Gray Filter Research Articles

Application of Gray Wolf Particle Filter Algorithm Based on Golden Section in Wireless Sensor Network Mobile Target Tracking

In order to address the issue of low tracking accuracy caused by particle depletion in the particle filter, a mobile target tracking algorithm tailored for wireless sensor networks (WSNs) is presented. This algorithm, based on the golden-section gray wolf particle filter (PF), represents a novel approach to target tracking. The algorithm’s originality lies in its ability to guide the particle swarm toward regions of higher weights, thereby striking a balance between global and local exploration capabilities. This not only alleviates issues related to sample depletion and local extrema but also enhances the diversity of the particle swarm, significantly improving tracking performance. To assess the effectiveness of the proposed algorithm, a series of simulation experiments were conducted, comparing it with the extended Kalman filter (EKF) and the standard PF algorithm. The experiments employed a constant velocity circular motion model (CM) for filtering and tracking. The root mean square error metric demonstrated a significant reduction in error of 57% and 37% in comparison to the extended Kalman filter (EKF) and the particle filter (PF), respectively. This serves to illustrate the superiority of our method in enhancing tracking accuracy.

Vision-related quality of life, photoaversion, and optical rehabilitation in achromatopsia.

We report on photoaversion and patient-reported quality of life in Danish patients with achromatopsia and evaluate the best optical rehabilitation. Our results contribute to the evaluation of outcome measures in therapy trials and aid in providing the best optical rehabilitation for patients with this and clinically similar conditions. This study aimed to investigate the vision-related quality of life, the impact of photoaversion on daily living, and the best optical rehabilitation in a cohort of achromatopsia patients, including testing the hypothesis that red light-attenuating filters are generally preferred. Patients with genetically verified achromatopsia were recruited. Investigations included the 25-item Visual Function Questionnaire and supplementary questions regarding photoaversion and visual aids. Patients were evaluated by a low vision optometrist and given the choice between different light-attenuating filters. First, two specially designed red and gray filters both transmitting 6% light, and then a pre-defined broader selection of filters. Best-corrected visual acuity and contrast sensitivity were measured without filters and with the two trial filters. Twenty-seven patients participated. Median 25-item Visual Function Questionnaire composite score was 73, with the lowest median score in the subscale near vision (58) and the highest in ocular pain (100). The majority of patients (88%) reported that light caused them discomfort, and 92% used aid(s) to reduce light. Ninety-six percent (26 of 27) preferred the gray filter to the red indoors; 74% (20 of 27) preferred the gray filter. Contrast sensitivity was significantly better with the gray filter compared with no filter (p=0.003) and the red filter (p=0.002). Our cohort has a relatively high vision-related quality of life compared with other inherited retinal diseases, but photoaversion has a large impact on visual function. Despite what could be expected from a theoretical point of view, red filters are not generally preferred.

Evaluating Implementation Outcomes of a Home Garden Intervention in Rural Guatemala Using the RE-AIM Framework

ObjectivesTo identify implementation barriers and facilitators for a home gardening intervention in rural Guatemala. MethodsHouseholds were eligible if they had a child 6–24 months of age with stunting. Households received seeds and seedlings for 16 crops, garden construction materials, agronomist-delivered education and technical assistance, and a standard-of-care nutrition program for 6 months. A mixed-methods implementation study was conducted from January 2019 to July 2020 using the RE-AIM (Reach, Effectiveness, Adoption, Implementation, Maintenance) framework. Data were collected from program records, staff observations, household surveys (n = 70), and interviews or focus groups with staff (n = 4), participants (n = 6) and community stakeholders (n = 3). Descriptive statistics were calculated for quantitative outcomes and qualitative data were organized into themes. ResultsReach: Seventy of 78 (90%) eligible households participated. Many households in the community expressed interested in participating but did not have a young child with stunting. Effectiveness: Participants and stakeholders felt the intervention increased access to diverse foods. Over the six-month period, households cultivated, on average, an additional 5 species of crops (95% CI: 4–6) overall and 2 species (95% CI: 1–3) that they self-reported consuming. Adoption: Concerns about water use for garden irrigation were a barrier, with only 1 of 4 candidate communities authorizing the project. Implementation: There was good adoption of agricultural best practices (e.g., fencing, staggered planting, adding organic material, saving seeds, intercropping). Gray water filters (facilitating use of recycled water for garden irrigation) and flexible implementation promoted participation. Maintenance: At 6 months, crops failure rates were low, with only 3 of the 16 crops having failure rates above 25%. Costs for garden materials and program delivery were ∼773 USD per household. There were concerns about seed access outside of the project. ConclusionsInterest and engagement with a home garden intervention in rural Guatemala were high. Future projects should plan to address gaps between garden production and consumption, water use, and seed sourcing. Funding SourcesAcademy of Nutrition and Dietetics Foundation.

