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With the development of high-frequency carriers and high-capacity communications, ultrahigh-speed spectrum analyzer based on all-optical methods has been demonstrated in many fields. Limited by current data processing algorithms, ultrahigh-speed spectrum analyzers based on time-lens, such as frequency-domain light intensity spectrum analyzer (f-LISA) and parametric spectro-temporal analyzer (PASTA), are not yet to automatically process acquired data, which hinders their practicality. In this paper, a frame segmentation algorithm based on time-domain waveform analysis is proposed to achieve the automatic data processing of these analyzers, which can directly generate the spectro-temporal image of the signal under test. In a single-soliton test based on the f-LISA platform, the algorithm achieves an accuracy of 93.28% when the soliton changes and 90.04% when the soliton is stable. And in this test, the single-frame processing time of the algorithm is 419us. We believe the proposed algorithm will facilitate the practical application of spectrum analyzers like f-LISA and PASTA and provide a reference for other real-time measurement schemes.

Ground penetrating radar (GPR) signal processing has used mode decomposition methods such as empirical mode decomposition (EMD), variational mode decomposition, and their variations, but these methods are typically constrained by manual mode selection for the extraction of real signals and unwanted noises. In this study, we propose an automatic mode selection scheme for GPR signal denoising, which is demonstrated by the combination of the improved complete ensemble EMD (ICEEMD) and the multiscale principal component analysis (MSPCA). For GPR B-scan data, the intrinsic mode functions (IMFs) are automatically screened using our predefined criteria to remove noise after their spectra and the relationship between signals are first analyzed. Next, the nonlinear and nonstationary signals are adaptively decomposed into many time series by the proposed automated ICEEMD. Each A-scan dataset is then screened and subjected to MSPCA before being reassembled into the final denoised result. The synthetic and real GPR data prove our denoising scheme’s superiority over more established denoising techniques like MSPCA and median filtering. [ABSTRACT FROM AUTHOR]

Nowadays, a large number of inverter-based resources (IBRs) are integrated into the grid at a single connection point as an IBR plant. In this article, a virtual dynamic grid impedance concept is introduced to evaluate the harmonics and stability for grid integration of an IBR plant containing multiple IBRs. First, a detailed theoretical study is conducted to build a foundation of the virtual grid impedance concept, which is a dynamic impedance that changes with the number of IBRs added into an IBR plant. Then, based on the new virtual dynamic grid impedance concept, frequency spectrum analysis is performed to explore harmonic impact at the interconnection point for an IBR plant with IBRs having L, LC, and LCL filters, respectively. An electromagnetic transient (EMT) simulation model of a grid-connected IBR plant is developed to explore the harmonics and stability of the IBR plant connected to the grid as well as the reliable operation of IBRs within the plant from the novel virtual equivalent dynamic grid impedance point of view. Hardware experiments are conducted to validate the EMT simulation evaluation. The results show that the number of IBRs added into an IBR plant influences the grid impedance, i.e., grid strength, and the grid impedance variation has multiple impacts on the IBR plant and IBRs within the plant depending on the grid-connected filters of the IBRs. [ABSTRACT FROM AUTHOR]

Multi-path transmission control protocol (MPTCP) is an extension of TCP that enables the concurrent transmission of information through different network interfaces (e.g., Cellular, Wi-Fi, 802.11p, and so on) available at terminal side. It is well known that MPTCP can provide significant advantages in bandwidth aggregation and transmission stability. Unfortunately, path diversity can limit bandwidth aggregation efficiency and incur higher delays. These issues become critical when in presence of emerging mobile AR and VR applications, which are bandwidth hungry, time-sensitive and exhibit abrupt variations of the bitrate. To address these issues, we propose the Reinforcement Learning-based mobile AR/VR multipath transmission with streaming Power Spectrum Density analysis (RL-PSD). RL-PSD analyses the Power Spectrum Density (PSD) of the AR/VR input stream to extract its features. Then, both the input stream and network features are considered to model the MPTCP congestion control as an reinforcement learning process. Finally, a two-stage reinforcement algorithm is proposed to optimize transmission performance. RL-PSD has been tested in both single-terminal and multi-terminal scenarios: results show that it outperforms the other advanced solutions conceived to support the multipath transmission of AR/VR streams. [ABSTRACT FROM AUTHOR]

Rotary encoders are commonly used for dynamic control and positioning of industrial robots. Results of this study suggested that rotary encoder signal can also be used to monitor the efficiency of industrial robot systems effectively after proper processing. A novel strategy using singular spectrum analysis (SSA) integrated with hierarchical hyper-Laplacian prior prototype (HHLP) is proposed in this study for defect detection in industrial robots. Sparsity-assisted techniques are efficient flaw-based extraction techniques that have been extensively investigated in recent years. However, the selection of an appropriate sparse prior from the point of view of probability theory remains unverified. First, SSA enables the separation of complicated encoding signals to several interpretable components, including a trend, a set of cyclic oscillations, and residual oscillations (noise). Second, we describe HHLP by maximizing posterior probability of robot flaw diagnosis. HHLP is proposed to extract noise interference, cyclic pulse, and harmonic interference from the residual signal using the SSA technique. We infer that the hyper-Laplacian prior in the prototype may present a more efficient prototype flaw than the Laplacian prior. In addition, HHLP incorporates physical characteristics necessary to distinguish harmonic intervention. This study primarily establishes a modern prototype that represents the former dispersed from the perspective of maximizing the probability of the latter. Meanwhile, generalized minimax-concave regularization inductive and kurtosis-based weighted sparse prototypes are compared and spectral kurtosis is used to confirm the efficacy of HHLP. Note to Practitioners—This study aims to solve the problem of industrial robot fault diagnosis during the operation process to avoid production delays. Rotary encoder sensor is attached to each joint to collect raw information and identify the robot position. Data from the rotary encoder sensor can also be used for the efficient health status assessment of the performance of the industrial robot system after proper processing. Therefore, a new approach using singular spectrum analysis combined with hierarchical hyper-Laplacian pre-induced prototype is proposed in this study. The residual signal extracted using the singular spectrum analysis method for processing in a hierarchical hyper-Laplacian pre-induced prototype is used to improve the weak fault feature. We describe a hierarchical hyper-Laplacian preprototype by maximizing the posterior probability of the flaw diagnosis. We introduce a hierarchical hyper-Laplacian prior that integrates physical characteristics to distinguish between harmonic interferences. The distribution of coefficients acquired through other dictionaries or transformations and selection of the optimal priority for the extraction of flaw characteristics will be the foci of future investigations. [ABSTRACT FROM AUTHOR]

Steady-state visual evoked potential (SSVEP) is widely used in brain computer interface (BCI), medical detection, and neuroscience, so there is significant interest in enhancing SSVEP features via signal processing for better performance. In this study, an image processing method was combined with brain signal analysis and a sharpening filter was used to extract image details and features for the enhancement of SSVEP features. The results demonstrated that sharpening filter could eliminate the SSVEP signal trend term and suppress its low-frequency component. Meanwhile, sharpening filter effectively enhanced the signal-to-noise ratios (SNRs) of the single-channel and multi-channel fused signals. Image sharpening filter also significantly improved the recognition accuracy of canonical correlation analysis (CCA), filter bank canonical correlation analysis (FBCCA), and task-related component analysis (TRCA). The tools developed here effectively enhanced the SSVEP signal features, suggesting that image processing methods can be considered for improved brain signal analysis.

