Analysis and Evaluation of the Family of Sign Adaptive Algorithms

In this thesis, four novel sign adaptive algorithms proposed by the author were analyzed and evaluated for floating-point arithmetic operations. These four algorithms include Sign Regressor Least Mean Fourth (SRLMF), Sign Regressor Least Mean Mixed-Norm (SRLMMN), Normalized Sign Regressor Least Mean Fourth (NSRLMF), and Normalized Sign Regressor Least Mean Mixed-Norm (NSRLMMN). The performance of the latter three algorithms has been analyzed and evaluated for real-valued data only. While the performance of the SRLMF algorithm has been analyzed and evaluated for both cases of real- and complex-valued data. Additionally, four sign adaptive algorithms proposed by other researchers were also analyzed and evaluated for floating-point arithmetic operations. These four algorithms include Sign Regressor Least Mean Square (SRLMS), Sign-Sign Least Mean Square (SSLMS), Normalized Sign-Error Least Mean Square (NSLMS), and Normalized Sign Regressor Least Mean Square (NSRLMS). The performance of the latter three algorithms has been analyzed and evaluated for both cases of real- and complex-valued data. While the performance of the SRLMS algorithm has been analyzed and evaluated for complex-valued data only. The framework employed in this thesis relies on energy conservation approach. The energy conservation framework has been applied uniformly for the evaluation of the performance of the aforementioned eight sign adaptive algorithms proposed by the author and other researchers. In other words, the energy conservation framework stands out as a common theme that runs throughout the treatment of the performance of the aforementioned eight algorithms. Some of the results from the performance evaluation of the four novel sign adaptive algorithms proposed by the author, namely SRLMF, SRLMMN, NSRLMF, and NSRLMMN are as follows. It was shown that the convergence performance of the SRLMF and SRLMMN algorithms for real-valued data was similar to those of the Least Mean Fourth (LMF) and Least Mean Mixed-Norm (LMMN) algorithms, respectively. Moreover, it was also shown that the NSRLMF and NSRLMMN algorithms exhibit a compromised convergence performance for realvalued data as compared to the Normalized Least Mean Fourth (NLMF) and Normalized Least Mean Mixed-Norm (NLMMN) algorithms, respectively. Some misconceptions among biomedical signal processing researchers concerning the implementation of adaptive noise cancelers using the Sign-Error Least Mean Fourth (SLMF), Sign-Sign Least Mean Fourth (SSLMF), and their variant algorithms were also removed. Finally, three of the novel sign adaptive algorithms proposed by the author, namely SRLMF, SRLMMN, and NSRLMF have been successfully employed by other researchers and the author in applications ranging from power quality improvement in the distribution system and multiple artifacts removal from various physiological signals such as ElectroCardioGram (ECG) and ElectroEncephaloGram (EEG)

Optimization of received power and SNR for an indoor attocells network in visible light communication

White LEDs Visible Light Communication (VLC) is applied in communication and illumination simultaneously. It provides unrestrained frequency spectrum and a large bandwidth that produces a higher transmission rate and speed in short-range communication. Also, VLC was considered as a promising alternative technology to the radio frequency in the next generation of communication systems. In this paper, the optical attocells configuration and LEDs distribution are proposed for optimizing the received power and Signal-to-Noise Ratio (SNR) in the Line of Sight (LOS) propagation link. Besides that, the trade-off between minimum SNR and received power are investigated. The simulation results showed that the proposed model can save 6.25% of the total transmitted power, and the optical received power versus semi-angle and field of view have with about increased 16.5% and 27.54% respectively. Moreover, the SNR also has 7.4% improvement. Hence, the proposed configuration model has improved the performance of VLC systems and has widen the window for future improvement

A novel fixed-point leaky sign regressor algorithm based adaptive noise canceller for PLI cancellation in ECG signals

In this paper, a novel fixed-point Leaky Sign Regressor Algorithm (LSRA) based adaptive noise canceller has been employed for the cancellation of 60 Hz Power Line Interference (PLI) from the ElectroCardioGram (ECG) signal. A sufficient condition for the convergence in the mean of the LSRA algorithm is also derived. The fixed-point LSRA-based adaptive noise canceller employed in this work is fully quantized using an in-house quantize function. The most effective number of quantization bits required for the various parameters are found to be 6-bits and are determined through rigorous simulations. The filtered ECG signal free from 60 Hz PLI is successfully recovered using a novel 6-bit fixed-point LSRA-based adaptive noise canceller

