Complex Signal Processing Research Articles

Globalization and the erosion of geo-ethnic checkpoints: evolving signal-boundary systems at the edge of chaos

Spatial boundaries, thermodynamic–economic specialization, and signal processing are at the core of evolution’s major transitions. Centered on these three dimensions, a proposed evolutionary informatics model roots ethnic and racial cleavages in zero-sum contests over rivalrous resources within geophysical sites. As the geophysical boundaries and signal-processing complexity of social systems coevolved, zero-sum contests centered on metropoles extracting resources from hinterlands. In this colonial extraction process, racialization arose from non-market spatial segregation of populations tagged with hinterland lineage. Subsequent post-industrial erosion—and greater permeability—of racial and ethnic boundaries has been enabled by the progressive uncoupling of more highly evolved complex adaptive systems from geophysical location (non-territorial adaptation). Signal and physical topologies are becoming more distinct. This uncoupling from physical location is driven by cybernetic parallelism in complex adaptive systems: diverse and independent agents learning from their mutual exchange of signals. Cybernetic parallelism has generated epistemic and geopolitical challenges to formal apartheid and racializing immigration policies, but not without friction or reversals.

DNA damage response and repair pathway modulation by non-histone protein methylation: implications in neurodegeneration.

Protein post-translational modifications (PTMs) have emerged to be combinatorial, essential mechanisms used by eukaryotic cells to regulate local chromatin structure, diversify and extend their protein functions and dynamically coordinate complex intracellular signalling processes. Most common types of PTMs include enzymatic addition of small chemical groups resulting in phosphorylation, glycosylation, poly(ADP-ribosyl)ation, nitrosylation, methylation, acetylation or covalent attachment of complete proteins such as ubiquitin and SUMO. Protein arginine methyltransferases (PRMTs) and protein lysine methyltransferases (PKMTs) enzymes catalyse the methylation of arginine and lysine residues in target proteins, respectively. Rapid progress in quantitative proteomic analysis and functional assays have not only documented the methylation of histone proteins post-translationally but also identified their occurrence in non-histone proteins which dynamically regulate a plethora of cellular functions including DNA damage response and repair. Emerging advances have now revealed the role of both histone and non-histone methylations in the regulating the DNA damage response (DDR) proteins, thereby modulating the DNA repair pathways both in proliferating and post-mitotic neuronal cells. Defects in many cellular DNA repair processes have been found primarily manifested in neuronal tissues. Moreover, fine tuning of the dynamicity of methylation of non-histone proteins as well as the perturbations in this dynamic methylation processes have recently been implicated in neuronal genomic stability maintenance. Considering the impact of methylation on chromatin associated pathways, in this review we attempt to link the evidences in non-histone protein methylation and DDR with neurodegenerative research.

Highly Sensitive 1T‐MoS2 Pressure Sensor with Wide Linearity Based on Hierarchical Microstructures of Leaf Vein as Spacer

AbstractFlexible pressure sensors have attracted considerable interest for their promising applications in future robotics and wearable devices. Pressure sensors with high sensitivity and a wide linear range do not require complex signal processing, and greatly enhance device miniaturization and decrease power consumption. High‐performance pressure sensors are prepared based on 1T molybdenum disulfide within polydimethylsiloxane foams (MoS2‐PDMS) as conductive active layer and with hierarchical micro‐network leaf vein as spacers. Due to the excellent electrical conductivity of 1T MoS2‐PDMS and the novel structural design, they exhibit better properties than previously reported devices, including a high sensitivity of 1036.04 kPa−1 over a wide linear range from 1 to 23 kPa, fast response time (<50 ms), ultralow detectable pressure limit of 6.2 Pa, and excellent repetitive load and unloading stability (10 000 cycles). In addition, its applications in sensing subtle pressure and non‐contact breeze and in monitoring different human motion activities and physiological signals are explored. Some new applications including water pressure testing and subtle fluctuations in water caused by small fish swimming are also investigated. The results demonstrate the tested applications of layered 2D materials in pressure sensors that can be feasibly adopted in next‐generation electronic skin and artificial intelligence.

