Filter Weights Research Articles

Magnification-arbitrary depth super-resolution with multiscale consistency deformable alignment

For many years, depth map super-resolution has been a problem worthy of studying in 3D reconstruction. Although many excellent algorithms have been developed to solve this problem, depth map super-resolution at any scale has not attracted much attention. In this paper, we propose a magnification-arbitrary depth super-resolution method based on Meta-SR framework. Different from the single spatial location guidance in Meta-SR, the multiscale consistency alignment module learns a hierarchical registered discontinuities and semantic feature guidance to aggregate more structural information of the depth map and its corresponding color image. Specially, the dense pyramid deformable convolution realizes the scale expansion and improves the precision of sub-pixel upsampling from coarse to fine. A collaborative geometrical-spatial attention based weight prediction module is proposed to measure the correlation between geometrical features, spatial location and the upsampling weights, and predict a set of weight filters for reconstruction. A large number of experiments show that our method achieves satisfactory subjective and objective results on both integral and fractional scales compared with existing methods.

Filter pruning via feature map clustering

With the help of network compression algorithms, deep neural networks can be applied on low-power embedded systems and mobile devices such as drones, satellites, and smartphones. Filter pruning is a sub-direction of network compression research, which reduces memory and computational consumption by reducing the number of parameters of model filters. Previous works utilized the “more-simple-less-important” criterion for pruning filters. That is, filters with the smaller norm or more sparse weights in the network are preferentially pruned. In this paper, we found that feature maps are not fully positively correlated with the sparsity of filter weights by observing the visualization of feature maps and the corresponding filters. Hence, we came up with the idea that the priority of filter pruning should be determined by redundancy rather than sparsity. The redundancy of a filter is the measure of whether the output of the filter is repeated with other filters. Based on this, we defined a criterion called redundancy index to rank the filters and introduced it into our filter pruning strategy. Extensive experiments demonstrate the effectiveness of our approach on different model architectures, including VGGNet, GoogleNet, DenseNet, and ResNet. The models compressed with our strategy surpass the state-of-the-art in terms of Floating Point Operations Per Second (FLOPs), parameters reduction, and classification accuracy.

Equalizer of Amplitude-Frequency Response of Acoustic Channels and its Adaptive Filter Based on Recursive Least Squares Algorithm with Diagonalized Correlation Matrix

The paper presents an equalizer of the amplitude-frequency response of the acoustic wave propagation channels in the rooms. The considered equalizer uses the so-called modified architecture based on the adaptive filter with the input signal filtering, which weights are calculated using the Recursive Least Squares (RLS) algorithm. The equalizer of the acoustic channels amplitude-frequency response usually requires the usage of the adaptive filters with the large number of weighs that means the large computational complexity of the adaptive filter. The adaptive filters based on the fast RLS algorithms are efficient in the terms of their complexity but often unstable if the number of adaptive filter weights is large. Therefore, the algorithms are poorly suited for usage in the amplitude-frequency response equalizers of the acoustic channels. Adaptive filters based on the RLS algorithm, which use the Matrix Inversion Lemma (MIL), are stable, but computationally complex. The equalizer presented in this paper uses a simplified adaptive filter based on the MIL RLS algorithm with a diagonalized correlation matrix of the filter input signal. In addition, in the filter, each of the square submatrices on the simplified correlation matrix diagonal is inverted using a computationally efficient version of the MIL that takes into account the symmetrical structure of the mentioned submatrices relatively their main diagonals. Computer simulation confirms the performance of the proposed equalizer and demonstrates its efficiency comparing to the unsimplified equalizer. The demo includes the graphs of the equalizer steady-state amplitude-frequency response, the acoustic channel amplitude-frequency responses before and after the equalization, and the power spectral density plots of the equalizer input signal and the signals passed through the equalizer and the acoustic medium. The equalizer can be used in the equipment for the high-quality voice and music reproduction in the conditions of the limited computing resources for the implementation of the equalizer.

