Fully digital microscopes are becoming more and more common in surgical applications. In addition to high-resolution stereoscopic images of the operating field, which can be transmitted over long distances or stored directly, these systems offer further potentials by supporting the surgical workflow based on their fully digital image processing chain. For example, the image display can be adapted to the respective surgical scenario by adaptive color reproduction optimization or image overlays with additional information, such as the tissue topology. Knowledge of this topology can be used for computer-assisted or AR-guided microsurgical treatments and enables additional features such as spatially resolved spectral reconstruction of surface reflectance. In this work, a new method for high-resolution depth measurements in digital microsurgical applications is proposed, which is based on the principle of laser triangulation. Part of this method is a sensor data fusion procedure to properly match the laser scanner and camera data. In this context, a strategy based on RBF interpolation techniques is presented to handle missing or corrupt data, which, due to the measuring principle, can occur on steep edges and through occlusion. The proposed method is used for the acquisition of high-resolution depth profiles of various organic tissue samples, proving the feasibility of the proposed concept as a supporting technology in a digital microsurgical workflow.

We present comprehensive measurements of the evaporation behaviour, E, of a thinning film during a hydrodynamic-evaporative spincoating experiment. E, ω (rotational speed), and \nu (viscosity) are the main control parameters of the process. The evolution of the entire film thinning process can be described theoretically quite well based on the bulk value of \nu of the deposited liquid and with a process-specific (constant) E. The weighing in values of \nu are easily accessible (calculations, literature values, simple measurements). E is specific for the experimental conditions and values cannot be found in literature. There is also no generally accepted strategy to calculate E in advance. We analyzed our experimental results in view of a theoretical prediction for E, which was presented already some time ago by Bornside, Macosco, and Scriven, but never tested experimentally. We find good agreement between theory and experiment for many solvents and different! Accordingly, this approach permits in advance the calculation of the evolution of the entire hydrodynamic-evaporative film thinning in a spincoating process. In addition, we present a general formula, which allows in the case of spincoating mixtures of volatile solvents and nonvolatile solutes the prediction of the final solute deposit based on literature data only.

The space debris management and alleviation in the microgravity environment is a dynamic research theme of contemporary interest. Herein, we provide a theoretical proof of the concept of a lucrative energy conversion system that is capable for changing the space debris into useful powders in the international space station (ISS) for various bids. A specially designed broom is adapted to collect the space debris of various sizes. An optical sorting method is proposed for the debris segregation in the ISS by creating an artificial gravitational field using frame-dragging or gravitomagnetism. An induction furnace is facilitated for converting the segregated metal-scrap into liquid metal. A fuel-cell aided water atomization method is proposed for transforming the liquid debris into metal powder. The high-energetic metal powders obtained from the space debris could be employed for producing propellants for useful aerospace applications, and the silicon powder obtained could be used for making soil for fostering the pharmaceutical-flora in the space lab in the future aiming for the scarce-drug discoveries for high-endurance health care management. The proposed energy conversion system is a possible alternative for the space debris extenuation, and its real applications in orbiting laboratories through the international collaboration for the benefits to humanity.

Autonomous dishwasher loading is a benchmark problem in robotics that highlights the challenges of robotic perception, planning and manipulation in an unstructured environment. Current approaches resort to a specialized solution, however, these technologies are not viable in a domestic setting. Learning-based solutions seem promising for a general purpose solutions, however, they require large amounts of catered data, to be applied in real-world scenarios. This paper presents a novel solution based on pre-trained object detection networks. By developing a perception, planning and manipulation framework around an off-the-shelf object detection network, we are able to develop robust pick-and-place solutions that are easy to develop and general purpose requiring only a RGB feedback and a pinch gripper. Analysis of a real-world canteen tray data is first performed and used for developing our in-lab experimental setup. Our results obtained from real-world scenarios indicate that such approaches are highly desirable for plug-and-play domestic applications with limited calibration. All the associated data and code of this work is shared in a public repository.

