Multiple Signal Processing Research Articles

Utilizing Multiple Triboelectric Nanogenerator Sensors and Signal Processing Technology for Monitoring Periodic Leg Movements of Sleep.

High-quality sleep is essential for both physiological and cognitive functions. However, periodic leg movements of sleep (PLMS), an involuntary phenomenon during sleep, affects millions of people worldwide, contributing to sleep fragmentation and functional impairments. The accurate monitoring of PLMS is important for identifying and addressing these issues. Traditional methods, such as polysomnography (PSG), which monitor the bare tibialis muscle movements in clinical environments, may not adequately reflect the natural sleep patterns at home. They are costly and unsuitable for long-term studies. In recent years, there has been growing interest in using flexible sensors for sleep monitoring. Previous studies have applied triboelectric nanogenerators (TENGs) as flexible sensors to detect muscle movements during sleep. However, distinguishing true PLMS from false signals caused by external factors, such as blankets, remains a challenge. This study proposes a method using three TENG sensors placed on the dorsum, ankle, and tibialis, respectively, along with signal processing techniques to enhance the accuracy of PLMS detection. This study provides a cost-effective, comfortable method for PLMS monitoring, with the potential for widespread use in home-based sleep studies and long-term care in the future.

Predicting Spontaneous Termination of Atrial Fibrillation Based on Analysis of Standard Electrocardiograms: ASystematic Review.

Forward prediction of atrial fibrillation (AF) termination is a challenging technical problem of increasing significance due to rising AF presentations to emergency departments worldwide. The ability to non-invasively predict which AF episodes will terminate has important implications in terms of clinical decision-making surrounding treatment and admission, with subsequent impacts on hospital capacity and the economic cost of AF hospitalizations. MEDLINE, EMCare, CINAHL, CENTRAL, and SCOPUS were searched on 29 July 2023 for articles where an attempt to predict AF termination was made using standard surface ECG recordings. The final review included 35 articles. Signal processing techniques fit into three broad categories including machine learning (n = 14), entropy analysis (n = 12), and time-frequency/frequency analysis (n = 9). Retrospectively processed ECG data was used in all studies with no prospective validation studies. Most studies (n = 33) utilized the same ECG database, which included recordings that either terminated within 1 min or continued for over 1 h. There was no significant difference in accuracy between groups (H(2) = 0.058, p-value = 0.971). Only one study assessed recordings earlier than several minutes preceding termination, achieving 92% accuracy using the central 10 s of paroxysmal episodes lasting up to 174. No studies attempted to forward predict AF termination in real-time, representing an opportunity for novel prospective validation studies. Multiple signal processing techniques have proven accurate in predicting AF termination utilizing ECG recordings sourced from a database retrospectively.

Fully Integrated Multiplexed Wristwatch for Real-Time Monitoring of Electrolyte Ions in Sweat.

Considerable progress has already been made in sweat sensors based on electrochemical methods to realize real-time monitoring of biomarkers. However, realizing long-term monitoring of multiple targets at the atomic level remains extremely challenging, in terms of designing stable solid contact (SC) interfaces and fully integrating multiple modules for large-scale applications of sweat sensors. Herein, a fully integrated wristwatch was designed using mass-manufactured sensor arrays based on hierarchical multilayer-pore cross-linked N-doped porous carbon coated by reduced graphene oxide (NPCs@rGO-950) microspheres with high hydrophobicity as core SC, and highly selective monitoring simultaneously for K+, Na+, and Ca2+ ions in human sweat was achieved, exhibiting near-Nernst responses almost without forming an interfacial water layer. Combined with computed tomography, solid-solid interface potential diffusion simulation results reveal extremely low interface diffusion potential and high interface capacitance (598 μF), ensuring the excellent potential stability, reversibility, repeatability, and selectivity of sensor arrays. The developed highly integrated-multiplexed wristwatch with multiple modules, including SC, sensor array, microfluidic chip, signal transduction, signal processing, and data visualization, achieved reliable real-time monitoring for K+, Na+, and Ca2+ ion concentrations in sweat. Ingenious material design, scalable sensor fabrication, and electrical integration of multimodule wearables lay the foundation for developing reliable sweat-sensing systems for health monitoring.

