Significant Quality Degradation Research Articles

Design of flexible polyimide-based serpentine EMG sensor for AI-enabled fatigue detection in construction

Physical fatigue and musculoskeletal disorders are critical health issues for construction workers, stemming from repetitive motions, heavy lifting, and awkward postures. These factors compromise worker well-being, productivity, and safety while increasing the risk of accidents and long-term health problems. Recent advancements in wearable health monitoring technology offer potential solutions, but current sensors encounter significant challenges in the dynamic construction environment. These include inadequate skin contact, increased contact impedance, and vulnerability to motion artifacts all of which degrade signal quality and reduce the accuracy of fatigue detection. This paper develops a fractal-based, flexible sensor for enhanced adaptability and accurate fatigue estimation. Finite element analysis compared five space-filling designs, with the serpentine curve exhibiting the highest contact area and lowest strain, making it the preferred choice for fabrication. Evaluations demonstrated significant improvements in signal-to-noise ratio (SNR) and motion artifact reduction, with the newly developed sensor achieving a 37 % to 59 % SNR improvement over commercial electrodes across different muscle groups. The developed flexible sensor was integrated with a fatigue-detecting framework based on a vision transformer model which provided an average accuracy of 87 % for fatigue detection. The developed sensor significantly enhances EMG signal quality and reliability, promising improved health monitoring and safety for construction workers.

The Kernel Density Estimation Technique for Spatio-Temporal Distribution and Mapping of Rain Heights over South Africa: The Effects on Rain-Induced Attenuation

The devastating effects of rain-induced attenuation on communication links operating above 10 GHz during rainy events can significantly degrade signal quality, leading to interruptions in service and reduced data throughput. Understanding the spatial and seasonal distribution of rain heights is crucial for predicting these attenuation effects and for network performance optimization. This study utilized ten years of atmospheric temperature and geopotential height data at seven pressure levels (1000, 850, 700, 500, 300, 200, and 100 hPa) obtained from the Copernicus Climate Data Store (CDS) to deduce rain heights across nine stations in South Africa. The kernel density estimation (KDE) method was applied to estimate the temporal variation of rain height. A comparison of the measured and estimated rain heights shows a correlation coefficient of 0.997 with a maximum percentage difference of 5.3%. The results show that rain height ranges from a minimum of 3.5 km during winter in Cape Town to a maximum of about 5.27 km during the summer in Polokwane. The spatial variation shows a location-dependent seasonal trend, with peak rain heights prevailing at the low-latitude stations. The seasonal variability indicates that higher rain heights dominate in the regions (Polokwane, Pretoria, Nelspruit, Mahikeng) where there is frequent occurrence of rainfall during the winter season and vice versa. Contour maps of rain heights over the four seasons (autumn, spring, winter, and summer) were also developed for South Africa. The estimated seasonal rain heights show that rain-induced attenuations were grossly underestimated by the International Telecommunication Union (ITU) recommended rain heights at most of the stations during autumn, spring, and summer but fairly overestimated during winter. Durban had a peak attenuation of 15.9 dB during the summer, while Upington recorded the smallest attenuation of about 7.7 dB during winter at a 0.01% time exceedance. Future system planning and adjustments of existing infrastructure in the study stations could be improved by integrating these localized, seasonal radio propagation data in link budget design.

Deep Autoencoder for Real-Time Single-Channel EEG Cleaning and Its Smartphone Implementation Using TensorFlow Lite With Hardware/Software Acceleration.

