Higher Number Of Degrees Of Freedom Research Articles

Robotic system for nasopharyngeal swab sampling based on remote center of motion mechanism.

In this study, a robotic system is proposed for nasopharyngeal (NP) swab sampling with high safety and efficiency. Most existing swab-sampling robots have more than six degrees of freedom (DOFs). However, not all six DOFs are necessarily required for NP swab sampling. A high number of DOFs can cause safety problems, such as collisions between the robot and patient. We developed a new type of robot with four DOFs for NP swab sampling that consists of a two DOFs remote center of motion (RCM) mechanism, a two DOFs insertion mechanism, and a nostril support unit. With the nostril support unit, the robot no longer needs to adjust the insertion position of the swab. The proposed robot enables the insertion orientation and depth to be adjusted according to different postures or facial shapes of the subject. For intuitive and precise remote control of the robot, a dedicated master device for the RCM and a visual feedback system were developed. The effectiveness of the robotic system was demonstrated by repeatability, RCM accuracy, tracking accuracy, and in vitro phantom experiments. The average tracking error between the master device and the robot was less than 2mm. The contact force exerted on the swab prior to reaching the nasopharynx was less than 0.04 N, irrespective of the phantom's pose. This study confirmed that the RCM-based robotic system is effective and safe for NP swab sampling while using minimal DOFs.

Semihyper-Reduction for Finite Element Structures With Nonlinear Surface Loads on the Basis of Stress Modes

Abstract Model reduction via projection is a common method to accelerate time integration of finite element (FE) structures by reducing the number of degrees-of-freedom (DOFs). However, nonlinear state-dependent surface loads are usually computed based on the nonreduced DOFs of the FE model. When a considerably high number of DOFs are involved in the nonlinear surface loads, their computation becomes a bottleneck. This paper presents a general approach for reduced time integration and reduced force computation for FE models. The required force trial vectors can be computed easily and systematically out of deformation trial vectors, commonly called “modes.” Those force trial vectors, which we call “stress modes,” can be determined a priori so that a nonlinear computation of the full system is not necessary. The new idea in this contribution is that stress recovery is used to decrease the number of equations for the force computation. A general framework for semihyper-reduction (SHR) is developed and its practical implementation is discussed. The term SHR is introduced because it is an intermediate approach between the straight-forward method of using the FE DOFs and pure hyper-reduction (HR) where the FE DOFs are omitted for computing state-depended surface loads. In order to demonstrate the proposed SHR approach practically, a numerical example of a planar crank drive is given, where a hydrodynamic lubrication film separates piston and cylinder. Thereby, very good result quality has been observed in comparison to a finite difference reference solution.

DOA Estimation Using Compressed Sparse Array

Sparse arrays, such as nested arrays and coprime arrays, can achieve a high number of degrees of freedom (DOFs) for direction of arrival (DOA) estimation with a reduced number of antennas. On the other hand, the compressive measurement method provides an effective way to reduce the number of front-end circuit chains. In this paper, we generalized current works on the two categories of methods to a compressed sparse array (CSA) scheme, which combines the compressive measurement method, and the sparse array together to significantly reduce the system complexity. After introducing the proposed scheme, the Cramer-Rao bound (CRB) of the proposed CSA scheme is derived. We then determine the corresponding existing conditions of the CRB, based on which the number of DOFs is derived and examined for the first time. It is proved that, for a CSA which compressed the output of an $L$ -element sparse array to $M chains, a higher number of DOFs is obtained as compared to that of the $M$ -element array with the same sparse structure. Furthermore, the DOA estimation accuracy using the $M$ -chain CSA is higher than that using the $M$ -element sparse array due to the extended array aperture. Numerical simulations verify the superiority of the proposed CSA scheme.

Performance Analysis for Uniform Linear Arrays Exploiting Two Coprime Frequencies

Sparse arrays can achieve a higher number of degrees of freedom (DOFs) compared with uniform linear array (ULA) counterparts. To further reduce the number of physical sensors while keeping a high number of DOFs, a direction of arrival (DOA) estimation algorithm by exploiting coprime frequencies base on a sparse ULA is recently proposed. However, the performance of such approach is not properly analyzed. In this letter, we analyze the Cramer–Rao bound (CRB) as the lower bound of the DOA estimation performance. The difference between the results presented in this letter and the recent CRB results on sparse arrays lies primarily in the additional phases occurred when utilizing different frequencies. It is shown in this letter that the phases affect the covariance matrix of the received data vector and, as a result, change the number of resolvable sources and alter the achieved CRB. We first demonstrate the effect of the additional phases with an example of two closely spaced sources, and the CRB for a sparse ULA exploiting two coprime frequencies is then derived. Numerical simulations are provided to validate the analyses.

Numerical Optimisation of Electro Hybrid Powertrains

In the future, the number of plug-in hybrid electric vehicles will further increase in order to fulfi l CO2 legislation. The complexity of electrifi ed powertrains makes it diffi cult to optimise the vehicle performance and the thermal management effi ciently due to the high number of degreesof-freedom. Therefore, a tailor-made optimisation tool was developed at the Vienna University of Technology to optimise the effi ciency of an electrifi ed powertrain while considering comfort functions like the heating of the driver cabin. AUTHORS

CHANNEL BASED DESIGN OF SYSTEMS WITH MULTIPLE ANTENNAS

In this article a method of optimizing wireless communication systems using multiple antennas is presented. This method focuses on the synthesis of antenna radiation patterns that are optimized in terms of mutual information, taking into account the specific limitations of the antenna design, such as the available space (for the antenna structure), polarization, number and arrangement of the antennas. Multiple-input-multiple-output (MIMO) antenna systems have the potential to increase the mutual information of wireless communication, through additional antennas, hence more channels and hardware. In contrast to the antennas, which are usually quite inexpensive, the size, complexity and price of the front-ends scale with the number of antennas. Accordingly, it is of interest for commercial applications, to use as few system inputs and outputs as possible to reduce the hardware for cost saving. The performance of a MIMO-antenna system is a direct result of the channel transfer matrix H, which in turn depends on the propagation environment and the antennas. Since the environment usually cannot be changed, the choice of antennas is crucial for the overall system behavior. The antennas determine inherently the correlation between the transmission coefficients of the channel matrix and, consequently, the mutual information of the system. The number of degrees of freedom (NDF) in the antenna design are: the spatial separation of the antennas, their polarization and their respective complex directional characteristics. The interaction of the propagation environment with the antennas and the high NDFs make the development of such a system especially time-consuming and expensive. This is the reason why, in most cases, a heuristic approach is used. This means that a certain antenna configuration is chosen based on general assumptions about the propagation environment, knowing that by doing so it is not guaranteed whether this choice increases the potential mutual information or not. Afterwards a prototype set-up with several possible combinations and a subsequent verification by measurements is omitted typically due to cost and time constraints. In contrast to the heuristic approach, our proposal includes full consideration of the propagation environment. The simulations performed during the proposed antenna synthesis method are reproducible and we can expect a resulting antenna system that is optimized in terms of mutual information in time variant mobile channels. Our promise is that this will allow the use of MIMO systems in industrial applications which are (typically) not realizable due to cost and space reasons and due to the high complexity. The basic idea behind the synthesis presented in this article is to optimize antenna systems by using channel knowledge already in the design process of the system. This enables us to determine fixed directional patterns that are optimized in terms of mutual information for any given set of constraints such as the number of antennas, their geometrical sizes and/or their polarization. By doing so hardware costs can be reduced since a system with fewer but more optimized antennas may have the same performance as a heuristically designed system with multiple antennas.