A cascaded algorithm based on GMM, GKF, and data fusion for mobile tracking in wireless sensor network

SummaryWireless sensor network (WSN) is formed with numerous communication nodes, which plays an important role in the Internet of Things (IoT). Location‐based service is critically important for WSN; however, the nonline of sight (NLOS) condition can deteriorate the positioning precision significantly. In this paper, a robust localization algorithm based on the range measurements to deal with the mixed line of sight (LOS)/NLOS environment is proposed. Firstly, a hypothesis testing based on the data deviation is used to detect the transmission condition. For the identified LOS propagation, the measured data are processed by Gaussian mixture model (GMM) to calculate the mean and standard deviation, which can update the likelihood probability and residual by data fusion. Moreover, for the identified NLOS propagation data, the mean of every Gaussian component is processed by gray Kalman filter (GKF) to discard large outliers. Finally, maximum likelihood (ML) is adopted to derive final coordinates. The simulation results have shown that the proposed algorithm has great advantages in dealing with severe NLOS interference and has higher accuracy compared with existing classic algorithms.

Illumination uniformity correction by using dynamic gray filters in a lithography system.

Illumination-integrated nonuniformity (IINU) is a key factor in determining resolution and critical dimension uniformity, which are important performance parameters in advanced lithography systems. To further reduce the IINU, a uniformity correction technology was adopted. In this paper, a low-cost and simple-structure approach for uniformity correction with higher flexibility and better correction capability is proposed. The method is composed of two dynamic gray filters with a specific transmittance distribution, which can form different correction curves by controlling the displacement of the gray filters. The frequency limitation of the defocus uniformity correction system is analyzed. A uniformity correction system design method based on the particle swarm optimization algorithm is introduced. Based on the proposed method, a dynamic gray filter uniformity correction system is applied to an illumination optical system. The experimental results show that the value of the corrected IINU reaches less than 0.7%, which satisfies the IINU requirements of advanced lithography systems. This verifies the higher flexibility and better correction capability of the proposed method.

Mixed-Methods Implementation Study of a Home Garden Intervention in Rural Guatemala Using the RE-AIM Framework

BackgroundHome gardening is a strategy to improve nutrition and food security. More information is needed about optimizing gardens in different contexts. ObjectiveThe aim was to identify implementation barriers and facilitators for a home gardening intervention in rural Guatemala and inform future larger-scale interventions in the region. DesignA mixed-methods implementation study using the RE-AIM (Reach, Effectiveness, Adoption, Implementation, Maintenance) framework was conducted from January 2019 to July 2020. Participants/settingFamilies (n = 70) in rural Guatemala participated in the intervention. Staff (n = 4), families (n = 6), and community stakeholders (n = 3) participated in interviews or focus groups. InterventionParticipating households received seeds and seedlings for 16 crops, garden construction materials, agronomist-delivered education and assistance, and a standard-of-care nutrition program. Main outcome measuresImplementation data were collected from program records and observations, participant surveys, and interviews and focus groups. Crop count and nutritional functional diversity of home gardens were assessed. Statistical analyses performedDescriptive statistics were calculated for quantitative outcomes. Qualitative data were double-coded and organized into overarching themes. ResultsReach: Ninety percent of eligible households participated. Child nutritional eligibility criteria was a barrier to reach. Effectiveness: Participants and stakeholders felt the intervention improved access to diverse foods. Cultivated crops increased an average of five species (95% confidence interval [CI], 4–6) at 6 months, although not all were consumed. Adoption: The main community adoption barrier was water sourcing for garden irrigation. Implementation: Raised beds were the most common gardening method, with good adoption of agricultural best practices. Gray water filters and flexible implementation were important for participation. Maintenance: Crops failure rates were low. Seed availability was a sustainability challenge. Direct costs were 763 USD per household. ConclusionsInterest and engagement with a home garden intervention in Guatemala were high. Gaps between garden production and consumption, access to water, and seed sourcing should be addressed in future work.

