realize the automatic detection of defects, avoiding the risks and computational costs of manual detection. Lightweight embedded devices such as drones and robots belong to small devices with limited computational resources, while deep learning mostly relies on deep neural networks with huge computational resources. And semantic features of deep networks are richer, which are also critical for accurately classifying morphologically similar defects for detection, helping to identify differences and classify transmission line components. Therefore, we propose a method to obtain advanced semantic features even in shallow networks. Combined with transfer learning, we change the image features (e.g., position and edge connectivity) under self-supervised learning during pre-training. This allows the pre-trained model to learn potential semantic feature representations rather than relying on low-level features. The pre-trained model then directs a shallow network to extract rich semantic features for downstream tasks. In addition, we introduce a category semantic fusion module (CSFM) to enhance feature fusion by utilizing channel attention to capture global and local information lost during compression and extraction. This module helps to obtain more category semantic information. Our experiments on a self-created transmission line defect dataset show the superiority of modifying low-level image information during pre-training when adjusting the number of network layers and embedding of the CSFM. The strategy demonstrates generalization on the publicly available PASCAL VOC dataset. Finally, compared with state-of-the-art methods on the synthetic fog insulator dataset (SFID), the strategy achieves comparable performance with much smaller network depths. [ABSTRACT FROM AUTHOR]

In this study, a low-cost space mapping (SM) modeling method with mesh deformation is proposed for microwave components. In this approach, the coarse-mesh model with mesh deformation is developed as the coarse model, and the fine-mesh model is simulated as the fine model. The SM technique establishes the mapping relationship between the coarse-mesh model and the fine-mesh model. This approach enables us to combine the computational efficiency of the coarse model with the accuracy of the fine model. The automatic mesh deformation technology is embedded in the coarse model to avoid the discontinuous change in the electromagnetic response. The proposed model consisting of the coarse model and two mapping modules can represent the features of the fine model more accurately, and predict the electromagnetic response of microwave components quickly. The proposed mesh SM modeling technique is applied to the four-pole waveguide filter. The value for the training and test errors in the proposed model is less than 1%, which is lower than that for the ANN models and the existing SM models trained with the same data. Compared with HFSS software, the proposed model can save about 70% CPU time in predicting a set of 100 data. The results show that the proposed method achieves a good modeling accuracy and efficiency with few training data and a low computational cost. [ABSTRACT FROM AUTHOR]

In this paper, an improved empirical formula modeling method using neuro-space mapping (Neuro-SM) for coupled microstrip lines is proposed. Empirical formulas with correction values are used for the coarse model, avoiding a slow trial-and-error process. The proposed model uses mapping neural networks (MNNs), including both geometric variables and frequency variables to improve accuracy with fewer variables. Additionally, an advanced method incorporating simple sensitivity analysis expressions into the training process is proposed to accelerate the optimization process. The experimental results show that the proposed model with its simple structure and an effective training process can accurately reflect the performance of coupled microstrip lines. The proposed model is more compatible than models in existing simulation software. [ABSTRACT FROM AUTHOR]

Small target detection has always been a hot and difficult point in the field of target detection. The existing detection network has a good effect on conventional targets but a poor effect on small target detection. The main challenge is that small targets have few pixels and are widely distributed in the image, so it is difficult to extract effective features, especially in the deeper neural network. A novel plug-in to extract location features of the small target in the deep network was proposed. Because the deep network has a larger receptive field and richer global information, it is easier to establish global spatial context mapping. The plug-in named location feature extraction establishes the spatial context mapping in the deep network to obtain the global information of scattered small targets in the deep feature map. Additionally, the attention mechanism can be used to strengthen attention to the spatial information. The comprehensive effect of the above two can be utilized to realize location feature extraction in the deep network. In order to improve the generalization of the network, a new self-distillation algorithm was designed for pre-training that could work under self-supervision. The experiment was conducted on the public datasets (Pascal VOC and Printed Circuit Board Defect dataset) and the self-made dedicated small target detection dataset, respectively. According to the diagnosis of the false-positive error distribution, the location error was significantly reduced, which proved the effectiveness of the plug-in proposed for location feature extraction. The mAP results can prove that the detection effect of the network applying the location feature extraction strategy is much better than the original network. [ABSTRACT FROM AUTHOR]

