Signal Processing Algorithms Research Articles

Research on the Advantages and Challenges of Replacing LEDs with Lasers in Functional Near-Infrared Spectroscopy Systems Based on Advanced Signal Processing Algorithms

Abstract: Functional near infrared spectroscopy (fNIRS) has received increasing attention as a non-invasive, portable brain hemodynamic monitoring tool due to its potential for use in natural Settings. Traditional fNIRS systems commonly use light-emitting diodes (LEDs) as light sources, which have become the mainstream choice because of their low cost, easy integration, low heat output, and availability of a variety of near-infrared wavelengths. However, LEDs are limited by their low optical output power and broad wavelength range, which restrict their effectiveness in deep tissue penetration and signal quality. In contrast, Laser Diodes (LD) have the advantages of good monochromaticity and high optical output power, which can provide deeper tissue penetration and higher signal stability. Therefore, this study explores the feasibility of using lasers instead of leds in fNIRS systems and their impact on signal quality and imaging depth. By comparing the performance of LED and laser-based fNIRS systems, it is evident that lasers provide significant improvements in signal quality and imaging depth, although they face challenges in terms of cost, thermal management, and safety. The research in this paper provides an important reference for the future design of fNIRS system, especially in the application scenarios requiring high precision imaging.

An in-air synthetic aperture sonar dataset of target scattering in environments of varying complexity

This paper describes a synthetic aperture sonar (SAS) dataset collected in-air consisting of four types of targets in four environments of different complexity. The in-air laboratory based experiments produced data with a level of fidelity and ground truth accuracy that is not easily attainable in data collected underwater. The range of complexity, high level of data fidelity, and accurate ground truth provides a rich dataset with acoustic features on multiple scales. It can be used to develop new signal-processing and image reconstruction algorithms, as well as machine learning models for object detection and classification. It may also find application in model verification and validation for acoustic simulators. The dataset consists of raw acoustic time series returns, associated environmental conditions, hardware configuration, array motion, as well as the reconstructed imagery.

Research on hydroacoustic signal processing algorithm based on B-spline and Hilbert transform

Purpose This paper aims to solve the problems of baseline drift, susceptibility to abnormal data interference during baseline drift processing, and phase inconsistency in underwater acoustic target detection and signal processing of single microelectromechanical systems (MEMS) vector hydrophone. To this end, this paper proposes a baseline drift removal algorithm based on Huber regression model with B-spline interpolation (H-BS) and a phase compensation algorithm based on the Hilbert transform. Design/methodology/approach First, the Huber regression model is innovatively introduced into the conventional B-spline interpolation (B-spline) to solve the control point vectors more accurately and to improve the anti-interference ability of the abnormal data when the B-spline interpolation fitting removes baseline drift and the baseline drift components in the signals are fitted accurately and removed by the above method. Next, the Hilbert transform is applied to the three-channel output signals of the MEMS vector hydrophone to calculate the instantaneous phase and the phase compensation is performed on the vector X/Y signals with the scalar P signal as the reference. Findings Through simulation experiments, it is found that H-BS proposed in this paper has smaller processing error and better robustness than variational modal decomposition and B-spline, but the H-BS algorithm takes slightly longer than the B-spline. In the actual lake test experiments, the H-BS algorithm can effectively remove the baseline drift component in the original signal and restore the signal waveform, and after the Hilbert transform phase compensation, the direction of arrival estimation accuracy of the signal is improved by 1°∼2°, which realizes high-precision orientation to the acoustic source target. Originality/value In this paper, the Huber regression model is introduced into B-spline interpolation fitting for the first time and applied in the specialized field of hydroacoustic signal baseline drift removal. Meanwhile, the Hilbert transform is applied to phase compensation of hydroacoustic signals. After simulation and practical experiments, these two methods are verified to be effective in processing hydroacoustic signals and perform better than similar methods. This study provides a new research direction for the signal processing of MEMS vector hydrophone, which has important practical engineering application value.