Segmental Degradation RUL Prediction of IGBT Based on Combinatorial Prediction Algorithms

Aiming at the segmentation nonlinear degradation characteristics of IGBT, the traditional single remaining useful lifetime (RUL) method has low accuracy. This paper proposes a method combining gray prediction and particle filter algorithm. The gray prediction model is used for slow degradation trends prediction in the early stage. When the health precusor parameters reach the fault warning line, the improved particle filter algorithm is used for this stage’s prediction with the characteristics of fast nonlinear degradation. The comparison analysis result shows that the combinatorial prediction algorithms used in this paper can be better for tracking the degradation trends of IGBT, and the prediction accuracy is higher than either of the two single prediction methods.

Improved gray wolf particle filtering and high‐fidelity second‐order autoregressive equivalent modeling for intelligent state of charge prediction of lithium‐ion batteries

The rapid development of new energy vehicles puts forward higher requirements for the lithium-ion batteries model construction, high-efficiency condition monitoring and collaborative estimation. An improved high-fidelity second-order autoregressive model is proposed and constructed, and the autoregressive model is integrated with the second-order equivalent circuit model, which can achieve an accurate and reliable description of the batteries internal dynamic change process. To achieve the accurate expression of the battery's external characteristics and internal state, the forgetting factor is combined with the recursive least square algorithm to improve the parameter identification accuracy and optimality while reducing the space complexity of the algorithm. A novel gray wolf particle filtering algorithm is proposed, which eliminates the particles severe degradation in traditional algorithms and enhances the ability of particles to resist degradation. The algorithm superiority and generalization are verified under complex working conditions. The experimental results show that the accuracy of the high-fidelity second-order autoregressive model can reach 99%, which can well simulate the complex chemical reaction process inside the lithium-ion battery. Experimental simulation is performed under constant current conditions. Compared with the extended Kalman filter, unscented Kalman filter, and particle filter algorithms, the gray wolf particle filter algorithm has reduced the root mean square error by 3.39%, 0.90%, and 2.84%, and the mean absolute error has reduced by 1.95%, 0.51%, and 2.22%. Under dynamic stress test conditions, the root mean square error is reduced by 1.54%, 0.33%, and 0.78%, and the average absolute error is reduced by 1.4%, 0.22%, and 0.76%. In addition, when tested under different environmental conditions, although the improved algorithm has a relatively long running time, the estimation accuracy of the algorithm is greatly improved and the execution efficiency is high. The improved algorithm provides a theoretical basis for the reliability and stability of the onboard operation of lithium-ion batteries. Highlights A novel lithium-ion battery high-fidelity second-order autoregressive equivalent model is proposed. The gray wolf particle filter is employed to improve the observation accuracy. A recursive calculation method is derived by the time-based parameters.

New image reconstruction algorithm for CCERT: LBP + Gaussian mixture model (GMM) clustering

This work focuses on the study of the image reconstruction algorithm of capacitively coupled electrical resistance tomography (CCERT). With the combination of a linear back projection (LBP) algorithm and an unsupervised Gaussian mixture model (GMM) algorithm, a new image reconstruction algorithm for CCERT is proposed. The LBP algorithm is used to implement the initial image reconstruction. The GMM algorithm is adopted to acquire the gray level threshold which will be used for the establishment of the gray level threshold filter. The final reconstructed image can be obtained with the thresholding operation. With a developed 12-electrode CCERT prototype system, the new image reconstruction algorithm is tested in image reconstruction experiments. The experimental results show that the proposed new image reconstruction algorithm is effective. The image reconstruction results are satisfactory. Compared with the conventional image reconstruction algorithms, the new image reconstruction algorithm (LBP + GMM) can obtain better reconstructed images with smaller relative image errors. It can obtain the reconstructed images with fewer empirical preset parameters and less manual intervention. In addition, with the introduction of the GMM algorithm, a relatively more suitable and reasonable gray level threshold can be obtained because the GMM algorithm implements the clustering process by utilizing both mean and variance information on the gray level distribution. Thus, better image reconstruction results can be obtained.

