Signal-to-noise Research Articles

Improving MR axon radius estimation in human white matter using spiral acquisition and field monitoring.

To compare MR axon radius estimation in human white matter using a multiband spiral sequence combined with field monitoring to the current state-of-the-art echo-planar imaging (EPI)-based approach. A custom multiband spiral sequence was used for diffusion-weighted imaging at ultra-high -values. Field monitoring and higher order image reconstruction were employed to greatly reduce artifacts in spiral images. Diffusion weighting parameters were chosen to match a state-of-the art EPI-based axon radius mapping protocol. The spiral approach was compared to the EPI approach by comparing the image signal-to-noise ratio (SNR) and performing a test-retest study to assess the respective variability and repeatability of axon radius mapping. Effective axon radius estimates were compared over white matter voxels and along the left corticospinal tract. Increased SNR and reduced artifacts in spiral images led to reduced variability in resulting axon radius maps, especially in low-SNR regions. Test-retest variability was reduced by a factor of approximately 1.5 using the spiral approach. Reduced repeatability due to significant bias was found for some subjects in both spiral and EPI approaches, and attributed to scanner instability, pointing to a previously unknown limitation of the state-of-the-art approach. Combining spiral readouts with field monitoring improved mapping of the effective axon radius compared to the conventional EPI approach.

Performance evaluation of magic square based spatial modulation in UAV communication

This study proposes a method to reduce the bit error rate (BER) in communications by unmanned aerial vehicles (UAVs). The method involves using specific active antennas that transmit at precise times with a particular mapping, ensuring orthogonality among transmitting antennas. This technique can accommodate numerous transmit antennas and amplitude-phase modulation orders. When applied to spatial modulation (SM) and generalized spatial modulation (GSM), it demonstrates clear superiority and significantly improved results compared to traditional SM and GSM methods, especially at high altitudes where conventional methods result in high BER. Introducing a new modulation method called magic square (MS) further enhances the results. The MS method provides a unique orthogonality pattern for the transmitting antenna sets. Additionally, the study introduces the utilization of magic squares in the modulation field. The MS-based SM model shows a BER rate almost 103 times lower than traditional SM at the 25 dB signal-to-noise ratio (SNR) level for various fly altitudes. Similarly, the MS-based GSM model demonstrates a BER rate almost 100 times lower than traditional GSM. The proposed method yields better results as the altitude increases, making it suitable for UAV communication at various altitudes.

Adaptive tunicate swarm optimization with partial transmit sequence for phase optimization in MIMO-OFDM

<span>Multiple-input multiple-output (MIMO) and orthogonal frequency division multiplexing (OFDM) are widely utilized in wireless systems and maximum data rate communications. The MIMO-OFDM technology increases the efficiency of spectrum utilization. The peak-to-average-power ratio (PAPR) minimization in MIMO-OFDM is a complex task in wireless communications systems. In this research, an adaptive tunicate swarm optimization with partial transmit sequence (ATSO-PTS) algorithm is proposed for a reduction of PAPR in MIMO-OFDM. The nonsquare-matrix-based differential space time coding (N-DSTC) scheme is used for the encoding and decoding process of MIMO-OFDM. The N-DSTC encoding and decoding are linear error-correcting codes that are utilized for message transmission over noisy channels. The pre-specified quadrate phase shift keying (QPSK) symbol is deployed for the modulation and demodulation scheme. On the receiver side, the serial to parallel (S/P) conversion, and fast Fourier transform (FFT) are accomplished, alongside the received data bits being demodulated to obtain the output bits. The proposed ATSO-PTS method achieves better results according to performance metrices PAPR, bit error rate (BER) and signal-to-noise-ratio (SNR), with values of about 2.9, 0.01 and 0.025, respectively. This ensures superior results when compared to the existing methods of twin symbol hybrid optimization applied to partial transmit sequence (TSHO-PTS), selective level mapping and PTS (SLM-PTS), and particle swarm and grey wolf (PS-GW) with PTS, respectively.</span>

Identification of Continental Mantle Earthquakes Using Regional Waves Propagating Into a Thinned Continental Crust

AbstractWe evaluate three identifiers of continental mantle earthquakes (CMEs) motivated by surface‐wave normal‐mode theory: the amplitude ratio of Sn to Lg, and the frequency content of Sn and of Lg, after wave propagation through continental crustal thinning. These flexible and easily applicable methods allow for potential new discoveries of CMEs. They rely on guided waves whose propagation is dependent on the uniformity of their waveguides. For a range of Moho models, we perform 2.5D axisymmetric simulations that reach the conventional distance and frequency ranges of observational studies; we compare results from four different source depths straddling the Moho. Our synthetics, and six south‐Tibet earthquakes recorded by an array in Bangladesh, show our Sn/Lg identifier is robust in the presence of crustal thinning, but the identifying frequency contents of Sn and Lg are easily obscured. These results strengthen the utility of Sn/Lg methods for global studies of CMEs.

