Signal Recovery Research Articles

NIMG-37. “SYNTHETIC” PERFUSION MRI WITH ADJUSTABLE ACQUISITION PARAMETERS IN BRAIN TUMORS USING DYNAMIC SPIN-AND-GRADIENT-ECHO ECHOPLANAR IMAGING

Abstract INTRODUCTION Dynamic susceptibility contrast (DSC) perfusion MRI can assess normalized relative cerebral blood volume (nrCBV) and percentage of signal recovery (PSR) in brain tumors, which can aid in differential diagnosis, molecular profiling, and identification of disease progression. However, CBV-optimized and PSR-optimized protocols differ in terms of acquisition parameters. Additionally, DSC dependency on acquisition parameters limits the universalizability of diagnostic cutoffs for nrCBV and PSR across institutions and scanners. This study aims to generate “synthetic” DSC datasets with adjustable synthetic acquisition parameters using dynamic spin-and-gradient-echo echoplanar imaging (dynamic SAGE-EPI) to: 1) simultaneously generate nrCBV and PSR with optimal acquisition parameters, 2) compare DSC datasets with heterogeneous external cohorts. METHODS Thirty-eight patients with contrast-enhancing brain tumors were prospectively imaged with dynamic SAGE-EPI during a non-preloaded single-dose contrast injection. Multiple synthetic DSC datasets with desired pulse sequence parameters were generated using the Bloch equations applied to the dual-echo gradient-echo (GE) data extracted from dynamic SAGE-EPI datasets, with or without optional preload simulation. For each patient, synthetic protocols were set to match guideline-compliant CBV-optimized protocols, PSR-optimized protocols, and protocols used in external cohorts from the literature, for comparison. RESULTS Dynamic SAGE-EPI allowed for simultaneous generation of CBV-optimized and PSR-optimized DSC datasets with a single contrast injection. Conversely, suboptimal PSR computation from guideline-compliant CBV-optimized protocols resulted in rank variations within the cohort (Spearman’s ρ=0.83-0.89, i.e. 31%-21% rank variation). Treatment-naïve glioblastoma exhibited lower parameter-matched PSR compared to the external cohorts of treatment-naïve primary CNS lymphomas (PCNSL) (p<0.0001), supporting a role of synthetic DSC for multicenter comparisons. CONCLUSIONS Dynamic SAGE-EPI allows for simultaneous generation of CBV-optimized and PSR-optimized DSC data with a single injection, facilitating use of a single perfusion protocol for all DSC applications. This approach may also be useful for comparisons of perfusion parameters across heterogeneous multicenter datasets.

Symptom Shifting From Nonsuicidal Self-Injury to Substance Use and Borderline Personality Pathology

A decline in nonsuicidal self-injury (NSSI) behavior is often viewed as an indication of mental health improvement when treating adolescents and young adults with borderline personality pathology. However, evidence shows that an initial decrease in NSSI behavior does not always signal recovery, and potential symptom shifting needs to be considered. To examine the codevelopment of NSSI and substance use in adolescents and young adults receiving treatment for NSSI behavior and to explore whether shifting from NSSI behavior to substance use is associated with emerging or persistent borderline personality pathology. This cohort study used data from the AtR!Sk study. The study included a consecutive sample of adolescents and young adults with NSSI behavior who presented to a specialized outpatient clinic for early intervention of borderline personality pathology. A baseline assessment (between May 3, 2016, and December 19, 2019) and 2 annual follow-up assessments were conducted. Data were analyzed from April 15, 2023, to September 5, 2024. The frequencies of NSSI behavior and substance use were self-reported. Diagnostic interviews were carried out to assess borderline personality pathology. Growth mixture models were specified to identify latent classes with different joint trajectories of NSSI behavior and substance use, and the classes were compared for the number of fulfilled borderline personality disorder (BPD) diagnostic criteria. Overall, 277 adolescents and young adults (249 [89.9%] female; mean [SD] age at baseline, 14.9 [1.5] years) were included in the study (number at first follow-up, 135; number at second follow-up, 82). Three latent classes were extracted from the data. A decline in NSSI behavior following treatment was common. Class 1 (estimated class count: 32.5; 11.7% of participants) was further characterized by a decline in substance use; class 2 (210.1; 75.9% of participants), by a moderate increase in substance use; and class 3 (34.4; 12.4% of participants), by a strong increase in substance use. The number of fulfilled BPD diagnostic criteria in class 1 (mean [SE], 4.64 [0.40]; comparison: χ2, 11.64; P < .001) and class 3 (mean [SE], 4.29 [0.41]; comparison: χ2, 5.98; P = .01) was greater than that in class 2 (mean [SE], 3.18 [0.15]) at baseline. The number of fulfilled BPD criteria remained high at the second follow-up assessment in class 3 (mean [SE], 5.15 [0.84]) but not in class 1 (mean [SE], 2.05 [0.54]). In this cohort study of adolescent patients with NSSI behavior, a decline in this behavior was commonly paired with an increase in substance use. This finding suggests that a decrease in NSSI behavior alone may be insufficient to indicate treatment success. Monitoring the joint trajectories of NSSI behavior and substance use may be a promising avenue toward early detection and targeted treatment of adolescent borderline personality pathology.

