Common Signal Processing Research Articles

Adaptive variable sampling T-SSD algorithm

Addressing the challenges of non-unique decomposition outcomes and prolonged decomposition durations in the fault feature adaptive extraction algorithm based on tensor decomposition, this paper presents a novel algorithm called the adaptive variable sampling tensor singular spectrum decomposition (T-SSD) algorithm. The proposed approach centers on decomposing multichannel time series with adaptive sampling frequency, leveraging tensor singular value decomposition. Initially, the embedding dimension and the number of resampling points were optimized by power spectral density analysis and adaptive sampling algorithm. Subsequently, a third-order tensor is constructed based on the principle of tensor-tensor-preserving order multiplication, combining trajectory tensor construction and embedding dimension. Finally, the signal decomposition and reconstruction of multichannel component signals are achieved through adaptive sampling, interpolation, and complementary back steps. Experimental signal analysis indicates that this algorithm can better extract fault characteristics from signals compared to common signal processing algorithms. In comparison with the traditional T-SSD algorithm, this method significantly improves decomposition efficiency, particularly for low-frequency components. It effectively tackles the efficiency challenge caused by data redundancy, enabling the organic fusion and adaptive decomposition of multichannel signals.

INVARIANT ZERO-WATERMARKING ALGORITHM IN DWT-DCT DOMAIN USING ROBUST FEATURES MATCHING

In order to protect the copyright of the digital contents, the robust watermarking techniques are employed based on frequency domains or combined frequency domains. Another techniques, called zero-watermarking which does not modify the original image to embed the watermark but create a watermark from its robust features, is a useful technique for resolving the tension between robustness and invisibility. In this paper, we propose a new zero-watermarking algorithm based on discrete wavelet transform and discrete cosine transform domain with significant feature points matching for improving the robustness. In our proposed method, the original image is firstly separated into three components (Y, Cr, Cb), then its Y-component is performed with discrete wavelet transform, afterwards its LL3 is transformed with discrete cosine transform. To generate the master share of the original image, the DCT-based image is binarized. After performing the Arnold transformation on the watermark, the owner share is generated by taking the XOR operation on the scrambled watermark and master share. The robustness of the our proposed algorithm for imaging processes is analyzed, and the results show that the proposed algorithm is robust to common signal processing such as noise, filtering, JPEG compression, geometric attacks such as rotation, scaling, translation and so on.

High-resolution 2D-DoA sequential algorithm of azimuth and elevation estimation in automotive distributed system of coherent MIMO radars

Objectives. One of the main tasks of radiolocation involves the problem of increasing spatial resolution of the targets in the case of limited aperture of the radar antenna array and short length of time samples (snapshots). Algorithms must be developed to provide high angular resolution and low computational complexity. In order to conform with the existing Advanced Driver Assistance Systems requirements, modern cars are equipped with more than one radar having a common signal processing scheme to improve performance during target detection, positioning, and recognition as compared to a single radar. The present study aims to develop a two-dimensional Direction-of-Arrival algorithm with low computation complexity as part of distributed coherent automotive radar system for cases involving short time samples (snapshots).Methods. A virtual antenna array formation algorithm is formulated according to the two-dimensional Capon method. A proposed modification of two-dimensional Capon algorithm is based on sequentially estimating the directions of arrival for the distributed radar system. The Monte Carlo method is used to compare the effectiveness of the considered algorithms.Results. The 2D-DoA sequential algorithm of azimuth and elevation estimation is proposed. The comparative analysis results for the developed algorithm and classical 2D Capon method based on numerical simulation using Monte Carlo method are presented. The proposed scheme of DoA estimation for coherent signal processing of distributed radars is shown to lead to an improvement of the main considered metrics representing the probability of correctly estimating the number of targets, mean square error, and square error compared to a single radar system. The proposed low-computational algorithm shows the gain in complexity compared to full 2D Capon algorithm.Conclusions. The proposed two-stage algorithm for estimating the directions of arrival of signals in azimuth and elevation planes can be applied to the distributed system of coherent radars with several receiving and transmitting antennas representing multiple input multiple output (MIMO) radars. The algorithm is based on sequentially estimating the directions of arrival, implying estimation in the azimuthal plane at the first stage and estimation in the vertical plane at the second stage. The performance of a coherent radar system with limited antenna array configuration of separate radar is close in characteristics to a high-performance 4D-radar with a large antenna array system.

