Signal-to-noise Ratio Values Research Articles

Efficiency energy analysis for 6G communication systems using intelligent reflecting surface architecture

The subject of this 6G communication system research is expected to form a hyper-connected network in which various electronic devices can connect continuously without interruption. Several technologies have been built to support 6G communication systems, such as intelligent reflecting surface (IRS). An IRS is a reflector equipped with several two-dimensional passive elements that perform a phase shift by each element, which can reflect electromagnetic (EM) waves coming from the base station (BS) to the user equipment (UE), which is controlled via a controller to increase the signal strength at the UE and overcome poor propagation conditions. IRS can be placed anywhere, such as on a wall or on the roof of a building. The aim of the IRS research is expected to reduce the energy consumption and increase the spectral efficiency of wireless networks using artificial intelligence (AI) with low costs, energy savings, no thermal noise, and fairly small levels of interference. The objective of this research is to evaluate and analyze the energy efficiency (EE), including the achievable rate (AR) and signal-to-noise ratio (SNR), by applying the IRS architecture in a 6G communications system, which uses an operating frequency of 95 GHz and a bandwidth of 800 MHz. Then, the method was based on computer simulation using the Matlab software. In this paper, 6G communication system modeling was proposed. This model uses an urban microcell (Umi) that consists of one base station (BS) with multiple antennas, varying the number of IRS reflecting elements and one user. In this research, AR, SNR, and EE, using the frequency of 95 GHz and simulations with MATLAB@2021a software. The results show that the number of elements as many as 400 is 39% more optimal than the number of elements as many as 40 for the AR results. The SNR results without electromagnetic interference (EMI) are higher than SNR values affected by EMI with SNR results of 100 dBm, and the number of reflecting elements is directly proportional to the SNR results. For 800 elements, the EE value is 26% higher than for 40 elements. Conclusions. The application of IRS in 6G communication systems can increase the AR, SNR, and EE.

Effect of Breath Training on Image Quality of Chest Magnetic Resonance Free-breathing Sequence.

Magnetic Resonance Imaging (MRI) plays a role in demonstrating substantial utility in lung lesion imaging, detection, diagnosis, and evaluation. Previous studies have found that free-breathing star VIBE sequences not only have high image quality but also have a high ability to detect and display nodules. However, in our routine clinical practice, we have encountered suboptimal image quality in the free-breathing sequences of certain patients. This study aims to assess the impact of breath training on the quality of chest magnetic resonance imaging obtained during free-breathing sequences. A total of 68 patients with lung lesions, such as nodules or masses detected via Computed Tomography (CT) examination, were prospectively gathered. They were then randomly divided into two groups: an observation group and a control group. Standard preparation was performed for all patients in both groups before the examination. The observation group underwent 30 minutes of breath training prior to the MRI examination additionally, followed by the acquisition of MRI free-breathing sequence images. The signal intensity (SI) and standard deviation (SD) of the lesion and adjacent normal lung tissue were measured, and the image signal-to-noise ratio (SNR) and contrast signal-to-noise ratio (CNR) of the lesion were calculated for objective image quality evaluation. The subjective image quality of the two groups of images was also evaluated using a 5-point method. MRI examinations were completed in both groups. Significantly better subjective image quality (edge and internal structure clarity, vascular clarity, breathing and cardiac artifacts, and overall image quality) was achieved in the observation group compared to the control group (P<0.05). In addition, higher SNR and CNR values for disease lesions were observed in the observation group compared to the control group (t=4.35, P<0.05; t=5.35, P<0.05). It is concluded that the image quality of free-breathing sequences MRI can be improved through breath training before examination.

