Simultaneous Signal Acquisition Research Articles

Bandwidth compensation for ultra-high-sensitivity SERF magnetometers in magnetocardiac sensing

The trade-off between sensitivity and bandwidth has limited the clinical application of spin exchange relaxation-free (SERF) atomic magnetometers in magnetocardiac measurements. This research addresses the issue by modeling the SERF magnetometer as a first-order inertial link and proposing a method that integrates transient response with tracking signals. The tracking-differential link enables the simultaneous acquisition of both transient and tracking signals, leading to effective noise suppression. Empirical mode decomposition is employed to extract features and filter out noise, ensuring high signal quality. This method extends the bandwidth of dual-beam magnetometers from 9.77 Hz to 240 Hz and single-beam magnetometers from 139 Hz to 289 Hz, while preserving sensitivities of 1 fT/Hz1/2 and 10 fT/Hz1/2, respectively. This research enables high-bandwidth measurements with sub-femtotesla magnetic field sensitivity, presenting promising application opportunities.

JQR-Net: joint quantitative reconstruction network for dual-modal photoacoustic and optical coherence tomography imaging.

Photoacoustic (PA) and optical coherence tomography (OCT) imaging are complementary imaging modalities with distinct contrast mechanisms, penetration depths, and spatial resolutions. Integrating these two modalities into a dual-modal PA-OCT imaging system enables the simultaneous acquisition of multimodal signals within a single scan. This integration supports quantitative reconstruction of tissue characteristics, offering a more precise and comprehensive analysis than single-modal imaging. In this paper, we propose a deep learning approach for joint quantitative reconstruction in dual-modal PA-OCT imaging, potentially advancing imaging capabilities for detailed tissue examination and disease analysis. We develop a deep neural network that performs end-to-end mapping from photoacoustically induced pressure signals and backscattered OCT signals to parametric images representing the spatial distribution of optical absorption and attenuation coefficients. This network provides both morphological and functional insights. To the best of our knowledge, this is the first deep learning model designed to simultaneously reconstruct multiple tissue characteristic parameters from dual-modal imaging signals, facilitating in-depth tissue characterization.

X-ray ghost imaging with a specially developed beam splitter.

X-ray ghost imaging with a crystal beam splitter has advantages in highly efficient imaging due to the simultaneous acquisition of signals from both the object beam and reference beam. However, beam splitting with a large field of view, uniform distribution and high correlation has been a great challenge up to now. Therefore, a dedicated beam splitter has been developed by optimizing the optical layout of a synchrotron radiation beamline and the fabrication process of a Laue crystal. A large field of view, consistent size, uniform intensity distribution and high correlation were obtained simultaneously for the two split beams. Modulated by a piece of copper foam upstream of the splitter, a correlation of 92% between the speckle fields of the object and reference beam and a Glauber function of 1.25 were achieved. Taking advantage of synthetic aperture X-ray ghost imaging (SAXGI), a circuit board of size 880 × 330 pixels was successfully imaged with high fidelity. In addition, even though 16 measurements corresponding to a sampling rate of 1% in SAXGI were used for image reconstruction, the skeleton structure of the circuit board can still be determined. In conclusion, the specially developed beam splitter is applicable for the efficient implementation of X-ray ghost imaging.

(Invited) Wearable Pocket-Sized Fully Non-Contact Cardiorespiratory Sensor

Cardiovascular and respiratory diseases have emerged as the leading cause of global mortality over the past decade, encompassing conditions such as atrial fibrillation and chronic obstructive pulmonary disease. Consequently, continuous monitoring plays a crucial role in managing these conditions, as it enables early detection, improves patient survival rates, and alleviates the burden on emergency medical resources. Our research group has developed a wearable pocket-sized fully non-contact cardiorespiratory sensor that utilizes electromagnetic induction technology to measure dynamic changes in electrical conductivity resulting from cardiac contractions and pulmonary volume variations. This enables the continuous acquisition of eddy current damping response signals to achieve cardiorespiratory monitoring. In comparison to commercially available ECGs, PPG smartwatches, and respiratory sensors, our proposed pocket-sized sensor offers three major advantages: exceptional concealment, convenience, and comfort for health monitoring due to its pocket-sized and micro-structured design; simultaneous acquisition of two-dimensional cardiac and respiratory signals, facilitating comprehensive cardiorespiratory health monitoring; and the ability to adjust sensing depth through frequency modulation of the electromagnetic field, thus eliminating variations in physiological structures among subjects and achieving optimized sensing and personalized medical benefits.