Intelligent bearing fault diagnosis using PCA–DBN framework

This paper studies the fault diagnosis problem for rolling element bearings. By casting the bearing fault diagnosis as a class of pattern classification problem, we propose a novel intelligent fault diagnosis approach based on principal component analysis (PCA) and deep belief network (DBN). The dimension of raw bearing vibration signals is reduced by adopting PCA method, which consequently extracts the fault signatures in terms of primary eigenvalues and eigenvectors. The modified samples are subsequently trained and tested by the DBN for fault classification and diagnosis. The distinctive feature of our approach is that it requires no complex signal processing on raw vibration data, rendering it easily achievable and widely applicable. The experimental results indicate the effectiveness of the proposed PCA–DBN fault diagnosis scheme compared with other methods.

Maximum likelihood approach to DoA estimation using lens antenna array

Massive antenna array has been proposed to improve the spectral efficiency and link reliability in wireless communication systems. However, using large antenna arrays incurs additional cost in terms of signal processing and hardware complexity. The electromagnetic (EM) lens-focusing antennas are introduced as a promising technique to reduce the hardware complexity and cost. On the other hand, determining the location of users in terms of their direction-of-arrival (DoA) using these lens array becomes of great interest for different 5G services. This paper addresses the issue of DoA estimation by adopting lens antenna array (LNA). We firstly derive an expression for the received signal with the adoption of LNA, and then a maximum likelihood (ML) estimator for the DoA has been obtained. Depending on the ability of the lens array to focus the signal power on a subset of antennas as a function of DoA. We propose using the antenna selection (AS) technology to select an antenna subset aiming to reduce the number of radio frequency (RF) chains and accordingly reducing the hardware cost. The simulation results show the the capability of the proposed method to avoid the phase ambiguity problem and provide high accurate DoA estimation of signals.

Digital Control of Bridgeless Interleaved PFC Boost Converter Based on Predicted Input Current

In this study, digital control of bridgeless interleaved power factor correction (PFC) boost converter is presented by using DSP (digital signal processor) for prediction of input current without any current sensing. The complex control methods which have current sensing are utilized to get minimum ripple for current and appropriate sharing of current for interleaved PFC converters that reduce input current ripple and improve power rating, so they are much more convenient than the classic PFC converters. The current signals do not need to be sampled for proposed predictive control approach due to sampling of the voltages of input and output. The sampled voltages of input and output are employed to calculate input current for the converter. The parameters of converter, calculated input current and reference current that acquired from the voltage loop compensator are employed for predicting following duty cycle. By this way, the complex signal processing and current measuring are taken out when compared with the conventional control methods. An experimental circuit of bridgeless interleaved PFC boost converter has been installed to apply the proposed predictive control method via DSP board. It is seen from the results that appropriate sharing of current, the low value of total harmonic distortion (THD) and high rate of power factor can be acquired thanks to using the proposed predictive control strategy.

SiCer: Meeting the Challenges of Tomorrow's Complex Electronic Systems [From the Guest Editors' Desk

A steady attempt to introduce new, more complex and demanding wireless transmission standards in various market segments is unfolding. In the communication market, a rising number of frequency bands of operation with increasingly complex modulation schemes is being proposed to satisfy the steady demand for higher channel capacities and data rates. Multiple antenna arrays matched with complex signal processing techniques are used to realize the enhanced multiple input, multiple output concepts of low-latency long-term evolution (LTE) systems. The push to reduce power consumption is driven by the need to increase the autarky of wearable devices and the operating time of all the physical devices that will form the upcoming Internet of Things. For future Industry 4.0 and Mobility 4.0 markets, sensors, actuators, and communication devices that can work independently, even under harsh environmental conditions, must be developed.

