Cardinality estimation schemes of Radio Frequency IDentification (RFID) tags using Framed Slotted ALOHA (FSA) based protocol are studied in this paper. Not as same as previous estimation schemes, we consider tag cardinality estimation problem under not only detection errors but also capture effect, where a tag's IDentity (ID) might not be detected even in a singleton slot, while it might be identified even in a collision slot due to the fading of wireless channels. Maximum Likelihood (ML) approach is utilized for the estimation of the detection error probability, the capture effect probability, and the tag cardinality. The performance of the proposed method is evaluated under different system parameters via computer simulations to show the method's effectiveness comparing to other conventional approaches.

In this paper, we analyze the effects of IQ (In-phase/Quadrature-phase) imbalance at both transmitter and receiver of OFDM (Orthogonal Frequency Division Multiplexing) system and show that more diversity gain can be achieved even though there are unwanted IQ imbalance. When mixed sub-carriers within an OFDM symbol due to the IQ imbalance undergo frequency selective channels, additional diversity effects are expected during the demodulation process. Simulation results on the symbol error rate (SER) performance with ML (Maximum Likelihood) and OSIC (Ordered Successive Interference Cancellation) receiver show that significant performance gain can be achieved with the diversity gain caused by the IQ imbalance combined with the frequency selective channels.

We develop a maximum likelihood estimation scheme for correcting the carrier frequency offsets prior to the general intercarrier interference (ICI) self-cancellation in the OFDM systems. Since the same data symbols employed for ICI self-cancellation are also used for frequency offset estimation, the proposed scheme does not consume additional bandwidth. The combined use of the estimation algorithm and ICI self-cancellation scheme provides both frequency offset compensation and ICI reduction hence improves the system performance greatly. The effectiveness of the proposed estimation-cancellation scheme is further verified by calculating the bit error rates of various OFDM receivers, and substantial improvements are found.

In this paper, a new maximum likelihood filter with finite impulse response (FIR) structures is proposed for state space signal models with both system and observation noises. This filter is called the maximum likelihood FIR (MLF) filter. The proposed MLF filter doesn't require a priori information of the window initial state and processes the finite observations on the most recent window linearly. The proposed MLF filter is first represented in a batch form, and then in an iterative form for computational advantage. The proposed MLF filter has good inherent properties such as time-invariance, unbiasedness, deadbeat, robustness. The validity of the proposed MLF filter is illustrated by a computer simulation on a sinusoidal signal.

Speech signals transmitted over telephone network often suffer from interference due to ambient noise and channel distortion. In this paper, a novel frame-dependent fuzzy channel compensation (FD-FCC) method employing two-stage bias subtraction is proposed to minimize the channel effect. First, through maximum likelihood (ML) estimation over the set of all word models, we choose the word model which is best matched with the input utterance. Then, based upon this word model, a set of mixture biases can be derived by averaging the cepstral differences between the input utterance and the chosen model. In the second stage, instead of using a single bias, a frame-dependent bias is calculated for each input frame to equalize the channel variations in the input utterance. This frame-dependent bias is achieved by the convex combination of those mixture biases which are weighted by a fuzzy membership function. Experimental results show that the channel effect can be effectively canceled even though the additive background noise is involved in a telephone speech recognition system.