Processing Algorithms Research Articles

Color in images: a machine vision approach to the measurement of CIEL*a*b* coordinates in bovine loins

Electronic machine vision systems bring together a set of technologies and techniques used to capture, process and analyze images to perform a specific task, such as object or measurement pattern recognition. These systems rely on image processing and machine learning algorithms to interpret visual information. Therefore, the objective of this research was the construction of an electronic machine vision system (SVA) for color analysis in bovine (longisimus dorsi) loins based on the CIEL*a*b* color space. The VAS implementation was carried out using the programming language Python3.9 programming language and the color parameters obtained were compared with those obtained on a Minolta CR-400 colorimeter (CM). Both systems were synchronized to provide the user with information about the color coordinates in the samples of loins stored for 6 days at 4°C. The results obtained showed no significant differences. The results obtained showed no significant differences in the values of the L* parameter, while b* and a* showed significant differences during the storage time of the loins. These results are attributed to the oxidation process of the myoglobin and to factors such as breed, feeding and slaughtering process of the cattle, which affect the color of the samples. The results obtained indicate that VAS could be used for the determination of color during the storage of beef loins in real time, offering a non-invasive and low-cost solution to the actors in the meat chain.Keywords: image analysis, beef, colorimeter, artificial vision system.

A real-time embedded system designed for NILM studies with a novel competitive decision process algorithm

A real-time embedded system designed for NILM studies with a novel competitive decision process algorithm

Revitalizing the single batch environment: a ‘Quest’ to achieve fairness and efficiency

In the realm of computer systems, efficient utilization of the CPU (Central Processing Unit) has always been a paramount concern. Researchers and engineers have long sought ways to optimize process execution on the CPU, leading to the emergence of CPU scheduling as a field of study. In this research, we have analyzed the single offline batch processing and investigated other sophisticated paradigms such as time-sharing operating systems and wildly used algorithms, and their shortcomings. Our work is directed toward two fundamental aspects of scheduling: efficiency and fairness. We propose a novel algorithm for batch processing that operates on a preemptive model, dynamically assigning priorities based on a robust ratio, employing a dynamic time slice, and utilizing periodic sorting to achieve fairness. By engineering this responsive and fair model, the proposed algorithm strikes a delicate balance between efficiency and fairness, providing an optimized solution for batch scheduling while ensuring system responsiveness.

Reconstruction of gene regulatory networks for Caenorhabditis elegans using tree-shaped gene expression data.

Constructing gene regulatory networks is a widely adopted approach for investigating gene regulation, offering diverse applications in biology and medicine. A great deal of research focuses on using time series data or single-cell RNA-sequencing data to infer gene regulatory networks. However, such gene expression data lack either cellular or temporal information. Fortunately, the advent of time-lapse confocal laser microscopy enables biologists to obtain tree-shaped gene expression data of Caenorhabditis elegans, achieving both cellular and temporal resolution. Although such tree-shaped data provide abundant knowledge, they pose challenges like non-pairwise time series, laying the inaccuracy of downstream analysis. To address this issue, a comprehensive framework for data integration and a novel Bayesian approach based on Boolean network with time delay are proposed. The pre-screening process and Markov Chain Monte Carlo algorithm are applied to obtain the parameter estimates. Simulation studies show that our method outperforms existing Boolean network inference algorithms. Leveraging the proposed approach, gene regulatory networks for five subtrees are reconstructed based on the real tree-shaped datatsets of Caenorhabditis elegans, where some gene regulatory relationships confirmed in previous genetic studies are recovered. Also, heterogeneity of regulatory relationships in different cell lineage subtrees is detected. Furthermore, the exploration of potential gene regulatory relationships that bear importance in human diseases is undertaken. All source code is available at the GitHub repository https://github.com/edawu11/BBTD.git.

Natural language processing algorithms for domain-specific data extraction in material science: Reseractor

Natural language processing algorithms for domain-specific data extraction in material science: Reseractor

Two-Dimensional Laplace NMR Reconstruction through Deep Learning Enhancement.