Quantitative assessment of the effectiveness of adaptive spatial processing algorithms in searching for low-noise underwater vehicles in surface shipping conditions of different density

The article describes the methodology and provides the results of a model quantitative assessment of the effectiveness of solving the problem of detecting and tracking a low-noise underwater object using three algorithms for spatial signal processing at the output of a multi-element antenna — the non-adaptive Bartlett algorithm, the Capon algorithm, and the Capon algorithm combined with a projection procedure limiting the signal power of strong local sources.

Chuprov invariant for vector-scalar fields of multipole sources in a shallow sea

A computational and theoretical study of the properties of the well-known waveguide invariant S.D. Chuprova (IC) was carried out in a plane-parallel Pekeris waveguide. Unlike earlier works, in which predominantly non-directional (monopole) sources were used as a source, and sound pressure fields (scalar fields) were studied, in this work not only scalar, but also vector fields formed in the waveguide by directional - combined multipole sources with directivity in both horizontal and vertical planes. A differential equation has been obtained that makes it possible to fairly accurately calculate the IC values under different conditions of signal propagation and different depths of sources and receivers. This makes it possible, in a simpler way than “full computer modeling,” to predict the invariance (stability) of the IC when varying both the hydrophysical conditions in the waveguide and the geometry of the experiment. It is shown that the directionality of sources in the horizontal plane has virtually no effect on the properties of the IC, and the directionality in the vertical plane leads to a shift in the fan structure of the signal amplitude fields, but has little effect on the IC values. The properties of the fan structure change in a similar way when using vertical projections of the oscillatory velocity vector - despite the fact that another analytical relation, different from scalar fields, is used to calculate the IC, the IC value is close to (+1) at all frequencies and distances, except those at which new modes or dislocations arise. At these frequencies and in these zones, alternating emissions with different signs and magnitudes occur. It is concluded that the stability of IC allows the application of signal processing algorithms developed for scalar fields and non-directional sources to vector-scalar fields generated, including using directional sources.

PEAK TO AVERAGE POWER COMPUTING AND OPTIMIZATION OF OPTICAL OTFS 5G WAVEFORM USING HYBRID FRACTAL-BASED SIGNAL PROCESSING ALGORITHM

Optical orthogonal time frequency space (OTFS) is better at handling Doppler shifts and multipath fading, making signals more reliable and valuable in places with much movement beyond the fifth generation (B5G). Using practical power amplifiers at the transmitter causes power inefficiency and signal distortion because of the OTFS system’s high peak-to-average power ratio (PAPR), severely reducing system efficiency. Combining partial transmit sequence (PTS) and selective mapping (SLM), a technique known as PTS+SLM, reduces peak power. While SLM generates numerous phase-modulated signal candidates and chooses the one with the lowest PAPR, PTS separates the signal into sub-blocks and optimizes their phases to decrease peak power. With few changes to the signal structure, this dual strategy effectively reduces PAPR while improving power spectral density (PSD) efficiency. As a result, we ensure the accuracy and dependability of the transferred data by maintaining the bit error rate (BER). Fractal optimization methods could be applied to these algorithms. For example, fractal-inspired optimization techniques might be used to explore the phase space more effectively or to discover new phase sequences that result in lower PAPR. According to the simulation findings, the suggested PTS+SLM method works better than the traditional PTS and SLM methods.

A TransISP Based Image Enhancement Method for Visual Disbalance in Low‐light Images

AbstractExisting image enhancement algorithms often fail to effectively address issues of visual disbalance, such as brightness unevenness and color distortion, in low‐light images. To overcome these challenges, we propose a TransISP‐based image enhancement method specifically designed for low‐light images. To mitigate color distortion, we design dual encoders based on decoupled representation learning, which enable complete decoupling of the reflection and illumination components, thereby preventing mutual interference during the image enhancement process. To address brightness unevenness, we introduce CNNformer, a hybrid model combining CNN and Transformer. This model efficiently captures local details and long‐distance dependencies between pixels, contributing to the enhancement of brightness features across various local regions. Additionally, we integrate traditional image signal processing algorithms to achieve efficient color correction and denoising of the reflection component. Furthermore, we employ a generative adversarial network (GAN) as the overarching framework to facilitate unsupervised learning. The experimental results show that, compared with six SOTA image enhancement algorithms, our method obtains significant improvement in evaluation indexes (e.g., on LOL, PSNR: 15.59%, SSIM: 9.77%, VIF: 9.65%), and it can improve visual disbalance defects in low‐light images captured from real‐world coal mine underground scenarios.

