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Soft-decision based redundant residue number system (RRNS) assisted error control coding is proposed and its performance is evaluated. An RRNS(n, k) code is a maximum-minimum distance block code, exhibiting identical distance properties to Reed-Solomon (RS) codes. Hence their error correction capability is given by t = (n − k)/2. Different bit mapping(More)
Following a brief historical perspective on channel coding the concept of near-instantaneously adaptive wireless transceivers is introduced as a countermeasure of mitigating the channel-quality fluctuations experienced in wireless communications. It is argued that channel coded adaptive modulation schemes can be viewed as a lower complexity alternative of(More)
— Video and voice transmission over wireless broadband has become popular and attracted more attention ever since. More and more hand phone owners use their phones to play video and voice over the Internet. Transmitting video and voice in a good quality over the wireless networks is a challenge to service providers. LTE, also known as one of the beyond 3G(More)
—Long Term Evolution (LTE) is a Quality of Service (QoS) provisioning wireless network for today's technology and as well as for future demands. There is a high demand for better network performance over LTE network, either for real-time or non-real-time traffic. Specifically, the existing scheduling algorithms for real-time application,(More)
A feedforward control based on neural networks to attenuate the effect of external vibrations on the positioning accuracy of hard disk drives (HDDs) is presented. The adaptive neural network compensator utilises accelerometer signals to detect external vibrations. No information on the plant, sensor and disturbance dynamics is needed in the design of the(More)
—Because of the causal nature of stock price changes, Causal network can be used to model the relationships between stocks. Unfortunately, there are very few works on the application of causal knowledge-driven approach in stock analysis; in particular, learning causal models from data. In this study, we introduce learning Bayesian networks from data as an(More)
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