Synergism in Binary Nanocrystals Enables Top-Illuminated HgTe Colloidal Quantum Dot Short-Wave Infrared Imager.

Thanks to their tunable infrared absorption, solution processability, and low fabrication costs, HgTe colloidal quantum dots (CQDs) are promising for optoelectronic devices. Despite advancements in device design, their potential for imaging applications remains underexplored. For integration with Si-based readout integrated circuits (ROICs), top illumination is necessary for simultaneous light absorption and signal acquisition. However, most high-performing traditional HgTe CQD photodiodes are p-on-n stack and bottom-illuminated. Herein, we report top-illuminated inverted n-on-p HgTe CQD photodiodes using a robust p-type CQD layer and a thermally evaporated Bi2S3 electron transport layer. The p-type CQD solid is achieved by exploring the synergism in binary HgTe and Ag2Te CQDs. These photodetectors show a room-temperature detectivity of 3.4 × 1011 jones and an EQE of ∼44% at ∼1.7 μm wavelength, comparable to the p-on-n HgTe CQD photodiodes. A top-illuminated HgTe CQD short-wave infrared imager (640 × 512 pixels) was fabricated, demonstrating successful infrared imaging.

An Algorithm for Ultrasonic Identification of Ceramic Materials and Virtual Prototype Realization

To prevent important items from being replaced by a forgery, an ultrasonic fingerprint identification algorithm is proposed and an identification program is developed. A virtual prototype for the ultrasonic identification of ceramics is developed based on an ultrasonic detection card. This virtual prototype allows for the simultaneous transmission and acquisition of signals. Numerous experimental tests were conducted using this virtual prototype. The results demonstrate that the virtual prototype achieves accurate identification of ceramics. This virtual prototype lays a good foundation for the development of intelligent, automated, integrated, and miniaturized ultrasonic identification systems.

Radio Frequency Measurements for Electrical Impedance Tomography

Electrical Impedance Tomography (EIT) is a medical imaging technique that employs small alternating currents to produce tomographic images. Usually, a single current frequency is used and the out-of-phase component of the signals is discarded. Here, we present a Radio Frequency (RF) approach based on a Vector Network Analyzer (VNA) for EIT for the first time. This approach allows the simultaneous acquisition of electrical complex signals over a broad frequency range between 10kHz and 1GHz. The results show that the real part of the signals gives the best-reconstructed images and images performed at frequencies higher than 10MHz are unreliable.

Simultaneous Acquisition of EIT and ECG Signals on Active EIT Electrodes

Simultaneous Acquisition of EIT and ECG Signals on Active EIT Electrodes

Exploring Biosignals for Quantitative Pain Assessment in Cancer Patients: A Proof of Concept

Perception and expression of pain in cancer patients are influenced by distress levels, tumor type and progression, and the underlying pathophysiology of pain. Relying on traditional pain assessment tools can present limitations due to the highly subjective and multifaceted nature of the symptoms. In this scenario, objective pain assessment is an open research challenge. This work introduces a framework for automatic pain assessment. The proposed method is based on a wearable biosignal platform to extract quantitative indicators of the patient pain experience, evaluated through a self-assessment report. Two preliminary case studies focused on the simultaneous acquisition of electrocardiography (ECG), electrodermal activity (EDA), and accelerometer signals are illustrated and discussed. The results demonstrate the feasibility of the approach, highlighting the potential of EDA in capturing skin conductance responses (SCR) related to pain events in chronic cancer pain. A weak correlation (R = 0.2) is found between SCR parameters and the standard deviation of the interbeat interval series (SDRR), selected as the Heart Rate Variability index. A statistically significant (p < 0.001) increase in both EDA signal and SDRR is detected in movement with respect to rest conditions (assessed by means of the accelerometer signals) in the case of motion-associated cancer pain, thus reflecting the relationship between motor dynamics, which trigger painful responses, and the subsequent activation of the autonomous nervous system. With the objective of integrating parameters obtained from biosignals to establish pain signatures within different clinical scenarios, the proposed framework proves to be a promising research approach to define pain signatures in different clinical contexts.