Optimizing Security and Cost Efficiency in N-Level Cascaded Chaotic-Based Secure Communication System

In recent years, chaos-based secure communication systems have garnered significant attention for their unique attributes, including sensitivity to initial conditions and periodic orbit density. However, existing systems face challenges in balancing encryption strength with practical implementation, especially for multiple levels. This paper addresses this gap by introducing a novel N-level cascaded chaotic-based secure communication system for voice encryption, leveraging the four-dimensional unified hyperchaotic system. Performance evaluation is conducted using various security metrics, including Signal-to-Noise Ratio (SNR), Peak Signal-to-Noise Ratio (PSNR), Percent Residual Deviation (PRD), and correlation coefficient, as well as Field-Programmable Gate Array (FPGA) resource metrics. A new Value-Based Performance Metrics (VBPM) framework is also introduced, focusing on both security and efficiency. Simulation results reveal that the system achieves optimal performance at N = 4 levels, demonstrating significant improvements in both security and FPGA resource utilization compared to existing approaches. This research offers a scalable and cost-efficient solution for secure communication systems, with broader implications for real-time encryption in practical applications.

A Novel Beam-Domain Direction-of-Arrival Tracking Algorithm for an Underwater Target

Underwater direction-of-arrival (DOA) tracking using a hydrophone array is an important research subject in passive sonar signal processing. In this study, a DOA tracking algorithm based on a novel beam-domain signal processing technique is proposed to ensure robust DOA tracking of an interested underwater target under a low signal-to-noise ratio (SNR) environment. Firstly, the beam-based observation is designed and proposed, which innovatively applies beamforming after array-based observation to achieve specific spatial directivity. Next, the proportional–integral–differential (PID)-optimized Olen–Campton beamforming method (PIDBF) is designed and proposed in the beamforming process to achieve faster and more stable sidelobe control performance to enhance the SNR of the target. The adaptive dynamic beam window is designed and proposed to focusing the observation on more likely observation area. Then, by utilizing the extended Kalman filter (EKF) tracking framework, a novel PIDBF-optimized beam-domain DOA tracking algorithm (PIDBF-EKF) is proposed. Finally, simulations with different SNR scenarios and comprehensive analyses are made to verify the superior performance of the proposed DOA tracking approach.

Detecting Detached Black Hole Binaries through Photometric Variability

Understanding the connection between the properties of black holes (BHs) and their progenitors is interesting in many branches of astrophysics. Discovering BHs in detached orbits with luminous companions (LCs) promises to help establish this connection since the LC and BH progenitor are expected to have the same metallicity and formation time. We explore the possibility of detecting BH–LC binaries in detached orbits using photometric variations of the LC flux, induced by tidal ellipsoidal variation, relativistic beaming, and self-lensing. We create realistic present-day populations of detached BH–LC binaries in the Milky Way (MW) using binary population synthesis where we adopt observationally motivated initial stellar and binary properties, star formation history, and the present-day distribution of these sources in the MW based on detailed cosmological simulations. We test detectability of these sources via photometric variability by Gaia and TESS missions by incorporating their respective detailed detection biases as well as interstellar extinction. We find that Gaia is expected to resolve ∼300–1000 (∼700–1500) detached BH–LC binaries with signal-to-noise ratio (SNR) ≥ 10 (1) depending on the photometric precision and details of supernova physics. Similarly, the number of resolved BH–LC binaries with TESS is ∼50–200 (∼140–350). We find that 136−15+15 BH–LC binaries would be common between Gaia and TESS. Moreover, ∼60–70 (∼50–200) BH–LC binaries identifiable using photometry with SNR ≥ 10 may also be resolved using Gaia’s radial velocity (astrometry).