Research on dynamic solution analysis and applications based on the Rydberg atom superheterodyne structure

This paper investigates the dynamic solution of the density matrix equation based on the Rydberg atom superheterodyne structure. Compared to the current analytical method relying on the steady-state solution, the dynamic solution is related to the Rabi frequency and the frequency of the signal to be measured. Therefore, it can comprehensively describe the instantaneous bandwidth and gain characteristics of the receiver and is in good agreement with experimental results. Additionally, we propose an atomic all-heterodyne receiver architecture that combines electric-field heterodyne and optical heterodyne techniques and demonstrates the reception and recovery of modulated signals under this architecture with linear frequency modulation (LFM) signals as an example. Our research offers interesting theoretical insights that can be applied to the performance analysis and system optimization of atomic receivers.

Multispatially Localized DNA Walker Coupling Covalent Organic Framework for Dual-Mode Detection of Nucleocapsid Protein Using the Walking-Recycling-Conversion Strategy.

DNA walkers have emerged as a powerful tool in bioanalysis; however, many existing approaches are still restricted by low reaction kinetics and inaccurate single-mode detection. Herein, a fluorescence (FL) and electrochemical (EC) dual-mode biosensor was proposed based on a multispatially localized DNA walker (m-DNA walker) coupling covalent organic framework (COF) using the walking-recycling-conversion strategy. Specifically, the functionalized COF not only served as a three-dimensional nanocarrier but also acted as an effective quencher of the walking tracks. In the presence of the target, the activated m-DNA walker moved fast along the numerous quenching tracks, leading to the cleavage of Cy3-H1 and the recovery of the FL signal. To further improve the detection sensitivity, the Cy3-H1 fragments' recycling process was implemented with the generation of a large amount of S1 and S2, which caused the assembly of DNA-Fe3+-polydopamine network amplifiers on the electrode. The rapid electrochemical conversion was introduced to convert DNA-Fe3+-polydopamine into electroactive Prussian Blue, providing a significant EC signal output. Using nucleocapsid protein (N-protein) as the model target, the designed biosensing platform produced a FL/EC dual-mode readout with the detection limits of 65.0 fg/mL for FL mode and 2.3 fg/mL for EC mode, which could eliminate the interference from different reactive pathways and improve the detection accuracy, holding potential application in early disease diagnosis and treatment.

Refined Iterative Method for a Common Variational Inclusion and Common Fixed-Point Problem with Practical Applications

This paper introduces a novel parallel method for solving common variational inclusion and common fixed-point (CVI-CFP) problems. The proposed algorithm provides a strong convergence theorem established under specific conditions associated with the CVI-CFP problem. Numerical simulations demonstrate the algorithm’s efficacy in the context of signal recovery problems involving various types of blurred filters. The results highlight the algorithm’s potential for practical applications in image processing and other fields.