Iterative Sparse Channel Estimation and Spatial Correlation Learning for Multichannel Acoustic OFDM Systems

This article addresses the problem of coherent detection of acoustic orthogonal frequency division multiplexing (OFDM) signals using a sparse channel estimation method based on a physical model of multipath propagation. Unlike the conventional sample-spaced and subsample-spaced methods, such as least squares and orthogonal matching pursuit (OMP), which target the taps of an equivalent discrete-delay channel response, the path identification (PI) method targets the physical propagation paths in a continuous-delay domain, and focuses on explicit estimation of delays and complex amplitudes of the channel paths in an iterative fashion. When multiple receive elements are available, two situations are possible: one in which the array elements see uncorrelated channel responses, and another in which the channel responses are correlated. In the first case, channel estimation must be accomplished element-by-element. This is done simply by applying the PI algorithm to each element individually. In the second case, correlation between the elements can be exploited. In doing so, our goal is to reduce the signal processing complexity without compromising the performance. Toward this goal, an adaptive precombining method is proposed. Without requiring any a priori knowledge about the spatial distribution of received signals, the method exploits spatial coherence between receive channels by linearly combining them into fewer output channels so as to reduce the number of subsequent channel estimators. The algorithm learns the spatial coherence pattern recursively over the carriers, thus effectively achieving broadband beamforming. The reduced-complexity precombining method relies on differential encoding that keeps the receiver complexity at a minimum and requires a very low pilot overhead. Using synthetic data as well as 210 experimental signals transmitted over a 3–7-km shallow-water channel in the 10.5–15.5-kHz acoustic band during a 3.5-h experiment, we study the system performance in terms of data detection mean-squared error (MSE), symbol error rate, and bit error rate (BER), and show that the PI algorithm achieves excellent MSE performance while its complexity is considerably lower than that of the OMP algorithm. We also demonstrate that the receiver equipped with the proposed reduced-complexity precombining scheme requires three times fewer channel estimators while achieving the same MSE and BER performance as the full-complexity receiver.

Rolling Element Bearing Fault Diagnosis Based on Deep Belief Network and Principal Component Analysis

Rolling element bearings are critical components in industrial rotating machines. Faults and failures of bearings can cause degradation of machine performance or even a catastrophe. Bearing fault diagnosis is therefore essential and significant to safe and reliable operation of systems. For bearing condition monitoring, acoustic emission (AE) signals attract more and more attention due to its advantages on sensitivity over the extensively used vibration signal. In bearing fault diagnosis and prognosis, feature extraction is a critical and tough work, which always involves complex signal processing and computation. Moreover, features greatly rely on the characteristics, operating conditions, and type of data. With consideration of changes in operating conditions and increase of data complexity, traditional diagnosis approaches are insufficient in feature extraction and fault diagnosis. To address this problem, this paper proposes a Deep Belief Network (DBN) and Principal Component Analysis (PCA) based fault diagnosis approach using AE signal. This proposed approach combines the advantages of deep learning and statistical analysis, DBN automatically extracts features from AE signal, PCA is applied to dimensionality reduction. Different bearing fault modes are identified by least squares support vector machine (LS-SVM) using the extracted features. An experimental case is conducted with a tapered roller bearing to verify the proposed approach. Experimental results demonstrate that the proposed approach has excellent feature extraction ability and high fault classification accuracy.

Optics-Simplified DSP for 50 Gb/s PON Downstream Transmission using 10 Gb/s Optical Devices

Directly-modulated laser (DML) is widely employed in intensity modulation and direct detection (IMDD) system due to its low cost and high output power. However, the corresponding frequency chirp is regarded as one of the main disadvantages for its application in passive optical networks (PONs). In this paper, we theoretically analyze the frequency response evolution of DML based system under different chirp and dispersion conditions, proving that the system bandwidth can be improved by interactions between negative dispersion and DML chirp. Based on this concept, we experimentally demonstrated downstream 50 Gb/s PAM4 signal transmission over 20 km single-mode fiber (SMF) access based on the 10 Gb/s DML operating at 1310 nm and avalanche photodiode (APD). A dispersion-shifted fiber (DSF) providing −150 ps/nm dispersion at 1310 nm in the optical line terminal (OLT) is used to pre-equalize the frequency response of bandwidth-limited directly modulated signals in the optical domain. Thanks to our proposed dispersion-supported equalization (DSE) technique, the system bandwidth can be improved by 5 GHz. Feed-forward equalization (FFE), decision feedback equalization (DFE) and Volterra filter are employed to evaluate the signal performance improvement, respectively. By evaluating the receiver sensitivity, the DSE combined with FFE scheme shows 2 dB improvement than the complex Volterra algorithm, indicating its potential to reduce the complexity of digital signal processing (DSP) and therefore a lower cost and power consumption in PON.