In the optical communication systems, the optical spectrum (OS) provides useful informations for optical performance monitoring and optical link diagnosis. In this paper, we investigate the neural-network based OS analysis (OSA) techniques able to simultaneously recognize the OS and estimate the optical signal to noise ratio (OSNR), cascaded filtering distortion (CFD) and carrier wavelength drift (CWD). We demonstrate that, compared with the multi-task artificial neural network (MT-ANN) and convolutional neural network (MT-CNN), the proposed multi-task cascaded ANNs (CANN) and cascaded CNNs (CCNN) can greatly improve the OSA performance and accelerate the training process by exploiting specific features and loss functions for different tasks. The CCNN able to deal with the high-resolution OS (HOS) can further improve the OSA performance compared with the CANN. The averaged OS recognition accuracy and OSNR, CFD and CWD estimation errors obtained with the CCNN (CANN) for the 15 kinds of optical signals are 99.32% (97.07%), 0.36 (0.55) dB, 0.24 (0.32) and 0.04 (0.06) GHz, respectively, even when the various OS distortions are present. The proposed CANN and CCNN with a better versatility, higher OSA accuracy and faster convergence speed are promising enabling techniques for the future intelligent OSA systems.

In this paper, a six-phase $\text{N}+2$ power converter for switched reluctance motor is studied. An open-circuit fault diagnosis and faulty devices location scheme based on midpoint current spectrum analysis is proposed. In order to realize fault diagnosis, five fault cases are distinguished, including single-phase fault, multiphase fault and midpoint switch fault. Taking the phase current frequency as the fundamental frequency, the DC component, the fundamental frequency component, and the second harmonic component have different change rules in the five fault cases. The third harmonic component is used as the denominator, three spectrum coefficient could be obtained and be used as the fault characteristic. Through this scheme, using a single current sensor to detect the midpoint current can ensure open-circuit faults accurately. After the fault case is confirmed, the amplitude of the harmonic component is used to define a fault location threshold, and the threshold is numerically compared with the midpoint current to realize the faulty bridge arm location. Finally, the complete process of fault diagnosis and location is described, then the effectiveness of the proposed diagnosis method is validated by the simulation and experiment.

In a pandemic, in order to slow down the spread of the virus, protect national health, and maintain the normal operation of economic activities, countries around the world will formulate public policies to limit the number of citizens that can gather. Our research focuses on how to achieve optimal public policy under different conditions. Traditional SIR and SEIR models can well reflect the transmission process and obtain credible prediction results from a macro perspective, but lack the sensitivity of micro data, and cannot assess the risk of epidemic transmission brought by close contacts and sub-close contacts. Based on the Barabási-Albert scale-free network and the Random spanning tree algorithm, we generate a simulated spread network for non-specific infectious diseases. At the same time, we also generate group networks under different gather constraints. The superposition of the two forms a composite contact network. Our research work on the contact network shows that after considering close contacts and sub-close contacts, the public policy optimization problem of slowing the spread of the epidemic can be answered by the spectrum analysis of the contact network. We perform computer simulations and theoretical proofs of this model, and conduct a transmission analysis of its process.

This article proposes a hybrid analytical modeling procedure for the performance parameter estimation of synchronous reluctance machine, including its operation in the region of magnetic saturation. The proposed model better estimated the torque profile through improved translation and stepped transition of rotor flux barriers. The non-linear operation is simulated by considering the global saturation and estimating the machine’s operating point on the magnetization curve of the core material. The procedure formulation focuses on improving the accuracy and rapidness of the iterative design process. The obtained results are validated against an already established design analysis technique, finite-element analysis, and the frequency spectrum analysis of practical machine results. With the improved accuracy and the least requirement of simulation runtime and computational resources, the modeling procedure can be utilized as an initial design iteration tool. [ABSTRACT FROM AUTHOR]

As an essential reprocessing method, dimensionality reduction (DR) can reduce the data redundancy and improve the performance of hyperspectral image (HSI) classification. A novel unsupervised DR framework with feature interpretability, which integrates both band selection (BS) and spatial noise reduction method, is proposed to extract low-dimensional spectral–spatial features of HSI. We proposed a new neighborhood grouping normalized matched filter (NGNMF) for BS, which can reduce the data dimension while preserving the corresponding spectral information. An enhanced 2-D singular spectrum analysis (E2DSSA) method is also proposed to extract the spatial context and structural information from each selected band, aiming to decrease the intraclass variability and reduce the effect of noise in the spatial domain. The support vector machine (SVM) classifier is used to evaluate the effectiveness of the extracted spectral–spatial low-dimensional features. Experimental results on three publicly available HSI datasets have fully demonstrated the efficacy of the proposed NGNMF-E2DSSA method, which has surpassed a number of state-of-the-art DR methods. [ABSTRACT FROM AUTHOR]

SO2F2 and SOF2 are important characteristic decomposition components of SF6. Accurate detection of these two gases is helpful for fault diagnosis of gas-insulated switchgear (GIS). In this article, the infrared characteristic peaks of SO2F2 and SOF2 were obtained by comparing the simulation and experimental infrared spectrum. Then, cross interference analysis was performed with SF6 characteristic components such as H2S, SO2, CO, and SF6 background gas to determine the appropriate detection bands for SO2F2 and SOF2. Tunable diode laser absorption spectroscopy (TDLAS) experiment platform was built based on quantum cascade laser with center wavelength at 1506.4 and 1340.2 cm−1. The concentration of SO2F2 and SOF2 is detected through this platform. And the concentration information of gas is deduced by detecting the voltage value of second harmonic signal. The experimental results show that, for SO2F2 gas between 1.01 and 19.88 ppm, the $R^{2}$ is 0.989 and the maximum relative error is 2.044%. For SOF2 gas between 1.03 and 9.00 ppm, the ${R}^{{2}}$ is 0.992 and the maximum relative error is 0.728%. SO2 gas may cause weak interference to SOF2 detection, which has some regularities, and the interference can be eliminated by selecting the appropriate SOF2 fitting linear equation. The research results provide a feasible scheme for the quantitative detection of SO2F2 and SOF2 gases. [ABSTRACT FROM AUTHOR]