New scheme of cooperative compressed spectrum sensing

Abstract This study addresses key challenges in sparse signal recovery and compressed spectrum sensing (CSS), focusing on low signal-to-noise ratios (SNR), and the computational complexity of cooperative systems. Motivated by the need for faster and more accurate recovery techniques, we first investigate and generalize the Reduced-Set Matching Pursuit (RMP) algorithm, which overcomes the speed and accuracy limitations of conventional greedy algorithms. Secondly, we propose a novel spatial averaging technique that enhances detection performance by exploiting data from multiple users to counteract low SNR. Lastly, we integrate cooperation into CSS, further improving the detection capabilities during the recovery process. Compared to existing techniques like Joint Sparse Recovery (JSR) and CoSaMP, which face computational and accuracy constraints in real-time applications, the RMP algorithm, combined with the Virtual method (data transformation) and AND fusion rule, delivers superior performance than JSR methods. Moreover, spatial averaging significantly increases the probability of cooperative detection Q d , with SNR increasing linearly by a factor of L − 1 per channel. The results are validated through the implementation of SDR. These findings demonstrate the potential of RMP and cooperation to overcome current limitations in CSS, advancing the state-of-the-art in spectrum sensing for collaborative networks.

High-Resolution Collaborative Forward-Looking Imaging Using Distributed MIMO Arrays

Airborne radar forward-looking imaging holds significant promise for applications such as autonomous navigation, battlefield reconnaissance, and terrain mapping. However, traditional methods are hindered by complex system design, azimuth ambiguity, and low resolution. This paper introduces a distributed array collaborative, forward-looking imaging approach, where multiple aircraft with linear arrays fly in parallel to achieve coherent imaging. We analyze signal model characteristics and highlight the limitations of conventional algorithms. To address these issues, we propose a high-resolution imaging algorithm that combines an enhanced missing-data iterative adaptive approach with aperture interpolation technique (MIAA-AIT) for effective signal recovery in distributed arrays. Additionally, a novel reference range cell migration correction (reference RCMC) is employed for precise range–azimuth decoupling. The forward-looking algorithm effectively transforms distributed arrays into a virtual long-aperture array, enabling high-resolution, high signal-to-noise ratio imaging with a single snapshot. Simulations and real data tests demonstrate that our method not only improves resolution but also offers flexible array configurations and robust performance in practical applications.

Advances in deep learning-based intelligent receivers

Abstract. This paper presents a summary of the progress of deep learning in the research of intelligent receivers and predicts its future development direction. First, the background of the era of intelligent receivers is introduced. With the rapid development of communication technology, intelligent receivers show insufficient flexibility, low accuracy, and spectral efficiency in detecting current radio signals. This is a challenge that is difficult to overcome in order to meet the needs of modern signal recovery. With the advent of artificial intelligence technologies such as deep learning, academia and industry began to explore the potential of applying these techniques to the field of communication receivers, leading to the emergence of intelligent receivers. This paper then introduces the fundamentals of wireless communication systems and deep learning neural networks, laying the groundwork for a deeper understanding of the subsequent content. Chapter 3 provides a comprehensive overview of the application of intelligent receivers in signal reception, including channel estimation, signal detection, modulation identification, demodulation and decoding. Finally, the paper considers the practical applications of intelligent receivers and speculates on their future development.

Sensitive detection of bisphenol A based on resonance energy transfer between zinc oxide nanoparticles enhanced luminol ECL and silver nanoparticles decorated TiVC MXene

This present work developed a highly sensitive electrochemiluminescence (ECL) sensor based on resonance energy transfer between ZnO nanoparticles (ZnONPs) enhanced luminol ECL as the energy donor and silver nanoparticle-modified TiVC MXene (AgNPs@TiVC) as the energy acceptor. ZnONPs were employed as catalysts to facilitate the reaction between luminol and dissolved oxygen, which could amplify the ECL signal of luminol without the need of additional co-reactants. The AgNPs@TiVC composite functioned as the energy acceptor, participating in ECL resonance energy transfer to quench the ECL emission. In the presence of bisphenol A (BPA), the specific recognition by the aptamer could detach the energy acceptor from the electrode surface, resulting in the recovery of ECL signal. The variation of ECL intensity exhibited a good linear relationship with the logarithm of BPA concentration in the range of 0.06 nM–0.08 μM, with a detection limit of 8.9 pM (3σ). This sensor owned satisfactory sensitivity and selectivity, which could further expand the utility of various MXenes in ECL sensing applications.

