Decrease In Signal-to-noise Ratio Research Articles

Dual-channel end-to-end network with prior knowledge embedding for improving spatial resolution of magnetic particle imaging

Magnetic particle imaging (MPI) is an emerging non-invasive medical imaging tomography technology based on magnetic particles, with excellent imaging depth penetration, high sensitivity and contrast. Spatial resolution and signal-to-noise ratio (SNR) are key performance metrics for evaluating MPI, which are directly influenced by the gradient of the selection field (SF). Increasing the SF gradient can improve the spatial resolution of MPI, but will lead to a decrease in SNR. Deep learning (DL) methods may enable obtaining high-resolution images from low-resolution images to improve the MPI resolution under low gradient conditions. However, existing DL methods overlook the physical procedures contributing to the blurring of MPI images, resulting in low interpretability and hindering breakthroughs in resolution. To address this issue, we propose a dual-channel end-to-end network with prior knowledge embedding for MPI (DENPK-MPI) to effectively establish a latent mapping between low-gradient and high-gradient images, thus improving MPI resolution without compromising SNR. By seamlessly integrating MPI PSF with DL paradigm, DENPK-MPI leads to a significant improvement in spatial resolution performance. Simulation, phantom, and in vivo MPI experiments have collectively confirmed that our method can improve the resolution of low-gradient MPI images without sacrificing SNR, resulting in a decrease in full width at half maximum by 14.8%–23.8 %, and the accuracy of image reconstruction is 18.2 %–27.3 % higher than other DL methods. In conclusion, we propose a DL method that incorporates MPI prior knowledge, which can improve the spatial resolution of MPI without compromising SNR and possess improved biomedical application.

Comparison of Reduced and Full Field of View in Diffusion-Weighted MRI on Image Quality: A Meta-Analysis.

Reduced field of view (rFOV) diffusion-weighted imaging (DWI) in MRI shows potential for enhanced image quality compared with traditional full field of view (fFOV) DWI. Evaluating rFOV DWI's impact on image quality is important for clinical adoption. To assess the efficacy of rFOV DWI in improving image quality, focusing on artifact reduction, signal-to-noise ratio (SNR) improvement, and lesion detectability. Meta-analysis. Systematic literature search was conducted in PubMed, Embase, the Cochrane Library, and Web of Science ending in January 2024. Thirteen studies with 765 participants focusing on DWI quality using rFOV was analyzed. SS-EPI, Rtr-SS-EPI, 2D-SS-EPI at 3.0 T. Two investigators performed the data extraction. QUADAS-2 assessed bias. The image quality assessment of rFOV and fFOV DWI were compared. Standardized mean difference (SMD) was utilized to evaluate and standardize MRI image quality. Heterogeneity was assessed using the I2 statistic and publication bias was evaluated with Egger's test. The QUADAS-2 analysis revealed that most studies exhibited a low risk of bias and minimal concerns regarding applicability. Statistical analysis indicated that rFOV DWI yielded higher subjective image quality scores (SMD = 0.535, 95% CI: 0.339, 0.731, I2 = 45.7%) compared with fFOV DWI and was more effective in reducing artifacts (SMD = 0.44, 95% CI: 0.209, 0.672, I2 = 42.3%) than fFOV DWI. However, a decrease in SNR was noted with rFOV DWI (SMD = -0.670, 95% CI: -1.187 to -0.152, I2 = 87.9%). Additionally, rFOV DWI demonstrated enhancements in lesion visibility (SMD = 0.432, 95% CI: -1.187, -0.152, I2 = 53.1%) and anatomical details (SMD = 0.598, 95% CI: 0.121, 1.075, I2 = 90.8%). rFOV DWI enhances MRI image quality by reducing artifacts and improving lesion visibility with a SNR trade-off. 3 TECHNICAL EFFICACY: Stage 1.

