Decrease In Processing Time Research Articles

FPGA Implementation of Archery Target Detection using Color Sequence Recognition Algorithm

In this paper, an implementation of image processing methods to extract and recognize a standard tri-colored archery target to a field-programmable gate array is demonstrated. Detection and recognition of the archery target was never been done on an FPGA platform. The platform used to realize the design was the ZedBoard™ Development Kit equipped with Xilinx Zynq®-7000 All Programmable system on chip. The algorithms used to extract the central region is based on color classification in HSV color space. Once each image pixels are classified, the color sequence recognition algorithm attempts to look for the target and extract the central region of the archery target if present. Image filtering techniques and analysis such as morphological filtering and contour feature analysis are used to properly identify the shape and location of the extracted pixels. Discussed next is the implementation of the algorithm both in the software and hardware aspects and a comparison between their response time and accuracy is demonstrated. There was about two-fold decrease in processing time when FPGA implementation was deployed. The accuracy of the system was also tested and able to reach an accuracy of 96.67% for near target distance. For far target distance, the accuracy degraded to 88.33% but the system has managed to maintain its specificity value despite the noise becoming dominant for smaller region occupied by the target.

Nanomaterial based flow of Prandtl-Eyring (non-Newtonian) fluid using Brownian and thermophoretic diffusion with entropy generation

Background: The augmentation of cooling or heating in a mechanical and industrial process may create a saving in energy, decrease process time, protract the working existence of hardware and raise thermal rating. A few procedures are even influenced subjectively by the action of increased heat transport. The advancement of high performance thermal frameworks for heat transport augmentation has turned out to be well known these days. Various works has been conducted to gain an understanding of heat transport execution for their viable application to heat transport enhancement. Consequently the appearance of high heat flow procedures has made huge interest for new innovations to increase the heat transport. Therefore, entropy generation in dissipative nanomaterial flow of Prandtl-Eyring nanofluid subject to heated stretchable surface. The impact of zero shear rate viscosity is discussed through Prandtl-Eyring fluid model. Through implementation of thermodynamics second law’s total entropy rate is calculated. Heat and mass transfer features are discussed using Brownian diffusion and thermophoresis. Homogeneous and heterogeneous chemical reactions are also accounted.Methods: Nonlinear partial differential systems are leads to ordinary systems through adequate similarity transformations. The obtained nonlinear ordinary systems are solved by Newton built in shooting technique.Results: Behaviors of different flow parameters on velocity, temperature, entropy generation rate, Bejan number and concentration are graphically discussed. Skin friction coefficient and heat transfer rate are discussed through tables. Entropy generation rate enhances for larger estimation of material parameter and Brinkman number. Bejan number is equal to one when Brinkman number is equal to zero and then progressively decreases for higher values of Brinkman number.Conclusions: A significant increment has been observed in the velocity field versus material parameter, while opposite trends is noticed forβ.Temperature field enhances against higher values of thermophoresis and Brownian parameters while it decays through larger Prandtl number. Mass concentration upsurges versus higher thermophoresis parameter and declined via larger Brownian parameter and homogeneous and heterogeneous parameters. Furthermore, entropy rate and Bejan number show contrast impact versus material parameter and Brinkman number.

A novel modified undersampling (MUS) technique for software defect prediction

AbstractBackground and aim: Many sophisticated data mining and machine learning algorithms have been used for software defect prediction (SDP) to enhance the quality of software. However, real‐world SDP data sets suffer from class imbalance, which leads to a biased classifier and reduces the performance of existing classification algorithms resulting in an inaccurate classification and prediction. This work aims to improve the class imbalance nature of data sets to increase the accuracy of defect prediction and decrease the processing time. Methodology: The proposed model focuses on balancing the class of data sets to increase the accuracy of prediction and decrease processing time. It consists of a modified undersampling method and a correlation feature selection (CFS) method. Results: The results from ten open source project data sets showed that the proposed model improves the accuracy in terms of F1‐score to 0.52 ∼ 0.96, and hence it is proximity reached best F1‐score value in 0.96 near to 1 then it is given a perfect performance in the prediction process. Conclusion: The proposed model focuses on balancing the class of data sets to increase the accuracy of prediction and decrease processing time using the proposed model.

