Discrete Wavelet Transform Scheme Research Articles

Improving Bit Error Rate of MIMO-OFDM System Using Discrete Wavelet Transformation

This paper has developed a discrete wavelet transform (DWT) for multicarrier modulation in MIMO-OFDM system. The proposed system used additive Gaussian white noise (AGWN) and Rayleigh flat fading channel with respect to SNR using binary phase shift keying (BPSK). The algorithms for various scenarios of MIMO-OFDM system have been developed and simulated in MATLAB environment. The performance metric of the system was based on bit error ratio (BER) variation over values of signal to noise ratio (SNR) for different transmit and receive antennas including single input multiple output (SIMO) arrangement. The simulation results showed that the performance of DWT based MIMO-OFDM outperformed DFT based MIMO-OFDM. For the 2×1, 2×2, 2×3 MISO and MIMO systems, the DWT scheme offered reduced BER of 1.803e-05, 1.502e-05, and 1.413e-05 against 0.005847, 0.005361, and 0.005325 for the DFT scheme with respect to signal to noise ratio (SNR) at 10 dB. Generally, the results showed that using DWT scheme in OFDM for wireless communication system does not require an extra subcarrier to provide cyclic prefix function that serves as additional cost burden and reduces bandwidth.

Deep image compression with lifting scheme: Wavelet transform domain based on high‐frequency subband prediction

Image compression is the most important image processing method extensively deployed in different appliances. “Discrete wavelet transform (DWT)” is one of the well-adopted transforming methods exploited for compressing images. The extremely deployed version of DWT is convolution-oriented. Nevertheless, the lifting-oriented DWT scheme requires more contemplation on more proficient performance and lesser computation cost. This paper intends to propose a deep learning-based image compression model with a lifting scheme for predicting high-frequency subbands. Moreover, the fine-tuning in lifting factorization is done by a new Sea Lion with Averaged Update Evaluation that includes new cosine estimation under the COordinate Rotation DIgital Computer algorithm. Similarly, this study defines a new single objective function that merges the multiconstraints, like, “Peak Signal to Noise Ratio (PSNR) and Compression Ratio (CR)”. At last, the supremacy of the presented approach is proved with respect to varied measures, like, CR, PSNR and so on.

Power Quality Events Segmentation in a Voltage Waveform using Joint Triggering Point Detection Scheme

Accurate and timely detection of power quality (PQ) events is imperative for adequate corrective measures to be taken. This paper presents a method of PQ event detection development called joint triggering point detection (JTPD) scheme with a view to achieving a more accurate PQ event detection in a voltage waveform. The JTPD combines the advantage of cumulative sum (CUSUM) algorithm for the statistical distribution of a signal waveform and the discrete wavelet transform (DWT) for change-point detection. The performance of JTPD scheme using detection rate was compared with CUSUM and DWT schemes and based on the analysis, the detection rate of JTPD, CUSUM and DWT were 100%, 95% and 50% respectively. With this, the proposed approach outperformed CUSUM and DWT schemes by 5% and 50% respectively. Hence, the proposed approach is suitable for accurate detection of voltage dip, swell and interruption PQ events Keywords: Cumulative Sum, Discrete Wavelet Transform, Joint Triggering Point Detection, Power Quality, Voltage Waveform

Analysis of Lifting and B-Spline DWT Implementations for Implantable Neuroprosthetics

The discrete wavelet transform (DWT) has been shown to provide exceptionally efficient data compression for neural records. This paper describes area-power minimized hardware implementations of the lifting and B-spline DWT schemes for multi-level, multi-channel DWT and analyzes their performance tradeoffs for implantable neuroprosthetics. The lifting scheme is shown to be increasingly superior for a larger number of input channels.