Continuous Signal Research Articles

Characterisation of Electrochemical Systems from Periodic Chronoamperometry Signals Using Computational Tools

Electrochemical systems are complex, involving various chemical and physical processes (e.g. electron transfer reactions, diffusive and convective mass transfer, charge transfer processes). In most cases, electrochemical systems are summarised using simple signals (e.g. amperometric or potentiometric signals), which do not, however, adequately capture their complexity. These mostly unidimensional signals are usually insufficient to facilitate the full understanding of the electrochemical system and its underlying, simultaneously occurring processes.Established electroanalytical methods are commonly utilised to characterise specific (electro-) chemical and physical processes of electrochemical systems (e.g. potentiometry, amperometry/voltammetry, electrochemical impedance spectroscopy, etc.). Despite the usefulness of these traditional electroanalysis techniques, they are often restricted to the analysis of a very specific set of system characteristics and parameters. Moreover, the experimental nature of these techniques, usually requiring a specific protocol, sets limits to their employment for on-line measurements of industrial electrochemical systems (e.g. reactors, fuel cells etc.).To avoid these limitations, a new alternative approach to understanding and characterising electrochemical systems is proposed. Computational techniques have the potential to extract various system parameters from periodic chronoamperometry signals of electrochemical systems. Such techniques include methods that are commonly used in chemometric investigations in addition to other modern signal processing tools.Early findings from this ongoing study include the separation of Faradaic and non-Faradaic current contributions from a variety of simulated chronoamperometric signals. Moreover, qualitative sensitivity studies appear promising for the subsequent quantitative recovery of multiple parameters from stagnant and hydrodynamic electrochemical systems (e.g. capacitance, resistance).As a consequence, this novel computer-aided approach can pave the way for real-time analyses of electrochemical applications. Furthermore, this strategy will likely reduce the need for specific electroanalytic experiments to determine certain parameters. Instead, complex electrochemical systems and their underlying chemical and physical processes have the potential to be characterised from simple, continuous chronoamperometric signals by means of computational techniques.

Triboelectric-Inertial Sensing Glove Enhanced by Charge-Retained Strategy for Human-Machine Interaction.

As technology advances, human-machine interaction (HMI) demands more intuitive and natural methods. To meet this demand, smart gloves, capable of capturing intricate hand movements, are emerging as vital HMI tools. Moreover, triboelectric-based sensors, with their self-powered, cost-effective, and material various characteristics, can offer promising solutions for enhancing existing glove systems. However, a key limitation of these sensors is that charge leakage in the measurement circuit results in capturing only transient signals, rather than continuous changes. To address this issue, a charge-retained circuit that effectively prevents triboelectric signal attenuation is developed, enabling accurate measurement of continuous finger movements. This innovation forms the foundation of a highly integrated smart glove system, enhancing HMI functionality by combining continuous triboelectric signals with inertial sensor data. The system showcases a diverse range of applications, including complex robotic control, virtual reality interaction, smart home lighting adjustments, and intuitive interface operations. Furthermore, by leveraging artificial intelligence (AI) techniques, the system achieves accurate recognition of complex sign language with an impressive 99.38% accuracy. This work presents a charge-retained approach for continuous sensing with triboelectric-based sensors, offering valuable insights for developing future multifunctional HMI and sign language recognition systems. The proposed smart glove system, with its dual-mode sensing and AI integration, holds great potential for revolutionizing various domains and enhancing user experiences.