Motion Analysis Using Global Navigation Satellite System and Physiological Data

Motion analysis by wearable sensors forms a very important research topic with a general mathematical background and applications in different areas including engineering, robotics, and neurology. This paper presents the use of the global navigation satellite system (GNSS) for detecting and recording the position of a moving body and the simultaneous acquisition of signals from further sensors. The application is related to the monitoring of physical activity and the analysis of the heart rate dynamics during the run at route segments of different slopes with changing speed, forming an alternative to the heart monitoring on the treadmill ergometer. The proposed computational method includes digital methods of data preprocessing, time synchronization, and data resampling to enable their correlation, feature extraction both in time and frequency domains, and classification. The datasets include signals acquired during ten experimental runs in the selected area. The detection of the patterns of motion includes segmenting the signals by analysing the GNSS data, evaluating the patterns, and classifying the motion signals under different terrain conditions. This method of classification provides a comparison of neural networks, support vector machine, Bayesian, and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> -nearest neighbour methods. The highest accuracy of 93.3 % was achieved by using combined features and a two-layer neural network for classification into three classes with different slopes. The proposed method and graphical user interface suggest the efficiency of multi-channel and multi-dimensional signal processing with applications in rehabilitation, fitness movement monitoring, neurology, cardiology, engineering, and robotic systems as well.

Motion Artifact Reduction in Electrocardiogram Signals Through a Redundant Denoising Independent Component Analysis Method for Wearable Health Care Monitoring Systems: Algorithm Development and Validation.

The quest for improved diagnosis and treatment in home health care models has led to the development of wearable medical devices for remote vital signs monitoring. An accurate signal and a high diagnostic yield are critical for the cost-effectiveness of wearable health care monitoring systems and their widespread application in resource-constrained environments. Despite technological advances, the information acquired by these devices can be contaminated by motion artifacts (MA) leading to misdiagnosis or repeated procedures with increases in associated costs. This makes it necessary to develop methods to improve the quality of the signal acquired by these devices. We aimed to present a novel method for electrocardiogram (ECG) signal denoising to reduce MA. We aimed to analyze the method's performance and to compare its performance to that of existing approaches. We present the novel Redundant denoising Independent Component Analysis method for ECG signal denoising based on the redundant and simultaneous acquisition of ECG signals and movement information, multichannel processing, and performance assessment considering the information contained in the signal waveform. The method is based on data including ECG signals from the patient's chest and back, the acquisition of triaxial movement signals from inertial measurement units, a reference signal synthesized from an autoregressive model, and the separation of interest and noise sources through multichannel independent component analysis. The proposed method significantly reduced MA, showing better performance and introducing a smaller distortion in the interest signal compared with other methods. Finally, the performance of the proposed method was compared to that of wavelet shrinkage and wavelet independent component analysis through the assessment of signal-to-noise ratio, dynamic time warping, and a proposed index based on the signal waveform evaluation with an ensemble average ECG. Our novel ECG denoising method is a contribution to converting wearable devices into medical monitoring tools that can be used to support the remote diagnosis and monitoring of cardiovascular diseases. A more accurate signal substantially improves the diagnostic yield of wearable devices. A better yield improves the devices' cost-effectiveness and contributes to their widespread application.

Strong light-matter interaction in hollow-core microfiber for multiplex sensing of environmental hazards

Hollow-core fibers with special micro-geometry have been heralded for years as a supreme medium for data transmission, quantum communications and laser power delivery, owing to their low loss in the air-guiding structure. The recent advances of non-touching hollow core anti-resonant fiber (HARF) have further minimized the loss and improved the modal properties. Here we exploit biochemical sensing performance of HARF. We address hybridization of state-of-the-art HARF with graphene oxide (GO) to achieve strong light-matter interaction and enable ultrasensitive and simultaneous quantification of multiple hazardous antibiotics in environmental samples. The GO-HARF shows a strict light confinement in the core region with 8 dB/km attenuation at 600 nm wavelength via numerical simulation. The broadband transmission (400–1000 nm) of GO-HARF allows simultaneous fluorescence signal acquisition at multiple wavelengths. The operating principle relies on detecting dose-dependent fluorescence changes in GO-HARF when an antibiotics complex is streamed through the fiber channel. Over three-orders-of-magnitude enhancement of sensitivity is achieved with the GO-HARF sensor compared with the limit of detection (LoD) of a microplate reader. GO-HARF offers a unique opportunity to measure picomolar analytes at multiplexed wavelengths in the visible spectral range, holding great potential for fast-response, ultrasensitive and low-cost biomedical analyses.

Analysis of the Significance of Changes in the Number and Energy Parameters of Acoustic Emission Signals on the Assessment of the Strength of Fibre-Cement Boards.