Thyroid Radiofrequency Ablation-Thermal Effects on Recurrent Laryngeal Nerve Using Continuous Intraoperative Neuromonitoring Animal Model.

When performing radiofrequency ablation for thyroid nodules, it is essential to avoid thermal injury to the recurrent laryngeal nerve. This porcine animal model study used continuous intraoperative neuromonitoring to investigate the thermal safety parameters of thyroid radiofrequency ablation. Porcine animal study. University animal laboratory. Twelve piglets were tested at different radiofrequency power levels, and the real-time electromyography signal changes were recorded under continuous intraoperative neuromonitoring. The spread heat study (8 piglets) included spontaneous recovery tests and cold water irrigation tests to investigate the safety distance from the recurrent laryngeal nerve to the active tip during 5-second activation with standard stimulation patterns. The residual heat study (4 piglets) investigated the safety cooling durations by touching the recurrent laryngeal nerve with the tip after a 5-second activation. In the spread heat study, substantial signal attenuation events were observed at an spread heat distance of 2, 3, 5, and 5 mm when the power was set as 10, 20, 30, and 50 W, respectively. No signal recovery could be observed in 20 minutes with or without cold water irrigation in the injured recurrent laryngeal nerve area. The residual heat study shows the residual thermal effect of the tip is minimal, and no substantial signal attenuation event was observed at all experiments. This innovative study established the thermal safety parameters for radiofrequency ablation in a porcine model at various power levels, which can potentially assist operators in delineating a precise ablation field and providing effective thyroid ablation treatment safely.

THE UTILITY OF MULTIPARAMETRIC MRI IN REDUCING DIAGNOSTIC UNCERTAINTY FOR PRIMARY CNS LYMPHOMA

Abstract AIMS A key limitation in the early treatment initiation in primary central nervous system lymphoma (PCNSL) is the diagnostic delay caused by lack of recognition of a lesion as a possible lymphoma, steroid initiation, and lesion involution, often resulting in inconclusive biopsy. We highlight the importance of multiparametric MRI (MPMRI), which incorporates diffusion weighted imaging (DWI), dynamic susceptibility contrast-enhanced perfusion weighted imaging (DSC-PWI) and proton magnetic resonance spectroscopy (1H-MRS) in addition to standard MRI sequences in resolving diagnostic uncertainty for PCNSL. METHOD We present a consecutive series of 10 patients at our centre with histology-proven PCNSL (specifically, diffuse large B-cell lymphoma of the CNS) who underwent multiparametric MRI. We retrospectively analyse the qualitative and semi-quantitative parameters and assess their radiological concordance for this diagnosis. RESULTS We note overall low apparent diffusion coefficient on DWI (mean ADCmin of 0.74), high percentage signal recovery on perfusion weighted imaging (mean 170%), a high choline-creatine ratio and a high-grade lipid peak on MRS giving a “twin-tower” appearance. Nine of ten patients had MRMRI findings concordant for PCNSL, defined as at least 3 of 4 parameters being consistent for PCNSL. CONCLUSION We propose that concordance between these imaging multiparametric modalities could be used as a radiological predictor of PCNSL, reducing diagnostic delays, providing a more accurate biopsy target, and resulting in quicker treatment initiation.

Remote physiological signal recovery with efficient spatio-temporal modeling.

