This paper proposes a dual-level low voltage differential signaling (DLVDS) circuit aimed at low power consumption and reducing transmission lines for LCD driver IC's. We apply two-bit binary data to the DLVDS circuit as inputs, and then the circuit converts these two inputs into two kinds of fully differential signal levels. In the DLVDS circuit, two transmission lines are sufficient to transfer two-bit binary inputs while keeping the conventional LVDS features. The receiver recovers the original two-bit binary data through a level decoding circuit. The proposed circuit was fabricated using a commercial 0.25 µm CMOS technology. Under a 2.5 V supply voltage, the circuit shows a data rate of 1-Gbps/2-line and power consumption of 35 mW.

The crosstalks between a single-ended line and a differential pair in parallel are analyzed using telegrapher's equations for multi-conductor lines. The crosstalk from the single-ended trace to the differential pair is estimated at shunt-arm resistors in T or Π termination networks. The analysis is conducted by incorporating the termination conditions with the solution of the telegrapher's equations. The time-domain characteristics of the crosstalk are obtained by using the fast inverse Laplace transform. The measurements are conducted easily by using a single-ended digital oscilloscope since the crosstalk is evaluated on the shunt-arm resistors. Both the calculated and measured results are presented, and the characteristics of the crosstalk are also investigated qualitatively.

In this paper, we consider the problem of bearing estimation for spatially distributed sources in unknown spatially-correlated noise. Assumed that the noise covariance matrix is centro-Hermitian, a differential denoising scheme is developed. Combined it with the classic DSPE algorithm, a differential denoising estimator is formulated. Its modified version is also derived. Exactly, the differential processing is first imposed on the covariance matrix of array outputs. The resulting differential signal subspace (DSS) is then utilized to weight array outputs. The noise components orthogonal to DSS are eliminated. Based on eigenvalue decomposition of the covariance matrix of weighted array outputs, the DSPE null spectrum is constructed. The asymptotic performance of the proposed bearing estimator is evaluated in a closed form. Moreover, in order to improve the performance of bearing estimation in case of low signal-to-noise ratio, a modified differential denoising estimator is proposed. Simulation results show the effectiveness of the proposed estimators under the low SNR case. The impacts of angular spread and number of sensors are also investigated.