Significant Quality Degradation Research Articles

Analysis of Quantum Dot LEDs for Enhancing Efficiency and Stability in Visible Light Communication

From the late 19th and the early 20th, people have focused a lot on the application of radio waves ranging from 3 kHz to 300 GHz. Different frequency bands are distributed to various applications. However, the limited range of radio frequency (RF) caused the insufficiency of communication resources across various areas of daily life, including military, medical, and other sectors. Engineers have developed numerous methods for managing and efficiently utilizing radio frequencies. Using Visible light Communication (VLC) is a solution to the RF crisis. This passage mainly researches the improvements that Quantum Dot LED(QLED) can do to increase the capability and stability of VLC compared to traditional LEDs. The purpose of this research is to leverage the advantages of QLED over traditional LEDs to address issues encountered in conventional VLC, such as signal quality degradation due to interference between signals and reduced transmission efficiency caused by indoor obstacles. Through a literature review, this paper finds that the various advantages of QLED make it perfectly suited for VLC and enhance its transmission capability.

Effect of Post-harvest Ozone Fumigation on Storage of Onion

The storage of commercial onions is essential to meet off-season demands; however, it often results in significant quality degradation and post-harvest losses, including physiological loss in weight, sprouting and rotting. Thus an attempt had been made to test efficacy of ozone fumigation on physical and quality attributes of onion cv. Nasik Red. Onions were fumigated with varying concentrations of ozone in airtight LDPE (Low Density Poly Ethylene) containers for specific duration followed by 2 hours exposure to atmospheric air to dissipate residual ozone. The treated onions were stored and examined for about 3 months. Among treatments, the onions treated with a lesser ozone concentration (3637.2µl L-1 ozone per 10 minutes) demonstrated superior results including significant reduction in physiological loss in weight, sprouting, rotting, black mold incidence with the highest percentage of marketable bulbs. These findings suggest that controlled ozone fumigation, particularly at lower concentration effectively preserve the onions quality during storage, offering promising post harvest management strategy.

Optimization of Pectin Extraction from Watermelon Rind and Characterization of the Extracted Pectin

Watermelon rind, comprising approximately 30% of the fruit weight, is commercially discarded as waste. This study explored the extraction of pectin from watermelon rind using citric acid, examining the effects of acid concentration, pH, and extraction temperature on yield using a Central Composite Design approach. The optimized conditions comprising of 3% citric acid, pH 1.28, 90°C extraction temperature, and a solid-to-extractant ratio of 1:25 for 1 hour, yielded a maximum pectin of 20.09%, closely matching the experimental yield of 19.02%, with an R2 value of 99.8%. Vacuum drying at 40°C and 550 mmhg-1 was determined to be ideal for drying the precipitated pectin. The extracted pectin exhibited high purity with a moisture content of 7.90%, methoxyl content of 6.86%, anhydrouronic acid content of 71.25%, ash content of 2.05%, and a degree of esterification (DE) of 55.24%, categorizing it as moderately high methoxyl pectin. The pectin could successfully be stored in low-density polyethylene (LDPE) pouches and borosilicate glass containers under ambient and refrigerated conditions for up to six months without significant quality degradation. The process outlined in this study provides a sustainable method for valorizing watermelon rind, demonstrating its potential as a raw material for high-quality pectin production.

Robust real-time single-pixel imaging based on a spinning mask via differential detection

Single-pixel imaging (SPI) has gained considerable attention over the past two decades but is still inadequate for imaging moving objects in practical scenarios. Recently, spinning masks have been employed for real-time SPI due to their fast modulation speeds. However, the intensity fluctuations caused by ambient light can overlap with the desired modulation from the mask, leading to a significant degradation in image quality, making it unable to function properly in environments with unstable lighting. Here we propose a rotating disc encoded with cyclic S-matrices, leveraging both reflective and transmissive signals for differential detection. Simulations and experiments demonstrate interference-resistant real-time SPI, achieving 31 fps at about 60 kHz modulation rates. This approach effectively suppresses external noise and is extendable to infrared and terahertz bands, advancing SPI’s practical applications.

Enhanced control of a brain-computer interface by tetraplegic participants via neural-network-mediated feature extraction.

To infer intent, brain-computer interfaces must extract features that accurately estimate neural activity. However, the degradation of signal quality over time hinders the use of feature-engineering techniques to recover functional information. By using neural data recorded from electrode arrays implanted in the cortices of three human participants, here we show that a convolutional neural network can be used to map electrical signals to neural features by jointly optimizing feature extraction and decoding under the constraint that all the electrodes must use the same neural-network parameters. In all three participants, the neural network led to offline and online performance improvements in a cursor-control task across all metrics, outperforming the rate of threshold crossings and wavelet decomposition of the broadband neural data (among other feature-extraction techniques). We also show that the trained neural network can be used without modification for new datasets, brain areas and participants.

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.

Assessment of Organic Pollutant Index and Biodegradability Index in Determining River Pollution Load

Abstract The Kupang River, a major watershed in Batang and Pekalongan, spans 53 kilometres and has been experiencing significant water quality degradation. Notably, the river’s colour changes to black and emits foul odours, especially during the dry season. This deterioration is attributed to extensive batik industries and residential areas along its banks, leading to severe pollution. This study aims to identify the primary factors contributing to the pollution of the Kupang River from 2021 to 2024 using organic pollutant and biodegradability index. Pollution loads were calculated across five monitoring segments to determine the river’s waste disposal capacity. The findings reveal potential agricultural pollution in the upstream segment, indicated by slow-biodegradable and non-biodegradable degradation. Thus, the downstream segment shows high levels of organic pollution, with degradation ranging from moderate to very strong polluted levels. Pollution load calculations indicate that Kupang River can accommodate pollution loads for TSS up to 30294,49 kg/day, BOD up to 763,03 kg/day, and COD up to 5935,96 kg/day. However, BOD’s low pollution load capacity should be a serious concern, highlighting severe contamination issues that necessitate immediate attention and remediation.

Recovery of encoded information from conjugate superimposed perfect vortex beams in scattering environments

Using orbital angular momentum (OAM) encoding for signal transmission enables optical communication in the spatial domain. However, during the transmission process of vortex optical communication, environmental factors such as atmospheric turbulence and haze cause scattering effects, resulting in the degradation of signal quality and increasing the complexity of decoding. Our goal is to design a framework that can recover the encoded signal from the speckle field, reducing the effects of scattering. We have designed a neural network model that combines a generative adversarial network (GAN) and U-Net, which utilizes the image segmentation capability of U-Net to guide the GAN in generating accurate information. We have demonstrated its effectiveness in experiments, with Pearson correlation coefficient (PCC) and peak signal-to-noise ratio (PSNR) values of 0.99 and 46.7, respectively. Compared to other works, our work focuses on the conjugate superimposed of perfect vortex beam (PVB), offering valuable insights into the beneficial aspects of vortex optical communication in long-distance transmission, interference resistance, and enhanced data transfer performance.

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.