Increase In Signal-to-noise Ratio Research Articles

Hammerstein-Wiener Motion Artifact Correction for Functional Near-Infrared Spectroscopy: A Novel Inertial Measurement Unit-Based Technique.

Participant movement is a major source of artifacts in functional near-infrared spectroscopy (fNIRS) experiments. Mitigating the impact of motion artifacts (MAs) is crucial to estimate brain activity robustly. Here, we suggest and evaluate a novel application of the nonlinear Hammerstein-Wiener model to estimate and mitigate MAs in fNIRS signals from direct-movement recordings through IMU sensors mounted on the participant's head (head-IMU) and the fNIRS probe (probe-IMU). To this end, we analyzed the hemodynamic responses of single-channel oxyhemoglobin (HbO) and deoxyhemoglobin (HbR) signals from 17 participants who performed a hand tapping task with different levels of concurrent head movement. Additionally, the tapping task was performed without head movements to estimate the ground-truth brain activation. We compared the performance of our novel approach with the probe-IMU and head-IMU to eight established methods (PCA, tPCA, spline, spline Savitzky-Golay, wavelet, CBSI, RLOESS, and WCBSI) on four quality metrics: SNR, △AUC, RMSE, and R. Our proposed nonlinear Hammerstein-Wiener method achieved the best SNR increase (p < 0.001) among all methods. Visual inspection revealed that our approach mitigated MA contaminations that other techniques could not remove effectively. MA correction quality was comparable with head- and probe-IMUs.

Partial Fourier in the presence of respiratory motion in prostate diffusion-weighted echo planar imaging.

To investigate the effect of respiratory motion in terms of signal loss in prostate diffusion-weighted imaging (DWI), and to evaluate the usage of partial Fourier in a free-breathing protocol in a clinically relevant b-value range using both single-shot and multi-shot acquisitions. A controlled breathing DWI acquisition was first employed at 3T to measure signal loss from deep breathing patterns. Single-shot and multi-shot (2-shot) acquisitions without partial Fourier (no pF) and with partial Fourier (pF) factors of 0.75 and 0.65 were employed in a free-breathing protocol. The apparent SNR and ADC values were evaluated in 10 healthy subjects to measure if low pF factors caused low apparent SNR or overestimated ADC. Controlled breathing experiments showed a difference in signal coefficient of variation between shallow and deep breathing. In free-breathing single-shot acquisitions, the pF 0.65 scan showed a significantly (p < 0.05) higher apparent SNR than pF 0.75 and no pF in the peripheral zone (PZ) of the prostate. In the multi-shot acquisitions in the PZ, pF 0.75 had a significantly higher apparent SNR than 0.65 pF and no pF. The single-shot pF 0.65 scan had a significantly lower ADC than single-shot no pF. Deep breathing patterns can cause intravoxel dephasing in prostate DWI. For single-shot acquisitions at a b-value of 800s/mm2, any potential risks of motion-related artefacts at low pF factors (pF 0.65) were outweighed by the increase in signal from a lower TE, as shown by the increase in apparent SNR. In multi-shot acquisitions however, the minimum pF factor should be larger, as shown by the lower apparent SNR at low pF factors.

Signal-to-noise ratio versus field strength for small surface coils.

The increasing signal-to-noise ratio (SNR) is the main reason to use ultrahigh field MRI. Here, we investigate the dependence of the SNR on the magnetic field strength, especially for small animal applications, where small surface coils are used and coil noise cannot be ignored. Measurements were performed at five field strengths from 3 to 14.1 T, using 2.2-cm surface coils with an identical coil design for transmit and receive on two water samples with and without salt. SNR was measured in a series of spoiled gradient echo images with varying flip angle and corrected for saturation based on a series of flip angle and T1 measurements. Furthermore, the noise figure of the receive chain was determined and eliminated to remove instrument dependence. Finally, the coil sensitivity was determined based on the principle of reciprocity to obtain a measure for ultimate SNR. Before coil sensitivity correction, the SNR increase in nonconductive samples is highly supralinear with B0 1.6-2.7, depending on distance to the coil, while in the conductive sample, the growth is smaller, being around linear close to the surface coil and increasing up to a B0 2.0 dependence when moving away from the coil. After sensitivity correction, the SNR increase is independent of loading with B0 2.1. This study confirms the supralinear increase of SNR with increasing field strengths. Compared with most human measurements with larger coil sizes, smaller surface coils, as mainly used in animal studies, have a higher contribution of coil noise and thus a different behavior of SNR at high fields.