An analytically separated neuro-space mapping (Neuro-SM) model of power transistors is proposed in this paper. Two separated mapping networks are introduced into the new model to improve the characteristics of the DC and AC, avoiding interference of the internal parameters in neural networks. Novel analytical formulations are derived to develop effective combinations between the mapping networks and the coarse model. In addition, an advanced training approach with simple sensitivity analysis expressions is proposed to accelerate the optimization process. The flexible transformation of terminal signals in the proposed model allows existing models to exceed their current capabilities, addressing accuracy limitations. The modeling experiment for the measurement data of laterally diffused metal-oxide-semiconductor transistors demonstrates that the novel method accurately represents the characteristics of the DC and AC of transistors with a simple structure and efficient training process. [ABSTRACT FROM AUTHOR]

An advanced Neuro-space mapping (Neuro-SM) multiphysics parametric modeling approach for microwave passive components is proposed in this paper. The electromagnetic (EM) domain model, which represents the EM responses with respect to geometrical parameters, is regarded as a coarse model. The multiphysics domain model, which represents the multiphysics responses with respect to both geometrical parameters and multiphysics parameters, is regarded as a fine model. The proposed model is constructed by the input mapping, the output mapping and the coarse model. The input mapping is used to map multiphysics parameters to EM parameters. The output mapping is introduced to further narrow the gap between the output of the coarse model and the multiphysics data. In addition, a three-stage training method is proposed for efficiently developing the proposed multiphysics model. The proposed technique, which combines the efficiency of EM analysis and the accuracy of multiphysics analysis, can achieve better accuracy with less multiphysics data than existing modeling methods. The developed Neuro-SM multiphysics model provides accurate and fast predictions of multiphysics responses. Therefore, the design cycle of microwave passive components is shortened while the modeling cost is significantly reduced. Two microwave filter examples are utilized to demonstrate the accuracy of the proposed parametric modeling technique. [ABSTRACT FROM AUTHOR]

A multi-wavelength erbium-doped fiber laser (MW-EDFL) with wide tuning range, switching and adjustable wavelength interval is designed and tested, which is based on a composite filter. The filter consists of a tapered microfiber coupler loop (TMCL) with a nested single mode fiber (SMF)-two mode fiber (TMF)-SMF (STS) structure, which has a comb spectrum with obvious envelope and uniform fluctuation. Our experimental and theoretical results show that the laser can output thirteen wavelengths, when the angles of two polarization controllers (PCs) in the TMCL are accurately set. Moreover, by adjusting the PCs, the tuning range of single- to sextuple-wavelength can reach about 40 nm. Six non-adjacent multi-wavelength outputs can be observed in some specific polarization states. The maximum side-mode suppression ratio (SMSR) of the output laser is 40.6 dB. Compared with other multi-wavelength EDFL, the output characteristics of the laser, such as the adjustability and flexibility of wavelength spacing and the switch-ability of wavelength number, have been improved. [ABSTRACT FROM AUTHOR]

Yield estimation and yield-driven design optimization play important roles in microwave design. Existing surrogate-based yield estimation/optimization methods need to develop separate surrogate models or a large surrogate model with high complexity to deal with multiport problems, which is computationally inefficient. This article proposes a novel surrogate-based method to expedite yield estimation/optimization of multiport microwave structures using combined quadratic mappings and matrix-valued transfer functions. For multiport structures, the responses of different transfer functions corresponding to different pairs of input–output ports are computed with separate residues, while the responses of transfer functions for all pairs of input–output ports are computed with a common set of poles. Taking advantage of this, we propose to formulate a set of quadratic functions to map the relationship between the separate residues and statistical/geometrical parameters while employing merely one quadratic function to map the relationship between the common poles and statistical/geometrical parameters. The resultant mappings together with the matrix-format transfer function form an efficient surrogate to expedite the yield estimation and optimization processes for microwave structures. Compared with existing surrogate-based methods, the proposed method can achieve similar yield estimation accuracy in a shorter time due to fewer electromagnetic (EM) simulations. Three microwave structures are used to demonstrate the advantages of the proposed method. Based on accurate yield estimations, we further perform yield-driven design optimization incorporating the proposed surrogate for all the three examples. [ABSTRACT FROM AUTHOR]

