Different Techniques For Application Research Articles

Constraint-Aware Multi-Technique Approximate High-Level Synthesis for FPGAs

Numerous approximate computing (AC) techniques have been developed to reduce the design costs in error-resilient application domains, such as signal and multimedia processing, data mining, machine learning, and computer vision, to trade-off computation accuracy with area and power savings or performance improvements. Selecting adequate techniques for each application and optimization target is complex but crucial for high-quality results. In this context, Approximate High-Level Synthesis (AHLS) tools have been proposed to alleviate the burden of hand-crafting approximate circuits by automating the exploitation of AC techniques. However, such tools are typically tied to a specific approximation technique or a difficult-to-extend set of techniques whose exploitation is not fully automated or steered by optimization targets. Therefore, available AHLS tools overlook the benefits of expanding the design space by mixing diverse approximation techniques toward meeting specific design objectives with minimum error. In this work, we propose an AHLS design methodology for FPGAs that automatically identifies efficient combinations of multiple approximation techniques for different applications and design constraints. Compared to single-technique approaches, decreases of up to \(30\% \) in mean squared error and absolute increases of up to \(6.5\% \) in percentage accuracy were obtained for a set of image, video, signal processing and machine learning benchmarks.

Conjugates-based design for microencapsulation of CO2 supercritical extract from red grape by-products to provide functional ingredients

In this study, red grape pomace from Cabernet variety was extracted by supercritical carbon dioxide using two sets of extraction conditions to recover the lipophilic and polyphenolic fractions. The extracts showed a different phytochemical profile, with a higher polyphenolic and carotenoids content in the extract obtained at a pressure of 45 MPa and temperature of 45 °C, whereas the extract obtained at 30 °C and 15 MPa showed a higher content in fatty acids, tocopherols, and phytosterols. The polyphenolic enriched extract showed good antibacterial activity against Staphylococcus aureus, Bacillus subtilis, and Pseudomonas fluorescens. The extracts were encapsulated in two conjugated designs, based on bovine β-lactoglobulin and lactose, with and without Maillard-induced glycosylation. The powders profile evidenced a higher emulsifying capacity of the conjugated powder, which allowed greater retention of fatty acids (264.13 ± 5.11 mg/g dry weight (DW)), whereas sample without heating showed a higher content of tocopherols (1.66 ± 0.39 μg/g DW) and phytosterols (77.01 ± 3.15 μg/g DW). Both powders showed a good retention index of polyphenols and flavonoids, with an enhanced protective effect registered for Maillard-induced powder. The results are valuable for providing a comprehensive phytochemical profile of the autochthon grape variety alongside improved solutions for bioactive stabilization techniques for different applications.

Implementation of Shared Laser–LED Sources in a Free Space Optics (FSO) Network under Environmental Impact

This paper is devoted to evaluating the combined coherent and incoherent sources (CCIS) technique for different applications in the optical domain and future optical code division multiple access (OCDMA) networks. Spectral amplitude coding (SAC) has gained significant attention in optical processing systems due to its increased capabilities in dealing with multiple-access interference (MAI) efficiently. Fixed right shift (FRS) is adopted as a signature code in this study. Furthermore, performance analysis is studied in terms of bit error rate (BER) for the system using CCIS in both the free space optics (FSO) and sky mesh network using an aerial altitude platform system (AAPS). Simulation results confirmed that a CCIS design significantly improves system performance with moderate cost. An acceptable BER value of 10−9 at 1.25 Gbps data rate and 60 km, 38 km, and 6 km distances for the laser, CCIS, and LED sources, respectively, can be supported. In particular, at Q-factor ~4.5, the FSO ranges under low haze, moderate haze, and heavy haze are, respectively, 3.7 km, 2.5 km, and 1.5 km. The reason is that a CCIS design causes an increase in the effective transmitted power. It can be summarized that a CCIS design can provide reliable solutions and an affordable cost for future optical fiber and wireless network applications.