Contactless physiological signal measurement has great applications in various fields, such as affective computing and health monitoring. Physiological measurements based on remote photoplethysmography (rPPG) are realized by capturing the weak periodic color changes. The changes are caused by the variation in the light absorption of skin surface during systole and diastole stages of a functioning heart. This measurement mode has advantages of contactless measurement, simple operation, low cost, etc. In recent years, several deep learning-based rPPG measurement methods have been proposed. However, the features learned by deep learning models are vulnerable to motion and illumination artefacts, and are unable to fully exploit the intrinsic temporal characteristics of the rPPG. This paper presents an efficient spatiotemporal modeling-based rPPG recovery method for physiological signal measurements. First, two modules are utilized in the rPPG task: 1) 3D central difference convolution for temporal context modeling with enhanced representation and generalization capacity, and 2) Huber loss for robust intensity-level rPPG recovery. Second, a dual branch structure for both motion and appearance modeling and a soft attention mask are adapted to take full advantage of the central difference convolution. Third, a multi-task setting for joint cardiac and respiratory signals measurements is introduced to benefit from the internal relevance between two physiological signals. Last, extensive experiments performed on three public databases show that the proposed method outperforms prior state-of-the-art methods with the Pearson's correlation coefficient higher than 0.96 on all three datasets. The generalization ability of the proposed method is also evaluated by cross-database and video compression experiments. The effectiveness and necessity of each module are confirmed by ablation studies.

Asymmetric spin echo multi-echo echo planar imaging (ASEME-EPI) sequence for pre-clinical high-field fMRI.

In functional magnetic resonance imaging (fMRI) of the blood oxygen level-dependent (BOLD) contrast, gradient-recalled echo (GRE) acquisitions offer high sensitivity but suffer from susceptibility-induced signal loss and lack specificity to microvasculature. In contrast, spin echo (SE) acquisitions provide improved specificity at the cost of reduced sensitivity. This study introduces Asymmetric Spin Echo Multi-Echo Echo Planar Imaging (ASEME-EPI), a technique designed to combine the benefits of both GRE and SE for high-field preclinical fMRI. ASEME-EPI employs a spin echo readout followed by two asymmetric spin echo (ASE) GRE readouts, providing an initial T2-weighted SE image and subsequent T2 * -weighted ASE images. A feasibility study for the technique was implemented on a 9.4 T pre-clinical MRI system and tested using a visual stimulation in northern tree shrews. Comparing ASEME-EPI with conventional GRE echo planar imaging (GRE-EPI) and SE echo planar imaging (SE-EPI) acquisitions, results showed that ASEME-EPI achieved BOLD contrast-to-noise ratio (CNR) comparable to GRE-EPI while offering improved specificity in activation maps. ASEME-EPI activation was more confined to the primary visual cortex (V1), unlike GRE-EPI which showed activation extending beyond anatomical boundaries. Additionally, ASEME-EPI demonstrated the ability to recover signal in areas of severe field inhomogeneity where GRE-EPI suffered from signal loss. The performance of ASEME-EPI is attributed to its multi-echo nature, allowing for SNR-optimized combination of echoes, effectively denoising the data. The inclusion of the initial SE also contributes to signal recovery in areas prone to susceptibility artifacts. This feasibility study demonstrates the potential of ASEME-EPI for high-field pre-clinical fMRI, offering a promising compromise between GRE sensitivity and SE specificity while addressing challenges of T2 * decay at high field strengths.

Signal processing for enhancing railway communication by integrating deep learning and adaptive equalization techniques.

With the increasing amount of data in railway communication system, the conventional wireless high-frequency communication technology cannot meet the requirements of modern communication and needs to be improved. In order to meet the requirements of high-speed signal processing, a high-speed communication signal processing method based on visible light is developed and studied. This method combines the adaptive equalization algorithm with deep learning and is applied to railway communication signal processing. In this research, the wavelength division multiplexing (WDM) and orthogonal frequency division multiplexing (OFDM) techniques are used, and fuzzy C equalization algorithm is used to softly divide the received signals, reduce signal distortion and interference suppression. The experimental results showed that increasing the step size could reduce the equalization effect, while increasing the modulation parameter will increase the bit error rate. Through deep learning to achieve channel equalization, visible light communication could effectively mitigate multi-path transmission and reflection interference, thereby reducing the bit error rate to the level of 0.0001. Under various signal-to-noise ratios, the system using the channel compensation method achieved the lowest bit error rate. This outcome was achieved by implementing hybrid modulation scheme, including Wavelength division multiplexing (WDM) and direct current-biased optical orthogonal frequency division multiplexing (DCO-OFDM) techniques. It has been proved that this method can effectively reduce the channel distortion when the receiver is moving. This study develops a dependable communication system, which enhances signal recovery, reduces interference, and improves the quality and transmission efficiency of railway communication. The system has practical application value in the field of railway communication signal processing.