The article presents the results of three-point bending tests carried out for samples cut from full-size fibre–cement boards subjected to typical and exceptional conditions. The tests were carried out with the simultaneous acquisition of acoustic emission signals. It has been noted that some factors significantly deteriorate the strength parameters of the samples as well as cause the occurrence of differences in the number of acoustic emission signals of various classes and their energy parameters. A statistical analysis was carried out in order to repeat the relationship between the strength parameters of the samples and the acoustic emission parameters. Based on the research, it was found that the MOR bending strength for specimens exposed to fire and high temperature is more than 50% lower than for air-dried specimens and specimens exposed to water. The increased number of freeze–thaw cycles also has an impact on the strength of the specimens. Components exposed to more than 10 freeze–thaw cycles had a strength more than 30% smaller than the reference specimens soaked in water and exposed to bath-drying cycles. A similar dependency was indicated by the number of signals of the individual classes, their energy parameters and their frequencies. The number, strength, duration and frequency also decreased along with the increase in the test case number. On this basis, conclusions were drawn concerning the suitability of acoustic emission for the evaluation of the strength of fibre–cement elements.

A Low-Cost Handheld Impedimetric Biosensing System for Rapid Diagnostics of SARS-CoV-2 Infections.

Current laboratory diagnostic approaches for virus detection give reliable results, but they require a lengthy procedure, trained personnel, and expensive equipment and reagents; hence, they are not a suitable choice for home monitoring purposes. This paper addresses this challenge by developing a portable impedimetric biosensing system for the identification of COVID-19 patients. This sensing system has two main parts: a throwaway two-working electrode (2-WE) strip and a novel read-out circuit, specifically designed for simultaneous signal acquisition from both working electrodes. Highly reliable electrochemical signal tracking from multiplex immunosensors provides a potential for flexible and portable multi-biomarker detection. The electrodes' surfaces were functionalized with SARS-CoV-2 Nucleocapsid Antibody enabling the selective detection of Nucleocapsid protein (N-protein) along with self-validation in the clinical nasopharyngeal swab specimens. The proposed programmable highly sensitive impedance read-out system allows for a wide dynamic detection range, which makes the sensor capable of detecting N-protein concentrations between 0.116 and 10,000 pg/mL. This lightweight and economical read-out arrangement is an ideal prospect for being mass-produced, especially during urgent pandemic situations. Also, such an impedimetric sensing platform has the potential to be redesigned for targeting not only other infectious diseases but also other critical disorders.

Design of Small Platform Sonar Data Acquisition and Storage System Based on NI-CRIO

The signal acquisition system is an important part of the sonar system. This paper presents a stable multi-channel underwater acoustic signal acquisition system based on NI-CompactRIO, aiming at the compact feature of the small platform sonar system. It realizes the simultaneous acquisition of multi-channel analog signals and the function of storing mass original data. The acquisition system basically comprises of sampling using the self-made acquisition module and storing using U disk. The software is programmed by LabVIEW. Finally, the stability of the system is verified by the experiment on the lake.

UStEMG: an Ultrasound Transparent Tattoo-based sEMG System for Unobtrusive Parallel Acquisitions of Muscle Electro-mechanics.

Human machine interfaces follow machine learning approaches to interpret muscles states, mainly from electrical signals. These signals are easy to collect with tiny devices, on tight power budgets, interfaced closely to the human skin. However, natural movement behavior is not only determined by muscle activation, but it depends on an orchestration of several subsystems, including the instantaneous length of muscle fibers, typically inspected by means of ultrasound (US) imaging systems. This work shows for the first time an ultra-lightweight (7 g) electromyography (sEMG) system transparent to ultrasound, which enables the simultaneous acquisition of sEMG and US signals from the same location. The system is based on ultrathin and skin-conformable temporary tattoo electrodes (TTE) made of printed conducting polymer, connected to a tiny, parallel-ultra-low power acquisition platform (BioWolf). US phantom images recorded with the TTE had mean axial and lateral resolutions of 0.90±0.02 mm and 1.058±0.005 mm, respectively. The root mean squares for sEMG signals recorded with the US during biceps contractions were at 57±10 µV and mean frequencies were at 92±1 Hz. We show that neither ultrasound images nor electromyographic signals are significantly altered during parallel and synchronized operation.Clinical relevance- Modern prosthetic engineering concepts use interfaces connected to muscles or nerves and employ machine learning models to infer on natural movement behavior of amputated limbs. However, relying only on a single data source (e.g., electromyography) reduces the quality of a fine-grained motor control. To address this limitation, we propose a new and unobtrusive device capable of capturing the electrical and mechanical behavior of muscles in a parallel and synchronized fashion. This device can support the development of new prosthetic control and design concepts, further supporting clinical movement science in the configuration of better simulation models.

Quantitative Local Probing of Polarization with Application on HfO2 -Based Thin Films.