ДИНАМИКА ПОКАЗАТЕЛЕЙ ФУНКЦИОНАЛЬНОГО СОСТОЯНИЯ ОРГАНА СЛУХА И СОСТАВА МИКРОФЛОРЫ ЛОР-ОРГАНОВ ЧЕЛОВЕКА В УСЛОВИЯХ НАЗЕМНОГО МОДЕЛИРОВАНИЯ ЭФФЕКТОВ МИКРОГРАВИТАЦИИ

Functioning of hearing organs and ENT microflora composition were investigated in 10 healthy male subjects (25–36 y.o.) who volunteered for a 7-day dry immersion as a model of the physiological effects of microgravity. The functional state of the hearing organs was assessed with the registration of distortion product oto-acoustic emission (DPOAE), and tympanometry. Specifically, assessed were the signal-to-noise ratio (SNR) of the oto-acoustic response (dB), middle ear pressure (daPa) and tympanogram maximal compliance (ml). To identify and count ENT microbial cultures, samples were gathered from the nasal and pharyngeal membranes and external skin of the auditory meatus. The eubiotic index was used to detect changes in the microflora composition. Changes in the hearing organ functioning included a SNR decrease in DPOAE of the low-frequency band (p ≤ 0.05), increase of the middle ear pressure (p ≤ 0.05) and an obvious downward trend of the maximal compliance. Dynamic microbiocenosis assessment discovered several negative changes such as a reduction in the number of protective microorganisms and growth of the number of opportunistic groups. The eubiotic index value was below one. These changes demonstrate a potential negative effect of modeled microgravity on the ENT microflora functioning and composition. Methods of dynamic assessment and control of the hearing organ and ENT microbiocenosis would be appropriate for use in monitoring of cosmonauts' health.

Satellite-to-ground optical downlink model using mode mismatching multi-mode photonic lanterns.

Photonics lanterns (PLs) provide an effective mode diversity solution to mitigate atmospheric turbulence interference in free-space optical communications (FSOC). This paper presents mode-mismatching multimode photonic lanterns (MM-PLs) for diversity receiver in satellite-to-ground downlink scenarios. Our study evaluates the coupling characteristics of the mode-selective PLs (MSPLs) and non-mode-selective PLs (NSPLs) for the influence of strong-to-weak turbulence and confirms that MSPLs outperform NSPLs under weak turbulence conditions. The research further explores the impact of fiber position error (FPE) on the spatial light-to-fiber coupling, including the optimal focal length deviation and lateral offset of receiving fiber devices. We have calculated and compared the coupling power and signal-to-noise ratio (SNR) of few-mode PLs (FM-PLs) and MM-PLs for various turbulence intensities. The results indicate that the optimal focal length tolerance, which corresponds to a decrease of approximately 1 dB in the average coupling power, is 2-3 m and 5-6 m for FM-PLs and MM-PLs, respectively. Furthermore, regardless of whether it is strong or weak turbulence, MM-PL exhibits a lateral offset tolerance exceeding 12 µm for a 0.5 dB drop in the mean coupled power, whereas the lateral offset tolerance of FM-PL is only 3 µm under weak turbulence. Additionally, the decrease in the average SNR of MM-PLs is gentle, only 0.67-1.16 dB at a 12 µm offset under weak turbulence, whereas there is a significant reduction of 6.50-8.49 dB in the average SNR of FM-PLs. These findings demonstrate the superiority of MM-PLs over FM-PLs in turbulence resistance and fiber position tolerance in the satellite-ground downlink.

Power limits with adjustable 10G receiver parameters for A-RoF transmission

Current paper characterizes the performance of the three-radio-frequency-channels, orthogonal-frequency-division-multiplexed (OFDM) radio-over-fiber (RoF) transmission system in terms of error-vector-magnitude and received optical signal power for a typical 10 GHz receiver obtained by the comprehensive simulations. The impact of the transmitted OFDM channels' signal-to-noise ratio (SNR) is also considered. We demonstrate that the SNR decrease from 40 dB to 25 dB results in an error vector magnitude (EVM) increase of up to 4 %. Furthermore, we propose the approach for allowing adjusting these results to other receiver sensitivity and responsivity values by introducing a delta in received optical signal power eliminating additional simulation or complicated calculations. The feasibility of the adjustments with a precision of up to 0.001% of the EVM is proved.

Speech Perception in Quiet and in the Presence of Noise in Children with Autism Spectrum Disorder: A Behavioral Study.