Online detection of rotor and load faults in permanent magnet synchronous motors (PMSM) based on spectrum analysis of current or vibration is not capable of identifying the root cause of the fault, since all faults produce identical fault signatures. The sensitivity of fault detection also depends on the motor and controller design making fault detection unreliable. In addition, operation under variable frequency and load limits the effectiveness of spectrum analysis based methods. In this article, airgap flux monitoring is investigated as an alternative for providing reliable identification of rotor and load faults in PMSMs. Based on the analysis of airgap flux under partial and uniform demagnetization, dynamic eccentricity, and load unbalance, an airgap search coil voltage based method for detection and classification of the faults is proposed. The claims made in the article are verified through experimental testing on an IPMSM under emulated fault conditions along with a comparison to vibration and current spectra-based detection. It is shown that the proposed method provides reliable online identification of the faults for cases where conventional spectrum analysis based methods fail. [ABSTRACT FROM AUTHOR]

It is well known that cognitive radio (CR) techniques have great potential to deal with the problem of radio spectrum scarcity. Spectrum sensing technique plays a critical role in enabling unlicensed secondary users (SUs) to utilize spectrum holes in cognitive radio networks (CRNs). However, a licensed primary user (PU) can be appropriately protected by simultaneously performing spectrum sensing and data transmission i.e., by using full-duplex (FD) mode. In this paper, we have proposed and analyzed a strategy which includes spectrum sensing and spectrum access mechanisms both. We consider heterogeneous traffic of real-time and non real-time SUs based on their different delay tolerance characteristics. We address the issue of false alarm rate (FAR) associated with FD sensing. Spectrum handoff and call buffering strategies with $p$ p -Retry policy are employed jointly so that SUs that would otherwise be blocked or forcibly dropped could be buffered and possibly served later. To evaluate the performance of the proposed strategy, five-dimensional continuous time Markov chain (CTMC) model is developed and the queueing-theoretic approach is utilized. Numerical results demonstrate the influence of spectrum sensing errors on the performance of such CRNs. Results also reveal that the provision of buffers under retrial policy increases the overall network resource utilization while decreasing blocking and dropping probabilities. [ABSTRACT FROM AUTHOR]

A robust vector multichannel singular spectrum analysis (MSSA) algorithm for denoising seismic data is introduced, and the initial results of applying this algorithm to denoise synthetic and real seismic data records are presented. In particular, the MSSA algorithm, originally applied to denoise scalar seismic wavefields, is generalized to denoise vector seismic wave fields. We also introduce a robust rank reduction algorithm within the MSSA denoising algorithm which attenuates erratic signals in the input wavefield without distorting the output. [ABSTRACT FROM AUTHOR]

Due to the absence of the natural current zero-crossing point, the dc interruption has been a critical issue in the dc system. The interruption schemes currently used are based on power electronics or precharged capacitors, leading to the high cost and complicated control strategy. In this article, a passive oscillation dc load current breaking scheme based on a vacuum interrupter is proposed. The external transverse magnetic field (ETMF) is applied to the vacuum interrupter to excite the oscillation of the arc voltage. According to the spectrum analysis of the arc voltage, a passive LC path is designed to realize resonant oscillation with the interrupter, by which 2 kA/10 kV interruption is realized within 2 ms. The influence of the gap separation, ETMF intensity, and arcing energy on the resonant oscillation process is investigated. At last, the practicability of applying the proposed scheme in HVdc interruption is discussed. [ABSTRACT FROM AUTHOR]

Accurately obtaining the spectrum characteristic parameters of switching devices is very significant for reduced electromagnetic interference (EMI) generation on the device switching process. This article proposed the universal analytical waveform (UAW) to equal the interference source in the device switching process, and analyzed its time and frequency-domain characteristic parameters. First, the time-domain equivalent method of UAW is proposed, and all the parameters of UAW are calculated with the switching process of the device. Then, the frequency-domain characteristic parameters of UAW are mathematically derived based on Fourier transform, and obtained the spectrum envelope functions. Subsequently, the experiments of Si IGBT and SiC mosfet have been performed to verify the correctness of the UAW, which provides the interference source for EMI prediction of the system where the device is located. Finally, taking SiC mosfet as an example, specific solutions to reduce the interference source are discussed, which can be used to diminish the EMI generation of the system where the device is located. [ABSTRACT FROM AUTHOR]

Based on spectrum analysis technology, a fast demodulation method is proposed to sense the acoustic and temperature with EFPI-FBG hybrid optical microphone accurately. Separating the EFPI and FBG spectrum from the obtained EFPI-FBG superimposed spectrum, frequency estimation based on total phase method and peak-searching based on quadratic polynomial fitting method were adopted to calculate the F-P cavity’s length and the central wavelength of the FBG. The detailed simulation showed that the proposed method has the capabilities to detect the change of the F-P cavity’s length and the drift of the FBG’s central wavelength with a high speed and accuracy. By applying the proposed method to a homemade diaphragm-based EFPI-FBG hybrid optical microphone, the acoustic and temperature could be recovered successfully. The acoustic sensing error caused by the temperature could be reduced from 75% to 10%. [ABSTRACT FROM AUTHOR]

Aiming at the technical difficulty of marine radar to detect small targets embedded in the sea clutter, this article proposed a three-feature fusion detection method based on singular spectrum analysis. First, considering that the number of coherent pulses used by radar in scanning mode is usually small (64 or less), this method combines the application of radar historical scan data and current frame data, transfers the feature extraction method from intraframe to interframe, and extracts three features that consist of cumulative major singular value (CMSV), linear degree of second singular vector (LDSSV), and linear degree of third singular vector (LDTSV) from singular space of the cell under test (CUT). Second, in view of the unideal distribution of sea clutter samples, a 3-D concave hull learning algorithm based on the geometry shape of sea clutter samples under the framework of anomaly detection is developed by improving the original convex hull algorithm, and target detection is realized in feature space using this algorithm. Under the same parameter condition, the measured CSIR data verify the two following points: first, the performance of detector using concave hull learning algorithm is better than that of convex hull learning algorithm; second, the detection performance of the proposed detector is obviously better than that of tri-time–frequency (TF)-feature detector, trifeature-based detector, consistency factor detector, and fractal-based detector. [ABSTRACT FROM AUTHOR]

Vibration signals always contain noise and irregularities, which makes spectrum analysis difficult to extract high-level features. Recently, graph theory has been applied to spectrum analysis to improve the performance of feature extraction. By converting the raw data into graphs, hidden structural and topological information can be obtained. In this article, a spatial-temporal graph-based feature extraction, called SuperGraph, for rotating machinery fault diagnosis is proposed. Specifically, graph theory-based spectrum analysis is used to construct the spatial-temporal graph. Then, the Laplacian matrix-based feature vector is extracted from the constructed spatial-temporal graph. By this means, the spatial-temporal graph is converted into the one-dimensional (1-D) vector for further constructing SuperGraph, where each node of the SuperGraph represents a spatial-temporal graph and the SuperGraph is composed of many local graphs. In the local graph, only the same type of nodes are connected to form a fully connected graph. Thus, the task of graph classification can be transformed into classifying the nodes in the SuperGraph. After graph convolutional network is established for learning and obtaining deep features, the label of nodes is identified from a $softmax$ model. Experiments are conducted on two benchmarking datasets and a practical experimental platform to verify effectiveness of the proposed fault diagnosis method. [ABSTRACT FROM AUTHOR]