Electrochemical Aptasensor for Progesterone Detection

Electrochemical biosensors have revolutionized point-of-care diagnostics through commercial accessibility while maintaining detection specificity and sensitivity. Medical biosensors are designed to detect biomarkers in bodily fluids (blood, urine, saliva, sweat) and commonly use enzymes, antibodies, or nucleic acids to bind biomarker targets. An emerging method of biomarker detection uses aptamers which are short, single-stranded DNA or RNA molecules that bind to specific target molecules with high affinity and selectivity. Aptamer sensors are an affordable alternative to antibody-based sensors, have a wider target scope compared to antibody- or enzyme-based assays, and inherently have high structure flexibility and design for redox robe modification. In particular, electrochemical aptamer biosensors (E-AB) represent a promising device architecture wherein an aptamer sequence with a redox probe is tethered to an alkane thiol self-assembled monolayer on a gold electrode (Au-SAM). Upon substrate binding, the conformational changes in the aptamer then enable fast electron transfer to the redox probe and allow rapid and continuous signal transduction of analyte concentrations. E-ABs have been used to target various biomarkers (cocaine, thrombin, tobramycin, vancomycin, etc.) and operate well in vivo, making them an attractive technology for point-of-care diagnostics.In this work, an E-AB is designed for progesterone monitoring. Progesterone is a key biomarker for monitoring reproductive health, as irregular levels are predictive of unsuccessful pregnancy, abnormal uterine bleeding, early embryonic mortality, or ectopic pregnancy. However, there are no continuous, non-invasive methods for progesterone detection. Current methods are limited to laboratory blood samples, ovulation prediction kits, and cycle tracking, but these methods fall short of achieving continuous, accurate, reliable, non-invasive, accessible, and comprehensive detection. Here, we develop an E-AB with a specific affinity for progesterone using the established Au-SAM architecture, a methylene blue redox reporter, and an aptamer sequence with a high affinity for progesterone. These experiments show proof of concept that an immobilized aptamer with methylene blue can detect progesterone electrochemically and is a promising platform for continuous monitoring of progesterone in vivo.

Probe set selection for targeted spatial transcriptomics

Targeted spatial transcriptomic methods capture the topology of cell types and states in tissues at single-cell and subcellular resolution by measuring the expression of a predefined set of genes. The selection of an optimal set of probed genes is crucial for capturing the spatial signals present in a tissue. This requires selecting the most informative, yet minimal, set of genes to profile (gene set selection) for which it is possible to build probes (probe design). However, current selections often rely on marker genes, precluding them from detecting continuous spatial signals or new states. We present Spapros, an end-to-end probe set selection pipeline that optimizes both gene set specificity for cell type identification and within-cell type expression variation to resolve spatially distinct populations while considering prior knowledge as well as probe design and expression constraints. We evaluated Spapros and show that it outperforms other selection approaches in both cell type recovery and recovering expression variation beyond cell types. Furthermore, we used Spapros to design a single-cell resolution in situ hybridization on tissues (SCRINSHOT) experiment of adult lung tissue to demonstrate how probes selected with Spapros identify cell types of interest and detect spatial variation even within cell types.

Indoor Positioning Method by CNN-LSTM of Continuous Received Signal Strength Indicator

This paper proposes an indoor positioning method based on Bluetooth Low Energy signals by Convolution Neural Network-Long Short-Term Memory (CNN-LSTM). The proposed method determines a receiver location based on distances from adjacent transmitters. The CNN-LSTM model estimates the distance from each transmitter using continuous signal strengths. To train and validate the model, the signal strengths are collected in several locations within various indoor environments. The positioning technique is adaptively selected based on the highest signal strength to avoid the interfering problem due to an excessively strong signal. If the signal strength exceeds a certain threshold, the location is determined using the proximity technique, which utilizes only the strongest signal instead of triangulation. In the experimental results, the proposed method demonstrated an average error of about 2.90 m, which is 34.2% better than a triangulation-based positioning method that does not utilize neural networks.

Accuracy study for over-the-air frequency synchronization of continuous wave signals

Abstract Future communication and radar sensing systems will require synchronization methods which are more versatile in terms of the systems involved in the synchronization process. We present an over-the-air frequency synchronization algorithm based on the standard and the generalized Kuramoto model which uses continuous wave (CW) signals. In contrast to other approaches, all nodes of the network participate equally, and synchronization can even be achieved in presence of a non-cooperative node. By changing the parameters of the radar or by modifying the synchronization algorithm, synchronization accuracy can be adjusted as well. All claims are supported by measurements conducted with CW radars. It will be demonstrated that our algorithm enables synchronization accuracies down to 1.92 ppb and thus could provide sufficient accuracy for velocity measurements on pedestrians.

