Fast Processing Time Research Articles

Microwave Imaging of Breast Skin Utilizing Elliptical UWB Antenna and Reverse Problems Algorithm.

Skin cancer is one of the most widespread and fast growing of all kinds of cancer since it affects the human body easily due to exposure to the Sun’s rays. Microwave imaging has shown better outcomes with higher resolution, faster processing time, mobility, and less cutter and artifact effects. A miniaturized elliptical ultra-wideband (UWB) antenna and its semi-spherical array arrangement were used for signal transmission and reception from the defected locations in the breast skin. Several conditions such as various arrays of three, six, and nine antenna elements, smaller tumor, multi-tumors, and skin on a larger breast sample of 30 cm were considered. To assess the ability of the system, a breast shape container with a diameter of 130 mm and height of 60 mm was 3D printed and then filled with fabricated skin and breast fat to perform the experimental investigation. An improved modified time-reversal algorithm (IMTR) was used to recreate 2D images of tumors with the smallest radius of 1.75 mm in any location within the breast skin. The reconstructed images using both simulated and experimental data verified that the system can be a reliable imaging system for skin cancer diagnosis having a high structural similarity index and resolution.

Comparative evaluation of different molecular methods for DNA extraction from individual Teladorsagia circumcincta nematodes

BackgroundThe purpose of this study was to develop a reliable DNA extraction protocol to use on individual Teladorsagia circumcincta nematode specimens to produce high quality DNA for genome sequencing and phylogenetic analysis. Pooled samples have been critical in providing the groundwork for T. circumcincta genome construction, but there is currently no standard method for extracting high-quality DNA from individual nematodes. 11 extraction kits were compared based on DNA quality, yield, and processing time.Results11 extraction protocols were compared, and the concentration and purity of the extracted DNA was quantified. Median DNA concentration among all methods measured on NanoDrop 2000™ ranged between 0.45–11.5 ng/μL, and on Qubit™ ranged between undetectable – 0.962 ng/μL. Median A260/280 ranged between 0.505–3.925, and median A260/230 ranged − 0.005 – 1.545. Larval exsheathment to remove the nematode cuticle negatively impacted DNA concentration and purity.ConclusionsA Schistosoma sp. DNA extraction method was determined as most suitable for individual T. circumcincta nematode specimens due to its resulting DNA concentration, purity, and relatively fast processing time.

Performant barcode decoding for herbarium specimen images using vector-assisted region proposals (VARP).

PremiseThe scale and associated costs of herbarium digitization make process automation appealing. One such process for many workflows is the association of specimen image files with barcode values stored with the specimen. Here, an innovation is presented that improves the speed and accuracy of decoding barcodes from specimen images.Methods and ResultsGeometric features common in barcodes are used to identify the regions of specimen images that are likely to contain a barcode. The proposed regions are then combined into a significantly reduced composite image that is decoded using traditional barcode reading libraries. Tested against existing solutions, this method demonstrated the highest success rate (96.5%) and the second fastest processing time (617 ms).ConclusionsThis method was developed to support a larger effort to automate specimen image post‐processing in real‐time, highlighting the importance of execution time. Although initially designed for herbarium digitization, this method may be useful for other high‐resolution applications.

Deep neural network ensemble for on-the-fly quality control-driven segmentation of cardiac MRI T1 mapping.

Recent developments in artificial intelligence have generated increasing interest to deploy automated image analysis for diagnostic imaging and large-scale clinical applications. However, inaccuracy from automated methods could lead to incorrect conclusions, diagnoses or even harm to patients. Manual inspection for potential inaccuracies is labor-intensive and time-consuming, hampering progress towards fast and accurate clinical reporting in high volumes. To promote reliable fully-automated image analysis, we propose a quality control-driven (QCD) segmentation framework. It is an ensemble of neural networks that integrate image analysis and quality control. The novelty of this framework is the selection of the most optimal segmentation based on predicted segmentation accuracy, on-the-fly. Additionally, this framework visualizes segmentation agreement to provide traceability of the quality control process. In this work, we demonstrated the utility of the framework in cardiovascular magnetic resonance T1-mapping - a quantitative technique for myocardial tissue characterization. The framework achieved near-perfect agreement with expert image analysts in estimating myocardial T1 value (r=0.987,p<.0005; mean absolute error (MAE)=11.3ms), with accurate segmentation quality prediction (Dice coefficient prediction MAE=0.0339) and classification (accuracy=0.99), and a fast average processing time of 0.39 second/image. In summary, the QCD framework can generate high-throughput automated image analysis with speed and accuracy that is highly desirable for large-scale clinical applications.