Metasurface-Integrated Photonic Platform for Versatile Free-Space Beam Projection with Polarization Control

Densely integrated photonic circuits enable scalable, complex processing of optical signals, including modulation, multiplexing, wavelength conversion, and detection. Directly interfacing such integrated circuits to free-space optical modes will enable novel optical functions, such as chip-scale sensing, interchip free-space interconnect and cooling, trapping, and interrogation of atoms. However, doing this within the limits of planar batch fabrication requires new approaches for bridging the large mode scale mismatch. Here, by integrating a dielectric metasurface with an extreme photonic mode converter, we create a versatile nanophotonic platform for efficient coupling to arbitrary-defined free-space radiation of 780 nm wavelength with well-controlled spatially-dependent polarization, phase, and intensity. Without leaving the chip, the high index photonic mode is converted first to a ≈ 200 μm wide, precisely collimated, linearly-polarized Gaussian beam, which is then modified by a planar, integrated, low-loss metasurface. We demonstrate high numerical aperture, diffraction limited focusing to an ≈ 473 nm spot at an ≈ 75 μm working distance, and combine it with simultaneous conversion from linear to elliptical polarization. All device components are lithographically defined and can be batch fabricated, facilitating future chip-scale low-cost hybrid photonic systems for bio-sensing, nonlinear signal processing and atomic quantum sensing, frequency references and memory.

A Sensor for Broken Wire Detection of Steel Wire Ropes Based on the Magnetic Concentrating Principle.

Electromagnetic testing is the most widely used technique for the inspection of steel wire ropes. As one of the electromagnetic detecting approaches, the magnetic flux leakage (MFL) method has the best effect for the detection of broken wires. However, existing sensors based on MFL method still have some problems. (1) The size of the permanent magnet exciter is usually designed according to experience or rough calculation, and there is not enough depth analysis for its excitation performance; (2) Since the detectable angular range for a single Hall component is limited, Hall sensor arrays are often employed in the design of MFL sensors, which will increase the complexity of the subsequent signal processing due to the extensive use of Hall components; (3) Although the new magneto-resistance sensor has higher sensitivity, it is difficult to be applied in practice because of the requirement of the micron-level lift-off. To solve these problems, a sensor for the detection of broken wires of steel wire ropes based on the principle of magnetic concentration is developed. A circumferential multi-circuit permanent magnet exciter (CMPME) is employed to magnetize the wire rope to saturation. The traditional Hall sensor array is replaced by a magnetic concentrator to collect MFL. The structural parameters of the CMPME are optimized and the performance of the magnetic concentrator is analyzed by the finite element method. Finally, the effectiveness of the designed sensor is verified by wire breaking experiment. 1–5 external broken wires, handcrafted on the wire rope with a diameter of 24 mm, can be clearly identified, which shows great potential for the inspection of steel wire ropes.

Analog Least Mean Square Loop With I/Q Imbalance for Self-Interference Cancellation in Full-Duplex Radios

Analog least mean square (ALMS) loop is a promising structure for self-interference (SI) mitigation in full-duplex radios due to its simplicity and adaptive capability. However, being constructed from in-phase/quadrature (I/Q) demodulators and modulators to process complex signals, the ALMS loop may face I/Q imbalance problems. Thus, in this paper, the effects of frequency-independent I/Q imbalance in the ALMS loop are investigated. It is revealed that I/Q imbalance affects the loop gain and the level of SI cancellation. The loop gain can be easily compensated by adjusting the gain at other stages of the ALMS loop. Meanwhile, the degradation on cancellation performance is proved to be insignificant even under severe conditions of I/Q imbalance. In addition, an upper bound of the degradation factor is derived to provide an essential reference for the system design. Simulations are conducted to confirm the theoretical analyses.