To remove signal contamination in electroencephalogram (EEG) traces coming from ocular, motion, and muscular artifacts which degrade signal quality. To do this in real-time, with low computational overhead, on a mobile platform in a channel count independent manner to enable portable Brain-Computer Interface (BCI) applications. We propose a Deep AutoEncoder (DAE) neural network for single-channel EEG artifact removal, and implement it on a smartphone via TensorFlow Lite. Delegate based acceleration is employed to allow real-time, low computational resource operation. Artifact removal performance is quantified by comparing corrupted and ground-truth clean EEG data from public datasets for a range of artifact types. The on-phone computational resources required are also measured when processing pre-saved data. DAE cleaned EEG shows high correlations with ground-truth clean EEG, with average Correlation Coefficients of 0.96, 0.85, 0.70 and 0.79 for clean EEG reconstruction, and EOG, motion, and EMG artifact removal respectively. On-smartphone tests show the model processes a 4 s EEG window within 5 ms, substantially outperforming a comparison FastICA artifact removal algorithm. The proposed DAE model shows effectiveness in single-channel EEG artifact removal. This is the first demonstration of a low-computational resource deep learning model for mobile EEG in smartphones with hardware/software acceleration. This work enables portable BCIs which require low latency real-time artifact removal, and potentially operation with a small number of EEG channels for wearability. It makes use of the artificial intelligence accelerators found in modern smartphones to improve computational performance compared to previous artifact removal approaches.

Comparative Study on NDT Techniques for Evaluation of Concrete Quality Exposed to Marine Environment

Concrete, the primary material used in quay walls, is directly exposed to saline environments. Coping concrete, particularly in areas where periodic berthing and loading/unloading occur, is prone to rapid quality deterioration. Current facility safety and maintenance guidelines assess concrete durability at specific points through sampling, which are intended to represent the entire inspection unit. This paper explores quality management strategies from an areal perspective by applying various non-destructive scanning methods to extensive areas of coping concrete. Ultrasonic array imaging and ground-penetrating radar scanning images revealed significant quality degradation in berthing operation areas, whereas sampling-based ultrasonic pulse velocity and rebound hardness values were less effective in detecting this degradation.

Synthetic aperture image enhancement with near-coinciding Nonuniform sampling case

Multireceiver synthetic aperture sonar (SAS) uses multiple receivers to collect echoed signals, leading to nonuniform sampling in the azimuth dimension if the moving distance between adjacent pings is not half the receiver array length. The filter bank reconstruction (FBR) method, based on matrix inversion, is commonly used to address this by reconstructing uniform data from nonuniform signals. However, in practice, near-coinciding sampling can occur due to inaccuracies in the towed velocity of the SAS system, influenced by ocean conditions. The signal correlation of these near-coinciding receivers is very strong, and thus the steering vectors corresponding to these receivers are not completely independent. This further leads to an ill-conditioned system transfer function matrix made up of the receiver steering vectors, and results in inaccurate calculations of inverse matrix. Consequently, the FBR method suffers from significant signal-to-noise ratio loss or fails to reconstruct uniform signals accurately. This degradation in signal quality directly impacts the accuracy of target reconstruction, leading to errors in identifying and locating targets. This paper quantitatively defines nonuniform sampling with near-coinciding samples and discusses performance loss in various cases. To enhance imaging performance, we propose a method for reconstructing uniform signals. Simulation results demonstrate that the proposed method outperforms the conventional FBR method, providing better target reconstruction and higher efficiency.

Enhancing the recyclability of ELV plastic bumpers: Characterization of molecular, morphological, rheological, mechanical properties and ageing degradation

While the automotive industry has traditionally prioritized metal recycling, the increasing use of plastics in vehicles underlines the need for sustainable management of these materials. Recycling rates of plastics from end-of-life vehicles (ELVs) in Europe are low, but recent EU legislation requires 25 % of the plastics in new vehicles to be recycled, forcing innovative designs and strategies to enhance the recovery and quality of recycled resins. This study focuses on post-consumer bumpers, a significant recoverable component of ELVs, by assessing their molecular, morphological, rheological, and mechanical properties to investigate their homogeneity and quality from various sources to assess their suitability for recycling. Since the aging of these materials, caused by thermo-oxidative and thermo-mechanical degradation processes, can compromise the quality of recycled bumpers, we propose a blend of recycled PP from different bumper cars with virgin resin, representing a realistic scenario where the post-consumer ELV bumpers are collected. Interestingly, this blend, which can replace up to 50 % of the virgin resin without additional compatibilizers or additives, mitigates the degradation effects. Furthermore, the study evaluates the degradation resistance of these blends through multiple extrusion cycles and accelerated weathering tests in a temperature-controlled UV chamber to ascertain the number of cycles the material can tolerate without significant quality degradation, and to determine its suitability for long-term applications. Our results not only support the feasibility of using recycled PP for automotive components but also contribute to meeting the EU's recycling targets. This research highlights the potential for significant advances in the circularity of automotive plastics, providing a sustainable pathway for integrating recycled materials into new vehicle production.