The contact properties between metal and monolayer chemical vapor deposition (CVD) graphene were investigated, and coplanar waveguides (CPWs) composed of CVD graphene-based signal lines and Au-based ground lines were fabricated. The reflection coefficients of the CPWs were experimentally measured from 1 to 15 GHz. The contact properties were represented using the equivalent circuit model, which consists of paralell contact resistance Rc and paralell contact capacitance Cc. The calculated reflection coefficients of the model nearly agreed with the measured ones, which indicated that this model is suitable for analyzing the contact properties between metal and graphene up to 15 GHz. Bacause the impedance of Cc (|1/(ωCc )| = 4.8×10-3 Ω) is four orders of magnitude lower than that of Rc (50 Ω) at 15 GHz, the current flow is more capacitive and efficient than that in the DC band. The ratio of power consumption and power storage in the microwave band to the total power consumption in the DC band decreased with increasing frequency and incresing Cc. Therefore, higher Cc is preferable in designing microwave devices with a metal/graphene-based feeding structure, such as antennas and transmission lines.

The hot deformation characteristics of Nickel-based corrosion resistant alloy was studied in the temperature range of 1050~1200oC and the strain rate range of 0.001~0.1s-1 by employing hot compression tests. The results show that the peak stress increases with decreasing temperature and increasing strain rate, and the activation energy is about 409kJ/mol. Basing on the Avrami equation through using the critical strain (εc) and the strain for 50% DRX (ε0.5), a kinetic model for dynamic recrystallization (DRX) was established, where the model parameters could be obtained using the modified Zener-Hollomon parameter (Z*). Applying the model, the predicted value of the steady state strain (εss) and the strain for maximum softening rate (εm) agree well with the experimental results. Accordingly, the relationship between ε m and ε 0.5 is established, which is mainly dependent on the Avrami exponent (n). When n <3.25, εm becomes less than ε0.5 and the difference in between decreases with increasing the strain rate or decreasing the deformation temperature. Finally, through observing DRX microstructure under different deformation conditions, a power law relation between DRX grain size (Ddrx) and Z*, with an exponent of -0.36, was found.

Random sampling is a ubiquitous tool in simulations and modeling in a variety of applications. There are efficient algorithms for these for several known distributions, but in general, one must resort to computing or approximating the inverse to the distribution to generate random samples, given a random number generator for a uniform distribution. In certain physical and biomedical applications with which we have been particularly concerned, it has proven to be more efficient to provide random times for a walk of a fixed length, rather than the conventional random step lengths in a given time step for the walker. For these, the hitting-time distributions which have to be sampled have been computed, and proved to be complicated expressions with no efficient method to compute the inverse. In this paper, we explore a well known probability (the F-ratio distribution) - whose inverses are efficiently computable - as an alternative to generating look-up tables and interpolations to obtain the required time samples. We find that this distribution approximates the hitting-time distribution well, and report on error measures for both the approximation to the desired, and the error in the generated time samples. Future Monte Carlo simulations in a number of fields of application may benefit from methods such as we report here.

Low-field nuclear magnetic resonance (NMR) relaxometry is an attractive approach for point-of-care testing medical diagnosis, industrial food science, and in situ oil-gas exploration. However, one of the problems is the inherently long relaxation time of the (liquid) sample (and hence low signal-to-noise ratio) which causes unnecessarily long repetition time. In this work, a new methodology is presented for a rapid and accurate object classification using NMR relaxometry with the aid of machine learning techniques. It is demonstrated that the sensitivity and specificity of the classification are substantially improved with a higher order of (pseudo)-dimensionality (e.g., 2D or multidimensional). This new methodology (the so-called Clustering NMR) may be extremely useful for rapid and accurate object classification (in less than a minute) using the low-field NMR.