ON PERFORMANCE OF ACCEPTANCE SAMPLING PLANS USING SEQUENTIAL PROBABILITY RATIO TEST BASED ON TRUNCATED LIFE TESTS USING SOME DISTRIBUTION FUNCTION

Sequential sampling procedure constitute a powerful tool to achieve experimental efficiency a sequence of sample are taken from the lot and allow the number of samples to be determined entirely by the result of the sampling process, it forms the basis of numerous sequential technique for different applications which was the special case for multiple acceptance plans, the performance of sequential probability ratio test provides better detection for a wide range of source strength and useful in choosing the best statistical test, which take less time to make decision, was developed on the software reliability measure based on failure intensity for the life time random variable for a truncated life test. In this paper, we considered the performance of acceptance sampling plans using sequential probability ration (SPRT) based on truncated life tests on Maxwell distribution and exponential distribution function in terms of obtaining the minimum number of sample sizes necessary to obtain specified average life time under a given consumer’s and producer’s risk. The SPRT provide an alternative to fix the plans that can help diminish producer and consumer risk of reaching at a wrong decision.

Novel hybrid ARIMA–BiLSTM model for forecasting of rice blast disease outbreaks for sustainable rice production

In recent years, the application of artificial intelligence (AI) in agriculture has grown to be the most important research domain. The proposed work focuses on forecasting rice blast disease outbreaks in paddy crops. Disease management in the farm fields is the most difficult problem on the planet. There is a variety of reasons for this, first, a lack of farmers’ experience in diagnosing diseases, second experts’ experience in detecting diseases visually, and third unfavorable climate. In recent days, researchers have offered a variety of time-series techniques in different applications. This study adds time-series techniques to the field of agriculture by forecasting crucial rice blast disease outbreaks in the paddy crop of the Davangere region based on daily weather data obtained from KSNDMC. The statistical time-series technique called ARIMA is trained by employing real data of blast disease outbreaks in the Davangere region from the period of 2015–2019. Meanwhile, the deep BiLSTM model is trained by employing real weather data and blast disease outbreaks of the Davangere region. Both models are evaluated by performance metrics, such as mean squared error and mean absolute error. The proposed research is focused on the hybrid model ARIMA–BiLSTM which is a combination of the statistical ARIMA model and deep BiLSTM model. The seasonal component of the rice blast disease outbreak feature is extracted from the additive decompose function used in the ARIMA model and fed as a dependent feature for the BiLSTM model. According to the results obtained, the hybrid approach can successfully forecast blast disease outbreaks in paddy crops with a mean squared error of 0.037 and a mean absolute error of 0.028 compared to the statistical ARIMA and deep BiLSTM model.

Modeling and Analysis of New Hybrid Clustering Technique for Vehicular Ad Hoc Network

Many researchers have proposed algorithms to improve the network performance of vehicular ad hoc network (VANET) clustering techniques for different applications. The effectiveness of the clustering model is the most important challenge. The K-Means clustering algorithm is an effective algorithm for multi-clusters that can be used in VANETs. The problems with the K-Means algorithm concern the selection of a suitable number of clusters, the creation of a highly reliable cluster, and achieving high similarity within a cluster. To address these problems, a novel method combining a covering rough set and a K-Means clustering algorithm (RK-Means) was proposed in this paper. Firstly, RK-Means creates multi-groups of vehicles using a covering rough set based on effective parameters. Secondly, the K-value-calculating algorithm computes the optimal number of clusters. Finally, the classical K-Means algorithm is applied to create the vehicle clusters for each covering rough set group. The datasets used in this work were imported from Simulation of Urban Mobility (SUMO), representing two highway scenarios, high-density and low-density. Four evaluation indexes, namely, the root mean square error (RMSE), silhouette coefficient (SC), Davies–Bouldin (DB) index, and Dunn index (DI), were used directly to test and evaluate the results of the clustering. The evaluation process was implemented on RK-Means, K-Means++, and OK-Means models. The result of the compression showed that RK-Means had high cluster similarity, greater reliability, and error reductions of 32.5% and 24.2% compared with OK-Means and K-Means++, respectively.

