Mixed-signal Research Articles

Joint short-time speaker recognition and tracking using sparsity-based source detection

A random finite set-based sequential Monte–Carlo tracking method is proposed to track multiple acoustic sources in indoor scenarios. The proposed method can improve tracking performance by introducing recognized speaker identities from the received signals. At the front-end, the degenerate unmixing estimation technique (DUET) is employed to separate the mixed signals, and the time delay of arrival (TDOA) is measured. In addition, a criterion to select the reliable microphone pair is designed to quickly obtain accurate speaker identities from the mixed signals, and the Gaussian mixture model universal background model (GMM-UBM) is employed to train the speaker model. In the tracking step, the update of the weight for each particle is derived after introducing the recognized speaker identities, which results in better association between the measurements and sources. Simulation results demonstrate that the proposed method can improve the accuracy of the filter states and discriminate the sources close to each other.

Recent IEEE SSCS Silicon Valley Chapter News: Seminars, Workshops, Distinguished Lectures, and Silicon Valley Engineering Hall of Fame Awards Ceremony Honoring Dr. Bill Dally, Nvidia and Stanford [Chapters

The IEEE Solid-State Circuits Society (SSCS) Santa Clara Valley (SCV) Chapter held several well-attended seminars, webinars, workshops, and Distinguished Lecturer (DL) talks over the past three years, throughout the pandemic, and recently attended the Silicon Valley Engineering Council (SVEC) Engineers Week Banquet and Hall of Fame Awards Ceremony, in February 2023, honoring Dr. Bill Dally, chief scientist, Nvidia, and adjunct professor, Stanford University, who is also well known in our SSCS community. This article highlights the wide variety of events covering topics from mixed-signal and analog circuits, high-speed links, machine learning acceleration, and the semiconductor industry. The speakers included professors and postdoctoral scholars from academia as well as start-up founders, technology leaders, and executives from industry. Please contact us if you are interested in presenting at our meetings, would like to attend Chapter events, or wish to volunteer. Full information on Chapter activities and upcoming seminars is available on the Chapter website: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://r6.ieee.org/scv-sscs/</uri> .

An Ultrahigh Voltage Shunt Reactor Acoustic Signal Separation Method Based on Masking Beamforming and Underdetermined Blind Source Separation

There are many types of acoustic sources in the ultrahigh voltage (UHV) substation. The acoustic signals of UHV shunt reactors obtained by direct measurement include many other phase reactor acoustic signals that are very similar to the subjected reactors in frequency domain characteristics, as well as cooling fans and random noise. An UHV shunt reactor acoustic signal separation method based on beamforming and undetermined blind source separation was proposed. In this method, the existing beamforming is improved to masking beamforming, which can separate those acoustic components differing from the spatial characteristics of the subjected reactor. Then, undetermined blind source separation was adopted to remove the acoustic components which are different from the frequency domain characteristics of the subjected reactor. Laboratory and substation tests were conducted, respectively. The results show that the method proposed in this research can obtain the acoustic signal with a power spectrum similarity of 0.924 from the original mixed acoustic signal with the power spectrum similarity of 0.617, which can verify the feasibility of the method.

