Search Results (15)

Tree nuts are rich in nutrients, and global production and consumption have doubled during the last decade. However, nuts have a range of quality defects that must be detected and removed during post-harvest processing. Tree nuts can develop hidden internal discoloration, and current sorting methods are prone to subjectivity and human error. Therefore, non-destructive, real-time methods to evaluate internal nut quality are needed. This study explored the potential for VNIR (400–1000 nm) hyperspectral imaging to classify brown center disorder in macadamias. This study compared the accuracy of classifiers developed using images of kernels imaged in face-up and face-down orientations. Classification accuracy was excellent using face-up (>97.9%) and face-down (>94%) images using ensemble and linear discriminate models before and after wavelength selection. Combining images to form a pooled dataset also provided high accuracy (>90%) using artificial neural network and support vector machine models. Overall, HSI has great potential for commercial application in nut processing to detect internal brown centers using images of the outside kernel surface in the VNIR range. This technology will allow rapid and non-destructive evaluation of intact nut products that can then be marketed as a high-quality, defect-free product, compared with traditional methods that rely heavily on representative sub-sampling. Full article

Blueberry (Vaccinium corymbosum L.) stands out among fruit in terms of three open physiological questions about its climacteric character, CO₂ uptake, and the absence or presence of stomata on its floral organs. The objective of the present study was to examine the structures of blueberry flowers and fruit to explain their contribution to CO₂ exchange and transpiration in order to clarify these discrepancies. Blueberries were dewaxed and the sepals/corolla removed for stomata counts, and their micromorphology was studied via LT-SEM. The fruit has stomata, contrary to beliefs in the literature, possibly because the stomata are occluded by the dense wax cover or ‘bloom’ and hidden on the distal part of the ovary in between and underneath the corolla. However, stomata were located on the distal part of the fruit surrounded by the sepals (calyx) and found predominantly on the abaxial sepals, while the adaxial side of the sepals and the proximal part of the ovary lacked stomata. The petals were devoid of stomata, trichomes, and chlorophyll and abscised after anthesis. In contrast, the sepals remained until maturity, contributing 5–7% to the berry surface but contributing to the majority of fruit stomata and chlorophyll. With 59–71% of the fruit’s chlorophyll, sepals were a significant source of the CO₂ uptake. Similarly, with 95% of the berry stomata, sepals were a significant source of water loss, measured via porometry of fruit with and without sepals. Overall, this study identified the ovary as a minor source and sepals as the dominant source of CO₂ and H₂O exchange in blueberries. Full article

Localising the sources of MEG/EEG signals often requires a structural MRI to create a head model, while ensuring reproducible scientific results requires sharing data and code. However, sharing structural MRI data often requires the face go be hidden to help protect the identity of the individuals concerned. While automated de-facing methods exist, they tend to remove the whole face, which can impair methods for coregistering the MRI data with the EEG/MEG data. We show that a new, automated de-facing method that retains the nose maintains good MRI-MEG/EEG coregistration. Importantly, behavioural data show that this “face-trimming” method does not increase levels of identification relative to a standard de-facing approach and has less effect on the automated segmentation and surface extraction sometimes used to create head models for MEG/EEG localisation. We suggest that this trimming approach could be employed for future sharing of structural MRI data, at least for those to be used in forward modelling (source reconstruction) of EEG/MEG data. Full article

In this study, the response surface methodology (RSM) and artificial neural network (ANN) were employed to study the adsorption process of 2,4-dichlorophenoxyacetic acid (2,4-D) by using modified hydrogel, i.e., activated carbon poly(dimethylaminoethyl methacrylate) (AC/PDMAEMA hydrogel). The effect of pH, the initial concentration of 2,4-D and the activated carbon content on the removal of 2,4-D and adsorption capacity were investigated through the face-centered composite design (FCCD), optimal design and two-level factorial design. The response surface plot suggested that higher removal of 2,4-D and adsorption capacity could be achieved at the higher initial concentration of 2,4-D and lower pH and activated carbon content. The modeling and optimization for the adsorption process of 2,4-D were also carried out by different design methods of RSM and different training methods of ANN. It was found that among the three design methods of RSM, the optimal design has the highest accuracy for the prediction of 2,4-D removal and adsorption capacity (R² = 0.9958 and R² = 0.9998, respectively). The numerical optimization of the optimal design found that the maximum removal of 2,4-D and adsorption capacity of 65.01% and 65.29 mg/g, respectively, were obtained at a pH of 3, initial concentration of 2,4-D of 94.52 mg/L and 2.5 wt% of activated carbon. Apart from the optimization of process parameters, the neural network architecture was also optimized by trial and error with different numbers of hidden neurons in the layers to obtain the best performance of the response. The optimization of the neural network was performed with different training methods. It was found that among the three training methods of the ANN model, the Bayesian Regularization method had the highest R² and lowest mean square error (MSE) with the optimum network architecture of 3:9:2. The optimum condition obtained from RSM was also simulated with the optimized neural network architecture to validate the responses and adequacy of the RSM model. Full article

