Simulation and performance assessment of a modified throttled load balancing algorithm in cloud computing environment

<span lang="EN-US">Load balancing is crucial to ensure scalability, reliability, minimize response time, and processing time and maximize resource utilization in cloud computing. However, the load fluctuation accompanied with the distribution of a huge number of requests among a set of virtual machines (VMs) is challenging and needs effective and practical load balancers. In this work, a two listed throttled load balancer (TLT-LB) algorithm is proposed and further simulated using the CloudAnalyst simulator. The TLT-LB algorithm is based on the modification of the conventional TLB algorithm to improve the distribution of the tasks between different VMs. The performance of the TLT-LB algorithm compared to the TLB, round robin (RR), and active monitoring load balancer (AMLB) algorithms has been evaluated using two different configurations. Interestingly, the TLT-LB significantly balances the load between the VMs by reducing the loading gap between the heaviest loaded and the lightest loaded VMs to be 6.45% compared to 68.55% for the TLB and AMLB algorithms. Furthermore, the TLT-LB algorithm considerably reduces the average response time and processing time compared to the TLB, RR, and AMLB algorithms.</span>

Comparative study of residual stress prediction methods in additive manufacturing processes

The additive manufacturing (AM) of products involves various processes, such as raising the temperature of a work-piece (part) and substrate to the melting point and subsequent solidification, using a movable source of heat. The work piece is subjected to repeated cycles of heating and cooling. The main objective of this work was to present an overview of the various methods used for prediction of the residual stresses and how their contributions can be used to improve current additive manufacturing methods. These novel methods of manufacturing have several merits, compared to conventional methods. Some of these merits include the lower costs, higher precision and accuracy of manufacturing, faster processing time and more eco-friendly approaches to processes involved.

Computer Vision Based Virtual Sketch Using Detection

In todays world during this pandemic situation Online Learning is the only source where one could learn. Online learning makes students more curious about the knowledge and so they decide their learning path . But considering the academics as they have to pass the course or exam given, they need to take time to study, and have to be disciplined about their dedication. And there are many barriers for Online learning as well. Students are lowering their grasping power the reason for this is that each and every student was used to rely on their teacher and offline classes. Virtual writing and controlling system is challenging research areas in field of image processing and pattern recognition in the recent years. It contributes extremely to the advancement of an automation process and can improve the interface between man and machine in numerous applications. Several research works have been focusing on new techniques and methods that would reduce the processing time while providing higher recognition accuracy. Given the real time webcam data, this jambord like python application uses OpenCV library to track an object-of-interest (a human palm/finger in this case) and allows the user to draw bymoving the finger, which makes it both awesome and interesting to draw simple thing. Keyword: Detection, Handlandmark , Keypoints, Computer vision, OpenCV

Design, Implementation & Performance of Vedic Multiplier Based on Look Ahead Carry Adder for Different Bit Lengths

This paper proposed the layout of Vedic Multiplier based totally on Urdhva Trigbhyam approach of multiplication. It is most effective Vedic sutras for multiplication. Urdhva triyagbhyam is a vertical and crosswise approach to discover product of two numbers. Multiplication is an essential quintessential feature in arithmetic logic operation. Computational overall performance of a DSP device is limited via its multiplication overall performance and since, multiplication dominates the execution time of most DSP algorithms. Multiplication is one of the simple arithmetic operations and it requires extensively extra hardware assets and processing time than addition and subtraction. Our work is to compare different bit Vedic multiplier structure using carry look ahead adder technique. Keywords: Carry Look Ahead Adder, Urdhva Trigbhyam, DSP algorithms, Vedic Multiplier

Dynamic Selection Techniques for Detecting GPS Spoofing Attacks on UAVs

Unmanned aerial vehicles are prone to several cyber-attacks, including Global Positioning System spoofing. Several techniques have been proposed for detecting such attacks. However, the recurrence and frequent Global Positioning System spoofing incidents show a need for effective security solutions to protect unmanned aerial vehicles. In this paper, we propose two dynamic selection techniques, Metric Optimized Dynamic selector and Weighted Metric Optimized Dynamic selector, which identify the most effective classifier for the detection of such attacks. We develop a one-stage ensemble feature selection method to identify and discard the correlated and low importance features from the dataset. We implement the proposed techniques using ten machine-learning models and compare their performance in terms of four evaluation metrics: accuracy, probability of detection, probability of false alarm, probability of misdetection, and processing time. The proposed techniques dynamically choose the classifier with the best results for detecting attacks. The results indicate that the proposed dynamic techniques outperform the existing ensemble models with an accuracy of 99.6%, a probability of detection of 98.9%, a probability of false alarm of 1.56%, a probability of misdetection of 1.09%, and a processing time of 1.24 s.