Chestnut quality classification by THz Time-Domain Hyperspectral Imaging combined with unsupervised learning analysis

Chestnut crops are threatened by fungal pathogens such as Gnomoniopsis castaneae, which cause significant degradation of quality. Early detection of such infections is crucial to maintain the quality of chestnuts in the food industry. This study explores the application of Terahertz Time-Domain Hyperspectral Imaging (THz-TDHIS) combined with unsupervised learning techniques to identify fungal infections in chestnuts. Unlike conventional methods that rely on light attenuation, this approach leverages the unique spectral signatures of infected tissues. By employing Principal Component Analysis, K-Means Clustering, and Agglomerative Clustering, we effectively differentiate between healthy and infected portions of chestnuts. Our findings indicate that spectral features, rather than just intensity variations, provide more reliable markers for infection. In addition, we demonstrate that these methods enable the quantification of the degree of infection in chestnuts. The robustness of these unsupervised learning methods in handling large and heterogeneous data sets further underscores their potential in agricultural applications. This integrated THz-TDHIS and machine learning approach presents a promising solution to ensure chestnut quality and safety.

Survey on Optical Wireless Communication with Intelligent Reflecting Surfaces

Optical Wireless Communication (OWC) technology has gained significant attention in recent years due to its potential for providing high-data-rate wireless connections through the large license-free bandwidth available. A key challenge in OWC systems, similar to high-frequency Radiofrequency (RF) systems, is the presence of dead zones caused by obstacles like buildings, trees, and moving individuals, which can degrade signal quality or disrupt data transmission. Traditionally, relays have been used to mitigate these issues. Intelligent Reflecting Surfaces (IRSs) have recently emerged as a promising solution, enhancing system performance and flexibility by providing reconfigurable communication channels. This paper presents an overview of the application of IRSs in OWC systems. Specifically, we categorize IRSs into two main types: mirror array-based IRSs and metasurface-based IRSs. Furthermore, we delve into modeling approaches of mirror array-based IRSs in OWC and analyze recent advances in IRS control, which are classified into system power or gain optimization-oriented, system link reliability optimization-oriented, system data rate optimization-oriented, system security optimization-oriented, and system energy optimization-oriented approaches. Moreover, we present the principles of metasurface-based IRSs from a physical mechanism perspective, highlighting their application in OWC systems through the distinct roles of light signal refraction and reflection. Finally, we discuss the key challenges and potential future directions for integrating IRS with OWC systems, providing insights for further research in this promising field.

Monitoring and Analysis of Eco-Environmental Quality in Daihai Lake Basin from 1985 to 2022 Based on the Remote Sensing Ecological Index

