Current Processing Methods Research Articles

Coronary artery segmentation from intravascular optical coherence tomography using deep capsules

The segmentation and analysis of coronary arteries from intravascular optical coherence tomography (IVOCT) is an important aspect of diagnosing and managing coronary artery disease. Current image processing methods are hindered by the time needed to generate expert-labelled datasets and the potential for bias during the analysis. Therefore, automated, robust, unbiased and timely geometry extraction from IVOCT, using image processing, would be beneficial to clinicians. With clinical application in mind, we aim to develop a model with a small memory footprint that is fast at inference time without sacrificing segmentation quality. Using a large IVOCT dataset of 12,011 expert-labelled images from 22 patients, we construct a new deep learning method based on capsules which automatically produces lumen segmentations. Our dataset contains images with both blood and light artefacts (22.8 %), as well as metallic (23.1 %) and bioresorbable stents (2.5 %). We split the dataset into a training (70 %), validation (20 %) and test (10 %) set and rigorously investigate design variations with respect to upsampling regimes and input selection. We show that our developments lead to a model, DeepCap, that is on par with state-of-the-art machine learning methods in terms of segmentation quality and robustness, while using as little as 12 % of the parameters. This enables DeepCap to have per image inference times up to 70 % faster on GPU and up to 95 % faster on CPU compared to other state-of-the-art models. DeepCap is a robust automated segmentation tool that can aid clinicians to extract unbiased geometrical data from IVOCT.

Research and Application of Rayleigh Wave 3-D Phase Velocity Imaging Based on Wavelet Transform

Rayleigh wave detection is a recently developed method for shallow seismic exploration. Current Rayleigh wave data processing and interpretation methods can only provide the transverse average wave velocity of rock-soil bodies under the geophone array range, resulting in a low lateral resolution of wave velocity. To solve this problem, this paper presents a Rayleigh wave data processing method based on wavelet transform. First, the Hankel matrix is constructed from the intercepted Rayleigh wave, and the effective singular value is preserved by singular value decomposition to filter the Rayleigh wave. Then, the appropriate center frequency is selected and the corresponding relationship between the time and frequency of the Rayleigh wave is obtained via wavelet transform. The waveform of each frequency component can be extracted and the complete time difference of each frequency component between two geophones will be obtained and used to calculate the phase velocity-depth profile of the Rayleigh wave in a rock-soil body. This method is applied to examine unfavorable geological bodies that are underground in a yard. By combining the phase velocity-depth profiles of several survey lines, the 3-D image of phase velocity of Rayleigh wave underground can be obtained. This method can provide the phase velocity distribution of the formation below the survey line by only one measurement, which greatly improves upon the work efficiency and lateral resolution of the traditional Rayleigh wave data processing method.

Porosity detection and localization during composite cure inside an autoclave using ultrasonic inspection

Composite materials offer unique benefits in aerospace applications such as increased strength-to-weight ratio and improved fatigue properties. They are increasingly being used in major commercial aircraft programs. However, current processing methods can lead to defects in composite parts, which must be detected using post-manufacturing inspection methods. Porosity (i.e., pores, voids) is a critical defect from the cure process that is detrimental to performance of the composite part. It is therefore necessary to understand and eliminate the formation of porosity defects. At NASA Langley Research Center, an in-situ cure monitoring system was developed to detect porosity defects as they form in real-time inside an autoclave. A capability to directly detect and localize porosity within a composite during cure did not exist before.This study is focused on an elevated-temperature ultrasonic inspection system to detect porosity defects in composites during autoclave cure. The ultrasonic inspection system operated inside an autoclave within an enclosure cooled by intermittent liquid nitrogen (LN2) injections. A high-temperature, 2.25 MHz, transducer transmitted ultrasonic waves through the tool plate and into the composite part and measured the amplitude and time of flight of the reflected waves with a 1 mm × 1 mm step size/areal resolution. Porosity was observed via the ultrasonic reflections, which experienced increased attenuation in regions of high porosity. Distinct regions of increased porosity were present due to uneven pressure across the panel, which was driven by an intentional misfit between the flat caul plate and the tapered composite panel with ply drops. The results were validated by post-cure ultrasonic inspection and micrographs. The in-situ inspection system was able to successfully provide porosity detection and localization in the tapered ply-drop panel. The successful results indicate the promise of this system for future implementation in the manufacturing of composite structures for aerospace applications.

