Minimum Processing Time Research Articles

Advanced depth-layering using a synthetic-depth map and deep neural network for real-world object-based holographic displays

We propose an advanced layering method for real-world object-based holographic displays, leveraging what we believe to be a novel synthetic-depth map and deep neural network. The proposed system aims to implement a holographic display that displays natural-like three-dimensional visualizations of real objects by enhancing data quantity and ensuring accurate depth layers. A simplified light-field image acquisition system combined with a deep neural network is employed to efficiently gather organized omnidirectional three-dimensional information from the object, achieving high quality while minimizing processing time. Subsequently, a novel high-accuracy synthetic-depth map containing data from both initial depth and position maps is estimated. Finally, the sub-holograms for each depth layer are generated and integrated as a single main hologram by encompassing comprehensive object information, which is displayed on the spatial light modulator of a holographic display system and illuminated by a coherent light source. Experimental results confirm the superiority of the proposed system, particularly demonstrating its effectiveness for objects with a wide depth range or multiple objects separated by considerable distances.

Performance evaluations of AI‐based obfuscated and encrypted malicious script detection with feature optimization

AbstractIn the digital security environment, the obfuscation and encryption of malicious scripts are primary attack methods used to evade detection. These scripts—easily spread through websites, emails, and file downloads—can be automatically executed on users' systems, posing serious security threats. To overcome the limitations of signature‐based detection methods, this study proposed a methodology for real‐time detection of obfuscated and encrypted malicious scripts using ML/DL models with feature optimization techniques. The obfuscated script datasets were analyzed to identify the unique characteristics, classified into 16 feature sets, to evaluate the optimal features for the best detection accuracy. Although the detection accuracy of these datasets was < 20%, when tested with commercial antivirus services, the experimental results using ML and DL models demonstrated that the proposed light gradient boosting model (LGBM) could achieve the best detection accuracy and processing speed. The LGBM outperformed other artificial intelligence models by achieving 97% accuracy and the minimum processing time in the decoded, obfuscated, and encrypted dataset cases.

Lightweight Machine Learning-Based DOS Attack Detection in Wireless Sensor Networks Using Decision Tree and Information Gain

Abstract—Wireless Sensor Networks (WSNs) are rapidly expanding across various domains due to their unique characteristics and performance capabilities. However, these networks are highly vulnerable to a range of security threats, particularly Denial-of-Service (DoS) attacks, which are among the most common in WSNs. This paper explores the vulnerabilities of WSNs, focusing on DoS threats, and reviews current techniques for their detection. It introduces a lightweight machine learning-based approach using a decision tree (DT) algorithm with the Information Gain (IG) feature selection method for efficient DoS detection. Tested on an enhanced WSN-DS dataset, the proposed method demonstrated high accuracy and minimal processing time compared to other classifiers, such as XGBoost, and RF. This efficiency makes the proposed method well-suited for real- time DoS attack detection in resource-constrained WSNs. Keywords—Machine Learning,Decision Tree (DT),Information Gain (IG),DoS attack detection

Enhanced Curvature-Based Fabric Defect Detection: A Experimental Study with Gabor Transform and Deep Learning

Quality control at every stage of production in the textile industry is essential for maintaining competitiveness in the global market. Manual fabric defect inspections are often characterized by low precision and high time costs, in contrast to intelligent anomaly detection systems implemented in the early stages of fabric production. To achieve successful automated fabric defect identification, significant challenges must be addressed, including accurate detection, classification, and decision-making processes. Traditionally, fabric defect classification has relied on inefficient and labor-intensive human visual inspection, particularly as the variety of fabric defects continues to increase. Despite the global chip crisis and its adverse effects on supply chains, electronic hardware costs for quality control systems have become more affordable. This presents a notable advantage, as vision systems can now be easily developed with the use of high-resolution, advanced cameras. In this study, we propose a discrete curvature algorithm, integrated with the Gabor transform, which demonstrates significant success in near real-time defect classification. The primary contribution of this work is the development of a modified curvature algorithm that achieves high classification performance without the need for training. This method is particularly efficient due to its low data storage requirements and minimal processing time, making it ideal for real-time applications. Furthermore, we implemented and evaluated several other methods from the literature, including Gabor and Convolutional Neural Networks (CNNs), within a unified coding framework. Each defect type was analyzed individually, with results indicating that the proposed algorithm exhibits comparable success and robust performance relative to deep learning-based approaches.

Quaqc: efficient and quick ATAC-seq quality control and filtering.