Improved Hierarchical Adaptive Deep Belief Network for Bearing Fault Diagnosis

Rotating machinery plays a vital role in modern mechanical systems. Effective state monitoring of a rotary machine is important to guarantee its safe operation and prevent accidents. Traditional bearing fault diagnosis techniques rely on manual feature extraction, which in turn relies on complex signal processing and rich professional experience. The collected bearing signals are invariably complicated and unstable. Deep learning can voluntarily learn representative features without a large amount of prior knowledge, thus becoming a significant breakthrough in mechanical fault diagnosis. A new method for bearing fault diagnosis, called improved hierarchical adaptive deep belief network (DBN), which is optimized by Nesterov momentum (NM), is presented in this research. The frequency spectrum is used as inputs for feature learning. Then, a learning rate adjustment strategy is applied to adaptively select the descending step length during gradient updating, combined with NM. The developed method is validated by bearing vibration signals. In comparison to support vector machine and the conventional DBN, the raised approach exhibits a more satisfactory performance in bearing fault type and degree diagnosis. It can steadily and effectively improve convergence during model training and enhance the generalizability of DBN.

Design of a High-Resolution Instantaneous Torque Sensor Based on the Double-Eccentric Modulation Principle

A novel non-contact instantaneous torque sensor is proposed in this paper. The mechanical structure of the torque sensor mainly consists of two eccentric sleeves rotating about an elastic shaft. The measurement of torque is transformed into the measurement of the phase difference between the eccentric sleeves. Eddy current sensors are used to measure distance changes between their probes and the eccentric sleeves. The phase is modulated by the distance changes when any torque applied to the elastic shaft the demodulation principle of the phase relies on solving simple trigonometric functions without any complex signal processing methods. Therefore, the acquisition of torque can be performed instantaneously without any accumulation of time or integer-period sampling. The proposed sensor has a simple structure with no electrical components within the rotational parts. Additionally, the proposed sensor facilitates the measurement of static torque, dynamic torque, and even reciprocating torque over a wide range of angular speeds. The sensor was calibrated by a torsion-testing setup and experimental results indicate that the sensitivity of the sensor is ${23.05N\centerdot }{m}/ ^\circ $ , the sum of squares due to error is 0.09449, and the root-mean-squared error is 0.1375. The non-linearity is 0.914%. The proposed sensor accuracy is 0.06%.

A dynamic and self-adaptive classification algorithm for motor imagery EEG signals

BackgroundBrain–computer interface (BCI) is a communication pathway applied for pathological analysis or functional substitution. BCI based on functional substitution enables the recognition of a subject's intention to control devices such as prosthesis and wheelchairs. Discrimination of electroencephalography (EEG) trials related to left- and right-hand movements requires complex EEG signal processing to achieve good system performance. New methodIn this study, a novel dynamic and self-adaptive algorithm (DSAA) based on the least-squares method is proposed to select the most appropriate feature extraction and classification algorithms couple for each subject. Specifically, the best couple identified during the training of the system is updated during online testing in order to check the stability of the selected couple and maintain high system accuracy. ResultsExtensive and systematic experiments were conducted on public datasets of 17 subjects in the BCI-competition and the results show an improved performance for DSAA over other selected state-of-the-art methods. Comparison with existing methodsThe results show that the proposed system enhanced the classification accuracy for the three chosen public datasets by 8% compared to other approaches. Moreover, the proposed system was successful in selecting the best path despite the unavailability of reference labels. ConclusionsPerforming dynamic and self-adaptive selection for the best feature extraction and classification algorithm couple increases the recognition rate of trials despite the unavailability of reference trial labels. This approach allows the development of a complete BCI system with excellent accuracy.