HybMED: A Hybrid Neural Network Training Processor with Multi-Sparsity Exploitation for Internet of Medical Things.

Cloud-based training and edge-based inference modes for Artificial Intelligence of Medical Things (AIoMT) applications suffer from accuracy degradation due to physiological signal variations among patients. On-chip learning can overcome this issue by online adaptation of neural network parameters for user-specific tasks. However, existing on-chip learning processors have limitations in terms of versatility, resource utilization, and energy efficiency. We propose HybMED, which is a novel neural signal processor that supports on-chip hybrid neural network training using a composite direct feedback alignment-based paradigm. HybMED is suitable for general-purpose health monitoring AIoMT devices. It improves resource utilization and area efficiency by the reconfigurable homogeneous core with heterogeneous data flow and enhances energy efficiency by exploiting sparsity at different granularities. The chip was fabricated by TSMC 40nm process and tested in multiple physiological signal processing tasks, demonstrating an average improvement in accuracy of 41.16% following online few-shot learning. The chip demonstrates an area efficiency of 1.17 GOPS/mm2 and an energy efficiency of 1.58 TOPS/W. Compared to the previous state-of-the-art physiological signal processors with on-chip learning, the chip achieves a 65× improvement in area efficiency and 1.48× improvement in energy efficiency, respectively.

A Hybrid Organic‐Crystal‐Based Curved Waveguide Network for Producing, Splitting, and Transducing Multi‐Color Outputs

AbstractHybrid optical components and circuits that deal with multiple signal generation and processing are quintessential for neural networking systems. Herein, the study reports the fabrication of one such component, a hybrid directional coupler (HDC) from blue emissive (4,4′‐bis(2,6‐di(1H‐pyrazol‐1‐yl)pyridin‐4‐yl)biphenyl) (BPP) and green emissive (E)‐1‐(((5‐bromopyridin‐2‐yl)imino)methyl)naphthalen‐2‐ol (BPyIN) pseudo‐plastic molecular crystals. Initially, a BPyIN microcrystal optical waveguide (OW1) is shaped into a strained waveguide with five bends using an atomic force microscopy cantilever‐tip aided mechanophotonics approach. Later, a singly bent BPyIN waveguide (OW2) is integrated into one of the bends at the coupling region on a strained waveguide to produce a 2 × 2 monolithic directional coupler (DC). Later, this 2 × 2 DC is extended to a HDC by integrating a blue emissive BPP waveguide (OW3) at another bend on the deformed waveguide. The fabricated HDC can effectively split the incident light into three parts with different split ratios and deliver multi‐color outputs depending on the port receiving the input signal. The spontaneous generation and processing of various signals produced in the circuit helps in understanding the functioning of complex optical neural networks. The demonstration of such innovative optical components manifests their utility for diverse applications in neural networking and quantum computing systems.

Multifunctional Intelligent Wearable Devices Using Logical Circuits of Monolithic Gold Nanowires.

Although multifunctional wearable devices have been widely investigated for healthcare systems, augmented/virtual realities, and telemedicines, there are few reports on multiple signal monitoring and logical signal processing by using one single nanomaterial without additional algorithms or rigid application-specific integrated circuit chips. Here, multifunctional intelligent wearable devices are developed using monolithically patterned gold nanowires for both signal monitoring and processing. Gold bulkand hollow nanowiresshow distinctive electrical properties with high chemical stability and high stretchability. In accordance, the monolithically patterned gold nanowires can be used to fabricate the robust interfaces, programmable sensors, on-demand heating systems, and strain-gated logical circuits. The stretchable sensors show high sensitivity for strain and temperature changes on the skin. Furthermore, the micro-wrinkle structures of gold nanowires exhibit the negative gauge factor, which can be used for strain-gated logical circuits. Taken together, this multifunctional intelligent wearable device would be harnessed as a promising platform for futuristic electronic and biomedical applications.