Technique for automated target object search in video stream from UAV in post-processing mode

Data obtaining tasks for correct and relevant management decision making pusposes are important. Particularly, in military and rescue areas such data is videodata from an unmanned aircraft vehicle (UAV) camera obtained during its flight over a territory-of-interest. In this case large size of obtained data means a significant problem because of complicating of its manual processing by operator (expert). In addition, data availability must be provided. In practice, the mentioned task is usually solved by recording target videodata onboard during the UAV flight followed by recorded videodata processing after the UAV landing e.g. on the groung control station, i.e. in offline mode. It is obviously to see that using this technique doesn’t solve the problem of complicated target data processing due to manual approach. As for automation of target data processing, as practice shows, every object detection method can potentially decrease processing time, but cannot increase processing quality in comparison with manual processing by operator (expert). Thus, task of ensuring an appropriate balance between availability of target data (videodata from UAV), automation and quality of its processing is relevant. This article i) proposes the technique for automated target object search in videodata from reconnaissance UAVs in post-processing mode by using an adaptive suspicious object search method as an automatic part, ii) describes the corresponding program implementation on C++ for detection method, C# for the user interface part and [standard] platform invoke technique for using the first code (C++) inside the last (C#), iii) shows quantitative characteristics calculated on the set of test videodata. The proposed technique is considered as an appropriate way to solve the specified task.

225Ac/213Bi generator based on inorganic sorbents

Abstract A scheme of an “inverse” generator based on an inorganic sorbent (annealed zirconium and yttrium mixture oxides) has been proposed and tested. The generator demonstrated high yield of the 213Bi product (up to 97 % in 0.5 mL of eluate), high degree of purification from the actinium isotopes (up to 10−2 % of initial 225Ac in 3 M NaNO3 solution), as well as the products of 227Ac decay, and low radiation impact on the sorbent. Application of circulating approach to the sorption of 213Bi provides decreasing processing time to 5 min at higher yield of the product.

A Hybrid Spoken Language Processing System for Smart Device Troubleshooting

The purpose of this work is to develop a spoken language processing system for smart device troubleshooting using human-machine interaction. This system combines a software Bidirectional Long Short Term Memory Cell (BLSTM)-based speech recognizer and a hardware LSTM-based language processor for Natural Language Processing (NLP) using the serial RS232 interface. Mel Frequency Cepstral Coefficient (MFCC)-based feature vectors from the speech signal are directly input into a BLSTM network. A dropout layer is added to the BLSTM layer to reduce over-fitting and improve robustness. The speech recognition component is a combination of an acoustic modeler, pronunciation dictionary, and a BLSTM network for generating query text, and executes in real time with an 81.5% Word Error Rate (WER) and average training time of 45 s. The language processor comprises a vectorizer, lookup dictionary, key encoder, Long Short Term Memory Cell (LSTM)-based training and prediction network, and dialogue manager, and transforms query intent to generate response text with a processing time of 0.59 s, 5% hardware utilization, and an F1 score of 95.2%. The proposed system has a 4.17% decrease in accuracy compared with existing systems. The existing systems use parallel processing and high-speed cache memories to perform additional training, which improves the accuracy. However, the performance of the language processor has a 36.7% decrease in processing time and 50% decrease in hardware utilization, making it suitable for troubleshooting smart devices.