In this study, we investigate sea state estimation from spaceborne GNSS-R. Due to the complex scattering of electromagnetic waves on the rough sea surface, the neural network approach is adopted to develop an algorithm to derive significant wave height (SWH) from CYGNSS data. Eighty-nine million pieces of CYGNSS data from September to November 2020 and the co-located ECMWF data are employed to train a three-hidden-layer neural network. Ten variables are considered as the input parameters of the neural network. Without the auxiliary of the wind speed, the SWH retrieved using the trained neural network exhibits a bias and an RMSE of −0.13 and 0.59 m with respect to ECMWF data. When considering wind speed as the input, the bias and RMSE were reduced to −0.09 and 0.49 m, respectively. When the incidence angle ranges from ${35}^{°}$ to ${65}^{°}$ and the SNR is above 7 dB, the retrieval performance is better than that obtained using other values. The measurements derived from the “Block III” satellite offer worse results than those derived from other satellites. When the distance is considered as an input parameter, the retrieval performances for the areas near the coast are significantly improved. A soft data filter is used to synchronously improve the precision and ensure the desired sample number. The RMSEs of the retrieved SWH are reduced to 0.45 m and 0.41 m from 0.59 m and 0.49 m, and only 16.0% and 14.9% of the samples are removed. The retrieved SWH also shows a clear agreement with the co-located buoy and Jason-3 altimeter data. Full article

Drug delivery to the anterior or posterior segments of the eye is a major challenge due to the protection barriers and removal mechanisms associated with the unique anatomical and physiological nature of the ocular system. The paper presents the preparation and characterization of drug-loaded polymeric particulated systems based on pre-emulsion coated with biodegradable polymers. Low molecular weight biopolymers (chitosan, sodium hyaluronate and heparin sodium) were selected due to their ability to attach polymer chains to the surface of the growing system. The particulated systems with dimensions of 190–270 nm and a zeta potential varying from −37 mV to +24 mV depending on the biopolymer charges have been obtained. Current studies show that particles release drugs (dexamethasone/pilocarpine/bevacizumab) in a safe and effective manner, maintaining therapeutic concentration for a longer period of time. An extensive modeling study was performed in order to evaluate the drug release profile from the prepared systems. In a multifractal paradigm of motion, nonlinear behaviors of a drug delivery system are analyzed in the fractal theory of motion, in order to correlate the drug structure with polymer. Then, the functionality of a SL(2R) type “hidden symmetry” implies, through a Riccati type gauge, different “synchronization modes” (period doubling, damped oscillations, quasi-periodicity and intermittency) during the drug release process. Among these, a special mode of Kink type, better reflects the empirical data. The fractal study indicated more complex interactions between the angiogenesis inhibitor Bevacizumab and polymeric structure. Full article

Recently, the size of models for real-time rendering has been significantly increasing for realism, and many graphics applications are being developed in mobile devices with relatively insufficient hardware power. Therefore, improving rendering speed is still important in graphics. Back-face culling is one of the core speed-up techniques to remove the back-facing polygons that are not drawn in the result image. In this paper, we present a mesh clustering and reordering method based on normal coherence for efficient back-face culling at an earlier stage than the current method, which removes back faces after the vertex shader on the GPU. In the pre-computation, our method first vertically clusters the mesh into multiple stripes based on the latitude of the face normal vector and sorts each stripe in ascending order of longitude. At runtime, our method computes a potentially visible set of faces at the current camera view by excluding back faces from the clustered and reordered faces list, and draws only the potentially visible set. Experiments have shown that the rendering using our method is more efficient than traditional methods, especially for large and static models. Full article

The 3D point cloud reconstruction from photos taken by an unmanned aerial vehicle (UAV) is a promising tool for monitoring and managing risks of cut-slopes. However, surface changes on cut-slopes are likely to be hidden by seasonal vegetation variations on the cut-slopes. This paper proposes a vegetation removal method for 3D reconstructed point clouds using (1) a 2D image segmentation deep learning model and (2) projection matrices available from photogrammetry. For a given point cloud, each 3D point of it is reprojected into the image coordinates by the projection matrices to determine if it belongs to vegetation or not using the 2D image segmentation model. The 3D points belonging to vegetation in the 2D images are deleted from the point cloud. The effort to build a 2D image segmentation model was significantly reduced by using U-Net with the dataset prepared by the colour index method complemented by manual trimming. The proposed method was applied to a cut-slope in Doam Dam in South Korea, and showed that vegetation from the two point clouds of the cut-slope at winter and summer was removed successfully. The M3C2 distance between the two vegetation-removed point clouds showed a feasibility of the proposed method as a tool to reveal actual change of cut-slopes without the effect of vegetation. Full article