Illusion of filtration: Evidence from tobacco industry documents.

We compared the design features of popular filtered and non-filtered cigarettes sold in the United States between 1960 and 1990, to assess the relationship between cigarette filter and tobacco weight. We analyzed data on the design features of six popular filtered and three non-filtered cigarette brands sold in the US including the weight of tobacco used provided in the Cigarette Information Reports produced by Philip Morris Tobacco Company between 1960 and 1990. We also collected information on other design features such as stick length and circumference, the percentage of reconstituted tobacco in the blend, among other product parameters. We used joinpoint regression to test for trends in outcome variables for each brand assessed between 1960 and 1990. In all years, filtered cigarettes had less tobacco by weight compared to non-filtered cigarettes. The lower average weight of tobacco found in filtered cigarettes appears to be due to a combination of factors including stick and filter length, and the amount of reconstituted tobacco in the blend. The average percentages of total alkaloids and expanded tobacco increased over time but were similar between filtered and non-filtered brands. While various design features of popular filtered and non-filtered brands changed between 1960 and 1990, the observed reduction in tobacco weight among filtered brands was perhaps the most salient in terms of disease risk. Less tobacco in a filtered cigarette calls into question the presumed exclusive role of cigarette filter tips in the reduced health risks of filtered versus non-filtered cigarette smoking.

A multi-dimensional trust model for misbehavior detection in vehicular ad hoc networks

Communication security is one of the focus issues of vehicular ad hoc networks (VANETs). Trust model is commonly used to identify malicious attackers or filter false messages in the network, which effectively guarantees secure communication between vehicles. Constrained by the distributed network architecture of VANETs, trust evaluation results may be biased due to uneven data distribution and lack of reference. By colluding with the partners, malicious vehicle launches imbalance attack and zigzag attack to bypass the detection of the trust model. To address these new intelligent attacks, we propose a multi-dimensional trust model (MDT) in this paper, which makes a combined evaluation of different trust attributes from vehicles. In MDT, data collection and trust calculation are deployed in vehicles and trust authority (TA), respectively. Since the model dynamically adjusts the corresponding weight coefficient according to the data fluctuations of each trust indicator by adopting entropy weight method and filters anomalous evaluation results by using the median absolute deviation (MAD) algorithm, it can effectively deal with complex and variable intelligent attacks. The legitimate identity of a vehicle will be revoked from the network by TA once its trust value is less than the threshold. We have conducted extensive experimental studies to evaluate the performance of MDT. The experimental results show that the MDT model can accurately and effectively calculate the global trust of vehicles and detect malicious attacks in the network under different scenarios and tests.

Full Electromagnetic Integration of Impedance-Balanced EMI Filters for Single-Phase Power Converters

Utilizing passive electromagnetic interference (EMI) filters is the most widely used approach to restrain the conducted emissions from power converters. However, the introduction of these filters will significantly raise the weight and size of the systems. Using the emerging flexible multi-layer foil (FMLF) technique, this paper investigates a complete electromagnetically integrated design concept of single-phase EMI filters based on EE-type cores, which can be applicable to engineering application scenarios with different EMI noise attenuation requirements. Two types of integrated structures are constructed with different well-designed FMLF-windings and terminal configurations. One is a common-mode (CM) choke with controllable leakage inductances, which is able to realize the integration design of CM inductor, CM capacitors and differential-mode (DM) inductors. The other is an EMI choke with integrating the CM and DM inductances & capacitances, which achieves electromagnetic integration of all the CM and DM filtering components. Compared to the existing UU-type core based integration methods, the proposed methods can ameliorate the noise suppression performance while reducing filter's weight and volume. Theoretical analyses for modeling and design of the EMI chokes is presented. Then prototypes of different FMLFs based EMI filters have been built for a 500W 100kHz SiC-MOSFET inverter, experiments demonstrate the feasibility and validity of the proposed structures.