Increasing yield of msc-evs in scalable xeno-free manufacturing

Background & Aim Extracellular vesicles (EVs) are increasingly being investigated as both drug delivery vehicles and as a cell-free therapy. EVs from mesenchymal stem/stromal cells (MSC-EVs), for example, have been shown to recapitulate many of the therapeutic effects of the cells themselves. A critical barrier in the development of MSC-EVs as a commercial therapy, however, is generating the large amount of EVs that will be required per dose. In a recent survey, the majority of those working with EVs were working with less than 100mL of sample (ISEV 2016). In this study, two strategies were investigated to maximize EV yield. First, timing parameters for culturing human bone-marrow derived MSCs and collecting MSC-EVs were evaluated to increase EV yield. Second, conditioning the cells was investigated by adding an EV activator during the EV collection period. Methods, Results & Conclusion MSCs were first cultured in an expansion media (RoosterNourishTM, RoosterBio) in a fed-batch system. The cultures were then switched to a collection media (RoosterCollectTM-EV, RoosterBio) with or without an EV activator (EV BoostTM, RoosterBio). After two days, the conditioned media was harvested. The EVs in the collected conditioned media were analyzed for particle concentration and particle size distributions by nanoparticle tracking analysis, protein expression, RNA expression, and wound healing capability. Particle concentration (particles per L of media) was used as a measure of EV yield. Particle concentration increased with increased culture time in expansion media, with confluent cultures generating the most EVs. The EV activator at a low concentration increased EV yield about 2-fold compared to without the activator after 24hrs. The EV yield from the low EV activator culture at 24hrs was still about 45% greater than the yield of the culture without the activator after 48hrs. After 6hrs of collection, the high EV activator concentration increased EV yield to about the same level as without the activator after 48hrs of collection. Optimizing protocols for EV yield will become increasingly important as MSC-EVs move towards the clinic and lot sizes of clinically-relevant doses are required. Pre-conditioning of cells or conditioning cells during EV collection offers one possibility to further increase EV yield and decrease process time.

On the effects of freeze-drying processes on the nutritional properties of foodstuff: A review

The aim of this paper is to review the effects of several different types of freeze-drying processes on some nutritional properties of foodstuff. Both the vacuum freeze-drying (VFD) process and the atmospheric freeze-drying (AFD) process have been considered, as well as the possibility of accelerating both using ultrasound (US), microwave (MWD), infrared (IR) heating, and other techniques. The effects of these processes on ascorbic acid, phenolic compounds, and total antioxidant capacity have been extensively reviewed in this paper, as these molecules were widely investigated in the literature. Finally, a summary of effects on other less recurrent studied compounds is also presented. It appears that for vitamin C, VFD most of the time gives the best results; MWD and IR combined processes with VFD seem to be able to decrease processing time, while having a mild effect on product quality. With respect to phenolic compounds and total antioxidant capacity, VFD and AFD seem to have fairly similar effects, with a mild effect of IR and US.

Robust, ultra-insulative and transparent polyethylene-based hybrid silica aerogel with a novel non-particulate structure

Aerogels derived from pre-polymerized vinyl trimethoxy silane (VTMS) precursor with nano-size particles are known to exhibit outstanding mechanical and insulation properties. However, the density reduction has been limited by the poor connectivity. This paper presents an innovative technology to generate a new class of VTMS-based hybrid silica aerogels that possess outstanding non-particulate, reticulated structure and superior properties. This technology relies on spinodal decomposition instead of conventionally exploited binodal decomposition, which leads to a particulate structure. This new aerogel technology has significantly increased the void fraction of the pre-polymerized VTMS-based aerogel, which could not be achieved previously using binodal decomposition. The increased void fraction in the form of nano-pores with an average pore size of 21.75 nm nullifies the gas thermal conductivity effectively. Another consequence of the non-particulate structure is decreased processing time by removing the aging step. These improvements are due to the non-particulate structure’s increased connectivity produced by spinodal decomposition. This novel structure was then compared to a particulate counterpart aerogel of the same material derived from the conventional binodal decomposition of the pre-polymerized VTMS precursor. To further decrease the processing cost, a lower molecular-weight polymeric precursor was synthesized under milder polymerization conditions. The effects of the polymeric precursor’s molecular weight on the mechanical and thermal properties of the aerogel created via spinodal decomposition were also investigated.

In situ analysis of the effect of high heating rates and initial microstructure on the formation and homogeneity of austenite

Decreasing processing time of a quench and temper heat treatment is of high interest for industry due to the possibility of cost reduction. One option to reduce processing time is to shorten the austenitizing cycle by applying high heating rates and minimum holding times. However, due to the high heating rates, the analysis of their influences on the formation kinetics of austenite and its crystallographic parameters is challenging. Thus, this work concentrates on the in situ analysis of the austenitization process by means of high-energy X-ray diffraction to study a range of heating rates applied to ferritic–pearlitic and soft annealed initial microstructures. The transformation kinetics from ferrite/pearlite and soft annealed state to austenite, the cementite dissolution behavior and the homogeneity of the freshly formed austenite were analyzed. The results indicate three distinct steps of austenite formation independent of initial microstructure and heating rate: (1) nucleation of carbon-rich austenite at cementite–ferrite interfaces, (2) growth of austenite phase fraction accompanied by a reduction of the carbon content, until reaching the mean carbon content of the steel, followed by growth of the austenite grain size, (3) regarding austenite homogeneity, the combination of austenitization temperature and initial microstructure are the main influencing factors.