Laplace NMR is a powerful tool for studying molecular dynamics and spin interactions, providing diffusion and relaxation information that complements Fourier NMR used for composition determination and structure elucidation. However, Laplace NMR demands sophisticated signal processing algorithms such as inverse Laplace transform (ILT). Due to the inherently ill-posed nature of ILT problems, it is generally challenging to perform satisfactory Laplace NMR processing and reconstruction, particularly for two-dimensional Laplace NMR. Herein, we propose a proof-of-concept approach that blends a physics-informed strategy with data-driven deep learning for two-dimensional Laplace NMR reconstruction. This approach integrates prior knowledge of mathematical and physical laws governing multidimensional decay signals by constructing a forward process model to simulate relationships among different decay factors. Benefiting from a noniterative neural network algorithm that automatically acquires prior information from synthetic data during training, this approach avoids tedious parameter tuning and enhances user friendliness. Experimental results demonstrate the practical effectiveness of this approach. As an advanced and impactful technique, this approach brings a fresh perspective to multidimensional Laplace NMR inversion.

Hybrid design scheme for enabling large-aperture diffractive achromat imaging

Diffractive achromats (DAs) combined with image processing algorithms offer a promising lens solution for high-performance ultra-thin imagers. However, the design of large-aperture DAs that align seamlessly with image processing algorithms remains challenging. Existing sequential methods, which prioritize focusing efficiency in DAs before selecting an algorithm, may not achieve a satisfactory match due to an ambiguous relationship between efficiency and final imaging quality. Conversely, image-quality-oriented end-to-end design often entails high computational complexity for both front-end optics and back-end algorithms, impeding the development of large-aperture designs. To address these issues, we present a hybrid design scheme that begins with end-to-end optimization of the DA with the simplest image processing algorithm, i.e., Wiener filter, significantly reducing the back-end complexity. Subsequently, we apply complex algorithm fine-tuning to further enhance image quality. We validate this hybrid design scheme through extensive investigations on several DA imagers. Our results demonstrate a reduction in memory requirement by approximately 50% while maintaining a high imaging quality with a reasonably large aperture. As a case in point, we simulated a DA imager with a 25 mm diameter aperture. Furthermore, our hybrid design scheme provides two crucial insights. Firstly, we find no strong linear correlation between focusing efficiency and imaging quality, which challenges the conventional understanding. Secondly, we establish a prediction formula for imaging quality, benefiting from the hybrid design scheme.

A Method for Extracting Joints on Mountain Tunnel Faces Based on Mask R-CNN Image Segmentation Algorithm

The accurate distribution of joints on the tunnel face is crucial for assessing the stability and safety of surrounding rock during tunnel construction. This paper introduces the Mask R-CNN image segmentation algorithm, a state-of-the-art deep learning model, to achieve efficient and accurate identification and extraction of joints on tunnel face images. First, digital images of tunnel faces were captured and stitched, resulting in 286 complete images suitable for analysis. Then, the joints on the tunnel face were extracted using traditional image processing algorithms, the commonly used U-net image segmentation model, and the Mask R-CNN image segmentation model introduced in this paper to address the lack of recognition accuracy. Finally, the extraction results obtained by the three methods were compared. The comparison results show that the joint extraction method based on the Mask R-CNN image segmentation deep learning model introduced in this paper achieved the best joint extraction effect with a Dice similarity coefficient of 87.48%, outperforming traditional methods and the U-net model, which scored 60.59% and 75.36%, respectively, realizing accurate and efficient acquisition of tunnel face rock joints. These findings suggest that the Mask R-CNN model can be effectively implemented in real-time monitoring systems for tunnel construction projects.

A Multiobjective Optimization Algorithm for Fluid Catalytic Cracking Process with Constraints and Dynamic Environments

The optimization problems in a fluid catalytic cracking process with dynamic constraints and conflicting objectives are challenging due to the complicated constraints and dynamic environments. The decision variables need to be reoptimized to obtain the best objectives when dynamic environments arise. To solve these problems, we established a mathematical model and proposed a dynamic constrained multiobjective optimization evolution algorithm for the fluid catalytic cracking process. In this algorithm, we design an offspring generation strategy based on minimax solutions, which can explore more feasible regions and converge quickly. Additionally, a dynamic response strategy based on population feasibility is proposed to improve the feasible and infeasible solutions by different perturbations, respectively. To verify the effectiveness of the algorithm, we test the algorithm on ten instances based on the hypervolume metric. Experimental results show that the proposed algorithm is highly competitive with several state-of-the-art competitors.

