In this paper, we present the design and implementation of a smart irrigation system using Internet of Things (IoT) technology, which can be used for automating the irrigation process in agricultural fields. It is expected that this system would create a better opportunity for farmers to irrigate their fields efficiently, as well as eliminating the field’s under-watering, which could stress the plants. The developed system is organized into three parts: sensing side, cloud side, and user side. We used Microsoft Azure IoT Hub as an underlying infrastructure to coordinate the interaction between the three sides. The sensing side uses a Raspberry Pi 3 device, which is a low cost, credit-card sized computer device that is used to monitor in near real-time soil moisture, air temperature and relative humidity, and other weather parameters of the field of interest. Sensors readings are logged and transmitted to the cloud side. At the cloud side, the received sensing data is used by the irrigation scheduling model to determine when and for how long the water pump should be turned on based on a user-predefined threshold. The user side is developed as an Android mobile app, which is used to control the operations of the water pump with voice recognition capabilities. Finally, this system was evaluated using various performance metrics, such as latency and scalability.
Early detection of increasing values of intraocular pressure (IOP) due to glaucoma can prevent sever ocular diseases and ultimately, prevent loss of vision. Currently, the need for an accurate, mobile measurement of intraocular pressure is unmet within the modern healthcare practices. There is a potential to utilize soundwaves as a mobile measurement method and therefore, the relationship between IOP and the reflection coefficient of sound waves is investigated. Simulations are conducted using COMSOL Multiphysics to provide theoretical confirmation of the worthiness of the experiment. An experimental demonstrated is presented to further investigate the relationship between the internal pressure of an object and its acoustic reflection coefficient. The experiment exploits the use of hydrostatic pressure to determine internal pressure, and the reflection coefficient is measured and analyzed. An initial experiment is conducted to identify the resonant frequency of the object and the optimal frequency for maximizing reflection. The experiment shows comprehensively that there is a relationship between the internal pressure of an object and its acoustic reflection coefficient, providing a confirmation of the theory that would allow mobile measurements of IOP to be conducted with the use of a smart phone.
Due to the soaring growth of the electric vehicles and grid energy storage markets, the high-safety and high-energy-density battery storage systems are urgent needed. Lithium metal anode with highest theoretical specific capacity (3860 mA·h·g−1) and the lowest electrochemical potential (−3.04 V vs standard hydrogen electrode) is regarded as the ultimate choice for the high energy density batteries. However, its safety problems as well as the low Coulombic efficiency during the Li plating and stripping processes significantly limit the commercialization of lithium metal batteries. Recently, Li-containing alloys have demonstrated vital roles in inhibiting lithium dendrite growth, controlling interfacial reactions and enhancing the Coulombic efficiency as well as cycle life. Accordingly, in this perspective, the progresses of lithium alloys for robust, stable and dendrite free anode for rechargeable lithium metal batteries are summarized. The challenges and future focus research of lithium-containing alloys in lithium metal batteries are also discussed.
Electrochemical machining (ECM) is a method for removing metal by anodic dissolution. At the interface between the workpiece surface and an electrically conductive ﬂuid (electrolyte), the material is dissolved locally without direct physical contact to the cathodic tool. Due to the force-free nature of the process, ECM is used for machining high-strength or hard materials, such as titanium aluminides, Inconel, Waspaloy, and high nickel, cobalt, and rhenium alloys.1 However, determining suitable process parameters remains challenging due to their interacting eﬀects on working distances during the machining process. Therefore a simulation-based approach to process design substantially reduces resource and time investment to achieve the desired geometry of the ﬁnished part. This methodology requires data about the materials electrochemical properties, such as removal velocity and current eﬃciency, which have to be obtained experimentally. In this study, a methodology for acquiring and processing this data as well as the development of multiphysics simulation models is presented for two use cases: (i) manufacturing a centrifugal impeller with a diameter of 14 mm consisting of the nickel alloy Inconel 713C for use in turbomachinery and (ii) the generation of a deﬁned surface micro structure into the novel Mg-Y-Zn alloy WZ73.
Goal: Fast Fourier transform (FFT), has been the main tool for EEG spectral analysis (SPA). As EEG can show nonlinear and non-stationary behavior, FFT may at times be meaningless. A novel method was developed for analyzing nonlinear and non-stationary signals using the Hilbert-Huang transform. Methods: We compared spectral analyses of EEG using FFT with Hilbert marginal spectra (HMS) with a multivariate empirical mode decomposition algorithm. Segments of continuous 60-sec EEGs recorded from 19 leads of 47 healthy volunteers were studied. Results: HMS showed a reduction of the alpha activity (-5.64%), with increments in the beta-1 (+1.67%), and gamma (+1.38%) fast activity bands, an increment in theta (+2.14%), and in delta (+0.45%) bands, and vice versa for the FFT method. For weighted mean frequencies, insignificant mean differences (lower than 1Hz) were observed between both methods for delta, theta, alpha, beta-1 and beta-2 bands, and only for gamma band values. The HMS were 3 Hz higher than the FFT method. Conclusion: HMS may be considered a good alternative for SPA of the EEG when nonlinearity or non-stationarity may be present.
Optical cables are enormous transmission media which carries high-speed data across transatlantic, intercontinental, international boundaries and cities. The optical cable is essential in data communication. The cable has become an indispensable component in optical communications infrastructure; hence, conscious efforts are always adopted to prevent or minimize faults in the optical network infrastructure. Typically, tracing fault in the underground optical network has been difficult even though optical time-domain reflectometer (OTDR) has been used to measure the distance of faults in the underground fiber cable. The methodologies deployed in the reviewed literature indicate a vast gap between the fault distance measured by the OTDR and the actual distance of fault. This paper observed the difficulties involved in tracing the actual spot of fault in the underground optical networks. The difficulty of tracing these underground faults mostly result in an undue delay and loss of revenue. This research presents a machine learning (ML) approach to predict the actual location of a fiber cable fault in an underground optical transmission link. Linear regression in the python sci-kit learn library was used to predict the actual location of a fault in an underground optical network. The MSE and MAE evaluation matrix used provided good accuracy results of 0.061291 and 0.080143, respectively. The result obtained in this paper indicates that faults in underground optical networks can be found quickly to avoid the delays in the fault tracing process, which leads to an excessive revenue loss.
This paper presents a complete design procedure, with an optimized feeding method, of two-dimensional slotted waveguide antenna arrays (2D SWAs). For a desired sidelobe level ratio, the proposed system provides a pencil shape pattern with a narrow halfpower beamwidth, large sidelobe level ratio (SLR), and very low sidelobe levels (SLL), which makes it suitable for high power microwave applications. The radiating slotted waveguide antennas use longitudinal slots, designed for a specified slidelobe level ratio and resonance frequency. The resulting two-dimensional slotted waveguide antenna array is formed by stacking a number of similarly designed radiating SWAs, and fed with an additional SWA. The proposed feeding method uses longitudinal coupling slots rather than the conventional inclined coupling slots, which can provide better values of SLR and easily obtain very low SLLs, in comparison with the conventional systems. The feeder dimensions and slots positions are deduced from the dimensions and total number of the radiating SWAs. For a desired SLR, the slots excitation in the radiating and feeder SWAs are calculated based on a specified distribution. Then, using simplified closed-form equations and for a desired resonance frequency, the slots lengths, widths, and their distribution along the length of the radiating SWAs and feeder SWA can be found. Two examples are illustrated with different number of slots and radiating elements, and one is fabricated and tested. Chebyshev distribution is used to estimate the excitations of the SWA slots in the examples. The obtained measured and simulated results are in accordance with the design objectives.
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.
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.
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.