Iterative image reconstruction algorithm analysis for optical CT radiochromic gel dosimetry

Background. Modern radiation therapy technologies aim to enhance radiation dose precision to the tumor and utilize hypofractionated treatment regimens. Verifying the dose distributions associated with these advanced radiation therapy treatments remains an active research area due to the complexity of delivery systems and the lack of suitable three-dimensional dosimetry tools. Gel dosimeters are a potential tool for measuring these complex dose distributions. A prototype tabletop solid-tank fan-beam optical CT scanner for readout of gel dosimeters was recently developed. This scanner does not have a straight raypath from source to detector, thus images cannot be reconstructed using filtered backprojection (FBP) and iterative techniques are required. Purpose. To compare a subset of the top performing algorithms in terms of image quality and quantitatively determine the optimal algorithm while accounting for refraction within the optical CT system. The following algorithms were compared: Landweber, superiorized Landweber with the fast gradient projection perturbation routine (S-LAND-FGP), the fast iterative shrinkage/thresholding algorithm with total variation penalty term (FISTA-TV), a monotone version of FISTA-TV (MFISTA-TV), superiorized conjugate gradient with the nonascending perturbation routine (S-CG-NA), superiorized conjugate gradient with the fast gradient projection perturbation routine (S-CG-FGP), superiorized conjugate gradient with with two iterations of CG performed on the current iterate and the nonascending perturbation routine (S-CG-2-NA). Methods. A ray tracing simulator was developed to track the path of light rays as they traverse the different mediums of the optical CT scanner. Two clinical phantoms and several synthetic phantoms were produced and used to evaluate the reconstruction techniques under known conditions. Reconstructed images were analyzed in terms of spatial resolution, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), signal non-uniformity (SNU), mean relative difference (MRD) and reconstruction time. We developed an image quality based method to find the optimal stopping iteration window for each algorithm. Imaging data from the prototype optical CT scanner was reconstructed and analysed to determine the optimal algorithm for this application. Results. The optimal algorithms found through the quantitative scoring metric were FISTA-TV and S-CG-2-NA. MFISTA-TV was found to behave almost identically to FISTA-TV however MFISTA-TV was unable to resolve some of the synthetic phantoms. S-CG-NA showed extreme fluctuations in the SNR and CNR values. S-CG-FGP had large fluctuations in the SNR and CNR values and the algorithm has less noise reduction than FISTA-TV and worse spatial resolution than S-CG-2-NA. S-LAND-FGP had many of the same characteristics as FISTA-TV; high noise reduction and stability from over iterating. However, S-LAND-FGP has worse SNR, CNR and SNU values as well as longer reconstruction time. S-CG-2-NA has superior spatial resolution to all algorithms while still maintaining good noise reduction and is uniquely stable from over iterating. Conclusions. Both optimal algorithms (FISTA-TV and S-CG-2-NA) are stable from over iterating and have excellent edge detection with ESF MTF 50% values of 1.266 mm−1 and 0.992 mm−1. FISTA-TV had the greatest noise reduction with SNR, CNR and SNU values of 424, 434 and 0.91 × 10−4, respectively. However, low spatial resolution makes FISTA-TV only viable for large field dosimetry. S-CG-2-NA has better spatial resolution than FISTA-TV with PSF and LSF MTF 50% values of 1.581 mm−1 and 0.738 mm−1, but less noise reduction. S-CG-2-NA still maintains good SNR, CNR, and SNU values of 168, 158 and 1.13 × 10−4, respectively. Thus, S-CG-2-NA is a well rounded reconstruction algorithm that would be the preferable choice for small field dosimetry.

Robust acoustic reflector localization using a modified EM algorithm

In robotics, echolocation has been used to detect acoustic reflectors, e.g., walls, as it aids the robotic platform to navigate in darkness and also helps detect transparent surfaces. However, the transfer function or response of an acoustic system, e.g., loudspeakers/emitters, contributes to non-ideal behavior within the acoustic systems that can contribute to a phase lag due to propagation delay. This non-ideal response can hinder the performance of a time-of-arrival (TOA) estimator intended for acoustic reflector localization especially when the estimation of multiple reflections is required. In this paper, we, therefore, propose a robust expectation-maximization (EM) algorithm that takes into account the response of acoustic systems to enhance the TOA estimation accuracy when estimating multiple reflections when the robot is placed in a corner of a room. A non-ideal transfer function is built with two parameters, which are estimated recursively within the estimator. To test the proposed method, a hardware proof-of-concept setup was built with two different designs. The experimental results show that the proposed method could detect an acoustic reflector up to a distance of 1.6 m with 60%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$60\\%$$\\end{document} accuracy under the signal-to-noise ratio (SNR) of 0 dB. Compared to the state-of-the-art EM algorithm, our proposed method provides improved performance when estimating TOA by 10%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$10\\%$$\\end{document} under a low SNR value.