In view of the poor effectiveness of existing potato cleaning methods in China and reflecting the findings of a research analysis of basic sizes and types of impurities on potato tubers, a gradient cleaning method for potato based on a multi-step dry-cleaning and wet cleaning operation was proposed. The method mainly consists of dry-cleaning and wet cleaning. The dry-cleaning stage, which combines vibration and brushing, could effectively remove impurities such as residual rhizomes, peeled potato skin, and large pieces of soil and crushed stone from the surface of potato tubers. The wet cleaning stage adopts the gradient cleaning method of pre-cleaning, rough cleaning and fine cleaning, which could further remove soil and crushed stone attached to the surface and hidden in the sprout eyes of potato tubers. The optimal parameter combination for the gradient cleaning method was determined as follows. The potato feeding amount was 3 t/h, the speed of the rubber chain rod mechanism was 25 r/min, the speed of the first and third brush roller was 40 r/min, the speed of the second and fourth brush roller was 56 r/min, the moving speed of the immersion mechanism conveying net chain was 0.04 m/s, the speed of the brush roller in the high pressure spray and brush roller combination mechanism was 40 r/min, the ultrasonic power was 1200 W, the ultrasonic frequency was 33 kHz, the bubble intensity was 300 L/min, and the moving speed of the conveying net chain in the ultrasonic and bubble combination mechanism was 0.05 m/s. Taking the impurity removal rate and damage rate of potato tuber as the test indexes, a potato cleaning performance test was carried out under the optimal parameters combination. The results showed that the average impurity removal rate and damage rate of potato tubers were 99.05% and 2.48%, respectively. Additionally, the operational performance fully met the requirements for potato cleaning. This study provides a new method for potato cleaning in China and can also provide a reference for cleaning other root and tuber crops. Full article

Urban sanitation is a major challenge during the rapid urbanization being experienced by developing countries, as a low sewerage infrastructure capacity and irregular onsite wastewater treatment raise the risk of surface water contamination. The application of specific sewage markers to characterize contaminant sources is therefore essential for managing urban sanitation issues. In this study, we investigated the concentrations of eight sewage markers (acetaminophen, caffeine, carbamazepine, cotinine, sulfamethoxazole, sulfapyridine, atenolol, and acesulfame) in various water sources within urban area of the Galle City, Sri Lanka. The total concentration of the eight markers was in the order of hospital discharge > sewage treatment plant (STP) influent > surface drainage system. Among the eight selected markers, acetaminophen was dominant in hospital discharge (70.2–123.6 µg/L) while caffeine was the largest contributor to STP influent (16.2–68.7 µg/L) and surface drainage (0.95–21.73 µg/L). We then proposed and tested a set of criteria for evaluating the applicability of markers, including removal efficiency, concentration magnitude, excretion rate, and wastewater burden. The labile markers caffeine and acetaminophen were suitable for characterizing domestic gray and black wastewater, respectively. These results imply that the city’s drainage system receives both domestic graywater and human excretion, likely due to insufficient on-site sanitation systems. The conservative marker carbamazepine was useful for tracking hospital residues over long distances; these results imply that hospital wastewater treatment was not working properly, accounting for pharmaceutical residues reaching surface water via a hidden discharges connected to the drainage system. Full article

Water-layer multiples pose a major problem in shallow water seismic investigations as they interfere with primaries reflected from layer boundaries or archaeology buried only a few meters below the water bottom. In the present study we evaluate two model-driven approaches (“Prediction and Subtraction” and “RTM-Deco”) to attenuate water-layer multiple reflections in very shallow water using synthetic and field data. The tests comprise both multi- and constant-offset data. We compare the multiple removal efficiency of the evaluated methods with two traditional methods (Predictive Deconvolution and SRME). Both model-driven approaches yield satisfactory results concerning the enhancement of primary energy and the attenuation of multiple energy. For the synthetic test cases, the multiple energy is reduced by at least 80% for the Prediction and Subtraction approach, and by more than 60% for the RTM-Deco approach. The application to two field data sets shows a significant amplification of primaries formerly hidden by the first water-layer multiple, with a reduction of multiple energy of up to 50%. The waveforms obtained from FD modeling match the true waveforms of the field data well and small deviations in time and amplitude can be removed by a time shift of the traces as well as an amplitude adaption to the field data. The field data examples should be emphasized, where the tested Prediction and Subtraction approach works significantly better than the traditional methods: the multiples are effectively predicted and attenuated while primary signals are highlighted. In conclusion, this shows that this method is particularly suitable in shallow water applications. Both evaluated multiple attenuation approaches could be successfully transferred to two other 3D systems used in shallow water near surface investigations. Especially the Prediction and Subtraction approach is able to enhance the primaries for both tested 3D systems with the multiple energy being reduced by more than 50%. Full article