A Morphological Approach to Development of a Process for Measurement Uncertainty Estimation

The problem of increasing the reliability of uncertainty estimation of measurement results is considered. The purpose of this work was to justify the application of the process approach to the formation of the algorithm of uncertainty estimation on the basis of morphological analysis. It is theoretically substantiated that from the standpoint of the system approach to achieving an acceptable degree of reliability of measurement uncertainty estimates it is necessary to implement a process approach to the formation of the estimation method as an algorithm of actions. The main stages of the estimation process are defined. It is established that each stage of the estimation process can be realized by alternative methods. The morphological box method as a realization of morphological analysis is proposed as a basis for its solution. A morphological box design of the uncertainty estimation process with an open architecture is presented, based only on commonly accepted methods and approaches for realizing each step of the process. Two aspects of the application of the morphological box method are identified. On the one hand, the morphological box allows to form an algorithm of the uncertainty assessment process, maximally acceptable for the laboratory conditions, as a combination of process steps, based on the task at hand, combining different variants of realization of these steps. On the other hand, the morphological box acts as a tool for development of new methods of realization of various stages of the uncertainty assessment process. Examples of using the morphological box method to develop alternative algorithms of the uncertainty estimation process of the same measurement method and to develop new methods of realization of different stages of the estimation process are considered.

ASQ-IT: Interactive explanations for reinforcement-learning agents

As reinforcement learning methods increasingly amass accomplishments, the need for comprehending their solutions becomes more crucial. Most explainable reinforcement learning (XRL) methods generate a static explanation depicting their developers' intuition of what should be explained and how. In contrast, literature from the social sciences proposes that meaningful explanations are structured as a dialog between the explainer and the explainee, suggesting a more active role for the user and her communication with the agent. In this paper, we present ASQ-IT – an interactive explanation system that presents video clips of the agent acting in its environment based on queries given by the user that describe temporal properties of behaviors of interest. Our approach is based on formal methods: queries in ASQ-IT's user interface map to a fragment of Linear Temporal Logic over finite traces (LTLf), which we developed, and our algorithm for query processing is based on automata theory. User studies show that end-users can understand and formulate queries in ASQ-IT and that using ASQ-IT assists users in identifying faulty agent behaviors.

An unsupervised coherent receiver digital signal processing algorithm based on spectral clustering with no data preamble

AbstractA coherent optical receiver digital signal processing (DSP) scheme is proposed based on spectral clustering, which utilises spectral clustering to cluster the signals outputted by the DSP. Compared to existing blind DSP algorithms for coherent optical receivers operating at over 100Gbps with PM‐mQAM, the proposed scheme effectively suppresses various physical impairments, achieving lower bit error rates (BERs) at the same transmission distance, thus enabling longer transmission distances under the same forward error correction threshold. Furthermore, simulations of the proposed scheme on a 14GBaud PM‐16QAM coherent optical transmission system show that, at a BER of 1.9E‐2, the maximum transmission distance increases from 1700 to 2000 km, and at a BER of 3.8E‐3, it increases from 700 to 900 km.

Supervised Factor Analysis Transfer: Calibration transfer with noise modeling and response variable integration

Multivariate calibration models often encounter challenges in extrapolating beyond the calibration instruments due to variations in hardware configurations, signal processing algorithms, or environmental conditions. Calibration transfer techniques have been developed to mitigate this issue. In this study, we introduce a novel methodology known as Supervised Factor Analysis Transfer (SFAT) aimed at achieving robust and interpretable calibration transfer. SFAT operates from a probabilistic framework and integrates response variables into its transfer process to effectively align data from the target instrument to that of the source instrument. Within the SFAT model, the data from the source instrument, the target instrument, and the response variables are collectively projected onto a shared set of latent variables. These latent variables serve as the conduit for information transfer between the three distinct domains, thereby facilitating effective spectra transfer. Moreover, SFAT explicitly models the noise variances associated with each variable, thereby minimizing the transfer of non-informative noise. Furthermore, we provide empirical evidence showcasing the efficacy of SFAT across three real-world datasets, demonstrating its superior performance in calibration transfer scenarios.