Improved recovery of cardiac auscultation sounds using modified cosine transform and LSTM-based masking.

Obtaining accurate cardiac auscultation signals, including basic heart sounds (S1 and S2) and subtle signs of disease, is crucial for improving cardiac diagnoses and making the most of telehealth. This research paper introduces an innovative approach that utilizes a modified cosine transform (MCT) and a masking strategy based on long short-term memory (LSTM) to effectively distinguish heart sounds and murmurs from background noise and interfering sounds. The MCT is used to capture the repeated pattern of the heart sounds, while the LSTMs are trained to construct masking based on the repeated MCT spectrum. The proposed strategy's performance in maintaining the clinical relevance of heart sounds continues to demonstrate effectiveness, even in environments marked by increased noise and complex disruptions. The present work highlights the clinical significance and reliability of the suggested methodology through in-depth signal visualization and rigorous statistical performance evaluations. In comparative assessments, the proposed approach has demonstrated superior performance compared to recent algorithms, such as LU-Net and PC-DAE. Furthermore, the system's adaptability to various datasets enhances its reliability and practicality. The suggested method is a potential way to improve the accuracy of cardiovascular diagnostics in an era of rapid advancement in medical signal processing. The proposed approach showed an enhancement in the average signal-to-noise ratio (SNR) by 9.6dB at an input SNR of - 6dB and by 3.3dB at an input SNR of 10dB. The average signal distortion ratio (SDR) achieved across a variety of input SNR values was 8.56dB.

Hybrid Encoding Schemes in Image Steganography Combined with Scrambling for Safeguarding Legal Asset Documents

Objective: The main objective of the study is to provide security to legal asset documents by including the details of ownership of the asset. Methods: This paper aims to improve security of legal asset documents using hybrid encoding schemes in Image Steganography combined with Image Scrambling. The proposed work uses images were gathered from various online sources grouped into a dataset of 40 images along with their 40 text files respectively. The techniques that were applied in the research are pre-processing for the noise removal, Steganography to embed data into image and Scrambling to introduce blur to the image. Finally, the objective metrics like Mean-Square Error (MSE), Peak Signal - Noise Ratio (PSNR) and Signal to Noise Ratio (SNR) values were calculated from them and compared with the acceptable values of the respective metrics. Findings: In the existing study, Steganography and Scrambling has been used separately for data and image security but in the proposed technique, Steganography has been applied with the land ownership data being hidden in the document image and the image being scrambled using different masks. The objective metrics gave the results such as the MSE value ranged between 0.0 and 10.0; the SNR value ranged between 14 dB to 80 dB; the PSNR value lies between 27 dB to 100 dB. The outcome of these values was mostly within the acceptable value range for all the measures here. Novelty: The proposed output dataset contains land asset documents that have details of ownership embedded in itself. Also, the hybrid encoding schemes of both the steganography and scrambling used here gives us data and image security together as proved in the outcome. Keywords: Image Steganography, Scrambling, Text Hiding, Legal Documents, Data Security

EMD AND WAVELET-BASED ECG SIGNAL DENOISING AND QRS COMPLEX DETECTION

This project presents a novel method for noise removal and QRS complex detection in electrocardiogram (ECG) signals, employing Empirical Mode Decomposition (EMD), windowing, and wavelet thresholding techniques. Noise is added to the ECG signal, and EMD is applied to obtain denoised components. Local minima detection and peak matching identify QRS complex boundaries. Windowing isolates the QRS regions, followed by wavelet thresholding for further denoising. Evaluation metrics like improved Signal-to-Noise Ratio, Mean Square Error, and Percent Root Mean Square Difference are calculated at different input SNR values, demonstrating the method's effectiveness in preserving the QRS complex while removing noise.. Keywords: ECG signal processing, QRS complex detection, Empirical Mode Decomposition (EMD), Windowing, Wavelet thresholding, QRS complex extraction, Signal-to-Noise Ratio (SNR), Mean Square Error (MSE), Percent Root Mean Square Difference (PRD).

