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In this article, a new technique to design a low-profile planar vortex beam generator is proposed based on microwave frequency selective surface (FSS), which is capable of generating orbital angular momentum (OAM) mode under circularly polarized incidence. Such OAM generator is composed of transmissive units distributed over a planar surface. Each unit cell, behaving as a spatial phase shifter, is composed of a stack of patches and grids separated by thin dielectric substrates. In order to reduce the thickness of the device, a rectangular patch is embedded in the middle layer to introduce an additional resonance. Compared to other resonant unit cells, this structure presents nearly perfect efficiency to convert circularly polarized incident wave to its cross-polarized component. A simple equivalent circuit model, composed of transmission lines coupled together with shunt capacitors and inductors, is presented to analyze this structure. The prototype of the proposed planar OAM generator operating in $x$ -band is designed, fabricated, and experimentally characterized. Good agreement is achieved between full-wave simulations and measurements. It is demonstrated that the generator can achieve an efficiency of 85% at 10.7 GHz with an overall thickness of $0.067\lambda _{0}$ .

In virtual vector control schemes for single-phase current-regulated voltage source inverters, many methods are proposed to generate a virtual orthogonal feedback current from the real feedback current. However, the generation stage causes mismatch between feedback and command currents, which may deteriorate dynamic current-tracking capability and power quality. A precise scalar model is developed to analyze the phenomenon. Based on this model, the mismatch is presented as a current disturbance signal introduced into the control loop. The mathematical analysis establishes that the current disturbance cannot be eliminated by any orthogonal signal generation (OSG) method. To solve the problem, a current precompensator is mooted. In this paper, the effect of the current precompensator is studied in detail. Furthermore, a voltage precompensator is proposed, which better suits OSG methods than the current precompensator. Experimental results of both the current and voltage precompensator are compared with the conventional solution to substantiate enhanced dynamic performance.

Understanding how we perceive differences in material compliance, or 'softness,' is a central topic in the field of haptics. The intrinsic elasticity of an object is the primary factor thought to influence our perceptual estimates. Therefore, most studies test and report the elasticity of their stimuli, typically as stiffness or modulus. However, many reported estimates are of very high magnitude for silicone-elastomers, which may be due to artifacts in characterization technique. This makes it very difficult to compare the perceptual results between the studies. The work herein defines a standardized and easy-to-implement way to characterize test stimuli. The procedure involves the unconstrained, uniaxial compression of a plate into cylindrical substrates 10 mm tall by 10 mm diameter. The resultant force-displacement data are straightforwardly converted into stress-strain data, from which a modulus is readily derived. This procedure was used to re-characterize stimuli from prior studies. The revised results from the validated method herein are 200-1,100 percent lower than modulus values either reported and/or approximated from stiffness. This is practically significant when differences of 10-15 percent are perceptually discriminable. The re-characterized estimates are useful in comparing prior studies and designing new studies. Furthermore, this characterization methodology may help more readily bridge studies on perception with those designing technology.

This paper presents the design and implementation of RT-Fall, a real-time, contactless, low-cost yet accurate indoor fall detection system using the commodity WiFi devices. RT-Fall exploits the phase and amplitude of the fine-grained Channel State Information (CSI) accessible in commodity WiFi devices, and for the first time fulfills the goal of segmenting and detecting the falls automatically in real-time, which allows users to perform daily activities naturally and continuously without wearing any devices on the body. This work makes two key technical contributions. First, we find that the CSI phase difference over two antennas is a more sensitive base signal than amplitude for activity recognition, which can enable very reliable segmentation of fall and fall-like activities. Second, we discover the sharp power profile decline pattern of the fall in the time-frequency domain and further exploit the insight for new feature extraction and accurate fall segmentation/detection. Experimental results in four indoor scenarios demonstrate that RT-fall consistently outperforms the state-of-the-art approach WiFall with 14 percent higher sensitivity and 10 percent higher specificity on average.