In a paper manufacturing system, it can be substantially important to detect machine failure before it occurs and take necessary maintenance actions to prevent a detrimental breakdown of the system. Multiple sensor data collected from a machine provides useful information on the system's health condition. However, it is hard to predict the system condition ahead of time due to the lack of clear ominous signs for future failures, a rare occurrence of failure events, and a wide range of sensor signals which might be correlated with each other. In this paper, we present two versions of feature extraction techniques based on the nearest neighbor combined with machine learning algorithms to detect a failure of the paper manufacturing machinery earlier than its occurrence from the multi-stream system monitoring data. First, for each sensor stream, the time series data is transformed into the binary form by extracting the class label of the nearest neighbor. We feed these transformed features into the decision tree classifier for the failure classification. Second, expanding the idea, the relative distance to the local nearest neighbor has been measured, results in the real-valued feature, and the support vector machine is used as a classifier. Our proposed algorithms are applied to the dataset provided by IISE 2019 data competition, and the results show the better performance than the given baseline.

This research reports on an image processing technique used to merge Magnetic Resonance Imaging (MRI) or Magnetic Resonance Angiography (MRA) with their intensity-curvature functional (ICF). Given a two-dimensional MR image, six 2D model polynomial functions were fitted to the image, and six ICF images were calculated. The MR image and its ICF were direct Fourier transformed. The phase of MR image was estimated pixel-by-pixel as arctangent of ratio between imaginary and real components of k-space and is called phase ratio. The phase of ICF is the phase of inverse Fourier transformation and is called base phase. The two values of phase were summed up and used to reconstruct ICF images through inverse Fourier transformation. The reconstructed image is the combination of MR and ICF. Data obtained with T2-MRI and MRA indicates that the technique improves vessel detection in T2-MRI and contrast enhances T2-MRI and MRA.

The nascent wave of disruptive competition in the current business environment brought about by the fourth industrial revolution (Fashion 4.0 or Apparel 4.0) is enormous. Therefore, it is paramount important to apparel industry to be flexible enough to respond quickly to the unstable customers’ demand through continuous improvement of their process efficiency and productivity. This study aims at achieving an optimal trouser assembly line balancing using simulation-based optimization via design of experiment. The empirical study is conducted at Southern Range Nyanza Limited (NYTIL) garment facility and a complex trouser assembly line with 72 operations is considered. The discrete event simulation of the trouser assembly line is developed using Arena simulation software. The local optimal solution is obtained from simulation experimentation and is adopted for the optimization process. The OptQuest tool is utilized to solve a single objective function (throughput) optimization problem. The results show that average throughput increases from the existing design (490 pieces per day) to local optimal design (638) and global optimal design (762). Consequently, the line efficiency increases from 61.2% to 79.7% to 95.2% respectively. The high increase in line efficiency and average throughput confirms the suitability assembly line balancing using simulation-based optimization via design of experiment.

Under proper loading conditions, micro-to-nanoscale heterogeneities (i.e., the bond system) that are commonly found within the materials of a system can coalesce until causing macroscopic alterations of the system properties. The bond system is responsible for atypical and invariant-scale non-linear elastic processes in granular media, from laboratory-tested materials (mm) to the Earth’s crust (km). The unusual observed behavior involves slow recovery, or relaxation, of the elastic properties after dynamic loading. Several models have been designed to explain non-linear elasticity, although their physics is still partially unknown. Here, we show that recovery processes are also observed at intermediary scales (m) in civil engineering structures, and that they might be related to structural health due to the healing of cracks. For Japanese buildings subjected to earthquakes, we observe rapid co-seismic reductions of their resonance frequency, followed by fascinating recoveries over different time-scales: over short times (i.e. seconds) for a single earthquake; over intermediate times (i.e. months) for a sequence of aftershocks; and over long times (i.e. years) for a series of earthquakes. By comparing two buildings with different damage levels after the 2011 Tohoku earthquake, we show how relaxation models can characterize the level of cracking caused by damaging events. Our results bridge the gap between the laboratory and seismological observation scales, verifying in this way the universality of recovery processes, and demonstrating their value for the detection and characterization of damage.