Surface Modification of Graphene Nanoplatelets by Organic Acids and Ultrasonic Radiation for Enhance Uremic Toxins Adsorption.

Ultrasound energy is a green and economically viable alternative to conventional techniques for surface modification of materials. The main benefits of this technique are the decrease of processing time and the amount of energy used. In this work, graphene nanoplatelets were treated with organic acids under ultrasonic radiation of 350 W at different times (30 and 60 min) aiming to modify their surface with functional acid groups and to improve the adsorption of uremic toxins. The modified graphene nanoplatelets were characterized by Fourier transform infrared spectroscopy (FT–IR), thermogravimetric analysis (TGA), and X-ray photoelectron spectroscopy (XPS). The optimum time for modification with organic acids was 30 min. The modified nanoplatelets were tested as adsorbent material for uremic toxins using the equilibrium isotherms where the adsorption isotherm of urea was adjusted for the Langmuir model. From the solution, 75% of uremic toxins were removed and absorbed by the modified nanoplatelets.

Compressed kNN: K-Nearest Neighbors with Data Compression.

The kNN (k-nearest neighbors) classification algorithm is one of the most widely used non-parametric classification methods, however it is limited due to memory consumption related to the size of the dataset, which makes them impractical to apply to large volumes of data. Variations of this method have been proposed, such as condensed KNN which divides the training dataset into clusters to be classified, other variations reduce the input dataset in order to apply the algorithm. This paper presents a variation of the kNN algorithm, of the type structure less NN, to work with categorical data. Categorical data, due to their nature, can be compressed in order to decrease the memory requirements at the time of executing the classification. The method proposes a previous phase of compression of the data to then apply the algorithm on the compressed data. This allows us to maintain the whole dataset in memory which leads to a considerable reduction of the amount of memory required. Experiments and tests carried out on known datasets show the reduction in the volume of information stored in memory and maintain the accuracy of the classification. They also show a slight decrease in processing time because the information is decompressed in real time (on-the-fly) while the algorithm is running.

Assessment of technician barcode scanning verification compared to pharmacist verification.

This study aims to show the feasibility and sustainability of technician barcode scanning verification as an alternative to pharmacist visual verification of first dose medications in an acute care setting. A two-phase, noninferiority, single-center study was conducted to compare the accuracy of technician barcode scanning verification to pharmacist visual verification of pre-packaged first dose medications within a large acute care medical center. In phase 1, a pharmacy technician utilized barcode scanning as a means of verification. These preparations were then re-verified for accuracy by pharmacist visual verification. In phase 2, the verification order was reversed, starting with pharmacist visual verification of first doses, which were subsequently re-verified by a technician utilizing barcode scanning. Accuracy and efficiency (first dose processing time) of each phase was analyzed via error logs and retrospective dose tracking. A total of 12,328 first dose preparations were included in the analysis and showed no difference between technician barcode scanning verification and pharmacist visual verification. Retrospective time study showed a 4-minute decrease in processing time when doses were verified by technician barcode scanning. Based on initial study outcomes, a variance was granted to pilot first dose tech-check-tech by the Wisconsin State Board of Pharmacy. This study determined that there is no difference in verification accuracy between technician barcode scanning verification and pharmacist visual verification of first doses in an acute care setting. Through leveraging technology and skill mix, Froedtert Hospital was able to provide the same level of patient safety while decreasing pharmacy processing time, developing our technician workforce, and reallocating pharmacist staff from distributive roles in central pharmacy to decentralized clinical activities.