"quaqc" allows for ATAC-seq-specific quality control and read filtering of NGS data with minimal processing time and extremely low memory overhead. An efficient scaling implementation allows for a wide range of use cases, from processing individual samples processed on personal laptops to handling thousands of samples processed in parallel on compute clusters. The helper R package "quaqcr" allows for interactive program execution and exploration of results. Source code and documentation are freely available for download from https://github.com/bjmt/quaqc and https://github.com/bjmt/quaqcr under the GPLv3 license. "quaqc" is implemented in C and has been tested on both macOS and Linux. The "quaqcr" helper package only requires the R programming language. Fixed versions of the programs and code associated with this manuscript can be found at https://zenodo.org/records/13833437.

Dynamic optimization of boiler for minimizing energy consumption in the intentionally transient process operation: effect of different interval number

Abstract Certain manufacturing or industrial processes may involve variable conditions, and intentionally transient boiler operation allows optimal adaptation to these variations. This helps maintain efficiency and reduce energy consumption during different process phases. Transient operation is inherent during the start-up and intermittent phases in reaching the pressure required for boiler operation. Optimizing these transient states can reduce energy consumption. Dynamic optimization of boilers is crucial for several reasons, especially in industrial and power generation settings. Boilers are used to produce steam or hot water for various processes, and optimizing their performance can lead to increased efficiency, reduced energy consumption, and improved overall system reliability. The dynamic optimization problems were solved using the orthogonal collocation method. Three problem optimizations were considered in this study: minimize process time (P1), minimize energy consumption without optimized final time (P2), minimize energy consumption with optimized final time (P3). The control/decision variables applied were firing rate, Q, and water feed flowrate, q f. From the simulation results, the control trajectories of P3 were chosen to be the most effective control operation to achieve the minimum energy consumption for reaching target pressure, i.e., 10.2 MPa, with a reasonable intermittent unit time. In practice, the selection of the number of intervals is often determined through a combination of domain knowledge, computational resources, and the desired level of accuracy. Sensitivity analysis and testing with different interval sizes can help in understanding the impact of this parameter on the optimization results. A greater interval time will decrease energy consumption.

Exploring Biomass Conversion Technologies: From Raw Materials to Valuable Products

The global demand for eco-friendly energy has propelled biomass into the spotlight. This report delves into biomass conversion technologies, including biochemical and thermochemical processes, with a focus on hydrothermal conversion. It highlights challenges related to cost-effectiveness and commercial viability. Thermochemical conversion processes, such as pyrolysis and combustion, unlock energy from organic matter. Hydrothermal processing's three approaches and their efficiency are discussed, particularly in biofuels, chemicals, and biochar production. The review analyzes hydrothermal gasification, emphasizing its efficiency and minimal processing time. Carbon and hydrogen gasification efficiencies are crucial in determining gas yields in supercritical conditions. Yield distribution and the influence of feedstock nature and composition on product yield are examined. In conclusion, this report offers insights into biomass conversion technologies and their sustainability for energy and chemical needs.

Optimization and extraction of annatto pigments obtained from Bixa orellana L. using supercritical fluid extraction

Traditionally, various techniques like water, vegetable oil, and solvent extraction are used to extract annatto pigments, a natural coloring agent derived from the Bixa orellana L. shrub. These traditional extraction methods can be time consuming and yield minimal pigment concentrations. This study explores an innovative approach to extract the maximum pigments from the annatto seed using supercritical fluid extraction (SFE). Fresh, extracted aqueous, and peeled samples of annatto powder were used at different temperatures (40, 45, and 50 °C) using different solvents of ethanol, methanol, and ethyl acetate with 99 % purity. The optimal extraction condition in supercritical fluid extraction is obtained with an aqueous sample treated with methanol solvent at 45 °C that yields 4.62 % norbixin, 2.49 % bixin, and a hue angle of 24.85⁰, achieving a high desirability value (0.949). Response surface method (RSM) was used to optimize the extraction procedure to obtain a higher yield of annatto pigments. In addition, for optimized conditions, the total phenols and antioxidants assay yielded 3.8 mg GAE/g and 51 % inhibition. HPLC analysis confirmed norbixin in the extracts obtained with a retention time of 38.40 min. This method offers a significant boost in pigment extraction efficiency, but also minimizes processing time. These combined advantages position it as a potentially superior approach for producing annatto pigments with enhanced concentration and purity.

