Real-time Signal Research Articles

Feasibility of volumetric-modulated arc therapy gating for cardiac radioablation using real-time ECG signal acquisition and a dynamic phantom.

Stereotactic arrythmia radioablation (STAR) is a noninvasive technique to treat ventricular tachycardia (VT). Management of cardiorespiratory motion plays an essential role in VT-STAR treatments to improve treatment outcomes by reducing positional uncertainties and increasing dose conformality. Use of an electrocardiogram (ECG) signal, acquired in real-time, as a surrogate to gate the beam has the potential to fulfil that intent. To investigate the gated delivery of volumetric-modulated arc therapy (VMAT) for STAR on a TrueBeam linear accelerator (linac) using a real-time acquired ECG signal and a dynamic cardiac phantom. Dosimetric characteristics of a 6 MV flattening filter free (FFF) beam from a Varian TrueBeam linac were initially evaluated under high-frequency gating scenarios relevant to cardiac rhythms with respect to dose linearity, beam output, and energy quality. A microcontroller board was used to interface and gate the linac, sending a beam on/off signal. For real-time cardiac gated measurements, an AD8232 Heart Monitor board was used to acquire the ECG signal and synchronize the VMAT delivery to an ArcCHECK detector to a specific phase of the cardiac cycle. Gated dose distributions were compared against those acquired for a non-gated delivery mode. An in-house dynamic cardiac phantom was developed to simulate cardiorespiratory motion that correlates target position with the signal to gate the beam. Measured dose distributions using gafchromic film were also compared against the static (reference) mode in different scenarios with and without gating. Maximum difference in dose per monitor unit (MU) was found to be no greater than 1% as compared to static mode with variation in the chamber response within 0.2% in the range of 50 MUs to 200 MUs. Maximum percentage differences for the beam output and beam qualiy index (TPR20,10) between gated and non-gated modes were 0.91% and -0.44%, respectively. Comparison of delivered dose distributions for the VMAT plan without gating versus ECG synchronized gating modes provided a passing rate 98% for the gamma analysis with 1% relative dose difference, 1mm distance-to-agreement criteria. For the synchronization of dose delivery with target position, passing rates were 98%, 97%, and 99% for the axial, coronal, and sagittal planes, respectively, when gating the beam based on target position for cardiac motion only, for 3%, 3mm tolerance as compared to static mode. Without gating the beam, passing rates of the respective plans are 97%, 94%, and 99% for the cardiac motion only, and 67%, 57%, and 55% when including respiratory component of motion. A 6 MV-FFF TrueBeam is stable for performing gating in STAR under high-frequency gate windows within typical cardiac cycles. Agreement between measured dose distributions for a VMAT plan in static and ECG-synchronized deliveries and between static and target-position gated modes shows that the proposed methodology is feasible and can be implemented on a TrueBeam platform.

A Novel System for Simultaneous Real-Time Determination of 15N-Enriched Ammonia, Hydroxylamine, Nitrite, and Nitrate.

Ammonium (NH4+), hydroxylamine (NH2OH), nitrite (NO2-), and nitrate (NO3-) account for the most important reactive nitrogen (N) species in the N cycle, playing a key role in N elimination and N retention, as well as the production of nitrogenous trace gases. However, it is still challenging to fulfill simultaneous real-time determination of all four N compounds enriched in 15N. This study successfully established a novel system by coupling an automatic simultaneous sample preparation unit to a membrane inlet mass spectrometer (4n-ASSP-MIMS) for rapid online 15N fraction analysis of all four key compounds in the N cycle. The limit of detection (LOD) was 20.22, 0.38, 0.17, and 0.25 μM for NH4+, NH2OH, NO2-, and NO3-, respectively, and relative error for 15N fraction determination was within 5% when 15N enrichment was higher than 5 atom %. This 4n-ASSP-MIMS system provides a first online analytical tool to capture the real-time isotopic signals during biogeochemical transformations of NH4+, NH2OH, NO2-, and NO3-, using the 15N tracing technique. Application of this system will dramatically advance our understanding of mechanisms involved in the N cycle.