Magnetic anomaly detection of moving objects

Magnetometry is commonly used to detect and locate stationary as well as moving objects made of magnetic materials. The problem consists of detecting and determining any anomaly in the geomagnetic field generated by the presence and/or motion of a magnetic object, with the latter presenting a more challenging case. The data collected by a sensor or an array of sensors are treated by common signal and data processing techniques or by the objective base function (OBF) optimization methods which is the method employed in this work. A novel fluxgate sensor developed by the Sensors Laboratory is used to collect magnetic field data in an appropriately designed setup in the laboratory. The data are generated by a weakly magnetized object moving at a constant velocity and various distances from the sensor. The OBF method is used to construct the energy detection criterion. Results show that the energy value decreases at longer distances but the object is clearly detected even when the signal is buried in background noise. The performance of the overall system is promising for detecting and tracking moving objects in adverse environments such as marine traffic.

A Fourier-based explanation of 1D-CNNs for machine condition monitoring applications

Neural networks (NN) have generated extensive interest in the field of machine condition monitoring (MCM). Many applications are however adapting structures and approaches from fields where NNs are highly established (e.g., image classification). This is done despite many neural network layers presenting strong similarities to signal processing steps that are well established in MCM. This paper aims at providing a mathematical analysis of typical NN architectures and layers, with specific attention to similarities to common signal processing steps such as filtering, downsampling, and enveloping. The set of strong analogies between NN and traditional MCM tools enables this work to provide MCM-specific guidelines for the design and tuning of NN architectures for MCM-specific applications. Most MCM applications are often characterised by periodic and cyclostationary signals, a situation not frequently encountered in many other NN applications. Hence, the Fourier-based explanation of the typical NN layers will be specific to the solution of typical MCM problems (e.g., diagnostics of gears and bearings). The study is aligned with the current trends of explainable AI and physics informed neural networks (PINN), in the sense that it aims at producing mathematical rules for a rational design of the network, drawing on the expertise accumulated in decades of signal processing for MCM. This is however done without upsetting traditional NN architectures, but rather trying to provide clear reasons for the setting of network parameters. The analytical work will be verified with numerical tests, which will also enable practical considerations, and by application of the suggested guidelines to the popular Case Western Reserve University (CWRU) experimental dataset.

Reliability Analysis of Nuclear Power Plant Electrical System Considering Common Cause Failure Based on GO-FLOW

The reliability of nuclear power plant electrical systems is an important guarantee of nuclear safety, and the common fault failure problem arising from redundant design and intelligent control may greatly affect reliability assessment results. Combined with the features of repairability, multi-state characteristics, and common fault failure of nuclear power plant electrical systems, a reliability analysis method of nuclear power plant electrical systems based on the GO-FLOW method considering common fault failure is proposed. This study firstly constructs the algorithmic model of combining operators of repairable components and the equivalent model of reliability parameters of multi-mode repairable components, then establishes a probability calculation model of common fault failure for repairable systems by considering the quantitative computation of the common signaling system model, and finally, quantitatively calculates the reliability of nuclear power plant electrical systems and their influencing factors. The example simulation calculates the reliability of the external power supply system and the electrical system of the nuclear power plant, analyzes the influence of the common signal processing and the common fault failure factors on the reliability of the electrical system of the nuclear power plant, and verifies the validity of the proposed method. The results show that the common fault failure factors have a large impact on the system reliability analysis; the common fault failure of the standby diesel generator set will seriously reduce the reliability of the electrical system, which can be improved by installing additional standby diesel generators.

Physical Layer Authenticated Image Encryption for IoT Network Based on Biometric Chaotic Signature for MPFrFT OFDM System.