SDR DESIGN AND IMPLEMENTATION OF DIFFERENTIAL STBC TRANSMISSION

Multiple-input-multiple-output (MIMO) technology uses multiple antennas and advanced signal processing to increase the communication system capacity, reliability and data rate without sacrificing energy consumption or RF bandwidth. In some scenarios, the MIMO communication system need to be designed without knowledge of the channel state information. One possible solution is differential space–time block coding (DSTBC). Ettus USRPs (Universal Software Radio Peripherals) are used in this research work to prototype a DSTBC-based 2 × 1 MISO (multiple-input-single-output) communication system. The signal processing is performed by software with MATLAB. This software defined radio (SDR) approach allows a reconfigurable implementation of the Differential STBC algorithm with tuneable parameters.

Multiple Antenna Systems in Mobile 6G: Directional Channels and Robust Signal Processing

In mobile communications, the trend since later fourth generation systems has been toward significantly larger numbers of antennas and higher carrier frequencies. This has led to numerous refinements and extensions of the channel models used, to account for newly encountered propagation characteristics. With increasing antenna numbers, the acquisition of channel state information (CSI) becomes more challenging, such that robust multiple antenna signal processing is required to cope with CSI imperfections. This article discusses important wireless channel characteristics and modeling aspects of fifth and sixth generation (6G) mobile, examines how these developments impact corresponding CSI acquisition techniques, addresses recent robust multiple antenna signal processing approaches, and specifically highlights features and potentials of 6G technologies.

A Portable and Convenient System for Unknown Liquid Identification With Smartphone Vibration

Traditional liquid identification instruments are often unavailable to the general public. This paper shows the feasibility of identifying unknown liquids with commercial lightweight devices, such as a smartphone. The wisdom arises from the fact that different liquid molecules have various viscosity coefficients, so they need to overcome dissimilitude energy barriers during relative motion. With this intuition in mind, we introduce a novel model that measures liquids’ viscosity based on active vibration. The idea sounds straightforward, yet, it is challenging to build up a robust system utilizing the built-in accelerometer in smartphones. Practical issues include under-sampling, self-interference, and volume change impact. Instead of using machine learning techniques, we tackle these issues through multiple signal processing stages to reconstruct the original signals and cancel out the interference. Our approach achieved the liquid viscosity estimates with a mean relative error of 2.3% and distinguish 30 kinds of liquid with an average accuracy of 97.33%.

Power Quality Detection and Categorization Algorithm Actuated by Multiple Signal Processing Techniques and Rule-Based Decision Tree

This paper introduces a power quality (PQ) detection and categorization algorithm actuated by multiple signal processing techniques and rule-based decision tree (RBDT). This is aimed to recognize PQ events of simple nature and higher order multiplicity with less computational time using hybridization of the signal processing techniques. A voltage waveform with a PQ event (PQE) is processed using the Stockwell transform (ST) to compute the Stockwell PQ detection index (SPDI). The voltage waveform is also processed using the Hilbert transform (HT) to compute the Hilbert PQ detection index (HPDI). A voltage waveform is also decomposed using the Discrete Wavelet transform (DWT) to compute the classification feature index (CFI) [CFI1 to CFI4]. A combined PQ detection index (CPDI) is computed by multiplication of the SPDI, the HPDI and CFI1 to CFI4. Incidence of a PQE on a voltage signal is located with the help of a location PQ disturbance index (LPDI) which is computed by differentiating the CPDI with respect to time. CFI5, CFI6 and CFI7 are computed from the SPDI, the HPDI and the CPDI, respectively. Categorization of PQ events is performed using CFI1 to CFI7 by the rule-based decision tree (RBDT) with the help of simple decision rules. We conclude that the proposed algorithm is effective to identify the PQE with an accuracy of 98.58% in a noise-free environment and 97.62% in the presence of 20 dB SNR (signal-to-noise ratio) noise. Ten simple nature PQEs and eight combined PQ events (CPQEs) with multiplicity of two, three and four are effectively detected and categorized using the algorithm. The algorithm is also tested to detect a sag PQ event due to a line-to-ground (LG) fault incident on a practical distribution utility network. The performance of the investigated method is compared with a DWT-based technique in terms of accuracy of classification with and without noise, maximum computational time of PQ detection and multiplicity of PQE which can be effectively detected. A simulation is performed using the MATLAB software. MATLAB codes are used for modelling the PQE disturbances and the proposed algorithm using mathematical formulations.