A Systematic Review of Sensor Fusion Methods Using Peripheral Bio-Signals for Human Intention Decoding

Humans learn about the environment by interacting with it. With an increasing use of computer and virtual applications as well as robotic and prosthetic devices, there is a need for intuitive interfaces that allow the user to have an embodied interaction with the devices they are controlling. Muscle–machine interfaces can provide an intuitive solution by decoding human intentions utilizing myoelectric activations. There are several different methods that can be utilized to develop MuMIs, such as electromyography, ultrasonography, mechanomyography, and near-infrared spectroscopy. In this paper, we analyze the advantages and disadvantages of different myography methods by reviewing myography fusion methods. In a systematic review following the PRISMA guidelines, we identify and analyze studies that employ the fusion of different sensors and myography techniques, while also considering interface wearability. We also explore the properties of different fusion techniques in decoding user intentions. The fusion of electromyography, ultrasonography, mechanomyography, and near-infrared spectroscopy as well as other sensing such as inertial measurement units and optical sensing methods has been of continuous interest over the last decade with the main focus decoding the user intention for the upper limb. From the systematic review, it can be concluded that the fusion of two or more myography methods leads to a better performance for the decoding of a user’s intention. Furthermore, promising sensor fusion techniques for different applications were also identified based on the existing literature.

Understanding the Mechanism of Abrasive-Based Finishing Processes Using Mathematical Modeling and Numerical Simulation

Recent advances in technology and refinement of available computational resources paved the way for the extensive use of computers to model and simulate complex real-world problems difficult to solve analytically. The appeal of simulations lies in the ability to predict the significance of a change to the system under study. The simulated results can be of great benefit in predicting various behaviors, such as the wind pattern in a particular region, the ability of a material to withstand a dynamic load, or even the behavior of a workpiece under a particular type of machining. This paper deals with the mathematical modeling and simulation techniques used in abrasive-based machining processes such as abrasive flow machining (AFM), magnetic-based finishing processes, i.e., magnetic abrasive finishing (MAF) process, magnetorheological finishing (MRF) process, and ball-end type magnetorheological finishing process (BEMRF). The paper also aims to highlight the advances and obstacles associated with these techniques and their applications in flow machining. This study contributes the better understanding by examining the available modeling and simulation techniques such as Molecular Dynamic Simulation (MDS), Computational Fluid Dynamics (CFD), Finite Element Method (FEM), Discrete Element Method (DEM), Multivariable Regression Analysis (MVRA), Artificial Neural Network (ANN), Response Surface Analysis (RSA), Stochastic Modeling and Simulation by Data Dependent System (DDS). Among these methods, CFD and FEM can be performed with the available commercial software, while DEM and MDS performed using the computer programming-based platform, i.e., “LAMMPS Molecular Dynamics Simulator,” or C, C++, or Python programming, and these methods seem more promising techniques for modeling and simulation of loose abrasive-based machining processes. The other four methods (MVRA, ANN, RSA, and DDS) are experimental and based on statistical approaches that can be used for mathematical modeling of loose abrasive-based machining processes. Additionally, it suggests areas for further investigation and offers a priceless bibliography of earlier studies on the modeling and simulation techniques for abrasive-based machining processes. Researchers studying mathematical modeling of various micro- and nanofinishing techniques for different applications may find this review article to be of great help.

A heuristic approach on predictive maintenance techniques: Limitations and scope

In view of the trend towards Industry 4.0, intelligent predictive monitoring and decision-making processes have become a crucial requirement in today’s manufacturing industries to safeguard data exchange and industrial assets from damage that would thus prevent the achievement of overall company goals. For enhanced reliability and safe operation of machines, frequent maintenance of the process equipment and the linked auxiliaries in a plant is highly desirable. Poor maintenance of assets can add to downtime, which can in turn affect the overall cost-effectiveness of the plant. With traditional maintenance strategies and planned or timed-based maintenance, one replaces the faulty systems when they are found to be damaged or broken. However, an early and proactive prediction of machine or equipment fault and failure state enables the industry to take the necessary action to replace the faulty system well before it stops operating entirely. This paper briefly reviews the available predictive maintenance techniques for different applications from the perspective of Industry 4.0. Furthermore, the associated challenges and opportunities are identified and discussed.