High-Fidelity OC-Seislet Stacking Method for Low-SNR Seismic Data

Seismic stacking is a core technique in seismic data processing, aimed at enhancing the signal-to-noise ratio (SNR) of data by utilizing seismic data acquisition with multifold geometry. Traditional stacking methods always have certain limitations, such as the reliance on the accuracy of velocity analysis for dip moveout (DMO) in common midpoint (CMP) stacking. The seislet transform, a compression technique tailored to nonstationary seismic data, can compress and stack along the prediction direction of seismic data, which provides a new technical idea for high-fidelity seismic imaging based on the effectiveness of the compression. This paper introduces a high-order OC-seislet stacking method for low-SNR seismic data, capable of achieving the high-fidelity stacking of reflection and diffraction waves simultaneously. With the multi-scale analysis advantages of the seislet transform, this method addresses the dependency of DMO stacking on velocity analysis accuracy. In the frequency–wavenumber–scale domain, the correction compensation of the high-order CDF 9/7 basis function is used to obtain the compression coefficients of the high-order OC-seislet transform. This approach simultaneously stacks frequency–wavenumber information of reflection and diffraction waves with high fidelity while implementing DMO processing. After normalizing the weighting coefficients and applying soft thresholding for denoising, the final result is transformed back to the original time–space domain, yielding high-fidelity stacking sections. The results of applying this method to both synthetic and field data show that, compared with conventional DMO stacking methods, the high-order OC-seislet stacking technique reasonably represents dipping layers and fault amplitudes, and it can achieve a balance of a high SNR and high fidelity in stacked profiles.

Exploring the feasibility of FOCUS DWI with deep learning reconstruction for breast cancer diagnosis: A comparative study with conventional DWI.

This study compared field-of-view (FOV) optimized and constrained undistorted single-shot diffusion-weighted imaging (FOCUS DWI) with deep-learning-based reconstruction (DLR) to conventional DWI for breast imaging. This study prospectively enrolled 49 female patients suspected of breast cancer from July to December 2023. The patients underwent conventional and FOCUS breast DWI and data were reconstructed with and without DLR. Two radiologists independently evaluated three images per patient using a 5-point Likert scale. Objective evaluations, including signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and apparent diffusion coefficient (ADC), were conducted using manual region of interest-based analysis. The subjective and objective evaluations were compared using the Friedman test. The scores for the overall image quality, anatomical details, lesion conspicuity, artifacts, and distortion in FOCUS-DLR DWI were higher than in conventional DWI (all P < 0.001). The SNR of FOCUS-DLR DWI was higher than that of conventional and FOCUS DWI (both P < 0.001), while FOCUS and conventional DWI were similar (P = 0.096). Conventional, FOCUS, and FOCUS-DLR DWI had similar CNR and ADC values. Our findings indicate that images produced by FOCUS-DLR DWI were superior to conventional DWI, supporting the applicability of this technique in clinical practice. DLR provides a new approach to optimize breast DWI.

Selective Word Stress Effect for Speech Perception Enhancement in Hearing-Impaired Older Listeners

Purpose: Although it is known that communication problem of the older adults might be overcome by using slower speech or talking in a less noisy environment, there is still a lack of detailed skills. This study aimed to identify word stress benefit in speech perception for older listeners when different levels of background noise and speech rate were presented.Methods: Forty elderly (20 for hearing loss and 20 for normal hearing) were randomly recruited. Both groups conducted sentence tests at quiet and three signal-to-noise ratio (SNR) (+6, +3, 0 dB) and did them under four timealteration conditions (± 30% and ± 15%). Further, all sentences of both the noise and time alteration conditions had a 4, 6, or 8 dB up emphasized key word.Results: As expected, the hearing-impaired group had lower scores than the group with normal hearing in quiet and three SNR conditions. In quiet, both groups performed best at 6 dB emphasis. However, there was no significant difference in performance across 4, 6, and 8 dB up stress conditions when background noise was present. As time compression increased, speech perception scores decreased in both groups. Notably, at 30% expansion (slower speed), the normal hearing group scored the highest at 4 dB up emphasis, while the hearing-impaired group showed the best performance best at 8 dB up emphasis.Conclusion: Based on the current results, rather than simply speaking slowly, it would be more helpful for older adults with hearing loss to control their speech rate also emphasize key words during the conversation.