A method for dynamically adjusting the difficulty of rehabilitation training tasks driven by attention level.

Enhancements in the rehabilitation of motor and cognitive functions are significantly attainable through proactive patient engagement. The difficulty of rehabilitation tasks and the environment in which they are conducted directly impact patient motivation. Consequently, this study introduces a dynamic difficulty adjustment method for rehabilitation training tasks based on attention levels, designed to adjust task difficulty in real-time and augment the focus of participants on their training tasks. 
Approach: EEG signals from participants were harnessed to train an attention classification model, enabling the acquisition of real-time attention level signals. Task difficulty levels were adjusted based on the fluctuating attention levels. A cohort of 30 participants was engaged to evaluate: (1) the impact on engagement when attention levels are utilized as dynamic difficulty 18 triggers; (2) the influence of various task environments on concentration. The experiment was assessed through EEG signals and questionnaire data, with frequency domain analysis conducted on EEG signals to calculate concentration values and statistical analysis performed on additional data. 
Main Results: The findings reveal that within an identical virtual reality (VR) environment, leveraging attention levels as triggers for difficulty adjustment markedly improves participants' task concentration. Compared to 2D environments, VR environments substantially enhance participants' sense of immersion, interest, and flow state, albeit with increased physical exertion during training. The integration of VR and attention level feedback is deemed the most effective strategy. 
Significance: These exploratory insights indicate that the proposed method paves a novel path for boosting patient engagement in rehabilitation. Immersive rehabilitation training, driven by attention levels, promises a more effective and captivating patient experience. This study advances the field by offering data-driven, personalized rehabilitation approaches, potentially culminating in superior patient outcomes and enhanced quality of life.

REGULATORS OF FINITE STABILIZATION FOR HYBRID LINEAR CONTINUOUS-DISCRETE SYSTEMS

For hybrid linear autonomous continuous-discrete systems, methods for designing two types of regulators that provide finite stabilization are proposed. The implementation of one of them, a regulators for finite stabilization by state, is based on knowledge of the values of the control system solution at discrete moments of time, multiples of the quantization step. For this purpose, an observer has been built that makes it possible to obtain the necessary solution values based on the observed output signal in real time and with zero error. The second type of regulator — the regulator of finite stabilization by output — uses the observed output signal as feedback, and its design is a modification of the finite state stabilization regulator by state by including the above observer in its circuit.

Research on Deterioration State Evaluation of Hydropower Units based on Successive Variational Mode Decomposition and Mahalanobis Distance

Abstract In order to better acquire the real-time operating status of hydropower units and realize early fault warning, a deterioration state evaluation method for hydropower units based on Successive Variational Mode Decomposition (SVMD) and Mahalanobis Distance (MD) is proposed. In the offline stage, SVMD is optimized with Dispersion Entropy (DE) as the fitness function, and historical health data is used to obtain a health baseline. In the online stage, the real-time monitoring signal is input into the optimized SVMD model first. The features of the Intrinsic Mode Functions (IMFs) are then extracted. Subsequently, Synthetic Detection Index (SDI) and Detection Index (DI) are utilized for feature parameter selection. Finally, a degradation indicator is constructed based on Gaussian mixture model (GMM) and MD, and the degradation curve is drown to evaluate the real-time deterioration state of the unit. Experimental results demonstrate that the proposed method can effectively characterize the real-time operating status of units, identify abnormal changes, and issue timely warnings.

