Curricula comparison of mechanical engineering technology and similarly named programmes

In this article, the author presents a curricula comparison study of the historically Accreditation Board for Engineering and Technology (ABET) accredited Mechanical Engineering Technology programme at Applied College in the University of Hafr Al Batin, the currently ABET-accredited Mechanical Maintenance Engineering Technology programme at Jubail Industrial College, and the currently ABET-accredited Mechanical Maintenance Technology programme at Yanbu Industrial College. In this curricula comparison, the author has analysed in finer details the respective general and core requirements of these engineering technology programmes offered at different colleges in the Kingdom of Saudi Arabia. The analysis presented in this article may be applied in a similar manner to other engineering technology programmes preparing for ABET accreditation

Removal of multiple artifacts from ECG signal using cascaded multistage adaptive noise cancellers

Although cascaded multistage adaptive noise cancellers have been employed before by researchers for multiple artifact removal from the ElectroCardioGram (ECG) signal, they all used the same adaptive algorithm in all the cascaded multi-stages for adjusting the adaptive filter weights. In this paper, we propose a cascaded 4-stage adaptive noise canceller for the removal of four artifacts present in the ECG signal, viz. baseline wander, motion artifacts, muscle artifacts, and 60 Hz Power Line Interference (PLI). We have investigated the performance of eight adaptive algorithms, viz. Least Mean Square (LMS), Least Mean Fourth (LMF), Least Mean Mixed-Norm (LMMN), Sign Regressor Least Mean Square (SRLMS), Sign Error Least Mean Square (SELMS), Sign-Sign Least Mean Square (SSLMS), Sign Regressor Least Mean Fourth (SRLMF), and Sign Regressor Least Mean Mixed-Norm (SRLMMN) in terms of Signal-to-Noise Ratio (SNR) improvement for removing the aforementioned four artifacts from the ECG signal. We employed the LMMN, LMF, LMMN, LMF algorithms in the proposed cascaded 4-stage adaptive noise canceller to remove the respective ECG artifacts as mentioned above. We succeeded in achieving an SNR improvement of 12.7319 dBs. The proposed cascaded 4-stage adaptive noise canceller employing the LMMN, LMF, LMMN, LMF algorithms outperforms those that employ the same algorithm in the four stages. One unique and powerful feature of our proposed cascaded 4-stage adaptive noise canceller is that it employs only those adaptive algorithms in the four stages, which are shown to be effective in removing the respective ECG artifacts as mentioned above. Such a scheme has not been investigated before in the literature

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

Steady-state and tracking analysis of the SSLMS algorithm

This paper presents expressions for the steady-state mean-square error (MSE), the optimum stepsize, and the corresponding minimum value of the tracking MSE of the sign-sign least mean square (SSLMS) algorithm for the case of real-valued data. Then, simulation results are presented to support our analytical findings and found to corroborate them very well. Also, the performance of the SSLMS algorithm is compared to that of the LMS algorithm in the case where the noise statistics are uniformly distributed

Curricula comparison of electrical and electronics engineering technology and similarly named associate degree programmes

The associate degree programmes discussed in this article are offered in the Kingdom of Saudi Arabia. In the first part of the article is presented a curriculum comparison study of the historically Accreditation Board for Engineering and Technology (ABET) accredited Electrical and Electronics Engineering Technology (EEET) associate degree programme at Applied College, Hafr Al Batin, the currently ABET-accredited Instrumentation and Control Engineering Technology (ICET) associate degree programme at Jubail Industrial College (JIC), and the currently ABET-accredited Electronics and Communication Technology (ECT) and Instrumentation and Control Technology (ICT) associate degree programmes at Yanbu Industrial College (YIC). The currently ABET-accredited ICET programme at JIC, and ECT and ICT programmes at YIC have served as a regional benchmark for the historically ABET-accredited EEET programme at Applied College. In the second part of the article, the indirect assessment process of ABET student outcomes and evaluation of ABET programme criteria of the EEET programme at Applied College is described

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

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

Analysis of the Normalized Sign-Sign LMS Algorithm

This work repots results of the convergence analysis of the normalized sign-sign least mean square (NSSLMS) algorithm when the input is real-valued data. The results includes expressions for different parameters, such as the steady-state mean-square error, and the tracking mean-square error. Moreover, the performance of the normalized sign-sign LMS algorithm is compared with that of the sign-sign LMS algorithm. The convergence behavior includes the rate of convergence. Finally, simulation results suggest that the normalized sign-sign LMS algorithm can be used as a good replacement for the sign-sign LMS algorithm as the former algorithm offers comparatively much faster rate of convergence than the latter algorithm

Tracking analysis of the ε-NSRLMMN algorithm

In this work, expressions for the tracking excess-mean-square error (EMSE) and optimum step-size of the ε-normalized sign regressor least mean mixed-norm (NSRLMMN) adaptive algorithm are derived. Finally, extensive simulation results performed are found to corroborate very closely with the theoretical results for correlated Gaussian data