SIGMA: Spectral Interpretation Using Gaussian Mixtures and Autoencoder

AbstractIdentification of unknown micro‐ and nano‐sized mineral phases is commonly achieved by analyzing chemical maps generated from hyperspectral imaging data sets, particularly scanning electron microscope—energy dispersive X‐ray spectroscopy (SEM‐EDS). However, the accuracy and reliability of mineral identification are often limited by subjective human interpretation, non‐ideal sample preparation, and the presence of mixed chemical signals generated within the electron‐beam interaction volume. Machine learning has emerged as a powerful tool to overcome these problems. Here, we propose a machine‐learning approach to identify unknown phases and unmix their overlapped chemical signals. This approach leverages the guidance of Gaussian mixture modeling clustering fitted on an informative latent space of pixel‐wise elemental data points modeled using a neural network autoencoder, and unmixes the overlapped chemical signals of phases using non‐negative matrix factorization. We evaluate the reliability and the accuracy of the new approach using two SEM‐EDS data sets: a synthetic mixture sample and a real‐world particulate matter sample. In the former, the proposed approach successfully identifies all major phases and extracts background‐subtracted single‐phase chemical signals. The unmixed chemical spectra show an average similarity of 83.0% with the ground truth spectra. In the second case, the approach demonstrates the ability to identify potentially magnetic Fe‐bearing particles and their background‐subtracted chemical signals. We demonstrate a flexible and adaptable approach that brings a significant improvement to mineralogical and chemical analysis in a fully automated manner. The proposed analysis process has been built into a user‐friendly Python code with a graphical user interface for ease of use by general users.

SGL-PCA: Health Index Construction With Sensor Sparsity and Temporal Monotonicity for Mixed High-Dimensional Signals

With advancements in sensor technology, high dimensional signals such as functional curves and images are typically collected from multiple sensors to characterize the degradation of a system. Data fusion methods are employed to integrate multisensor signals generated from the system into a scalar health index (HI) to understand the degradation status of the system. This paper develops sparse group LASSO-principal component analysis (SGL-PCA), a method that constructs HIs for image and profile data. First, we remove the smooth background from each sensor signal. Then, we solve the degradation patterns and the degradation paths through a rank-one matrix approximation problem, with the consideration of the sparsity of the measurements related to the degradation process and the monotonicity of the degradation paths. Results from a simulation study and a case study illustrate that the HI constructed by the proposed method outperforms the benchmark methods in identifying the measurements subject to the degradation process and predicting the remaining useful life of the system. Note to Practitioners—In practice, sensors generating multiple high-dimensional curves and images are often installed in systems to characterize their degradation status. Compared with scalar sensor signals, the information that associates with the degradation process often appears in sparse regions from the sensor signals. Therefore, identifying the degradation information accurately is important in the health index (HI) construction for degradation modeling and prognostic analysis. This article proposes a method that simultaneously selects the degradation information and estimates the optimal weights for integrating multi-sensor signals in constructing the HI. The proposed method is applicable in the case where the systems degrade under a single failure mode, and multiple sensors are used to monitor the degradation processes. Practitioners can implement our method to predict the remaining useful lives of in-service systems through three steps: (1) estimate the backgrounds of each sensor and derive data-fusion model using a historical dataset; (2) construct the HIs of in-service systems; (3) predict the remaining useful lives of these systems based on the developed HIs.

BUILT-IN SELF-TEST AND SELF-CORRECTION METHOD FOR MIXED-SIGNAL INTEGRATED CIRCUITS

In modern integrated circuits the number of devices have strongly increased. As a result, identifying the issues which have an impact on their performance stands out as a very complicated and challenging problem. In digital integrated circuits there are methods which are known, as well as some others which are in the active development stages targeted to enable the built-in self-tests, identify, and report those issues. At that, for mixed signal circuits, where the calculations or functions are performed based on the voltage levels, it is much complicated to develop self-testing mechanisms. A novel method of identifying the lack of clock signal and its duty cycle variation is proposed in this paper. The developed architectures and solutions are capable of detecting the lack of the clock signals, as well as informing the digital parts of the systems about the requirement for the coarse or fine tunings of the clock generation systems outside their autocalibration and loops.

The Design of An LDO Regulator

In today’s modern systems on chip (SOCs), a crucial power management circuit is the low-dropout (LDO) regulator. Of course, the need for supply voltage regulation, goes back many years in the past since the circuits have been designed. Today, LDO based voltage regulators are frequently used in a number of mixed-signal systems to produce local supply voltages that feed different building blocks. LDOs try to isolate the noise of the circuit and noise from the global supply and try to reduce their effect on device performance. For the state of desired achievement, each LDO’s architecture is circuited to the specific cell it feeds. An LDO designed to feed a flash analog-to-digital converter, for instance, differs greatly from one designed to input a VCO. In this paper, we direct an LDO for a VCO of 5-GHz LC and point the particulars of 1.2 V as input voltage, produces 1V as output voltage, 5 mA of maximum output current, power supply rejection greater than 40 dB up to 10 MHz and noise voltage present at output that is less than 25nV/√Hz at 1 MHz.