A study on the processing technology for Rhizoma Coptidis

Background The present study intends to optimize the processing technology for the wine-processing of Rhizoma Coptidis, using alkaloids as indicators. Method In the present study, the Box–Behnken design method was adopted to optimize the processing technology for Rhizoma Coptidis, using the alkaloid component quantities as the index. 100 g of Rhizoma Coptidis slices and 12.5 g of Rhizoma Coptidis wine were used. After full mixing, box-Behnken design method was used to optimize the processing time, processing temperature and processing time of coptis chinensis by taking alkaloid content as index. After mixing well, these components were fried in a container at 125 °C for 6 min and exhibited good parallelism. Results The content of alkaloids in coptis chinensis was the highest after roasting at 125 °C for 6 min. The characteristic components were berberine hydrochloride, and the relative content was about 15.96%. And showed good parallelism. The effective components of Rhizoma Coptidis were primarily alkaloids. Conclusion The optimized processing technology for Rhizoma Coptidis is good.

Electrohydrodynamic Drying in Agribusiness: Literature Review

In agribusiness, drying is a unitary operation that optimizes the production and preservation of products and raw materials. Drying is performed through different traditional methods, one of the most recently studied is the electrohydrodynamic drying EHD which uses an electric field that allows decreasing the processing time thus increasing the drying speed of raw materials and consuming less energy. In this article, a review was carried out through Scopus using a search equation with the keywords “Electrohydrodynamic drying,” “food” and “AGRI” which resulted in a total of 145 articles; which were analyzed through in-depth reading, analyzing aspects such as year, author, keywords, countries, quartile, journal, relationship with agroindustry, mathematical models used and applications in agro-industrial products, this analysis was complemented with the application of Vantage Point software through co-occurrence matrices and cluster analysis. Recent applications were found in Carrot, Chicken, Sea Cucumber, Goji Berry, Peppermint Leaf, Quince, Potato, Blueberry, Aquatic Products, Banana Slices, Grape Pomace, Blueberry, Apple, Mushroom, Wheat, and Mushroom Slices, mathematical models with application in EHD drying were also found, such as Henderson and Pabis, Page, Logarithmic, Quadratic, Newton/Lewis, Diffusion and exponential.

Litter Detection with Deep Learning: A Comparative Study

Pollution in the form of litter in the natural environment is one of the great challenges of our times. Automated litter detection can help assess waste occurrences in the environment. Different machine learning solutions have been explored to develop litter detection tools, thereby supporting research, citizen science, and volunteer clean-up initiatives. However, to the best of our knowledge, no work has investigated the performance of state-of-the-art deep learning object detection approaches in the context of litter detection. In particular, no studies have focused on the assessment of those methods aiming their use in devices with low processing capabilities, e.g., mobile phones, typically employed in citizen science activities. In this paper, we fill this literature gap. We performed a comparative study involving state-of-the-art CNN architectures (e.g., Faster RCNN, Mask-RCNN, EfficientDet, RetinaNet and YOLO-v5), two litter image datasets and a smartphone. We also introduce a new dataset for litter detection, named PlastOPol, composed of 2418 images and 5300 annotations. The experimental results demonstrate that object detectors based on the YOLO family are promising for the construction of litter detection solutions, with superior performance in terms of detection accuracy, processing time, and memory footprint.

Optimal Order Batching in Warehouse Management: A Data-Driven Robust Approach

Optimizing warehouse processes has direct impact on supply chain responsiveness, timely order fulfillment, and customer satisfaction. In this work, we focus on the picking process in warehouse management and study it from a data perspective. Using historical data from an industrial partner, we introduce, model, and study the robust order batching problem (ROBP) that groups orders into batches to minimize total order processing time accounting for uncertainty caused by system congestion and human behavior. We provide a generalizable, data-driven approach that overcomes warehouse-specific assumptions characterizing most of the work in the literature. We analyze historical data to understand the processes in the warehouse, to predict processing times, and to improve order processing. We introduce the ROBP and develop an efficient learning-based branch-and-price algorithm based on simultaneous column and row generation, embedded with alternative prediction models such as linear regression and random forest that predict processing time of a batch. We conduct extensive computational experiments to test the performance of the proposed approach and to derive managerial insights based on real data. The data-driven prescriptive analytics tool we propose achieves savings of seven to eight minutes per order, which translates into a 14.8% increase in daily picking operations capacity of the warehouse.

Utilization of Secondary Jet in Cavitation Peening and Cavitation Abrasive Jet Polishing

The cavitation peening (CP) and cavitation abrasive jet polishing (CAJP) processes employ a cavitating jet to harden the surface or remove surface irregularities. However, a zero incidence angle between the jet and the surface limits the efficiency of these two processes. This limitation can be improved by introducing a secondary jet. The secondary jet interacts with the main jet, carrying bubbles to the proximity of the workpiece surface and aligning the disordered bubble collapse events. Through characterizing the treated surface of AL6061 in terms of the hardness distribution and surface roughness, it was found out that the secondary jet can increase the hardening intensity by 10%, whereas the material removal rate within a localized region increased by 66%. In addition, employing multiple secondary jets can create a patched pattern of hardness distribution. Another finding is that the hardening effect of the cavitation increases with the processing time at first and is then saturated.