This article proposes a novel surrogate-assisted multistate tuning-driven electromagnetic (EM) optimization technique to address the challenges of microwave tunable filter design with multiple tuning states. The desired multiple tuning states are satisfied simultaneously using the proposed surrogate-assisted technique. The proposed surrogate model is composed of several subsurrogate models. Each subsurrogate model is developed to perform the optimization for each tuning state. The subsurrogate models share the same values of nontunable parameters and possess different values of tunable parameters. The overall surrogate model is developed to find a single set of optimal solutions for nontunable parameters and multiple sets of optimal solutions for tuning parameters simultaneously. Parallel computation scheme is exploited to generate the training samples for establishing the proposed surrogate model. Furthermore, a new trust-region updating formulation specifically for multistate tuning is proposed to improve the convergence of the proposed optimization algorithm. Using the proposed optimization technique, different tuning states are considered together and optimized simultaneously. The values of nontunable design parameters are constrained by all tuning states and consequently there is a higher chance that more suitable solutions can be found to satisfy all the desired tuning states simultaneously. The proposed technique for the tunable filter design with multiple tuning states has a better capability of avoiding local minima and can reach the optimal solution more effectively in comparison with the existing optimization method. Two microwave examples are used to validate the proposed technique. [ABSTRACT FROM AUTHOR]

Objective: Major depressive disorder (MDD) is one of the most common psychiatric disorders, the diagnosis and treatment of MDD are major clinical issues. However, there is a lack of effective biomarkers and drugs diagnosis and therapeutics of MDD. In the present study, bioinformatics analysis combined with an experimental verification strategy was used to identify biomarkers and paeoniflorin targets for MDD diagnosis and treatment. Methods: Based on network pharmacology, we obtained potential targets and pathways of paeoniflorin as an antidepressant through multiple databases. We then constructed a protein-protein interaction network and performed enrichment analyses. According to the results, we performed in vivo and in vitro experimental validation. Results: The results showed that paeoniflorin may exert an antidepressant effect by regulating cell inflammation, synaptic function, NF-κB signaling pathway, and intestinal inflammation. Conclusion: NPM1, HSPA8, HSPA5, HNRNPU, and TNF are the targets of paeoniflorin treatment. In addition, we demonstrated that paeoniflorin inhibits inflammatory cytokine production via the p38MAPK/NF-κB pathway and has neuroprotective effects on the synaptic structure. Our findings provide valuable evidence for the diagnosis and treatment of MDD. [ABSTRACT FROM AUTHOR]

Plant fermentation extracts (PFEs) produced from fermenting various fruits and vegetables by microorganisms have traditionally been consumed as healthy beverages in Asia. Herein, the immunomodulatory activities of a novel polysaccharide (AP) isolated from PFE were examined in RAW264.7 cells. The results showed that AP was composed of Rha, Xyl, Man and Glu in a molar ratio of 9.7:27.6:8.81:53.85 and the average molecular weight was 4.81 × 105 Da. Our data showed that AP could significantly enhance the phagocytic activity of RAW264.7 cells and their ability to release NO, TNF-α, IL-6 and IL-10. Furthermore, AP could significantly up-regulate the mRNA expression of iNOS, cytokines (TNF-α, IL-6, IL-10) and pattern recognition receptors (TLR2, MR, GR, CR3 and SR). Western blotting demonstrated that the regulation of NO and TNF-α were mediated through the NF-κB and MAPK signalling pathways. These findings helped to elucidate the immunomodulatory properties of the polysaccharide AP and PFE. [ABSTRACT FROM AUTHOR]