Deep learning image reconstruction of diffusion-weighted imaging in evaluation of prostate cancer focusing on its clinical implications.

Image-based assessment of prostate cancer (PCa) is increasingly emphasized in the diagnostic workflow for selecting biopsy targets and possibly predicting clinically significant prostate cancer (csPCa). Assessment is based on Prostate Imaging-Reporting and Data System (PI-RADS) which is largely dependent on T2-weighted image (T2WI) and diffusion weighted image (DWI). This study aims to determine whether deep learning reconstruction (DLR) can improve the image quality of DWI and affect the assessment of PI-RADS ≥4 in patients with PCa. In this retrospective study, 3.0T post-biopsy prostate magnetic resonance imaging (MRI) of 70 patients with PCa in Korea University Ansan Hospital from November 2021 to July 2022 was reconstructed with and without using DLR. Four DWI image sets were made: (I) conventional DWI (CDWI): DWI with acceleration factor 2 and conventional parallel imaging reconstruction, (II) DL1: DWI with acceleration factor 2 using DLR, (III) DL2: DWI with acceleration factor 3 using DLR, and (IV) DL3: DWI with acceleration factor 3 and half average b-value using DLR. Apparent diffusion coefficient (ADC) value, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were measured by one reviewer, while two reviewers independently assessed overall image quality, noise, and lesion conspicuity using a four-point visual scoring system from each DWI image set. Two reviewers also performed PI-RADSv2.1 scoring on lesions suspected of malignancy. A total of 70 patients (mean age, 70.8±9.7 years) were analyzed. The image acquisition time was 4:46 min for CDWI and DL1, 3:40 min for DL2, and 2:00 min for DL3. DL1 and DL2 images resulted in better lesion conspicuity compared to CDWI images assessed by both readers (P<0.05). DLR resulted in a significant increase in SNR, from 38.4±14.7 in CDWI to 56.9±21.0 in DL1. CNR increased from 25.1±11.5 in CDWI to 43.1±17.8 in DL1 (P<0.001). PI-RADS v2.1 scoring for PCa lesions was more agreeable with the DL1 reconstruction method than with CDWI (κ value CDWI, DL1; 0.40, 0.61, respectively). A statistically significant number of lesions were upgraded from PI-RADS <4 in CDWI image to PI-RADS ≥4 in DL1 images for both readers (P<0.05). Most of the PI-RADS upgraded lesions were from higher than unfavorable intermediate-risk groups according to the 2023 National Comprehensive Cancer Network guidelines with statistically significant difference of marginal probability in DL1 and DL2 for both readers (P<0.05). DLR in DWI for PCa can provide options for improving image quality with a significant impact on PI-RADS evaluation or about a 23% reduction in acquisition time without compromising image quality.

Evaluation of the Pulmonary Arteries on CTPA With Dual Energy CT: Objective Analysis and Subjective Preferences in a Multireader Study.