Signal Fusion of Multi-Frequency Continuous Wave Ground Penetrating Radar

Abstract Carrier-less pulse radar, with its short pulse duration, low average power, and weak anti-interference capability, poses significant limitations to the application of ground penetrating radar (GPR). Fusing continuous wave signals from multiple frequency components forms the basis for the design and data interpretation of continuous wave radar. We firstly introduce two fusion methods: the application of the Inverse Fast Fourier Transform (IFFT) to multi-frequency signals and direct superposition. And we demonstrate these methods by synthesizing three basic sinusoidal signals and analyse their time-frequency relationship. Furthermore, we perform Fast Fourier Transform (FFT) on the transmit waveform of pulse radar to obtain a set of frequency components. By superimposing three sets of frequency components with different numbers of frequency points, we obtain the fused waveform and compare it with the original Ricker wavelet. The results show that while reducing the number of frequency points may increase the error in the synthesized signal slightly, it remains capable of accurately synthesizing the Ricker wavelet. However, this reduction also decreases the time window of the synthesized signal and may result in temporal overlapping. These findings offer insights into selecting parameters for frequency components in multi-frequency continuous wave radar and lay a foundation for signal fusion in such radar systems.

Multifunctional enhancement strategy for MXene-based fire alarm sensors via interlocked “brick/mortar” structure of dimension-hybrid nanoparticle system

MXene-based fire-responsive composites are promising candidates for next-generation fire alarm sensors with high sensitivity and good mechanical properties. However, the low dispersibility and inherent thermal oxidation of MXene combined with the unsuitable spatial scale of polymer particles in hybrid systems impede the rapid response and alarm duration when suffering a fire. Herein, a strategy based on an interlocked “brick/mortar” structure was proposed for integrating faster response, high sensitivity, and longer duration in flames via a dimension-hybrid nanoparticle network, which was assembled by 2D MXene, 1D cellulose nanocrystals (CNCs), and ammonium polyphosphate (APP). The CNCs with a high aspect ratio of 114 and close length to MXene assisted uniform dispersion of MXene and coupled with APP impeding thermal oxidation of MXene. This assembled MXene/CNC@APP sensor can quickly trigger an alarm signal via substance conversion from insulating to conductive within 1.79 s when suffering a high thermal of 260 °C from fire, thus sensitive fire identification and rapid response were obtained. This MXene/CNC@APP film also maintained good mechanical performance with a tensile strength of 52.5 MPa and elongation at a break of 7.6%, which combined excellent flame retardancy leading to a continuous fire alarm signal over 442 s of the sensor in the fire. This multifunctional integrated enhancement strategy for MXene-based sensors may be conducive to the upgrade of fire alarm systems for better safeguarding of life and property.

Understanding perioperative risk determinants in carotid endarterectomy: the impact of compromised circle of Willis morphology on inter-hemispheric blood flow indices based on intraoperative internal carotid artery stump pulse pressure and backflow patterns.

Carotid artery stenosis (CAS) often requires surgical intervention through carotid endarterectomy (CEA) to prevent stroke. Accurate cerebrovascular risk assessments are crucial in CEA, as poor collateral circulation can lead to insufficient interhemispheric blood flow compensation, resulting in ischemic complications. Therefore, understanding perioperative risk determinants is vital. This study aims to determine the impact of compromised circle of Willis (CoW) morphology on inter-hemispheric blood flow, focusing on indices based on intraoperative internal carotid artery stump pulse pressure and backflow patterns. In 80 CAS patients who underwent CEA, preoperative CT angiography for CoW was conducted. Patients were categorized into five subgroups based on their CoW anatomy and three additional groups based on intraoperative internal carotid artery (ICA) stump backflow patterns evaluated by the surgeon. Continuous blood pressure signals, including systolic, diastolic, mean, and pulse pressure values, were recorded during the procedure. The relationship between CoW anatomical variants and the systolic and diastolic segments of the averaged pressure waveforms, particularly diastolic pressure decay, was analyzed. The correlation between CoW anatomy and stump backflow intensity was also examined. Significant variability in ICA stump backflow and pressure values was evident across CoW variants. Patients with compromised CoW morphology exhibited weaker backflow patterns and lower ICA stump pulse pressure values, consistent with impaired interhemispheric blood flow. Notably, ICA stump diastolic pressure decay was consistent across most CoW variant groups, indicating developed collateral circulation in cases with CoW anomalies. Thus, impaired CoW integrity is associated with compromised interhemispheric blood flow indices based on intraoperative ICA stump pulse pressure and backflow patterns during CEA. Integrating intraoperative pulse waveform analysis with preoperative CT angiography provides a more detailed assessment of cerebrovascular risk, guiding the selective use of shunts. This combined approach may improve surgical outcomes and patient safety by identifying patients at increased risk of perioperative neurological events due to CoW anomalies.