In quest of the optimal coincidence resolving time in TDCR LSC

This paper presents studies of the influence of the coincidence resolving time on the activity calculated by the Triple-to-Double Coincidences Ratio (TDCR) method in Liquid Scintillation (LS) counting. Recently, published methods for the correction for accidental coincidences in TDCR counting open the possibility to use resolving times up to several μs, long enough not to miss true coincidences and to study the effects of delayed fluorescence.3H, 14C, 55Fe and 63Ni LS-sources in UltimaGold (UG), UG LLT and Toluene+PPO cocktails were measured using a TDCR counter connected to a digitizer working in list-mode. The necessary resolving time to include 99.9% of the logical sum of double (D) coincidences was found to be 1.2 μs for 3H, 1 μs for 55Fe and 500 ns for 63Ni in UG. The activity of all LS-sources was calculated using the TDCR method for resolving times from 10 ns to 2 μs and a significant dependence between the calculated activity and resolving time was observed. A dedicated Monte Carlo (MC) code was used to simulate list-mode data from TDCR measurements. The simulation results suggest that the 3H activity calculated by the TDCR method is overestimated regardless of the used resolving time if delayed fluorescence is present which is not described by the used ionization quenching function.Efficiency variation measurements of 3H in UG LLT show a strong dependence of the optimal kB parameter on the used resolving time: 85 μm/MeV at 40 ns and 110 μm/MeV at 200 ns, leading to 2.5% difference in calculated activity. In the framework of this study the efficiency variation methods by chemical quenching and gray filters were compared and a difference of 60 μm/MeV between the two was observed. The results from this article demonstrate that regardless of the available corrections for accidental coincidences, it is not advisable to increase the resolving time beyond what is necessary to register all prompt fluorescence events. Moreover, even for short coincidence resolving times, delayed fluorescence could have a significant influence on the activities calculated by the TDCR method.

Effect of a photoselective filter on the yield and postharvest quality of 'Viroflay' baby spinach (Spinacia oleracea L.) leaves cultivated in a hydroponic system

Consumers are demanding foods with high sensory and nutritional quality including a higher content of compounds that help maintain good health. Given this scenario, new technologies are emerging to produce fresh salads that have the characteristics currently in demand. Shade nettings or photoselective filters on fruit and vegetable crops are a technology that provides protection, modifies the light spectrum resulting in a higher accumulation of antioxidant compounds, making them more desirable to consumers. The objective of this study was to compare the effect of shade netting on the fresh quality parameters of baby leaf spinach grown in a hydroponic system at harvest and after a period of 10 days at 4 °C. By using different shade netting colors, the average photosynthetic photon flux density (PPFD) that reaches the plants was modified during the growing period of baby leaf spinach (red: 118.35 μmol m−2 s−1; blue:117.96 μmol m−2 s−1; gray: 63.18 μmol m−2 s−1 and control without shade nettings: 278.12 μmol m−2 s−1). At harvest, baby leaf spinach grown under the red shade netting reached the highest yield. However, with the blue filter, leaves showed a value of 9.3 % dry weight, significantly higher than the values from red and gray filters with values of 7.3 and 6.3 %, respectively. The phenolic compound contents reached significantly higher values of 485.5 mg gallic acid equivalent (GAE) per 100 g−1 FW for the baby leaf spinach grown under the red filter compared to the blue, gray and control filters with values of 472.5; 387.6 and 316.5 mg GAE per 100 g−1 FW, respectively. The antioxidant capacity was significantly higher under the red filter. The sensory quality parameters indicated that spinach grown under color filters did not show off-flavors and maintained its turgidity and appearance at harvest. In the postharvest period, baby leaf spinach grown under red filter maintained the highest total phenol content and antioxidant activity after 10 days at 4 °C with similar appearance and turgidity as the control.