Preparation of aerogels with SBS@rGO structure by Pickering emulsion method for innovative applications in pressure sensors

Pressure sensors based on graphene conductive fillers show great promise for application in the field of smart wearable devices. The SBS@rGO structured aerogel sensors covered herein were prepared by the Pickering emulsion method based on a solution of styrene-butadiene-styrene (SBS) and reduced graphene oxide (rGO) dispersion. The mechanical properties of SBS@rGO aerogel were tested with 100 independent loading-unloading cycles in a graded compressive strain range of 10–40 %. The results indicate that the SBS@rGO aerogel have fast and slightly destructive resilience after unloading at low compressive strains (ε ≤ 30 %). Moreover, the presence of rGO endowed SBS@rGO with pressure sensing properties such as high electrical conductivity (2.294 S/m), high sensitivity (0.016 kPa−1 at 6.36–100 kPa), short signal response time (60 ms) and recovery time (30 ms). The SBS@rGO pressure sensors were integrated into shoe soles to simulate walking scenes, whose response signals had excellent reproducibility and could be differentiated according to three different walking states. The potential and feasibility of SBS@rGO aerogel as wearable flexible pressure sensors has been demonstrated.

Research of the impact of noise reduction methods on the quality of audio signal recovery

The subject of the study is the analysis of various filtering algorithms for the quality of the resulting audio files. The importance of audio line filtering has grown significantly in recent years due to its key role in a variety of applications such as speech reduction and artificial intelligence. Taking into account the growing demand for solving problems related to speech recognition, the processing of audio series becomes important for determining the accuracy and efficiency of the obtained solution.The purpose of the work is to study the impact of noise suppression methods on the quality of restoration of an audio signal, which was alternately noisy with one of five types of noise - white, pink, brown, impulse, Gaussian with different power. To achieve the goal, the following tasks were solved: an analysis of the types of noise was carried out and analysis of noise reduction and filtering methods. A generalized model of noise reduction and filtering was developed, and an experiment was planned depending on the type and power of noise. Simulation of the experiment was performed by comparing the parameters of the signal-to-noise ratio before and after the experiment and the peak signal-to-noise ratio in the processed file. The following methods are used: spectral subtraction, filtering based on frequency filters and wavelet transformation.The following results were obtained: depending on the selected noises and algorithms, it was possible to achieve the lowest value of the peak signal-to-noise ratio of 21.52db, and the signal-to-noise ratio increased, which allowed further work with these audio files. The practical significance of this work is the increase in the number of available audio files for further work.Conclusions: the analysis of the obtained results showed that filtering based on frequency filters only worsened the output signal, that is, not only noise, but also useful information is filtered. In all runs, the SNR deteriorates to - 18dB. which is worse than no filtering. Algorithms of spectral subtraction and wavelet transformation improved SNR parameters and output audio files noisy with the most powerful noises in the range of 20dB, which can be considered acceptable for further processing. The results highlight the importance of using denoising and filtering for complex audio processing tasks, particularly neural network training tasks.