A novel approach toward optimized image processing using sigma delta modulation

Image processing has widespread uses practically in every branch of science and arts. Processing images is more difficult than processing sound or data as there are more bits in the high pixel quality image. It requires more space to store the image, more bandwidth to transmit it, and more time and resources to process. An image's complexity may decrease if its bit size is decreased. Sigma-delta modulation, or SDM for short, is an alternative method of minimizing data-word length to compression. Digital signal processing (DSP) systems can be made simpler by using the SDM approach, which was first created for analog to digital conversion (ADC). This paper suggests a novel way to use SDM in MATLAB for improved image processing. Consequently, the suggested single-bit SDM-based image arithmetic architecture is tested and compared with the traditional image arithmetic techniques. Additionally, to see the noisy channel influence on the traditional and proposed systems, some statistical metrics are also studied at different noise variance values, such as signal to noise ratio (SNR), mean square error (MSE), and Peak SNR value. The suggested architecture for the SDM-based image arithmetic precisely matches the addition and subtraction results of the conventional design, even yielding a higher SNR and the same Peak SNR as the traditional methods. In contrast, the outcomes of division and multiplication fall within an acceptable range. For better results the over-sampling ratio (OSR), an inherent characteristic of SDM must be increased at the cost of more processing cycles. Therefore, the trade-off between fewer resources, limited transmission bandwidth, and comparatively more cycles is provided by the SDM-based technique.

Feasibility study of real-time imaging of an Ir-192 source using compact gamma camera with curved diverging collimator for brachytherapy

Brachytherapy is a treatment method that requires the accurate positioning of a radioactive source to deliver high doses to a tumor while minimizing exposure to surrounding tissues. Herein, we propose a compact gamma camera system based on a diverging collimator for real-time source positioning during brachytherapy. In the process of developing and fabricating such a gamma camera system, Monte Carlo simulations for diverging and pinhole collimators are performed under conditions that are similar to the actual detection environment, with the camera-to-source distance set at 50 cm to verify the feasibility of the gamma camera. Full width at half maximum (FWHM) and signal-to-noise ratio (SNR) values are analyzed based on the horizontal and vertical profiles at each location as the source shifts stepwise from the center to the right and diagonal direction. On average, the diverging collimator had FWHM values of 18 and 13 mm and SNR values of 30 and 31, while the pinhole collimator had FWHM values of 26 and 25 mm and SNR values of 47 and 46 when profiled horizontally and vertically. The diverging collimator has a lower SNR than pinhole collimator but performs better in terms of spatial resolution. Additionally, to test the performance of the manufactured gamma camera, the distance between the camera and the source was set to 100 cm and an experiment was conducted. The experimental results exhibit a trend similar to the simulation. Numerically, the average FWHM value were 39 mm in the vertical direction and 71 mm in the horizontal direction. Additionally, the average SNR values were 27 for the vertical direction and 17 for the horizontal direction. Based on these results, we confirm the possibility of Ir-192 source imaging.

The application value of a vendor-specific deep learning image reconstruction algorithm in "triple low" head and neck computed tomography angiography.

