Some of important issues in acoustic echo cancellation (AEC) using adaptive filters are the sparseness of the acoustic path impulse responses and strong dependency of the convergence performance of adaptive algorithm to the eigenvalue spread of the input signal correlation matrix. These issues result in a performance degradation of the adaptive AEC systems. In this paper, to improve the performance of the LMS/Newton adaptive algorithm in AEC, the matrix inverse computation is modified. To this end, the matrix inversion lemma is employed such that the contribution of the matrix inverse in the weight update is initially high and as a result, the dependency of the adaptive algorithm to the eigenvalue spread is low during the initial convergence. In addition, for the step-size adjustment, an improved proportionate method is applied such that during the convergence, the contribution of those weights having higher amplitudes in the adaptation process is gradually varied to become identical at the end of convergence. The proposed adaptive proportionate method, results in both convergence rate and steady-state performance improvement for identification of sparse acoustic impulse responses. Simulation results using a colored speech-like signal shows the steady-state misalignment of the proposed algorithm is typically 6. 5 dB lower than that of the LMS/Newton algorithm. Moreover, the convergence of the proposed algorithm is typically 3. 6 sec faster than that of the PNLMS algorithm, to achieve a misalignment of-17 dB. Theoretical misalignment analyses in the transient and steady state are presented and verified with simulation results.

The least mean square (LMS) adaptive algorithm is widely used in acoustic noise cancellation (ANC) scenario. In this scenario, speech signals usually have high amplitude and sudden variations that are modeled by impulsive disturbances and it is well known that the acoustic channels usually have been sparse impulse response. Impulsive noise and sparsity of the acoustic channel are two important challenges in the ANC scenario that have paid special attention, recently. This paper presents a novel adaptive noise cancellation algorithm, to address the poor performance of the LMS algorithm in presence of impulsive noise along with a sparse impulse response. In order to eliminate impulsive noise from speech signal, the information theoretic criterion is used in the proposed cost function and the zero norm is also employed to deal with the sparsity feature of the acoustic channel impulse response. Simulation results indicates the superiority of the proposed algorithm in presence of impulsive noise along with sparsity condition of acoustic channel.

In this paper SYS-PNLMS algorithm is proposed. The analysis reveals that it performs a faster convergence rate compared to that of the recently introduced SPNLMS, PNLMS algorithms. Compared with its proportionate counterparts e.g. PNLMS and SPNLMS, the proposed SYS-PNLMS algorithm not only results in a faster convergence rate for both white and colored noise inputs, but also preserves its initial fast convergence rate until it reaches to its steady state condition. It also presents a higher tracking behavior for quasi-stationary inputs such as speech signal in addition to better performance in terms of computational complexity and resulting ERLE. In addition, the proposed SYS-PNLMS algorithm is also evaluated with previously proposed algorithms in a theoretical framework which validates the computer simulation results in terms of CPU time and number of iterations needed for each algorithm to get converged. Finally, a region of convergence for the proposed algorithm is derived for different input cases including white, colored noise and speech signal. This region is also compared with the practical value usually used in echo cancellation application.

In recent years, Brain-Computer Interface (BCI) has been noted as a new means of communication between the human brain and his surroundings. In order to set up such a system, the collaboration of several blocks, such as data recording, signal processing and user interface are needed. The signal processing block, includes two units of preprocessing and pattern recognition. Pattern recognition block itself involves two phases: feature extraction and classification. In this paper, the sparse representation based classification (SRC) has been used in the classification block. There are two important issues in using the SRC. These are creating an appropriate dictionary matrix and adopting a proper method for finding the sparse solution for an input data. In this research study, the dictionary matrix is formed by extracting an optimal set of features from the training data. Toward this goal, the common spatial patterns algorithm (CSP) is first used. Sensitivity to noise and the over learning phenomena are the main drawbacks of the CSP algorithm. In order to remove these problems, the regularized common spatial patterns algorithm (RCSP) is employed. In previous studies in within the BCI framework, the standard BP algorithm has been used to find a sparse solution. The main disadvantage of the BP algorithm is that the method is computationally expensive. To overcome this weakness, a recently proposed algorithm namely the SL0 approach is used instead. Our experimental results show that when the number of training samples is limited, the RCSP algorithm outperforms the CSP one. Using the features derived from the RCSP, the average detection rate is in average increased by a factor of 7.53 %. Our classification results also show that using the SL0 algorithm, the classification process is highly speeded up as compared to the BP algorithm while an almost equivalent accuracy is achieved.

