Less Number Of Computations Research Articles

Estimate-Merge-Technique-based algorithms to track an underwater moving target using towed array bearing-only measurements

Bearing-only passive target tracking is a well-known underwater defence issue dealt in the recent past with the conventional nonlinear estimators like extended Kalman filter (EKF) and unscented Kalman filter (UKF). It is being treated now-a-days with the derivatives of EKF, UKF and a highly sophisticated particle filter (PF). In this paper, two novel methods based on the Estimate Merge Technique are proposed. The Estimate Merge Technique involves a process of getting a final estimate by the fusion of a posteriori estimates given by different nonlinear estimates, which are in turn driven by the towed array bearing-only measurements. The fusion of the estimates is done with the weighted least squares estimator (WLSE). The two novel methods, one named as Pre-Merge UKF and the other Post-Merge UKF, differ in the way the feedback to the individual UKFs is applied. These novel methods have an advantage of less root mean square estimation error in position and velocity compared with the EKF and UKF and at the same time require much lesser number of computations than that of the PF, showing that these filters can serve as an optimal estimator. A testimony of the afore-mentioned advantages of the proposed novel methods is shown by carrying out Monte Carlo simulation in MATLAB R2009a for a typical war time scenario.

Multi‐objective Taguchi approach for optimal DG integration in distribution systems

This study presents a new multi-objective Taguchi approach for optimal integration of distributed generations (DGs) in small and large scale distribution networks. The Taguchi method (TM) is a statistical method and employs orthogonal arrays (OAs) to estimate the output response in less number of computations. In every cycle, OA is updated according to mean response of each parameter at its respective levels in the previous cycle. A new node priority list is proposed to guide TM to select promising nodes. For multi-objective problems, a trade-off is developed between various objectives using the technique for order of preference by similarity to ideal solution that reduces Euclidean distances of various objectives from their best solutions and increases Euclidean distances from their worst solutions. A multi-objective DG integration problem is formulated to demonstrate the applicability of the proposed approach and tested on IEEE 33-bus, 118-bus and a practical 201-bus radial distribution systems. The simulation results are compared with existing multi-objective optimisation techniques used for optimal DG integration problems in the literature and found to be promising.

Efficient architecture for global elimination algorithm for H.264 motion estimation

This paper presents a fast block matching motion esti mation algorithm and its architecture. The proposed architecture is based on Global Elimination (GE) Algorithm, which uses pixel averaging to reduce complexity of motion search while keeping performance close to that of full search. GE uses a preprocessing stage which can skip unnecessary Sum Absolute Difference (SAD) calculations by comparing minimum SAD with sub-sampled SAD (SSAD). In the second stage SAD is computed at roughly matched candidate positions. GE algorithm uses fixed sub-block sizes and shapes to compute SSAD values in preprocessing stage. Complexity of this GE algorithm is further reduced by adaptively changing the sub-block sizes depending on the macro-block features. In this paper adaptive Global Elimination algorithm has been implemented which reduces the computational complexity of motion estimation algorithm and thus resulted in low power dissipation. Proposed architecture achieved 60% less number of computations compared to existing full search architecture and 50% high throughput compared to existing fixed Global Elimination Architecture.

VLSI implementation of a new LMS-based algorithm for noise removal in ECG signal

ABSTRACTLeast mean square (LMS)-based adaptive filters are widely deployed for removing artefacts in electrocardiogram (ECG) due to less number of computations. But they posses high mean square error (MSE) under noisy environment. The transform domain variable step-size LMS algorithm reduces the MSE at the cost of computational complexity. In this paper, a variable step-size delayed LMS adaptive filter is used to remove the artefacts from the ECG signal for improved feature extraction. The dedicated digital Signal processors provide fast processing, but they are not flexible. By using field programmable gate arrays, the pipelined architectures can be used to enhance the system performance. The pipelined architecture can enhance the operation efficiency of the adaptive filter and save the power consumption. This technique provides high signal-to-noise ratio and low MSE with reduced computational complexity; hence, it is a useful method for monitoring patients with heart-related problem.

A New LMS Based Noise Removal and DWT Based R-peak Detection in ECG Signal for Biotelemetry Applications

ECG signals are non-stationary pseudo periodic in nature and whose behavior changes with time. The proper processing of ECG signal and its accurate feature extraction is very much essential since it determines the condition of the heart. Least mean square (LMS) based adaptive filters are widely deployed for removing artifacts in ECGs due to less number of computations. But they posses high mean square error (MSE) under noisy environment. The MSE can be reduced by transform domain variable step size LMS algorithm at the cost of computational complexity. In this paper, a variable step size delayed LMS adaptive filter is used to remove the artifacts from ECG signal for improved feature extraction. Moreover, the extraction of R peak in ECG is carried out using discrete wavelet transform based QRS detection algorithm. Due to the high speed of our method, the ECG de-nosing and R Peak extracting could be both realized at real time, which is an effective method to monitor the patients in biotelemetry applications.

Spectro-temporal directional derivative based automatic speech recognition for a serious game scenario

Speech is one of the important modalities in a serious game platform. Serious game can be very useful for the rehabilitation of individuals with voice disorders. Therefore, we need an efficient and high-performance automatic speech recognition (ASR) system. In this paper, we propose a spectro-temporal directional derivative (STDD) feature that requires less number of computations in the modeling and yet gives high recognition accuracy in the ASR system. The proposed STDD feature is achieved by applying different directional derivative filters in the spectro-temporal domain. The feature dimension is then compressed by discrete cosine transform. The experiments are performed with voice samples of Arabic numerals spoken by persons with and without voice pathology. The experimental results show that the STDD feature outperforms the conventional mel-frequency cepstral coefficients both in clean and noisy environments.