5-D data is the original recorded form in the 3-D seismic acquisition, which includes sufficient information from all five dimensions. However, environmental and economic logistic difficulties often severely impact the data acquisition geometry, leading to raw data with missing traces and strong contaminating random noise. This deficiency often causes troubles in subsequent processing. Thus, an efficient interpolation and denoising method is required to recover useful signals. Unfortunately, practical applications of many existing reconstruction algorithms are limited by their intensive computational cost when applied to the 5-D data. Additionally, the stability of these algorithms is also challenged by complex geological structures, which often degrades the reconstruction performance. To seek solutions to the aforementioned problems, we design a simple and effective framework for fast reconstruction and denoising of undersampled 5-D seismic data via a two-step process. First, we prepare the initial model from the original recordings by constructing a 3-D gather at each common offset point. This step effectively interpolates the missing traces in 3-D common offset gathers by exploiting the data coherency in the adjacent areas (i.e., nearby mid-points). In the second step, the processed 5-D data is reorganized into 3-D common midpoint gathers, with each of them further sorted into a 2-D section according to absolute offset values. Then a conventional 2-D processing algorithm (e.g., F-X prediction, wavelet thresholding, or multichannel singular spectrum analysis) is invoked to filter the obtained 2-D section. The proposed workflow has a low overall computational cost and preserves signal fidelity. We use this framework to simultaneously denoise and interpolate the low-quality and extremely sparse seismic data. The synthetic and field examples both demonstrate the superb performance of the proposed framework in comparison with conventional methods. [ABSTRACT FROM AUTHOR]

Compared with traditional onsite wind turbine condition monitoring systems (CMSs), the remote CMSs can use better computational resources to process data with more advanced algorithms and, thus, can provide more advanced condition monitoring capabilities, but may suffer from a data loss problem, especially when wireless data transmission is used. To solve this problem, this article proposes a compressive sensing-based missing-data-tolerant fault detection method for remote condition monitoring of wind turbines. First, the condition monitoring signals collected from wind turbines are conditioned to increase their sparsity. Then, a compressive-sensing-based sampling algorithm is designed to sample the conditioned signals. The resulting data samples, called measurements of the conditioned signals are transmitted wirelessly during which some data samples are possibly lost. At the data receiving end, the conditioned signals are reconstructed from the received data samples, which might be incomplete, via a compressive-sensing-based signal reconstruction algorithm. Finally, spectrum analysis is performed on the reconstructed signals for wind turbine fault detection via fault characteristic frequency identification. The proposed method is validated for bearing fault detection of a Skystream 3.7 wind turbine and an Air Breeze wind turbine by using the data of a generator current signal collected from each wind turbine remotely while considering different data loss rates. [ABSTRACT FROM AUTHOR]

Pipeline transportation systems play an increasingly essential role in the development of the economy. An accurate location of leakage is important to reduce economic losses and maintain the safe operation of pipelines. In this article, a pipeline leakage detection method based on infrasound and negative pressure waves (NPWs) is adopted. This method can locate the leakage only by collecting the infrasound and pressure signals at the first station of the pipeline, which effectively solves the disadvantages of low positioning accuracy of a signal and easily produces false alarms. In this article, the signal denoising method of cross-spectral analysis combined with ensemble empirical mode decomposition (EEMD) was adopted. First, the leakage signals of sound waves and NPWs are processed by EEMD to obtain a series of intrinsic mode functions (IMFs). The characteristic frequency band is obtained by cross-spectral analysis of the leakage signal. Within this frequency band, the dominant component of the leakage source signal is divided by correlation analysis, and the IMF component is reconstructed to complete the signal denoising. When compared to the single signal pipeline leak detection method, the positioning accuracy of this article showed a 1–3 m improvement on average. [ABSTRACT FROM AUTHOR]

We solve the simultaneous source separation problem by adopting the projected gradient descent (PGD) method to iteratively estimate the data one would acquire via a conventional seismic acquisition. The projection operator is a windowed robust singular spectrum analysis (SSA) filter that suppresses source interferences in the $f-x$ (frequency-space) domain. We reformulate the SSA filter as a robust optimization problem solved via a bifactored gradient descent (BFGD) algorithm. Robustness becomes achievable by adopting Tukey’s biweight loss function for the design of the robust SSA filter. The SSA filter requires breaking down common-receiver gathers or common offset gathers into small overlapping windows. The traditional SSA method needs the filter rank as an input parameter, which can vary from window to window. The latter has been a shortcoming for the application of classical SSA filtering to complex seismic data processing. The proposed robust SSA filter is less sensitive to rank-selection, making it appealing for deblending applications that require windowing. Additionally, the robust SSA projection provides an effective attenuation of random source interferences during the initial iterations of the PGD method. Comparing classical and robust SSA filters, we also report an acceleration of the PGD method convergence when we adopt the robust SSA filter. Finally, we provide synthetic and real data examples, and discuss heuristic strategies for parameter selection. [ABSTRACT FROM AUTHOR]

Steady-state visual evoked potential (SSVEP) is widely used in brain computer interface (BCI), medical detection, and neuroscience, so there is significant interest in enhancing SSVEP features via signal processing for better performance. In this study, an image processing method was combined with brain signal analysis and a sharpening filter was used to extract image details and features for the enhancement of SSVEP features. The results demonstrated that sharpening filter could eliminate the SSVEP signal trend term and suppress its low-frequency component. Meanwhile, sharpening filter effectively enhanced the signal-to-noise ratios (SNRs) of the single-channel and multi-channel fused signals. Image sharpening filter also significantly improved the recognition accuracy of canonical correlation analysis (CCA), filter bank canonical correlation analysis (FBCCA), and task-related component analysis (TRCA). The tools developed here effectively enhanced the SSVEP signal features, suggesting that image processing methods can be considered for improved brain signal analysis. [ABSTRACT FROM AUTHOR]