INTEGRATED RADAR AND COMMUNICATION SYSTEMS FOR HIGHLY AUTOMATED VEHICLES

The application of radar has spread to numerous civilian automotive purposes like adaptive cruise control, automotive emergency braking, blind spot detection, lane change assist, and so on. The need for ever-increasing rates in wireless communications has moved the frequency spectrum toward millimeter range, usually assigned to automotive radar. This has led to an integrated automotive radar and communication system, which results in limited electromagnetic pollution and lower cost, size, weight, and power. Improved massive multiple input, multiple output signal processing techniques are required as integration of these two systems will be based on large antenna arrays. Similar to the see-through technique of 3GPP Release 16, the authors have conducted a simulation experiment where data of multiple targets have been obtained with automotive radar and then sent to a gateway. From there, data are transmitted to a fifth-generation communication model. Final data obtained at a receiver of the communication system matches the original radar output, which proves that data have been successfully transferred from the radar system to the communication system, thus making it an integrated system. This system is very useful in the case of vehicle platooning.

Research on coal gangue recognition based on multi-layer time domain feature processing and recognition features cross-optimal fusion

Accurate and rapid recognition of coal gangue is an important link to realize automatic mining, which is helpful to improve the efficiency of coal mining and the safety of mining workers. Relevant studies have confirmed the effectiveness of coal gangue recognition based on tail beam vibration signal, but the recognition accuracy of a single point source vibration signal of tail beam is slightly low. In order to improve the practicability of coal gangue recognition method based on tail beam vibration signal, this paper takes the single source tail beam vibration signal as the object, improves the coverage of tail beam vibration characteristics by conducting experiments and extracting multiple vibration signals at different positions, and conducts the research on coal gangue recognition technology by multi-layer time domain feature processing and cross-optimal fusion of multiple signals. Firstly, through analysis of classification algorithm ability and coal gangue recognition principle, the coal gangue recognition process is formulated and the classification model is constructed. Then, based on the two data samples of a single signal eigenvector and multi-signal eigenmatrix obtained by Direct Data Statistics (DDS), the recognition research is carried out, and the recognition effectiveness of the eigenmatrix is verified. Based on this conclusion, the three-layers eigenmatrix data samples of DDS, EMD Data Statistics (EDS) and HHT Data Statistics (HDS) are obtained by multi-layer time domain feature processing, and the coal gangue recognition based on multi-layer eigenmatrix is carried out respectively. DDS is the most accurate signal processing method. On this basis, the construction method of DDS + EDS and DDS + HDS recognition eigenmatrix based on array series is studied, and the recognition ability of DDS + HDS recognition eigenmatrix construction method and Logic Regression algorithm is proved. Recognition accuracy based on DDS + HDS recognition eigenmatrix and Logic Regression algorithm is up to 95.25 %. Finally, in order to further improve the recognition accuracy and response speed, this paper proposes a method of feature fusion based on cross-optimal selection (FFCOS). Features of DDS and HDS are sorted by recognition sensitivity, and then cross-selected and fused according to this sort. The results show that the recognition accuracy of the serial construction method of DDS + HDS recognition eigenmatriX array and the Logistic Regression algorithm can reach 97 %, which proves the effectiveness of the FFCOS method and realizes the accurate recognition of coal gangue based on the vibration signal of single source tail beam.

Computational Methods for the Design of Recombinase Logic Circuits with Adaptable Circuit Specifications.

Synthetic biology aims at engineering new biological systems and functions that can be used to provide new technological solutions to worldwide challenges. Detection and processing of multiple signals are crucial for many synthetic biology applications. A variety of logic circuits operating in living cells have been implemented. One particular class of logic circuits uses site-specific recombinases mediating specific DNA inversion or excision. Recombinase logic offers many interesting features, including single-layer architectures, memory, low metabolic footprint, and portability in many species. Here, we present two automated design strategies for both Boolean and history-dependent recombinase-based logic circuits. One approach is based on the distribution of computation within multicellular consortia, and the other is a single-cell design. Both are complementary and adapted for non-expert users via a web design interface, called CALIN and RECOMBINATOR, for multicellular and single-cell design strategies, respectively. In this book chapter, we are guiding the reader step by step through recombinase logic circuit design, from selecting the design strategy fitting to their final system of interest to obtaining the final design using one of our design web interfaces.