Evaluating antenna phase center variation effects on tropospheric delay retrieval using a low-cost dual-frequency GNSS receiver

Abstract This study examines the impact of Phase Center Variation (PCV) corrections on Zenith Wet Delay (ZWD) accuracy using a low-cost U-blox ZED-F9P receiver paired with three different antenna configurations: the high-grade TRM57971 antenna, the moderate-grade AS-ANT3BCAL antenna, and the low-cost ANN-MB-00 antenna. Among the three antennas evaluated, the low-cost antenna exhibited the largest PCV magnitude and a pronounced elevation angle dependence. In contrast, the other two antennas demonstrated lower levels of PCV variation. Without PCV corrections, the low-cost antenna showed significant ZWD biases compared to reference values. Applying PCV corrections significantly improved its accuracy, reducing bias and RMS by 88% and 79%, respectively. Moderate- and high-grade antennas experienced minimal improvement with correction. All antennas exhibited remarkable day-to-day repeatability in their residual patterns, despite variations observed in the RMS of phase residuals. This observed repeatability is likely attributable to the presence of unmodeled multipath contributions. The variations in RMS, in turn, can be primarily ascribed to inherent differences in multipath resistance among the antenna designs. This study highlights the critical role of PCV corrections for accurate ZWD estimation with low-cost GNSS receivers. Future research should prioritize the acquisition of manufacturer-provided calibration data for low-cost antennas to streamline and enhance the accuracy of PCV correction applications. Moreover, efforts should be directed toward developing innovative solutions, such as low-cost, multipath-resistant antennas or advanced signal processing algorithms, to mitigate the impact of multipath errors. By addressing these areas, low-cost GNSS solutions can become more reliable and cost-effective tools for tropospheric delay estimation.

Enhanced Performance Analysis of 120 Gbps Single‐Channel IQ Modulation Based PDM‐FSO Transmission System Using 64‐QAM Signals

ABSTRACTThe ever‐augmenting demand of data channel‐bandwidth by the end users has resulted in the exploration of new transmission techniques for wireless transmission networks. In this work, we have demonstrated the modeling of a single‐channel free space optics (FSO) transmission system by employing polarization‐division‐multiplexing (PDM) technique. Further, in‐phase‐quadrature modulation (IQM) has been used to increase the transmission efficiency of the system. To decrease the baud‐rate of the system, we have proposed the use of 64‐level quadrature amplitude modulation (QAM) signals. The net transmission rate of the system is 120 Gbps with a baud‐rate of 10 Gbps. Furthermore, the overcome the signal degradation of the 120 Gbps signal propagating through atmospheric channel, we have reported the use of advanced digital signal processing (DSP) algorithms. The system's performance is investigated for adverse external climate weather conditions and the results reported show reliable 120 Gbps data transmission along range between 870 m and 16.25 km with good performance of BER and EVM 7.5%.

Zigbee-Based Wireless Sensor Network of MEMS Accelerometers for Pavement Monitoring

In this paper, we propose a wireless sensor network for pavement health monitoring exploiting the Zigbee technology. Accelerometers are adopted to measure local accelerations linked to pavement vibrations, which are then converted into displacements by a signal processing algorithm. Each device consists of an on-board unit buried in the roadway and a roadside unit. The on-board unit comprises a microcontroller, an accelerometer and a Zigbee module that transfers acceleration data wirelessly to the roadside unit. The roadside unit consists of a Raspberry Pi, a Zigbee module and a USB Zigbee adapter. Laboratory tests were conducted using a vibration table and with three different accelerometers, to assess the system capability. A typical displacement signal from a five-axle truck was applied to the vibration table with two different displacement peaks, allowing for two different vehicle speeds. The prototyped system was then encapsulated in PVC packaging, deployed and tested in a real-life road situation with a fatigue carousel featuring rotating truck axles. The laboratory and on-road measurements show that displacements can be estimated with an accuracy equivalent to that of a reference sensor.