Hyperspectral image super-resolution (SR) methods based on deep learning have achieved significant progress recently. However, previous methods lack the joint analysis between spectrum and horizontal or vertical direction. Besides, when both 2D and 3D convolution are in the network, the existing models cannot effectively combine the two. To address these issues, in this article, we propose a novel hyperspectral image SR method by exploring the relationship between 2D/3D convolution (ERCSR). Our method alternately employs 2D and 3D units to solve the problem of structural redundancy by sharing spatial information during reconstruction for existing model, which can enhance the learning ability of 2D spatial domain. Importantly, compared with the network using 3D units, i.e., 2D units are replaced by 3D units, it can not only reduce the size of the model but also improve the performance of the model. Furthermore, to exploit the spectrum fully, the split adjacent spatial and spectral convolution (SAEC) is designed to parallelly explore information between spectrum and horizontal or vertical direction in space. Experiments on widely used benchmark datasets demonstrate that the proposed approach outperforms state-of-the-art SR algorithms across different scales in terms of quantitative and qualitative analysis. [ABSTRACT FROM AUTHOR]

Muscle fatigue is required to be assessed in real-time to maintain the best physical condition, especially for sports and rehabilitation areas. In recent years, numerous studies proposed muscle fatigue estimation methods with non-invasive surface electromyography (sEMG). However, the previous approaches were limited to discerning whether muscle fatigue occurs and were unable to quantify the fatigue level due to individual differences in muscle characteristics. In this study, we propose a novel method for quantitative muscle fatigue estimation that is applicable for various people without individual calibration. Because muscle mass is closely related to muscular endurance, it is utilized as a standard parameter in our assessment process. We introduce a new concept of muscle fatigue score (MFS), based on the cosine similarity between muscle mass and representative fatigue indicators. The MFS exhibits a high correlation coefficient (|R| = 0.7398) with key muscle characteristics compared to previous representative muscle fatigue indicators calculated from sEMG: mean frequency (|R| = 0.2848), median frequency (|R| = 0.1972), and low-frequency ratio (|R| = 0.0346).

An analog, low-profile and shielded spectrum analyzer is proposed for operation at mm-wave frequencies around the 60 GHz band based on a novel side-fire leaky-wave antenna (LWA) configuration. The proposed side-fire periodic LWA is systematically developed from a conventional three-port waveguide T-junction which is modified to a LWA unit cell with an internal matching mechanism to suppress the stopband and enable broadside radiation based on unit cell symmetry considerations. The resulting periodic side-fire antenna radiates in the plane of the antenna, whereby the leakage power can either be allowed to radiate in free-space or kept confined inside a parallel-plate waveguide (PPW) structure. The proposed side-fire structure can therefore be completely shielded, making it useful as a broadband spectrum analyzer that is compatible with substrate-integrated waveguide (SIW) technology. Furthermore, a convex side-fire antenna is demonstrated to focus the radiated beams in the near-field of the structure to make the entire system compact. The integrated spectrum analyzer is experimentally demonstrated between 59 and 66 GHz providing 1 GHz frequency resolution. Furthermore, a simple mathematical model consisting of an array of line sources is used to efficiently model the beam-scanning characteristics of the curved side-fire LWA in the near-field of the structure. [ABSTRACT FROM AUTHOR]

Featuring unprecedentedly high data rates and high connection densities, 3GPP New Radio (NR) vehicle-to-everything (V2X) communications have been regarded as the most promising remedy to support the next generation driving use cases. For this purpose, although dedicated bands on 5.9 GHz spectrum have been allocated for Intelligent Transportation Systems (ITS), the limited bandwidth may not afford the desired service requirements. This engineering concern thus drives the utilization of unlicensed bands on 5 GHz spectrum adjacent to the ITS bands. However, subject to the fairness constraint in sharing unlicensed spectrum, various communication regulations have been imposed, and four different types of multi-band channel access procedures are consequently developed both in 3GPP LTE-A Licensed-Assisted Access (LAA) and NR on unlicensed spectrum (NR-U), to lead to a phenomenon that occupying more unlicensed bands may not suggest a proportional growth in the throughput. To empower high-throughout and high-density V2X communications, in this paper, we consequently propose the optimum channel access strategies for each V2X transmitter to decide the optimum number of unlicensed bands to access. To this end, analytic models for four types of multi-band channel access procedures in 3GPP NR-U are provided, which capture both inter-system and intra-system interference from coexisting WiFi transmitters and V2X transmitters. Subsequently, game-theoretically optimum strategies for each type of multi-band channel access procedures are derived. Performance evaluation results fully demonstrate the optimality of the proposed strategies, to serve the urgent demands in supporting the next generation driving use cases on unlicensed spectrum. [ABSTRACT FROM AUTHOR]

It is of great significance in managing human health, preventing and curing diseases such as heart disease to measure and monitor the physiological parameters accurately and robustly. However, imaging photoplethysmography (iPPG) can be easily affected by the ambient illumination variations or the subject's motions. In this paper, therefore, a novel framework of heart rate (HR) measurement robust to both illumination and motion artefacts is proposed, which combines the projection-plane-switching-based iPPG method (2PS) with the singular spectrum analysis (SSA). Based on the estimation of the head motion state, one reasonable projection plane is firstly determined, the temporally normalized red-green-blue signals are projected onto the plane and a pulse signal is obtained by alpha-tuning. After that, singular spectrum analysis (SSA) is applied to the obtained pulse signal and the normalized B-channel signal of the facial region of interest (ROI) to remove the artefacts remained in the pulse signal. For the self-collected database and the public PURE database, Bland-Altman plots show that the proposed 2PS-SSA has better agreement than the five compared methods, where the mean biases are 0.59 beat per minute (bpm) and 0.034 bpm, with 95% limits from −2.59 bpm to 3.78 bpm and from -1.97 bpm to 2.04 bpm, respectively. [ABSTRACT FROM AUTHOR]

This article presents a multistage singular spectrum analysis (SSA) method to denoise a signal efficiently and adaptively. The multistage SSA is to apply the basic SSA recursively toward the noisy signal. Therefore, the noise-dominated components will be extracted out from the noisy signal stage by stage. However, the denoised signal is still noisy if the number of stages is small. On the other hand, after all the noise-dominated components have been extracted out by enough number of stages, some signal-dominated components begin to be moved into the noise group as the multistage SSA-based denoising proceeds. An adaptive criterion based on the correlation measure is proposed to terminate the multistage SSA-based denoising algorithm to prevent moving the signal-dominated components into the noise group. The multistage SSA-based denoising algorithm is efficient since the window length is selected as a small positive integer in every stage. Also, the adaptive stop criterion makes the proposed method avoid inappropriate parameter selection effectively. Computer numerical simulation results show that the proposed method has better denoising performance toward different test signals under different noise levels and needs less processing time when compared with the conventional SSA denoising method. [ABSTRACT FROM AUTHOR]

