A Time-Reversal ( TR) imaging method for multiple targets that incorporates fewer transmitters is suggested using temporal filtering and Frequency Focusing (FF) matrices. This study is intended for the detection of malignant breast tumors with minimal microwave radiation. A couple of scenarios with two and four tumors are considered here. Using only two transmitters and twelve receivers, we show that two tumors could be detected in an inhomogeneous tissue breast. The results are compared with conventional TR Multiple Signal Classification method (MUSIC), which requires more transmitters. It is shown that several well-resolved tumors that exceed transmitters could be detected by subregion Spatio-Temporal Filtering (SSTF). In our proposed method, we used the orthogonal subspace instead of the signal subspace to have a higher Signal to Clutter Ratios (SCR). Since the Born approximation is not used, multiple scattering is considered. In addition, white Gaussian noise is added to measurement simulations. A 3-GHz modulated Gaussian pulse with a 1-GHz bandwidth is employed for illuminating the medium. Finite-Element Time-Domain method (FETD) is used in the measurement simulations, and the analytic Green’s function of the background is utilized for backpropagation. Several simulations are provided that show the effectiveness of this approach.
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.
With the enormous platforms available in present days, consumers communicate and interconnect online with web users all around the world to share their experiences. Thus, online platform has become a major source of reviews about different entities. People presently travel frequently around the world for different purposes. Seeking good hotels for accommodation is a prime concern. Customer reviews on hotels help future customers to take decisions about their accommodation as well as help hotel owners to rethink about designing customer facilities. However, many online reviews are biased due to different factors. Many hotel owners come up with attractions like referral rewards, coupons, bonus points etc. to the reviewers to motivate them in writing biased reviews. We have worked on US’s 100 hotel and found 952 incentivized reviews out of 19175 reviews, which is 4.96% of total reviews. A categorization on incentivized reviews is performed as well. Furthermore, hotels are distinguished based on real and incentivized reviews found on them. Results are verified using machine learning algorithms. Random Forest, K-Nearest Neighbor and Support Vector Machine are applied as machine learning algorithms to validate the accuracy of our model and their prediction results are compared. Random Forest outperforms with 94.4% prediction accuracy.
In this work, we propose a rate control algorithm (RCA) which regards characteristics of multiview video coding (MVC). The proposed RCA is designed for real-time applications of MVC and optimized to provide high quality compressed video bit streams with optimal utilization of channel bandwidth and buffering delay. The proposed RCA uses a fuzzy rate controller and a deterministic quality controller to define a quantization parameter (QP) for a Group of Pictures (GOP) based on given target rate, buffer, and quality constraints. The Key point in the proposed algorithm is to provide a variable bit rate multiview video bit stream with minimum fluctuations in QP and thereafter in quality while the buffer constraints are satisfied. The experimental results show that it can control the bitrate of all views according to the specified target bit rates for each view while the buffering constraints are completely obeyed and it provides compressed video bit streams with high visual quality.
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 analysis report is an attempt to differentiate various mitigation techniques for soil liquefaction that are currently in practice around the world. Through the different data- obtained from separate researches, this report strives to determine the most efficient and economical mitigation measure for low-story (3,4 floors) residential buildings in the Kathmandu valley. The disastrous earthquake of April 2015 left many residents of Kathmandu valley awestruck. The houses in the Imadol, Manamaiju, Manahara, Ramkot, and Changu Narayan were hugely damaged. This report is an effort to raise awareness, among engineers and other people, that geotechnical failures such as liquefaction are significant aspects of structural stability, despite being overshadowed by the superstructure.
As a high-precision measuring instrument, laser tracker is widely used in the field of geometric error detection of CNC machine tools. However, the employment of this laser tracker will lead to high cost as well as low measurement accuracy caused by the angle error. In order to solve these problems, the passive 3D laser tracking ball bar based on the principle of laser interference is introduced in this paper. The following measurement is realized by the passive stretching of the telescopic mechanism, and the space attitude adjustment of the laser is ensured by two precise rotating shafts. Moreover, the deflection caused by the telescopic guideway is an important factor affecting the accuracy of the device. Therefore, the telescopic mechanism is designed by the maximum deviation of the laser obtained by the experiment, and the finite element analysis is carried out. The results showed that the accuracy requirements are met. The main error model of the device is established and the influence of each error is analyzed. Moreover, the simulation results showed that the vertical axis offset angle error has the greatest impact on the device. At last, the reason of different influence of errors on the device is analyzed.
