An Improved Proportionate Normalized Least Mean Square Algorithm for Sparse Impulse Response Identification

In this paper after analyzing the adaptation process of the proportionate normalized least mean square(PNLMS) algorithm, a statistical model is obtained to describe the convergence process of each adaptive filter coefcient. Inspired by this result, a modified PNLMS algorithm based on precise magnitude estimate is proposed. The simulation results indicate that in contrast to the traditional PNLMS algorithm, the proposed algorithm achieves faster convergence speed in the initial convergence state and lower misalignment in the stead stage with much less computational complexity.

Application of SALTMED and HYDRUS-1D models for simulations of soil water content and soil salinity in controlled groundwater depth

Salinization is a gradual process that should be monitored.Modelling is a suitable alternative technique that saves time and cost for the field monitoring.But the performance of the models should be evaluated using the measured data.Therefore,the aim of this study was to evaluate and compare the SALTMED and HYDRUS-1D models using the measured soil water content,soil salinity and wheat yield data under different levels of saline irrigation water and groundwater depth.The field experiment was conducted in 2013 and in this research three controlled groundwater depths,i.e.,60(CD60),80(CD80)and 100(CD100)cm and two salinity levels of irrigation water,i.e.,4(EC4)and 8(EC8)dS/m were used in a complete randomized design with three replications.Soil water content and soil salinity were measured in soil profile and compared with the predicted values by the SALTMED and HYDRUS-1D models.Calibrations of the SALTMED and HYDRUS-1D models were carried out using the measured data under EC4-CD100 treatment and the data of the other treatments were used for validation.The statistical parameters including normalized root mean square error(NRMSE)and degree of agreement(d)showed that the values for predicting soil water content and soil salinity were more accurate in the HYDRUS-1D model than in the SALTMED model.The NRMSE and d values of the HYDRUS-1D model were 9.6%and 0.64 for the predicted soil water content and 6.2%and 0.98 for the predicted soil salinity,respectively.These indices of the SALTMED model were 10.6%and 0.81 for the predicted soil water content and 11.0%and 0.97 for the predicted soil salinity,respectively.According to the NRMSE and d values for the predicted wheat yield(9.8%and 0.91,respectively)and dry matter(2.9%and 0.99,respectively),we concluded that the SALTMED model predicted the wheat yield and dry matter accurately.

Chip Layout for Adaptive Line Enhancer Design using Adaptive Filtering Algorithms and Metrics Computation for Auscultation Signal Separation

Currently,the growth of micro and nano(very large scale integration-ultra large-scale integration)electronics technology has greatly impacted biomedical signal processing devices.These high-speed micro and nano technology devices are very reliable despite their capacity to operate at tremendous speed,and can be designed to consume less power in minimum response time,which is particularly useful in biomedical products.The rapid technological scaling of the metal-oxide-semi-conductor(MOS)devices aids in mapping multiple applications for a specific purpose on a single chip which motivates us to design a sophisticated,small and reliable application specific integrated circuit(ASIC)chip for future real time medical signal separation and processing(digital stetho-scopes and digital microelectromechanical systems(MEMS)microphone).In this paper,ASIC level implementation of the adaptive line enhancer design using adaptive filtering algorithms(least mean square(LMS)and normalized least mean square(NLMS))integrated design is used to separate the real-time auscultation sound signals effectively.Adaptive line enhancer(ALE)design is imple-mented in Verilog hardware description language(HDL)language to obtain both the network and adaptive algorithm in cadence Taiwan Semiconductor Manufacturing Company(TSMC)90 nm standard cell library environment for ASIC level implementation.Native compiled simulator(NC)sim and RC lab were used for functional verification and design constraints and the physical design is implemented in Encounter to obtain the Geometric Data Stream(GDS II).In this architecture,the area occupied is 0.08 mm,the total power consumed is 5.05 mW and the computation time of the proposed system is 0.82μs for LMS design and the area occupied is 0.14 mm,the total power consumed is 4.54 mW and the computation time of the proposed system is 0.03μs for NLMS design that will pave a better way in future electronic stethoscope design.

FORECASTING AUTOMOBILE WARRANTY PERFORMANCE IN PRESENCE OF ‘MATURING DATA’ PHENOMENA USING MULTILAYER PERCEPTRON NEURAL NETWORK

Automobile companies that spend billions of dollars annually towards warranty cost, give high priority to warranty reduction programs. Forecasting of automobile warranty performance plays an important role towards these efforts. The forecasting process involves prediction of not only the specific months-in-service (MIS) warranty performance at certain future time, but also at future MIS values. However, 'maturing data' (also called warranty growth) phenomena that causes warranty performance at specific MIS values to change with time, makes such a forecasting task challenging. Although warranty forecasting methods such as log-log plots and dynamic linear models appear in literature, there is a need for applications addressing the well recognized issue of ‘maturing data’. In this paper we use an artificial neural network for the forecasting of warranty performance in presence of ‘maturing data’ phenomena. The network parameters are optimized by minimizing the training and testing errors using response surface methodology. This application shows the effectiveness of neural networks in the forecasting of automobile warranty performance in the presence of the ‘maturing data’ phenomena.