Artificial neural network modeling techniques have been recognized as important vehicles in the microwave computer‐aided design (CAD) area in addressing the growing challenges of designing next generation microwave device, circuits, and systems. This article provides an overview of recent advances in knowledge‐based neural network model generation and extrapolation techniques for microwave applications. We first introduce the unified knowledge‐based neural network structure optimization technique. Using the distinctive property for feature selection of l1 optimization, this unified modeling technique efficiently determines the type and topology of the mapping structure in a knowledge‐based model. This knowledge‐based model structure optimization technique is more flexible and systematic, and can further speed up the knowledge‐based neural model development. As a further advancement, we also discuss the advanced multi‐dimensional extrapolation technique for neural‐based microwave modeling. The purpose is to make the neural network model can be reliably used not only inside the training range but also outside the training range. Multi‐dimensional cubic polynomial extrapolation formulation and optimization over grids outside the training range are utilized to make neural models more robust and reliable when they are used outside the training range. The validity of these techniques is demonstrated by microwave modeling examples. [ABSTRACT FROM AUTHOR]

A dual-wavelength switchable perfect absorber, comprised of a continuous Au film, an alumina (Al2O3) spacer, an indium tin oxide (ITO) layer, double-layer Dysprosium-doped cadmium oxide (CdO:Dy) films, and a gold ring array from bottom to top, is numerically designed in this paper. The epsilon-near-zero (ENZ) properties are determined by the carrier concentration of these ENZ materials. As for ITO material, the carrier (electron) concentration can be electrically modified by applying a biasing voltage V. And different growth conditions afford significant variation of carrier concentration in CdO:Dy layers. Via changing the biasing voltage V , we can achieve broadband and multifrequency absorption in our infrared absorber. Especially, the proposed infrared absorber demonstrates excellent electrical regulation performance, enabling bidirectional switching of "ON" and "OFF" states at dual-wavelength. We also further reveal the absorption mechanism by establishing quasi-Fabry–Pérot cavity resonance model. In addition, it is shown that the infrared absorber can tolerate a wide range of incident angles as well as has polarization insensitive features by verification. This device has great potential in numerous optoelectronic applications, such as invisibility cloaking, sub-diffraction imaging, and thermal emission. [ABSTRACT FROM AUTHOR]

Space mapping is a recognized surrogate-based optimization method to accelerate the electromagnetic (EM) design. In this article, for the first time, space mapping is elevated from solving the problem of EM optimization to solving the problem of multiphysics optimization for high power microwave filters. Multiphysics analysis, which involves the EM domain with other physics domains, is increasingly important for high-performance microwave components to obtain an accurate system design. To speed up the multiphysics design, a space-mapping-based surrogate model including a coarse model and two mapping functions is proposed in this article. We propose to use EM single physics responses as the coarse model to provide good approximations to fine model multiphysics responses. To avoid repetitive EM simulations during the surrogate model training and optimization process, the coarse model is developed using an artificial neural network (ANN). Frequency mapping and explicit input mapping are further performed to develop the proposed surrogate model. Multiple EM and multiphysics training samples are evaluated in parallel to develop the surrogate model. A trust-region algorithm, tailored to the space-mapping-based multiphysics optimization technique, is proposed to improve the convergence. By exploiting the knowledge of the coarse model established by relatively inexpensive EM data, the proposed technique can provide a larger and more efficient optimization update in each optimization iteration, consequently obtaining optimal solutions faster than the existing multiphysics optimization without space mapping. Two examples of multiphysics optimization of high-power microwave filters are used to validate the proposed technique. [ABSTRACT FROM AUTHOR]

Inverse modeling of microwave components plays an important role in microwave design and diagnosis or tuning. Since the analytical function or formula of the inverse input‐output relationship does not exist and is difficult to obtain, artificial neural network (ANN) becomes an efficient tool to develop inverse models for microwave components. This paper provides an overview of recent advances in neural network‐based inverse modeling techniques for microwave applications. We review two different shallow neural network‐based inverse modeling techniques, including the comprehensive neural network inverse modeling methodology and the multivalued neural network inverse modeling technique. Both techniques address the problem of nonuniqueness in inverse modeling. We also provide an overview of recently developed hybrid deep neural network modeling technique and the application to inverse modeling. For the inverse modeling problem with high‐dimensional inputs, the relationship between the inputs and the outputs of the inverse model will become more complicated and the inverse modeling problem will become harder. The deep neural network becomes a practical choice. The hybrid deep neural network structure is presented. The recently proposed activation function, specifically for microwave application, and a three‐stage deep learning algorithm for training the hybrid deep neural network are reviewed. [ABSTRACT FROM AUTHOR]