This article provides a novel approach for discriminating the interturn short circuit (ITSC) fault in a salient pole synchronous generator (SPSG) based on the use of noninvasive sensors. A stray magnetic field is used as a signal that can provide valuable data regarding the health condition of the SPSG. Generally, the captured signal under an ITSC fault is examined using a classical signal processing tool based on fast Fourier transforms (FFTs). The amplitude of the side-bands to the frequency spectrum is increased by ITSC, eccentricity, and broken damper-bar faults. Therefore, determining the type of fault is not possible by analyzing the FFT side-bands. A unique feature is introduced using a short-time Fourier transform to identify the ITSC in its early stage. The proposed feature can locate and recognize the ITSC fault with high accuracy in either the no-load or the full-load operation of the SPSG. This novel methodology is verified by applying an ITSC fault to a 100 $kVA$ custom-made SPSG. [ABSTRACT FROM AUTHOR]

The resonant controller (RC), as a promising candidate for high-speed nonraster nanopositioning applications, can track the sinusoidal reference with zero steady-state error. This article presents a controller composed of several RCs in parallel for tracking nonraster sequential scanning trajectories. The selection for each RC in the parallel array is based on two considerations: one is the spectrum of the reference signal and the other is the harmonics caused by the nonlinearities of the nanopositioning stage. The performance of RC is highly dependent on the accurate placement of the resonant poles, but unfortunately, many existing digital implementation methods could cause a deviation of the resonant poles from their initial locations. To address this problem, a modified Tustin (MTus) method is proposed in this article to implement the controller with better accuracy. Furthermore, the fractional-order (FO) calculus is introduced to improve the transient performance of the RCs. To validate the proposed methods, a comprehensive examination of several types of the nonraster sequential scanning trajectories with a wide frequency range has been carried out on a nanopositioning stage. The results have been compared with other methods, showing that the tracking errors are reduced significantly under the controller implemented by the MTus method especially in high-frequency conditions and that the application of the FO calculus reduces the settling time of the controller by more than 30% in most cases. Note to Practitioners—The demand for high-speed atomic force microscope (AFM) increases rapidly. However, the commonly used raster trajectory limits the achievable scan speed of the AFM. An effective way to improve the scanning and imaging speed of the AFM is the application of the sequential nonraster scanning methods. The trajectories of sequential nonraster scanning patterns mainly composed of few sinusoid signals with different frequencies. Therefore, the resonant controller (RC) is introduced in this article as it is capable to track the sinusoidal reference with zero steady-state error. Several RCs are selected first based on the spectrum analysis of the reference and the consideration of the harmonics caused by the system nonlinearities, and then, they are connected in parallel to form the controller for precise tracking of the reference. In order to realize the digital implementation of the designed controller, a modified Tustin discretization method is proposed, which ensures the accurate resonant pole placement of the RC and thus maintains the tracking performance of the RC. In addition, the fractional-order (FO) calculus is introduced to speed up the convergence of the designed controller while preserving the tracking accuracy, and this parallel-structure FO RC (PSFORC) design can be implemented to other systems that require high-speed and high-accuracy tracking of the periodic signals. [ABSTRACT FROM AUTHOR]

Batteryless, wireless, and packageless acoustic wave sensors are particularly desirable for harsh high-temperature environments. In this letter, an acoustic wave sensor based on a lithium niobate (Y + 128° cut, abbreviated LN-Y128) substrate with a buried platinum interdigital transducer (IDT) in an aluminum nitride (AlN) overlayer is investigated. Previously, it was demonstrated theoretically that due to the specific properties of LN-Y128, Rayleigh-type guided waves can propagate at the AlN/IDT(Pt)/LN-Y128 interface. Here, this structure is, for the first time, studied experimentally, including the growth and properties of the AlN layer onto irregular platinum IDTs. Both Shear Horizontal and Rayleigh-type waves have been identified after the AlN deposition and the velocities are consistent with the fitted SDA-FEM-SDA (a combination of finite element modeling with spectral domain analysis) simulations. Electrical measurements with a surface perturbation and temperature measurements show that the AlN/IDT(Pt)/LN-Y128 bilayer structure is promising as a packageless high-temperature sensor. [ABSTRACT FROM AUTHOR]

This work assessed the possible correlation between the refractive index of a SiOxNy passivation film on a surface acoustic wave (SAW) device and the temperature coefficient of frequency (TCF) of the device itself. The data demonstrate that the refractive index does correlate with the TCF as well as the frequency of the one-port resonator. SiOxNy passivation films having an optimal refractive index can potentially suppress the frequency shifts caused by the deposition of such layers, and can change the TCF from that for a Si3N4 film to that for SiO2. The results also show that the coupling coefficient of the one-port resonator increases when using a SiOxNy film with a lower refractive index, which changes the TCF such that this value approaches that for a SiO2 film. Finite-element method spectral domain analyses established that the frequency responses of the one-port resonators were affected by the velocity and temperature coefficient of velocity of the dielectric films deposited on the interdigital transducer electrodes. Thus, adjusting the refractive index of the SiOxNy film can be used to control the properties of an SAW device, including the TCF. [ABSTRACT FROM AUTHOR]

Acoustic emission signal processing has always been a key problem in damage assessment of components and materials, and parameter analysis and waveform analysis are two important means of acoustic emission signal processing. In this paper, the parameter analysis method in signal processing and the meaning and characteristics of each parameter are introduced. Then, the waveform analysis method is introduced and the basic theory of wavelet transform and its spectrum analysis in waveform analysis method are mainly analyzed. The comparison between wavelet transform and Fourier transform is made: traditional Fourier transform signal processing methods can only be used to process transient stationary signals, and the transient non-stationary signals cannot be analyzed locally. The wavelet transform can take into account the characteristics of both the time and frequency domains, and it can also reflect the local signal well. Through the analysis of the signals in the process of concrete damage, it is found that the two signal processing methods mentioned above can well characterize the damage evolution of materials and damage precursors.

We present an investigation on the intrinsic optical properties of superconducting FeSe thin films deposited on CaF2 substrates. It is found that the samples respond strongly to the near-infrared (NIR) irradiation in different temperature regimes. Thus, we are able to measure the optical reflectivity of the in-plane (ab-plane) thin films from room temperature to 5 K. A critical behavior near the superconducting transition temperature (Tc~10 K) can be observed optically in the temperature-dependent reflectivity spectrum. We carry out the Kramers-Kronig analysis of the experimental data and discuss the basic optical properties of superconducting FeSe thin films in different structural phases above and below the transition temperature Ts~100 K. By fitting experimental data with the Drude-Smith and Drude-Lorentz formulas, we obtain the key sample parameters such as the electronic relaxation time and dc conductivity. The interesting and important findings obtained from this study not only shed new light on the pairing mechanism in FeSe but also demonstrate that the NIR reflection experiment is a powerful and convenient optical technique for contactless characterization and investigation of superconducting thin film materials.