Resonant Cell-Based 1f Photoacoustic Gas Analyzer Immune to Light Power Fluctuation and Frequency Mismatch.

To overcome the light power fluctuation and frequency mismatch in photoacoustic spectroscopy (PAS), we proposed a self-corrected 1f-only resonant cell-based PAS (1f-RCPAS) gas analyzer. Based on the theoretical analysis of the 1f signal, a signal processing algorithm considering laser power-current nonlinearity is proposed. The 1f-only algorithm is well-tailored for the resonant systems, requiring no time-division multiplexing. The algorithm is further improved to extend the dynamic range. The T-type resonant cell incorporating a graphene sticker is utilized for effectively amplifying the acoustic signals from both the gas and solid to achieve normalization. No optical path alignment is needed. For the low resonance frequency, a digital orthogonal-vector lock-in amplifier is used, further simplifying the system setup. The gas analyzer is used to measure methane (CH4) with the near-infrared absorption peak at 1651 nm. The experiments demonstrated immunity to fiber coupling loss, laser power drift over time, and frequency mismatch caused by property differences between air and standard gases. The R2 value in the concentration calibration reaches 0.99995, and the minimum detection limit given by the Allan variance reaches 3.5 ppb at an average time of 105 s.

A self-adaptive fractional-order PID controller for the particle velocity regulation in a pneumatic conveying system

This paper presents the methodical development of a state-error-driven self-adaptive fractional-order proportional–integral–derivative (AFOPID) control algorithm to efficiently regulate the velocity of pneumatically conveyed particles at the desired set point and to prevent the blockage of particles in a pipe due to an imbalanced combination of their velocity and corresponding mass flow rate. The proposed fractional control law is constituted by adaptively modulating fractional orders of the integral and differential operators in the control based on the state-error variations in the velocity of solid particles. The particle’s velocity is measured and updated via electrostatic sensors in conjunction with cross-correlation signal processing algorithms. All the other fixed hyper-parameters associated with the proportional–integral–derivative (PID) control scheme are meta-heuristically optimized by using a genetic algorithm. The proposed AFOPID is benchmarked against conventional integer-order PID and the fractional-order proportional–integral–derivative (FOPID) controllers. Experiments are performed on a laboratory-scale test rig to comparatively analyze the aforesaid control schemes, where each controller is examined for three velocity set points and three disturbance levels. The experimental results validate the superior time optimality and robustness of the proposed AFOPID controller against bounded disturbances and abrupt velocity set-point variations by manifesting relatively faster settling time, low overshoots (and undershoots), and smaller steady-state fluctuations.

A cloud-based simulation framework for high-fidelity room impulse response dataset generation: case study, validation and computational performance

Increased cost-effective computational resources in the cloud in combination with state-of-the-art simulation algorithms has opened doors for generating large quantities of simulated high-fidelity room impulse responses. Such datasets can, e.g., be used for training machine learning based audio signal processing algorithms such as speech enhancement or dereverberation, as well as for carrying out virtual prototyping of audio devices in different acoustic conditions. The authors have previously presented a cloud-based simulation engine that is well suited for such large-scale dataset generation tasks. The simulation engine leverages a hybrid wave/geometrical solver, in combination with various geometry pre-processing technology and spatial audio post-processing technology. In this paper, we present a dataset generation case study using this simulation engine, as well as a novel validation study into certain aspects of the spatial audio post-processing. Finally, we present an analysis into computational performance when leveraging multi-node computational architectures to run extremely large wave-based simulations using the simulation framework. To the best of our knowledge, this is the first time a multi-node wave-based acoustics solver presented, enabling wave-based simulations with billions of degrees of freedom.