Head and neck computed tomography angiography (CTA) technology has become the noninvasive imaging method of choice for the diagnosis and long-term follow-up of vascular lesions of the head and neck. However, issues of radiation safety and contrast nephropathy associated with CTA examinations remain concerns. In recent years, deep learning image reconstruction (DLIR) algorithms have been increasingly used in clinical studies, demonstrating their potential for dose optimization. This study aimed to investigate the value of using a DLIR algorithm to reduce radiation and contrast doses in head and neck CTA. A total of 100 patients were prospectively enrolled and randomly divided into two groups. Group A (50 patients) consisted of those who underwent 70-kVp CTA with a low contrast volume and injection rate and who were classified according to the reconstruction algorithm into subgroups A1 [DLIR at high weighting (DLIR-H)], A2 [DLIR at low weighting (DLIR-L)], and A3 [volume-based adaptive statistical iterative reconstruction with 50% weighting (ASIR-V50%)]. Meanwhile, group B (50 patients) consisted of those who underwent standard radiation and contrast doses at 100 kVp with ASIR-V50% reconstruction. The computed tomography (CT) attenuation, background noise, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and subjective image quality score (SIQS) were statistically compared for several vessels among the four groups. Group A showed significant reductions in contrast dosage, injection rate, and radiation dose of 36.09%, 20.88%, and 47.80%, respectively, compared to group B (all P<0.001). The four groups differed significantly in terms of background noise (all P<0.05) with group A1 having the lowest value. Group A1 also had significantly higher SNR and CNR values compared to group B in all vessels (all P<0.05) except the M1 of the middle cerebral artery for the SNR. Group A1 also had the highest SIQS, followed by the A2, B, and A3 groups. The SIQS showed good agreement between the two reviewers in all groups, with κ values between 0.88 and 1. Compared to the standard-dose protocol using 100 kVp and ASIR-V50%, a protocol of 70 kVp combined with DLIR-H significantly reduces the radiation dose, contrast dose, and injection rate in head and neck CTA while still significantly improving image quality for patients with a standard body size.

Towards standardisation of surface electromyography measurements in the horse: Bipolar electrode location

The use of surface electromyography in the field of animal locomotion has increased considerably over the past decade. However, no consensus exists on the methodology for data collection in horses. This study aimed to start the development of recommendations for bipolar electrode locations to collect surface electromyographic data from horses during dynamic tasks.Data were collected from 21 superficial muscles of three horses during trot on a treadmill using linear electrode arrays. The data were assessed both quantitatively (signal-to-noise ratio (SNR) and coefficient of variation (CoV)) and qualitatively (presence of crosstalk and activation patterns) to compare and select electrode locations for each muscle.For most muscles and horses, the highest SNR values were detected near or cranial/proximal to the central region of the muscle. Concerning the CoV, there were larger differences between muscles and horses than within muscles. Qualitatively, crosstalk was suspected to be present in the signals of twelve muscles but not in all locations in the arrays.With this study, a first attempt is made to develop recommendations for bipolar electrode locations for muscle activity measurements during dynamic contractions in horses. The results may help to improve the reliability and reproducibility of study results in equine biomechanics.

Exploring the impact of microlensing on gravitational wave signals: Biases, population characteristics, and prospects for detection

ABSTRACT In this study, we investigate the impact of microlensing on gravitational wave (GW) signals in the LIGO−Virgo sensitivity band. Microlensing caused by an isolated point lens, with (redshifted) mass ranging from MLz ∈ (1, 105) M⊙ and impact parameter y ∈ (0.01, 5), can result in a maximum mismatch of $\sim 30~{{\ \rm per\ cent}}$ with their unlensed counterparts. When y &amp;lt; 1, it strongly anticorrelates with the luminosity distance enhancing the detection horizon and signal-to-noise ratio (SNR). Biases in inferred source parameters are assessed, with in-plane spin components being the most affected intrinsic parameters. The luminosity distance is often underestimated, while sky-localization and trigger times are mostly well-recovered. Study of a population of microlensed signals due to an isolated point lens primarily reveals: (i) using unlensed templates during the search causes fractional loss (20 per cent to 30 per cent) of potentially identifiable microlensed signals; (ii) the observed distribution of y challenges the notion of its high improbability at low values (y ≲ 1), especially for y ≲ 0.1; (iii) Bayes factor analysis of the population indicates that certain region in MLz − y parameter space have a higher probability of being detected and accurately identified as microlensed. Notably, the microlens parameters for the most compelling candidate identified in previous microlensing searches, GW200208_130117, fall within a 1σ range of the aforementioned higher probability region. Identifying microlensing signatures from MLz &amp;lt; 100 M⊙ remains challenging due to small microlensing effects at typical SNR values. Additionally, we also examined how microlensing from a population of microlenses influences the detection of strong lensing signatures in pairs of GW events, particularly in the posterior-overlap analysis.