Suppressive Interference Suppression for Airborne SAR Using BSS for Singular Value and Eigenvalue Decomposition Based on Information Entropy

Suppressive interference is a common interference signal for synthetic aperture radar (SAR) that can seriously affect the target identification and imaging results of SAR. This paper proposes a method for suppressing suppressive jamming using blind source separation (BSS) for singular value and eigenvalue decomposition based on information entropy. First, we developed an airborne SAR imaging geometry model and a suppressive interference signal mixing model. Next, we perform blind signal separation of the interfered mixed signal by means of BSS based on singular value and eigenvalue decomposition. Then, we image the different signals we have extracted. Finally, we extract the features of the image domain for the separated signals and set the information entropy threshold by the difference of information entropy to identify the jamming signal and the source signal and obtain the source signal. This method uses eigenvalue and singular value decomposition for BSS and extracts the image domain features of the signal after BSS by information entropy and identifies the source signal by information entropy thresholding. This method compensates for the uncertainty in the decomposition of the signal by means of BSS. The signal loss is minimal and the similarity of the separated signal and the original signal is very high. Simulated and measured data demonstrate the feasibility of this algorithm.

High-resolution imaging and micro-motion feature extraction of space multiple targets

In radar applications, different micro-motion forms can be used as the basis of target recognition. However, the radar echo signal of multiple spatial targets is overlapping in the time-frequency and time-range domains, which increases the difficulty of micro-motion feature extraction. In this paper, a high-resolution imaging and micro-motion feature extraction framework based on a multiple joint-domain radar tool is proposed to address this mixed signal. First, an accurate and suitable micro-motion model of cone-shaped space multiple targets is built. Then, the two-dimensional adaptive regularized smoothed L0 norm algorithm (2D AReSL0) based on sparse reconstruction is formulated to directly reconstruct the inverse synthetic aperture radar (ISAR) image. Additionally, the range-frequency-time radar data cube can be extracted from the ISAR movie by the CLEAN algorithm. To solve the scattering point association problem in the radar data cube, a 3D segmentation Viterbi algorithm is designed to extract the micro-motion features. Finally, simulation and experiment results demonstrate the effectiveness of the proposed framework.

Diagnosis of Skin Vascular Complications Revealed by Time-Frequency Analysis and Laser Doppler Spectrum Decomposition.

Nowadays, photonics-based techniques are used extensively in various applications, including functional clinical diagnosis, progress monitoring in treatment, and provision of metrological control. In fact, in the frame of practical implementation of optical methods, such as laser Doppler flowmetry (LDF), the qualitative interpretation and quantitative assessment of the detected signal remains vital and urgently required. In the conventional LDF approach, the key measured parameters, index of microcirculation and perfusion rate, are proportional to an averaged concentration of red blood cells (RBC) and their average velocity within a diagnostic volume. These quantities compose mixed signals from different vascular beds with a range of blood flow velocities and are typically expressed in relative units. In the current paper we introduce a new signal processing approach for the decomposition of LDF power spectra in terms of ranging blood flow distribution by frequency series. The developed approach was validated in standard occlusion tests conducted on healthy volunteers, and applied to investigate the influence of local pressure rendered by a probe on the surface of the skin. Finally, in limited clinical trials, we demonstrate that the approach can significantly improve the diagnostic accuracy of detection of microvascular changes in the skin of the feet in patients with Diabetes Mellitus type 2, as well as age-specific changes. The results obtained show that the developed approach of LDF signal decomposition provides essential new information about blood flow and blood microcirculation and has great potential in the diagnosis of vascular complications associated with various diseases.