Hydroinformatics based technique for leak identification purpose – An experimental analysis

Analysis of single point pressure transient signal has the potential of providing information on the pipeline system. The technique uses the frequency response and experiments are satisfactory for low-frequency bandwidth. The concept behind this approach is that reflections are detected by leak-induced disturbances. Such reflections are often very difficult to identify, particularly because of excessive noise. The common approach to boost the analysis is to use a data denoising approach. The wavelet transforms for leak detection in water pipelines are proposed in this article. The wavelet filter is optimally calibrated using maximal kurtosis values and is chosen for leakage detection. The suggested approach is tested using high-noise laboratory test signals. The findings show that the method is superior to current signal processing methods for the conditions used. The system permits not only the detection of leakage but also the location and evaluation of its magnitude with errors of less than 7%.

Deep-learned time-signal intensity pattern analysis using an autoencoder captures magnetic resonance perfusion heterogeneity for brain tumor differentiation

Current image processing methods for dynamic susceptibility contrast (DSC) magnetic resonance imaging (MRI) do not capture complex dynamic information of time-signal intensity curves. We investigated whether an autoencoder-based pattern analysis of DSC MRI captured representative temporal features that improves tissue characterization and tumor diagnosis in a multicenter setting. The autoencoder was applied to the time-signal intensity curves to obtain representative temporal patterns, which were subsequently learned by a convolutional neural network. This network was trained with 216 preoperative DSC MRI acquisitions and validated using external data (n = 43) collected with different DSC acquisition protocols. The autoencoder applied to time-signal intensity curves and clustering obtained nine representative clusters of temporal patterns, which accurately identified tumor and non-tumoral tissues. The dominant clusters of temporal patterns distinguished primary central nervous system lymphoma (PCNSL) from glioblastoma (AUC 0.89) and metastasis from glioblastoma (AUC 0.95). The autoencoder captured DSC time-signal intensity patterns that improved identification of tumoral tissues and differentiation of tumor type and was generalizable across centers.

Prediction of concentration for microalgae using image analysis

Maintaining the optimum growth rate and estimating the concentration of microalgae are critical in improving microalgae production. An efficient concentration assessment of microalgae is essential for a timely and effective determination of the harvest period. This study proposes the luminance and viscosity methods to predict the concentration of microalgae. Image analysis was applied to measure the concentration of native microalgae: Desmodesmus sp., Scenedesmus sp., Dictyosphaerium sp., and Klebsormidium sp. The experiments were performed using different concentrations of the dry cell weight (DCW) of these microalgae species. A dual-camera device was used to capture the images of the DCW solution in a flask. For the confirmation of viscosity, a viscometer was used to determine the concentration of microalgae. A comparative analysis was performed between the data from the image analysis and viscosity method. The results from the viscosity method showed a higher accuracy with R2 = 0.9784 and the luminance method with R2 = 0.8266. Further investigations revealed that the brightness of the DCW image had a limitation at a specific concentration where the color was unrecognized. The current image processing method has the potential to be applied in an outdoor cultivation facility for real-time data acquisition. Both methods have advantages in terms of required time and experimental costs. The image analysis method provides an alternative way to efficiently monitor the cultivation and harvesting of microalgae.