The EEG multiverse of schizophrenia.

Research on schizophrenia typically focuses on one paradigm for which clear-cut differences between patients and controls are established. Great efforts are made to understand the underlying genetical, neurophysiological, and cognitive mechanisms, which eventually may explain the clinical outcome. One tacit assumption of these "deep rooting" approaches is that paradigms tap into common and representative aspects of the disorder. Here, we analyzed the resting-state electroencephalogram (EEG) of 121 schizophrenia patients and 75 controls. Using multiple signal processing methods, we extracted 194 EEG features. Sixty-nine out of the 194 EEG features showed a significant difference between patients and controls, indicating that these features detect an important aspect of schizophrenia. Surprisingly, the correlations between these features were very low. We discuss several explanations to our results and propose that complementing "deep" with "shallow" rooting approaches might help in understanding the underlying mechanisms of the disorder.

Enhancing deep learning nuclear quadrupole resonance detection using transfer learning and autoencoders

Nuclear quadrupole resonance is a highly specific spectroscopy technique for analyzing solid substances with applications ranging from laboratory analysis to security screening screening for prohibited substances. The technique has the drawback of a very low signal-to-noise ratio and multiple signal processing and analysis solutions have been proposed for noise rejection and detection. Among these, the deep learning approach using the AlexNet network was recently shown to outperform previous solutions. This paper proposes the enhancement of deep learning detection using transfer learning to extend the applicability of the detection algorithm to other spectrometers and denoising autoencoders to improve its performance at very low signal-to-noise ratios. The transfer learning technique is demonstrated by training the AlexNet network on a simulated data set and transferring the gained knowledge to a real data set. The resulting model achieves a detection accuracy of 98%, close to that obtained by the initial model trained on the real data. Two denoising architectures are proposed, such as deep neural network-based autoencoder and convolutional autoencoder. A comparative evaluation is performed at multiple signal-to-noise ratio conditions in the range [-30, 20] dB, and the convolutional autoencoder is shown to provide the best results, by significantly increasing the detection accuracy by approx. 20% at −30 dB.

Development of a thermal excitation source used in an active thermographic UAV platform

ABSTRACT This work aims to address the effectiveness and challenges of using active infrared thermography (IRT) onboard an unmanned aerial vehicle (UAV) platform. The work seeks to assess the performance of small low-powered forms of excitation which are suitable for active thermography and the ability to locate subsurface defects on composites. An excitation source in multiple 250 W lamps is mounted onto a UAV and is solely battery powered with a remote trigger to power cycle them. Multiple experiments address the interference from the UAV whilst performing an active IRT inspection. The optimal distances and time required for a UAV inspection using IRT are calculated. Multiple signal processing techniques are used to analyse the composites which help locate the sub-surface defects. It was observed that a UAV can successfully carry the required sensors and equipment for an Active thermographic NDT inspection which can provide access to difficult areas. Most active thermographic inspection equipment is large, heavy, and expensive. Furthermore, using such equipment for the inspection of complex structures is time-consuming. For example, a cherry picker would be required to inspect the tail of an aircraft. This solution looks to assist engineers in inspecting complex composite structures and could potentially significantly reduce the time and cost of a routine inspection.

Sub-Nanosecond Digital Signal Processing of Photomultiplier Tube Response Enabling Multiphoton Counting in Raman Spectroscopy.

This paper introduces a novel approach to achieve multiple photon counting for Raman spectroscopy. The multiphoton counting process is made possible by recording and analyzing the photomultiplier tube response to each pulse of a pulsed laser in a time-resolved Raman spectroscopy system. Conventional Raman spectroscopy typically considers photon arrivals as binary events assessed by a single threshold. Hence, the conventional algorithm ignores the fact that multiple photons could arrive within the same response, sacrificing potential signal gain. In this work, a high-speed data acquisition system and multiple threshold digital signal processing counting algorithm are employed to facilitate multiphoton counting, a data processing approach that differentiates photon arrival events by amplitude and time and contributes to improved Raman detection sensitivity. The multiphoton counting algorithm enables lower concentration detection, greater sensitivity, shortens experiment duration, and improves noise rejection. Results from analyses of aqueous solutions of nitrate, isopropanol, and rhodamine 6G demonstrate the versatility and effectiveness of this algorithm. The algorithm increased system sensitivity by ∼ 2.0-, 2.0-, and 3.1-fold, compared to traditional single-threshold analyses of the same data for tests performed on nitrate, isopropanol, and rhodamine 6G, respectively. Results also demonstrated that the multiphoton counting algorithm increases the upper analysis limit for high Raman-yield compounds, shifting the saturation threshold to a higher concentration in typical concentration versus intensity calibration curves.