In this paper, a new physical layer authenticated encryption (PLAE) scheme based on the multi-parameter fractional Fourier transform-Orthogonal frequency division multiplexing (MP-FrFT-OFDM) is suggested for secure image transmission over the IoT network. In addition, a new robust multi-cascaded chaotic modular fractional sine map (MCC-MF sine map) is designed and analyzed. Also, a new dynamic chaotic biometric signature (DCBS) generator based on combining the biometric signature and the proposed MCC-MF sine map random chaotic sequence output is also designed. The final output of the proposed DCBS generator is used as a dynamic secret key for the MPFrFT OFDM system in which the encryption process is applied in the frequency domain. The proposed DCBS secret key generator generates a very large key space of 22200. The proposed DCBS secret keys generator can achieve the confidentiality and authentication properties. Statistical analysis, differential analysis and a key sensitivity test are performed to estimate the security strengths of the proposed DCBS-MP-FrFT-OFDM cryptosystem over the IoT network. The experimental results show that the proposed DCBS-MP-FrFT-OFDM cryptosystem is robust against common signal processing attacks and provides a high security level for image encryption application.

Analysis and effectiveness of deeper levels of SVD on performance of hybrid DWT and SVD watermarking

In this paper, an analysis on hybrid Discrete Wavelet Transform (DWT) and Singular Value Decomposition (SVD) for image watermarking is carried out to investigate the effect of a deeper level of the SVD on imperceptibility and robustness to resist common signal processing and geometric attacks. For this purpose, we have designed two hybrid watermarking schemes, the first one with DWT and first level of SVD, whereas, in the second scheme, the same design is employed with a second level of SVD. In this experiment, a comprehensive analysis is performed on the two designed schemes and the effect of robustness and imperceptibility is compared in the first and second levels of SVD in each DWT sub-band. Having analyzed more than 100 medical and non-medical images in standard datasets and real medical samples of patients, the experimental outcomes show a remarkable increase in both imperceptibility and robustness in the second level of SVD, in comparison to the first level. In addition, the achieved result shows that the SVD2 scheme offers the highest imperceptibility in the LL sub-band (more than 60 dB on average PSNR), with satisfactory robustness against noise attacks, but less persistence in some geometric attacks such as cropping. For the HH sub-band, strong robustness against all types of tested of attacks is obtained, though its imperceptibility is slightly lower than the achieved PSNR in the LL sub-band. In HH sub-band, an average growth of 5 dB in PSNR and 2% in NC can be observed from the second level of SVD in comparison to the first level. These results make SVD2 a good candidate for content protection, especially for medical images.

A Highly Robust Reversible Watermarking Scheme Using Embedding Optimization and Rounded Error Compensation

The robust reversible watermarking (RRW) requires high robustness and capacity on the condition of reversibility and imperceptibility, which still remains a big challenge nowadays. In this paper, we propose a two-stage RRW scheme that improves robustness and capacity through embedding optimization and rounded error compensation. The first stage inserts a robust watermark into the selected Pseudo-Zernike moments (PZMs) by using an adaptive normalization method and an optimized embedding strategy. Specifically, the adaptive normalization method achieves both an invariance to pixel amplitude variation and a balance between robustness and imperceptibility, and the optimized embedding strategy reduces embedding distortions remarkably. The watermarked PZMs are inversely transformed to generate the robustly watermarked image, in which rounded errors caused in the inverse transformation is compensated elaborately and thus a larger capacity can be obtained at the same embedding distortion. The second stage embeds a reversible watermark consisting of errors between the robust watermark embedded image and the original one, aiming at achieving the reversibility in case of no attacks. Extensive experimental simulations show that the proposed scheme provides strong robustness against common signal processing, including AWGN, salt-and-pepper noise, JPEG, JPEG2000, median filtering, mean filtering, geometrical transformations involving rotation and scaling, and a compressive sensing attack exemplified by two-dimensional compressive sensing, which outperforms the state-of-the-art schemes. Our code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/yichao-tang/PZMs-RRW</uri> .