On the role of oral feedback in ESL postgraduate thesis writing supervision

The provision of oral feedback to ESL students at the postgraduate level plays an interventionist role in their development as writers. It is through such feedback that the students are expected to be guided towards achieving their writing goals. Supervisors provide written feedback and this is usually supported with oral feedback which appears to play a crucial role in the supervision process in that it helps in the formation of scholarly identities, scaffolds students' academic writing and learning, fosters autonomy, equality, and learning skills among ESL learners, develops students' dialogical skills, helps students focus on their research, and guides them to conform with dissertation/thesis writing. However, ESL students' own cultural background and social circumstances may affect the efficacy of the oral feedback process. Some limitations of existing studies are discussed and key directions for future research on the role of oral feedback in ESL postgraduate supervision settings are suggested

Online multiclass EEG feature extraction and recognition using modified convolutional neural network method

Many techniques have been introduced to improve both brain-computer interface (BCI) steps: feature extraction and classification. One of the emerging trends in this field is the implementation of deep learning algorithms. There is a limited number of studies that investigated the application of deep learning techniques in electroencephalography (EEG) feature extraction and classification. This work is intended to apply deep learning for both stages: feature extraction and classification. This paper proposes a modified convolutional neural network (CNN) feature extractorclassifier algorithm to recognize four different EEG motor imagery (MI). In addition, a four-class linear discriminant analysis (LDR) classifier model was built and compared to the proposed CNN model. The paper showed very good results with 92.8% accuracy for one EEG four-class MI set and 85.7% for another set. The results showed that the proposed CNN model outperforms multi-class linear discriminant analysis with an accuracy increase of 28.6% and 17.9% for both MI sets, respectively. Moreover, it has been shown that majority voting for five repetitions introduced an accuracy advantage of 15% and 17.2% for both EEG sets, compared with single trials. This confirms that increasing the number of trials for the same MI gesture improves the recognition accurac

Design of cyclic prefix characteristic-based OFDM system for WiMAX technology

Worldwide interoperability for microwave access (WiMAX) offers the wireless connectivity using orthogonal frequency division multiplexing (OFDM) modulation is a proficient wireless technology that capacities high-speed data transmission facilities. The existing WiMAX techniques have the problem of increase in inter-symbol interference (ISI) and bit error rate (BER) at reduced power spectrum that degrades the performance of WiMAX system due to high data rate transmission. The utilization of different adaptive modulation techniques seen as a potential solution to reduce the ISI and BER for high data rate transmission. In this paper, OFDM is adapted using advanced modulation technique for WiMAX system. The technique proposes the cyclic prefix (CP) is utilized that include supplementary bits at the stage of the transmitter. The proposed technique offers minimization of ISI and improvement in BER. It is defined that performance of the existing CP system is equated with the designed single cyclic prefix (SCP) and double cyclic prefix (DCP) and non-cyclic prefix (NCP). BER, probability of error, and power spectral density are utilized to analyse the performance of the designed system. The OFDM based SCP and DCP and NCP for WiMAX are demonstrated for modulation techniques such as; QPSK, BPSK, and QAM. It is determined that BPSK has the smallest BER when compared to QPSK, 16-QAM, and 64-QAM modulations. It is also demonstrated that QPSK is also very competent, however, it has a higher BER as compared to BPSK modulation. It is also observed that 16-QAM and 64-QAM are less efficient in terms of BER compared to QPSK and BPKS modulations. 64-QAM offers the high data rates, and due to high SNR ratio. The designed system is tested for under AWGN and Rayleigh fading channel, and effect power spectral density of signal to noise ratio on OFDM for rayleigh fading channel are demonstrated for SCP and DCP and NCP. It is determined that the OFDM transmitter with proposed DCP for random signals is efficiently reducing the BER and ISI for WiMAX system

A New Fixed Point Noise Cancellation Method for Suppressing Power Line Interference in Electrocardiogram Signals

In this article, a new fixed point Leaky Sign Regressor Least Mean Mixed Norm (LSRLMMN) powered adaptive noise cancellation technique is being used for eliminating the Power Line Interference (PLI) noise embedded in the ElectroCardioGram (ECG) signal. The fixed point LSRLMMN powered noise cancellation technique used in this article has been completely quantized. The intention for the extensive quantization study and modeling approach was with a view to the physical integrated circuit implementation. All the modeling and simulation studies were carried out at the bit-level with various loss of precision schemes to ensure compliance with the set specification. The filter coefficients and all the data paths are quantized in order to establish at a high-level behavioral level of the parameters for a decreased complexity in integrated circuit implementation