Four-wave mixing properties in bilayer graphene nanosystem

Graphene thin films are often used to manufacture various optoelectronic nanodevices owing to their advantages such as light weight, small size, high quality factor, and good conductivity. So far, there have been few studies of the four-wave mixing characteristics in a bilayer graphene nanosystem, especially theoretical research. In this work, we study theoretically the four-wave mixing properties in a bilayer graphene nanosystem. Firstly, the analytical formula of the four-wave mixing signal is derived by quantum mechanical method, which is divided into three steps. 1) Total Hamiltonian of the system is written in the rotating wave approximation. 2) By using the Heisenberg equation of motion and the commutation relations between different operators, the corresponding density matrix equations are obtained. 3) To solve these density matrix equations, we make an ansatz and obtain the analytical formula of the four-wave mixing signal. Secondly, we explore the dependence of the four-wave mixing signal on the phonon-exciton coupling strength, pumping intensity and the detuning between the exciton and the pump field. The calculated results show that the lineshape of four-wave mixing spectrum can be switched among two-peaked, three-peaked, four-peaked, five-peaked and six-peaked by adjusting the phonon-exciton coupling strength, the pumping intensity, and the detuning between the exciton and the pump field. In a weak phonon-exciton coupling regime (i.e. phonon-exciton coupling strength &lt;i&gt;g&lt;/i&gt; &lt; dephasing rate of exciton &lt;i&gt;Γ&lt;/i&gt;&lt;sub&gt;2&lt;/sub&gt;), the intensity of the left peak and right peak of four-wave mixing signal first increase and then decrease with the increase of the pumping intensity &lt;inline-formula&gt;&lt;tex-math id="M1"&gt;\begin{document}$ {\varOmega }_{{\text{pu}}}^{\text{2}} $\end{document}&lt;/tex-math&gt;&lt;alternatives&gt;&lt;graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="12-20230012_M1.jpg"/&gt;&lt;graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="12-20230012_M1.png"/&gt;&lt;/alternatives&gt;&lt;/inline-formula&gt;. In the intermediate and strong phonon-exciton coupling regime (i.e. &lt;i&gt;g&lt;/i&gt; = &lt;i&gt;Γ&lt;/i&gt;&lt;sub&gt;2&lt;/sub&gt; and &lt;i&gt;g&lt;/i&gt; &gt; &lt;i&gt;Γ&lt;/i&gt;&lt;sub&gt;2&lt;/sub&gt;), the four-wave mixing spectrum exhibits a two-peaked structure. The maximum values of these two peaks increase as &lt;inline-formula&gt;&lt;tex-math id="M2"&gt;\begin{document}$ {\varOmega }_{{\text{pu}}}^{\text{2}} $\end{document}&lt;/tex-math&gt;&lt;alternatives&gt;&lt;graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="12-20230012_M2.jpg"/&gt;&lt;graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="12-20230012_M2.png"/&gt;&lt;/alternatives&gt;&lt;/inline-formula&gt; increases, and their spacing is equal to 2&lt;i&gt;g&lt;/i&gt;. Especially, for a given pumping intensity &lt;inline-formula&gt;&lt;tex-math id="M3"&gt;\begin{document}$ {\varOmega }_{{\text{pu}}}^{\text{2}} $\end{document}&lt;/tex-math&gt;&lt;alternatives&gt;&lt;graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="12-20230012_M3.jpg"/&gt;&lt;graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="12-20230012_M3.png"/&gt;&lt;/alternatives&gt;&lt;/inline-formula&gt; (= 10 THz&lt;sup&gt;2&lt;/sup&gt;), the maximum value of the peak for &lt;i&gt;g&lt;/i&gt; = 4 THz becomes 0.4% of that for &lt;i&gt;g&lt;/i&gt; = 1 THz, indicating that the phonon-exciton coupling inhibits the enhancement of the four-wave mixing signal to a certain extent. Our findings can not only offer an efficient way to measure the phonon-exciton coupling strength in the bilayer graphene system, but also help one to further explore the underlying physical mechanism in such a nanosystem.