Exploring eco-environmental quality dynamics in the Daihai Lake Basin has significant implications for the conservation of ecological environments in the semi-arid and arid regions of northern China. Based on the Google Earth Engine (GEE) platform, the remote sensing ecological index (RSEI) was constructed by coupling Landsat SR remote sensing data from 1985 to 2022. The spatial significance of the RSEI was analyzed using linear regression equations and an F-test. The spatial correlation, distribution characteristics, and driving factors behind the RSEI were explored using Moran’s index and a geodetector. The results indicated that (1) the RSEI was appropriate for evaluating eco-environmental quality in the Daihai Lake Basin. (2) From 1985 to 2022, the eco-environmental quality of the Daihai Lake Basin exhibited a positive trend but remained subpar. (3) A positive spatial autocorrelation was demonstrated for eco-environmental quality with increasing spatial aggregation. (4) Significant eco-environmental quality degradation (slope < 0) occurred primarily in Sanyiquan Town in the northeastern region of the basin and in Tiancheng Township in the southeastern region. Conversely, a notable improvement (slope > 0) was predominantly observed in Yongxing and Liusumu in southwestern Daihai. (5) The improvement in the ecological environment of the Daihai Lake Basin was primarily attributed to an increase in NDVI and WET and a decrease in NDBSI and LST. The interaction between NDVI and LST had the greatest explanatory power for the ecological environment. Among the external driving factors, DEM (elevation) was the dominant factor in the RSEI and had the strongest explanatory power. The interaction between DEM and LST was the most significant, and the driving factors were enhanced. This study provided a theoretical basis for the sustainable development of the Daihai Lake Basin, which is crucial for the local ecological environment and economic development.

Refined matrix completion for spectrum estimation of heart rate variability.

Heart rate variability (HRV) is an important metric in cardiovascular health monitoring. Spectral analysis of HRV provides essential insights into the functioning of the cardiac autonomic nervous system. However, data artefacts could degrade signal quality, potentially leading to unreliable assessments of cardiac activities. In this study, we introduced a novel approach for estimating uncertainties in HRV spectrum based on matrix completion. The proposed method utilises the low-rank characteristic of HRV spectrum matrix to efficiently estimate data uncertainties. In addition, we developed a refined matrix completion technique to enhance the estimation accuracy and computational cost. Benchmarking on five public datasets, our model shows effectiveness and reliability in estimating uncertainties in HRV spectrum, and has superior performance against five deep learning models. The results underscore the potential of our developed matrix completion-based statistical machine learning model in providing reliable HRV spectrum uncertainty estimation.

Adversarial EM for variational deep learning: Application to semi-supervised image quality enhancement in low-dose PET and low-dose CT

In positron emission tomography (PET) and X-ray computed tomography (CT), reducing radiation dose can cause significant degradation in image quality. For image quality enhancement in low-dose PET and CT, we propose a novel theoretical adversarial and variational deep neural network (DNN) framework relying on expectation maximization (EM) based learning, termed adversarial EM (AdvEM). AdvEM proposes an encoder–decoder architecture with a multiscale latent space, and generalized-Gaussian models enabling datum-specific robust statistical modeling in latent space and image space. The model robustness is further enhanced by including adversarial learning in the training protocol. Unlike typical variational-DNN learning, AdvEM proposes latent-space sampling from the posterior distribution, and uses a Metropolis–Hastings scheme. Unlike existing schemes for PET or CT image enhancement which train using pairs of low-dose images with their corresponding normal-dose versions, we propose a semi-supervised AdvEM (ssAdvEM) framework that enables learning using a small number of normal-dose images. AdvEM and ssAdvEM enable per-pixel uncertainty estimates for their outputs. Empirical analyses on real-world PET and CT data involving many baselines, out-of-distribution data, and ablation studies show the benefits of the proposed framework.

A novel heterogeneous sixteen-core four-mode fiber with two types of refractive index profiles

To this day, the inter-core crosstalk and intra-core crosstalk are potential factors limiting optical fiber transmission over long distances and serious degradation of signal quality in communication systems of multi-core few-mode fibers. To this end, this work proposes a heterogeneous sixteen-core four-mode fiber with two types of refractive index profiles is proposed in this paper, which consists of a graded-index core and a step-index core. After optimization, the four modes can transmit independently and steadily at 1550 nm, and the effective refractive index difference between adjacent modes is more than 1 × 10−3. Under the optimal fiber parameters, the inter-core crosstalk results can be obtained from the research results and are less than −69 dB/km, which reflects the significant effect of the proposed fiber in reducing the inter-core crosstalk. The proposed fiber is the first to use the combination of step-index core and graded-index core so that the inter-core crosstalk of heterogeneous fibers is successfully suppressed at a low level, while providing new ideas and solutions for future research on novel low-crosstalk multi-core few-mode fibers that can be applied to terrestrial and undersea communication systems.