Paintable, Fast Gelation, Highly Adhesive Hydrogels for High-fidelity Electrophysiological Monitoring Wirelessly.

High-fidelity wireless electrophysiological monitoring is essential for ambulatory healthcare applications. Soft solid-like hydrogels have received significant attention as epidermal electrodes because of their tissue-like mechanical properties and high biocompatibility. However, it is challenging to develop a hydrogel electrode that provides robust contact and high adhesiveness with glabrous skin and hairy scalp for high-fidelity, continuous electrophysiological signal detection. Here, a paintable, fast gelation, highly adhesive, and conductive hydrogel is engineered for high-fidelity wireless electrophysiological monitoring. The hydrogel, consisting of gelatin, gallic acid, sodium citrate, lithium chloride, glycerol, and Tris-HCl buffer solution exhibits a reversible thermal phase transition capability, which endows it with the attributes of on-skin applicability and fast in situ gelation with 15 s, thereby addressing the aforementioned limitations. The introduction of gallic acid enhances the adhesive properties of the hydrogel, facilitating secure electrode attachment to the skin or hairy scalp. To accentuate the potential applications in at-home electrophysiological health monitoring, the hydrogel electrodes are demonstrated for high-fidelity electrocardiogram recording for one hour during various daily activities, as well as in simultaneous electroencephalogram and electrocardiogram recording during a 30 min nap.

A Review of the Basic Principles and Methods of Signal Sampling Technology

In modern life, communication is an essential human activity. In the process of communication, various information spreads around the world in the form of signals. In order to optimize the communication, the reasonable processing of the signal becomes an important research problem. Signal processing is an important step during signal processing. It is one of two processes that convert an analog signal into a digital equivalent signal. It can be considered that signal sampling transforms the time axis of the signal into a set of discrete time moments, that is, the process of converting a signal in a continuous time domain to a discrete time domain through a series of methods. It is used to convert continuous time signals into digital signals for relevant processing and storage of digital signals in subsequent operations. This paper will summarize the basic principles and methods of signal sampling technology, introduce the sampling theorem, sampling frequency, sampling depth, sampling method and sampling error, and finally discuss the future development of signal adoption technology.

Segmentation window of speech information processing in the human auditory cortex

Humans perceive continuous speech signals as discrete sequences. To clarify the temporal segmentation window of speech information processing in the human auditory cortex, the relationship between speech perception and cortical responses was investigated using auditory evoked magnetic fields (AEFs). AEFs were measured while participants heard synthetic Japanese words /atataka/. There were eight types of /atataka/ with different speech rates. The durations of the words ranged from 75 to 600 ms. The results revealed a clear correlation between the AEFs and syllables. Specifically, when the durations of the words were between 375 and 600 ms, the evoked responses exhibited four clear responses from the superior temporal area, M100, that corresponded not only to the onset of speech but also to each group of consonant/vowel syllable units. The number of evoked M100 responses was correlated to the duration of the stimulus as well as the number of perceived syllables. The approximate range of the temporal segmentation window limit of speech perception was considered to be between 75 and 94 ms. This finding may contribute to optimizing the temporal performance of high-speed synthesized speech generation systems.

Parametry akustyczne ochronników słuchu i metody ich pomiaru

Acoustic parameters of hearing protectors and methods of their measurement The article presents how the basic parameter of hearing protectors, i.e. sound attenuation, is determined using a method based on the active participation of people (subjective method). In addition, objective methods for measuring acoustic parameters of hearing protectors, such as insertion loss, based on the use of miniature microphones and acoustic test fixture are described. The results of measurements of sound pressure level under hearing protectors carried out using an acoustic test fixture reproducing anatomical shape of a human head (including concha and ear canal), used as a standard for measuring acoustic parameters of hearing protectors in impulse noise situations are also presented. These measurements were carried out to verify the possibility of using this acoustic test fixture, to measure the sound pressure level reaching under the hearing protectors in the case of continuous noise. The tests showed that in the case of measurements with microphones that are standard equipment of the acoustic test fixture, dedicated to impulse noise, the use of these microphones is limited to high-level continuous signals. Microphones that are optional equipment on the acoustic test fixture were a more suitable solution for the intended purpose.