To perform qualitative and quantitative evaluation of low-monoenergetic images (50KeV) compared with conventional images (120kVp) in pulmonary embolism (PE) studies and to determine the extent and clinical relevance of these differences as well as radiologists' preferences. One hundred fifty CT examinations for PE detection conducted on a single-source dual-energy CT were retrospectively evaluated. Attenuation, contrast-to-noise-ratio, and signal-to-noise-ratio were obtained in a total of 8 individual pulmonary arteries on each exam-including both central (450/1200=37.5%) and peripheral (750/1200=62.5%) locations. Results were compared between the conventional and low-monoenergetic images. For quality assessment, 41 images containing PE were presented side-by-side as pairs of slices in both conventional and monoenergetic modes and evaluated for ease in embolus detection by 9 radiologists: cardiothoracic specialists (3), noncardiothoracic specialists (3), and residents (3). Paired samples t tests, a-parametric Wilcoxon test, McNemar test, and kappa statistics were performed. Monoenergetic images had an overall statistically significant increased average ratio of 2.09 to 2.26 (P<0.05) for each measured vessel attenuation, with an increase in signal-to-noise ratio (23.82±9.29 vs. 11.39±3.2) and contrast-to-noise ratio (17.17±6.7 vs 7.27±2.52) (P<0.05). Moreover, 10/150 (6%) of central pulmonary artery measurements considered suboptimal on conventional mode were considered diagnostic on the monoenergetic images (181±14.6 vs. 387.7±72.4 HU respectively, P<0.05). In the subjective evaluation, noncardiothoracic radiologists showed a preference towards low-monoenergetic images, whereas cardiothoracic radiologists did not (74.4% vs. 57.7%, respectively, P<0.05). The SNR and CNR increase on monoenergetic images may have clinical significance particularly in the setting of sub-optimal PE studies. Noncardiothoracic radiologists and residents prefer low monoenergetic images.

A novel outlier detection method based on Bayesian change point analysis and Hampel identifier for GNSS coordinate time series

The identification and removal of outliers in time series are important problems in numerous fields. In this paper, a novel method (BCP-HI) is proposed to enhance the accuracy of outlier detection in GNSS coordinate time series by combining Bayesian change point (BCP) analysis and the Hampel identifier (HI). By using BCP, change points (cps) in the time series are lidentified, and so the time series is divided into subsegments that have properties of a normal distribution. In each of these separated segments, outliers are detected using HI. Each data element identified as an outlier is corrected by a median filter of window size (w) to obtain the corrected signal. The BCP-HI method was tested on both simulated and real GNSS coordinate time series. Outliers from three different synthetic test datasets with different sampling frequencies and outlier amplitudes were detected with approximately 98% accuracy after processing. After this process, Signal-to-Noise Ratio (SNR) increased from 0.0084 to 10.8714 dB and Root Mean Square (RMS) decreased from 24 to 23 mm. Similarly, for real GNSS data, approximately 98% accuracy was achieved, with an increase in SNR from 0.0003 to 4.4082 dB and a decrease in RMS from 7.6 to 6.6 mm observed. In addition, the output signals after BCP-HI were examined graphically using Lomb–Scargle periodograms and it was observed that clearer power spectrum distributions emerged. When the input and output signals were examined using the Kolmogorov–Smirnov (KS) test, they were found to be statistically similar. These results indicate that the BCP-HI algorithm effectively removes outliers, and enhances processing accuracy and reliability, and improves signal quality.

Speech Enhancement with Background Noise Suppression in Various Data Corpus Using Bi-LSTM Algorithm

Noise reduction is one of the crucial procedures in today’s teleconferencing scenarios. The signal-to-noise ratio (SNR) is a paramount factor considered for reducing the Bit error rate (BER). Minimizing the BER will result in the increase of SNR which improves the reliability and performance of the communication system. The microphone is the primary audio input device that captures the input signal, as the input signal is carried away it gets interfered with white noise and phase noise. Thus, the output signal is the combination of the input signal and reverberation noise. Our idea is to minimize the interfering noise thus improving the SNR. To achieve this, we develop a real-time speech-enhancing method that utilizes an enhanced recurrent neural network with Bidirectional Long Short Term Memory (Bi-LSTM). One LSTM in this sequence processing framework accepts the input in the forward direction, whereas the other LSTM takes it in the opposite direction, making up the Bi-LSTM. Considering Bi-LSTM, it takes fewer tensor operations which makes it quicker and more efficient. The Bi-LSTM is trained in real-time using various noise signals. The trained system is utilized to provide an unaltered signal by reducing the noise signal, thus making the proposed system comparable to other noise-suppressing systems. The STOI and PESQ metrics demonstrate a rise of approximately 0.5% to 14.8% and 1.77% to 29.8%, respectively, in contrast to the existing algorithms across various sound types and different input signal-to-noise ratio (SNR) levels.