Signal-to-noise ratio (SNR) is one of the important parameters to be considered for the effective perception of speech. Many researchers indicate that children with autism spectrum disorder (ASD) have reduced capacity to integrate sensory information across different modalities and show speech understanding difficulty in the presence of background speech or noise. So, this present study was undertaken with the aimed to evaluate and compare the speech perception ability in quiet and in the presence of noise for children with and without ASD and also to compare across different noise conditions. Speech perception in noise was measured for 15 children with ASD and 15 age-matched children without ASD in the age range of 8 to 12 years. The stimulus includes standardized bisyllabic and trisyllabic Kannada words in quiet and at different SNR conditions. The result showed that children with ASD had poor performance in all the listening conditions (quiet, speech babble, and speech noise) and the syllable conditions (bisyllables and trisyllables) compared to children without ASD. When compared across quiet and different SNR conditions for individuals with ASD, the result showed the best performance in quiet conditions followed by different SNR conditions. The performance deteriorated with a decrease in SNR for both groups. Children with ASD showed poor performance in quiet and in the presence of noise compared to children without ASD. Speech perception evaluation in the presence of noise provides a more reliable predictor of the communication difficulty faced by children with ASD than evaluating only in quiet conditions.

Signal-to-noise ratio penalties from a loss of stimulated echoes when using slab-selective excitation in three-dimensional fast spin echo imaging with long echo trains.

Three-dimensional fast spin echo imaging with long echo trains combines high resolution with reasonable acquisition times and reduced specific absorption rate due tolow refocusing flip angles. Typically, an entire volume is encoded (nonselective excitation) or localization can be performed with slab select excitation, which uses a long 90° pulse for precise localization, followed by a preliminary nonselective 180° pulse bounded by spoiler gradients to destroy signal outside of the volume of interest. Subsequent flip angles in the train are nonselective and identical between the two methods. The inclusion of the initial selective pulse and spoiler gradients results in a signal-to-noise ratio (SNR) penalty for slab selection, beyond the slice-averaging dependence, arising from a loss of stimulated echoes. SNR differences are explored using Bloch equation simulations of a T2-weighted 96 echo train sequence with varying parameters including T2, T1, and B1+ and compared with phantom and in vivo brain, neck, and knee experiments. In vivo SNR measurements in the three regions showed a maximum decrease of selective SNR by 29% (gastrocnemius muscle), 25% (pons), and 22% (globus pallidus), despite similar experimental parameters to nonselective experiments. Decreased SNR was compounded by B1+ variation affecting prescribedflip angles with further smaller reductions with T2 and T1 times. In conclusion, the elimination of coherences via the preliminary nominal 180° pulse and spoiler gradients in addition to the extended echo timing from the long excitation pulse resulted in a reduction in SNR compared with the nonselective case. Consideration of the required SNR and chosen anatomy as well as sequence restrictions should be weighed before choosing slab-selective excitation.

Application of the long axial field-of-view PET/CT with low-dose [18F]FDG in melanoma

AimThe recent introduction of long axial field-of-view (LAFOV) PET/CT scanners has yielded very promising results regarding image quality and sensitivity in oncological patients. We, herein, aim to determine an appropriate acquisition time range for the new long axial field of view Biograph Vision Quadra PET/CT (Siemens Healthcare) using low dose [18F]FDG activity in a group of melanoma patients.MethodologyForty-nine melanoma patients were enrolled in the study. All patients underwent total body PET/CT from the top of the head through the feet in two bed positions (field-of-view 106 cm) after i.v. injection of 2.0 MBq/kg [18F]FDG. The PET images of the first bed position (head to upper thigh; PET-10) were reconstructed and further split into 8-min (PET-8), 6-min (PET-6), 5-min (PET-5), 4-min (PET-4), and 2-min (PET-2) duration groups. Comparisons were performed between the different reconstructed scan times with regard to the visual evaluation of the PET/CT scans using the PET-10 images as reference and by calculating the 95%-CI for the differences between different time acquisitions. Moreover, objective evaluation of PET/CT image quality was performed based on SUV calculations of tumor lesions and background, leading to calculation of liver signal-to-noise ratio (SNR), and tumor-to-background ratio (TBR).ResultsA total of 60 scans were evaluated. Concerning visual analysis, 49/60 (81.7%) PET-10 scans were pathological, while the respective frequencies were 49/60 (81.7%) for PET-8 (95%-CI: − 0.0602–0.0602), 49/60 (81.7%) for PET-6 (95%-CI: − 0.0602–0.0602), 48/60 (80%) for PET-5 (95%-CI: − 0.0445–0.0886), 46/60 (76.7%) for PET-4 (95%-CI: − 0.0132–0.1370), and 45/60 (75%) for PET-2 (95%-CI: 0.0025–0.1593). In 18 PET-10 scans, the extent of metastatic involvement was very large, rendering the accurate calculation of [18F]FDG-avid tumor lesions very complicated. In the remaining 42 PET-10 scans, for which the exact calculation of tumor lesions was feasible, a total of 119 tumor lesions were counted, and the respective lesion detection rates for shorter acquisitions were as follows: 97.5% (116/119) for PET-8 (95%-CI: 0–1), 95.0% (113/119) for PET-6 (95%-CI: 0–1), 89.9% (107/119) for PET-5 (95%-CI: 0–2), 83.2% (99/119) for PET-4 (95%-CI: 1–2), and 73.9% (88/119) for PET-2 (95%-CI: 2–4). With regard to objective image quality evaluations, as a general trend, the reduction of acquisition time was associated with a decrease of liver SNR and a decrease of TBR, although in lesion-based analysis the change in TBR and tumor SUVmean values was non-significant up to 6 and 5 min acquisitions, respectively.ConclusionsIn melanoma, low-dose LAFOV PET/CT imaging is feasible and can reduce the total scan time from head to upper thigh up to 5 min providing comparable diagnostic data to standard lengths of acquisition. This may have significant implications for the diagnostic work-up of patients with melanoma, given the need for true whole-body imaging in this type of cancer.