Optimization Algorithms for Real-Time Image Processing in IoT Networks

This study explores the application of optimization algorithms, with a focus on Particle Swarm Optimization (PSO), to enhance real-time image processing in Internet of Things (IoT) networks. Given the constraints of computational power and energy resources in IoT devices, efficient processing of image data is a critical challenge. The proposed PSO-based framework aims to minimize processing time and energy consumption while maintaining high accuracy in image analysis tasks. Experimental results demonstrate that PSO can reduce processing time by 10% and energy consumption by 15% on average, with a slight improvement in accuracy. Comparisons with traditional and other evolutionary optimization techniques reveal that PSO provides a superior balance between efficiency and performance, making it particularly well-suited for real-time IoT applications. The findings suggest that the integration of advanced optimization algorithms like PSO into IoT systems can significantly enhance their operational efficiency, scalability, and effectiveness, particularly in resource-constrained environments.

Влияние параметров сушки некоторых видов овощей на продолжительность процесса и органолептическую оценку

Abstract. Vegetables are an important part of the people's diet, and their production is also increasing as the population grows. However, vegetables have a relatively short shelf life and are also sensitive to storage and transportation conditions. Because of this, losses of the harvested crop can reach significant values, and in order to reduce them, a quality preservation method such as drying can be used. There are various drying methods with their own advantages and disadvantages, therefore, when organizing the process, it is important to select optimal conditions that would quickly achieve the required humidity and microbiological safety indicators, and would not be accompanied by excessive energy consumption and excessive deterioration in the quality of raw materials. In the course of the study, the influence of drying parameters of certain types of vegetables (red beet, carrots, courgettes, eggplants, tomatoes, white cabbage, parsley) on the duration of the process and the organoleptic assessment of the resulting product was studied. Optimal drying parameters were determined (allowing to obtain a product with the best organoleptic characteristics and minimal process time) of vegetables of selected varieties / hybrids, depending on the drying method: for example, for carrots – drying in a dehydrator for 180 minutes at a temperature of 65 °C and 150 minutes at a temperature of 70 °C; for eggplants – drying for minutes at a temperature of 60 °C; for tomatoes – drying in a dehydrator for 220 minutes at a temperature of 60 °C and for 200 minutes at a temperature of 65 °C. The data obtained can be used to improve existing methods of drying the abovementioned vegetables. Key words: red beet, carrot, courgettes, eggplants, tomatoes, white cabbage, parsley, drying, organoleptic characteristics

Robot Motion Planning Based on an Adaptive Slime Mold Algorithm and Motion Constraints

The rapid advancement of artificial intelligence technology has significantly enhanced the intelligence of mobile robots, facilitating their widespread utilization in unmanned driving, smart home systems, and various other domains. As the scope, scale, and complexity of robot deployment continue to expand, there arises a heightened demand for enhanced computational power and real-time performance, with path planning emerging as a prominent research focus. In this study, we present an adaptive Lévy flight–rotation slime mold algorithm (LRSMA) for global robot motion planning, which incorporates LRSMA with the cubic Hermite interpolation. Unlike traditional methods, the algorithm eliminates the need for a priori knowledge of appropriate interpolation points. Instead, it autonomously detects the convergence status of LRSMA, dynamically increasing interpolation points to enhance the curvature of the motion curve when it surpasses the predefined threshold. Subsequently, it compares path lengths resulting from two different objective functions to determine the optimal number of interpolation points and the best path. Compared to LRSMA, this algorithm reduced the minimum path length and average processing time by (2.52%, 3.56%) and (38.89%, 62.46%), respectively, along with minimum processing times. Our findings demonstrate that this method effectively generates collision-free, smooth, and curvature-constrained motion curves with the least processing time.

Efficient and Secure Color Image Encryption System with Enhanced Speed and Robustness Based on Binary Tree

Recently, there has been a growing demand for image encryption techniques that offer robust protection and minimize processing time. The proposed paper proposes an efficient color image encryption system that excels in speed and security. The encryption system comprises three fundamental phases. The initial phase generates a unique encryption key by combining user-defined input with the original image and applying various operations and hash functions. In the confusion phase, the image is divided into blocks, forming a Binary Tree (BT) using primary color blocks, ensuring that the root and leaves belong to different colors. The confused matrix is derived through an inorder traversal that ensures non-adjacency of pixels of the same color, introducing an added layer of security. Finally, each pixel is scrambled by applying BT to its binary form to add more security and complexity. A DNA sequence is generated, and operations are executed based on two different chaotic maps, enhancing unpredictability and attack resistance. Extensive testing has validated the effectiveness of the proposed system, revealing a remarkable 28–45% reduction in processing time compared to recent techniques. Moreover, the system successfully withstands various attacks, as demonstrated through rigorous evaluations, including high-performance, visual perception, and cryptosystem strength evaluations. These results underscore the practical applicability and robust security offered by our efficient color image encryption solution, which provides a practical solution for applications prioritizing efficiency.