Existing gain estimation methods for radio frequency (RF) antennas often rely on rigorous and expensive experimental facilities or are only implemented for classic structure. They perform limited application scopes. To address the challenges, this study provides an accessible method for gain estimation of heteromorphic RF identification (RFID) antennas. There are three main innovations in the proposed method. An estimation framework is proposed based on neuro-space mapping technique which effectively reduces the time consumption and avoids laborsome measurement processes. A diverse extraction integration strategy is designed for training data acquisition, to balance the estimation accuracy and the training data size. A new adaptive particle swarm optimiser embedded with scale elaboration strategy is developed, which tackles the approximation problem from the gain estimation model to the gain from high-fidelity simulations. The proposed method is tested by four types of RF antennas. Simulations results demonstrate the method possesses high accuracy and strong applicability. [ABSTRACT FROM AUTHOR]

Gradient-based optimization algorithms are popularly used in electromagnetic (EM)-based design optimizations. Among the gradient-based optimization algorithms, Newton’s method is not practically applicable to EM optimization because it is very time consuming to obtain the Hessian matrix containing the second-order derivatives of the EM responses with respect to the geometrical variables. This article addresses this situation and proposes an efficient gradient-based EM optimization technique using the combined Lagrangian method with Newton’s method. EM optimizations can be reformulated into constrained optimizations when the finite element method (FEM) is applied to perform EM simulations. In this article, we propose to elevate the Lagrangian method (i.e., a popular constrained optimization method) to EM optimization. By using the Lagrangian method to perform the EM optimization, the Hessian matrix can be obtained efficiently without the time-consuming evaluations of second-order derivatives of the EM responses with respect to the geometrical variables. With the efficiently calculated the Hessian matrix, Newton’s method can be applied. We derive new formulations of Newton’s method specifically for the EM optimization with the Lagrangian method. The proposed EM optimization using the combined Lagrangian method with Newton’s method can converge faster than direct EM optimizations with other gradient-based optimization methods. The proposed technique is demonstrated by two EM optimization examples of microwave components. [ABSTRACT FROM AUTHOR]

For the high-performance microwave component and system design, besides electromagnetic (EM) physics domain, we also need to consider the operation of the real-world multiphysics (MP) environment that contains the effects of other physics domains. EM-centric MP analysis and design optimization become very important. In this article, for the first time, we develop a novel parallel EM-centric multiphysics optimization (MPO) technique. In our proposed technique, the pole/residue-based transfer function is exploited to build an effective and robust surrogate model. A group of modified quadratic mapping functions is formulated to map the relationships between pole/residues of the transfer function and the design variables. Multiple EM-centric MP evaluations are performed in parallel to generate the training samples for establishing the surrogate model. Using our proposed technique, the surrogate model can be valid in a relatively large neighborhood, which makes an effective and large optimization update in each optimization iteration. The trust region algorithm is performed to guarantee the convergence of the proposed MPO algorithm. Our proposed MPO technique takes a small number of iterations to obtain the optimal EM-centric MP response. Two microwave filter examples are used to demonstrate the validity of the proposed technique. [ABSTRACT FROM AUTHOR]

This article proposes a multifeature-assisted neuro-transfer function (neuro-TF) surrogate-based electromagnetic (EM) optimization technique exploiting trust-region algorithms for microwave filter design. The proposed optimization technique addresses the situation where the response of the starting point is far away from the design specifications. We propose to utilize multiple feature parameters to help move the passband of the filter response into the range of design specifications. The pole–zero-based neuro-TF is introduced in this article to help extract the multiple feature parameters when the feature parameters of filter responses are not explicitly identified. Furthermore, we propose to derive new optimization objective functions to involve the multiple feature parameters. A new trust-region updating formulation for the modified optimization objective functions is derived to guarantee the optimization convergence. With the assistance of multiple feature parameters, the proposed surrogate-based EM optimization has a better capability of avoiding local minima and can reach the optimal EM solution faster than the surrogate-based EM optimizations without feature assistance. Three examples of EM optimizations of microwave filters are used to demonstrate the proposed technique. [ABSTRACT FROM AUTHOR]