A deep learning approach for RGB image-based powdery mildew disease detection on strawberry leaves

In this study, Deep Learning (DL) was used to detect powdery mildew (PM), persistent fungal disease in strawberries to reduce the amount of unnecessary fungicide use, and the need for field scouts. This study optimised and evaluated several well-established learners, including AlexNet, SqueezeNet, GoogLeNet, ResNet-50, SqueezeNet-MOD1, and SqueezeNet-MOD2. Data augmentation was carried out from among 1450 healthy and infected leaf images to prevent overfitting and to consider the various shapes and direction of the leaves in the field. A total of eight clockwise rotations (0°; the original data, 45°, 90°, 135°, 180°, 225°, 270°, and 315°) was performed to generate 11,600 data points. Overall, the six DL algorithms that were used in this study showed on average of >92% in classification accuracy (CA). ResNet-50 gave the highest CA of 98.11% in classifying the healthy and infected leaves; however, considering the computation time, AlexNet had the fastest processing time, at 40.73 s, to process 2320 images with a CA of 95.59%.When considering the memory requirements for hardware deployment, SqueezeNet-MOD2 would be recommended for PM detection on strawberry leaves with a CA of 92.61%.

Stemmer and phonotactic rules to improve n-gram tagger-based indonesian phonemicization

A phonemicization or grapheme-to-phoneme conversion (G2P) is a process of converting a word into its pronunciation. It is one of the essential components in speech synthesis, speech recognition, and natural language processing. The deep learning (DL)-based state-of-the-art G2P model generally gives low phoneme error rate (PER) as well as word error rate (WER) for high-resource languages, such as English and European, but not for low-resource languages. Therefore, some conventional machine learning (ML)-based G2P models incorporated with specific linguistic knowledge are preferable for low-resource languages. However, these models are poor for several low-resource languages because of various issues. For instance, an Indonesian G2P model works well for roots but gives a high PER for derivatives. Most errors come from the ambiguities of some roots and derivative words containing four prefixes: 〈ber〉, 〈meng〉, 〈peng〉, and 〈ter〉. In this research, an Indonesian G2P model based on n-gram combined with stemmer and phonotactic rules (NGTSP) is proposed to solve those problems. An investigation based on 5-fold cross-validation, using 50 k Indonesian words, informs that the proposed NGTSP gives a much lower PER of 0.78% than the state-of-the-art Transformer-based G2P model (1.14%). Besides, it also provides a much faster processing time.

Flame Image Processing and Classification Using a Pre-Trained VGG16 Model in Combustion Diagnosis.

Nowadays, despite a negative impact on the natural environment, coal combustion is still a significant energy source. One way to minimize the adverse side effects is sophisticated combustion technologies, such as, e.g., staged combustion, co-combustion with biomass, and oxy-combustion. Maintaining the combustion process at its optimal state, considering the emission of harmful substances, safe operation, and costs requires immediate information about the process. Flame image is a primary source of data which proper processing make keeping the combustion at desired conditions, possible. The paper presents a method combining flame image processing with a deep convolutional neural network (DCNN) that ensures high accuracy of identifying undesired combustion states. The method is based on the adaptive selection of the gamma correction coefficient (G) in the flame segmentation process. It uses the empirically determined relationship between the G coefficient and the average intensity of the R image component. The pre-trained VGG16 model for classification was used. It provided accuracy in detecting particular combustion states on the ranging from 82 to 98%. High accuracy and fast processing time make the proposed method possible to apply in the real systems.

ICS: Interoperable Communication System for Inter-Domain Routing in Internet-of-Things

The Internet-of-Things consists of heterogeneous smart appliances connected by global network with self-configuring capabilities requiring interoperable communication schemes while performing inter-domain routing. A review of existing interoperable approaches shows that there is still a large scope of improving IoT interoperability. The proposed system introduces Interoperable Communication System (ICS) by developing a novel inter-domain routing in IoT using two schemes. Preemptive and Non-Preemptive Communication scheme targets mainly emergency-based routing, which demands faster transmission, and dedicated transmission, demanding accountability in communication. A simulation study carried out for the proposed system shows that it offers approximately 90% reduced delay, 57% increased packet delivery ratio, and 98% faster processing time when compared with existing approaches to accomplish interoperability in IoT.