Multi-agent reinforcement learning framework based on information fusion biometric ticketing data in different public transport modes

In smart cities, biometric technologies have become extensively used for ticket authentication on public transport. Information fusion plays a key role in biometric ticketing, allowing ticket validation with more data source validation in different public transport modes. This paper proposes a novel biometric technology-based mobile ticket application-based system. We formulate the problem as a multi-agent reinforcement learning framework for biometric ticketing in multi-transport environments. Specifically, we propose the Asynchronous Advantage Critic Biometric Ticketing Framework (A3CBTF) algorithm, which consists of different schemes based on the proposed system. The proposed algorithm framework operates in hybrid transport modes using a parallel reinforcement learning scheme. A key advantage of A3CBTF is that it enables passengers to use a single ticket for various public transport modes. Additionally, even when a passenger’s mobile device is stolen, lost, or has a dead battery, they can still validate their tickets through different information fusion sources, such as fingerprint and face recognition. A3CBTF is a multi-agent system that integrates mobile, transport, edge, and cloud servers to facilitate ticket validation in a distributed environment. By optimizing both convex and concave optimizations, A3CBTF ensures efficient ticket validation with minimal processing time and maximizes validation rewards across different biometric technologies. Experimental results demonstrate that A3CBTF outperforms mobile off with other options such as fingerprint and face recognition in public transport as compared to other ticketing systems.

An Efficient Feature Selection and Extraction using Metaheuristic Technique for Diabetic Retinopathy

Diabetic retinopathy (DR) is associated with diabetes, which causes harm to the retina as a result of persistent elevated blood sugar levels, resulting in symptoms such as impaired vision. Regular eye examinations are crucial for the timely identification of any issues. The process of selecting characteristics in DR involves optimizing important features using a combination of Hybridized Weight-Optimized Particle Swarm Optimization and Whale Optimization Algorithm (HWOPSO-WOA). This leads to an enhancement in the accuracy of detection, which is crucial for appropriate diagnosis and therapy. Pre-processing encompasses the utilization of linear filters such as Sobel and Prewitt for image manipulation, mean filters for the reduction of noise, and Gaussian filters for the purpose of smoothing. Feature selection employs an objective function that considers significant performance measure, which utilizes binary encoding and conducts fitness evaluation. The methodology is utilized for the analysis of DR utilizing the Indian Diabetic Retinopathy Image Dataset. The results comprise convergence graphs, and a comparative analysis, which emphasize the greater accuracy and efficiency of the Proposed technique. Visual representations, such as fundus images with selected features, highlight the importance of the chosen features in detecting DR. The proposed HWOPSO-WOA achieves highest accuracy of 97.3% and minimal processing time 98.45seconds that outperforms the state-of-art techniques.

Empowering Manets with Advanced Multimodal Biometric Authentication and Encryption

In a mobile ad hoc network (MANET), nodes communicate wirelessly, facing unique challenges. Traditional MANETs suffer from issues like erroneous transmission and vulnerability to unauthorized nodes joining the network, posing security risks. Authentication within MANETs is a significant security concern, prompting ongoing research for enhancements. Our solution integrates multimodal biometric authentication with RSA and AES encryption, providing robust security for user authentication and data protection in MANETs. This approach effectively addresses risks such as unauthorized access and data tampering, crucial for secure communication in dynamic, resource-limited MANET environments. Our proposed system utilizes a combination of face and fingerprint biometrics for encryption, enhancing network security. Through testing, our system demonstrates a high authentication rate of 92.42% with minimal processing times: 0.042 ms for key generation, 0.019 ms for encryption, and 0.032 ms for decryption, based on a 1024-bit key size. These practical results showcase the resilience and efficiency of our secure system.

Perfectly dense ceramics of highly pure calcium manganese oxide

Immensely thermally stable and perfectly dense ceramics of highly pure calcium manganese oxide (CaMnO3) powders is realized at optimized minimal processing temperature and time using cost-effective ball-milling and hot-press techniques. The effect of ball milling time on the calcination time to prepare highly pure CaMnO3 powder and sintering time on the temperature adequate enough to achieve perfectly dense CaMnO3 ceramics is studied thoroughly and systematically. The as-synthesized single phase CaMnO3 is attained by mixing precursors of calcium carbonate and manganese oxide, using high energy ball milling for longer time followed by calcination for shorter time duration and vice-versa. A decrease in particle size in as-synthesized CaMnO3 is noticed for powders prepared with longer ball milling and shorter calcination times. The minimal time required for achieving highly pure and fine-grained powders containing sub-micrometer particles of CaMnO3 is accomplished by ball milling the precursor mixture for 12 h and calcining at 1273 K for 2 h. Relative density of 99.03% is achieved for the as-synthesized CaMnO3 powders hot pressed at 1073 K for 30 min.