Highly Sensitive Detection of Melanin in Melanomas Using Multi-harmonic Low Frequency EPR.

Low frequency EPR can noninvasively detect endogenous free radical melanin in melanocytic skin lesions and could potentially discriminate between benign atypical nevi and malignant melanoma lesions. We recently succeeded in demonstrating the ability of clinical EPR to noninvasively detect the endogenous melanin free radical in skin lesions of patients. However, the signal-to-noise ratio (SNR) was extremely low warranting further research to boost the sensitivity of detection. In the present study, we assessed the performance of a clinical EPR system with the capability to perform multi-harmonic (MH) analysis for the detection of melanin. The sensitivity of MH-EPR was compared with a classical continuous wave (CW)-EPR (1st harmonic) detection in vitro in melanin phantoms, in vivo in melanoma models with cells implanted in the skin, in lymph nodes and having colonized the lungs, and finally on phantoms placed at the surface of human skin. In vitro, we observed an increase in SNR by a factor of 10 in flat melanin phantoms when using MH analysis compared to CW combined with an increase in modulation amplitude. In B16 melanomas having grown in the skin of hairless mice, we observed a boost in sensitivity in vivo similar to that observed in vitro with the capability to detect melanoma cells at an earlier stage of development. MH-EPR was also able to detect non-invasively the melanin signal coming from melanoma cells present in lymph nodes as well as in lungs. We also observed a boost of sensitivity using phantoms of melanin placed at the surface of human skin. Overall, our results are paving the way for new clinical trials that will use MH clinical EPR for the characterization of pigmented skin lesions.

Research on the deconvolution algorithm for the laser-induced fluorescence diagnosis based on the maximum entropy principle

Abstract Laser-induced fluorescence (LIF) spectroscopy is employed for plasma diagnosis, necessitating the utilization of deconvolution algorithms to isolate the Doppler effect from the raw spectral signal. However, direct deconvolution becomes invalid in the presence of noise as it leads to infinite amplification of high-frequency noise components. To address this issue, we propose a deconvolution algorithm based on the maximum entropy principle. We validate the effectiveness of the proposed algorithm by utilizing simulated LIF spectra at various noise levels (signal-to-noise ratio, SNR = 20–80 dB) and measured LIF spectra with Xe as the working fluid. In the typical measured spectrum (SNR = 26.23 dB) experiment, compared with the Gaussian filter and the Richardson-Lucy (R-L) algorithm, the proposed algorithm demonstrates an increase in SNR by 1.39 dB and 4.66 dB respectively, along with a reduction in root-mean-square error (RMSE) by 35% and 64% respectively. Additionally, there is a decrease in spectral angle (SA) by 0.05 and 0.11 respectively. In the high quality spectrum (SNR = 43.96 dB) experiment, the results show that the running time of the proposed algorithm is reduced by about 98% compared with the R-L iterative algorithm. Moreover, the maximum entropy algorithm avoids parameter optimization settings and is more suitable for automatic implementation. In conclusion, the proposed algorithm can accurately resolve Doppler spectrum details while effectively suppressing noise, thus highlighting its advantage in LIF spectral deconvolution applications.