Compact Spatial Heterodyne Spectrographs for Future Space-Based Observations: Instrument Modeling and Applications.

High-resolution spectroscopy employing spatial heterodyne spectrographs (SHS) holds significant promise for forthcoming space missions, building upon its established track record in science applications. Notably, it offers exceptional performance and cost- effectiveness in the ultraviolet-visual (UV-Vis) region compared to contemporary instruments. SHS instruments provide high-resolution capabilities and substantially larger etendues than similar resolving power instruments. This study introduces a comprehensive Python-based SHS model integrated with a user-friendly web scraping interface for target star selection, parameter generation, and 2D interferogram creation. Our SHS model demonstrates double the resolving power of a grating spectrometer and a throughput comparable to a Fourier transform spectrometer (FTS) but without moving parts, enhancing robustness for deployment in space. The interferogram processing algorithm includes flat-fielding, bias removal, apodization, and an inverse Fourier transform (IFT) for accurate spectrum retrieval. Despite bandwidth limitations due to resolving power constraints, SHS models excel in applications requiring high spectral resolution over narrow wavelength ranges, such as studying isotopic emission lines. The model provides optimization results and trade-offs for system parameters, ensuring precise spectral recovery with realistic signal-to-noise ratio (SNR) values. SHS is versatile and effective for various scientific applications, including investigating atomic and molecular emissions from comets, planetary atmospheres, the Earth's atmosphere, the Sun, and the interstellar medium (ISM). This research significantly contributes to expediting the development and deployment of SHS instruments, demonstrating their potential across numerous scientific domains.

Utilizing color analysis and image processing for the assessment of the color of block para rubber

This research explores the use of image processing and color analysis as a reliable approach for evaluating para rubber color quality. An automated color comparator prototype, based on the CIELAB color method and employing various image processing algorithms, is introduced. The research aims to enhance the precision of color analysis for block para rubbers (BPRs), a critical raw material in the rubber industry, impacted by environmental conditions during the sun-drying process. The study follows international standards for BPR specimen preparation and employs an image acquisition system, threshold techniques, and algorithms for color analysis. The proposed method aligns with Rubber Research Institute standards. The outcomes demonstrate the effectiveness of the automated system in accurately assessing the color values of BPRs, providing valuable insights for the rubber industry. This image processing technique offers advantages in natural rubber production for products requiring specific color characteristics.

Automated image processing algorithm for 3D OCT images of fouling in spacer-filled membrane filtration channels

OCT imaging is an important technique to study fouling in spacer-filled channels of reverse osmosis systems for seawater desalination. However, OCT imaging of membrane filtration channels with feed spacers is challenging because the spacer material can be (partly) transparent, making it difficult to detect and possibly mistaken for fouling, and the longer optical pathway through the spacer material distorts the image below the spacer. This study presents an automated 3D OCT image processing method in MATLAB for visualization and quantification of biofouling in spacer-filled channels. First, a spacer template of arbitrary size and rotation was generated from a CT scan of the feed spacer. Second, background noise and file size were reduced by representing the OCT image with a list of discrete reflectors. Finally, the spacer template was overlayed with the feed spacer in the 3D OCT image, enabling automated visualization of the feed spacer and correction of the distortions. Moreover, the method allows the selection of datasets with the same location relative to the position of the spacer, enabling systematic comparison between datasets and quantitative analysis.•A spacer template of arbitrary size and rotation was generated from a CT scan.•The background noise was removed, and the file size was reduced by representing the OCT dataset with a list of discrete reflectors.•The spacer template was overlayed with the feed spacer in the 3D OCT image.