ParaDist-HMM: A Parallel Distributed Implementation of Hidden Markov Model for Big Data Analytics using Spark

Big Data is an extremely massive amount of hetero-geneous and multisource data which often requires fast processing and real time analysis. Solving big data analytics problems needs powerful platforms to handle this enormous mass of data and efficient machine learning algorithms to allow the use of big data full potential. Hidden Markov models are statistical models, rich and widely used in various fields especially for time varying data sequences modeling and analysis. They owe their success to the existence of many efficient and reliable algorithms. In this paper, we present ParaDist-HMM, a parallel distributed imple-mentation of hidden Markov model for modeling and solving big data analytics problems. We describe the development and the implementation of the improved algorithms and we propose a Spark-based approach consisting in a parallel distributed big data architecture in cloud computing environment, to put the proposed algorithms into practice. We evaluated the model on synthetic and real financial data in terms of running time, speedup and prediction quality which is measured by using the accuracy and the root mean square error. Experimental results demonstrate that ParaDist-HMM algorithms outperforms other implementations of hidden Markov models in terms of processing speed, accuracy and therefore in efficiency and effectiveness.

Comparison of an extracellular v. total DNA extraction approach for environmental DNA-based monitoring of sediment biota

Monitoring sediment biota is an essential step for the quality assessment of aquatic ecosystems. Environmental DNA-based approaches for biomonitoring are increasing in popularity; yet, commercial kits and protocols for extracting total DNA from sediments remain expensive and time-consuming. Furthermore, they can accommodate only small amounts of sediments, potentially preventing an adequate representation of local biodiversity, especially for macro-organisms. Here, we assessed the reliability of a cost- and time-effective extracellular DNA extraction approach able to account for large volumes of starting material, for characterising bacterial, eukaryote and metazoan communities in three sedimentary environments. DNA concentrations extracted with the extracellular approach were at least similar to those obtained with the commercial kit. Local diversity estimates were not biased towards any particular extraction method, although specific responses were observed depending of the sediment type. Community composition and β-diversity patterns were moderately affected by the extraction approach and the initial amount of starting material; differences being more important for macro- than microorganisms. Thus, the extracellular DNA approach appears as robust and efficient as those based on the commercially available kit for biomonitoring sedimentary communities. Its low cost and fast processing time make it a promising alternative for large-scale ecological assessments of aquatic environments.

Generalized self‐tuning system for adaptive threshold estimators in cognitive radio systems using swarm and evolutionary‐based approaches

AbstractParameter‐based adaptive threshold estimators (ATEs) are widely used for signal detection in cognitive radio (CR) systems. However, their performance deteriorates under dynamic spectra conditions owing to a lack of valid methods to accurately self‐tune the different parameters of such ATEs. In this article, we address this limitation by proposing a generalized system for self‐tuning the parameters of any ATE based only on the input signal measured per time. We adopt swarm and evolutionary‐based metaheuristic optimization techniques to effectively search for the optimal parameter values of any ATE. Our system controls the search process by applying the between‐class variance function adapted from Otsu's algorithm as the objective function. We tested the system using five different metaheuristic optimization algorithms (MOAs) to self‐tune two different ATEs, namely the recursive one‐sided hypothesis testing (ROHT) technique and the histogram partitioning algorithm under Rayleigh and Rician fading channels, as well as under different modulation schemes, including the 4‐quadrature amplitude modulation and 4‐phase shift keying schemes. Our findings suggest that our proposed system yields generally a small error rate irrespective of the MOA used. In addition, the ROHT‐cuckoo search optimization configuration yielded a reasonably high and low probability of detection and probability of false alarm, respectively, as a function of the signal‐to‐noise‐ratio of the input signal at a fast average processing time of 0.0699 seconds. We concluded that our system presents an effective mechanism that can be used to automatically tune the parameters of any ATE for useful signal detection in CR.

Building Balinese Part-of-Speech Tagger Using Hidden Markov Model (HMM)

Part-of-Speech tagging or word class labeling is a process for labeling a word class in a word in a sentence. Previous research on POS Tagger, especially for Indonesian, has been done using various approaches and obtained high accuracy values. However, not many researchers have built POS Tagger for Balinese. In this article, we are interested in building a POS Tagger for Balinese using a probabilistic approach, specifically the Hidden Markov Model (HMM). HMM is selected to deal with ambiguity since it gives higher accuracy and fast processing time. We used k-fold cross-validation (with k = 10) and tagged corpus around 3669 tokens with 21 tags. Based on the experiments conducted, the HMM method obtained an accuracy of 68.56%.