Image quality of abdominal photon-counting CT with reduced contrast media dose: Evaluation of reduced contrast media protocols during the COVID19 pandemic supply shortage

Rationale and objectivesAim of this study was to assess the impact of contrast media dose (CMD) reduction on diagnostic quality of photon-counting detector CT (PCD-CT) and energy-integrating detector CT (EID-CT). MethodsCT scans of the abdominal region with differing CMD acquired in portal venous phase on a PCD-CT were included and compared to EID-CT scans. Diagnostic quality and contrast intensity were rated. Additionally, readers had to assign the scans to reduced or regular CMD. Regions-of-interest (ROIs) were placed in defined segments of portal vein, inferior vena cava, liver, spleen, kidneys, abdominal aorta and muscular tissue. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated. ResultsOverall 158 CT scans performed on a PCD-CT and 68 examinations on an EID-CT were analyzed. Overall diagnostic quality showed no significant differences for PCD-CT with standard CMD which scored a median 5 (IQR:5-5) and PCD-CT with 70% CMD scoring 5 (4–5). (For PCD-CT, 71.69% of the examinations with reduced CMD were assigned to regular CMD by the readers, for EID-CT 9.09%. Averaged for all measurements SNR for 50% CMD was reduced by 19% in PCD-CT (EID-CT 34%) and CNR by 48% (EID-CT 56%). Virtual monoenergetic images (VMI)50keV for PCD-CT images acquired with 50% CMD showed an increase in SNR by 72% and CNR by 153%. ConclusionsDiagnostic interpretability of PCD-CT examinations with reduction of up to 50% CMD is maintained. PCD-CT deducted scans especially with 70% CMD were often not recognized as CMD reduced scans. Compared to EID-CT less decline in SNR and CNR is observed for CMD reduced PCD-CT images. Employing VMI50keV for CMD-reduced PCD-CT images compensated for the effects.

Multiscale dilated denoising convolution with channel attention mechanism for micro-seismic signal denoising

Denoising micro-seismic signals is paramount for ensuring reliable data for localizing mining-related seismic events and analyzing the state of rock masses during mining operations. However, micro-seismic signals are commonly contaminated by various types of complex noise, which can hinder micro-seismic accurate P-wave pickup and analysis. In this study, we propose the Multiscale Dilated Convolutional Attention denoising method, referred to as MSDCAN, to eliminate complex noise interference. The MSDCAN denoising model consists of an encoder, an improved attention mechanism, and a decoder. To effectively capture the neighborhood features and multiscale features of the micro-seismic signal, we construct an initial dilated convolution block and a multiscale dilated convolution block in the encoder, and the encoder focuses on extracting the relevant feature information, thus eliminating the noise interference and improving the signal-to-noise ratio (SNR). In addition, the attention mechanism is improved and introduced between the encoder and decoder to emphasize the key features of the micro-seismic signal, thus removing the complex noise and further improving the denoising performance. The MSDCAN denoising model is trained and evaluated using micro-seismic data from Stanford University. Experimental results demonstrate an impressive increase in SNR by 11.237 dB and a reduction in root mean square error (RMSE) by 0.802. Compared to the denoising results of the DeepDenoiser, CNN-denoiser and Neighbor2Neighbor methods, the MSDCAN denoising model outperforms them by enhancing the SNR by 2.589 dB, 1.584 dB and 2dB, respectively, and reducing the RMSE by 0.219, 0.050 and 0.188, respectively. The MSDCAN denoising model presented in this study effectively improves the SNR of micro-seismic signals, offering fresh insights into micro-seismic signal denoising methodologies.

Inhomogeneity mitigation for diffusion weighted MRI at 7T using TR-FOCI pulses.