A Review of Optimization Techniques Application for Building Performance Analysis

Optimization techniques (OT) are tools to find the best solution during a decision making process. Each of these techniques has its own advantages and disadvantages depending on their objectives and focus areas. Early application of OT was recorded as early as the 1930s with the introduction of the Monte Carlo Method, which is widely used in business studies. The idea was conceived due to poly-objectives, or multi-objectives, in identifying the best solution. OT theories and methods have evolved to cover various fields of study. This paper aims to provide a brief review of OT through a comparison of the pros and cons of each OT technique. The findings emphasize the suitability of each technique for different applications in various fields of study. Finally, this study aims to select the most suitable OT for building performance for energy optimization. In conclusion, the summary of findings and recommendations from Tables 2, 3, and 4 need to be combined during the process of selecting the most suitable OT. Regardless of the various categories and their multiple applications, it is the summary of characteristics of each optimization technique that determines their suitability for adoption depending on the research objectives, strength of the researcher, and availability of data. ANN is suitable for optimization for building energy performance covering energy use, energy cost and energy prediction which offer a high level of accuracy. However, it is a complex model that requires historical data input and can produce only short-term predictions. For broader optimization objectives which cover energy load, a hybrid of ANN and kNN is recommended. Doi: 10.28991/CEJ-2022-08-04-014 Full Text: PDF

Metal additive manufacturing using friction stir engineering: A review on microstructural evolution, tooling and design strategies

Solid state metal additive manufacturing (SS-MAM) techniques based on friction stir engineering (FSE) have gained significant attention since the last decade and act as a bridge for many existing MAM shortfalls. In this article, SS-MAM techniques based upon FSE especially additive friction stir (AFS) and friction stir additive manufacturing (FSAM) are discussed. There is meagre research with respect to tooling associated with these processes since they involve expertise in two fields i.e., AM and FSE. The current article bridges this gap with a detailed discussion on all relevant fundamentals of AFS and FSAM. The recent developments with respect to tooling design and parameters are discussed in-depth. Also, this article discusses metallurgy, microstructural aspects and mechanical properties of deposited layers via these techniques. A detailed timeline summarizing the development with respect to these techniques has been presented. A complete section is dedicated to utilization of these techniques for different applications. Recent developments and trends are also discussed in detail.

Feature Extraction in Six Blocks to Detect and Recognize English Numbers

The Fuzzy Logic method was implemented to detect and recognize English numbers in this paper. The extracted features within this method make the detection easy and accurate. These features depend on the crossing point of two vertical lines with one horizontal line to be used from the Fuzzy logic method, as shown by the Matlab code in this study. The font types are Times New Roman, Arial, Calabria, Arabic, and Andalus with different font sizes of 10, 16, 22, 28, 36, 42, 50 and 72. These numbers are isolated automatically with the designed algorithm, for which the code is also presented. The number’s image is tested with the Fuzzy algorithm depending on six-block properties only. Groups of regions (High, Medium, and Low) for each number showed unique behavior to recognize any number. Normalized Absolute Error (NAE) equation was used to evaluate the error percentage for the suggested algorithm. The lowest error was 0.001% compared with the real number. The data were checked by the support vector machine (SVM) algorithm to confirm the quality and the efficiency of the suggested method, where the matching was found to be 100% between the data of the suggested method and SVM. The six properties offer a new method to build a rule-based feature extraction technique in different applications and detect any text recognition with a low computational cost.

Off-Target Analysis in Gene Editing and Applications for Clinical Translation of CRISPR/Cas9 in HIV-1 Therapy.

As genome-editing nucleases move toward broader clinical applications, the need to define the limits of their specificity and efficiency increases. A variety of approaches for nuclease cleavage detection have been developed, allowing a full-genome survey of the targeting landscape and the detection of a variety of repair outcomes for nuclease-induced double-strand breaks. Each approach has advantages and disadvantages relating to the means of target-site capture, target enrichment mechanism, cellular environment, false discovery, and validation of bona fide off-target cleavage sites in cells. This review examines the strengths, limitations, and origins of the different classes of off-target cleavage detection systems including anchored primer enrichment (GUIDE-seq), in situ detection (BLISS), in vitro selection libraries (CIRCLE-seq), chromatin immunoprecipitation (ChIP) (DISCOVER-Seq), translocation sequencing (LAM PCR HTGTS), and in vitro genomic DNA digestion (Digenome-seq and SITE-Seq). Emphasis is placed on the specific modifications that give rise to the enhanced performance of contemporary techniques over their predecessors and the comparative performance of techniques for different applications. The clinical relevance of these techniques is discussed in the context of assessing the safety of novel CRISPR/Cas9 HIV-1 curative strategies. With the recent success of HIV-1 and SIV-1 viral suppression in humanized mice and non-human primates, respectively, using CRISPR/Cas9, rigorous exploration of potential off-target effects is of critical importance. Such analyses would benefit from the application of the techniques discussed in this review.