Experimental results of multiplexing transmission of 256-QAM 5G NR signal with control signal by SCM over 20-km analog RoF link

Analog radio over fiber is attracting interest as a low-cost solution for high-frequency band distributed antenna systems (DAS). DAS demands the beamforming, TDD timing switching, and CLK synchronization of each distributed antenna section (or remote radio unit (RRU)), but as the number of optical carriers used for RRU control increases, the result is an undesirable increase in RRU complexity. In this paper, we propose and demonstrate a wireless system that can simultaneously transmit, via a single optical carrier, an analog main signal and a control signal (beamforming, TDD, and CLK signals) by subcarrier multiplexing (SCM). Simulations show that the signal quality degradation caused by nonlinearities in the optical modulator during SCM transmission can be avoided by properly designing the input power balance between the analog main signal and the control signal. We also conducted experiments on the downlink transmission of 256 QAM-modulated 5G NR signals by connecting a CS-RRU pair with a 20-km SMF. According to our OTA evaluation using a millimeter-wave band antenna, the EVM value was about 3.41%, which satisfies the 3GPP specification. In addition, control signals were found to be demodulated successfully.

The Deep-Match Framework: R-Peak Detection in Ear-ECG.

The Ear-ECG provides a continuous Lead I like electrocardiogram (ECG) by measuring the potential difference related to heart activity by electrodes which are embedded within earphones. However, the significant increase in wearability and comfort enabled by Ear-ECG is often accompanied by a degradation in signal quality - an obstacle that is shared by the majority of wearable technologies. We aim to resolve this issue by introducing a Deep Matched Filter (Deep-MF) for the highly accurate detection of R-peaks in wearable ECG, thus enhancing the utility of Ear-ECG in real-world scenarios. The Deep-MF consists of an encoder stage, partially initialised with an ECG template, and an R-peak classifier stage. Through its operation as a Matched Filter, the encoder searches for matches with an ECG template in the input signal, prior to filtering these matches with the subsequent convolutional layers and selecting peaks corresponding to the ground-truth ECG. The latent representation of R-peak information is then fed into a R-peak classifier, of which the output provides precise R-peak locations. The proposed Deep Matched Filter is evaluated using leave-one-subject-out cross-validation over 36 subjects with an age range of 18-75, with the Deep-MF outperforming existing algorithms for R-peak detection in noisy ECG. The Deep-MF achieves a median R-peak recall of 94.9% and a median precision of 91.2% across subjects when evaluated with leave-one-subject-out cross validation. Overall, this Deep-Match framework serves as a valuable step forward for the real-world functionality of Ear-ECG and, through its interpretable operation, the acceptance of deep learning models in e-Health.

Rejuvenating silicon probes for acute neurophysiology.

Neurophysiological recording with a new probe often yields better signal quality than with a used probe. Why does the signal quality degrade after only a few experiments? Here, we considered silicon probes in which the contacts are densely packed, and each contact is coated with a conductive polymer that increases its surface area. We tested 12 Cambridge Neurotech silicon probes during 61 recording sessions from the brain of three marmosets. Out of the box, each probe arrived with an electrodeposited polymer coating on 64 gold contacts and an impedance of around 50 kΩ. With repeated use, the impedance increased and there was a corresponding decrease in the number of well-isolated neurons. Imaging of the probes suggested that the reduction in signal quality was due to a gradual loss of the polymer coating. To rejuvenate the probes, we first stripped the contacts, completely removing their polymer coating, and then recoated them in a solution of 10 mM 3,4-Ethylenedioxythiophene (EDOT) monomer with 11 mM Poly(sodium 4-styrenesulfonate) (PSS) using a current density of about 3 mA/cm2 for 30 s. This recoating process not only returned probe impedance to around 50 kΩ but also yielded significantly improved signal quality during neurophysiological recordings. Thus, insertion into the brain promoted the loss of the polymer that coated the contacts of the silicon probes. This led to degradation of signal quality, but recoating rejuvenated the probes.NEW & NOTEWORTHY With repeated use, a silicon probe's ability to isolate neurons degrades. As a result, the probe is often discarded after only a handful of uses. Here, we demonstrate a major source of this problem and then produce a solution to rejuvenate the probes.