The purpose of this study is to improve the image quality of diffusion-weighted images obtained with a single RF transmit channel 7 T MRI setup using time-resampled frequency-offset corrected inversion (TR-FOCI) pulses to refocus the spins in a twice-refocused spin-echo readout scheme. We replaced the conventional Shinnar-Le Roux-pulses in the twice refocused diffusion sequence with TR-FOCI pulses. The slice profiles were evaluated in simulation and experimentally in phantoms. The image quality was evaluated in vivo comparing the Shinnar-Le Roux and TR-FOCI implementation using a b value of 0 and of 1000 s/mm2. The b0 and diffusion-weighted images acquired using the modified sequence improved the image quality across the whole brain. A region of interest-based analysis showed an SNR increase of 113% and 66% for the nondiffusion-weighted (b0) and the diffusion-weighted (b = 1000 s/mm2) images in the temporal lobes, respectively. Investigation of all slices showed that the adiabatic pulses mitigated inhomogeneity globally using a conventional single-channel transmission setup. The TR-FOCI pulse can be used in a twice-refocused spin-echo diffusion pulse sequence to mitigate the impact of inhomogeneity on the signal intensity across the brain at 7 T. However, further work is needed to address SAR limitations.

Developing analytical models for the post‐detection SINR of TSVD‐based techniques with imperfect CSI

SummaryIn massive multiple input multiple output (m‐MIMO) uplink, the performance gap between zero forcing (ZF) or direct pseudo‐inverse and minimum mean square error (MMSE) detection schemes increases with an increase in the load factor. To report this issue, truncated singular value decomposition (TSVD) is used in both ZF and direct pseudo‐inverse detection techniques using dynamic threshold. Moreover, the analytical models for the post‐detection signal‐to‐interference‐plus‐noise ratio (PDSINR) of TSVD‐based detection schemes are derived with imperfect channel state information (CSI), and subsequently, bit error rate (BER) is evaluated. Later, an optimum hard threshold for the TSVD‐based detection schemes is deduced from the empirical analysis. Further, the tightness of the analysis is verified through Monte Carlo trails. The simulation result shows that at lower values of average signal‐to‐noise ratio (SNR), TSVD‐based detection schemes with imperfect CSI offer comparable performance and low complexity when compared with the MMSE technique. However, the BER of TSVD‐based detection schemes decreases and gets saturated with an increase in the average SNR, whereas the BER of the MMSE detection scheme increases and approaches the BER of ZF or direct pseudo‐inverse detection schemes.

First in-human evaluation of [1-13C]pyruvate in D2O for hyperpolarized MRI of the brain: A safety and feasibility study.

To investigate the safety and value of hyperpolarized (HP) MRI of [1-13C]pyruvate in healthy volunteers using deuterium oxide (D2O) as a solvent. Healthy volunteers (n = 5), were injected with HP [1-13C]pyruvate dissolved in D2O and imaged with a metabolite-specific 3D dual-echo dynamic EPI sequence at 3T at one site (Site 1). Volunteers were monitored following the procedure to assess safety. Image characteristics, including SNR, were compared to data acquired in a separate cohort using water as a solvent (n = 5) at another site (Site2). The apparent spin-lattice relaxation time (T1) of [1-13C]pyruvate was determined both in vitro and in vivo from a mono-exponential fit to the image intensity at each time point of our dynamic data. All volunteers completed the study safely and reported no adverse effects. The use of D2O increased the T1 of [1-13C]pyruvate from 66.5 ± 1.6 s to 92.1 ± 5.1 s in vitro, which resulted in an increase in signal by a factor of 1.46 ± 0.03 at the time of injection (90 s after dissolution). The use of D2O also increased the apparent relaxation time of [1-13C]pyruvate by a factor of 1.4 ± 0.2 in vivo. After adjusting for inter-site SNR differences, the use of D2O was shown to increase image SNR by a factor of 2.6 ± 0.2 in humans. HP [1-13C]pyruvate in D2O is safe for human imaging and provides an increase in T1 and SNR that may improve image quality.