Composite Method for Image Retrieval

In the present day to day life the retrieval of images from the larger database has become important like Google search. Presently, CBIR is trending method. In this paper images are retrieved in three stages with color, texture and shape feature extracted in each respective stage. The color feature extraction is done through finding different parameters for an RGB image, combined texture feature extraction through Gray level co-occurrence matrix GLCM and Gabor filter and shape feature extraction through region growing. The experiment is done on images present in coral database. The simulation results have been compared and it has been shown that the proposed method show high retrieval rate in terms of the average precision and average recall.

Dynamic mainlobe interference suppression method based on monopulse with gray model Kalman filter

The architecture and processing algorithm of mainlobe interference suppression method is described for nulling the signal from mainlobe electronic jammer and multiple sidelobe electronic jammers while maintaining monopulse angle estimation accuracy on the target. The existing mainlobe interference suppression method is based on monopulse Wiener filtering. However, the Wiener filtering is more suitable to remove static interference. Therefore, the authors present the dynamic mainlobe interference suppression method with Gray Model Kalman filter to resolve this problem. The simulations verify the proposed method.

Study on image reconstruction of capacitively coupled electrical impedance tomography (CCEIT)

The aim of this work is to study the image reconstruction of capacitively coupled electrical impedance tomography (CCEIT) by using total impedance of a gas–liquid two-phase flow. The results of image reconstruction that utilise the real part, the imaginary part, the amplitude of the impedance, and the image reconstruction results by making use of different parts of the total impedance are investigated and discussed. By combining a linear back projection (LBP) algorithm and a K-means clustering algorithm, a new image reconstruction algorithm for CCEIT is proposed. The LBP is used to obtain the original reconstructed image. The K-means is adopted to automatically obtain the gray level threshold. The final reconstructed image is acquired with an established gray level threshold filter. With the new algorithm (LBP + K-means), the images reconstructed by using different parts of the impedance are obtained. To make full use of total impedance information, an image fusion method based on the weighted combination method is presented. With a developed 12-electrode CCEIT prototype, the image reconstruction experiments are conducted. Experimental results show that all three parts of the impedance can reflect the phase distribution characteristics of the gas–liquid two-phase flow from different aspects. Image fusion results indicate that making full use of the different parts of the total impedance of the gas–liquid two-phase flow can effectively improve the quality of the image reconstruction. The experimental results also verify the feasibility and the effectiveness of the proposed new image reconstruction algorithm. Compared with conventional algorithms, the proposed image reconstruction method can obtain comparable/better image reconstruction results with less prior knowledge. The influence of manual intervention on the image reconstruction results can be effectively avoided.

Diseño e implementación de un sistema de visión por computadora para el reconocimiento de objetos en movimiento

In this work we developed a computer vision system that allow the geometric figures recognition in real time using a web camera, also the identification of the objects size using a known object. To make this possible we used several techniques of the digital image processing as gray scale filters because that’s the first step to start the image processing, threshold to make the image transformation in binary form where 1 is the white color and 0 the black color, edge detection to detect the edges of the figures therefore an important parameter is the noise cancelation of the obtained signals, that’s the reason of the using of digital filters because those allow the oscillation cancelation in the signals. The possible application can be the microorganism detection in controlled environments accord of its morphology also the change of its area in real time.

Convolutional sparse coding for demosaicking with panchromatic pixels

Image demosaicking is the problem of reconstructing color images from raw images captured by a digital camera covered by a Color Filter Array (CFA). CFAs allow only part of the incident light to transmit, so they will lose some incident light. Recently, CFAs with panchromatic pixels are used to avoid excessive loss of light. These CFAs’ color is sampled at a sparse set of locations, making demosaicking more challenging. In this paper, we propose a Convolutional Sparse Coding based method for Demosaicking (CSCD) with panchromatic pixels. The CSCD learns a set of filters to decompose training images into color sparse feature maps, then it estimates a set of feature maps of the to-be-reconstructed raw images and gets color images. Convolutional Sparse Coding (CSC) commonly operates on gray images. Naively extending CSC to color images will make the learned filters to be gray, which means that the values of the red, green, and blue channels are almost equal. To address this issue, we add a regularization term to penalize gray filters. We use the Alternating Direction Method of Multipliers (ADMM) to solve the CSCD. We compare the proposed CSCD with the demosaicking methods that are applicable to CFAs with panchromatic pixels. Experimental results validate its advantages in terms of CPSNR and visual quality.