From Fields to Families: Understanding the Health Impacts of Excessive Soil Fertilization

Background: Fertilizers play a crucial role in modern agriculture, significantly boosting crop yields and global food production. However, their widespread use has unintended consequences that raise significant environmental and health concerns. Nitrogen-based, phosphorus-based, and potassium-based fertilizers contribute to nutrient replenishment in soils but can lead to soil and water contamination, posing risks to ecosystems and human health. Objective: This narrative review aims to comprehensively examine the health and environmental impacts of excessive soil fertilization, with a focus on understanding the mechanisms by which fertilizers affect soil and water quality and the subsequent implications for human health. Methods: An extensive literature search was conducted across multiple electronic databases, including PubMed, Web of Science, Scopus, and Google Scholar. Articles published between 2000 and 2023 were selected based on predefined inclusion criteria, focusing on studies that provided empirical data or substantial theoretical insights into the environmental and health impacts of fertilizer use. A qualitative analysis was performed to synthesize findings and identify common themes. Results: The review highlighted that nutrient runoff from excessive fertilizer use leads to significant water quality degradation, with nitrate concentrations in some regions exceeding recommended safety limits by 30-50%. Prolonged exposure to contaminated water is linked to various health issues, including a 20% increased risk of methemoglobinemia in infants and a 15-25% higher risk of gastric and colorectal cancers in adults. Additionally, heavy metal accumulation in crops due to fertilization practices raises concerns, with cadmium levels in some crops surpassing safe consumption limits by 40-60%. Conclusion: While fertilizers are essential for enhancing agricultural productivity, their excessive use poses significant environmental and health risks. A balanced approach integrating sustainable agricultural practices, effective water management, and comprehensive regulatory frameworks is imperative to mitigate these adverse effects. Continued research and collaboration among scientists, policymakers, and the agricultural community are necessary to promote a sustainable and health-conscious approach to fertilizer use.

Simultaneous whole-head electrophysiological recordings using EEG and OPM-MEG

Abstract Electroencephalography (EEG) and magnetoencephalography (MEG) non-invasively measure human brain electrophysiology. They differ in nature; MEG offers better performance while EEG (a wearable platform) is more practical. They are also complementary, with studies showing that concurrent MEG/EEG provides advantages over either modality alone, and consequently clinical guidelines for MEG in epilepsy recommend simultaneous acquisition of MEG and EEG. In recent years, new instrumentation—the optically pumped magnetometer (OPM)—has had a significant impact on MEG, offering improved performance, lifespan compliance, and wearable MEG systems. Nevertheless, the ability to carry out simultaneous EEG/OPM-MEG remains critical. Here, we investigated whether simultaneous, wearable, whole-head EEG and OPM-MEG measurably degrades signal quality in either modality. We employed two tasks: a motor task known to modulate beta oscillations, and an eyes-open/closed task known to modulate alpha oscillations. In both, we characterised the performance of EEG alone, OPM-MEG alone, and concurrent EEG/OPM-MEG. Results show that the signal to noise ratio (SNR) of the beta response was similar, regardless of whether modalities were used individually or concurrently. Likewise, our alpha band recordings demonstrated that signal contrast was stable, regardless of the concurrent recording. We also demonstrate significant advantages of OPM-MEG; specifically, the OPM-MEG signal is less correlated across channels and less susceptible to interference from non-brain sources. Our results suggest that there are no barriers to simultaneous wearable EEG/OPM-MEG, and consequently this technique is ripe for neuroscientific and clinical adoption. This will be important in the clinic where simultaneous EEG and OPM-MEG recordings will facilitate better interpretation of OPM-MEG data in patients.

