Sharif Digital Repository

The main focus of this paper is on the estimation of target motion parameters using bearing measurements. Here, based on a linearization of measurements, a recursive least squares (RLS) solution is developed. The performance of the proposed method is compared to PLE which uses the same approach in measurement linearization. Simulation results show the excellence of the proposed method over the PLE, and RLS results are in vicinity of the CRLB  

A novel architecture for QR decomposition-based recursive least squares is presented. It offers low latency for applications where the channel equalization and adaptive filtering are mandatory. This approach reduces the computations by rewriting the equations in a manner that lets intense hardware resource sharing by reusing similar values in different computations. Moreover, precision range conversion allows for combining complex operations such as root square and division with minimum effect on the overall quantization error. Hence, an efficient lookup table-based solution has highly enhanced the performance of the design by 2.7 times with respect to the previous works  

Conventional models of cardiovascular system (CV) frequently lack required detail. Once utilized to study the heart function, these models focus primarily on the overall relationship between pressure, flow and volume. This study proposes a localized and regional model of the CV system. It utilizes non-invasive blood flow and pressure seed data and temporal cardiac muscle regional activation to predict the operation of the heart. Proposed localized analysis considers specific regions of the heart, namely base, mid and apex sections of the left ventricle. This modular system is based on a hydraulic electric analogy model, estimating desired parameters, namely resistance (R), compliance (C),... 

In this paper, a low order recursive linear prediction method and recursive least square as an adaptive filter (LP-RLS) are introduced to predict the speech and the excitation signals. In real-time packet-based communication systems, one major problem is misrouted or delayed packets which results in degraded perceived voice quality. If packets are not available on time, the packet is known as lost packet. The easiest task of a network terminal receiver is to replace silence for the duration of lost speech segments. In a high quality communication system, to avoid quality reduction due to packet loss, a suitable method and/or algorithm is needed to replace the missing segments of speech. The... 

Fault detection has always an important role in maintaining the system stability and assuring satisfactory and safe operation. In this paper a method based on system identification is used for fault detection on a nuclear reactor. The combination of Extended Kalman Filter and recursive Least Square are used to identify the system parameters. Another goal of this paper is design of a fault tolerant control system for a nuclear reactor during power change operation. The proposed controller is an adaptive neuro-fuzzy controller based on emotional learning. Performance of the controller in term of transient response and robustness against failure is very good and outstanding  

This paper proposes a novel error-feedback lattice recursive least-squares (EF-LRLS) filter for online estimation of power system oscillatory modes. The EF-LRLS filter is applied to ambient data provided by phasor measurement units to identify the autoregressive (AR) model parameters. This filter has a modular structure; accordingly, if the length of the filter equals N, it identifies AR(1) to AR(N) models concurrently. In the proposed method, removing very low and high frequencies and re-sampling steps are fulfilled in an online fashion. This adaptive filter has less computational complexity than standard RLS filter, making it an appropriate choice for online system identification. The... 

This paper proposes a novel error-feedback lattice recursive least-squares (EF-LRLS) filter for online estimation of power system oscillatory modes. The EF-LRLS filter is applied to ambient data provided by phasor measurement units to identify the autoregressive (AR) model parameters. This filter has a modular structure; accordingly, if the length of the filter equals $N$, it identifies AR(1) to ${m{AR(}}N)$ models concurrently. In the proposed method, removing very low and high frequencies and resampling steps are fulfilled in an online fashion. This adaptive filter has less computational complexity than standard RLS filter, making it an appropriate choice for online system identification.... 

Online monitoring of inter-area oscillations is a vital need for secure operation of large-scale power systems. This paper aims at online estimation of inter-area modes by a novel stabilized fast transversal recursive least squares (SFTRLS) filter running on ambient power system data received from phasor measurement units. The SFTRLS filter is a fast finite-impulse response adaptive filter that is widely used in communication applications, such as noise and echo cancelation. It has the lowest computational complexity among least-squares filters and directly calculates coefficients of the parametric model selected for identifying a system under study. In the proposed method, the SFTRLS filter... 

This paper presents a model-based method for applying online proactive generators redispatch to improve damping of the critical electromechanical oscillations of power system. The proposed method comprises two stages: 1) monitoring modal characteristics of oscillatory modes in ambient condition, and 2) applying generators redispatch based on sensitivities of the critical mode to the generators active power changes using a new analytic method. An online identification method such as error feedback lattice recursive least square adaptive filter is applied for online estimation of the oscillatory modes. Then, whenever the damping ratio of an identified mode is less than a preset threshold, its... 

Conventional models of cardiovascular system frequently lack required detail and focus primarily on the overall relationship between pressure, flow and volume. This study proposes a localized and regional model of the cardiovascular system. It utilizes noninvasive blood flow and pressure seed data and temporal cardiac muscle regional activity to predict the operation of the heart under normal and congestive heart failure conditions. The analysis considers specific regions of the heart, namely, base, mid and apex of left ventricle. The proposed method of parameter estimation for hydraulic electric analogy model is recursive least squares algorithm. Based on simulation results and comparison...