Hybrid method for noise rejection from breath sound using transient artifact reduction algorithm and spectral subtraction.

Computerized breath sound based diagnostic methods are one of the emerging technologies gaining popularity in terms of detecting respiratory disorders. However, the breath sound signal used in such automated systems used to be too noisy, which affects the quality of the diagnostic interpretations. To address this problem, the proposed work presents the new hybrid approach to reject the noises from breath sound. In this method, 80 chronic obstructive pulmonary disease (COPD), 75 asthmatics and 80 normal breath sounds were recorded from the participants of a hospital. Each of these breath sound data were decontaminated using hybrid method of Butterworth band-pass filter, transient artifact reduction algorithm and spectral subtraction algorithm. The study examined the algorithms noise rejection potential over each category of breath sound by estimating the noise rejection performance metrics, i.e.,mean absolute error (MAE), mean square error (MSE), peak signal to noise ratio (PSNR), and signal to noise ratio (SNR). Using this algorithm, the study obtained a high value of SNR of 70 dB and that of PSNR of 72 dB. The study could definitely a suitable one to suppress noises and to produce noise free breath sound signal.

Exploration of Digital Filter on Cardiac Monitor through Carotid Pulse and PCG (Phonocardiogram)

Heart defect early detection and correct diagnosis have become important healthcare priorities. Tools for monitoring cardiac problems are constantly being developed, with the PCG (Phonocardiogram) and Cardiac Monitor via Carotid Pulse essential for heart evaluation. Although condenser microphones embedded in electric stethoscopes have been used in past research as PCG sensors, more advancements are still required to reduce received noise. This study investigates how well a Chebyshev type-II digital filter works to reduce noise on the cardiac monitor using PCG and carotid pulse. The PCG sensor is the GY-MAX 9814 module, which is interfaced with an Arduino Uno microcontroller. Matlab, Visual Studio (used as a graph viewer), and Doppler Simulator (used as a phantom cardiac signal) are used. SNR (Signal to Noise Ratio) is used in the analysis to assess the effectiveness of two digital filter orders. The average SNR value for the Doppler Simulator is 0.001404 dB at order 2, however, it climbs dramatically to 18.60023 dB at level 4 according to the results of the SNR analysis. The average SNR value in human signals is 11.50718 dB before the filter, 0.001404 dB after the post-order 2 filter, and 12.0009 dB after the post-order 4 filter. According to the results, the digital filter of order 4 is more effective in reducing noise. This study highlights the possibility of an order 4 digital filter to improve the Cardiac Monitor through PCG and Carotid Pulse. Through enhanced signal quality, the creation of this gadget holds the potential for streamlining the identification of cardiac problems. Future developments in this technology could lead to more precise and trustworthy cardiac exams, which would help with early diagnosis and treatment of cardiovascular health.

Modeling the effects of power efficient approximate multipliers in radio astronomy correlators

Large scale Radio Telescopes for Radio Astronomy highly depend on the availability of large (digital) processing capacities for imaging. Estimates concerning power efficiency for future Radio Telescopes lead to anticipated power consumption numbers beyond feasibility. To reduce the power budget, the use of approximate multipliers within the correlator is explored. A baseband equivalent executable model of a radio synthesis telescope is constructed to assess the effects of approximate multipliers. Besides ideal multipliers with floating point accuracy, the use of accurate 8-bit multipliers and 4 different types of approximate multipliers is explored. For each of these multipliers, the energy efficiency of an individual multiplier is known and used to determine the energy efficiency improvement of a correlator when using approximate multipliers. The effects of approximation are quantified by 3 metrics (Signal-to-Noise-Ratio (SNR), Spurious-Free-Dynamic-Range (SFDR) and Root-Mean-Square (RMS) level) derived from maps constructed by the executable model based on an empty sky with only a single point source. This is considered to be the worst case scenario. For illustration purposes, a more realistic input is processed by the model as well. The metrics have been determined based on different SNR levels at the input of each antenna element. For input SNR levels up to 10 dB, all types of approximate multipliers used in this paper can be exploited to improve energy efficiency of correlators, leading to a maximum energy reduction of 19 %. For input SNR values up to 30 dB an energy improvement up to 12 % can be achieved. These percentages are based on implementations in a 40nm low power IC technology at 1 GHz.