Low-Complexity Neural Network-Based Processing Algorithm for QCM-D Signals

Low-Complexity Neural Network-Based Processing Algorithm for QCM-D Signals

Polymer-metal film induced ultrahigh output TVNG and self-powered intelligent sensor system assisted by EEMD

Tribovoltaic nanogenerators (TVNGs) based on tribovoltaic effect can output DC current directly, and has the advantages of high output current density and low internal resistance. While TVNGs have been restricted by its low output voltage and power. Herein, we have developed a metal-polymer film with a micro-uneven structure on the surface, much higher corrosion resistance and wear resistance than metal. On this basis, a centimeter-level sliding structure metal-EP/GaN-TVNG has been fabricated, which shows a high and stable long-term DC electrical output (a maximized power output of 4.19 W/m2 when matched with a 0.07 MΩ resistor, a stable output current of 2.25 A/m2 and a peak Voc of 27 V). Further, a TVNG vehicle travelling intelligent sensor and system which can integrate electrical signals collected by TVNG into digital signal processing algorithms and then feed backing to the driving system, has been designed and manufactured. As an effective method for deep analysis of signals, Ensemble Empirical Mode Decomposition (EEMD) has been used for the first time for deep processing of nanogenerator signals in this work. This work provides a new model of further development of high output TVNG and a new deep signal processing methods of TVNG/TENG signal.

Evaluating ASD in children through automatic analysis of paintings

Autism spectrum disorder (ASD) is a hereditary neurodevelopmental disorder affecting individuals, families, and societies worldwide. Screening for ASD relies on specialized medical resources, and current machine learning-based screening methods depend on expensive professional devices and algorithms. Therefore, there is a critical need to develop accessible and easily implementable methods for ASD assessment. In this study, we are committed to finding such an ASD screening and rehabilitation assessment solution based on children’s drawings. From an ASD painting database, 375 drawings from children with ASD and 160 drawings from typically developing children were selected, and a series of image signal processing algorithms based on typical characteristics of children with ASD were designed to extract features from images. The effectiveness of extracted features was evaluated through statistical methods, and they were then classified using a support vector machine (SVM) and XGBoost (eXtreme Gradient Boosting). In 5-fold cross-validation, the SVM achieved a recall of 94.93%, a precision of 86.40%, an accuracy of 85.98%, and an AUC of 90.90%, while the XGBoost achieved a recall of 96.27%, a precision of 93.78%, an accuracy of 92.90%, and an AUC of 98.00%. This efficacy persists at a high level even during additional validation on a set of newly collected paintings. Not only did the performance surpass that of participated human experts, but the high recall rate, as well as its affordability, manageability, and ease of implementation, indicates potentiality in wide screening and rehabilitation assessment. All analysis code is public at GitHub: dishangti/ASD-Painting-Pub.

Implementation of Wearable Wireless Chest Patch Monitoring Terminal

A wearable wireless chest patch monitoring terminal is designed to realize the acquisition, processing, and wireless transmission of ECG, respiration, and body temperature signals. The analog front-end ADS1292R, which integrates respiratory impedance and ECG front-end, is utilized to collect human ECG and respiratory signals. The body temperature is collected using a low-power, high-precision digital temperature sensor MAX30208. A filter algorithm for signal processing and wireless transmission is designed through a low-power nRF52840 Bluetooth SoC with an Arm Cortex-M4F kernel. The experimental results show that the designed monitoring terminal can monitor the ECG, respiration, and body temperature parameters of the human body in real-time and send the monitoring results via Bluetooth, with a continuous working time of more than 13 hours. The wearable wireless chest patch monitoring terminal features good portability, long standby time, and high measurement accuracy, and it has promising application prospects in the fields of family health monitoring, mobile medical treatment, and smart healthcare.