New Inversion Approach for Interpreting Gravity Data Caused by Dipping Faults

AbstractA new inversion algorithm of gravity data utilizing the particle swarm optimization (PSO) algorithm is used to interpret dipping faults models. The PSO technique is stochastic in nature; its development was motivated by the common in‐flight performance of birds looking for food. Particles or models represent birds. Individual particles have a location and a velocity vector. The parameter value represents the position vectors. PSO is adjusted by random particles or models and searches for targets by updating generations. This algorithm determines the dipping faults different parameters (amplitude factor, depth to the center of the upper part of the layer, depth to the center of the lower part of the layer, fault dip angle, and the origin of the fault trace). Herein, the PSO algorithm is applied to noise‐free synthetic data, synthetic data contaminated with different random noise levels (5%, 10%, and 15%) and real field gravity data from Egypt. The applicability and efficiency of the PSO inversion algorithm are well demonstrated for synthetic and field gravity data. The errors of the different estimated parameters are calculated for synthetic data, also, the root mean square error is calculated for synthetic and real data. The parameters estimated from real data matches well with that resulted from different published techniques. From the results obtained by using the present technique, we can apply the proposed technique in different applications, like mining and ore exploration.

Ordering heuristics for k -clique listing

Listing all k -cliques in a graph is a fundamental graph mining problem that finds many important applications in community detection and social network analysis. Unfortunately, the problem of k -clique listing is often deemed infeasible for a large k , as the number of k -cliques in a graph is exponential in the size k. The state-of-the-art solutions for the problem are based on the ordering heuristics on nodes which can efficiently list all k -cliques in large real-world graphs for a small k (e.g., k ≤ 10). Even though a variety of heuristic algorithms have been proposed, there still lacks a thorough comparison to cover all the state-of-the-art algorithms and evaluate their performance using diverse real-world graphs. This makes it difficult for a practitioner to select which algorithm should be used for a specific application. Furthermore, existing ordering based algorithms are far from optimal which might explore unpromising search paths in the k -clique listing procedure. To address these issues, we present a comprehensive comparison of all the state-of-the-art k -clique listing and counting algorithms. We also propose a new color ordering heuristics based on greedy graph coloring techniques which is able to significantly prune the unpromising search paths. We compare the performance of 14 various algorithms using 17 large real-world graphs with up to 3 million nodes and 100 million edges. The experimental results reveal the characteristics of different algorithms, based on which we provide useful guidance for selecting appropriate techniques for different applications.

Evaluation of Particle Size Techniques to Support the Development of Manufacturing Scale Nanoparticles for Application in Pharmaceuticals

The measurement of nanoparticle size, and size distribution, is important to the development of pharmaceutical nanoparticle products and their manufacturing processes. In this work we report on the use of 4 widely-used liquid-phase techniques, dynamic light scattering, differential centrifugal sedimentation, particle tracking analysis, and tuneable resistive pulse sensing to measure 4 different batches of AZD2811NPs. The techniques rely on different physical principles to measure nanoparticle size. The batches cover a range of different manufacturing scales and different sites of manufacture, and were made to support toxicity, clinical, and engineering studies. The results from the different techniques and different batches are compared in terms of the average size, and size distribution, measured. In addition, we discuss the suitability of techniques for different applications, for example, QC and process understanding.

Ontology expansion based on UWN reusability

In the present era of big data the demand for developing efficient knowledge representation techniques for different applications is expanding steadily. Knowledge representation is largely based on ontology in the Semantic Web. However, one of the major difficulties to construct a new ontology require users the high cost and long time. Collecting complete knowledge about the domain of opening is time consuming and cannot guarantee expected results too. Therefore, reuse of existing resources can offer a cost and time benefit than building a new one from scratch. Hence, an approach is proposed to reuse the existing ontologies available on the web or online libraries then enrich it. This paper proposed Uyghur domain ontology constructing method based on UWN reusability, which can extract domain ontology factors from UWN and get the initial ontology in which the concepts and relations are already relative standard, then expand it automatically via Word2Vec models, at last construct the final higher quality version after revising and improving. The method is simple, but effective on ontology enrichment. After evaluating the accuracy, the experimental results show that our method is feasible and effective.