Performance investigation of elevated source EBG TFET based photosensor for near-infrared light sensing applications

In this manuscript, an elevated source TFET with extended back gate (ES-EBG-TFET) based photosensor is designed to offer improvement in optical performance for detecting incident light of narrow-spaced wavelengths (∼100 nm) and low luminous intensity (<0.9 W/cm2) in the near-infrared (IR) range. The proposed photosensor combines the advantages provided by both the elevated top gate and extended back gate in the sense of increasing the line tunneling region at the source-channel (S–C) interface, resulting to improvement in illumination current (ILight), threshold voltage (Vth) and average subthreshold swing (SSavg) under the light state. Furthermore, the inclusion of an L-shaped pocket around the source region helps to reduce the corner effects at S–C interface in the sub-threshold region, thereby improving the signal-to-noise ratio (SNR) of the proposed photosensor for on-chip applications. Due to improved optical generation in the photosensing gate, a high spectral sensitivity (Sn) of ∼54 is achieved for ES-EBG-TFET based photosensor because the variation in wavelength (λ) of incident light from 1050 to 750 nm shows a two order change in ILight (ID at Vgs = 0.2V under light state) from 3.1 × 10−15 A/μm to 1.72 × 10−13 A/μm, respectively. Since the elevated source provides a space to create larger illumination window above the channel region, a high optical generation rate is observed for the proposed photosensor under the incident light with low intensity (<0.9 W/cm2). The maximum value of SNR is observed to be 66.4 dB at a λ of 750 nm, while the quantum efficiency (ƞ) and responsivity (R) are reported to be 74% and 0.59 A/W, respectively, around λ of 1050 nm. Moreover, an optimum length of back-gate underlap with drain (LBGUD) is found to enhance the magnitude of SNR (at λ of 750 nm) and Sn (750–1050 nm) from 25.6 to 63.1 dB and 14 to 52.8, respectively when the photosensor is operated at the ambipolar state (Vgs = −0.6 V and Vds = 0.5 V). Finally, the photosensing parameters such as Sn and SNR are analyzed under the presence of acceptor and donor interface traps and it is found that there is insignificant degradation in the optical performance of the proposed photosensor.

Single-petal rosette trajectory for 2D functional lung imaging

Purpose: The purpose of this study was to investigate the use of a 2D rosette trajectory for breath-hold and free-breathing real-time imaging of the lungs.Methods: Eight healthy volunteers underwent breath-hold magnetic resonance imaging (MRI) using two different parametrizations of the proposed single-petal rosette (SPR) trajectory, as well as radial ultra-short echo time (UTE) acquisition combined with the tiny golden-angle acquisition scheme. The additional free-breathing acquisitions of all trajectories were performed. The proposed technique was compared with the conventional radial UTE technique regarding image sharpness, signal-to-noise ratio (SNR), regional fractional ventilation (FV) for breath-hold and retrospective image-based self-gating, and real-time imaging capabilities.Results: Image sharpness significantly increased for gated SPR images compared to radial UTE for the end-expiratory stage; no significant difference was found for the self-gated end-inspiratory stage and the breath-hold acquisitions. The SPR trajectory performs significantly better than radial UTE concerning the SNR for breath-hold imaging, whereas no significant difference was found for self-gated images. Fractional ventilation values were comparable between SPR and radial UTE. The SPR real-time data showed a marked reduction in the aliasing artifacts with no apparent streaks.Conclusion: The second half of the petal of the SPR trajectory accomplishes rephasing while acquiring non-redundant k-space data, thus leading to a more efficient sampling of the k-space than that with the radial UTE sampling scheme, with a minimal increase in TR. This leads to the need of less read-outs to achieve the same SNR values and, thus, a reduction in scan time. For real-time application, the SPR approach resulted in a marked reduction of aliasing artifacts when compared to UTE.