A new beam switch antenna based on a composite right/left-handed (CRLH) Butler matrix is presented and experimentally validated. The CRLH transmission line (TL) is proposed to increase the number of beams. The proposed CRLH TL has more than 100◦ phase differences using variable bias voltages. Different combinations of phase shifts are achieved by applying different bias voltages between 0 and 8V. The CRLH TL is added to the conventional Butler matrix to increase the progressive phase difference between adjacent ports, and consequently, the beam pattern. A 5◦ beam resolution within a spatial range of 100◦ is achieved. The measurement results are in good agreement with the simulations.

Many scientific researches have shown an obvious fact that the quality control charts with variable sampling schemes are more effective than the classical ones in improving statistical measures. The average number of false alarms (ANF), the average number of samples (ANS), the average number of inspected items (ANI), and the adjusted average time to signal (AATS) are the most important statistical measures that have always been attending in the evaluation of control charts. In this paper, a comprehensive analytical review on the U control chart by the statistical measures have been explained. For this purpose, different levels of the possible factors are determined and presented the results of calculating the statistical measures with the obtained parameters on the sampling schemes of the U control chart. It is shown that the variable U control charts are able to improve the effectiveness statistical, especially for detecting shifts and number of false alarms.

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Temperature, time and particle size effects on Irvingia gabonensis kernel oil (IGKO) yield, as well as the kinetics and thermodynamics parameters were investigated. Highest oil yield of 68.80 % (by weight) was obtained at 55 °C, 150 min., and 0.5 mm. Evaluated physicochemical properties of IGKO indicated that viscosity, acidity, dielectric strength, flash and pour points were 19.37 mm2s-1, 5.18 mg KOHg-1, 25.83 KV, 285 °C, and 17 °C, respectively, suggesting its feasibility as transformer fluid upon further treatment. Of the pseudo second order (PSO) and hyperbolic kinetic models studied, the former gave better fit to the experimental data. ∆H, ∆S and ∆G values of IGKO extraction at 0.5 mm and 328 K were, 251.81 KJ/mol, 1.08 KJ/mol and -105.49 KJ/mol, respectively, indicating the endothermic, irreversible and spontaneous nature of the process. Kinetic model equations that describe the process were successfully developed for both models based on the process parameters.

This work focused on the chemical synthesis and characterization of palm kernel oil (PKO) for bio-lubricant production using transesterification of palm kernel methyl ester (PKME) with trimethylolpropane (TMP) and epoxidation-esterification methods. The PKO was extracted using solvent extraction method. The physicochemical characteristics of the PKO and produced bio-lubricant samples were determined using standard methods. Fourier Transform Infrared (FTIR) spectrometry and Gas Chromatographic analyses, were respectively, used to determine the predominant functional groups and fatty acids of PKO and the produced bio-lubricant samples. At 55 °C, 150 min and 0.5 mm particle size, kernel oil yield was 49.82 % (by weight). The viscosities at 40 °C, 100 °C, viscosity index, pour and flash points of the bio-lubricants produced by transesterification of TMP (PKBLT) and epoxidation-esterification (PKBLE) methods, were [42.53 cSt, 10.65 cSt, 139, - 11 °C, 235 °C] and [44.69 cSt, 11.42 cSt, 132, - 12 °C, 240 °C], respectively. Time, mole ratio and temperature effects were the main factors that significantly influenced the transesterification and epoxidation processes. The obtained physicochemical properties of PKBLE and PKBLT samples showed conformity with ISO VG 32 standard, hence, their possible application as bio-lubricant basestock.

We present a novel radar signal processing technique to identify the presence or absence of a living body in a vehicle using a mm-wave frequency-modulated continuous-wave radar. Unlike traditional detection methods which are mostly based on constant false alarm rate (CFAR), our proposed method extracts and monitors the consistent Doppler effect of received signals from the radar antenna resulting from the consistent breathing of living bodies over time. The proposed method works in all types of cars without the need for threshold definition for tracking as well as no need for training. Hence, the algorithm is more robust, accurate and fast. We assess our proposed signal processing with two phantoms mimicking the breathing of children and with adults in the vehicle in various conditions. The system has been proven to be robust in extensive studies over the course of multiple months.