Is the Purpose of Cohesion Policy Changing?

An effective cohesion policy is the backbone of the common market and European economy in general. It mitigates the negative effects of economic integration by strengthening the weakest links in the chain of European economic cooperation. The significance of cohesion policy is evident, however, there are worrying tendencies weakening its effectiveness. Not only does the Multiannual Financial Framework have less nominal resources allocated for cohesion in general, but the focus, objectives, and recipients seem to be shifting as well. By introducing the objectives of other policy fields into cohesion policy and deviating its resources for short-term crisis management, the development of regions and their cohesion seems to become a secondary, subsidiary objective. Without cohesion policy, the costs of accelerated economic integration can become unbearable for certain parts of the EU. Even though the European Parliament does recognise this tendency, it sends mixed signals during legislative and non-legislative procedures. There is only one solution for the change in the nature of cohesion policy and thus its hollowing-out – a strict separation of cohesion and non-cohesion goals and their allocation in the EU budget.

Guest Editors' Words

Electronic Design Automation has been the driving force of VLSI design since those circuits are far more complex beyond what a human designer can comprehend. Today, the most advanced circuits have billions of components, mostly transistors, arranged in random logic, memory, data path, analog, mixed signal, IPs, etc. The process of automation of a large VLSI chip is extremely complex, generally decomposed into several categories and steps that are part of most existing commercial tool flows. Furthermore, timing and power optimization enable a wide range of applications we see today such as computing, mobile, internet of things (IoT), etc., and can only happen with creative innovations in EDA. For its complexity, any researcher in the field must invest heavily in finding and reading appropriate literature. In this special edition, the Journal of Integrated Circuits and Systems is collecting and surveying the key topics, algorithms and approaches that pertain to the current state-of-the-art in EDA research.

Chaotic-based Orthogonal Frequency Division Multiplexing with Index Modulation

Orthogonal frequency division multiplexing with index modulation (OFDM-IM), stands out among conventional communication technologies, as it uses the indices of the available transmit entities. Thanks to such an approach, it offers a novel method for the transmission of extra data bits. Recent years have seen a great interest in chaos-based communications. The spectrum-spreading signals used in chaotic signal modulation technologies are orthogonal to the existing mixed signals. This paper presents how well a non-coherent differential chaos shift keying communication system performs across an AWGN. Different types of detection methods are simulated, bit error rate and power spectral density are calculated and then compared with standard OFDM with index modulation. The results of simulations concerning the performance of a DCSK system, adding to the security of the proposed solution and offering a comparable bit error rate performance, are presented in the paper as well.

Spatial variations in age-0+ Merluccius hubbsi (Patagonian stock; 45º-47ºS) feeding conditions through fatty acids composition

Fatty acids (FA) play critical roles in trophic interactions in aquatic food webs, transferring from phytoplankton to fish, and being modulated by oceanographic conditions and productivity. FA are a well-established approach for studying fish feeding preferences, and can indicate habitat quality and ecosystem condition. Thus, they provide valuable information in the context of fisheries management. The aim of this research was to determine the FA profiles of age-0 + hake Merluccius hubbsi and their zooplankton prey (Euphausia spp., Munida gregaria, Themisto gaudichaudii), identify FA trophic markers, infer their diet in comparison with stomachs information, and evaluate spatial changes in FA indicators with respect to the austral spring environmental variability in the Patagonian nursery ground (San Jorge Gulf, SJG; 45º-47ºS, 65º30’−67º30’W). We hypothesize that age-0 + hake and zooplankton FA composition relate to frontal structures and chlorophyll-a concentration, revealing diatoms predominance in the SJG spring food web. Major FA in hake and zooplankton were 16:0, 16:1n7, 18:1n9, 20:5n3 and 22:6n3. Age-0 + hake profiles mostly resembled euphausiids FA composition, in agreement with the stomachs analysis. Bacterial (15:0 + 17:0 + 17:1 + 18:1n7) and a mixed signal from both diatoms (16:1n7 + 20:5n3) and dinoflagellates (18:4n3 + 22:6n3) were recorded in the profiles; these suggest the existence of a classical food web in central coastal waters of the gulf and an omnivorous one towards the north and south extremes. Results are useful for a better understanding of the SJG food web and the key role age-0 + hake play in energy transfer, contributing to ongoing M. hubbsi recruitment studies in the region.