Efficient Aerodynamic Shape Optimization of the Hypersonic Lifting Body Based on Free Form Deformation Technique

Aerodynamic shape optimization (ASO) of hypersonic lifting body has become a significant research topic due to its significant performance advantages. As a universal parameterization, the free form deformation (FFD) technique has benefits including geometric independence, random deformation, and mesh synchronization. In this paper, an effective design method to apply the FFD technique in the ASO of a hypersonic lifting body is presented. Some commonly used basis functions are researched in FFD modeling of the windward side of a typical lifting body, including the Bernstein polynomial, the B-spline function, and the non-uniform rational B-spline (NURBS) function. An efficient aerodynamic simulation method combining Euler equations (non-viscous component) and skin friction drag (viscous component based on the compressible turbulence model) is then developed to minimize computational requirements. The accuracy of the proposed method is validated, and a significant decrease in processing time is observed. In addition, a kriging surrogate model combined with infilling sampling expected improvement (EI) criterion is developed to improve optimization efficiency. To obtain a lifting body with high lift-to-drag ratio that satisfies inner loading constraint, a baseline is optimized by manipulating its shape using the NURBS-based FFD technique. The results show that the optimal shape displays outstanding aerodynamic performance, and the effective design method can provide practical support for ASO of the hypersonic lifting body.

Optimization of electrostatic powder coat cure oven process: A capstone senior design research project

Abstract This paper describes one successful example of a capstone senior design research project that deals with optimization of the electrostatic powder coat cure process. Optimizing any manufacturing process provides a company with extensive benefits obtained through increased productivity, reduced manufacturing costs, and quicker time to market. Along with the explicit production cost benefits, a company will also reap the benefits of a decreased environmental impact and increased market competitiveness accruing from increased productivity. Therefore, companies are incentivized to continually improve, and optimize their production processes. One such process that many products, throughout numerous industries, go through is the painting process. While painting a product can aid in easily identifying certain brands (e.g. John Deere green, Caterpillar yellow, New Holland blue, International Harvester red, etc.), the main purpose of providing a painted surface is to protect the product from environmental corrosion, and wear in addition to providing a drastically longer life cycle to the product. One of the most important steps of the painting process is the curing of the paint on the surface of the product. This is normally done through an industrial oven set at appropriate temperatures and times depending on the paints used and the products to be painted. Using DOE, ANOVA, and other statistical analysis tools for data collection and analysis, a cure oven set at 450 °F for 40 minutes was optimized to 450 °F for 38 minutes. This provides a 5% decrease in both process time and natural gas usage while continuing to maintain the same level of quality. This demonstrates the effectiveness of DOE tools and statistical analysis of collected data coupled with the principles of process optimization in increasing productivity and reducing overall production costs.

Evaluation of two culture-based methods for the early detection of methicillin-resistant Staphylococcus aureus nasal carriage in pre-operative neurosurgical patients

Background: Pre-operative screening for nasal carriage of methicillin-resistant Staphylococcus aureus(MRSA) is practiced to reduce the risk of post-operative MRSA infections. Although controversies prevail on the necessity for screening, we perform MRSA screening in all pre-operative neurosurgical patients due to the benefits incurred. Aim: The aim of this study was to evaluate two different culture-based methods for rapid screening of MRSA nasal carriage among pre-operative neurosurgical patients. Methods: This cross-sectional, single-centre study was conducted in a tertiary healthcare facility in South India. All pre-operative neurosurgery patients were screened for nasal carriage of MRSA using two culture-based methods. Advantage of the newer screening method using chromogenic media over the conventional method was evaluated. A positive screening test was considered an indication for decolonisation using mupirocin and chlorhexidine prior to surgery. A repeat negative screening was ensured before the surgical procedure. Results: MRSA colonisation rate among pre-operative neurosurgical patients in our study was 2.7%. Good correlation was observed between the two screening methods evaluated. Newer method using chromogenic media on comparison with conventional culture showed better turnaround time and shorter pre-operative stay of patients. Because the turnaround time is reduced, earlier contact isolation practice and treatment initiation was achieved for better care of MRSA carriers. Conclusion: Chromogenic media usage for screening the nasal carriage of MRSA is beneficial to patients, surgeons and laboratory personnel. Decrease in turnaround time and processing time and early initiation of isolation along with major reduction in reagent usage are the benefits achieved.