Feasibility of using toroidal transceivers for acquiring intraoperative MR images around deep brain stimulation electrodes

IntroductionMagnetic resonance imaging (MRI) provides excellent soft tissue contrast for visualizing of deep brain stimulation (DBS) targets, allowing validation of the electrode placement, and assessing complications such as microhemorrhage and edema. However, the presence of the electrodes can introduce challenges such as radiofrequency (RF) induced current artifacts and excessive heating of the electrode contacts. Additionally, extended procedure times are also considered a disadvantage when using MRI as an intraoperative imaging modality following DBS electrode placement. MethodWe propose a novel approach of using toroidal resonators to inductively couple the shaft of the electrode to the scanner's transmit-receive chain thereby utilizing it as a localized imaging antenna. The small extent of the field generated by the electrode antenna allows fast imaging with smaller field-of-views (FOVs) spanning only a few centimeters. Furthermore, we present a fast and accurate safety monitoring strategy that can be used to predict the temperature increase at the electrical contacts of the electrode. Results and DiscussionImaging with the toroidal transceiver yields a higher signal-to-noise ratio (SNR) efficiency in proximity to the electrodes. This approach reduced the RF induced current artifacts around the electrode which enhanced the visibility of the shaft and improved electrode localization. Moreover, the limited sensitivity around the electrode can be exploited to perform fast scans with small FOVs. The predicted heating around DBS contacts was in quantitative agreement with the experimental heating in swine studies with a normalized root-mean-square error (NRMSE) ≤ 0.09.

Ultra-high-resolution photon-counting detector CT for visualization of the brachial plexus

ObjectivesTo investigate the use of photon-counting detector CT (PCD-CT) to improve brachial plexus depiction. Materials and MethodsThis retrospective study included patients who underwent neck CT from March to December 2023. To assess the optimal reconstruction condition in PCD-CT, the signal-to-noise ratios (SNRs) on images using various quantitative regular (Qr) kernels and strengths of quantum iterative reconstruction (QIR) were evaluated. Next, images obtained by ultra-high-resolution mode in PCD-CT (PCD-UHR), standard mode in PCD-CT (PCD-STD), and standard mode in energy-integrating detector CT (EID-STD) of 20 patients each were compared regarding brachial plexus depiction. A qualitative evaluation was performed using a 5-point Likert scale regarding sharpness, noise, and overall image quality. The standard deviations (SDs), SNRs, and contrast-to noise ratios (CNRs) were quantitatively evaluated. ResultsOverall, 60 patients (mean age, 63 years ± 18; 30 males) were included. The SNRs for the Qr40 and QIR4 (means ± SDs) were 3.6 ± 1.1 and 4.1 ± 1.2, respectively, significantly higher than others (P < 0.05). The scores for overall image quality were 4 [4–5], 3 [3–4], and 2 [2–3], and those for sharpness were 4 [3–5], 3 [3–3], and 2 [1–3] for PCD-UHR, PCD-STD, and EID-STD, respectively (all, P < 0.05). Those for noise were 3 [3–4], 4 [3–4], and 2 [2–2], the SDs were 6.6 ± 1.6, 5.4 ± 0.8, and 8.8 ± 1.7, SNRs were 5.0 ± 1.4, 6,1 ± 1.2, and 3.5 ± 1.6, and CNRs were 5.6 ± 1.9, 7.9 ± 1.7, and 4.4 ± 1.8, respectively (between either of the PCD groups and EID-STD, P < 0.05). ConclusionPCD-CT showed superior delineation for brachial plexus to EID-CT.

Radiofrequency Enhancer to Recover Signal Dropouts in 7 Tesla Diffusion MRI.

Diffusion magnetic resonance imaging (dMRI) allows for a non-invasive visualization and quantitative assessment of white matter architecture in the brain by characterizing restrictions on the random motion of water molecules. Ultra-high field MRI scanners, such as those operating at 7 Tesla (7T) or higher, can boost the signal-to-noise ratio (SNR) to improve dMRI compared with what is attainable at conventional field strengths such as 3T or 1.5T. However, wavelength effects at 7T cause reduced transmit magnetic field efficiency in the human brain, mainly in the posterior fossa, manifesting as signal dropouts in this region. Recently, we reported a simple approach of using a wireless radiofrequency (RF) surface array to improve transmit efficiency and signal sensitivity at 7T. In this study, we demonstrate the feasibility and effectiveness of the RF enhancer in improving in vivo dMRI at 7T. The electromagnetic simulation results demonstrated a 2.1-fold increase in transmit efficiency with the use of the RF enhancer. The experimental results similarly showed a 1.9-fold improvement in transmit efficiency and a 1.4-fold increase in normalized SNR. These improvements effectively mitigated signal dropouts in regions with inherently lower SNR, such as the cerebellum, resulting in a better depiction of principal fiber orientations and an enhanced visualization of extended tracts.