Simulation Study of Accuracy of Calculated Single Photon Event Position with Sub-pixel Spatial Resolution in Charge Couple Device

It has been a long-term effort to get higher energy resolution for experiment detector. Usually the energy resolution can be converted from spatial resolution though mechanical setup. To shrink pixel size of detector takes a lot of efforts, time and is limited by integrated circuit technology. So centroid algorithm is widely used to increase spatial resolution. Through electron cloud diffusion, the excited electrons are distributed among nearby pixels. Using centroid algorithm, the photon impact point can be calculated. However, the accuracy of calculated position is affected by relative impact position within pixel and signal to noise ratio (SNR). There is a systematic bias offset, or edge effect, toward pixel center when the impact point is near perimeter of pixel. Also the SNR is an important factor which affects the accuracy of calculated position. In this study, we try to calculate the simulated photon event positions by three algorithms, 3x3 centroid, 5x5 centroid and two-dimensional Gaussian profile fit. A simulation 2000 x 1000 pixels’ image with specific SNR is filled with noise first and then sparsely and randomly distributed with 200 Gaussian shaped photon impact events to avoid pile up. Since the original impact points are well known, the calculated positions of photon event can be compared with the original data to verify their accuracy. The noise, or SNR, can also be adjusted for different images to see its effects on the accuracy. The simulation shows the systematic bias toward pixel center for centroid algorithm as seen in the previous experiment using 3x3 centroid. For 3x3 centroid, the bias will not disappear even when SNR is high. 5x5 centroid is better than 3x3 centroid when SNR is high but the position accuracy degrades faster when image SNR decreases. The 2D-Gaussian fit can provide better position estimation at the same SNR and systematic bias of position at perimeter of pixel is minimized. However, the iteration process will diverge and no optimized answer can be attained when SNR is too low. The accuracy of calculated position, defined as inverse of position error standard deviation, is proportional to SNR for 5x5 and 2-D Gaussian fit.

Performance analysis of wireless network aided by discrete-phase-shifter IRS

Discrete phase shifter of intelligent reflecting surface (IRS) generates phase quantization error (QE) and degrades the receive performance at the receiver. To make an analysis of the performance loss (PL) caused by IRS with phase QE, based on the law of large numbers, the closed-form expressions of signal-to-noise ratio (SNR) PL, achievable rate (AR), and bit error rate (BER) are successively derived under line-of-sight (LoS) channels and Rayleigh channels. Moreover, based on the Taylor series expansion, the approximate simple closed form of PL of IRS with approximate QE is also given. The simulation results show that the performance losses of SNR and AR decrease as the number of quantization bits increases, while they gradually increase with the number of IRS phase shifter elements increases. Regardless of LoS channels or Rayleigh channels, when the number of quantization bits is larger than or equal to 3, the performance losses of SNR and AR are less than 0.23 dB and 0.08 bits/s/Hz, respectively, and the BER performance degradation is trivial. In particular, the performance loss difference between IRS with QE and IRS with approximate QE is negligible when the number of quantization bits is not less than 2.