Uniform Analysis of Multipath Components From Various Scenarios With Time-Domain Channel Sounding at 300GHz

Knowledge of the wireless channel remains crucial for the development of applications such as joint communication and sensing, intelligent reflective surfaces or terahertz communications that are currently discussed as fundamental part of the sixth generation of mobile systems. To benefit from synergies, a uniform and collaborative data evaluation of measurement sets from two different time-domain channel sounders is presented in this paper. Based on a common signal processing that extracts discrete multipath components, the properties of the propagation channel are analyzed. The channels show a great variety of characteristics depending on the scenario, the line-of-sight condition and the signal-to-noise ratio requirements of the prospective communication system. Considering the impact of a realistic antenna array, the radio channel that is ultimately relevant for the communication system is examined. Having less impact due to the spatial filtering, the different characteristics of multipath propagation still emphasize the brought range of terahertz channels. The extracted multipath components are published as research data to serve the community for future analytical studies and simulations in relevant scenarios for the sixth generation of mobile systems.

SSVS-SSVD Based Desynchronization Attacks Resilient Watermarking Method for Stereo Signals

Most of the audio signals in real-world applications are stereo signals. However, the previous desynchronization attacks resilient watermarking methods cannot preserve perceptual quality or achieve robustness when constrained by high embedding rates and stereo host. In this paper, based on two novel features segmental singular values summation (SSVS) and segmental singular values difference (SSVD) that are generated using discrete cosine transform (DCT) and singular value decomposition (SVD), we present a robust watermarking method for stereo signals that not only is robust to desynchronization attacks and common signal processing attacks but also has a larger embedding rate compared with the previous methods. In the proposed method, we first apply DCT and SVD on each segment of the host signal to extract the SSVS feature and the SSVD feature. Then we generate the adaptive embedding parameters and embed watermark bits via optimized embedding strategies based on these features. Due to the use of the adaptive embedding parameters and the optimized embedding strategies, the proposed method significantly increases the embedding rate without compromising the robustness and perceptual quality. Analysis results show our proposed method outperforms the state-of-the-art methods by a large margin, where the perceptual quality improvement is over 14%, and the robustness against desynchronization attacks is improved by more than 49% when the embedding rate is 70 bps.

WiFi Sensing on the Edge: Signal Processing Techniques and Challenges for Real-World Systems

In this work, we evaluate the feasibility of deploying ubiquitous WiFi sensing systems at the edge and consider the applicability of existing techniques on constrained edge devices and what challenges still exist for deploying WiFi sensing devices outside of laboratory environments. Through an extensive survey of existing literature in the area of WiFi sensing, we discover common signal processing techniques and evaluate the applicability of these techniques for online edge systems. Based on these techniques, we develop a topology of components required for a low-cost WiFi sensing system and develop a low-cost WiFi sensing system using ESP32 IoT microcontroller edge devices. We perform numerous real world WiFi sensing experiments to thoroughly evaluate machine learning prediction accuracy by performing Tree-structured Parzen Estimator (TPE) hyperparameter optimization to independently identify optimal hyperparameters for each method. Additionally, we evaluate our system directly on-board the ESP32 with respect to computation time per method and overall sample throughput rate. Through this evaluation, we demonstrate how an edge WiFi sensing system enables online machine learning through the use of on-device inference and thus can be used for ubiquitous WiFi sensing system deployments.

ECSS: High-Embedding-Capacity Audio Watermarking with Diversity Reception.

Digital audio watermarking is a promising technology for copyright protection, yet its low embedding capacity remains a challenge for widespread applications. In this paper, the spread-spectrum watermarking algorithm is viewed as a communication channel, and the embedding capacity is analyzed and modeled with information theory. Following this embedding capacity model, we propose the extended-codebook spread-spectrum (ECSS) watermarking algorithm to heighten the embedding capacity. In addition, the diversity reception (DR) mechanism is adopted to optimize the proposed algorithm to obtain both high embedding capacity and strong robustness while the imperceptibility is guaranteed. We experimentally verify the effectiveness of the ECSS algorithm and the DR mechanism, evaluate the performance of the proposed algorithm against common signal processing attacks, and compare the performance with existing high-capacity algorithms. The experiments demonstrate that the proposed algorithm achieves a high embedding capacity with applicable imperceptibility and robustness.