Efficient Convolutional Processing of Spiking Neural Network With Weight-Sharing Filters

The importance of implementing an efficient convolutional neural network (CNN) is increasing. A weight-sharing spiking CNN inference system (WS-SCNN) employing efficient convolution layers (ECLs) is proposed and modeled to enable the compact convolutional processing of the spiking neural network (SNN) inference. The proposed ECL efficiently maps convolutional features between inputs and filter weights. The ECL does not replicate the synaptic filter array with respect to input sliding, which minimizes the number of synaptic devices required to implement hardware SNNs. A four-bit weight quantization capability of a fabricated charge-trap flash synaptic device is used to verify the accurate multiplication and summation of weights in the ECL. Moreover, a nine-layer WS-SCNN consisting of multiple ECLs is modeled, and the benefits of the WS-SCNN in terms of the area and energy are evaluated. Simulation results show that the WS-SCNN has 5.68 and 103.5 times higher energy and area efficiency than conventional SCNN systems, respectively.

Adaptive qXE-LMF Filter for Improving Power Quality Using Grid-Supporting Photovoltaic System

In practice, nonlinear electrical loads pose various power quality (PQ) challenges. Driven by these challenges, a parallel combination of a nonlinear load and a photovoltaic (PV) inverter with adaptive quantum normalized-least mean fourth ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</i> XE-LMF) filter is proposed and experimentally validated to detect and compensate for the harmonics in harmonically contaminated currents. A scheme with an adaptive <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</i> XE-LMF using fundamental weight components is used to generate the harmonics in the inverter output current thereby improving the PQ of the grid supply. The resulting grid current is pure and balanced; the PV inverter connected in parallel with the nonlinear load provides reactive and harmonic current support. Practically, the adaptive feature enables the online learning of filter weights of the load current harmonics to guarantee unconditionally balanced sinusoidal grid current. The proposed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</i> XE-LMF filter ensures fast convergence and minimum steady-state oscillations in the grid reference currents. An improved anti-windup PI compensator is used to ensure dc-link voltage stabilization. The practicality of the proposed adaptive <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</i> XE-LMF filter is experimentally demonstrated using a three-phase four-wire uncontrolled rectifier-based nonlinear load. The experimental results also demonstrate the robustness of the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</i> XE-LMF filter against changes in solar insolation level and nonlinear loads.

An Adaptive Beam Forming Algorithm for Reduction of Coexistence- Interference and Improved Performance in 5G Communication Systems

Abstract: A couple of Wi-Fi systems coexisting in a 5G community might produce interference within the same frequency band, degrading the obtained sign's overall performance. This paper proposes a singular set of rules in antenna array processing to address interference-coexistence verbal exchange. We adopt a linear filter which is referred to as a Linearly confined minimal Variance (LCMV) filter out. We impose a log-sum penalty at the coefficients and upload it to the price feature based on traditional singly linearly restrained least suggest square (LC-LMS). The iterative formula for filter weights is derived. We show that the brand-new approach's convergence price is quicker than the conventional one in the usage of simulations in an antenna environment with a signal of hobby, noise, and interference. Furthermore, the proposed approaches suggest rectangular errors (MSE) are shown. Our approach has a lower MSE than the traditional LC-LMS set of rules, in line with the findings of the experiments. The cautioned adaptive beam forming approach may be used in a 5G gadget to cope with sign and interference coexistence.

Strategy to implement a convolutional neural network based ideal model observer via transfer learning for multi-slice simulated breast CT images