Influence of grey and color filters on the electrical properties of the dye-sensitized solar cells

During the last decade, generating renewable energy has become more and more important, and so did solar cells. This led to a swift development of dye-sensitized solar cells (DSSCs). The highest efficiencies are achieved by cells which use synthetic ruthenium-based dyes; however, DSSCs can also be prepared from low-purity and nontoxic materials. Extracting anthocyanins from, e.g., fruit or commercially available tea offers an economic and ecological alternative to toxic dyes, while the solar cell efficiency is significantly reduced. This article reports on the possibility of using anthocyanin-based DSSCs under ambient light, which was modified by different grey and color filters. Dyes were tailored by modifying the pH value of the dye solution. While generally the lowest pH values resulted in the highest efficiencies, the performance of all DSSCs depended strongly on the light spectrum, for some color filters even favoring the original dye solution without pH modification.

Automatic segmentation of coronary lumen based on minimum path and image fusion from cardiac computed tomography images

Segmentation of the coronary lumen is a challenging aspect in the clinical application of cardiac computed tomography. In this paper, a new method is proposed for automatic segmentation of the coronary lumen. The proposed method is based on the directional minimum path and level-set segmentation on a cross-sectional fusion image. The directional minimum path is first used to automatically track the centerlines of the main coronary branches along a region of interest, providing the location of the center of the coronary lumen. Then, based on this centerline, cross-sectional images of the cardiac computed tomography volume are calculated to reconstruct a three-dimensional stacked image. To increase the success rate of lumen segmentation, the three-dimensional stacked image was enhanced using the proposed fusion method. The proposed fusion method included a gray filter that decreases noise, and a measure of the Vesselness that can enhance the coronary structure. Adaptive weighted fusion is then proposed to fuse the enhanced images. Finally, a level-set algorithm was used to segment the coronary lumen in the cross-sectional fused images. The proposed method was validated for the three main coronary branches. The Dice coefficients were 83.8% for the right coronary artery, 82.5% for the left anterior descending artery, and 83.9% for the left circumflex artery, respectively.

An efficient hybrid eye detection method

Eye detection is the most important and critical task of diverse applications such as face detection and recognition. However, most eye detection methods do not fully consider detection robustness to people with glasses, illumination variation, head pose change, and eye occlusions. This paper proposes an efficient hybrid eye detection method based on a gray intensity variance filter (VF) and support vector machines (SVMs). Firstly, the VF is used for eliminating most of noneye region images to keep less candidate eye regions. Then accurate two eye regions are determined easily through the trained SVM classifier. Moreover, this paper provides an assessment of the sensitivity of obtained parameters in the SVM classifier on eye detection accuracy. The proposed method was evaluated on different face databases. The experimental results show that the method can improve the performance of eye detection and achieve a higher detection accuracy compared with state-of-the-art methods.

56Mn, 60Co, 18F and 22Na activity measurements by coincidence technique at VNIIM

For modernization of the Russian national activity standard at the Ionizing Radiation Department of the D.I. Mendeleyev Institute for Metrology, a prototype 4π(LS)-γ(NaI) coincidence arrangement was created, and applied to the standardization of long-lived 60Co and 22Na and short-lived 56Mn and 18F. Efficiency variation was performed using “grey filters”, high voltage variation and variation of low threshold. The main metrological characteristics of the setup were determined: long-term stability, background, dead time, resolution time and temperature dependence. The results obtained have practical applications. The 18F solution with well-known activity is required for calibration of ionizing chamber used in nuclear medicine. The 56Mn is used for calibration of manganese bath equipment used in neutron laboratory. The results obtained are in good agreement with 4πγ(NaI)-counting and 4πβ(PC)-γ(NaI) methods of Russian national radioactivity standard. The combined uncertainty (k=2) of results was estimated in the range 1–2%.