Advanced Signal Processing Techniques for Real-Time Systems in Edge Computing

With the rapid expansion of edge computing, real-time systems are increasingly deployed in environments such as IoT, autonomous vehicles, and industrial applications, where efficient signal processing is crucial for performance and decision-making. However, real-time signal processing in edge computing environments faces significant challenges, including limited computational resources, data transmission constraints, and strict latency requirements. Objective: This paper aims to explore advanced signal processing techniques specifically tailored for real-time systems in edge computing. The goal is to propose novel algorithms and strategies to enhance processing efficiency, reduce latency, and optimize resource usage in these environments. Methods: We propose a combination of traditional signal processing methods and emerging techniques, including: Adaptive Filtering: Improving real-time performance in dynamic environments. Compressed Sensing: Leveraging sparsity to reduce data processing and transmission overhead. Deep Learning Models: Applying convolutional and recurrent neural networks for accurate signal classification and anomaly detection. Distributed Signal Processing: Offloading computation tasks to distributed edge devices to balance load and reduce latency. Results: Experimental results demonstrate that the proposed techniques outperform traditional methods in terms of signal processing speed, resource utilization, and accuracy. In particular, the integration of deep learning models significantly improves classification accuracy for real-time signal detection, while compressed sensing reduces bandwidth requirements without compromising signal quality. Conclusion: The proposed signal processing methods effectively address the unique challenges posed by real-time systems in edge computing environments. These techniques can be applied to a wide range of applications, such as smart cities, industrial IoT, and autonomous systems, providing a solid foundation for the development of efficient, low-latency real-time systems.

An unobserved lava fountain deciphered in real-time by high-precision borehole strainmeter and contribution to hazard evaluation: the Etna 21 May 2023 eruption

A powerful lava fountain took place on 21 May 2023 at Mt. Etna after a year of repose, preceded a few days earlier by a shallow seismic swarm. The main critical issue with the eruption was the difficulty to track this eruptive event with conventional remote sensing devices, such as webcams and satellite instruments, due to the bad weather conditions and especially the dense cloud cover. Despite the sophisticated monitoring available at the volcano, the eruption effectively remained “hidden”. It was here that the borehole dilatometers excelled: as with all recent lava fountains at Etna, these high-precision instruments detected significant strain changes and proved a valuable contribution to real-time monitoring of the event. Through recently implemented approaches, the analysis of the strain data allowed us to decipher the eruption: namely identify the timing of the events, evaluate the “size” of the fountain (i.e. its eruptive intensity), and also estimate the erupted volumes in near real-time. This provided a useful support during the Civil Protection emergency meeting at the Prefecture of Catania, where these results were presented and jointly discussed. Overall, the 21 May 2023 lava fountain was an important showcase, demonstrating the strategic contribution that real-time high-precision strain signals may have in defining and assessing the hazard associated with an eruptive event even in adverse weather conditions, when remote sensing systems may be unable to furnish direct information on the ongoing phenomenon.

Comparison of analysis methods for separating and recognizing multicomponent radar signals

This paper proposes a model for separating and recognizing a mixture of radar signals using combined multiresolution methods and convolution neural networks. The model involves three main steps: separating the signal into individual components using the Multiresolution analysis (MRA) methods: Empirical mode decomposition (EMD), Variational mode decomposition (VMD), and Maximal overlap discrete wavelet packet transform (MODWPT); transforming these components into the time-frequency domain using Wigner-Ville distribution (WVD) and storing them as images; and then feeding these images into the SqueezeNet for recognition. These multiresolution methods are then compared based on three criteria: The number of successful separations, the SNR ratio of the input signal, and the correlation between the separated signal components and the original signal components. Additionally, we evaluate the performance of the SqueezeNet with real-time signals.

Hardware simulation of real-time wavelength corrected phase projection

We demonstrate the real-time signal processing operation of a dispersion-free phase projection algorithm intended for atmospheric correction of multi-aperture optical phased arrays. It uses interferometric phase measurements at multiple sensing wavelengths, offset by 50 GHz, to compute a phase correction at a third, remote wavelength. This is useful where phase sensing cannot be implemented at the wavelength of interest, enabling interferometric level control from wavelength offset targeting beacons or guidestars. The digital signal processing implementation we demonstrate has a residual temporal phase error of 4×10−4rad/Hz while being capable of 100 MHz throughput with 0.53 µs latency, making it a viable approach for either feedback or feed-forward atmospheric correction in segmented piston-phase control systems.

EKFNet: edge-based Kalman filter network for real-time EEG signal denoising.