Subsynchronous oscillation (SSO) phenomena caused by interactions between controllers of Doubly-fed induction generators (DFIGs) and series capacitor compensators has been well recognized. However, SSO can also happen on a DFIG-based wind farm without series compensation as reported in a recent real event. This paper firstly analyzes the recorded voltage waveform during this SSO event. Then an equivalent model of the system including the wind farm, static synchronous compensator (STATCOM) and the power grid was established. The paper derives the subsynchronous frequency response of the DFIG converter together with the STATCOM in detail and gives the criterion for SSO to occur, which is verified by both time domain simulation and spectrum analysis. The methodology adopted in this paper can be generalized to analyze the causes of a type of SSO that involves DFIGs and STATCOM but not series compensation. The paper also provides practical suggestions on adjustments of control parameters with the DFIG and STATCOM to prevent this type of SSO.

This article suggests that the correlation information, hidden in spatial configuration and temporal dynamic of frequencies, is an important indication for bearing health condition. To consider this information, we extend graph-modeling strategy, and introduce a bearing fault detection and diagnosis technique based on temporal-spatio graph. First, short-time periodogram is extracted from vibration signal, and, then, modeled by a temporal-spatio graph. In fault detection phase, the spectrum of temporal channel graph is used to map short-time periodogram to acquire the so-called graph-mapped spectrum (GMS). The principal frequency in resulting GMS is found highly related with the health condition of monitored bearing. Thus, any change of health condition can be detected by checking this principal frequency over time. Once a fault is detected, the spatio channel graph is fed to K-nearest neighbor classifier, coupled with a specific graph distance metric, for fault type identification. Comprehensive experiments on two benchmarking datasets along with theoretical interpretation demonstrate the superiority of proposed method over state of the arts. The proposed temporal-spatio graph provides a significant extension of existing spectrum analysis for fault detection and diagnosis. [ABSTRACT FROM AUTHOR]

Due to increasing demands for spectral resources in both communication and radar systems, the radio frequency electromagnetic spectrum is becoming more and more crowded with interfering nuisances. In order to tackle the scarcity of available spectral intervals, in recent years a multitude of sensing algorithms have been developed for improving spectrum sharing. Among these, two-dimensional (2-D) spectrum sensing can be used to obtain space–frequency electromagnetic spectrum awareness. Specifically, this approach makes it possible to optimize the spectrum usage of certain spectrum portions whose occupancy varies both temporally and spatially. In this article, we evaluate the effectiveness of certain space–frequency map recovery algorithms relying on the use of commercially available hardware. To this end, we employ an inexpensive four-channel coherent receiver, using software-defined radio components, for emitter localization. Hence, after proper calibration of the receiving system, the acquired samples are used to evaluate the performance of different signal processing strategies which exploits the inherent block-sparsity of the overall profile. At the analysis stage, results reveal the effectiveness of such algorithms. [ABSTRACT FROM AUTHOR]

We propose a high-resolution compact spectrometer based on a multimode waveguide (MMW) integrated with a planar photonic lantern (PL). Four modes were launched into the MMW via asymmetric directional couplers (ADCs) and individually switched by four modulators. The mode field of the MMW was spatially sampled by four separate detectors. Measurement of the 64 possible states (16 switch states $\times $ 4 modes) allowed the spectrum of the optical signal to be recovered. Experimentally, we demonstrated spectral correlation width of 0.16-nm in the measurement at 1560 nm using a MMW with 0.5 mm length for both sparse and continuous spectra retrieval. To scale up, the spectrometer resolution can be estimated by $\delta ~\lambda = \lambda ^{\mathbf {2}} / \Delta ~{n} _{\mathbf {eff}}~{L}$ while the operational bandwidth can be estimated to be $2^{\mathbf {N}} \times {N} \times \delta \,\,\lambda $ , with ${N}$ denoting the number of modes supported in the whole system. This work may lead to ultra-compact and high-resolution spectrometer systems. [ABSTRACT FROM AUTHOR]

Cutting chatter is violent self-excited vibration between tool and work piece, which will seriously degrade the machining surface quality and health of machine tool. Online monitoring of chatter phenomenon can feed back the control system to adjust processing parameters dynamically for chatter suppression. In this article, a short-time difference spectrum analysis (STDSA) is proposed for early chatter identification, which can recognize chatter frequency and track its amplitude from low SNR vibration signal at early stage. The method is verified by both simulated vibration signals and machining vibration signals collected from an axle lathe. A comparison between classic variational mode decomposition (VMD) and STDSA is given to illustrate its performance. The results indicate that the method is robust to noise and has a little warning time delay, which makes it suitable for the online monitoring of cutting chatter in an industrial production environment. [ABSTRACT FROM AUTHOR]

Synchrosqueezing transform (SST) is an effective time–frequency analysis (TFA) approach for the processing of nonstationary signals. The SST shows a satisfactory ability of the TF localization of the nonlinear signal with a slowly time-varying instantaneous frequency (IF). However, for the signal of which ridge curves in the TF domain are fast varying, or even almost parallel to the frequency axis, the SST will provide a blurred TF representation (TFR). To solve this issue, the transient-extracting transform (TET) was recently put forward. The TET can effectively characterize and extract transient features in the much concentrated TFR for the strongly frequency-modulated (FM) signal, especially the impulse-like signal. However, contrary to the SST, it is not suitable for weak FM modes. In this study, we propose a TFA method called the time-synchroextracting general chirplet transform (TEGCT). The TEGCT can achieve a highly concentrated TFR for strong FM signals as well as weak FM ones. Quantized indicators, the concentration measurement and the peak signal-to-noise ratio, are used to analyze the performances of the proposed method compared with those of other methods. The comparisons show that the TEGCT can provide a result with better TF localization. Then, the proposed method was applied to the spectrum analysis of the seismic data for oil reservoir characteristics. The horizontal slices of the offshore 3-D seismic data show that the TEGCT delineates more distinct and continuous subsurface channels in a fluvial-delta deposition system. All the results illustrate that our proposed method is a good potential tool for seismic processing and interpretation in the geoscience. [ABSTRACT FROM AUTHOR]

The application of ultrasound imaging to the diagnosis of lung diseases is nowadays receiving growing interest. However, lung ultrasound (LUS) is mainly limited to the analysis of imaging artifacts, such as B-lines, which correlate with a wide variety of diseases. Therefore, the results of LUS investigations remain qualitative and subjective, and specificity is obviously suboptimal. Focusing on the development of a quantitative method dedicated to the lung, in this work, we present the first clinical results obtained with quantitative LUS spectroscopy when applied to the differentiation of pulmonary fibrosis. A previously developed specific multifrequency ultrasound imaging technique was utilized to acquire ultrasound images from 26 selected patients. The multifrequency imaging technique was implemented on the ULtrasound Advanced Open Platform (ULA-OP) platform and an LA533 (Esaote, Florence, Italy) linear-array probe was utilized. RF data obtained at different imaging frequencies (3, 4, 5, and 6 MHz) were acquired and processed in order to characterize B-lines based on their frequency content. In particular, B-line native frequencies (the frequency at which a B-line exhibits the highest intensity) and bandwidth (the range of frequencies over which a B-line shows intensities within −6 dB from its highest intensity), as well as B-line intensity, were analyzed. The results show how the analysis of these features allows (in this group of patients) the differentiation of fibrosis with a sensitivity and specificity equal to 92% and 92%, respectively. These promising results strongly motivate toward the extension of the clinical study, aiming at analyzing a larger cohort of patients and including a broader range of pathologies. [ABSTRACT FROM AUTHOR]