Design and experimental research of abrasive particle detection sensor based on coil magnetic field

This paper develops a dual-coil sensor integrated with high-permeability materials based on inductive sensor technology to detect wear particles in oil. This method is mainly used for online detection and fault analysis of pollutants in hydraulic and lubricating oil systems. The sensor innovatively embeds permalloy into the sensing unit of the sensor to generate high gradient magnetic field in the sensing area, consequently improving the detection sensitivity of the sensor. The detection unit consists of two pieces of permalloy and two plane coils. The permalloys have a rectangular groove at the center, which is placed in line with the inner hole of the coil, thereby forming the detection area. Particles in the microchannel can be detected as they flow through the detection area. The article theoretically analyzes the working principle of the sensor and establishes a verification experiment system. The experimental results show that after adding permalloy to the sensing unit, the signal-to-noise ratio (SNR) of iron particles is increased by more than 40%, and the SNR of copper particles is increased by more than 30%. As the particle size increases, the SNR decreases. Using this design, the range of the lower limit detection for ferromagnetic metal particles increased to 10–15 μm, while that of non-ferromagnetic metal particles increased to 60 μm. Compared with traditional inductive sensors, the addition of permalloy greatly improves the sensor's performance, which significantly boosts the sensitivity of the dual-coil type sensor.

Brillouin Frequency Shift Extraction Based on AdaBoost Algorithm.

The Brillouin Optical Time-Domain Analyzer assisted by the AdaBoost Algorithm for Brillouin frequency shift (BFS) extraction is proposed and experimentally demonstrated. The Brillouin gain spectrum classification under different BFS is realized by iteratively updating the weak classifier in the form of a decision tree, forming several base classifiers and combining them into a strong classifier. Based on the pseudo-Voigt curve training set with noise, the performance of the AdaBoost Algorithm is studied, and the influence of different signal-to-noise ratio (SNR), frequency range, and frequency step is also studied. Results show that the performance of BFS extraction decreases with the decrease in SNR, the reduction in frequency range, and the increase in frequency step.

Feasibility of In-Line Raman Spectroscopy for Quality Assessment in Food Industry: How Fast Can We Go?

Raman spectroscopy is a viable tool within process analytical technologies due to recent technological advances. In this article, we evaluate the feasibility of Raman spectroscopy for in-line applications in the food industry by estimating the concentration of the fatty acids EPA + DHA in ground salmon samples (n = 63) and residual bone concentration in samples of mechanically recovered ground chicken (n = 66). The samples were measured under industry like conditions: They moved on a conveyor belt through a dark cabinet where they were scanned with a wide area illumination standoff Raman probe. Such a setup should be able to handle relevant industrial conveyor belt speeds, and it was studied how different speeds (i.e., exposure times) influenced the signal-to-noise ratio (SNR) of the Raman spectra as well as the corresponding model performance. For all samples we applied speeds that resulted in 1 s, 2 s, 4 s, and 10 s exposure times. Samples were scanned in both heterogenous and homogenous state. The slowest speed (10 s exposure) yielded prediction errors (RMSECV) of 0.41%EPA + DHA and 0.59% ash for the salmon and chicken data sets, respectively. The more in-line relevant exposure time of 1 s resulted in increased RMSECV values, 0.84% EPA + DHA and 0.84% ash, respectively. The increase in prediction error correlated closely with the decrease in SNR. Further improvements of model performance were possible through different noise reduction strategies. Model performance for homogenous and heterogenous samples was similar, suggesting that the presented Raman scanning approach has the potential to work well also on intact heterogenous foods. The estimation errors obtained at these high speeds are likely acceptable for industrial use, but successful strategies to increase SNR will be key for widespread in-line use in the food industry.

Reduced Semantic Context and Signal-to-Noise Ratio Increase Listening Effort As Measured Using Functional Near-Infrared Spectroscopy