Owing to their switchable spontaneous polarization, ferroelectric materials have been applied in various fields, such as information technologies, actuators, and sensors. In the last decade, as the characteristic sizes of both devices and materials have decreased significantly below the nanoscale, the development of appropriate characterization tools became essential. Recently, a technique based on conductive atomic force microscopy (AFM), called AFM-positive-up-negative-down (PUND), is employed for the direct measurement of ferroelectric polarization under the AFM tip. However, the main limitation of AFM-PUND is the low frequency (i.e., on the order of a few hertz) that is used to initiate ferroelectric hysteresis. A significantly higher frequency is required to increase the signal-to-noise ratio and the measurement efficiency. In this study, a novel method based on high-frequency AFM-PUND using continuous waveform and simultaneous signal acquisition of the switching current is presented, in which polarization-voltage hysteresis loops are obtained on a high-polarization BiFeO3 nanocapacitor at frequencies up to 100kHz. The proposed method is comprehensively evaluated by measuring nanoscale polarization values of the emerging ferroelectric Hf0.5 Zr0.5 O2 under the AFM tip.

LightSpeed: A Compact, High-Speed Optical-Link-Based 3D Optoacoustic Imager.

Wide-scale adoption of optoacoustic imaging in biology and medicine critically depends on availability of affordable scanners combining ease of operation with optimal imaging performance. Here we introduce LightSpeed: a low-cost real-time volumetric handheld optoacoustic imager based on a new compact software-defined ultrasound digital acquisition platform and a pulsed laser diode. It supports the simultaneous signal acquisition from up to 192 ultrasound channels and provides a hig-bandwidth direct optical link (2x 100G Ethernet) to the host-PC for ultra-high frame rate image acquisitions. We demonstrate use of the system for ultrafast (500Hz) 3D human angiography with a rapidly moving handheld probe. LightSpeed attained image quality comparable with a conventional optoacoustic imaging systems employing bulky acquisition electronics and a Q-switched pulsed laser. Our results thus pave the way towards a new generation of compact, affordable and high-performance optoacoustic scanners.

INVESTIGATION OF SYNCHRONIZED ACQUISITION OF ELECTROCARDIOGRAM AND PHONOCARDIOGRAM SIGNALS TOWARDS ELECTROMECHANICAL PROFILING OF THE HEART

Cardiac diseases are the leading cause of death in the world. Electrocardiogram (ECG and Phonocardiogram (PCG signals play a significant role in the diagnosis of various cardiac diseases. Simultaneous acquisition of ECG and PCG signals can open new avenues of signal processing approaches for electromechanical profiling of the heart. However, there are no standard approaches to ensure high fidelity synchronous data acquisition to enable the development of such novel technologies. In this work, the authors report results on various data capture positions that could lead to standardization of simultaneous ECG and PCG data collection. Presence of lung sounds, variations in posture, depth and frequency of breathing can lead to differences in the ECG-PCG signals recorded. This necessitates a standard approach to record and interpret the data collected. The authors recorded ECG-PCG simultaneously in six healthy subjects using a digital stethoscope to understand the differences in signal quality in various recording positions (prone, supine, bending, semi recumbent, standing, left lateral and sitting with normal and deep breathing conditions. The collected digitized signals are processed offline for signal quality using custom MATLAB software for SNR. The results indicate minimal differences in signal quality across different recording positions. Validation of this technique with larger dataset is required. Future work will investigate changes in characteristic ECG and PCG features due to position and breathing patterns.

Simultaneous Acquisition of Ultrasound and Gamma Signals with a Single-Channel Readout.

We propose an integrated front-end data acquisition circuit for a hybrid ultrasound (US)-gamma probe. The proposed circuit consists of three main parts: (1) a preamplifier for the gamma probe, (2) a preprocessing analog circuit for the US, and (3) a digitally controlled analog switch. By exploiting the long idle time of the US system, an analog switch can be used to acquire data of both systems using a single output channel simultaneously. On the nuclear medicine (NM) gamma probe side, energy resolutions of 18.4% and 17.5% were acquired with the standalone system and with the proposed switching circuit, respectively, when irradiated with a Co-57 radiation source. Similarly, signal-to-noise ratios of 14.89 and 13.12 dB were achieved when US echo signals were acquired with the standalone system and with the proposed switching circuit, respectively. Lastly, a combined US-gamma probe was used to scan a glass target and a sealed radiation source placed in a water tank. The results confirmed that, by using a hybrid US-gamma probe system, it is possible to distinguish between the two objects and acquire structural information (ultrasound) alongside molecular information (gamma radiation source).