Objective. In this work, we propose a convolutional neural network (CNN)-based multi-slice ideal model observer using transfer learning (TL-CNN) to reduce the required number of training samples. Approach. To train model observers, we generate simulated breast CT image volumes that are reconstructed using the FeldkampDavisKress algorithm with a ramp and Hanning-weighted ramp filter. The observer performance is evaluated on the background-known-statistically (BKS)/signal-known-exactly task with a spherical signal, and the BKS/signal-known-statistically task with random signal generated by the stochastic grown method. We compare the detectability of the CNN-based model observer with that of conventional linear model observers for multi-slice images (i.e. a multi-slice channelized Hotelling observer (CHO) and volumetric CHO). We also analyze the detectability of the TL-CNN for different numbers of training samples to examine its performance robustness to a limited number of training samples. To further analyze the effectiveness of transfer learning, we calculate the correlation coefficients of filter weights in the CNN-based multi-slice model observer. Main results. When using transfer learning for the CNN-based multi-slice ideal model observer, the TL-CNN provides the same performance with a 91.7% reduction in the number of training samples compared to that when transfer learning is not used. Moreover, compared to the conventional linear model observer, the proposed CNN-based multi-slice model observers achieve 45% higher detectability in the signal-known-statistically detection tasks and 13% higher detectability in the SKE detection tasks. In correlation coefficient analysis, it is observed that the filters in most of the layers are highly correlated, demonstrating the effectiveness of the transfer learning for multi-slice model observer training. Significance. Deep learning-based model observers require large numbers of training samples, and the required number of training samples increases as the dimensions of the image (i.e. the number of slices) increase. With applying transfer learning, the required number of training samples is significantly reduced without performance drop.

On the Use of the Kolmogorov–Wiener Filter for Heavy-tail Process Prediction

This paper is devoted to the investigation of the applicability of the Kolmogorov–Wiener filter to the prediction of heavy-tail processes. As is known, telecommunication traffic in systems with data packet transfer is considered to be a heavy-tail process. There are a lot of rather sophisticated approaches to traffic prediction; however, in the rather simple case of stationary traffic sophisticated approaches may not be needed, and a simple approach, such as the Kolmogorov–Wiener filter, may be applied. However, as far as we know, this approach has not been considered in recent papers. In our previous papers, we theoretically developed a method for obtaining the filter weight function in the continuous case. The Kolmogorov–Wiener filter may be applied only to stationary processes, but in some models telecommunication traffic is treated as a stationary process, and thus the use of the Kolmogorov–Wiener filter may be of practical interest. In this paper, we generate stationary heavy-tail modeled data similar to fractional Gaussian noise and investigate the applicability of the Kolmogorov–Wiener filter to data prediction. Both non-smoothed and smoothed processes are investigated. It is shown that both the discrete and the continuous Kolmogorov–Wiener filter may be used in a rather accurate short-term prediction of a heavy-tail smoothed stationary random process. The paper results may be used for stationary telecommunication traffic prediction in systems with packet data transfer.

Analysis on population-based algorithm optimized filter for non-invasive fECG extraction

Metaheuristic algorithms (MAs) have become one of the primary tools for optimization in diverse domains, including non-invasive fetal electrocardiogram (fECG) extraction. Research reveals that hyperparameters affect the performance of MAs differently to problems, and some algorithms are problem-specific designed and may produce bad results for different problems. Hence, three questions arise, (1) how much can we trust MAs when solving boxed-constrained fECG extraction problems, and (2) which type of MAs are suitable and adequate for fECG extraction? (3) do MAs find acceptable solutions to a problem that does not formulate the problem comprehensively with an imperfect objective function? This paper focuses on these three inquiries and proposes a framework providing an MA-assisted adaptive filter for non-invasive fECG extraction. The proposed framework has three key components: data pre-processing, MA-based adaptive filter, and post-processing. The pre-processing starts to process the signals detected by pregnant women and extract the desired signal by independent component analysis. Such output signals are then passed to a filter and optimized by population-based algorithms, producing an optimal solution. After that, this solution, as filter weights, will be used for signal extraction and be processed by post-processing. Importantly, the proposed framework is user-friendly that can import any MA algorithm to run and disassemble as a separate assisting tool. This work investigated eight classic and disparate MA algorithms on the Abdominal and Direct Fetal ECG Database (ADFECGDB) dataset, offering comprehensive experimental analysis. We infer from the results that the recordings r01,r02,r03,r05,r08, and r09, where the signals are less noisy and can be solved well. While for recordings r04,r07,r06, and r10, the performance can be influenced by hyperparameters for any test algorithms, such as the population size, window size, and other parameters in MAs. Meanwhile, we found that MA may not be the primary target influencing the performance vary.