Objective.Signal denoising methods based on deep learning have been extensively adopted on electroencephalogram devices. However, they are unable to deploy on edge-based portable or wearable (P/W) electronics due to the high computational complexity of the existed models. To overcome such issue, we propose an edge-based lightweight Kalman filter network (EKFNet) that does not require manual prior knowledge estimation.Approach.Specifically, we construct a multi-scale feature fusion module to capture multi-scale feature information and implicitly compute the prior knowledge. Meanwhile, we design an adaptive gain estimation module that incorporates long short-term memory and sequential channel attention module to dynamically predict the Kalman gain. Furthermore, we present an optimization strategy utilizing operator fusion and constant folding to reduce the model's computational overhead and memory footprint.Main results. Experimental results show that the EKFNet reduces the sum of the square of the distances by at least 12% and improves the cosine similarity by at least 2.2% over the state-of-the-art methods. Besides, the model optimization shortens the inference time by approximately 3.3×. The code of our EKFNet is available athttps://github.com/cathnat/EKFNet.Significance.By integrating Kalman filter with deep learning, the approach addresses the parameter-setting challenges in traditional algorithms while reducing computational overhead and memory consumption, which exhibits a good tradeoff between algorithm performance and computing power.

Multiplexed microfluidic chip based flow injection analysis system for simultaneous determination of Fe(III), Mn(II) and Cu(II)

The development of microfluidic flow injection analysis (FIA) system for simultaneous and real-time detection of multiple metal ions is of great importance for environmental monitoring, food safety and industrial field. In this work, a multiplexed microfluidic chip based FIA system was fabricated for simultaneous determination of three metal ions, by using Fe(III), Mn(II) and Cu(II) as models. The multiplexed microfluidic chip based FIA system is composed of three parts, including a multi-channel FIA system for solution manipulation, a group of microfluidic chips for reagents mixing and reaction, and one fiber optic spectrometer for fast and real-time absorbance signal detection. The sample solution and chromogenic reagents were injected into the microfluidic chips to carry out specific colorimetric reactions. Fe(III), Mn(II) and Cu(II) reacts with 5-sulfosalicylic acid, formaldoxime, and bis(cyclohexanone) oxalyldihydrazone, respectively, to generate orange, ember and blue colored solution with peak absorbance at 420 nm, 450 nm, and 600 nm, respectively. The absorbance signal of the generated three channels of solution can be sequentially detected by using only one mini spectrometer. The linear detection range for Fe(III), Mn(II) and Cu(II) is 0.1–20 mg/L, 0.1–12 mg/L and 0.2–12 mg/L, respectively. The limit of detection for Fe(III), Mn(II) and Cu(II) is 0.07, 0.02 and 0.1 mg/L respectively. And the limit of quantification for Fe(III), Mn(II) and Cu(II) is 0.2, 0.07 and 0.3 mg/L, respectively. The recoveries of spiked Fe (III), Mn (II), and Cu (II) in real water samples are between 95.3 % to 106 %. Finally, the developed microfluidic chip based FIA system was successfully applied to simultaneous determination of three metal ions in environmental water samples and zinc smelting solution from zinc hydrometallurgy industry with satisfied results.

Processing of real-time surface electromyography signals during knee movements of rehabilitation participants

Processing of real-time surface electromyography signals during knee movements of rehabilitation participants

Implementation of global navigation satellite system softwaredefined radio baseband processing algorithms in system on chip

<p>The global navigation satellite system (GNSS) is an international navigation system that determines users' locations globally using a constellation of satellites. Conventional hardware-based receivers often face challenges related to cost-effectiveness and lack of reconfigurability. To address these issues, GNSS software receivers have emerged, executing baseband processing methods on host computers. However, host PC-based GNSS software receivers encounter obstacles during real-time signal acquisition, such as computational complexity and data loss. This research paper introduces a real-time system on chip (SoC)-based GNSS software receiver to mitigate these concerns. The receiver utilizes the USRP N210 radio frequency (RF) front end to acquire GNSS signals in real-time. Baseband processing algorithms are executed using the Zynq 7000 SoC board, with modifications applied to the acquisition module. The effectiveness of the SoC-based GNSS receiver is evaluated under both stationary and dynamic conditions. Experimental outcomes indicate that the receiver provides precise user localization and facilitates prototype development. This methodology not only overcomes the limitations of conventional hardware-based receivers but also leverages the benefits of SoC architecture to process GNSS signals in a flexible and efficient manner.</p>