Strategically Acquired Gradient Echo (STAGE) Imaging, part IV: Constrained Reconstruction of White Noise (CROWN) Processing as a Means to Improve Signal-to-Noise in STAGE Imaging at 3 Tesla

Increasing the signal-to-noise ratio (SNR) has always been of critical importance for magnetic resonance imaging. Although increasing field strength provides a linear increase in SNR, it is more and more costly as field strength increases. Therefore, there is a major effort today to use signal processing methods to improve SNR since it is more efficient and economical. There are a variety of methods to improve SNR such as averaging the data at the expense of imaging time, or collecting the data with a lower resolution, all of these methods, including imaging processing methods, usually come at the expense of loss of image detail or image blurring. Therefore, we developed a new mathematical approach called CROWN (Constrained Reconstruction of White Noise) to enhance SNR without loss of structural detail and without affecting scanning time. In this study, we introduced and tested the concept behind CROWN specifically for STAGE (strategically acquired gradient echo) imaging. The concept itself is presented first, followed by simulations to demonstrate its theoretical effectiveness. Then the SNR improvement on proton spin density (PSD) and R2⁎ maps was investigated using brain STAGE data acquired from 10 healthy controls (HCs) and 10 patients with Parkinson's disease (PD). For the PSD and R2* maps, the SNR and CNR between white matter and gray matter were improved by a factor of 1.87 ± 0.50 and 1.72 ± 0.88, respectively. The white matter hyperintensity lesions in PD patients were more clearly defined after CROWN processing. Using these improved maps, simulated images for any repeat time, echo time or flip angle can be created with improved SNR. The potential applications of this technology are to trade off the increased SNR for higher resolution images and/or faster imaging.

Block Diagonalization in the 5G SA Network

In this paper, we did programming regarding the Block diagonalization technology in the 5G standalone SA network, in this program, we have created a 5G site with 16 antennas(minimum of Massive MIMO) and 4 active users equipped of 4 antennas, this system is called Multi Users Massive MIMO system, the link that was chosen is the downlink,we have calculated the maximum throughput in the 5G downlink where we have obained a value of 1673864 b/ms, this value is divided by the number of Massive MIMO layers which worth 16 to get a transport block size of 104616 b/ms (no Cyclic redundancy check CRC). The Block Error rate BLER is null (no detection of errors in reception) because we are in the case of no crc and no channel coding (uncoded transmission), the signal of each user among 4 to be transmitted consists of 4 vectors, each vector has a length of 52308 that corresponds to the number of symbols which are the outputs of Quadrature Phase Shift Keying QPSK Mapping Operation. The received signal at each user equipment UE has a form which can be represented by the multiplication of preconding matrix of this UE with the channel matrix between this UE and the 5G site plus the noise received at the antennas of this UE. the results show that the product of channel gain between UE and the 5G site(known in emission) with the precoding matrix of the other UE gives a matrix which composes of imaginary elements each of which has a real part and imaginary part which both tend to zero(the inter users interferences IUI is canceled). The results show also that when the Signal to Noise Ratio SNR increases(several transmissions) the Bit Error Rate decreases.

Improving BFS measurement accuracy of BOTDR based on Cauchy proximal splitting

A novel approach based on the Cauchy proximal splitting (CPS) algorithm is proposed to improve Brillouin frequency shift (BFS) measurement accuracy in Brillouin optical time domain reflectometry system. The CPS algorithm utilizes proximal splitting to handle the data and the penalty function based on Cauchy distribution to accurately estimate the target signal to promote sparsity, achieving significant improvement in signal-to-noise ratio (SNR). Experimental results confirm that with a SNR increase of 12.7 dB, an increase in BFS measurement accuracy from 4.78 MHz to 0.43 MHz is achieved over a 10.3 km sensing fiber. The denoising effects of the CPS, wavelet denoising (WD) and non-local mean (NLM) algorithms are further compared, demonstrating that the CPS algorithm has the lowest root mean square error (0.43 MHz) and no deterioration in spatial resolution rather than the WD and NLM algorithms does.