Deep learning-based instance segmentation of cracks from shield tunnel lining images

This paper presents a deep learning (DL)-based method for the instance segmentation of cracks from shield tunnel lining images using a mask region-based convolutional neural network (Mask R-CNN) incorporated with a morphological closing operation. The Mask R-CNN herein is divided into a backbone architecture, a region proposal network (RPN), and a head architecture for specification, and the implementation details are introduced. Compared with the current image processing methods, the proposed DL-based method efficiently detects cracks in an image while simultaneously generating a high-quality segmentation mask for each crack. A shield tunnel lining image dataset is established for crack instance segmentation task. The established dataset contains a total of 1171 labelled crack instances in 761 images. The morphological closing operation was incorporated into a Mask R-CNN to form an integrated model to connect disjoint cracks that belong to one crack. Image tests were carried out among four trained models to explore the effect of the morphological closing operation, network depth, and feature pyramid network on crack segmentation performance, and a relative optimal model is found. The relative optimal model achieves a balanced accuracy of 81.94%, a F1 score of 68.68%, and an intersection over union (IoU) of 52.72% with respect to 76 test images.

Eco-Friendly Polymer Solar Cells: Advances in Green-Solvent Processing and Material Design.

Despite the recent breakthroughs of polymer solar cells (PSCs) exhibiting a power conversion efficiency of over 17%, toxic and hazardous organic solvents such as chloroform and chlorobenzene are still commonly used in their fabrication, which impedes the practical application of PSCs. Thus, the development of eco-friendly processing methods suitable for industrial-scale production is now considered an imperative research focus. This Review provides a roadmap for the design of efficient photoactive materials that are compatible with non-halogenated green solvents (e.g., xylenes, toluene, and tetrahydrofuran). We summarize the recent development of green processing solvents and the processing methods to match with the efficient photoactive materials used in non-fullerene solar cells. We further review progress in the use of more eco-friendly solvents (i.e., water or alcohol) for achieving truly sustainable and eco-friendly PSC fabrication. For example, the concept of water- or alcohol-dispersed nanoparticles made of conjugated materials is introduced. Also, recent important progress and strategies to develop water/alcohol-soluble photoactive materials that completely eliminate the use of conventional toxic solvents are discussed. Finally, we provide our perspectives on the challenges facing the current green processing methods and materials, such as large-area coating techniques and long-term stability. We believe this Review will inform the development of PSCs that are truly clean and renewable energy sources.

PSpice modeling of the pulsed electroacoustic method for dispersive polymer sample application.

In space applications, space charge inside dielectric materials can lead to unwanted phenomena such as breakdown and electrostatic discharge. To prevent such incidents, the pulsed electroacoustic (PEA) device is employed to measure the space charge of dielectric samples. Unfortunately, most of the current PEA signal processing methods are inaccurate in studying the dielectric materials that attenuate and disperse the acoustic wave, as well as studying multi-layer or thin samples. A model under the PSpice software is developed in this work with the aim of improving the PEA signal processing. This model is an evolution of previous works, including the attenuation and dispersion of a polymer sample using the nearly local Kramers-Kronig relation. This model of the polymer sample is implemented into PSpice as a frequency dependent transmission line using Branin's method of characteristics model. A comparison of a model simulation and an experimental PEA signal obtained from a PTFE sample is presented.

Distributed Query Processing in the Edge-Assisted IoT Data Monitoring System

The massive amount of data generated by the Internet-of-Things (IoT) devices places enormous pressure on sensory data query processing. Due to the limitations of computation and data transmission capabilities in traditional wireless sensor networks (WSNs), the current query processing methods are no longer effective. Furthermore, processing vast amount of sensory data also overloads the cloud. To address these problems, we investigate query processing in an edge-assisted IoT data monitoring system (EDMS). Multiaccess edge computing (MEC) is an emerging topic in IoTs. Unlike WSNs, the edge servers in an EDMS can deploy the computation and storage resources to nearby IoT devices and offer data processing services. Therefore, queries toward massive sensory data can be processed in an EDMS in a distributed manner and the edge servers can handle the sensory data in a distributed manner, reducing the workload of the cloud. In this article, we define a query processing problem in an EDMS, which aims to derive a distributed query plan with the minimum query response latency. We prove that this problem is NP-Hard and propose a corresponding approximation algorithm. The performance of the proposed algorithm is bounded. Furthermore, we evaluate the performance of the proposed algorithm through extensive simulations.