A new and effective nonparametric variable step-size normalized least-mean-square algorithm and its performance analysis

This paper presents a new and effective nonparametric variable step-size normalized least-mean-square (VSS-NLMS) algorithm which provides excellent performance in various signal-to-noise ratio (SNR) scenarios. The step-size update expressions are based on steady-state step-size which derives from the filter weight update equations. Specifically, the step-size adjustment rules employ the square root of the estimated error power and the system noise power to enhance the effectiveness of the proposed algorithm. Detailed transient-state and steady-state analyses are presented for the Gaussian white input signal and suggest that the steady-state performance of the proposed algorithm is independent of the smoothing parameter (used in the step-size update expressions). In addition, performance comparisons between the proposed algorithm and other well-known VSS-NLMS algorithms show the improvements of the proposed algorithm. Furthermore, theoretical analyses and simulated behaviors of the proposed algorithm are in good agreement for both transient-state and steady-state. Through simulation results, the irrelevance between the steady-state performance and the smoothing parameter is verified.

Frequency-Domain RF Self-Interference Cancellation for In-Band Full-Duplex Communications

Wireless backhaul has recently gained a significant amount of interest as a cost-effective solution in comparison with conventional backhaul technologies with dedicated microwave links or fiber optics. Self-interference cancellation (SIC) is an enabling technology that allows wireless backhaul to operate in the more spectrum-efficient in-band full-duplex (IBFD) operation mode instead of the out-of-band mode. Compared to Wi-Fi IBFD transceivers, wireless in-band backhaul systems face some unique challenges, such as significantly higher transmission power and much larger propagation delay spread for the self-interference signal, especially in the low-frequency bands under 1 GHz, which often prevent accurate SIC performance. The SIC is often implemented with an interference-cancelling filter, where the filter weights are essentially the channel estimates of the self-interference signals. In this paper, a frequency-domain Radio Frequency (RF) SIC (RF-SIC) framework with a novel filter weight optimization algorithm is proposed to tackle the challenges of wireless in-band backhaul. The proposed RF-SIC does not require a dedicated training phase which needs to stop the transmission of the backhaul signal. Moreover, it has the capability of tracking the self-interference channel variation since the filter weights are updated in a block-by-block fashion.

Tolerance of Siamese Networks (SNs) to Memory Errors: Analysis and Design

This article considers memory errors in a Siamese Network (SN) through an extensive analysis and proposes two schemes (using a weight filter and a code) to provide efficient hardware solutions for error tolerance. Initially the impact of memory errors on the weights of the SN (stored as floating-point (FP) numbers) is analyzed; this shows that the degradation is mostly caused by outliers in weights. Two schemes are subsequently proposed. An analysis is pursued to establish the filter's bounds selection by the maximum/minimum values of the weight distributions, by which outliers can be removed from the operation of the SN. A code scheme for protecting the sign and exponent bits of each weight in an FP number, is also proposed; this code incurs in no memory overhead by utilizing the 4 least significant bits (LSB) to store parity bits. Simulation shows that the filter has a better performance for multi-bit errors correction (a reduction of 95.288% in changed predictions), while the code achieves superior results in single-bit errors correction (a reduction of 99.775% in changed predictions). The combined method that uses the two proposed schemes, retains their advantages, so adaptive to all scenarios; The ASIC-based FP designs of the SN using serial and hybrid implementations are also presented; these pipelined designs utilize a novel multi-layer perceptron (MLP) (as branch networks of the SN) that operates at a frequency of 681.2 MHz (at a 32nm technology node), so significantly higher than existing designs found in the technical literature. The proposed error-tolerant approaches also show advantages in overheads comparing with for example traditional error correction code (ECC). These error-tolerant MLP-based designs are well suited to hardware/power-constrained platforms.