Hardware Implementation of a 2D Chaotic Map-Based Audio Encryption System Using S-Box

This paper presents a hardware-based audio encryption system using a 2D chaotic map and dynamic S-box design implemented on an Artix-7 FPGA platform. Three distinct chaotic maps—logistic–fraction (2D-LF), logistic–sine (2D-LS), and fraction–sine (2D-FS)—were investigated and implemented on an FPGA. The 2D-LF map was employed in the encryption system for its throughput and power efficiency performance. The proposed encryption system benefits from the randomness of chaotic sequences for block permutation and S-box substitution to enhance the diffusion and confusion properties of the encrypted speech signal. The system’s encryption strength is validated through performance evaluations, using the mean squared error (MSE), signal-to-noise ratio (SNR), correlation coefficients, and NIST randomness tests, which confirm the unpredictability of the encrypted speech signal. The hardware implementation results show a throughput of 2880 Mbps and power consumption of 0.13 W.

Combined Effect of Noise Reduction, FBS-Based Spectral Splitting, and Dynamic Range Compression of Speech Signal on Source Localization in Binaural Hearing Aids

Localizing sound sources in three spatial dimensions (azimuth, elevation, and distance) is critical for human hearing comfort. It relies on two binaural cues: interaural time difference (ITD) and interaural level difference (ILD). Cochlear or auditory nerve injury can result in sensorineural hearing loss (SNHL). Hearing aids enable people with sensorineural hearing loss to converse more effectively and hear better. However, there is an apprehension that the binaural hearing aids may degrade the localization cues, thus affecting the source localization. In response to this concern, the current study investigates how the binaural hearing aid algorithm affects source localization by adopting a cascaded structure of noise reduction technique (wiener filter) followed by filter bank summation (FBS) based spectral splitting and dynamic range compression for binaural dichotic presentation. Listening tests for seven different azimuth angles (-90⁰, -60⁰, -30⁰, 0⁰, 30⁰, 60⁰, and - 90⁰) were conducted on six listeners with normal hearing under different signal-to-noise ratio (SNR) conditions as well as on six subjects with mild bilateral sensorineural hearing impairment. Test stimuli included background glass-breaking sound and broadband noise for participants with normal hearing. In an experiment with hearing-impaired subjects, the glass-breaking sound served as one of the test stimuli. The result showed that these binaural hearing aid algorithms had no adverse effects on localization ability.

Adopting Buffer Layer for Improving Signal‐to‐Noise Ratio of Broadband Photomultiplication Type Organic Photodetectors

AbstractThe photomultiplication type organic photodetectors (PM‐OPDs) are prepared with structure of ITO/PNDIT‐F3N/F8BT/Y6‐1O:P3HT (100:3, wt/wt)/Al, containing hole traps formed with P3HT surrounded by Y6‐1O in active layers. The PM‐OPDs exhibit external quantum efficiency (EQE)&gt;100% in the spectral response range from 310 to 910 nm, resulting from electron tunneling injection assisted by trapped hole near ITO electrode. The incorporation of F8BT buffer layers can induce the markedly decreased dark current density (JD) due to the large electron injection barrier. The light current density (JL) of PM‐OPDs exhibits slightly decreased by inserting F8BT buffer layers due to the enhanced electron tunneling injection assisted by the more trapped holes near ITO electrode. The signal‐to‐noise ratio (SNR) of PM‐OPDs achieves over 40‐fold increment by inserting appropriate thickness of F8BT buffer layers, resulting from the markedly decreased JD and reasonably high JL. The optimal PM‐OPDs exhibit excellent photodetection capability with EQE of 4200% at 360 nm and 6600% at 850 nm, associated with the specific detectivity of 3.9 × 1011 Jones at 360 nm and 9.7 × 1011 Jones at 850 nm.

Deep Learning for Opportunistic Rain Estimation via Satellite Microwave Links.

Accurate precipitation measurement is critical for managing flood and drought risks. Traditional meteorological tools, such as rain gauges and remote sensors, have limitations in resolution, coverage, and cost-effectiveness. Recently, the opportunistic use of microwave communication signals has been explored to improve precipitation estimation. While there is growing interest in using satellite-to-earth microwave links (SMLs) for machine learning-based precipitation estimation, direct rainfall estimation from raw signal-to-noise ratio (SNR) data via deep learning remains underexplored. This study investigates a range of machine learning (ML) approaches, including deep learning (DL) models and traditional methods like gradient boosting machine (GBM), for estimating rainfall rates from SNR data collected by interactive satellite receivers. We develop real-time models for rainfall detection and estimation using downlink SNR signals from satellites to user terminals. By leveraging a year-long dataset from multiple locations-including SNR measurements paired with disdrometer and rain-gauge data-we explore and evaluate various ML models. Our final models include ensemble approaches for both rainfall detection and cumulative rainfall estimation. The proposed models provide a reliable solution for estimating precipitation using Earth-satellite microwave links, potentially improving precipitation monitoring. Compared to the state-of-the-art power-law-based models applied to similar datasets reported in the literature, our ML models achieve a 46% reduction in the root mean squared error (RMSE) for event-based cumulative precipitation predictions.