Opening the envelope: Efficient envelope-based PPG denoising algorithm

Photoplethysmography (PPG) signals obtained from the skin’s surface offer valuable insights into blood volume fluctuations. With the rising interest in continuous non-invasive physiological monitoring, PPG has garnered significant attention. However, PPG signals are often affected by various forms of noise, impeding reliable feature extraction. Robust data pre-processing approaches are vital for both retrospective and real-time analysis. Existing denoising methods, including recent machine learning techniques, often suffer from implementation challenges, computational inefficiency, and limited interpretability. Addressing this challenge, we propose a novel PPG denoising algorithm. The algorithm was evaluated using a dataset representing approximately 81,015.99 min or 1360.27 h of PPG data collected from 31 patients. The evaluation involved the calculation and analysis of five key metrics: Signal-to-Noise Ratio (SNR), Variance, Total Variation (TV), Shannon entropy, and Instances-per-second (IPS). Our results demonstrate a notable increase in SNR after denoising, indicating effective noise reduction while preserving signal content. Variance and TV values showed a reduction post-denoising, suggesting smoother and less variable signals, validating the noise suppression efficacy. Additionally, Shannon entropy exhibited a decrease after denoising, indicating successful noise reduction and enhanced signal regularity. The nonparametric Wilcoxon signed-rank test (a = 0.05) was employed to assess the statistical significance of the observed differences of these metrics before and after denoising. Furthermore, the computational speed analysis revealed the EPDA’s potential for efficient processing of large datasets and real-time applications. This comprehensive evaluation approach allows for a thorough understanding of the EPDA’s effectiveness in denoising PPG data, fostering advancements in non-invasive physiological monitoring and promoting the broader adoption of PPG-based healthcare technologies.

Compensation of concomitant field effects in double diffusion encoding by means of added oscillating gradients

Maxwell or concomitant fields imprint additional phases on the transverse magnetization. This concomitant phase may cause severe image artifacts like signal voids or distort the quantitative parameters due to the induced intravoxel dephasing. In particular, double diffusion encoding (DDE) schemes with two pairs of bipolar diffusion-weighting gradients separated by a refocusing radiofrequency (RF) pulse are prone to concomitant field-induced artifacts. In this work, a method for reducing concomitant field effects in these DDE sequences based on additional oscillating gradients is presented. These oscillating gradient pulses obtained by constrained optimization were added to the original gradient waveforms. The modified sequences reduced the accumulated concomitant phase without significant changes in the original sequence characteristics. The proposed method was applied to a DDE acquisition scheme consisting of 60 pairs of diffusion wave vectors. For phantom as well as for in vivo experiments, a considerable increase in the signal-to-noise ratio (SNR) was obtained. For phantom measurements with a diffusion weighting of b = 2000 s/mm2 for each of the gradient pairs, an SNR increase of up to 40% was observed for a transversal slice that had a distance of 5 cm from the isocenter. For equivalent slice parameters, in vivo measurements in the brain of a healthy volunteer exhibited an increase in SNR of up to 35% for b = 750 s/mm2 for each weighting. These findings are supported by corresponding simulations, which also predict a positive effect on the SNR. In summary, the presented method leads to an SNR gain without additional RF refocusing pulses.

A novel energy efficient IRS-relay network for ITS with Nakagami-m fading channels

In this paper, we have investigated the performance of energy efficiency (EE) for Intelligent Transportation Systems (ITS), which recently emerged and advanced to preserve speed as well as safe transportation expansion via a cooperative IRS-relay network. To improve the EE, the relay model has been integrated with an IRS block consisting of a number of passive reflective elements. We analyze the ITS in terms of EE, and achievable rate, with different signal-to-noise ratio (SNR) values under Nakagami-m fading channel conditions that help the system to implement in a practical scenario. From the numerical results it is noticed that the EE for the only relay, IRS, and proposed cooperative relay-IRS-aided network at SNR value of 100 dBm is 30, 17, and 48 bits/joule respectively. In addition, we compare the impact of multi-IRS with the proposed cooperative IRS-relay and conventional relay-supported ITS. Simulation results show that both the proposed cooperative IRS-relay-aided ITS network and multi-IRS-aided network outperform the relay-assisted ITS with the increase in SNR.