Fast and Accurate Linear Fitting for an Incompletely Sampled Gaussian Function With a Long Tail [Tips &amp; Tricks

Fitting experiment data onto a curve is a common signal processing technique to extract data features and establish the relationship between variables. Often, we expect the curve to comply with some analytical function and then turn data fitting into estimating the unknown parameters of a function. Among analytical functions for data fitting, Gaussian function is the most widely used one due to its extensive applications in numerous science and engineering fields. To name just a few, Gaussian function is highly popular in statistical signal processing and analysis, thanks to the central limit theorem [1]; Gaussian function frequently appears in the quantum harmonic oscillator, quantum field theory, optics, lasers, and many other theories and models in Physics [2]; moreover, Gaussian function is widely applied in chemistry for depicting molecular orbitals, in computer science for imaging processing and in artificial intelligence for defining neural networks.

Intra-beat biomarker for accurate continuous non-invasive blood pressure monitoring

Accurate continuous non-invasive blood pressure (CNIBP) monitoring is the holy grail of digital medicine but remains elusive largely due to significant drifts in signal and motion artifacts that necessitate frequent device recalibration. To address these challenges, we developed a unique approach by creating a novel intra-beat biomarker (Diastolic Transit Time, DTT) to achieve highly accurate blood pressure (BP) estimations. We demonstrated our approach’s superior performance, compared to other common signal processing techniques, in eliminating stochastic baseline wander, while maintaining signal integrity and measurement accuracy, even during significant hemodynamic changes. We applied this new algorithm to BP data collected using non-invasive sensors from a diverse cohort of high acuity patients and demonstrated that we could achieve close agreement with the gold standard invasive arterial line BP measurements, for up to 20 min without recalibration. We established our approach's generalizability by successfully applying it to pulse waveforms obtained from various sensors, including photoplethysmography and capacitive-based pressure sensors. Our algorithm also maintained signal integrity, enabling reliable assessments of BP variability. Moreover, our algorithm demonstrated tolerance to both low- and high-frequency motion artifacts during abrupt hand movements and prolonged periods of walking. Thus, our approach shows promise in constituting a necessary advance and can be applied to a wide range of wearable sensors for CNIBP monitoring in the ambulatory and inpatient settings.

A sophisticated and provably grayscale image watermarking system using DWT-SVD domain.

Digital watermarking has attracted increasing attentions as it has been the current solution to copyright protection and content authentication in today’s digital transformation, which has become an issue to be addressed in multimedia technology. In this paper, we propose an advanced image watermarking system based on the discrete wavelet transform (DWT) in combination with the singular value decomposition (SVD). Firstly, at the sender side, DWT is applied on a grayscale cover image and then eigendecomposition is performed on original HH (high–high) components. Similar operation is done on a grayscale watermark image. Then, two unitary and one diagonal matrices are combined to form a digital watermarked image applying inverse discrete wavelet transform (iDWT). The diagonal component of original image is transmitted through secured channel. At the receiver end, the watermark image is recovered using the watermarked image and diagonal component of the original image. Finally, we compare the original and recovered watermark image and obtained perfect normalized correlation. Simulation consequences indicate that the presented scheme can satisfy the needs of visual imperceptibility and also has high security and strong robustness against many common attacks and signal processing operations. The proposed digital image watermarking system is also compared to state-of-the-art methods to confirm the reliability and supremacy.

Gas sensing technology for meat quality assessment: A review

AbstractMeat is rich in many nutritional elements and is one of the most basic foods for human beings. The demand for meat is increasing year by year, but at the same time, meat is highly susceptible to spoilage during storage and transportation, and consumption of such meat can be hazardous to human health. Gas sensing technology provides a nondestructive, less time‐consuming, and convenient detection method compared with traditional meat quality testing techniques. In this review, we briefly describe the working principle, the common sensors, signal processing, and pattern recognition for gas sensing technology. Then, we discuss the most relevant contributions of gas sensing technology to meat freshness evaluations and authenticity assessments using different data processing methods in recent years. Finally, the problems, prospects, and likely future development of gas sensing technology are highlighted.Practical applicationsThis article analyzes each key technology of gas sensing, points out some existing defects and future trends, reviews the practical applications of gas sensing technology in meat quality inspection in recent years, and puts forward feasible technology enhancement suggestions.

