Low Noise Measurement Systems

One of the most challenging problems related to single- ended line testing (SELT) is the estimation of the noise power spectral density (PSD) at the customer-premises (CP) side of the line under test, because only measurements at the central-office side (CO) are possible. A solution for the noise caused by crosstalk could consist of first identifying the disturbers present in the cable binder of the line under test based on the measured noise PSD at the CO side and secondly estimating the noise PSD at the CP side by means of crosstalk models and the transmit PSD's of the identified disturbers. In this paper we focus on the first part of the solution, the disturber identification. For the identification non-parametric and parametric modeling approaches are explored. The estimator is based on a maximum-likelihood (ML) cost function. The proposed algorithms have been experimentally verified by means of measurements on a France Telecom cable.

This paper examines and contrasts the feasibility of joint state and parameter estimation of noise-driven chaotic systems using the extended Kalman filter (EKF), ensemble Kalman filter (EnKF), and particle filter (PF). In particular, we consider the chaotic vibration of a noisy Duffing oscillator perturbed by combined harmonic and random inputs ensuing a transition probability density function (pdf) of motion which displays strongly non-Gaussian features. This system offers computational simplicity while exhibiting a kaleidoscope of dynamical behavior with a slight change of input and system parameters. An extensive numerical study is undertaken to contrast the performance of various nonlinear filtering algorithms with respect to sparsity of observational data and strength of model and measurement noise. In general, the performance of EnKF is better than PF for smaller ensemble size, while for larger ensembles PF outperforms EnKF. For moderate measurement noise and frequent measurement data, EKF is able to correctly track the dynamics of the system. However, EKF performance is unsatisfactory in the presence of sparse observational data or strong measurement noise.

In this paper a closed-loop control algorithm is developed for blood glucose regulation in type I diabetes mellitus patients. The control technique incorporates expert knowledge about treatment of disease by using Mamdani-type fuzzy logic controller to stabilize the blood glucose concentration in normoglycaemic level of 70 mg/dl. Controller performance is assessed in terms of its ability to reject the multiple meal disturbances resulting from food intake, on an averaged nonlinear patient model. Robustness of the controller is tested over a group of patients with model parameter varying considerably from the average model. In addition, proposed controller provides the possibility of more accurate control of blood glucose level in the patient in spite of uncertainty in model and measurement noise. Simulation results show the superiority of the proposed scheme in terms of robustness to uncertainty in comparison with other researches.

We have designed and implemented an UWPE (ultra-weak photon emission) measurement system - a dark chamber and low dark count PMT module - oriented to environmental control studies. The system is totally controlled by software - power supply, counting board, temperature control and shutter for light excitation. The system's architecture and performance are described here. In terms of SNR the proposed system has presented a great improvement related to previous development, also in terms of resources provided as full automatic control of power supply, sample illumination and temperature control capability yet having low implementation costs.

Unmanned Aerial Vehicles (UAVs) are playing an important role both in military as well as civilian applications. However, their role in civilian applications is hampered by the lack of adequate guidelines and operational requirements. In this paper a set of flight, operational and performance requirements for the use of fixed wing UAVs in civilian applications are collated from several resources

A large-aperture, electromagnetic model for coherent microscopy is presented and the inverse scattering problem is solved. Approximations to the model are developed for near-focus and far-from-focus operations. These approximations result in an image-reconstruction algorithm consistent with interferometric synthetic aperture microscopy (ISAM): this validates ISAM processing of optical-coherence-tomography and optical-coherencemicroscopy data in a vectorial setting. Numerical simulations confirm that diffraction-limited resolution can be achieved outside the focal plane and that depth of focus is limited only by measurement noise and/or detector dynamic range. Furthermore, the model presented is suitable for the quantitative study of polarimetric coherent microscopy systems operating within the first Born approximation.

The major concentration of this study is to describe the structure of a C++/MEX solution for robust detection and delineation of arterial blood pressure (ABP) signal events. Toward this objective, the original ABP signal was pre-processed by application of à trous discrete wavelet transform (DWT) to extract several dyadic scales. Then, a sliding window with fixed length was moved on the appropriately selected scale. In each slid, mean, variance, Skewness and Kurtosis values of the excerpted segment were superimposed to generate a newly defined multiple higher order moments (MHOM) metric to be used as the detection decision statistic (DS). Then, after application of an adaptive-nonlinear transformation for making the DS baseline static, the histogram parameters of the enhanced DS were used for regulation of the α-level Neyman–Pearson classifier aimed for false alarm probability (FAP)-bounded delineation of the ABP events. The proposed method was applied to all 18 subjects of the MIT-...

One of the most interesting questions in control theory is that of constructing observers. Observers compute estimates of the internal states of a dynamical system, using data provided by measurement probes or partial state information. For linear systems, Luenberger observers (also known as "deterministic Kalman filters" since they amount to Kalman filters designed without regard to the statistics of measurement noise) provide a general solution, but, for nonlinear systems, establishing generally applicable conditions for existence and convergence of observers is an open and active area of research. This paper provides a necessary and sufficient condition for detectability, and an explicit construction of observers when this condition is satisfied, for chemical reaction networks of the Feinberg-Horn-Jackson zero deficiency type.

Model Free Adaptive Control (MFAC) has been used to reject the sensor noise measuring the controlled output. The MFAC is trained using feedback error learning law. The measurement noise makes the control error (used by feedback error learning) very noisy which results in increased control activity as well as corruption of the past learning. The MFAC is modeled as a Fuzzy Relational Model (FRM). FRM can be trained to represent the sensor noise occurring at the plant output in the fuzzy control signal. This representation of the sensor noise can be effectively utilized to reduce the control activity; hence increasing the life span of an actuator and contributing towards enhanced control performance. The control activity can be reduced by using conditional defuzzification which defuzzifies the fuzzy control signal by taking into consideration the uncertainty representation. By employing conditional defuzzification the controller will not respond to the noisy measurement of the plant output. The superiority of the conditional defuzzification used in conjunction with MFAC is demonstrated by the fact that with increased sensor noise the control activity reduces while maintaining the same control performance.