Real Time Traffic Management System Using Machine Learning

The increasing quantity of automobiles on the road presents difficulties for conventional traffic control strategies. With heavy traffic levels, these traditional methods find it difficult to handle. They only work well in light traffic situations; when there is a large difference in traffic volume on one side of the road or when vehicle density increases on one side, they are unable to adjust. Therefore, by adding dynamic signal switching capabilities, we want to modernise the static signal system. The updated system will continuously check and modify the timing of the signals in real time. In this project, we want to use real-time image detection to precisely measure traffic density and figure out the best times to switch signals, especially when traffic is heavy. Putting this strategy into practice should effectively reduce traffic jams and accelerate traffic flow.

Automatic ECG Arrhythmia Recognition using ANN and CNN

Present research highlights the need for more patient-oriented monitoring systems for cardiac health, especially in the aftermath of COVID-19. The study introduces a contactless and affordable ECG device capable of recording heart arrhythmias for remote monitoring, which is vital in managing the rising incidence of untimely heart attacks. Two deep learning algorithms have been developed to design the system: RCANN (Real-time Compressed Artificial Neural Network) and RCCNN (Real-time Compressed Convolutional Neural Network), respectively, based on ANN and CNN. These methods are designed to classify and analyze three different forms of ECG datasets: raw, filtere and filtered + compressed signals. These were developed in this study to identify the most suitable type of dataset that can be utilized for regular/remote monitoring. This data is prepared using online ECG signals from Physionet(ONLINE) and the developed real-time signals from Arduino ECG sensor device. Performance is analysed on the basis of accuracy, sensitivity, specificity and F1 score for all kinds of designed ECG databases using both RCCNN and RCANN. For raw data, accuracy is 99.2%, sensitivity is 99.7%, specificity is 99.2%, and F1-Score is 99.2%. For RCCNN, accuracy is 93.2%, sensitivity is 91.5%, specificity is 95.1%, and F1-Score is 93.5% for RCANN. For Filtered Data, accuracy is 97.7%, sensitivity is 95.9%, specificity is 99.4%, and F1-Score is 97.6%. For RCCNN, accuracy is 90.5%, sensitivity is 85.8%, specificity is 96.4%, and F1-Score is 90.9% for RCANN. For Filtered + compressed data, accuracy is 96.6%, sensitivity is 97.6%, specificity is 95.7%, and F1-Score is 96.5%. For RCCNN, accuracy is 85.2%, sensitivity is 79.2%, specificity is 94.5%, and F1-Score is 86.7% for RCANN. The performance evaluation shows that RCCNN with filtered and compressed datasets outperforms other approaches for telemonitoring and makes it a promising approach for individualized cardiac health management.

A Real-Time Signal Measurement System Using FPGA-Based Deep Learning Accelerators and Microwave Photonic

A Real-Time Signal Measurement System Using FPGA-Based Deep Learning Accelerators and Microwave Photonic

Real-time 60 Gb/s PS-64QAM SSB-DMT transceiver deploying a low-complexity distribution matcher in band-limited IM-DD system

In this work, we have implemented a low-complexity distribution matcher in real-time for bandwidth-limited intensity modulation and direct detection (IM/DD) system based on field programmable gate array (FPGA). For the hardware implementation of probabilistic shaping (PS), a simple and effective distribution matcher is the key to achieving the symbol of the target probability. Compared to traditional PS distribution matchers, our proposed distribution matcher has lower hardware implementation complexity and only requires a small amount of look-up table and registers to achieve target probability distribution. Meanwhile, an optical tunable filter is used to generate single sideband discrete multitone (SSB-DMT) signals for 50-km standard single-mode fiber (SSMF) transmission. By using PS technology and SSB modulation scheme, we successfully real-time realize 11-GHz PS-64QAM SSB-DMT signals over 25/50-km SSMF transmission in band-limited IM-DD system, achieving a line rate of approximately 65.7 Gbit/s.