This paper presents a new processing method for denoising interferograms obtained by digital holographic speckle pattern interferometry (DHSPI) to serve in the structural diagnosis of artworks. DHSPI is a non-destructive and non-contact imaging method that has been successfully applied to the structural diagnosis of artworks by detecting hidden subsurface defects and quantifying the deformation directly from the surface illuminated by coherent light. The spatial information of structural defects is mostly delivered as local distortions interrupting the smooth distribution of intensity during the phase-shifted formation of fringe patterns. Distortions in fringe patterns are recorded and observed from the estimated wrapped phase map, but the inevitable electronic speckle noise directly affects the quality of the image and consequently the assessment of defects. An effective method for denoising DHSPI wrapped phase based on deep learning is presented in this paper. Although a related method applied to interferometry for reducing Gaussian noise has been introduced, it is not suitable for application in DHSPI to reduce speckle noise. Thus, the paper proposes a new method to remove speckle noise in the wrapped phase. Simulated data and experimental captured data from samples prove that the proposed method can effectively reduce the speckle noise of the DHSPI wrapped phase to extract the desired information. The proposed method is helpful for accurately detecting defects in complex defect topography maps and may help to accelerate defect detection and characterization procedures. Full article

This paper deals with the development and comparison of prediction models established using response surface methodology (RSM) and artificial neural network (ANN) for a wire electrical discharge machining (WEDM) process. The WEDM experiments were designed using central composite design (CCD) for machining of Inconel 718 superalloy. During experimentation, the pulse-on-time (T_ON), pulse-off-time (T_OFF), servo-voltage (SV), peak current (I_P), and wire tension (WT) were chosen as control factors, whereas, the kerf width (Kf), surface roughness (R_a), and materials removal rate (MRR) were selected as performance attributes. The analysis of variance tests was performed to identify the control factors that significantly affect the performance attributes. The double hidden layer ANN model was developed using a back-propagation ANN algorithm, trained by the experimental results. The prediction accuracy of the established ANN model was found to be superior to the RSM model. Finally, the Non-Dominated Sorting Genetic Algorithm-II (NSGA- II) was implemented to determine the optimum WEDM conditions from multiple objectives. Full article

We have developed an output device for a computer-generated hologram (CGH) named a fringe printer, which can output a 0.35- $\mathsf{\mu }$ m plane-type hologram. We also proposed several CGH with a fringe printer. A computer-generated rainbow hologram (CGRH), which can reconstruct a full color 3D image, is one of our proposed CGH. The resolution of CGRH becomes huge (over 50 Gpixels) due to improvement of the fringe printer. In the calculation, it is difficult to calculate the whole fringe pattern of CGRH at the same time by a general PC. Furthermore, since the fine pixel pitch provides a wide viewing angle in CGRH, object data, which are used in fringe calculation, should be created from many viewpoints to provide a proper hidden surface removal process. The fringe pattern of CGRH is calculated in each horizontal block. Therefore, the object data from several view points should be organized for efficient computation. This paper describes the calculation algorithm for huge resolution CGRH and its output results. Full article

Time-frequency analysis is usually used to reveal the appearance of different frequency components varying with time, in signals, of which time-frequency spectrogram is an important visual tool to display the information. The Mesh Surface Generation (MSG) algorithm is widely used in three-dimensional (3D) modeling. Removing hidden lines from the mesh plot is an essential process that produces explicit depth information. In this paper, a fast and effective method has been proposed for a time-frequency Spectrogram Mesh Surface Generation (SMSG) display, especially, based on the painter’s algorithm. In addition, most portable fault diagnosis devices have little function to generate a 3D spectrogram, which generally needs a general computer to realize the complex time-frequency analysis algorithms and a 3D display. However, general computer is not portable and then not suitable for field test. Hence, the proposed SMSG algorithm is applied to an embedded fault diagnosis device, which is light, low-cost, and real-time. The experimental results show that this approach can realize a high degree of accuracy and save considerable time. Full article