AIRIS:Artificial Intelligence Enhanced Signal Processing in Reconfigurable Intelligent Surface Communications

Reconfigurable intelligent surface(RIS)is an emerging meta-surface that can provide additional communications links through reflecting the signals,and has been recognized as a strong candidate of 6G mobile communications systems.Meanwhile,it has been recently admitted that implementing artificial intelligence(AI)into RIS communications will extensively benefit the reconfiguration capacity and enhance the robustness to complicated transmission environments.Besides the conventional model-driven approaches,AI can also deal with the existing signal processing problems in a data-driven manner via digging the inherent characteristic from the real data.Hence,AI is particularly suitable for the signal processing problems over RIS networks under unideal scenarios like modeling mismatching,insufficient resource,hardware impairment,as well as dynamical transmissions.As one of the earliest survey papers,we will introduce the merging of AI and RIS,called AIRIS,over various signal processing topics,including environmental sensing,channel acquisition,beamforming design,and resource scheduling,etc.We will also discuss the challenges of AIRIS and present some interesting future directions.

Pattern recognition of surface electromyography signal based on wavelet coefficient entropy

This paper introduced a novel, simple and ef-fective method to extract the general feature of two surface EMG (electromyography) signal patterns: forearm supination (FS) surface EMG signal and forearm pronation (FP) surface EMG signal. After surface EMG (SEMG) signal was decomposed to the fourth resolution level with wavelet packet transform (WPT), its whole scaling space (with frequencies in the interval (0Hz, 500Hz]) was divided into16 frequency bands (FB). Then wavelet coefficient entropy (WCE) of every FB was calculated and corre-spondingly marked with WCE(n) (from the nth FB, n=1,2,…16). Lastly, some WCE(n) were chosen to form WCE feature vector, which was used to distinguish FS surface EMG signals from FP surface EMG signals. The result showed that the WCE feather vector consisted of WCE(7) (187.25Hz, 218.75Hz) and WCE(8) (218.75Hz, 250Hz) can more effectively recog-nize FS and FP patterns than other WCE feature vector or the WPT feature vector which was gained by the combination of WPT and principal components analysis.

Investigation on global positioning system signal scattering and propagation over the rough sea surface

This paper is devoted to the study of polarization properties, scattering properties and propagation properties of global positioning system （GPS） scattering signal over the rough sea surface. To investigate the polarization and the scattering properties, the scattering field and the bistatic scattering coefficient of modified Kirchhoff approximation using the tapered incident wave is derived in detail. In modeling the propagation properties of the GPS scattering signal in the evaporation duct, the initial field of parabolic equation traditionally computed by the antenna pattern using fast Fourier transform （FFT） is replaced by the GPS scattering field. And the propagation properties of the GPS scattering signal in the evaporation duct with different evaporation duct heights and elevation angles of GPS are discussed by the improved discrete mixed Fourier transform taking into account the sea surface roughness.

Multifractal analysis of surface EMG signals for assessing muscle fatigue during static contractions

This study is aimed at assessing muscle fatigue during a static contraction using multifractal analysis and found that the surface electromyographic (SEMG) signals characterized multifractality during a static contraction. By applying the method of direct determination of the f(α) singularity spectrum, the area of the multifractal spectrum of the SEMG signals was computed. The results showed that the spectrum area significantly increased during muscle fatigue. Therefore the area could be used as an assessor of muscle fatigue. Compared with the median frequency (MDF)―the most popular indicator of muscle fatigue, the spectrum area presented here showed higher sensitivity during a static contraction. So the singularity spectrum area is considered to be a more effective indicator than the MDF for estimating muscle fatigue.

Characterization of surface EMG signals using improved approximate entropy

An improved approximate entropy (ApEn) is presented and applied to characterize surface electromyography (sEMG) signals. In most previous experiments using nonlinear dynamic analysis, this certain processing was often confronted with the problem of insufficient data points and noisy circumstances, which led to unsatisfactory results. Compared with fractal dimension as well as the standard ApEn, the improved ApEn can extract information underlying sEMG signals more efficiently and accu- rately. The method introduced here can also be applied to other medium-sized and noisy physiological signals.