Split-Aperture 2-in-1 Computational Cameras

While conventional cameras offer versatility for applications ranging from amateur photography to autonomous driving, computational cameras allow for domain-specific adaption. Cameras with co-designed optics and image processing algorithms enable high-dynamic-range image recovery, depth estimation, and hyperspectral imaging through optically encoding scene information that is otherwise undetected by conventional cameras. However, this optical encoding creates a challenging inverse reconstruction problem for conventional image recovery, and often lowers the overall photographic quality. Thus computational cameras with domain-specific optics have only been adopted in a few specialized applications where the captured information cannot be acquired in other ways. In this work, we investigate a method that combines two optical systems into one to tackle this challenge. We split the aperture of a conventional camera into two halves: one which applies an application-specific modulation to the incident light via a diffractive optical element to produce a coded image capture, and one which applies no modulation to produce a conventional image capture. Co-designing the phase modulation of the split aperture with a dual-pixel sensor allows us to simultaneously capture these coded and uncoded images without increasing physical or computational footprint. With an uncoded conventional image alongside the optically coded image in hand, we investigate image reconstruction methods that are conditioned on the conventional image, making it possible to eliminate artifacts and compute costs that existing methods struggle with. We assess the proposed method with 2-in-1 cameras for optical high-dynamic-range reconstruction, monocular depth estimation, and hyperspectral imaging, comparing favorably to all tested methods in all applications.

Deep Sketch Vectorization via Implicit Surface Extraction

We introduce an algorithm for sketch vectorization with state-of-the-art accuracy and capable of handling complex sketches. We approach sketch vectorization as a surface extraction task from an unsigned distance field, which is implemented using a two-stage neural network and a dual contouring domain post processing algorithm. The first stage consists of extracting unsigned distance fields from an input raster image. The second stage consists of an improved neural dual contouring network more robust to noisy input and more sensitive to line geometry. To address the issue of under-sampling inherent in grid-based surface extraction approaches, we explicitly predict undersampling and keypoint maps. These are used in our post-processing algorithm to resolve sharp features and multi-way junctions. The keypoint and undersampling maps are naturally controllable, which we demonstrate in an interactive topology refinement interface. Our proposed approach produces far more accurate vectorizations on complex input than previous approaches with efficient running time.

A three-degrees-of-freedom motion error measurement system based on Mach–Zehnder interferometry

This paper proposes a three-degree-of-freedom motion error measurement system based on Mach–Zehnder interferometry. The system consisted of an optical flat, beam-splitting prisms, and a reflector. When the stage was moving, the straightness error of the linear guides changed the optical path difference, which in turn caused a phase shift in the interferogram. This phase shift could be calculated by utilizing an image processing algorithm that was also developed during this study. The 3-DOF straightness errors, which included one linear error and two angular errors could be calculated. A commercial laser interferometer and a chromatic confocal sensor were used as reference sensors. The experimental results showed that the linear and angular measurement errors of the system were within ± 0.05 μm and ± 0.3″, respectively over measurement ranges of 100 mm and 60″. The repeatability, which was expressed by the standard deviation, was within 40 nm and 0.3″ for the linear and angular measurements, respectively. The proposed Mach-Zehnder interferometer has been successfully applied in a high-accuracy 3D surface topographic measurement system. It was able to effectively compensate the motion error and improve the accuracy for surface profiling.

Large-area magnetic skin for multi-point and multi-scale tactile sensing with super-resolution

The advancements in tactile sensor technology have found wide-ranging applications in robotic fields, resulting in remarkable achievements in object manipulation and overall human-machine interactions. However, the widespread availability of high-resolution tactile skins remains limited, due to the challenges of incorporating large-sized, robust sensing units and increased wiring complexity. One approach to achieve high-resolution and robust tactile skins is to integrate a limited number of sensor units (taxels) into a flexible surface material and leverage signal processing techniques to achieve super-resolution sensing. Here, we present a magnetic skin consisting of multi-direction magnetized flexible films and a contactless Hall sensor array. The key features of the proposed sensor include the specific magnetization arrangement, K-Nearest Neighbors (KNN) clustering algorithm and convolutional neural network (CNN) model for signal processing. Using only an array of 4*4 taxels, our magnetic skin is capable of achieving super-resolution perception over an area of 48400 mm2, with an average localization error of 1.2 mm. By employing neural network algorithms to decouple the multi-dimensional signals, the skin can achieve multi-point and multi-scale perception. We also demonstrate the promising potentials of the proposed sensor in intelligent control, by simultaneously controlling two vehicles with trajectory mapping on the magnetic skin.