Optimization of monitoring systems power cable lines

RELEVANCE. The length and complexity of the geography of medium voltage cable lines is at a high level. Such lines extend underground, on supports in the air. In order to constantly maintain the reliability of city power supply at a high level, any interruptions and accidents should be promptly corrected.TARGET. The main goal of the work is to develop the theory of medium voltage CL research, to practically and theoretically substantiate the search for the most convenient and effective installation for CL diagnostics, to study and develop possible modifications of CL diagnostic installations.METHODS. A variant of CL diagnostics based on the CPDA-60 installation is proposed, which makes it possible to find and localize the places where defects occur in the insulation based on the measurement and analysis of partial discharges (PD). Suitable for insulation monitoring in all types of high voltage cables. The CPDA installation can be used when testing new cable lines being put into operation, and to analyze the condition of old cables in operation.RESULTS. Cable lines require an integrated approach to diagnostics and monitoring, since the reliability of modern authentication systems for the generation and distribution of electricity is largely determined by the electrical reliability of electrical equipment. Technical diagnostics of equipment is a key link, the quality of which determines the efficiency of the processes of organizing production activities, strategic planning and renovation of electric grid assets.CONCLUSION. The study and analysis of the presented data and research allows us to form a conclusion regarding the method of measuring and localizing partial discharges (PD) in power cable lines (CL) using the Online Wire Testing System (OWTS) diagnostic system. The OWTS system allows real-time measurements without interrupting cable lines, making it especially valuable to the energy industry. Thanks to the introduction of advanced technologies and signal processing algorithms, the method has high accuracy and sensitivity to minimal manifestations of private discharges, which allows not only to detect, but also to accurately localize the location of defects in the insulation. The use of this method can significantly increase the service life of power cables, reduce the likelihood of sudden accidents and, as a result, reduce the cost of repair and maintenance of electrical power equipment. Ultimately, improvements in diagnostic and monitoring techniques, including the method of measuring and localizing PD in power lines using OWTS, represent a significant step towards improving the reliability and safety of electrical power systems. This will not only reduce operating costs, but also ensure uninterrupted and high-quality power supply to consumers.

Thermal desorption-photoionization ion mobility-electronic nose (TD-PIM-Nose) with distance-probability joint decision SVM algorithm: A novel system for Daqu Grade identification

Electronic nose is a bionic technology that uses sensor arrays and pattern recognition algorithms to mimic the human olfactory system. This study developed a thermal desorption-photoionization ion mobility-electronic nose (TD-PIM-Nose) system, employing thermal desorption for direct sampling and humidity control, with a photoionization ion mobility tube as virtual sensor array for component separation and detection, and pattern recognition algorithms for signal processing to differentiate and identify samples. Furthermore, it was applied to assess four quality grades of Daqu samples (“Excellent+”, “Excellent”, “Grade I”, and “Grade II”) determined by the Check-All-That-Apply (CATA) method. Characteristic compound differences among these grades were identified using fingerprint spectra and reduced mobility values. A distance-probability joint decision support vector machine (SVM) algorithm model was established, validated against sensory CATA standards. Results showed identification accuracies: 90 %, 90 %, 96.88 %, and 100 % for respective grades. These findings demonstrated the promising potential of the TD-PIM-Nose system in Daqu quality grading.

High-Resolution Sea Surface Target Detection Using Bi-Frequency High-Frequency Surface Wave Radar

The monitoring of the sea surface, whether it is the state of the sea or the position of targets (ships), is an up-to-date research topic. In order to determine localization parameters of ships, we propose a high-resolution algorithm for primary signal processing in high-frequency surface wave radar (HFSWR) which operates at two frequencies. The proposed algorithm is based on a high-resolution estimate of the range–Doppler (RD-HR) map formed at every antenna in the receive antenna array, which is an essential task, because the performance of the entire radar system depends on its estimation. We also propose a new focusing method allowing us to have only one RD-HR map in the detection process, which collects the information from both these carrier frequencies. The goal of the bi-frequency mode of operation is to improve the detectability of targets, because their signals are affected by different Bragg-line interference patterns at different frequencies, as seen on the RD-HR maps during the primary signal processing. Also, the effect of the sea (sea clutter) manifests itself in different ways at different frequencies. Some targets are masked (undetectable) at one frequency, but they become visible at another frequency. By exploiting this, we increase the probability of detection. The bi-frequency architecture (system model) for the localization of sea targets and the novel signal model are presented in this paper. The advantage of bi-frequency mode served as a motivation for testing the detectability of small boats, which is otherwise a very challenging task, primarily because such targets have a small radar reflective surface, they move quickly, and often change their direction. Based on experimentally obtained results, it can be observed that the probability of detection of small boats can also be significantly improved by using a bi-frequency architecture.