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.
Automotive radar is one of the key sensor technologies for active safety and comfort advanced driver assistance systems(ADAS). Vehicles equipped with radar sensors can determine the range, velocity and angle of arrival of multiple targets simultaneously in a highly dynamic environment. At 77 GHz, road infrastructure and buildings are an ever present source of clutter that can affect crucial target detection. Guardrails present a unique clutter challenge due to their ubiquity, proximity to ego vehicle and extremely large radar cross section(RCS). Due to their large RCS, guardrails can mask the existence of soft targets such as pedestrians in their vicinity. Therefore, it is crucial for sensor perception algorithms to identify and filter out the effects of guardrails. This paper presents a full-physics, simulation-based study of several full-scale road traffic scenes with different guardrail arrangements. By studying the Range-Doppler(RD) plots of each of the scenes at 77 GHz, we demonstrate the distinctly different radar signatures of guardrails in four key road settings that normally occur in driving. Using the results from this study, we characterize both the range and velocity behavior of various guardrail sections. Results from this study can be used to train perception algorithms to accurately identify and filter out guardrail systems in different driving scenarios and thus potentially prevent future accidents.
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.
In the present study, polyacrylonitrile (PAN)-co-polymer nanofibers as well as PAN-co-polymer nanofibers reinforced with functionalized single - walled carbon nanotubes (F-SWCNTs) were produced by electrospinning and stabilized. The samples were evaluated using DSC, FTIR, SEM and XRD. In the sample containing F-SWCNT the amount of heat released during the stabilization reactions was lower than that of pure PAN nanofibers. This indicates that the F-SWCNT prevents the sudden release of heat and damage to the nanofibers during stabilization. The carbon nanotubes greatly prevent the reduction of the diameter of the nanofibers as well as the decrease in the size of the crystals and the decrease of the arrangement of the nanofibers during stabilization.
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.
Walking and running are common types of physical activities people do in day to day living, to improve health and physical fitness or for recreation. During a physical activity, rate and depth of breathing increase because working muscles need extra oxygen in order to produce energy. In this study, computational fluid dynamics (CFD) simulations were used to investigate respiratory airflow flow dynamics in human upper airways response to walking and running. The numerical simulations were done in a realistic CT-scan airway model using ANSYS Fluent 19.0 software. Flow fields were characterized numerical and flow patterns were investigated in the airway model during inspiration and expiration in response to walking and running. The axial velocity distribution and secondary flow patterns were analyzed response to the two physical activities at different cross-sections of the airway model. The maximum velocity, wall pressure, and wall shear stress values for running were respectively 3.2, 9.4 and 5.9 times higher than that of walking during inspiration. More mixing of streamlines was observed during running than walking because of the occurrence of greater turbulence. More skewed flows at airway curvatures were observed at the inspiration than expiration. The result of this study supported the fact that running is a more intensive activity than walking from respiratory dynamics point of view.
‘Waiting’ and ‘Motion’ are two wastes of the seven wastes considered in the lean manufacturing concept. They both consume valuable operating time and slow down the production cycle. The main goal of this paper is to incorporate 5S, a lean manufacturing method with a view to reducing these two wastes. 5S was implemented to develop a system of organization for blowing Sizers and printing blocks in the work area. 5S derived from five Japanese words, when translated, mean sort, set in order, shine, standardize, and sustain. Research has been carried out in a poly bag manufacturing industry in Bangladesh with the goal of increasing productivity by minimizing non-value - added operational time. The manufacturing of poly bags consists mainly of 3 operations; blowing, printing and sealing. Following the implementation of 5S in these areas, the lead time for blowing operation decreased by 8% and the lead time for printing operation decreased by 18%. The assessment was successfully conducted and established in this study. A scope for improvement has been opened through this research, which may inspire other researchers to consider implementing the 5S tool in their respective research areas as a tool to reduce non-value - added operational time.
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.