Two-phase flow experiments with Coriolis Mass Flow Metering using complex signal processing

Abstract— Two-phase (gas/liquid) flow is common in many industrial applications but its measurement remains challenging for Coriolis mass flow meters (CMFM), especially for high Gas Void Fraction (GVF). In this paper, we present experimental results applying previously developed complex signal processing techniques for tracking the rapidly changing sensor signals generated by two-phase flow. The techniques are implemented in a new System-on-Chip (SOC) prototype transmitter connected to a commercial Coriolis flow tube. Experiments have been carried out over a range of single phase and two-phase (water/air) flows. The signal tracking as well as mass flow rate and density measurement performance is compared with that of a laboratory version of a commercial Coriolis transmitter with two-phase flow capability. The results show that the complex bandpass algorithms, coupled with flowtube control algorithms, reduce the standard deviation of the mass flow measurement by a factor of 3 or more in 50% of the experiments undertaken. For the density measurement, the corresponding reduction in standard deviation is by a factor of 6.

Strain‐Visualization with Ultrasensitive Nanoscale Crack‐Based Sensor Assembled with Hierarchical Thermochromic Membrane

AbstractAs eidetic signal recognition has become important, displaying mechanical signals visually has imposed huge demands for simple readability and without complex signal processing. Such visualization of mechanical signals is used in delicate urgent medical or safety‐related industries. Accordingly, chromic materials are considered to facilitate visualization with multiple colors and simple process. However, the response and recovery time is very long, such that rapid regular signals are unable to be detected, i.e., physiological signals, such as respiration. Here, the simple visualization of low strain ≈2%, with ultrasensitive crack‐based strain sensors with a hierarchical thermochromic layer is suggested. The sensor shows a gradient color change from red to white color in each strain, which is attributed to the hierarchical property, and the thermal response (recovery) time is dramatically minimized within 0.6 s from 45 to 37 °C, as the hierarchical membrane is inspired by termite mounds for efficient thermal management. The fast recovery property can be taken advantage of in medical fields, such as monitoring regular respiration, and the color changes can be delicately monitored with high accuracy by software on a mobile phone.

Blind time domain nonlinear compensator embedded in the constant modulus algorithm.

In this paper, a blind nonlinear compensator is proposed for the compensation of fiber nonlinearity. The nonlinear compensator is embedded in the constant modulus algorithm (CMA) which consists of two stages. In the first stage, the linear filter coefficients are calculated while the nonlinear CMA is performed in the second stage. After the nonlinear coefficients reach the stable state, the two-stage CMA switches to the conventional CMA followed by a fixed time domain nonlinear equalizer because the fiber nonlinearity within the transmission link does not vary as the polarization rotation does. This greatly reduces the digital signal processing (DSP) complexity and saves the computational efforts. In comparison with the existing nonlinear compensation techniques such as the back-propagation (BP) method and the Volterra series based nonlinear equalizer (VSNE), the proposed method does not need fast Fourier transform (FFT) or iterative procedures, which significantly reduces the computational complexity. Since the method is performed blindly, no prior information about the transmission link is required, which greatly facilitates the implementation of the nonlinear compensation technique. In addition to these, the coefficients of the nonlinear compensator are optimized adaptively, and it outperforms the existing methods. Finally, the method can work in conjunction with the existing nonlinear compensators, such as the BP method, and significant performance improvement is found over the original nonlinear compensator.

Analysis of sum spectrum efficiency in multiple antennas multi-user MIMO cellular system for millimeter wave communications

In this paper, sum spectrum efficiency of two cell multiple-input multiple-output (MIMO) system having multiple antennas at base station (BS) and multiple user equipments (UEs) is analysed. The maximum ratio (MR) and multi-cell minimum mean squared error (M-MMSE) techniques are utilized for detection/precoding in the uplink/downlink scenario. In this paper, two boundary cases of propagation channel model: line of sight (LoS) and non-line of sight (NLoS), and generalized propagation channel model of millimeter wave (mmWave) are compared using MR and MMSE detection techniques. The closed bound expressions of sum spectrum efficiency of MR are expressed and simulated, whereas for M-MMSE, Monte-Carlo simulations are performed on the basis of its weighting vector. The fundamental limits on the system performance, using large antenna arrays and many UEs satisfying practical constraints like desired signal to noise ratio (SNR), low complexity in signal processing, gain of inter cell interference and finite-dimensional channels are analysed.