This paper presents a novel Neurospace Mapping (Neuro-SM) method for packaged transistor modeling. A new structure consisting of the input package module, the nonlinear module, the output package module, and the S-Matrix calculation module is proposed for the first time. The proposed method can develop the model only using the terminal signals, instead of the internal and physical structure information of the transistors. An advanced training method utilizing the different parameters to adjust the different characteristics of the packaged transistors is developed to make the proposed model match the device data efficiently and accurately. Measured data of radio frequency (RF) power laterally diffused metal-oxide semiconductor (LDMOS) transistor are used to verify the capability of the proposed Neuro-SM method. The results demonstrate that the novel Neuro-SM model is more accurate and efficient than existing device models. [ABSTRACT FROM AUTHOR]

This paper proposes a novel technique to develop a low-cost electromagnetic (EM) centric multiphysics parametric model for microwave components. In the proposed method, we use space mapping techniques to combine the computational efficiency of EM single physics (EM only) simulation with the accuracy of the multiphysics simulation. The EM responses with respect to different values of geometrical parameters in nondeformed structures without considering other physics domains are regarded as coarse model. The coarse model is developed using the parametric modeling methods such as artificial neural networks or neuro-transfer function techniques. The EM responses with geometrical and nongeometrical design parameters as variables in the practical deformed structures due to thermal and structural mechanical stress factors are regarded as fine model. The fine model represents the behavior of EM centric multiphysics responses. The proposed model includes the EM domain coarse model and two mapping neural networks to map the EM domain (single physics) to the multiphysics domain. Our proposed technique can achieve good accuracy for multiphysics parametric modeling with fewer multiphysics training data and less computational cost. After the modeling process, the proposed model can be used to provide accurate and fast prediction of EM centric multiphysics responses of microwave components with respect to the changes of design parameters within the training ranges. The proposed technique is illustrated by a tunable four-pole waveguide filter example at 10.5–11.5 GHz and an iris coupled microwave cavity filter example at 690–720 MHz. [ABSTRACT FROM AUTHOR]

Network planning plays a critical role in the performance of ultra-high frequency radio-frequency identification (UHF RFID) system. Existing works mainly focus on the impact of reader antennas’ location with regard to coverage, quality of service, and cost, rather than localization accuracy. Moreover, since the link budget of stated solutions was investigated based on omnidirectional antennas, most research findings cannot be directly applied to practical directional localization scene. Hence, a novel deployment optimization approach for the directional reader antennas is proposed in this paper. By investigating the gain characteristic of patch antenna and dipole antenna, a propagation model is established in the first place. Then, we build a new network planning model for identifying the placement of a fixed number of reader antennas, which maximizes coverage and minimizes location error as well as interference. Finally, an improved chicken swarm optimization algorithm, termed as MPCSO, is developed for this problem. Simulation results show that the proposed approach achieves much better performance than other classic algorithms. [ABSTRACT FROM PUBLISHER]

Blind image deblurring is an important topic which is widely used in many research fields such as photography, optics, astronomy, medical images, monitoring, military and so on. Although many algorithms have been proposed to improve the deblurring result in the past years, most of them cannot perform perfectly in some challenging cases. This study presents a novel blind deblurring method based on an adaptive weighted total variation (TV) algorithm. The blur kernel estimation is based on the image structure, the sparsity and continuity prior of point spread function is also taken into account. To get better effect of removing the ringing artefacts, adaptive weight calculated according to the property of the higher‐order partial derivatives in the local image is proposed in TV algorithm to alleviate the ill‐posed inverse problem and stabilise the solution for latent image restoration. The experimental results prove that the proposed algorithm can suppress the ringing artefacts to a great extent in the latent image, and can get much better effect in both vision and theoretical results than traditional algorithms. [ABSTRACT FROM AUTHOR]

