Mohammed Mujahid Ulla Faiz

Ultra-fast Microwave Synthesis of ZnO Nanowires and their Dynamic Response Toward Hydrogen Gas

Ultra-fast and large-quantity (grams) synthesis of one-dimensional ZnO nanowires has been carried out by a novel microwave-assisted method. High purity Zinc (Zn) metal was used as source material and placed on microwave absorber. The evaporation/oxidation process occurs under exposure to microwave in less than 100 s. Field effect scanning electron microscopy analysis reveals the formation of high aspect-ratio and high density ZnO nanowires with diameter ranging from 70 to 80 nm. Comprehensive structural analysis showed that these ZnO nanowires are single crystal in nature with excellent crystal quality. The gas sensor made of these ZnO nanowires exhibited excellent sensitivity, fast response, and good reproducibility. Furthermore, the method can be extended for the synthesis of other oxide nanowires that will be the building block of future nanoscale devices.

Recursive least-squares adaptive channel estimation for spatial modulation systems

In this paper, a recursive least-squares (RLS) adaptive channel estimation scheme is applied for spatial modulation (SM) system over a block fading multiple-input-multiple-output (MIMO) channel. The performance of spatial modulation with channel estimation is compared to vertical Bell Labs layered space-time (V-BLAST) and maximum ratio combining (MRC) transmission schemes for different pilot rates and a fixed 3-b/s/Hz spectral efficiency. Computer simulations carried out demonstrate the superiority of SM over V-BLAST and MRC schemes. In addition, the results in this study show that SM is more robust against channel estimation errors than the other MIMO schemes.

Analysis of the Sign Regressor Least Mean Fourth Adaptive Algorithm

A novel algorithm, called the signed regressor least mean fourth (SRLMF) adaptive algorithm, that reduces the computational cost and complexity while maintaining good performance is presented. Expressions are derived for the steady-state excess-mean-square error (EMSE) of the SRLMF algorithm in a stationary environment. A sufficient condition for the convergence in the mean of the SRLMF algorithm is derived. Also, expressions are obtained for the tracking EMSE of the SRLMF algorithm in a nonstationary environment, and consequently an optimum value of the step-size is obtained. Moreover, the weighted variance relation has been extended in order to derive expressions for the mean-square error (MSE) and the mean-square deviation (MSD) of the proposed algorithm during the transient phase. Computer simulations are carried out to corroborate the theoretical findings. It is shown that there is a good match between the theoretical and simulated results. It is also shown that the SRLMF algorithm has no performance degradation when compared with the least mean fourth (LMF) algorithm. The results in this study emphasize the usefulness of this algorithm in applications requiring reduced implementation costs for which the LMF algorithm is too complex.

Rapid microwave synthesis of high aspect-ratio ZnO nanotetrapods for swift bisphenol A detection.

Highly crystalline and high aspect-ratio ZnO nanotetrapods were grown by a novel and swift microwave synthesis. FESEM analysis revealed that each tetrapod has four thin arms and are derived from the midst of the crystal. The diameter of each arm is larger at the base and smaller at the tip. Structural analysis revealed that these nanotetrapods are single crystalline and have a wurtzite hexagonal crystal structure. These ZnO nanotetrapods were used for the detection of BPA. The electrochemical sensor based on the ZnO nanotetrapods modified electrode showed electrocatalytic activity in terms of significant improvement of the anodic current of BPA and lowering of the detection limit. Under optimized conditions, the squarewave oxidation peak current of BPA was linear over the concentration range of 12.4 nM to 1.2 μM with the detection limit of 1.7 nM and sensitivity of 5.0 μA nM(-1) cm(-2). This sensor showed high sensitivity and response compared with other electrochemical sensors reported for the detection of BPA.

Analysis of the complex sign regressor least mean fourth adaptive algorithm

In this paper, expressions are derived for the steady-state and tracking excess-mean-square error (EMSE) of the complex sign regressor least mean fourth (SRLMF) adaptive algorithm. In addition, an expression for optimum step-size is also derived. Finally, it is shown that the theoretical results are consistent with the simulation results.

Convergence and tracking analysis of the ε-NSRLMF algorithm

In this work, the convergence and tracking behavior of the ε-normalized sign regressor least mean fourth (NSRLMF) algorithm are analyzed in the presence of white and correlated Gaussian data. Furthermore, the stability bound on the step-size of the ε-NSRLMF algorithm to ensure convergence in the mean, which also leads us to the mean convergence of the ε-normalized sign regressor least mean mixed-norm (NSRLMMN) algorithm is derived. Finally, simulation results are conducted to confirm the validity and performance of the proposed adaptive algorithm for both white and correlated Gaussian regressors.

The ∈-normalized sign regressor least mean fourth (NSRLMF) adaptive algorithm

In this paper, a new algorithm, the ϵ-normalized sign regressor least mean fourth (NSRLMF) algorithm is presented as a substitute for the ϵ-normalized least mean fourth (NLMF) algorithm. This new algorithm reduces significantly the computational load. Moreover, the proposed algorithm has similar convergence properties as those of the ϵ-NLMF algorithm. Finally, simulations corroborate very well the theoretical findings.

A steady-state analysis of the ε-normalized sign-error least mean square (NSLMS) adaptive algorithm

In this work, expressions are derived for the steady-state excess-mean-square error (EMSE) of the ε-normalized sign-error least mean square (NSLMS) adaptive algorithm for both cases of real- and complex-valued data. Moreover, a comparison between the computational load of the ε-NSLMS algorithm and the ε-normalized least mean square (NLMS) algorithm is also presented. Finally, simulation results to substantiate the theoretical findings are presented.

Insights into the convergence and steady-state behaviors of the SLMF and its variants

In this paper, we provide some insights into the convergence and steady-state behaviors of the sign-error least mean fourth (SLMF), sign-sign least mean fourth (SSLMF), normalized sign-error least mean fourth (NSLMF), and normalized sign-sign least mean fourth (NSSLMF) algorithms for both cases of real- and complex-valued data. Moreover, we also report the equivalence algorithms of the block-based normalized sign-error least mean fourth (BBNSLMF) and block-based normalized sign-sign least mean fourth (BBNSSLMF) algorithms. Finally, simulations are conducted for both cases of real- and complex-valued data to provide us with more insights into the performance of the SLMF, SSLMF, NSLMF, and NSSLMF algorithms.

Adaptive channel equalization using the sign regressor least mean fourth algorithm

In this paper, the performance analysis of the least mean fourth (LMF) algorithm and the sign regressor least mean fourth (SRLMF) algorithm is investigated in an adaptive channel equalization scenario. The simulation results indicate that both the LMF and the SRLMF algorithms exhibit similar bit error rate (BER) performance. Moreover, the results show that the SRLMF algorithm has a slight performance degradation in terms of convergence behavior when compared with the LMF algorithm.