Localization with orientation using RSSI measurements: RF map based approach

Localization of RFIDs in the indoor environment will entail determining both the position and the orientation of the user. This paper develops estimator using RSSI measurements to predict the position and orientation of a transmitter in an indoor environment. The best estimator tried was an K-nearest neighbours model that gave an accuracy of approximately 83% for position prediction and 93% for orientation prediction. It was also found that the RSSI values change throughout the day, meaning that an adaptive estimator is necessary for localization.

A calibration for surface analysis on aluminium alloys by RF-Glow-Discharge Optical Emission Spectrometry

Glow-Discharge Optical Emission Spectrometry (GD-OES) is a powerful technique for the rapid analysis of elements in a solid surface as a function of depth. DC-GD-OES allows depth profiling on electrically conductive surfaces only, and has proven to be difficult for the analysis of insulating layers, such as oxides. However, the technique of radio-frequency (RF) GD-OES has the advantage of being able to depth profile through multiple layers, both conducting and insulating. In this work, a LECO GDS- 850A spectrometer was calibrated for aluminium, oxygen, and other elements, with the RF source installed. A quantitative depth profile for a sample of tempered aluminium alloy 7475 is presented and compared with earlier work[1,2].

RF communication between surface and underwater robotic swarms

In order for underwater robots to communicate with land and air based robots on an equal basis, high speed communications is required. If the robots are not to be tethered then wireless communications is the only possibility. Sonar communications is too slow. Unfortunately radio waves are rapidly attenuated under water due to phenomena such as skin depth. These experiments attempt to extend the range of underwater radio communications.

Pi-shaped MEMS architecture for lowering actuation voltage of RF switching

A wide band low actuation capacitive coupling electrostatic RF MEMS switching device is presented in this paper. The device includes a pi-shaped matching architecture containing two switches connected by a high impedance short transmission line. The device can act as a switch for any desired frequency whilst requiring only 12volts for actuation. By optimizing the length and the characteristic impedance of the transmission line, the switch can be tailored for desired frequency bands. The switch is calculated and simulated for Ka to V frequency bands demonstrating excellent improvements of RF characteristics.

Design and simulation of a low voltage wide band RF MEMS switch

This paper presents design of an electrostatic wide band shunt capacitive coupling RF MEMS switch with low actuation voltage. The key factors of the RF MEMS switch design are the proper scattering parameters, low actuation voltage, and the cost of the fabrication process. An overview of the recent low actuation voltage RFMEMS switches has been presented. These designs still suffer from the complexity of process, lack of reliability, limitation of frequency band, and process cost. RF characteristics of a shunt RF MEMS switches are specified mostly by coupling capacitor in upstate position of the membrane Cu. This capacitor is in trade-off with actuation voltage. In this work, the capacitor is eliminated by using two short high impedance transmission lines, at the input and output of the switch. The simulation results demonstrate an improvement in the RF characteristic of the switch.

Design and simulation of a RF MEMS shunt switch for Ka and V bands and the impact of varying its geometrical parameters

RF MEMS plays an important role in microwave switching. The high performance of RF MEMS shunt such as high bandwidth, low insertion loss, and high isolation have made these switches well suitable for high performing microwave and millimeter wave circuits. This paper presents a RF MEMS shunt capacitive switch for Ka and V band application. This paper investigates the effect of various geometrical parameters on RF characteristics of the switch. The simulation results are presented and discussed.

Determining RF MEMS switch parameter by neural networks

A challenge in designing a RF MEMS switch is the determination of its parameters to satisfy the application requirements. Often this is done through a set of comprehensive time consuming simulations. This paper employs neural networks and develops a supervised learner that is capable of determining S11 parameter for a RF MEMS shunt switch. The inputs are the length its L and the height of its gap. The outputs are S11s for eight different frequency points from 0 to V band. The developed learner helps prevent repetitive simulations when designing the specified switch. Simulation results are presented.