Objectives: Understanding speech-in-noise can be highly effortful. Decreasing the signal-to-noise ratio (SNR) of speech increases listening effort, but it is relatively unclear if decreasing the level of semantic context does as well. The current study used functional near-infrared spectroscopy to evaluate two primary hypotheses: (1) listening effort (operationalized as oxygenation of the left lateral PFC) increases as the SNR decreases and (2) listening effort increases as context decreases. Design: Twenty-eight younger adults with normal hearing completed the Revised Speech Perception in Noise Test, in which they listened to sentences and reported the final word. These sentences either had an easy SNR (+4 dB) or a hard SNR (−2 dB), and were either low in semantic context (e.g., “Tom could have thought about the sport”) or high in context (e.g., “She had to vacuum the rug”). PFC oxygenation was measured throughout using functional near-infrared spectroscopy. Results: Accuracy on the Revised Speech Perception in Noise Test was worse when the SNR was hard than when it was easy, and worse for sentences low in semantic context than high in context. Similarly, oxygenation across the entire PFC (including the left lateral PFC) was greater when the SNR was hard, and left lateral PFC oxygenation was greater when context was low. Conclusions: These results suggest that activation of the left lateral PFC (interpreted here as reflecting listening effort) increases to compensate for acoustic and linguistic challenges. This may reflect the increased engagement of domain-general and domain-specific processes subserved by the dorsolateral prefrontal cortex (e.g., cognitive control) and inferior frontal gyrus (e.g., predicting the sensory consequences of articulatory gestures), respectively.

Optimization of SPECT/CT imaging protocols for quantitative and qualitative 99mTc SPECT

BackgroundThe introduction of hybrid SPECT/CT devices enables quantitative imaging in SPECT, providing a methodological setup for quantitation using SPECT tracers comparable to PET/CT. We evaluated a specific quantitative reconstruction algorithm for SPECT data using a 99mTc-filled NEMA phantom. Quantitative and qualitative image parameters were evaluated for different parametrizations of the acquisition and reconstruction protocol to identify an optimized quantitative protocol.ResultsThe reconstructed activity concentration (ACrec) and the signal-to-noise ratio (SNR) of all examined protocols (n = 16) were significantly affected by the parametrization of the weighting factor k used in scatter correction, the total number of iterations and the sphere volume (all, p < 0.0001). The two examined SPECT acquisition protocols (with 60 or 120 projections) had a minor impact on the ACrec and no significant impact on the SNR.In comparison to the known AC, the use of default scatter correction (k = 0.47) or object-specific scatter correction (k = 0.18) resulted in an underestimation of ACrec in the largest sphere volume (26.5 ml) by − 13.9 kBq/ml (− 16.3%) and − 7.1 kBq/ml (− 8.4%), respectively. An increase in total iterations leads to an increase in estimated AC and a decrease in SNR. The mean difference between ACrec and known AC decreased with an increasing number of total iterations (e.g., for 20 iterations (2 iterations/10 subsets) = − 14.6 kBq/ml (− 17.1%), 240 iterations (24i/10s) = − 8.0 kBq/ml (− 9.4%), p < 0.0001). In parallel, the mean SNR decreased significantly from 2i/10s to 24i/10s by 76% (p < 0.0001).ConclusionQuantitative SPECT imaging is feasible with the used reconstruction algorithm and hybrid SPECT/CT, and its consistent implementation in diagnostics may provide perspectives for quantification in routine clinical practice (e.g., assessment of bone metabolism). When combining quantitative analysis and diagnostic imaging, we recommend using two different reconstruction protocols with task-specific optimized setups (quantitative vs. qualitative reconstruction). Furthermore, individual scatter correction significantly improves both quantitative and qualitative results.

Optimization of Computed Tomography Angiography Protocols for Follow-Up Type B Aortic Dissection Patients by Using 3D Printed Model

Thoracic endovascular aortic repair (TEVAR) is a life-saving therapy for type B aortic dissection (TBAD). However, surveillance computed tomography (CT) scans in post-TEVAR patients are associated with high radiation dose, thus resulting in potential risk of radiation-induced malignancy. In this study, we developed a patient-specific three-dimensional (3D) printed phantom with stent grafts in situ, then scanned the phantom with different CT protocols to determine the optimal scanning parameters for post-treatment patients. The CT scans were conducted with different kVp and pitch values (80, 100, 120 kVp and pitch of 1.2, 1.5, 2.0, 2.5), resulting in a total of 12 datasets. Signal-to-noise ratio (SNR) was measured to determine and compare the image quality between different datasets. Results showed no significant differences in SNR between different kVp when the pitch value was 1.2. At low pitch values, a decrease in kVp from 120 to 80 led to a significant effective dose reduction by more than 20%. SNR decreased by 30% when pitch was increased from 1.2 to 2.5 at 80 kVp, and 20% at 120 kVp. In contrast, there was only a 3.9% decrease in SNR when kVp was reduced from 120 to 80 at pitch 1.2, and 15.9% at pitch 2.5. High pitch with 100 kVp can effectively reduce the dose while maintaining image quality.

