Negative feedback system reduces pump oscillations

External negative feedback system counteracts low frequency oscillations in rocket engine propellant pumps. The system uses a control piston to sense pump discharge fluid on one side and a gas pocket on the other

Artificial-feedback system

System suppresses spurious sinusoidal responses of any sinusoidally driven amplifier showing time-dependent phase shift versus frequency function. System is applicable to any sinusoidally driven amplifier. Technique eliminates, or reduces, unwanted spurious vibrations during tests to determine dynamic frequency responses of mechanical systems

Performance analysis of layered random beamforming OFMDA with feedback reduction

This paper presents a downlink performance analysis of a Layered Random Beamforming (LRB) - MIMO-OFDMA Physical Layer (PHY) with feedback reduction as applicable to future generation wireless communication systems. OFDMA is a popular multiple access candidate for future generation cellular communication systems which facilitates multi-user diversity by enabling multiple access in the frequency domain. LRB enables the exploitation of spatial multi-user diversity gain, spatial multiplexing capacity gain and layer spatial multi-user diversity gain, which is achieved by enabling the multiplex of data transmitted simultaneously to different destinations. Unlike a conventional beamforming system, an LRB system only requires effective signal to interference and noise ratios (ESINR) as feedback from every spatial layer of the MIMO channels and thus has potentially lower feedback requirements than a system which requires feedback of more detailed channel information. By combining the LRB technique with OFDMA, LRB-OFDMA can achieve an additional spectral multi-user diversity gain compared to the single carrier LRB system. However, in this case ESINR feedback on a per-sub-carrier basis is required in principle and the feedback requirements may thus increase substantially. This feedback requirement can be reduced by generating the feedback information on a cluster (group of sub-carriers) basis rather than on an individual sub-carrier basis. In this way, the system can exploit any correlation in the frequency response of the channel. The design of an LRB-OFDMA system is presented in this paper and the performance of the system is evaluated for different degrees of feedback reduction using various statistical channel models

Breathing feedback system with wearable textile sensors

Breathing exercises form an essential part of the treatment for respiratory illnesses such as cystic fibrosis. Ideally these exercises should be performed on a daily basis. This paper presents an interactive system using a wearable textile sensor to monitor breathing patterns. A graphical user interface provides visual real-time feedback to patients. The aim of the system is to encourage the correct performance of prescribed breathing exercises by monitoring the rate and the depth of breathing. The system is straightforward to use, low-cost and can be installed easily within a clinical setting or in the home. Monitoring the user with a wearable sensor gives real-time feedback to the user as they perform the exercise, allowing them to perform the exercises independently. There is also potential for remote monitoring where the user’s overall performance over time can be assessed by a clinician

Bayesian feedback versus Markovian feedback in a two-level atom

We compare two different approaches to the control of the dynamics of a continuously monitored open quantum system. The first is Markovian feedback as introduced in quantum optics by Wiseman and Milburn [Phys. Rev. Lett. {\bf 70}, 548 (1993)]. The second is feedback based on an estimate of the system state, developed recently by Doherty {\em et al.} [Phys. Rev. A {\bf 62}, 012105 (2000)]. Here we choose to call it, for brevity, {\em Bayesian feedback}. For systems with nonlinear dynamics, we expect these two methods of feedback control to give markedly different results. The simplest possible nonlinear system is a driven and damped two-level atom, so we choose this as our model system. The monitoring is taken to be homodyne detection of the atomic fluorescence, and the control is by modulating the driving. The aim of the feedback in both cases is to stabilize the internal state of the atom as close as possible to an arbitrarily chosen pure state, in the presence of inefficient detection and other forms of decoherence. Our results (obtain without recourse to stochastic simulations) prove that Bayesian feedback is never inferior, and is usually superior, to Markovian feedback. However it would be far more difficult to implement than Markovian feedback and it loses its superiority when obvious simplifying approximations are made. It is thus not clear which form of feedback would be better in the face of inevitable experimental imperfections.Comment: 10 pages, including 3 figure