Ocean surface current retrieval and imaging with a new shore-based X-band radar based on time-shifted up-and-down linear frequency modulated signal

This paper proposes a multifunction radar that can not only measure sea currents but also perform sea-surface imaging.The fundamental aspect of the proposed radar comprises transmitting time-shifted up-and-down continuous wave linear frequency modulated signals that allow for the offset of two one-dimensional range images of the sea surface that respectively correspond to the upward linear frequency modulated(LFM)signal and the downward LFM signal.Owing to the Doppler frequency shift from the sea surface,a range offset,which is proportional to the radial velocity of the sea surface,occurs between the upward and downward LFM signals.By using the least-squares linear fitting method in the transformed domain,the range offset can be measured and the current velocity can be retrieved.Finally,we verify the accuracy of current measurement with simulation results.

The influences of large earthquake signals on the recovery of surface waves from ambient noise cross-correlation functions

Ambient noise tomography(ANT)has been widely used to image crust and upmost mantle structures.ANT assumes that sources of ambient noise are diffuse and evenly distributed in space and the energy of different modes is equipartitioned.At present,the sources of the primary and the secondary microseisms are well studied,but there are only a few on the studies of long-period ambient noise sources.In this study,we study the effects of large earthquake signals on the recovery of surface waves from seismic ambient noise data recorded by seismic stations from the US permanent networks and Global Seismographic Network(GSN).Our results show that large earthquake signals play an important role on the recovery of long-period surface waves from ambient noise cross-correlation functions.Our results are consistent with previous studies that suggest the contribution of earthquake signals to the recovery of surface waves from cross-correlations of ambient noise is dominant at periods larger than 20–40 s.

Remote Sensing of Sea Surface Wind of Hurricane Michael by GPS Reflected Signals

In this paper, the propagating geometry and the w av eform of the GPS reflected signals are expatiated in detail. Furthermore, the pr inciple and the method of retrieving sea surface wind are presented. In order to test the feasibility of retrieval, the experiment data obtained by NASA in Hurr icane Michael are used. The result shows that the retrieval accuracy of wind spe ed is about 2 m/s.

Classification of surface EMG signal with fractal dimension

Surface EMG (electromyography) signal is a complex nonlinear signal with low signal to noise ratio (SNR). This paper is aimed at identifying different patterns of surface EMG signals according to fractal dimension. Two patterns of surface EMG signals are respectively acquired from the right forearm flexor of 30 healthy volunteers during right forearm supination (FS) or forearm pronation (FP). After the high frequency noise is filtered from surface EMG signal by a low-pass filter, fractal di-mension is calculated from the filtered surface EMG signal. The results showed that the fractal dimensions of filtered FS surface EMG signals and those of filtered FP surface EMG signals distribute in two different regions, so the fractal dimensions can rep-resent different patterns of surface EMG signals.

The Change of Spectral Energy Distribution of Surface EMG Signal During Forearm Action Process

Spectral energy distribution of surface EMG signal is often used but difficultly and effectively control artificial limb, because the spectral energy distribution changes in the process of limb actions. In this paper, the general characteristics of surface EMG signal patterns were firstly characterized by spectral energy change. 13 healthy subjects were instructed to execute forearm supination (FS) and forearm pronation (FP) with their right forearms when their forearm muscles were “fatigue” or “relaxed”. All surface EMG signals were recorded from their right forearm flexor during their right forearm actions. Two sets of surface EMG signals were segmented from every surface EMG signal appropriately at preparing stage and acting stage. Relative wavelet packet energy (symbolized by pnp and pna respectively at preparing stage and acting stage, n denotes the nth frequency band) of surface EMG signal firstly was calculated and then, the difference (Pn=pna-pnp) were gained. The results showed that Pn from some frequency bands can effectively characterize the general characteristics of surface EMG signal patterns. Compared with Pn in other frequency bands, P4, the spectral energy change from 93.75 to 125 Hz, was more appropriately regarded as the features.