In this paper, at the first time, dynamic response of double curved, single curved, flat and circular cylindrical composite shells with simply supported and clamped boundary conditions subjected to multi- mass impacts were studied based on the first-order shear deformation theory (FSDT). The mechanical strains were used according to Sander’s theory. Using a new linearized two degrees of freedom spring-mass system (linear form of Hertzian contact law) and complete solution model (nonlinear form of Hertzian contact law), the results are derived and compared with together. For additional verification, the results are compaired with the Abaqus finite element software and the latest available literature. The presented formulation is general and capable to analyses the cylindrical composite shells subjected to multi-mass impacts with arbitrary different masses, initial velocities and impact locations. In this investigation, the effect of geometrical and mechanical parameters, such as length to width ratio (a/b), length to radius ratio (L/R) and velocity of the impactor masses on the impacts response from both presented models are studied and compared with together.

The manner by which the shock waves, generated by an explosion, propagate in the medium surrounding an underground structure is very complex. This complexity is due to the interaction between the structure and the surrounding soil and the attenuation of the shock waves in the soil layers. In this research, by simulating the shock waves equivalent to the explosion of 900 kg of TNT, the effects of different parameters such as soil type, concrete type, and buried depth on maximum strain at the top and on the mid-length of the structure is investigated. For simulating the loading due to explosion, the finite element software, ABAQUS, has been utilized. Also, to numerically simulate the nonlinear behavior of the medium surrounding the underground structure, the Mohr-Columb model has been used. In view of the results obtained from the numerical analysis of various models, it was found that the parameter of underground structure depth has the greatest effect on the amount of maximum strain produced in the structure, followed by soil and concrete type.

An experimental procedure is used to determine the transient response of an elastomeric isolator under the impact loading conditions and a numerical procedure is developed to evaluate the corresponding acceleration transmission ratio and shock response spectrum. In the experimental analysis the elastomeric isolator is connected to a resonance beam subjected to the shock loading of a pendulum striker and the shock level is measured using acceleration sensors mounted along three orthogonal directions in the basement and free end of isolator. The shock response spectrum diagram and the level of wave attenuation are determined based on the measured acceleration levels for a wide frequency range. Finite element model based on mode superposition approach is developed to analyze the impact response of elastomeric isolator using the mode shapes with frequency in the range of impact excitation spectrum. Due to the importance of longitudinal response of isolators, the numerical model is employed to evaluate the longitudinal output acceleration time history of isolator. The number of elements, time step for motion equation integration and the number of mode shapes are studied and the optimized corresponding values are selected based on the convergence of the numerical results. The calculated results for wave attenuation level and shock response spectrum diagrams correlate well with the experimental measurements under two different impact loading conditions and the present model can be used to evaluate the performance of isolators depending on the level of impact loads, transmission acceleration and displacement ratios in the output of elastomeric isolators.

Manufacturing products using powder compaction is one of the most widely used methods in the industry. In this paper, dynamic compaction of aluminum powder under low-velocity impact loading was investigated using a drop hammer testing machine along with the optimization of effective parameters in this process. In this series of experiments, the green density and green strength of compacted products were measured. The response surface methodology was used to study the influential parameters in the powder compaction process. In this method, the effects of independent parameters including the grain particle size, the hammer mass, and the standoff distance of the hammer on the green density and green strength were evaluated. In the current study, two separate analyses were performed for each output response and the obtained results were summarized in ANOVA tables. The results showed that the p-value for the model is less than 0. 05, which means that the model is significant. The values of R2 for the green density and green strength are equal to 0. 9956 and 0. 9912, respectively. The results of the optimization section indicate that the optimum case, the maximum green density as well as green strength at the same time, occurs when the grain particle size, the hammer mass and the standoff distance of the hammer have the maximum values. The factors o standoff distance of hammer and grain particle size have the highest and least effect on responses.