ABSTRACT A new technique for behavioral modeling of power amplifier (PA) with short- and long-term memory effects is presented here using recurrent neural networks (RNNs). RNN can be trained directly with only the input-output data without having to know the internal details of the circuit. The trained models can reflect the behavior of nonlinear circuits. In our proposed technique, we extract slow-changing signals from the inputs and outputs of the PA and use these signals as extra inputs of RNN model to effectively represent long-term memory effects. The methodology using the proposed RNN for modeling short-term and long-term memory effects is discussed. Examples of behavioral modeling of PAs with short- and long-term memory using both the existing dynamic neural networks and the proposed RNNs techniques are shown. © 2014 Wiley Periodicals, Inc. Int J RF and Microwave CAE 25:289-298, 2015. [ABSTRACT FROM AUTHOR]

This letter presents a novel dynamic Neuro-space mapping (Neuro-SM) technique for nonlinear device modeling. This is an advance over the existing static Neuro-SM which aims to map a given approximate device model towards an accurate model. The proposed technique retains the ability of static Neuro-SM in modifying the effects of nonlinear resistors and current sources. The proposed technique can also make up for any capacitive effects and non-quasi-static effects that maybe missing in the given model, which is not achievable by the existing static Neuro-SM. In this way, the dynamic Neuro-SM model can exceed the accuracy limit of the static Neuro-SM. The validity and efficiency of the proposed approach are verified through two high-electron mobility transistor (HEMT) modeling examples. [ABSTRACT FROM PUBLISHER]

ABSTRACT A novel Wiener‐type dynamic neural network (Wiener‐type DNN) method for large signal modeling of power amplifiers (PAs) is proposed in this paper. In this method, the proposed model structure for the PA contains two Wiener‐type DNNs to describe the input impedance and the amplification efficiency, respectively. The Wiener‐type DNN includes a simplified linear dynamic equation module and a nonlinear static equation module. For the simplification process of the linear dynamic equation, it is proposed to process fundamental frequency components of the large signal harmonic data of the PA by vector fitting. The neural network is used to implement the static equation in the Wiener‐type DNN. The formulas for training the proposed Wiener‐type DNN model of the PA using large signal data are derived. The training algorithm of the PA model is proposed to improve the training efficiency. A Motorola MOSFET PA example and a Freescale lateral double‐diffused MOSFET PA example are present to validate the proposed Wiener‐type DNN method. For the Motorola MOSFET PA, a dynamic neural network (DNN) model and a time‐delay deep neural network (TDDNN) are established for comparison. For the Freescale PA, the DNN model is used for comparison experiment. The comparison figures show that the Wiener‐type DNN model has better convergence properties than the DNN model in the nonlinear region of the PA. The established accurate PA model is conducive to the design of subsequent communication circuits. [ABSTRACT FROM AUTHOR]

Ultra high frequency radio frequency identification (UHF RFID)-based indoor localization technology has been a competitive candidate for context-awareness services. Previous works mainly utilize a simplified Friis transmission equation for simulating/rectifying received signal strength indicator (RSSI) values, in which the directional radiation of tag antenna and reader antenna was not fully considered, leading to unfavorable performance degradation. Moreover, a k-nearest neighbor (kNN) algorithm is widely used in existing systems, whereas the selection of an appropriate k value remains a critical issue. To solve such problems, this paper presents an improved kNN-based indoor localization algorithm for a directional radiation scenario, IKULDAS. Based on the gain features of dipole antenna and patch antenna, a novel RSSI estimation model is first established. By introducing the inclination angle and rotation angle to characterize the antenna postures, the gains of tag antenna and reader antenna referring to direct path and reflection paths are re-expressed. Then, three strategies are proposed and embedded into typical kNN for improving the localization performance. In IKULDAS, the optimal single fixed rotation angle is introduced for filtering a superior measurement and an NJW-based algorithm is advised for extracting nearest-neighbor reference tags. Furthermore, a dynamic mapping mechanism is proposed to accelerate the tracking process. Simulation results show that IKULDAS achieves a higher positioning accuracy and lower time consumption compared to other typical algorithms. [ABSTRACT FROM AUTHOR]