A Perspective on Developing a Plant 'Holobiont' for Future Saline Agriculture.

Soil salinity adversely affects plant growth and has become a major limiting factor for agricultural development worldwide. There is a continuing demand for sustainable technology innovation in saline agriculture. Among various bio-techniques being used to reduce the salinity hazard, symbiotic microorganisms such as rhizobia and arbuscular mycorrhizal (AM) fungi have proved to be efficient. These symbiotic associations each deploy an array of well-tuned mechanisms to provide salinity tolerance for the plant. In this review, we first comprehensively cover major research advances in symbiont-induced salinity tolerance in plants. Second, we describe the common signaling process used by legumes to control symbiosis establishment with rhizobia and AM fungi. Multi-omics technologies have enabled us to identify and characterize more genes involved in symbiosis, and eventually, map out the key signaling pathways. These developments have laid the foundation for technological innovations that use symbiotic microorganisms to improve crop salt tolerance on a larger scale. Thus, with the aim of better utilizing symbiotic microorganisms in saline agriculture, we propose the possibility of developing non-legume ‘holobionts’ by taking advantage of newly developed genome editing technology. This will open a new avenue for capitalizing on symbiotic microorganisms to enhance plant saline tolerance for increased sustainability and yields in saline agriculture.

A New High-Capacity Audio Watermarking Based on Wavelet Transform using the Golden Ratio and TLBO Algorithm

Digital watermarking is one of the best solutions for copyright infringement, copying, data verification, and illegal distribution of digital media. Recently, the protection of digital audio signals has received much attention as one of the fascinating topics for researchers and scholars. In this paper, we presented a new high-capacity, clear, and robust audio signaling scheme based on the DWT conversion synergy and golden ratio advantages using the TLBO algorithm. We used the TLBO algorithm to determine the effective frame length and embedded range, and the golden ratio to determine the appropriate embedded locations for each frame. First, the main audio signal was broken down into several sub-bands using a DWT in a specific frequency range. Since the human auditory system is not sensitive to changes in high-frequency bands, to increase the clarity and capacity of these sub-bands to embed bits we used the watermark signal. Moreover, to increase the resistance to common attacks, we framed the high-frequency bandwidth and then used the average of the frames as a key value. Our main idea was to embed an 8-bit signal simultaneously in the host signal. Experimental results showed that the proposed method is free from significant noticeable distortion (SNR about 29.68dB) and increases the resistance to common signal processing attacks such as high pass filter, echo, resampling, MPEG (MP3), etc.

Recognition, Processing, and Detection of Sensor Fault Signal Based on Genetic Algorithm

With the development of electronic information science and network transmission technology, signal processing technology is widely used in various fields. The processing of sensor coarse signal is the key of signal processing technology, in order to study the signal detection and transmission function of the sensor. A genetic algorithm-based sensor fault signal identification, processing, and detection are proposed, and three common signal analysis and processing methods are summarized. The methods of optimal arrangement of sensors are as follows: effective independent algorithm, genetic algorithm, simulated annealing algorithm, and ant colony algorithm principle are studied in detail; signal analysis methods are as follows: fast Fourier transform, wavelet transform, and HHT transform are studied in detail. In the experimental system of the sensor’s coarse signal processing mode, the optimal arrangement of the measurement points of the acceleration sensor in this experiment is directly related to the information collection effect of the monitoring system. Combined with numerical simulation and engineering cases, the soft computing (genetic algorithm, simulated annealing algorithm, and ant colony algorithm) is analyzed in detail, out of the MATLAB program for soft computing. Taking four typical functions as the numerical experimental platform, the three algorithms are used for comparative experimental analysis, and their optimized performance and application range are analyzed. Finally, the practical application performance of soft computing is tested by the practical application problem of optimal path optimization of measuring points. When there are only 10 measuring points, all three algorithms can quickly converge to the global optimal solution, but when there are 100 measuring points, only approximate solutions can be obtained.