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Narrowband jammer excision is formulated as an optimization problem in this paper. Optimal filter weights are calculated/searched for by the computational intelligence techniques. We compare the error rate performance, complexity, and implementation issues of various computational intelligence techniques like Particle Swarm Optimization (PSO), Genetic Algorithm, and Least Mean Square (LMS). These techniques update the excision filter weights iteratively till the convergence criteria has been achieved. Bit Error Rate performance shows these techniques effectively suppress the Narrow Band Interference. It has been observed that PSO-based algorithm with tuned parameters outperforms other schemes of PSO and the other algorithms. It approaches the optimum performance in fewer iterations with ease in implementation.

Direct-Sequence Code Division Multiple Access (DS-CDMA) is a digital method to spread spectrum modulation for digital signal transmission. We propose to detect signal in DS-CDMA communication using the learning mechanism. Initially, the user signals are spread using the respective pseudo-noise (PN) code where the input signal is multiplied with the code which is then modulated using the quadrature phase shift keying (QPSK) modulator. The modulated signal is then transmitted in a 3G/4G channel considering all types of fading. The transmitted signal is received by the antenna array which is performed by demodulation. We propose to adaptively assign the weights by employing Improved Whale Optimized Multi-Layer Perceptron Neural Network (IWMLP-NN)-based learning mechanism. To design IWMLP-NN, Improved Whale Optimization Algorithm is combined with multilayer perceptron neural network. This is used instead of the normal Multiple Signal Classification (MUSIC) and least mean squares (LMS)/root-mean-square (RMS) algorithms used in beam-forming networks. After assigning weight through IWMLP-NN-based learning mechanism, we de-spread to get the original user data. We have compared our proposed technique with the normal techniques with the help of plots of Bit Error Rate (BER) versus Signal-to-Noise Ratio (SNR). We use both the AWGN channel and fading channel for analysis. Experimental results prove that our proposed method achieves better BER performance results even with deep fading.

Electrocardiogram (ECG) is a graphical visualization of the electrical activity of the human heart that is recorded by placing a surface electrode at standardized position on a person’s chest. ECG signals suffer from artifacts/noises due to baseline wander (BW), electrode artifacts, muscle artifacts, power-line interference and channel noises during acquisition and transmission of the ECG signals. Reduction of these artifacts is crucial for efficient diagnosis and interpretation of the human heart condition. In this paper, an effective adaptive noise canceller (ANC) based on empirical mode decomposition (EMD)-Jaya algorithm is proposed for denoising electrocardiogram. In this approach, intrinsic mode functions (IMFs) produced by EMD are used as reference and Jaya algorithm is used to calculate optimum weights of finite impulse response (FIR) filter. This scheme is compared with EMD, wavelet transform (WT) thresholding, and hybrid EMD-least mean square (LMS) approaches through extensive simulation on noise corrupted ECG besides verifying the robustness with real ECG signals. The performance of the proposed technique is assessed using standard metric signal-to-noise ratio (SNR) with different contamination levels. The results obtained demonstrate the superiority of the hybrid when compared to other competing approaches.

The use of adaptive filters to alleviate the degradation caused by wind driven ambient noise in shallow water is considered in this paper. Since, underwater acoustic signals are greatly affected by the ocean interference and ambient noise disturbances when propagating through underwater channels, an effective adaptive filtering system is necessary for denoising the signal which are degraded by noise. Least mean square (LMS), normalized LMS (NLMS), Modified New LMS (MNLMS) and Kalman LMS (KLMS) based adaptive algorithms are analyzed in terms of their performance with the aid of performance measure characteristics such as signal to noise ratio (SNR) and mean square error (MSE). The MNLMS is developed by calculating an optimum learning parameter that best suits for the acoustic signal used. The analysis is carried out for a range of 100 Hz to 10 KHz source signals and the algorithm proves that any ambient noise signals against the source signal in this range can be eliminated and the source signal can be reconstructed. Our simulation results show that KLMS and MNLMS algorithms achieve remarkable performance even in the very low SNR region as compared to LMS and NMLS algorithms. Moreover, it is observed that the output convergence is also very fast for MNLMS and KLMS.

This paper presents a new random noise cancellation technique for cancelling muscle artifact effects from ECG using ALE in the transformed domain. For this a transform domain variable step size griffith least mean square (TVGLMS) algorithm is proposed. The technique is based on the adaptation of the gradient of the error surface. The method frees both the step size and the gradient from observation noise and reduces the gradient mis-adjustment error. The sluggishness introduced due to the averaging of the gradient in the time domain is overcome by the transformed domain approach. The proposed algorithm uses a discrete cosine transform (DCT)-based signal decomposition due to its improved frequency resolution compared to a discrete Fourier transform (DFT). Furthermore, as the data used symmetrical, DCT usage results in low leakage (bias and variance). The performance of the proposed method has been tested on ECG signals combined with WGN, extracted from MIT database, and compared with several existing techniques like LMS, NLMS, and VGLMS.

The rise of Web technology leads to the convenience of global information exchanges. Furthermore, E-Learning takes full advantage of cross area characteristic of the web, enabling online learning, at any time, at any place. Thus, SCORM (Sharable Content Object Reference Model) was result of such a trend. This study is based on CDA example teaching materials provided by international, HL7 organization, attempting to combine with SCORM standard, constructing a teaching material, which can be provided to hospital doctors, interns, or students while learning HL7/CDA. The teaching materials are based on SCORM standard. Besides in class use, the teaching materials can be shared and used on any SCORM-comptliant LMS (Learning Management Server) platform. In addition, the user can edit data of selected cases from HIS (Hospital Information System) directly on the teaching materials web pages, exporting into HL7/CDA compliant clinical documents, saving extra programming time to achieve inter-hospitals information sharing.

Fullan and Langworthy (2013) identified eight Deep Learning Skills in 2013. Michael Fullan later focuses on Key Future Skills or the 6 C’s, which students need to know in order to live in the modern times. Public schools, worldwide, are gradually adding Deep Learning Tasks to their curriculum in order to prepare their students for a modern life.

The main purpose of this paper is to investigate the effect of adding an online Learning Management System to a teacher training programme for language teachers who are not digitally as literate as their students to prepare these teachers to develop their students’ Key Future Skills.

This study was conducted over a period of 9 months with 50 primary and secondary school English language teachers in a rural school in Malaysia during a project by the British Council for the Ministry of Education. The teachers’ online behaviour was monitored and documented during this period. Data were collected through questionnaires (10 questions) at the end of the project and through interviews 6 months after the end of the project. The results show that the teachers became more vigilant towards Deep Learning Skills and their significance for life in the 21st century and the application of technology in their own teaching context. On a positive note, their active online presence and collaboration with each other on the selected platform also helped them feel more confident in preparing their own students to develop their 6 C’s independently.