Dual-band dynamic reconfigurable intelligent surface for signal enhancement in millimeter wave

Millimeter wave with large bandwidth,high transmission rate,and low delay is considered a reliable alternative to cope with the spectrum shortage.However,the fast attenuation and narrow beam characteristics make it difficult to achieve long-distance or wide-range applications.Here,a 1-bit dual-band reflective reconfigurable intelligent surface(RIS)for signal enhancement in millimeter wave with 16×16 elements is designed,fabricated,and measured.Different from most existent RIS,dynamic programming is realized at two separate frequency bands by integrating the PIN diodes and field-programmable gate array(FPGA).Particularly,the beam deflection,dual-beam,and multi-beam are created based on the coding theory and convolution operation,proving the effectiveness of wavefront manipulation.Moreover,the far-field patterns and signal power with different coding sequences are measured and compared.It is indicated that the received signal power is 6–7 dB stronger than that without coding,which shows good agreement with the desired expectations.The proposed reconfigurable metasurface exhibits great potential in beam forming,making it a promising candidate for progressive wireless communication applications.

The measurement of sea surface profile with X-band coherent marine radar

The line-of-sight velocity of scattering facets is related to the Doppler signals of X-band coherent marine radar from the oceanic surface. First, the sign Doppler Estimator is applied to estimate the Doppler shift of each radar resolution cell. And then, in terms of the Doppler shift, a retrieval algorithm extracting the vertical displacement of the sea surface has been proposed. The effects induced by radar look-direction and radar spatial resolution are both taken into account in this retrieval algorithm. The comparison between the sea surface spectrum of buoy data and the retrieved spectrum reveals that the function of the radar spatial resolution is equivalent to a low pass filter, impacting especially the spectrum of short gravity waves. The experimental data collected by McMaster IPIX radar are also used to validate the performance of the retrieval algorithm. The derived significant wave height and wave period are compared with the in situ measurements, and the agreement indicates the practicality of the retrieval technology.

A New Technique for Surface Roughness Testing

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Adaptive Radio Frequency Interference Mitigation for HF Surface Wave Radar

The paper analyses the characteristics of radio frequency interference （RFI） in HF surface wave radar （HFSWR） which adopts the linear frequency modulated interrupted continuous wave （FMICW）. RFI will influence all the range cells including all the positive frequency and negative frequency, and the negative frequency range cells contain only the interference information. Based on the above characteristics, we introduce and analyze a new adaptive interference mitigation beamforming algorithm using the negative frequency range cells samples to estimate the interference covariance matrix. Experimental results confirm that this general and robust algorithm can achieve effective RFI suppression using the data recorded by the HFSWR, located near Zhoushan in Zhejiang China.

Noise reduction and periodic signal extraction for GNSS height data in the study of vertical deformation

Global navigation satellite system(GNSS)technique has irreplaceable advantages in the continuous monitoring of surface deformation.Reducing noise to improve the signal-to-noise ratio(SNR)and extract the concerned signals is of great significance.As an improved algorithm of empirical mode decomposition(EMD),complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN)algorithm has better signal processing ability.Using the CEEMDAN algorithm,the height time series of 29GNSS stations in Chinese mainland were analyzed,and good denoising effects and extraction from periodic signals were achieved.The numerical results showed that the annual signal obtained with the CEEMDAN algorithm was significantly based on Lomb_Scargle spectrum analysis,and large differences in the long-term signals were found between the stations at different locations in Chinese mainland.With respect to data denoising,compared with the EMD and wavelet denoising algorithms,the CEEMDAN algorithm respectively improved the SNR by 29.35% and 36.54%,increased the correlation coefficient by 8.67% and 11.96%,and reduced root mean square error(RMSE)by 44.68% and 43.48%,indicating that the CEEMDAN algorithm had better denoising behavior than the other two algorithms.In addition,the results demonstrated that different denoising methods had little influence on estimating the annual vertical deformation velocity.The extraction of periodic signals showed that more components were retained by using the CEEMDAN algorithm than the EMD algorithm,which indicated that the CEEMDAN algorithm had advantages over frequency aliasing.In conclusion,the CEEMDAN algorithm was recommended for processing the GNSS height time series to analyze the vertical deformation due to its excellent features of denoising and the extraction of periodic signals.