Trends and variability of rainfall characteristics influencing annual streamflow: A case study of southeast Australia

AbstractThe trend and variability in rainfall characteristics that influence annual streamflow of southeast Australia is assessed using two methods, non‐parametric Kendall test and linear slope, and two gridded daily rainfall datasets (SILO and AWAP) and 1,196 station rainfall data for 1971–2021 period. The rainfall anomaly before, during and after the Millennium Drought (1997–2009) is compared to quantify the variability. The results show: (a) a declining rainfall trend is detected as the recent dry decades were preceded by wet decades. The declining trend largely occurs from autumn through to spring with the largest trend in August to October followed by April and May; (b) the trend is more significant for number of rainfall days and for multi‐day rainfall totals (and more so for longer accumulations) than for annual rainfall; (c) the very high extreme rainfall shows a small increasing trend in summer and mixed signal in the other seasons; (d) the streamflow in the post drought period is lower than the long‐term mean, despite the mean annual rainfall being slightly higher. This can be partly explained by the rainfall‐runoff relationship of catchments not having fully recovered from the prolonged drought and partly by changes in rainfall characteristics, like the wet spell length and multi‐day rainfall, influencing annual streamflow; (e) The general conclusions for analyses with different sources of daily rainfall data are the same. However, some differences do exist, with SILO gridded rainfall and station data having larger areas with statistically significant trend than the AWAP gridded rainfall, as well as larger declining trends in the high elevation areas. The general conclusions of this study are drawn from an Australia case study but could have implications for other regions to investigate the attributions of rainfall characteristics other than annual rainfall to the non‐stationary rainfall‐streamflow relationship.

Analog Compatible Logic Functions Using EXCCIIs and an Extended Application for Sigmoid Activation Function

This paper introduces the use of an extra X current conveyor (EXCCII) for realizing logic operations, suited for mixed signal design, without necessitating separate digital blocks. The NOR and OR logic functions are thus shown to be realized using the current-mode analog building block operable at a low voltage. The use of [Formula: see text][Formula: see text]V supply makes the new circuits operable at low voltage. The EXCCII bias current used in the design is 25[Formula: see text][Formula: see text]A. The input currents for defining logic 0 and logic 1 are, respectively, taken as 0[Formula: see text]mA and 0.5[Formula: see text]mA. The proposed circuits’ delay is found to be 10[Formula: see text]ns for low-to-high transition, while 2[Formula: see text]ns for high-to-low transition for NOR gate under capacitive loading conditions. The use of the current-mode approach makes the new proposed circuit apt for current-mode signal processing. The new proposed logic function circuits are compatible with analog circuits, thus providing an easy merger for mixed signal systems design, paving the way to a modular design approach. As an extended application in artificial neural networks, the proposed circuit is shown to generate a sigmoid activation function with convincing results. Several results are included by varying the reference current, and the circuit’s robustness aspect is also tested in the presence of fluctuation in the bias current of EXCCII. The novel approach of utilizing an analog building block for realizing digital functions is, therefore, verified with promising future applications.