Building convolutional neural network parameters using genetic algorithm for the croup cough classification problem

Croup cough is an infection in the upper airway typically occurs in children from age six month to 3 years. Symptoms of croup cough begin with a normal cold, fever and loud barking makes the child difficult to breath. These symptoms are relatively similar with a recent pandemic SARS-COV2. So, the common symptoms of croup cough and SARS-COV2 is urges the physicians to diagnose the infection at early stage. Typically, clinical professions Computer Aided Diagnose system (CADS) for detecting the abnormalities from chest X-Ray (PA View) and CT images of infants. Most of CADS adopted the deep learning technique for classification of radiograph images due to the its ability in term of accuracy rate. Classification accuracy of deep learning techniques like Convolution Neural Network (CNN) highly relays on the weights of convolution filters and fully connected layer. In this work, we propose the optimized CNN using Genetic algorithm (GA) for classification of croup cough images. This work includes optimizing weights of CNN with different batch size and iterations using genetic algorithm to identify the best weights for the classifier to generate maximum accuracy. The experiments were carried out with croup cough image dataset, and we show the promising performance of proposed method of 88.32% accuracy rate with smaller amount of dataset.

A Reconfigurable Spatial Architecture for Energy-Efficient Inception Neural Networks

Convolutional neural networks (CNNs) have been widely utilized in modern artificial intelligent (AI) systems. In particular, GoogLeNet, one of the most popular CNNs, consisting of a number of inception layers and max-pooling layers, has been intensively studied for mobile and embedded scenarios. However, the energy efficiency of GoogLeNet in hardware is still limited as the huge data movement between the processor and the memory. Therefore, designing a dataflow and the corresponding hardware architecture to achieve parallel processing with minimal data movement is rather critical to achieve high energy efficiency and throughput. In this paper, we propose a novel column stationary (CS) dataflow that maximally exploits the local data reuse of both the filter weights and feature maps. Moreover, a reconfigurable spatial architecture was proposed to map multiple convolution kernels (with different types and dimensions) in parallel to the processing engines (PEs) array. In this case, multiple convolution kernels can share the same input feature maps (activations) in computing process. In our hardware design, we utilize three typical convolution kernels (i.e., <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$5 \times 5$ </tex-math></inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3 \times 3$ </tex-math></inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1 \times 1$ </tex-math></inline-formula> , corresponding to the inception layers of GoogLeNet) as an example to test the efficiency of our proposed dataflow and hardware architecture. The accelerator was implemented for one inception layer of the GoogLeNet in a 55-nm foundry’s CMOS process. The test results show that our CS dataflow can reduce ~85% energy consumption for memory access and save area of 13% and power of 12% for computing. In summary, our CS dataflow is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.2\times $ </tex-math></inline-formula> to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2.5\times $ </tex-math></inline-formula> more energy-efficient compared to state-of-the-art dataflows.

Processes underlying the assembly of plant communities: The role of environmental filtering heterogeneity in a competition–dispersal experiment