Towards imagined speech: Identification of brain states from EEG signals for BCI-based communication systems

BackgroundThe electroencephalogram (EEG) based brain-computer interface (BCI) system employing imagined speech serves as a mechanism for decoding EEG signals to facilitate control over external devices or communication with the external world at the moment the user desires. To effectively deploy such BCIs, it is imperative to accurately discern various brain states from continuous EEG signals when users initiate word imagination. New methodThis study involved the acquisition of EEG signals from 15 subjects engaged in four states: resting, listening, imagined speech, and actual speech, each involving a predefined set of 10 words. The EEG signals underwent preprocessing, segmentation, spatio-temporal and spectral analysis of each state, and functional connectivity analysis using the phase locking value (PLV) method. Subsequently, five features were extracted from the frequency and time-frequency domains. Classification tasks were performed using four machine learning algorithms in both pair-wise and multiclass scenarios, considering subject-dependent and subject-independent data. ResultsIn the subject-dependent scenario, the random forest (RF) classifier achieved a maximum accuracy of 94.60 % for pairwise classification, while the artificial neural network (ANN) classifier achieved a maximum accuracy of 66.92 % for multiclass classification. In the subject-independent scenario, the random forest (RF) classifier achieved maximum accuracies of 81.02 % for pairwise classification and 55.58 % for multiclass classification. Moreover, EEG signals were classified based on frequency bands and brain lobes, revealing that the theta (θ) and delta (δ) bands, as well as the frontal and temporal lobes, are sufficient for distinguishing between brain states. ConclusionThe findings promise to develop a system capable of automatically segmenting imagined speech segments from continuous EEG signals.

Nanocomposite conductive r(GO/BSA) hydrogel as an effective dressing for rapid chronic diabetic wound healing

Conductive hydrogels, with their soft, hydrated interface and superior electrical conductivity, represent a groundbreaking solution for diabetic wound healing. However, developing a conductive hydrogel with excellent biocompatibility has proven to be challenging. This study presents the synthesis of a novel conductive hydrogel composed of bovine serum albumin (BSA) protein and graphene oxide (GO) for the treatment of diabetic wounds. The prepared hydrogel is mildly reduced by ascorbic acid to facilitate charge-transfer and enhance its conductivity. Hydrogel is optimized for enhanced mechanical and electrical properties by changing the concentration of GO within the BSA matrix. The nanocomposite hydrogel demonstrates superior antibacterial properties and enhanced biocompatibility compared to the BSA hydrogel. The prepared hydrogel is investigated for wound healing capacity in a rat model, where it demonstrates rapid healing of chronic diabetic wound. The improved therapeutic outcomes are attributed to the hydrogel's ability to facilitate cellular activities through enhanced electrical conductivity by maintaining continuous electrical signal at the wound site. The presented findings suggest that nanocomposite protein-based conductive hydrogel not only hold great potential for advanced diabetic wound care and have potential for future innovation in tissue engineering, regenerative medicine and beyond.

Continuous Signal Convolution Using FFT with Overlap for Transportation Robot Coordinate Determination

The paper discusses the development of a continuous discrete signal convolution algorithm using fast Fourier transform (FFT) with overlap for more accurate determination of the coordinates of a transportation robot. Two methods for determining the coordinates of a transportation robot in two-dimensional space are presented, where the convolution operation is used and where this algorithm can be applied. The algorithm is based on a method that allows the FFT to be performed on multiple computational units in parallel, such as on a TMS320F28377D digital signal processor, where three computational units are available that operate independently in parallel. The use of a FFT with overlap allows signal processing without data loss, which is critical for continuous signal processing.The study of the influence of the FFT window offset value on the frequency of obtaining the spectrum of the signal has been carried out, and as it was found that reducing the FFT window offset leads to an increase in the frequency of obtaining the spectrum of the signal, which, in turn, allows you to more accurately restore the amplitude of the original signal, that is, there is less loss of spectrum data. As results, the paper provides illustrations of the study in the form of three-dimensional graphs, where the spectra of the signal in time during the FFT with overlap, where as the initial signal is used a sinusoidal signal, which over time increases its frequency. Comparison of the results of the algorithm without overlap and with overlap at different offset values showed that the method with overlap provides significantly more accurate and frequent acquisition of spectrum data, avoiding data loss.The presented results are useful in robotics when it is required to accurately determine the coordinates of a transportation robot.