Defected microstrip structure-based near-end and far-end crosstalk mitigation in high-speed PCBs for mixed signals

PurposeThe design, fabrication and experimental validation of defected microstrip structure (DMS) are proposed to address the problem of near-end crosstalk (NEXT) and far-end crosstalk (FEXT) between the microstrip transmission lines in a printed circuit board.Design/methodology/approachThe proposed DMS evolved with the combination of spur line (L-shaped DMS) and U-shaped DMS topologies. This technique reduces the strength of electromagnetic coupling and suppresses crosstalk by optimizing the capacitive and inductive coupling ratio between the linked microstrip lines. The practical inductance value is much more significant in DMS than in defected ground structures (DGS), but the capacitance value remains the same.FindingsA DMS unit is etched on the aggressor microstrip line instead of the DGS circuit. Because there is no leakage via the ground plane and the circuit size is far smaller than with DGS, the enclosure issue is disregarded. DMS structures have a larger effective inductance and are resistant to electromagnetic interference. A tightly coupled transmission line structure with minimal separation between the coupled microstrip line is designed using DMS. Further research must be conducted to improve the NEXT, FEXT and spacing between the transmission lines.Originality/valueSimulation and actual measurement results show that the proposed DMS structure can effectively suppress crosstalk by analysing the S-parameters, namely, S_12, S_13 and S_14, with measured values of 1.48 dB, 20.65 dB and 21.099 dB, respectively. The data rate is measured to be 1.34 Gbps as per the eye diagram characterization. The results show that the NEXT and FEXT are reduced by approximately 20 dB in the frequency range of 1–11 GHz for mixed signals. The substantial measured results in the vector network analyser coincide with the computer simulation technology microwave studio suite simulation results.

Suppressing interferences of EIT on synchronous recording EEG based on comb filter for seizure detection.

The purpose of this study was to eliminate the interferences of electrical impedance tomography (EIT) on synchronous recording electroencephalography (EEG) for seizure detection. The simulated EIT signal generated by COMSOL Multiphysics was superimposed on the clinical EEG signal obtained from the CHB-MIT Scalp EEG Database, and then the spectrum features of superimposed mixed signals were analyzed. According to the spectrum analysis, in addition to high-frequency interference at 51.2 kHz related to the drive current, there was also low-frequency interference caused by switching of electrode pairs, which were used to inject drive current. A low pass filter and a comb filter were used to suppress the high-frequency interference and low-frequency interference, respectively. Simulation results suggested the low-pass filter and comb filter working together effectively filtered out the interference of EIT on EEG in the process of synchronous monitoring. As a result, the normal EEG and epileptic EEG could be recognized effectively. Pearson correlation analysis further confirmed the interference of EIT on EEG was effectively suppressed. This study provides a simple and effective interference suppression method for the synchronous monitoring of EIT and EEG, which could be served as a reference for the synchronous monitoring of EEG and other medical electromagnetic devices.

A fully blind and adaptive filter method to solve the inverse problem in vibration-based gear damage detection

In vibration-based gear damage detection, the fault signatures are usually corrupted by noise and convolution of vibration transmission channels. Deconvolution techniques have been widely studied to solve the inverse problem for restoring fault signatures. The mainstream methods, however, often fail due to the requirement of prior knowledge about fault signatures or the experience-based determination of filter parameters. In this paper, a fully blind and adaptive filter method is proposed. Specifically, a new criterion is defined, and the filter method is formulated as a maximization problem based on the criterion. The detailed deduction and pseudo-code for iteratively solving the problem are presented. Moreover, the filter method embeds an advanced swarm intelligence algorithm—that is, the grey wolf optimizer to achieve the adaptive determination of the optimal filter parameters according to the signals. In this context, it is a fully blind and adaptive filter method without requiring any prior knowledge and experiences about the target signatures to pre-determine filter parameters. Analysis results of a simulated signal and the comparisons with several state-of-the-art methods show the effectiveness of the proposed method for restoring the repetitive fault impulses from the mixed signal containing complex interferences. Fault-injection experiment and engineering application are carried out and the results demonstrate the performance of the proposed method for solving the inverse problem in gear damage detection.