Gesture recognition based on multi‐modal feature weight

SummaryWith the continuous development of sensor technology, the acquisition cost of RGB‐D images is getting lower and lower, and gesture recognition based on depth images and Red‐Green‐Blue (RGB) images has gradually become a research direction in the field of pattern recognition. However, most of the current processing methods for RGB‐D gesture images are relatively simple, ignoring the relationship and influence between its two modes, and unable to make full use of the correlation factors between different modes. In view of the above problems, this paper optimizes the effect of RGB‐D information processing by considering the independent features and related features of multi‐modal data to construct a weight adaptive algorithm to fuse different features. Simulation experiments show that the method proposed in this paper is better than the traditional RGB‐D gesture image processing method and the gesture recognition rate is higher. Comparing the current more advanced gesture recognition methods, the method proposed in this paper also achieves higher recognition accuracy, which verifies the feasibility and robustness of this method.

Metal additive manufacturing: Technology, metallurgy and modelling

This paper provides a comprehensive review of metal additive manufacturing, a rapidly evolving field with innovative technologies and processes. The purpose of this review paper is to provide a complete picture of the current research on metal AM and its capabilities. An overview of metal AM and the current processing methods are provided, along with a brief introduction to the complex physics behind the melt pool formation. Common metal AM characteristic defects are discussed as well as the current metals and alloys that are commercially available. Furthermore, process optimization techniques and computational modelling methods are reviewed. Lastly, various post-processing methods to improve surface roughness, mechanical properties and dimensional precision are discussed. Although the library for printable alloys is increasing, there is still a need for alloy development outside of the commercial setting. Furthermore, there is currently not a complete numerical model of the AM process which is mainly due to the computational costs. Although metal AM is still in its infancy, the frequency and significance of new developments are driving AM to mainstream adoption.

Methods to assess the effect of meat processing on viability of Toxoplasma gondii: towards replacement of mouse bioassay by in vitro testing

Consumption of meat containing viable tissue cysts is considered one of the main sources of human infection with Toxoplasma gondii. In contrast to fresh meat, raw meat products usually undergo processing, including salting and mixing with other additives such as sodium acetate and sodium lactate, which affects the viability of T. gondii. However, the experiments described in the literature are not always performed in line with the current processing methods applied in industry. It was our goal to study the effect of salting and additives according to the recipes used by industrial producers. Mouse or cat bioassay is the ‘gold standard’ to demonstrate the presence of viable T. gondii. However, it is costly, time consuming and for ethical reasons not preferred for large-scale studies.Therefore, we first aimed to develop an alternative for mouse bioassay that can be used to determine the effect of processing on the viability of T. gondii tissue cysts. The assays studied were (i) a cell culture method to determine the parasite’s ability to multiply, and (ii) a propidium monoazide (PMA) dye-based assay to selectively detect DNA from intact parasites. Processing experiments were performed with minced meat incubated for 20 h with low concentrations of NaCl, sodium lactate and sodium acetate. NaCl appeared to be the most effective ingredient with only one or two out of eight mice infected after inoculation with pepsin-digest of portions processed with 1.0, 1.2 and 1.6% NaCl. Results of preliminary experiments with the PMA-based method were inconsistent and did not sufficiently discriminate between live and dead parasites. In contrast, the cell culture method showed promising results, but further optimization is needed before it can replace or reduce the number of mouse bioassays needed. In future, standardised in vitro methods are necessary to allow more extensive testing of product-specific processing methods, thereby providing a better indication of the risk of T. gondii infection for consumers.