This article presents the design, development, and characterization of a double-bridge electrostatically actuated micromirror for multiobject spectroscopy (MOS). The proposed structure is an improvement over single-bridge micromirrors in terms of aperture size, pull-in voltage, degrees of freedom, and fill factor. The two-axis symmetric rotation mechanism of the proposed micromirror is achieved by bending and twisting action of the suspended cantilevers which is contrary to the twisting cantilevers of a conventional one-axis torsional micromirror. The placement of anchor and cantilevers under the mirror plate results in a high fill factor when arranged in a 2-D array. The analytical modeling, design optimization, and static and dynamic analysis are done using finite-element method (FEM) in Coventorware. An optimized design is fabricated using a simple approach of surface micromachining and electroplating. For a micromirror of size 200μm × 200 μm and the actuation gap of 2.5~μm, the device exhibits a tilt angle of 1.5° at a pull-in voltage of 23.1 V, a switching time of 38~μm, and the resonance frequency of 35.23 kHz. A 3 × 3 array of the micromirror is demonstrated with a fill factor of more than 95%. The deflection range can be increased by simply increasing the thickness of the sacrificial layer and without any major process modification. [ABSTRACT FROM AUTHOR]

Cardiac Magnetic Resonance Imaging (MRI) is time-consuming and error-prone. To ease the patient’s burden and to increase the efficiency and robustness of cardiac exams, interest in methods based on continuous steady-state acquisition and self-gating has been growing in recent years. Self-gating methods extract the cardiac and respiratory signals from the measurement data and then retrospectively sort the data into cardiac and respiratory phases. Repeated breathholds and synchronization with the heart beat using some external device as required in conventional MRI are then not necessary. In this work, we introduce a novel self-gating method for radially acquired data based on a dimensionality reduction technique for time-series analysis (SSA-FARY). Building on Singular Spectrum Analysis, a zero-padded, time-delayed embedding of the auto-calibration data is analyzed using Principle Component Analysis. We demonstrate the basic functionality of SSA-FARY using numerical simulations and apply it to in-vivo cardiac radial single-slice bSSFP and Simultaneous Multi-Slice radiofrequency-spoiled gradient-echo measurements, as well as to Stack-of-Stars bSSFP measurements. SSA-FARY reliably detects the cardiac and respiratory motion and separates it from noise. We utilize the generated signals for high-dimensional image reconstruction using parallel imaging and compressed sensing with in-plane wavelet and (spatio-)temporal total-variation regularization. [ABSTRACT FROM AUTHOR]

The optoelectronic inertial stabilization platform is widely used in the fields of astronomical observation, monitoring and search, quantum communication and other fields, which all need strong vibration suppression and tracking capability. To realize high-precision angular measurement throughout the wide bandwidth for inertial stabilization platform, the fusion algorithm is studied to fuse the signals of magnetohydrodynamic (MHD) and microelectromechanical (MEMS) gyroscope. The basic adaptive Kalman filter experiences signal-to-noise ratio and fusion-frequency jitter problems because of the measurement transfer matrix deviation, making it unsuitable for quick dynamic systems. This paper proposes an adaptive Kalman filter enhanced with spectrum analysis by connecting the measurement covariance with the signal frequencies. This method divides the trace of the modified measurement covariance into three parts according to frequency domain characteristics. Based on the frequency analysis, the filter output mainly depended on the MEMS gyroscope at low frequencies, fusion results of the two gyroscopes in the intermediate frequency-domain, and the MHD gyroscope at high frequencies. This theory is verified in the swept frequency experiment and comparative experiments, including multi-harmonic sinusoidal and step response tests compared with combing filter and closed-loop filter. The tests results show that the fusion signal is identical with the actual signal throughout the measurement bandwidth, and the fusion signal-to-noise ratio is improved. The frequency characteristics and noise level of the fusion algorithm satisfy the requirements of optoelectronic inertial stabilization vibration measurement. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the practical implication of employing virtual massive multiple-input-multiple output (MIMO) based distributed decision fusion (DF) for collaborative wideband spectrum sensing (WSS) in a cognitive radio (CR)-like network. Towards that end, an indoor-only measurement campaign has been conducted to capture the propagation statistics of a $4 \times 64$ massive MIMO system with one authorized primary user (PU) and 4 unauthorized secondary users (SUs) transmitting simultaneously over a 20 MHz band divided into 1200 subcarriers. The frequency subcarriers belong to an Orthogonal-frequency-division-multiplexing (OFDM)-like set-up without the addition of cyclic prefix (CP) to the transmit symbols. Measurements are accumulated for different relative positions of the SUs which are analyzed to extract fading, shadowing, noise and interference power statistics. Log-likelihood ratio (LLR) based fusion rule and three different sets of sub-optimum fusion rules along with their time-reversed versions are formulated for combining decisions on the availability of each subcarrier transmitted by the SUs. The extracted channel characteristics are incorporated in both analytical and simulated performance analysis of the devised fusion rules for comparison and testing the validity of distributed DF in realistic collaborative WSS scenario. [ABSTRACT FROM AUTHOR]

One-bit radar, performing signal sampling and quantization by a 1-bit analog-to-digital converter, is a promising technology for many civilian applications due to its low-cost and low-power consumptions. In this article, problems encountered by a 1-bit linear-frequency-modulated continuous-wave (LFMCW) radar are studied, and a two-stage target detection method termed as the dimension-reduced generalized approximate message passing (DR-GAMP) approach is proposed. First, the spectrum of 1-bit quantized signals in a scenario with multiple targets is analyzed. It is indicated that high-order harmonics may result in false alarms and cannot be neglected. Second, based on the spectrum analysis, the DR-GAMP approach is proposed to carry out target detection. Specifically, linear preprocessing methods and target predetection are first adopted to perform the dimension reduction, and then, the generalized approximate message passing algorithm is utilized to suppress high-order harmonics and recover true targets. Finally, numerical simulations are conducted to evaluate the performance of the 1-bit LFMCW radar under typical parameters. It is shown that compared to the conventional radar applying linear processing methods, the 1-bit LFMCW radar has about 1.3-dB performance gain when the input signal-to-noise ratios of targets are low. In the presence of a strong target, it has about 1.0-dB performance loss. [ABSTRACT FROM AUTHOR]