Optimization of received signal strength in visible light communication using the combination of heuristic algorithms

SummaryLEDs are a prominent technique for a pervasive communication system using visible light communication (VLC), since LED includes long life expectancy, fast switching, and being visible light and is less expensive, which is safe for the human body, though still many technical problems presented to be addressed, especially in an indoor environment. Due to this, the major aim of this task is to afford a hybridized optimization algorithm that is highly suitable for enabling the signal to noise ratio (SNR) and received signal strength (RSS) maximization in VLC. Here, the coordinate of each point of the receiver photo detector in the VLC system is tuned by the new hybridized algorithm. The fusion of two optimization algorithms like Deer Hunting Optimization Algorithm (DHOA) and Dragonfly Algorithm (DA) termed as Fitness‐oriented Dragonfly‐based DHOA (FD‐DHOA) is used to attain the objective concerning the RSS or received power and SNR. The proposed model tries to attain maximum RSS and SNR at the receiver instead of calculating it at every angle. Moreover, the experimental analysis proves that the developed model validates the orientation that generates an optimum RSS and SNR value at the receiver.

Effects of diagram plane on neural network based modulation recognition

Modulation recognition using deep learning presents challenges in effectively distinguishing high-order modulation schemes while maintaining a balance between complexity and recognition accuracy. In this study, we curate a comprehensive dataset in the rθ plane, encompassing eight distinct modulation schemes. Leveraging hyperparameter optimization and transfer learning, we explore the capabilities of various CNN-based architectures, including MobileNetV2, ResNet50V2, ResNet101V2, InceptionV3, ResNet152V2, Xception, and InceptionResNetV2, for the classification of modulation schemes. The simulation results demonstrate that with signal-to-noise ratio (SNR) values exceeding 5 dB, all models exhibit classification accuracies surpassing 50% and approach near-perfect accuracy at an SNR value of 20 dB. However, under low SNR conditions, such as 5 dB, the recognition accuracies of all models, except for ResNet152V2 and InceptionV3, show minimal variation. As the SNR increases by 5 dB from −5 dB to 20 dB, ResNet152V2 and InceptionV3 demonstrate remarkable classification accuracy improvements, exceeding 40%, 30%, 30%, 10%, and 15%, respectively. In contrast, the other models do not exhibit such robust responsiveness in accuracy enhancements. The remarkable performance improvements are achieved by fine-tuning pre-trained models through these processes.

Feasibility of Low-Dose and Low-Contrast Media Volume Approach in Computed Tomography Cardiovascular Imaging Reconstructed with Model-Based Algorithm.

Aim: To evaluate the dose reduction and image quality of low-dose, low-contrast media volume in computed tomography (CT) examinations reconstructed with the model-based iterative reconstruction (MBIR) algorithm in comparison with the hybrid iterative (HIR) one. Methods: We prospectively enrolled a total of 401 patients referred for cardiovascular CT, evaluated with a 256-MDCT scan with a low kVp (80 kVp) reconstructed with an MBIR (study group) or a standard HIR protocol (100 kVp-control group) after injection of a fixed dose of contrast medium volume. Vessel contrast enhancement and image noise were measured by placing the region of interest (ROI) in the left ventricle, ascending aorta; left, right and circumflex coronary arteries; main, right and left pulmonary arteries; aortic arch; and abdominal aorta. The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were computed. Subjective image quality obtained by consensus was assessed by using a 4-point Likert scale. Radiation dose exposure was recorded. Results: HU values of the proximal tract of all coronary arteries; main, right and left pulmonary arteries; and of the aorta were significantly higher in the study group than in the control group (p < 0.05), while the noise was significantly lower (p < 0.05). SNR and CNR values in all anatomic districts were significantly higher in the study group (p < 0.05). MBIR subjective image quality was significantly higher than HIR in CCTA and CTPA protocols (p < 0.05). Radiation dose was significantly lower in the study group (p < 0.05). Conclusions: The MBIR algorithm combined with low-kVp can help reduce radiation dose exposure, reduce noise, and increase objective and subjective image quality.