Fused Deposition modeling process parameters optimization and effect on mechanical properties and part quality: Review and reflection on present research

Abstract Additive manufacturing (AM) was developed initially as a technique for rapid prototyping, to visualize, test and authenticate a design, before end-user production of the design. In recent years, Additive Manufacturing (AM) technique Fused Deposition modeling (FDM), has developed to become a rapid manufacturing technique because of the ability to produce complex parts layer-by-layer in lesser production cycle time than as compared to conventional machining processes. FDM also offers the advantage of the lowest cost because of no tooling requirements. Despite these advantages, building parts by utilizing FDM for end-use is still a demanding endeavor. This is because FDM has multiple processing parameters, which affect the part quality, mechanical properties, build time and dimensional accuracy. These FDM processing parameters include air gap, build orientation, infill percentage, raster angle, layer thickness, etc. Depending upon the application, for which the part is manufactured, careful selection of these process parameters needs to be done. For a specific output requirement, some of the process parameters are significant than the rest, these significant process parameters need to be identified and optimized. Due to this, researchers have explored and utilized various experimental or statistical Design of Experiment (DOE) techniques for optimizing the FDM process parameters to improve the mechanical properties or part quality or both. Some of these DOE techniques include the Taguchi method, Genetic algorithm (GA), gray relational, Response surface method (RSM), fractional factorial, Artificial Neural networks (ANN), Fuzzy logic, ANOVA, etc. This article aims at reviewing the current research on the statistical and experimental design techniques for different applications or output responses such as enhancing mechanical properties, build time, part quality, etc.

An approach for realistically simulating the performance of scientific applications on high performance computing systems

Scientific applications often contain large, computationally-intensive, and irregular parallel loops or tasks that exhibit stochastic behavior leading to load imbalance. Load imbalance often manifests during the execution of parallel scientific applications on large and complex high performance computing (HPC) systems. The extreme scale of HPC systems on the road to Exascale computing only exacerbates the loss in performance due to load imbalance. Dynamic loop self-scheduling (DLS) techniques are instrumental in improving the performance of scientific applications on HPC systems via load balancing. Selecting a DLS technique that results in the best performance for different problem and system sizes requires a large number of exploratory experiments. Currently, a theoretical model that can be used to predict the scheduling technique that yields the best performance for a given problem and system has not yet been identified. Therefore, simulation is the most appropriate approach for conducting such exploratory experiments in a reasonable amount of time. However, conducting realistic and trustworthy simulations of application performance under different configurations is challenging. This work devises an approach to realistically simulate computationally-intensive scientific applications that employ DLS and execute on HPC systems. The proposed approach minimizes the sources of uncertainty in the simulative experiments results by bridging the native and simulative experimental approaches. A new method is proposed to capture the variation of application performance between different native executions. Several approaches to represent the application tasks (or loop iterations) are compared to establish their influence on the simulative application performance. A novel simulation strategy is introduced that applies the proposed approach, which transforms a native application code into simulative code. The native and simulative performance of two computationally-intensive scientific applications that employ eight task scheduling techniques (static, nonadaptive dynamic, and adaptive dynamic) are compared to evaluate the realism of the proposed simulation approach. The comparison of the performance characteristics extracted from the native and simulative performance shows that the proposed simulation approach fully captured most of the performance characteristics of interest. This work shows and establishes the importance of simulations that realistically predict the performance of DLS techniques for different applications and system configurations.

An Ensemble Voted Feature Selection Technique for Predictive Modeling of Malwares of Android

Each Android application requires accumulations of permissions in installation time and they are considered as the features which can be utilized in permission-based identification of Android malwares. Recently, ensemble feature selection techniques have received increasing attention over conventional techniques in different applications. In this work, a cluster based voted ensemble voted feature selection technique combining five base wrapper approaches of R libraries is projected for identifying most prominent set of features in the predictive modeling of Android malwares. The proposed method preserves both the desirable features of an ensemble feature selector, accuracy and diversity. Moreover, in this work, five different data partitioning ratios are considered and the impact of those ratios on predictive model are measured using coefficient of determination (r-square) and root mean square error. The proposed strategy has created significant better outcome in term of the number of selected features and classification accuracy.