Estimating false alarm rates of sub-dominant quasi-normal modes in GW190521

Abstract A major aim of gravitational wave astronomy is to test observationally the Kerr nature of black holes. The strongest such test, with minimal additional assumptions, is provided by observations of multiple ringdown modes, also known as black hole spectroscopy. For the gravitational wave merger event GW190521, we have previously claimed the detection of two ringdown modes emitted by the remnant black hole. In this paper we provide further evidence for the detection of multiple ringdown modes from this event. We analyse the recovery of simulated gravitational wave signals designed to replicate the ringdown properties of GW190521. We quantify how often our detection statistic reports strong evidence for a sub-dominant $(\ell,m,n)=(3,3,0)$ ringdown mode, even when no such mode is present in the simulated signal. We find this only occurs with a probability $\sim 0.02$, which is consistent with a Bayes factor of $56 \pm 1$ (1$\sigma$ uncertainty) found for GW190521. We also quantify our agnostic analysis of GW190521, in which no relationship is assumed between ringdown modes, and find that only 1 in 250 simulated signals without a $(3,3,0)$ mode yields a result as significant as GW190521. Conversely, we verify that when simulated signals do have an observable $(3,3,0)$ mode they consistently yield a strong evidence and significant agnostic results. We also find that constraints on deviations from the $(3,3,0)$ mode on GW190521-like signals with a $(3,3,0)$ mode are consistent with what was obtained from our previous analysis of GW190521. Our results support our previous conclusion that the gravitational wave signal from GW190521 contains an observable sub-dominant $(\ell,m,n)=(3,3,0)$ mode.

Quantifying the effects of neutron fluence on proton signal retention in CR-39.

This paper reports on investigations on the impact of higher neutron fluences on the detection efficiency of protons with CR-39, a charged particle track detector. CR-39 is widely used as a diagnostic for inertial fusion applications and is an integral component of numerous particle diagnostics at the OMEGA laser facility and National Ignition Facility. As experiments continue to produce higher and higher yields, existing diagnostics are impacted by higher particle fluences than they were originally designed for. This paper presents data from experiments measuring proton signal on pieces of CR-39 with different levels of neutron fluence with two different etch times. The experiments show a decrease in signal recovery with increased neutron fluence, which is exacerbated at longer etch times. At 3h etch time, data suggest a 17% ± 7% signal loss at 1.3 × 105 neutron-induced tracks per cm2 and a 67% ± 21% loss at 6h etch time. Careful signal isolation techniques can recover most of the proton tracks even with moderate neutron fluence.

Hidden data recovery using reservoir computing: Adaptive network model and experimental brain signals.

The problem of hidden data recovery is crucial in various scientific and technological fields, particularly in neurophysiology, where experimental data can often be incomplete or corrupted. We investigate the application of reservoir computing (RC) to recover hidden data from both model Kuramoto network system and real neurophysiological signals (EEG). Using an adaptive network of Kuramoto phase oscillators, we generated and analyzed macroscopic signals to understand the efficiency of RC in hidden signal recovery compared to linear regression (LR). Our findings indicate that RC significantly outperforms LR, especially in scenarios with reduced signal information. Furthermore, when applied to real EEG data, RC achieved more accurate signal reconstruction than traditional spline interpolation methods. These results underscore RC's potential for enhancing data recovery in neurophysiological studies, offering a robust solution to improve data integrity and reliability, which is essential for accurate scientific analysis and interpretation.

Direction of Arrival Estimation Based on DNN and CNN

The accuracy of Direction of Arrival (DOA) estimation primarily depends on the precision of the data. When the receiver uses a low-precision analog-to-digital converter (ADC), traditional DOA estimation algorithms exhibit poor accuracy. To face the challenge of multi-target DOA estimation in scenarios with low-precision ADC quantized sampling, this paper proposes a novel DOA estimation algorithm for quantized signals based on classification problems. A deep learning network was constructed using Deep Neural Networks (DNNs) and Convolutional Neural Networks (CNNs), divided into the quantized signal recovery framework and the DOA estimation framework. The DNN network is utilized to recover signals that have undergone low-precision quantization, while the CNN network addresses the classification problem to estimate the DOA from received data with an unknown number of signal sources. A comprehensive analysis of the impact of signal-to-noise ratio (SNR), the number of array elements, and the number of quantization bits on the proposed algorithm was conducted. Simulation results indicate that the proposed algorithm exhibits superior DOA estimation performance in low-precision scenarios, characterized by reduced computational complexity, thereby facilitating real-time DOA estimation.