Ultra high frequency radio frequency identification (UHF RFID)-based indoor localization technology has been a competitive candidate for context-awareness services. Previous works mainly utilize a simplified Friis transmission equation for simulating/rectifying received signal strength indicator (RSSI) values, in which the directional radiation of tag antenna and reader antenna was not fully considered, leading to unfavorable performance degradation. Moreover, a k -nearest neighbor ( k NN) algorithm is widely used in existing systems, whereas the selection of an appropriate k value remains a critical issue. To solve such problems, this paper presents an improved k NN-based indoor localization algorithm for a directional radiation scenario, IKULDAS. Based on the gain features of dipole antenna and patch antenna, a novel RSSI estimation model is first established. By introducing the inclination angle and rotation angle to characterize the antenna postures, the gains of tag antenna and reader antenna referring to direct path and reflection paths are re-expressed. Then, three strategies are proposed and embedded into typical k NN for improving the localization performance. In IKULDAS, the optimal single fixed rotation angle is introduced for filtering a superior measurement and an NJW-based algorithm is advised for extracting nearest-neighbor reference tags. Furthermore, a dynamic mapping mechanism is proposed to accelerate the tracking process. Simulation results show that IKULDAS achieves a higher positioning accuracy and lower time consumption compared to other typical algorithms.

We present here the draft genome sequence of Lactobacillus brevis strain 3M004, a probiotic that has potential for regulating quorum sensing. The strain was obtained from a type of aquafeed. The assembly consists of 2,459,326 bp and contains 33 contigs, with a G+C content of 45.10%., (Copyright © 2017 Li et al.)

Chinese bayberry (Morella rubra Sieb. et Zucc.) fruit have a diverse flavonoid composition responsible for the various medicinal activities, including anti-diabetes. In the present study, efficient simultaneous purification of four flavonoid glycosides, i.e., cyanidin-3-O-glucoside (1), myricetin-3-O-rhamnoside (2), quercetin-3-O-galactoside (3), quercetin-3-O-rhamnoside (4), from Chinese bayberry pulp was established by the combination of solid phase extract (SPE) by C18 Sep-Pak(®) cartridge column chromatography and semi-preparative HPLC (Prep-HPLC), which was followed by HPLC and LC-MS identification. The purified flavonoid glycosides, as well as different fractions of fruit extracts of six bayberry cultivars, were investigated for α-glucosidase inhibitory activities. The flavonol extracts (50% methanol elution fraction) of six cultivars showed strong α-glucosidase inhibitory activities (IC50 = 15.4-69.5 μg/mL), which were higher than that of positive control acarbose (IC50 = 383.2 μg/mL). Four purified compounds 1-4 exerted α-glucosidase inhibitory activities, with IC50 values of 1444.3 μg/mL, 418.8 μg/mL, 556.4 μg/mL, and 491.8 μg/mL, respectively. Such results may provide important evidence for the potential anti-diabetic activity of different cultivars of Chinese bayberry fruit and the possible bioactive compounds involved.

The present study investigated the possible anti-obesity and hypoglycemic effects of Poncirus trifoliata L. extracts. Mature fruit were divided into flavedo (PF) and juice sacs (PJ), and extracts from them were tested on C57BL/6 mice fed a high-fat diet (HFD) for thirteen weeks. Both fruit extracts (40 mg/kg body weight, respectively) showed anti-obesity and hypoglycemic effects. Consumption of PF and PJ extracts reduced body weight by 9.21% and 20.27%, respectively. Liver and adipose weights, fasting glucose, serum triglyceride (TG), and low density lipoprotein cholesterol (LDL-c) levels decreased significantly, while serum high density lipoprotein cholesterol (HDL-c) and oral glucose tolerance levels increased significantly in response to two fruit extracts. These effects were due in part to the modulation of serum insulin, leptin, and adiponectin. Furthermore, transcript levels of fatty acid synthase (FAS) and stearoyl-CoA desaturase 1 (SCD1) were reduced while those of carnitine palmitoyltransferase 1α (CPT1α) and insulin receptor substrate 2 (IRS2) were increased in the liver of C57BL/6 mice, which might be an important mechanism affecting lipid and glucose metabolism. Taken together, P. trifoliata fruit can be potentially used to prevent or treat obesity and associated metabolic disorders.