Plasma electrolytic polishing of additively manufactured metal parts: Optimizing the post processing workflow

Additive manufacturing of metal parts is gaining acceptance in industry due to its unique manufacturing capabilities. Higher surface roughness of printed parts is one of the major challenges of this advanced manufacturing technique, which requires a crucial post-processing stage before the components can be used in practice. This study investigates the influence of plasma electrolytic polishing (PeP) and the combination of PeP and particle blasting as a post processing technique for additively manufactured stainless steel samples. The results show a 70% improvement in surface roughness after 10 minutes of particle blasting and 3 minutes of PeP in sequence and presents the possibility of optimizing processing time to achieve better surface qualities with minimal post processing time.

Cost and performance aware scheduling technique for cloud computing environment

Recently, lot of interest have been put forth by researchers to improve workload scheduling in cloud platform. However, execution of scientific workflow on cloud platform is time consuming and expensive. As users are charged based on hour of usage, lot of research work have been emphasized in minimizing processing time for reduction of cost. However, the processing cost can be reduced by minimizing energy consumption especially when resources are heterogeneous in nature; very limited work have been done considering optimizing cost with energy and processing time parameters together in meeting task quality of service (QoS) requirement. This paper presents cost and performance aware workload scheduling (CPA-WS) technique under heterogeneous cloud platform. This paper presents a cost optimization model through minimization of processing time and energy dissipation for execution of task. Experiments are conducted using two widely used workflow such as Inspiral and CyberShake. The outcome shows the CPA-WS significantly reduces energy, time, and cost in comparison with standard workload scheduling model.

Fabrication of Superhydrophobic Nanostructures on Glass Surfaces Using Hydrogen Fluoride Gas.

Water-repellent glass surfaces have become increasingly important to ensure clear visibility in outdoor cameras, sensors, and automotive windows. In this study, we investigated a process for the formation of nanoscale structures on a glass surface using chemical reactions with hydrogen fluoride gas. Using this approach, nanostructures with superhydrophobicity, superhydrophilicity, and antireflective properties were formed on glass surfaces with minimal processing time. This mask-free method, working at atmospheric pressure, can be efficiently integrated within the float process, a mainstream manufacturing technique for flat glass, to introduce nanostructures onto the glass surface. Notably, after treatment with (1-H, 1-H, 2-H, 2-H-tridecafluorooctyl)trimethoxysilane (FAS-13), a typical hydrophobic agent, the resulting surface exhibited a maximum water contact angle of 162°. Owing to its low reflectivity and superhydrophobicity, this surface is anticipated to find applications in not only the design of architectural window glass and vehicle windows but also the development of solar panels and sensor cover glass for autonomous vehicles.

Pulsed Electric Field Technology for Recovery of Proteins from Waste Plant Resources and Deformed Mushrooms: A Review

Proteins are complex molecules, which play a vital role in our body’s function, the building of tissues, and the regulation of metabolic activity. They are crucial to children’s growth and serve as a key component in the body’s process of distributing oxygen. Proteins fuel the body by supplying the required nutrition and energy. Currently, there is an increasing demand for proteins on large scales with no detrimental effects. The adverse health effects of animal proteins have resulted in a growing preference for plant-based proteins, which offer a healthier daily dosage. Valuable proteins can be extracted from various parts of the plant, including stems, leaves, seeds, fruits, vegetables, and roots. Notably, protein extraction from waste plant and mushroom parts minimizes the product wastage and improves the overall production to support economic sustainability. There are several protein extraction techniques available, where the replacement of non-thermal methods with thermal ones is promising nowadays due to the appreciable retainment of protein quality. Pulsed Electric Field (PEF) technology is one of the most efficient non-thermal tools used to assist with extracting these proteins at the minimum processing time and energy consumption when compared with thermal techniques. It relies on the application of a high-voltage pulse between two electrodes to treat samples inside the treatment chamber. While electrode shapes and treatment chamber designs primarily govern the electric field’s application, optimizing process parameters such as electric field strength, pulse width, number of pulses, and pulse waveshape assists in obtaining a desirable enhancement in the protein yield. The primary objective of this review is to explain the PEF-assisted protein extraction process applicable to waste plant parts and deformed mushrooms. While PEF is not a novel concept, utilizing it as a pre-extraction treatment to the aforementioned waste resources would aid in improving the production of value-added protein products economically. So far, PEF has shown immense promise in assisting with protein extraction studies, but requires further research in order to establish this area for large-scale industrial applications.