Reprint of: Triboelectric nanogenerator-based wearable electronic devices and systems: Toward informatization and intelligence

Nowadays, wearable electronic devices with rich functions have significantly facilitated individual combat in the military and daily lives of people. To achieve implanting and sustainable wearable electronic systems, it is necessary to develop self-powered sensors utilizing environmental energy harvesting with superior mechanical stretchability and flexibility. Triboelectric nanogenerators (TENGs) can capture the low-frequency mechanical energy in human motion and convert it into electricity, which is expected to be a potential solution for this urgent need. In this review, based on advanced applications, the rich functions of TENG-based wearable devices are thoroughly discussed, including human body perception and human-machine interaction, personnel identification, as well as generation and recognition of coded information. Then, we elaborate on three crucial strategies for achieving an optimal design of TENG-based wearable devices, including selection and optimization of flexible materials, structural design and optimization, and synergistic information acquisition using multiple sensors. The representative signal processing methods are investigated to exploit the potential information behind the output signals, including basic qualitative waveform features and quantization thresholds, anti-interference processing and joint processing of multiple signals, and implementation of complex functions based on artificial intelligence. The review concludes with an overview of the remaining key challenges and potential technologies that can achieve the ultimate goal of a “fully self-powered wearable microsystem”.

CRISPR signal conductor 2.0 for redirecting cellular information flow

A key challenge in designing intelligent artificial gene circuits is generating flexible connections between arbitrary components and directly coupling them with endogenous signaling pathways. The CRISPR signal conductor based on conditionally inducible artificial transcriptional regulators can link classic cellular protein signals with targeted gene expression, but there are still problems with multiple signal processing and gene delivery. With the discovery and characterization of new Cas systems and long noncoding RNA (lncRNA) functional motifs, and because of the compatibility of guide RNA with noncoding RNA elements at multiple sites, it is increasingly possible to solve these problems. In this study, we developed CRISPR signal conductor version 2.0 by integrating various lncRNA functional motifs into different parts of the crRNA in the CRISPR-dCasΦ system. This system can directly regulate the expression of target genes by recruiting cellular endogenous transcription factors and efficiently sense a variety of protein signals that are not detected by a classical synthetic system. The new system solved the problems of background leakage and insensitive signaling responses and enabled the construction of logic gates with as many as six input signals, which can be used to specifically target cancer cells. By rewiring endogenous signaling networks, we further demonstrated the effectiveness and biosafety of this system for in vivo cancer gene therapy.

Set-membership affine projection algorithm based on the percentage change of the error signal and variable projection order

Nowadays, the use of adaptive filters plays an important role in multiple signal processing applications, such as active noise control, acoustic echo cancellers, system identifiers, channel equalizer, among others. Until date, many of the existing adaptive algorithms such as affine projection algorithms offer a high convergence speed. However, its computational cost is also high. Currently, several authors make extraordinary efforts to reduce its computational cost to be used in practical applications. In this paper, we propose a new set-membership affine projection algorithm based on the percentage change of the error signal and variable projection order (SMAP-PC-VO). Specifically, we propose two techniques to create this algorithm; 1) the new algorithm uses an error bound, which is obtained by calcuting the percentage change of the error signal, to avoid the computation of the variance of additive noise, since in existing approaches this parameter determines the error bound. In practical applications, the computation of the variance of additive noise is infeasible since this signal is not available; 2) we propose a new method to dynamically modify the projection order in the new algorithm. As a consequence, its computational cost is reduced. To demonstrate its performance, the proposed algorithm was successfully tested in different environments for system identification and active noise control for headphone applications. The simulation results demonstrate that the proposed algorithm presents good convergence properties. In addition, the proposed algorithm exhibits a low overall computational complexity.