一种基于循环时空深度神经网络的手势识别方法

针对表面肌电信号解码模型因缺乏时空信息等重要性表征,面临解码精度低、鲁棒性差等问题,提出了一种基于循环时空深度神经网络的手势识别模型,来提高挖掘表面肌电信号的表征能力。首先,设计多通道卷积神经网络,并融入双向循环神经网络来提取强判别力的时空特征信息。其次,采用通道注意力机制来捕捉时空特征中通道重要性信息,设计基于时空特征的注意力模块以进一步增强时空特征信息。同时,基于特征金字塔网络思想来设计多尺度特征融合模块,从多尺度、多角度获取多级特征信息,提高模型对肌电信号的解码能力。最后,将所提出的手势识别模型在大型手势识别数据库Ninapro上进行测试,结果表明所提方法能有效提高对表面肌电信号的表征挖掘能力,为人体手势动作识别的深度学习建模工作提供借鉴意义。

融合表面肌电和姿势信息的轮椅绩效评价方法

目的为客观评价轮椅的使用效益,使用表面肌电设备测试轮椅使用过程中的肌电信号,并融合姿势信号IMU来构建人机评价模型。方法分别对轮椅的折叠、刹车方式及行驶坡度进行试验来评估轮椅的使用绩效,通过对比用力肌群间的疲劳状况来判定较为舒适的轮椅折叠及刹车方式。实验要求被试者在执行轮椅任务时,分别使用两种折叠方式和三种刹车方式不同的轮椅进行实验,并在操作动作任务过程中采集sEMG和IMU信号,在实验任务结束后填写NASA-TLX量表。结果根据模型的评价指标对实验数据进行了比较与分析,横向收折式折叠和凹口式刹车(手刹位于前方)的轮椅疲劳度低,在3~4°坡度范围下轮椅使用者的受力最小,较为舒适,从而验证了模型在轮椅人机评价上的可行性,为优化轮椅设计提供参考。结论人机评价模型适用于评估产品绩效,同时提出的融合表面肌电和姿势信息的轮椅绩效评价方法具有较高的精度和准确性,能够有效地评估轮椅使用者的绩效水平。

基于差分结构的地面核磁共振数据噪声压制技术

地面核磁共振(surface nuclear magnetic resonance,SNMR)技术是一种可直接定性定量探测地下水的非侵害式地球物理方法,已广泛应用于资源勘探、地质灾害预警和环境检测等方面。但在实际应用中,复杂的环境噪声导致微弱的SNMR信号常常被淹没,很难获取有效的SNMR信号。针对这一问题,本文提出了一种基于差分结构的SNMR数据噪声压制技术,采用两个接收线圈等距设置在发射线圈上下位置。这种分布可以实时抵消大部分环境噪声以及消除收发线圈耦合影响。理论建模和仿真结果验证了新方法能够有效压制噪声,并可靠获取到早期自由感应衰减(free induction decay,FID)信号。

表面肌电与三轴信息融合的运动判断实验

为了提高基于表面肌电与三轴加速度信号的运动识别准确率,提出了一套多源信息融合处理的实验流程与方法。该方法利用5层离散小波变换对表面肌电信号进行分解,充分提取不同运动产生的肌电信号中各频域的特征信息;再将分解后的表面肌电信号与三轴加速度信号通过滑动窗口的方法进行特征融合,构造融合肌电与空间运动特征的特征图;最后用融合特征图对深度学习模型进行训练,并结合自动状态机进行最终运动状态的识别。实验结果表明,多源信息融合处理方法可以提高运动识别的准确性,总体识别精度分别达到了95.4%和89.2%。该方法在实时性与准确性上均有良好表现。