Joining oxide ceramic matrix composites by ionotropic gelation

AbstractThe production of complex‐shaped all‐oxide ceramic matrix composites (Ox‐CMC) is somewhat restricted by their current processing methods, as well as by the lack of applicable joining techniques. Thus, we present a new method for joining Ox‐CMCs based on the gelation of slurries with the polysaccharide polymer alginate. For this investigation, Nextel 610/alumina‐zirconia composites were produced using alginate as binder and aluminum acetate as gelling agent. The joining capabilities of this technique were investigated with microstructural analyses and single‐lap compression shear tests. For that, a slurry‐containing alginate was used to join two composite plates at different stages of the processing: gel state, dried green body and after sintering. Joining composites plates in their gel or green state was successful as the joints showed shear strength values similar to the interlaminar shear strength of the composites plates. The quality of the joints was attributed to the interactions between the alginate chains of the composite plates and the joint. We also show that even the joining of already sintered Ox‐CMCs is feasible. However, densification cracks and lower shear strength are observed for such cases.

Modified Distance Transformation for ImageEnhancement in NIR Imaging of Finger Vein System.

Most of the current image processing methods used in the near-infrared imaging of finger vascular system concentrate on the extraction of internal structures (veins). In this paper, we propose a novel approach which allows to enhance both internal and external features of a finger. The method is based on the Distance Transformation and allows for selective extraction of physiological structures from an observed finger. We evaluate the impact of its parameters on the effectiveness of the already established processing pipeline used for biometric identification. The new method was compared with five state-of-the-art approaches to features extraction (position-gray-profile-curve—PGPGC, maximum curvature points in image profiles—MC, Niblack image adaptive thresholding—NAT, repeated dark line tracking—RDLT, and wide line detector—WD) on the GustoDB database of images obtained in a wide range of NIR wavelengths (730–950 nm). The results indicate a clear superiority of the proposed approach over the remaining alternatives. The method managed to reach over identification accuracy for all analyzed datasets.

Calcification detection using convolutional neural network architectures in Intravascular ultrasound images

<p>Cardiovascular disease is the highest leading to death for Non-Communicable disease. Coronary artery calcification disease is part of cardiovascular disease. The built-in of the plaques and the calcification in the coronary artery inner wall make the blood vessel cross-section area narrow. The standard practice by the radiologists and medical clinical are by visual inspection to detect the calcification in the intravascular ultrasound image. Deep learning is the current image processing methods that have high potential to detect calcification analysis using convolutional neural network architecture and classifiers. To detect the absence of calcification and presence calcification on the intravascular ultrasound image, using k-fold =10, we compared the three types of convolutional neural network architectures and the seven types of classifiers with the provided ground truth from MICCAI 2011. We used two types of images named as Cartesian Coordinates image and polar reconstructed coordinate image. The classifiers such as Support Vector Machine, Discriminant analysis, Ensembles and Error-Correcting Output Codes obtained the perfect result with value one for Area Under Curve and all the performance measure result, accuracy, sensitivity, specificity, positive predictive value and negative predictive value. Area Under Curve for Naïve Bayes classifier is 0.9967 and for Decision Tree classifier is 0.9994, obtained using the polar reconstructed coordinate image for InceptionresNet-V2 architecture.</p>

Simulation Method on Three-Dimensional Shape Generation Process by Femtosecond Laser Ablation

Ablation processing using a femtosecond laser has the advantage of processing materials that are impossible to machine by conventional methods. However, it is difficult to predict the three-dimensional removal shape owing to a femtosecond laser irradiation and the current processing method, repeating through trial and error, is inefficient and expensive. For this reason, for processing materials to desired shapes using a femtosecond laser, development of a simulation system and a CAM (Computer-Aided Manufacturing) system are indispensable. In this paper, a simulation method for processing materials using a femtosecond laser is proposed and conducted to nano-polycrystalline diamond (NPD). It is confirmed that the proposed method can precisely simulate the femtosecond laser processing for three-dimensional shape generation. Therefore, the proposed method enables to predict the removal shape identified as the virtual tool shape for calculating a tool path on a CAM system. In addition, an automation process method including an on-machine real-time measurement system of the proposed method is described.