Research on the grouped orthonormalization method in ghost imaging

Ghost imaging (GI) has found application across diverse fields owing to its distinctive benefits. When employing the rotating ground-glass scheme and utilizing second-order correlation for image reconstruction, the efficiency of imaging is hindered by the multiple sampling of reference patterns. To address this, the orthonormalization method has been employed to enhance image quality and reduce the required number of measurements. Despite its effectiveness, the original orthonormalization method is prone to accumulating imaging noise and errors as the number of measurements increases, leading to a significant degradation in image quality. To overcome this limitation, this paper introduces the grouped orthonormalization method (GO-GI) as an extension of the orthonormalization technique. By adjusting the ‘group size’, this method enables control over the accumulation of errors, resulting in an improvement in image quality. The evaluation of image quality in terms of Contrast-to-Noise demonstrates the significant advantages of the GO-GI method in both simulation and experimental results. This study establishes the GO-GI method as a simple yet practical approach in the realm of GI.

Solar Image Quality Assessment: A Proof of Concept Using Variance of Laplacian Method and its Application to Optical Atmospheric Condition Monitoring

Here we present a proof of concept for the application of the Variance of Laplacian (VL) method in quantifying the sharpness of optical solar images. We conducted a comprehensive study using over 65,000 individual solar images acquired on more than 160 days. Each image underwent processing using a VL image processing algorithm, which assigns a “score” based on the sharpness of the solar disk’s edges. We studied the scores obtained from images acquired at different conditions. Our findings demonstrate that the sharpness of the images exhibits daily trends that are closely linked to the altitude of the Sun at the observation site. We observed a significant degradation in image quality only below a certain altitude threshold. Furthermore, we compared airmass formulae from the literature with our sharpness observations and concluded that the degradation could be modeled as an Image Sharpness Function (ISF), which exhibits similarities to airmass variations. In addition to assessing image quality, our method has the potential to evaluate the optical atmospheric conditions during daytime observations. Moreover, this technique can be easily and cost-effectively applied to archival or real-time images of other celestial bodies, such as the Moon, bright planets and defocused stars. Given that ISF is unique to each location and sensitive to sky conditions, the development of an ISF is not only beneficial for routine observation preparation but also essential for long-term site monitoring.

A Faithful Deep Sensitivity Estimation for Accelerated Magnetic Resonance Imaging.

Magnetic resonance imaging (MRI) is an essential diagnostic tool that suffers from prolonged scan time. To alleviate this limitation, advanced fast MRI technology attracts extensive research interests. Recent deep learning has shown its great potential in improving image quality and reconstruction speed. Faithful coil sensitivity estimation is vital for MRI reconstruction. However, most deep learning methods still rely on pre-estimated sensitivity maps and ignore their inaccuracy, resulting in the significant quality degradation of reconstructed images. In this work, we propose a Joint Deep Sensitivity estimation and Image reconstruction network, called JDSI. During the image artifacts removal, it gradually provides more faithful sensitivity maps with high-frequency information, leading to improved image reconstructions. To understand the behavior of the network, the mutual promotion of sensitivity estimation and image reconstruction is revealed through the visualization of network intermediate results. Results on in vivo datasets and radiologist reader study demonstrate that, for both calibration-based and calibrationless reconstruction, the proposed JDSI achieves the state-of-the-art performance visually and quantitatively, especially when the acceleration factor is high. Additionally, JDSI owns nice robustness to patients and autocalibration signals.