Mechanical properties and microstructural evolution of solder alloys fabricated using laser-assisted bonding

The mass reflow (MR) process is widely used in electronic packaging interconnection. However, the conventional MR process can cause thermal damage to components and requires a long process time. The laser-assisted bonding (LAB) process is considered as an alternative soldering process for overcoming these limitations due to its extremely fast process time and high thermal selectivity. The LAB process causes low thermal damage; hence, it can be used to fabricate flexible and stretchable applications. This study investigates the mechanical properties, microstructural variation, and intermetallic reaction between the SAC 305 solder and Cu electrodes. The mechanical properties are investigated using shear tests, while the microstructure is analyzed using scanning electron microscopy and X-ray scans. Furthermore, the reliability of the LAB- and MR-produced SAC 305 solders is assessed using a high-temperature storage test. As a result, the intermetallic compound layer is found to be considerably thinner, and the volume of voids in the solder matrix is lower in the LAB-produced solder than that in the MR-produced solder. The mechanical properties of the LAB-produced solders are enhanced by these microstructural advantages.

TAMI-64. PHYSICAL STRESSES IN BRAIN TUMORS

Abstract Solid stress, distinct from fluid pressure, is a physical force contained in and transmitted by solid components of the brain tumor, including cells and the matrix they produce. Solid stress has been shown to promote tumor progression, and decrease anticancer therapy efficacy. This is especially relevant in brain tumors, as the rigid skull results in these trapped forces, increasing intracranial pressure, and potentially leading to other complications, including neuronal cell death. Here we present a novel method of quantifying these physical stresses in situ in both mice (glioblastoma [U87], brain metastasis [BT474], and ependymoma models) and patients. Briefly, following a craniotomy, mechanical forces that include solid stress are released, which causes the tissue to deform in peaks (areas under compression) and valleys (areas originally under tension). This tissue deformation is imaged via high-resolution ultrasound, and analysed via custom code to produce an accurate 3D model of the entire mouse brain, including the tumour region. For human samples, a pre-operative MRI is used to generate a detailed 3D model of the human brain. During surgery, the trapped physical stresses results in a bulge of the dura post craniotomy, which is mapped via Brainlab. These deformations are analysed in an identical fashion to the murine model. We further show that in the brain metastases model, chemotherapy reduces compression stresses by 51%. Further, our technique results in fast processing time (~ 15 minutes), and has the potential to prevent the need for intraoperative MRI based on position simulations. As such, solid stress measurements provide a new class of mechanical biomarkers that can be correlated to clinical outcomes for predictive and prognostic value.

Appropriate search techniques to estimate Weibull function parameters in a Pinus spp. plantation

The Weibull function, a continuous probability distribution, is widely used for diameter distribution modelling, in which parameter estimation performance is affected by stand attributes and fitting methods. The Weibull cumulative distribution function is nonlinear, and classical fitting methods may provide a not optimal solution. Invoking the use of artificial intelligence by metaheuristics is reasonable for this optimisation task. Therefore, aimed and compared (1) the metaheuristics genetic algorithm and simulated annealing performance over the moment and percentile methods; (2) the hybrid strategy merging the metaheuristics tested and the percentile method and, (3) the metaheuristics fitness functions under basal area and density errors. A long-term experiment in a Pinus taeda stand subjected to crown thinning was used. According to our findings, all methods have a similar performance, independent of the thinning regimes and age. The pattern of the estimated parameters tends to be acceptable, as b increases over time and c increases after thinning. Overall, our findings suggest that methods based on metaheuristics have a higher precision than classical methods for estimating Weibull parameters. According to the classification test, the methods that involved simulated annealing stood out. The hybrid method involving this metaheuristic also stood out, with accurate estimates. Classical methods showed the poorest performance, and we therefore suggest the use of simulated annealing due to its faster processing time and high-quality solution.

A faster way to purify blood cells

Plasmodium vivax is the most common malaria-causing parasite in Asia and South America. It preferentially infects an immature red blood cell that accounts for only 1–2% of circulating blood. The limited supply of these cells, called reticulocytes, has made it difficult to study P. vivax in the lab. Now, researchers at the Singapore-MIT Alliance for Research and Technology report two microfluidic methods for purifying lab-grown reticulocytes, one of which calls to mind the game Plinko from the game show The Price Is Right ( Lab Chip 2020, DOI: 10.1039/c9lc01128e ). The team demonstrates that the cheap, microfluidic chip-based methods offer comparable purity and faster processing times than standard sorting methods. These purification methods could also help advance the field’s long-standing goal of manufacturing red blood cells for transfusions. Learn more at cenm.ag/cellsorter .