Mixed-Signal Physically Unclonable Function With CMOS Capacitive Cells

An electronic physically unclonable function usually includes an on-chip error-correcting code unit, which is vulnerable to security attacks and adds area, power, and data processing time overheads. This paper proposes a mixed-signal physically unclonable function circuit for authentication purposes, which we call the enhanced capacitive physically unclonable function. It divides the input challenge word over multiple computational groups to decrease processing time, increase security, and eliminate the need for error-correcting code units. Most of the challenge bits control capacitive networks grouped into several capacitive cells, while some are analogized through two digital-to-analog converters. One digital-to-analog converter controls the discharge loads of the capacitive cells; the other controls the reference voltage of comparator units. Each comparator controls a counter that digitizes the discharge time into a response chunk. Most of these counters operate at high frequencies for more precise time-to-digital conversion and are overflown to act as roulettes to promote unpredictability. One counter is not overflown to generate a reference response chunk to support error handling. The design allows for more intrinsic variations throughout the fabrication process, leading to unique response chunks. It applies an expanding challenge-response pair approach, generating a 128-bit response word for a 64-bit challenge word. The capacitive nature of the design supports various security features. Simulating the circuit using 45 nm complementary metal-oxide semiconductor technology resulted in an average power of 921.67 μW, a layout area of 22,470 μm 2 , and an average data processing time of 118 μs.

Time-domain analysis of a CRLH coupled-line coupler using the CN-FDTD method

In this study, the implicit Crank–Nicolson finite-difference time-domain (CN-FDTD) method is applied to discretize the governing telegrapher's equations of a composite right-/left-handed (CRLH) coupled-line coupler. The unconditionally stable CN-FDTD is compared with the conventional leap-frog (LF) FDTD method. The results obtained from the CN-FDTD scheme show up to 10 times increase in the temporal step size, reflecting in a dramatic decrease in processing time; in addition to having a good agreement with the LF method and the measurements.

Increasing productivity in SMEs Samiler crackers by implementing appropriate technology

This study was conducted on Small and Medium Enterprise (SMEs) Samiler crackers in Indonesia and aims creating designs of hygienic steamer and a scraper as well as testing the tools in order to ensure their performance, determine standard time, standard output, and piece work rate as the basis for designing incentive-giving policies. By using tools designed with appropriate technology such as stainless steel based material and using gas as production fuel, production capacity as well as the hygiene in production process can be optimized. The increase in productivity occurred at a decrease in processing time for scraping machines by 75%, increased production capacity by 300%, decreased fuel consumption costs by up to 43%, decreased processing time for steam engines up to 60%. The calculation results also indicate if the operator produces more than five products, it will get wages plus incentives of 1,049 IDR per additional product produced. Communication in designing the tools to meet the needs and function in the use becomes the key to secure the success in increasing productivity of SMEs. More broadly, the synergy between the government, universities and other stakeholders is believed to strengthen the position of SMEs at the national and even international level.

An Adaptive Geometry Transformation and Repair Method for Hybrid Manufacturing

Hybrid manufacturing has become particularly attractive for refurbishing of high-value freeform components. Components may experience unique geometric distortions and/or wear-driven material loss in service, which require the use of part-specific, adaptive repair strategies. The current work presents an integrated adaptive geometry transformation method for additive/subtractive hybrid manufacturing based on rigid and nonrigid registrations of parent region material and geometric interpolation of the repair region material. In this approach, rigid registration of nominal part geometry to actual part geometry is accomplished using iterative alignment of profiles in the parent material. Nonrigid registration is used to morph nominal part geometry to actual part geometry by transformation of the profile mean line. Adaptive additive and subtractive tool paths are then used to add material based on constant stock margin requirements, as well as to produce blend repairs with smooth transition between parent and repair regions. A range of part deformation conditions due to profile twist and length changes are evaluated for the case of a compressor blade/airfoil geometry. Accuracy of the resulting adaptive geometry transformation method were quantified by (1) surface comparisons of actual and transformed nominal geometry and (2) blend region surface accuracy. Performance of the adaptive repair strategy relative to a naïve strategy is evaluated by the consideration of material efficiency and process cycle time. It is shown that the adaptive repair strategy resulted in an increase in material efficiency by 42.2% and a decrease in process time by 17.8%, depending on the initial deformation imposed on the part geometry.