Preparation and Characterization of Chitosan obtained from Pacific White Shrimp Shells and its in vitro Antifungal Activity

In this article, a method for the processing of chitosan from Pacific white shrimp shells is developed which involves three steps viz. demineralization, deproteinization, and deacetylation. The samples of chitosan with more than 90% degree of deacetylation (DD%) were obtained by FTIR. This indicated that the current processing method of shrimp shells was beneficial for chitosan production. The morphology of chitosan sample was determined using scanning electron microscopy (SEM). X-ray diffraction (XRD) patterns exhibited two peaks of crystalline character approximately at 10º and 20º (2θ). The effect of 0.1% (w/v) of chitosan on the growth of Penicillium digitatum was tested by an in vitro assay and the results showed an almost complete inhibition (98% ± 0.56).

Modeling and methods for gear shaping process and cutting force prediction of variable transmission ratio rack

Variable transmission ratio rack shows great potentials in the automobile industry as a key component of vehicle variable transmission ratio steering gear to balance steering portability and steering sensitivity. The current processing method for variable transmission ratio rack is orbital forging, which has the advantages of high performance and high processing efficiency. However, orbital forging can only process small size rack and its processing accuracy is limited. Therefore, this paper proposes a novel processing method, i.e., gear shaping, to achieve high quality processing of variable transmission ratio rack by overcoming above limits of orbital forging. Firstly, an accurate envelope motion model between the tool and the workpiece is established based on the mapping relation of the speed as well as the feed of each machine axis between envelope motion and actual gear shaping process, so as to envelope final complex changing tooth shape of variable transmission ratio rack. Namely, the speed of each machine axis is determined strictly according to the linkage relationship of each machine axis with a variable transmission ratio function involved. Then, a specific tooth profile mathematical model of variable transmission ratio rack is obtained through simultaneous solution of the envelope motion model and the tool model. Meanwhile, since cutting force is a critical factor of gear shaping process, this paper proposes a new method for cutting force prediction which involves two innovative calculation algorithms for two major components (contact length b and chip area a) of the classical metal cutting force calculation model. Firstly, based on discrete representation, a new calculation algorithm of contact length b including specific dynamic selection criteria of cutting contact points is presented. Secondly, in order to precisely calculate chip area a, this paper proposes a discrete calculation algorithm with two pivotal cutting points selecting criteria involved. Finally, based on the classical metal cutting force calculation model, accurate predictions of cutting force in gear shaping process of variable transmission ratio rack are achieved. Gear shaping experiments as well as cutting force measurements are conducted and errors between the predictions and the experimental measurements of cutting force are obtained, which remain between 1% and 19%. Therefore, it is concluded that the envelope motion model and methods for cutting force prediction in gear shaping process of variable transmission ratio rack presented in this paper are feasible.

Challenges of Finite Element Analysis of Resolvers

Nowadays, position sensors are increasingly used in many applications. One of the most commonly used sensors are resolvers. There are different types of resolvers including wound rotor resolvers, variable air-gap length variable reluctance resolvers, and sinusoidal rotor variable reluctance resolvers. Regardless of the resolvers’ configurations, including linear, cylindrical and disk type, finite element analysis (FEA) is inseparable part of design and the performance evaluation of these magnetic sensors. The scope of this study is to discuss challenges in the FEA of resolvers. The influence of mesh quality, time steps, stop time, excitation methods including voltage source and current source and signal processing methods are investigated in detail. Then, a key question in analysis of resolvers is answered. The question is Is total harmonic distortion of induced voltages’ envelopes a suitable criterion for the performance evaluation of resolvers? Finally, four prototypes of different types of resolvers are used for experimental tests. Good agreements are obtained between simulation and test results indicating the accuracy of following discussions.