Kidney Boundary Detection Algorithm Based on Extended Maxima Transformations for Computed Tomography Diagnosis

This article describes the automated computed tomography (CT) image processing technique supporting kidney detection. The main goal of the study is a fully automatic generation of a kidney boundary for each slice in the set of slices obtained in the computed tomography examination. This work describes three main tasks in the process of automatic kidney identification: the initial location of the kidneys using the U-Net convolutional neural network, the generation of an accurate kidney boundary using extended maxima transformation, and the application of the slice scanning algorithm supporting the process of generating the result for the next slice, using the result of the previous one. To assess the quality of the proposed technique of medical image analysis, automatic numerical tests were performed. In the test section, we presented numerical results, calculating the F1-score of kidney boundary detection by an automatic system, compared to the kidneys boundaries manually generated by a human expert from a medical center. The influence of the use of U-Net support in the initial detection of the kidney on the final F1-score of generating the kidney outline was also evaluated. The F1-score achieved by the automated system is 84% ± 10% for the system without U-Net support and 89% ± 9% for the system with U-Net support. Performance tests show that the presented technique can generate the kidney boundary up to 3 times faster than raw U-Net-based network. The proposed kidney recognition system can be successfully used in systems that require a very fast image processing time. The measurable effect of the developed techniques is a practical help for doctors, specialists from medical centers dealing with the analysis and description of medical image data.

A validated method for the quantitative determination of sugars in honey using high-performance thin-layer chromatography

Sugars, in particular glucose and fructose, are the main constituents of honey, comprising up to 85% of its total weight. A high-performance thin-layer chromatography (HPTLC) method to identify and quantify common sugars (glucose, fructose and sucrose) in honey has been developed and fully validated according to the International Conference on Harmonisation guidelines. It allows to determine a honey’s fructose-to-glucose ratio, which is not only an important authentication and quality control parameter, but also an indication of its tendency to crystallise. The HPTLC analysis is easy to perform, accurate, precise, specific and sensitive and requires only minimal sample preparation. With a limit of detection/limit of quantification of 21.98 ng/66.62 ng for fructose, 33.00 ng/100.00 ng for glucose and 21.17 ng/64.15 ng for sucrose, the sensitivity of the method has been greatly improved compared to other HPTLC-based approaches. An additional advantage is the method’s simplicity and fast processing time as it only requires a single development step without plate or sample pre-treatment.

Continuous Delivery of Customized SaaS Edge Applications in Highly Distributed IoT Systems

Edge computing is a reality for the current IoT systems that need fast processing and quick response time to make real-time decisions and IoT systems without permanent connectivity to the cloud (e.g., car manufacturing, precision agriculture, or cattle raising). Additionally, these industries are facing the need for rapid and continuous innovation by accelerating the delivery of over-the-air (OTA) software updates in edge devices. DevOps promotes collaboration between development and operation teams and automation at all steps of software construction to achieve continuous delivery (CD) of business value. Although DevOps has demonstrated numerous successful cases in the Web domain, in the IoT domain and, more specifically, at the edge, there are few reported cases. This work presents a success case of CD of customized software as a service software as a service (SaaS) updates at the IoT Edge. This may enable new business models at the IoT Edge. This article presents an architectural model of a highly distributed (cloud and edge) IoT system and a CD process flow for customized SaaS applications in edge nodes. Both the architectural model and the CD process flow are instantiated in a case study for precision agriculture.

Fine Doppler Frequency Estimation of Windowed Complex Sinusoidal Signals for Radar Systems

This article presents a new fine Doppler frequency estimation method of complex sinusoidal signals for radar systems. The proposed method can work with nonrectangular window functions to suppress the interference signal caused by the undesired spectral leakage. This method is based on an interpolation curve that uses the sum and difference of two discrete Fourier transform (DFT) magnitudes, and can estimate an accurate frequency between the interpolation curve samples through precise linear approximation. The proposed method exhibits lower computational complexity than the conventional method, which compensates the bias of an arbitrary window function and exhibits high performance. Thus, the proposed method is applicable to radar systems requiring fast processing time. Furthermore, the simulation results show that the proposed method outperforms the conventional method for various window functions.