In this paper we discuss system-related aspects in embedded DRAM/logic designs. We focus on large embedded memories which have to be implemented as DRAMs.

We derive an exact solution to the problem of estimating the rotation of a rigid body from noisy 3D image data. Our approach is based on total least squares (TLS), but unlike previous work involving TLS, we include the constraint that the transformation matrix should be orthonormal. It turns out that the solution to the estimation problem has the same form as if the data are not noisy, and thus the solution to the standard Procrustes problem can be applied.

Considers the problem of reconstructing a continuous-tone (contone) image from its halftoned version, where the halftoning process is done by error diffusion. The authors present an iterative nonlinear decoding algorithm for halftone-to-contone conversion and show simulation results that compare the performance of the algorithm to that of conventional linear low-pass filtering. They find that the new technique results in subjectively superior reconstruction. As there is a natural relationship between error diffusion and SigmaDelta modulation, the reconstruction algorithm can also be applied to the decoding problem for SigmaDelta modulators.

Chaotic /spl Sigma//spl Delta/ modulators, in which at least one internal integrator is unstable, have recently been proposed to combat spurious, detrimental "tones" appearing in the modulator output. This paper employs a semi-analytical technique to solve the design problem for general chaotic double-loop modulators with constant inputs. For given integrator pole locations, scaling factors are found which maximize an approximate performance measure subject to bounds on internal state variables. The results clarify an explicit trade-off between tone rejection through chaos, system stability and practiced performance.>

The authors consider the application of Sigma Delta modulators to analog-to-digital conversion. They have previously shown that for constant input signals, optimal nonlinear decoding can achieve large gains in signal-to-noise ratio (SNR) over linear decoding. A similar result is shown for band-limited input signals. The new nonlinear decoding algorithm is based on projections onto convex sets (POCS) and alternates between a band limitation and a time-domain operation to find a signal invariant under both. The band limitations can be based on singular value decomposition of a certain matrix. The time-domain operation results in a quadratic programming problem. Simulation results are shown for the SNR performance of a POCS-based decoder and a linear decoder for the single-loop Sigma Delta modulator and for a specific fourth-order interpolative modulator. Improvements in SNR of up to 20 dB can be achieved for the single-loop modulator depending on the oversampling ratio, and up to 10 dB for the fourth-order modulator.>

New properties of single- and double-loop sigma-delta modulators with constant inputs are derived by exploiting the inherent structure os the output sequences or codewords that the modulators are capable of producing. Upper bounds are derived on the number of N-bit codewords for the single- and double-loop modulators. Analytical lower bounds on the mean squared error (MSE) obtainable by any decoder, linear or nonlinear, in approximating the constant input are also derived. Optimal nonlinear decoders for constant inputs based on a table lookup approach which operates directly on the nonuniform quantization intervals are considered. Using simulations is is found that the optimal nonlinear decoders perform better than linear decoders, by about 3 and 20 dB for the single- and double-loop modulators, respectively. A cascade structure specifically for constant inputs is introduced, and its corresponding decoding algorithm is derived. It is shown that for a fixed latency, the MSE performance of the cascade structure is 12 dB superior, and its throughput is twice that of the conventional two-stage MASH modulator.>