Radiofrequency Diagnostic of the Decaying Plasma in the “Gigantic” Coaxial Line at the Large Plasma Device

At the large-scale Krot facility (Gaponov-Grekhov Institute of Applied Physics of the Russian Academy of Sciences), a new device was developed for laboratory simulations of the effects of the interaction of ultrawideband electromagnetic pulses with the partially ionized atmosphere and ionosphere: the “gigantic” coaxial line with a length of 10 m and diameter of 1.4 m that is filled by the decaying plasma of the inductive discharge. Two radiofrequency methods of wave diagnostics used in the device are described, the cutoff method and the wave interferometer, which can be used to determine the electron density of the plasma in the line in a wide range of values, Ne = 107–1011 cm–3. The measurement results are compared with the values obtained by the contact diagnostic, a probe with a microwave resonator. The interferometric method is implemented taking into account the nonuniform distribution of plasma density both along and across the transmission line, which, in the working range of pulsed and continuous diagnostic signals, is an oversized waveguide. The specific features of application and the limitations of the contact (probe) and contactless (wave) methods of diagnostics are discussed, taking into account the nonuniform plasma distribution in the coaxial line and the specific features of its construction.

Instrumentation techniques for the measurement of gunfire (Part 3)

This paper is the third in a series concerning near-field measurement of sound pressure from small arms gunfire. It has been long established that exposure to gunfire events can lead to permanent threshold shift for an unprotected shooter. Close to a shooter's ear, the peak sound pressure level from a rifle or pistol can be 155-to-160 decibels with a pulse rise time on the order of ten microseconds. The U.S. Department of Defense has considered two approaches for assessing exposure limits from small arms gunfire - one involves the unweighted peak sound pressure and the other a series of A-weighted sound exposure events integrated to obtain an equivalent eight-hour dose. This dose is then compared to hearing damage risk criteria established for continuous noise signals. The first approach places great demands on the measurement system since the necessary bandwidth extends well into the ultrasonic range, thereby requiring fast slewing from the amplifiers combined with high sampling rates from the digitizer. The second approach requires the A-weighting filter to have specified time and frequency domain properties in the ultrasonic region. The paper discusses some laboratory experiments using actual gunfire signals to simulate the outcome from both approaches.

Seizure forecasting based on AI-supported analysis of multidien and circadian cycles in EEG and non-EEG long-term datasets

AbstractLong-term datasets in epilepsy encompassing weeks to months of continuous physiological signal recordings along with novel data analysis techniques have recently advanced the understanding of epilepsy in several aspects. Patterns of seizures, interictal discharges, and autonomous nervous system activity were observed to often exhibit long, multidien cycles that are often correlated with each other. These observations have provided the basis for new approaches to forecast seizure risk from electroencephalographic (EEG) and non-EEG data.

Emotion recognition using cross-modal attention from EEG and facial expression

The fusion of facial expression and Electroencephalogram (EEG) signals in a multi-modal framework is a comprehensive and accurate method for emotion recognition. However, current methods often tend to directly concatenate two modalities, thereby overlooking the interplay between modalities. Furthermore, the selection of single static facial images for extracting facial features neglected the dynamic changes in facial expressions. These limitations have been identified as factors that constrain the accuracy and stability of the model. In this study, an innovative multimodal emotion recognition network was introduced, which using continuous facial expressions and EEG signals. It incorporated the cross-modal attention fusion mechanism to establish robust correlations between modal feature vectors, thereby generating amalgamated vectors with mutual information. Additionally, a Self-Attention Convolutional Long Short-Term Memory (SA-ConvLSTM) was employed to capture spatiotemporal information from facial expression images. The model proposed in this paper was experimentally evaluated on the DEAP and MAHNOB-HCI datasets. Its recognition accuracy was higher than the existing advanced research methods. At the same time, it still performed well in the Leave-One-Subject-Out (LOSO) experiment based on DEAP dataset. The experimental results showed the effectiveness of the proposed model in the task of multimodal emotion recognition.