RF-based low-SNR classification of UAVs using convolutional neural networks

Unmanned Aerial Vehicles (UAVs), or drones, which can be considered as a coverage extender for Internet of Everything (IoE), have drawn high attention recently. The proliferation of drones will raise privacy and security concerns in public. This paper investigates the problem of classification of drones from Radio Frequency (RF) fingerprints at the low Signal-to-Noise Ratio (SNR) regime. We use Convolutional Neural Networks (CNNs) trained with both RF time-series images and the spectrograms of 15 different off-the-shelf drone controller RF signals. When using time-series signal images, the CNN extracts features from the signal transient and envelope. As the SNR decreases, this approach fails dramatically because the information in the transient is lost in the noise, and the envelope is distorted heavily. In contrast to time-series representation of the RF signals, with spectrograms, it is possible to focus only on the desired frequency interval, i.e., 2.4 GHz ISM band, and filter out any other signal component outside of this band. These advantages provide a notable performance improvement over the time-series signals-based methods. To further increase the classification accuracy of the spectrogram-based CNN, we denoise the spectrogram images by truncating them to a limited spectral density interval. Creating a single model using spectrogram images of noisy signals and tuning the CNN model parameters, we achieve a classification accuracy varying from 92% to 100% for an SNR range from -10 dB to 30 dB, which significantly outperforms the existing approaches to our best knowledge.

Spectrum sensing based on adaptive sampling of received signal

Spectrum sensing (SS) has been heatedly discussed due to its capacity to discover the idle registered spectrum bands, which effectively alleviates the shortage of spectrum by spectrum reuse. Energy detector (ED) is widely accepted for SS as its complexity is very low. In this paper, an adaptive sampling scheme is proposed to improve the sensing performance of ED, where the sampling point of the received signal is adaptively adjusted with the environment signal-to-noise ratio (SNR). When SNR decreases, the sensing performance can be maintained and even improved by the rise of the sampling point. When SNR increases, the improved ED is considered for idle spectrum detection. The SNR is evaluated based on the joint of convolutional neural network (CNN) and long short-term memory (LSTM) network. Both theoretical derivations and simulation experiments validate the effectiveness of the proposed scheme.

Modeling GPS Signal Propagation Through Volcanic Plumes

AbstractVolcanic plumes have been detected by monitoring for a drop in the Signal to Noise Ratio (SNR) of Global Positioning System (GPS) signals at receivers close to the erupting volcano. Understanding the physical processes causing this decrease in GPS SNR during eruptions can help to both refine the plume detection technique and to better understand the structure of the observed plume. However, there are multiple physical effects that may cause signal extinction within a plume, and understanding how GPS signals interact with tephra particles is critical to explaining the loss in signal strength. To uncover which conditions could significantly affect GPS signals, a forward model for electromagnetic scattering and absorption through a plume is developed. Plume geometry as well as particle composition, density, and charge is considered and evaluated for contribution to GPS signal extinction. The simulated results are compared against GPS data taken during two eruptions: the 23 November 2013 eruption at Etna Volcano and the 23 March 2009 eruption of Redoubt Volcano.

Denoising click-evoked otoacoustic emission signals by optimal shrinkage.

Click-evoked otoacoustic emissions (CEOAEs) are clinically used as an objective way to infer whether cochlear functions are normal. However, because the sound pressure level of CEOAEs is typically much lower than the background noise, it usually takes hundreds, if not thousands, of repetitions to estimate the signal with sufficient accuracy. In this paper, we propose to improve the signal-to-noise ratio (SNR) of CEOAE signals within limited measurement time by optimal shrinkage (OS) in two different settings: covariance-based optimal shrinkage (cOS) and singular value decomposition-based optimal shrinkage (sOS). By simulation, the cOS consistently enhanced the SNR by 1-2 dB from a baseline method that is based on calculating the median. In real data, however, the cOS cannot enhance the SNR over 1 dB. The sOS achieved a SNR enhancement of 2-3 dB in simulation and demonstrated capability to enhance the SNR in real recordings. In addition, the level of enhancement increases as the baseline SNR decreases. An appealing property of OS is that it produces an estimate of all single trials. This property makes it possible to investigate CEOAE dynamics across a longer period of time when the cochlear conditions are not strictly stationary.