AbstractQuestionInterspecific competition and seed dispersal are key mechanisms that may influence plant community assembly, but their relative importance remains difficult to disentangle as they can be affected by abiotic conditions (environmental filtering). We experimentally studied the balance between these processes in a Molinia wet meadow plant community in which abiotic constraints are mostly represented by flooding, while agricultural practices such as mowing prevent the dominance of a few competitive species. We hypothesized that (H1) mowing, by decreasing competitive exclusion and (H2) seed addition, by increasing immigration, both enhance plant species diversity, and that (H3) small variations in flooding intensity become secondary to these processes in determining species assembly.LocationFrench National Nature Reserve “Etang de Cousseau”, Nouvelle Aquitaine, France.MethodsIn a three‐year experiment, we added seeds of 17 species characteristic of Molinia wet meadows and managed above‐ground competition through mowing. We measured spatial variations in soil moisture as an indicator of the flooding regime. We recorded species relative cover, species richness and evenness annually, and measured light availability, linked with canopy openness, twice.ResultsFrom the beginning of the experiment, small‐scale variations in soil moisture mainly affected species richness, composition and evenness, and canopy openness. Mowing significantly increased light availability and evenness, and affected the relative cover of several species, but not species richness. The addition of seeds did not influence any of the variables monitored during the experiment.ConclusionOur study helps to disentangle processes underlying community assembly in wet meadows. It demonstrates significant and rapid effects of mowing on canopy opening and on community assembly, while seed addition had only a slight effect, possibly explained by an insufficient decrease in competitive interactions through mowing or unmeasured processes. It also shows that abiotic heterogeneity remains a primary driver of community assembly, even on a small scale. Finally, our results suggest that the respective weight of the different filters (dispersion, environment, competition) is subject to interannual variability or environmental stochasticity.

Noise Cancellation of a Train Electric Traction System Fan Based on a Fractional-Order Variable-Step-Size Active Noise Control Algorithm

In this study, an active noise control (ANC) algorithm based on fractional order with a variable step size is proposed to improve the convergence performance of an integer-order ANC algorithm and achieve noise cancellation of a train electric traction system fan. The algorithm optimized the convergence parameters by constructing a time-varying function related to the variation of an error signal to improve the convergence speed of the algorithm, and the introduction of fractional calculus in the algorithm to control the update iteration of the filter weight coefficients, which could improve the control accuracy of the algorithm and reduce the steady-state error. And we give proof of the convergence of the algorithm and compared the noise reduction effect of the system under different orders to obtain the optimal order. The algorithm is compared with other traditional algorithms, and it is proved that it is superior to traditional algorithms in terms of convergence speed, steady-state error, and error attenuation. The experimental results demonstrate that the algorithm exhibited a good noise reduction effect on the fan noise of the train electric traction system of 0–500 Hz and the average noise reduction was about 1.9–4.1 dB.

Channel Selectivity of Satellite Transponders with the Antenna Combined with a Size-Reduced Metallic Waveguide Bandpass Filter Having Thin Metamaterial Resonators.

Global and intercontinental networking relies on satellite communication. Its wireless communication system always has antennas and their feed assembly comprising waveguides. This makes the satellite payload heavy and costly. In this paper, a novel method is proposed to effectively reduce the size of a waveguide bandpass filter (BPF). Because the metallic cavities make the conventional waveguide end up with a large geometry, especially for high-order BPFs, very compact waveguide-type resonators having metamaterial zeroth-order resonance (WG-ZOR) are designed on the cross-section of the waveguide and substituted for the cavities. While the cavities are half-wavelength resonators, the WG-ZOR is shorter than one eighth of a wavelength. A substantial reduction in size and weight of the waveguide filter is observed as the resonators are cascaded in series through coupling elements in the X-band much longer than K- or Ka-band. An X-band of 7.25~7.75 GHz is chosen to verify the method as the passband with attenuation of 40 dB at 7.00 GHz and 8.00 GHz as the roll-off in the stopband. The BPF is manufactured using the CNC milling technique. The design is carried out with geometrical parameters, not of the level of 10 μm, but the level of 100 μm, which is good for manufacturers but a big challenge for component designers. The measurement of the manufactured metal waveguide filter reveals that the passband has about ≤1 dB and ≤-15 dB as insertion loss and reflection coefficient and the stopband has ≤-40 dB as attenuation, which are in good agreement with the results of the circuit and simulation. The proposed filter has a length of 3.5 λg as the eighth-order BPF, but the conventional waveguide is 5 λg as the seventh-order BPF for the same area of the cross-section. This metamaterial BPF is combined with a horn antenna. The filter enables the wide-band antenna to distinguish the band of transmission from that of noise suppression. This channel selectivity is obviously observed by the filter integrated antenna test.