Evaluation of a Commercially Available Radiochromic Film for Use as a Complementary Dosimeter for Rapid In-field Low Photon Equivalent Radiation Dose (≤50 mSv) Monitoring.

This work investigates the low photon radiation dose (≤50 mSv) response of commercially available radiochromic films as a potential field dosimeter that could be used by the Canadian Armed Forces to complement their existing personal radiation dosimeters. The films were exposed to various photon energies from x-ray devices and radioisotopes (cesium-137, cobalt-60, and americium-241), and their radiation signal was read using three methods: net optical density, UV/visible spectroscopy, and Fourier transform infrared spectroscopy. A complimentary film dosimeter for field usage should, for military use, display a visual color change and detect doses ≤50 mSv. Given the film's radiochromic properties, it was determined that the net optical density method was the most optimal read-out method, which ascertained a minimum detection dose limit of 4.5 mSv under exposure to a clinical orthovoltage operated at 100 kVp. The film presented an overall linear relationship between net optical density and radiation dose; however, they also portrayed a photon energy-dependent response between 0-100 mSv. Overall, the radiochromic films presented a real-time visual dose signal that could be interpreted rapidly in a mobile laboratory and possessed the ability to detect photon doses ≤50 mSv below the vendor's recommended limits, making it a suitable option as a complementary, disposable, military dosimetric tool. Future work includes the investigation of the film's response under multi- and unknown source environments and environmental-dependent factors such as UV/sunlight exposure and extreme temperatures.

Inter- and Intrahemispheric Sources of Vestibular Signals to V1.

Head movements are sensed by the vestibular organs. Unlike classical senses, signals from vestibular organs are not conveyed to a dedicated cortical area but are broadcast throughout the cortex. Surprisingly, the routes taken by vestibular signals to reach the cortex are still largely uncharted. Here we show that the primary visual cortex (V1) receives real-time head movement signals - direction, velocity, and acceleration - from the ipsilateral pulvinar and contralateral visual cortex. The ipsilateral pulvinar provides the main head movement signal, with a bias toward contraversive movements (e.g. clockwise movements in left V1). Conversely, the contralateral visual cortex provides head movement signals during ipsiversive movements. Crucially, head movement variables encoded in V1 are already encoded in the pulvinar, suggesting that those variables are computed subcortically. Thus, the convergence of inter- and intrahemispheric signals endows V1 with a rich representation of the animal's head movements.

High-performance conductive double-network hydrogel base on sodium carboxymethyl cellulose for multifunctional wearable sensors

Sodium carboxymethyl cellulose showed great potential in wearable intelligent electronic devices due to its low price and good biocompatibility. This research aimed to develop a novel conductive hydrogel with stretchable, self-healing, self-adhesive, antibacterial, 3D printable properties, for the development of multifunctional flexible electronic materials based on sodium carboxymethyl cellulose. A multifunctional conductive hydrogel based on sodium carboxymethyl cellulose (SCMC) was synthesized by simple polymerization of SCMC, acrylic acid (AA) and alkaline calcium bentonite (AC-Bt). The multifunctional hydrogels (PAA-SCMC) possess excellent mechanical property (stress: 0.25 MPa; strain: 1675.0 %), Young's modulus (75.6 kPa), and conductivity (2.25 S/m). The multifunctional PAA-SCMC hydrogels serve as strain sensors (Gauge Factor (GF) = 12.68), temperature sensors (temperature coefficient of resistance (TCR) = −0.887 % °C at 20 °C–60 °C), sweat sensors, and pressure sensors. Importantly, the obtained hydrogels exhibited exceptional self-healing capability, self-adhesive properties, antimicrobial properties and 3D printability. The printed hydrogel has good mechanical properties, conductivity and antibacterial properties. Moreover, the hydrogel sensor possessed prominent sensitivity and cyclic stability to accurately monitor human motion, emotional changes, physiological signals in real time, and a hydrogel-based flexible touch keyboard was also fabricated to recognize writing trajectories. Overall, this study provided novel insights into the simple and efficient synthesis and sustainable manufacturing of environmentally friendly multifunctional flexible electronic skin sensors.