The Effect of Noise and Reverberation on Spatial Perception in Sequential Bilateral Cochlear Implant Users

Abstract Background Spatial orientation is an executive function which includes vital activities and auditory organization according to daily bodily movements, directionality, and environmental information. It is directly linked to the vision and hearing and used throughout life, building complex relationships with these systems, based on learning. Purpose Our purpose in our study is to try to see the effects of noise and reverberation on the users by comparing the localization and auditory performances of the cochlear implant (CI) user individuals in a silent, noisy environment and reverberation. Research Design All subjects were subjected to immitancemetric/audiological tests, language development test (TIFALDI, Receptive/Expressive Language Test score 7 years and above), localization determination in noise, and localization determination test in reverberation. Study sample: In our study, 18 female and 16 male bilateral CI users with profound sensorineural hearing loss were included. The age range of subjects was 8 years 4 months and 10 years 11 months. Data Collection and Analysis Data from subjects were collected prospectively. Data analysis was analyzed with SPSS 21 program. Results It was observed that the subjects did not have difficulty in determining the direction in silent condition, but they had a significant difficulty in localizing the 135-, 225-, and 315-degree angles especially when the noise was signal-to-noise ratio (SNR) –10 dB and the reverberation was 06 and 09 second (p ≤ 0.005). Subjects' performances were significantly altered in sequential implanted users both when the SNR was changed and in the presence of reverberation (p &lt; 0.05). Conclusion As a result of our study, it is thought that individuals with hearing loss will experience intense difficulties, especially in noisy and reverberant environments such as schools, and using assistive listening devices in these environmental conditions will contribute positively to their academic development.

Image quality of the non-contrast BOOST cardiac magnetic resonance sequence to visualize the pulmonary veins in patients with atrial fibrillation

Abstract Background In patients with atrial fibrillation (AF), imaging examinations have an important role in planning ablation by imaging the pulmonary veins (PV). The PV anatomy is conventionally assessed before ablation using computer tomography (CT) or 3 dimensional (3D) contrast-enhanced cardiac magnetic resonance (CMR) imaging. The new 3-dimensional, free-breathing BOOST (Bright-blood and black-blOOd phase SensiTive inversion recovery) cardiac magnetic resonance (CMR) sequence is suitable for imaging the left atrium without the addition of contrast agent. Purpose The aim of our study was to investigate the BOOST sequence for the imaging of PVs, and the effect of heart rate and rhythm on image quality. Methods Forty-seven patients underwent and BOOST CMR before ablation, 25 of them had also left atrial CT angiography (CTA). The BOOST sequence was performed using T2 preparation pre-pulse (T2prep) and magnetization transfer preparation (MTC) techniques. The image quality was analyzed using a subjective Likert scale and quantitatively by determining the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR). Heart rate and rhythm were assessed by 12-lead ECG. Results MTC-BOOST images were suitable for PVs analysis in all cases, whereas the assessment of PVs on T2prep-BOOST images was inadequate in the majority of cases due to artifacts. CTA showed the same PV anatomy as assessed with BOOST CMR. Based on the operating electrophysiologists’ subjective opinion, the quality of the CTA scans was better compared with BOOST sequence. Still, they found the CMR images appropriate for procedural planning in 43 out of 47 cases (91%). SNR and CNR values of the MTC-BOOST bright-blood images were higher if patients had sinus rhythm. We found a significant or nearly significant negative correlation between heart rate and the SNR and CNR values of MTC-BOOST bright-blood images. Conclusions MTC-BOOST images are suitable for visualisation of PVs, producing diagnostic image quality in 100% of cases. In the majority of cases, MTC-BOOST images were appropriate for procedural planning before ablation. Image quality is affected by the patients' heart rate and frequency. Figure 1: MTC BOOST bright blood image of the left atrium and the right pulmonary veins (arrows)

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.