Mansour A. Aldajani

Stability and performance analysis of an adaptive sigma-delta modulator

This work develops an adaptive sigma-delta modulator that is based on adapting the quantizer step-size using estimates of the quantizer input rather than the modulator input. The adaptive modulator with a first-order noise shaping filter is shown to be bounded-input bounded-output stable. Moreover, an analytical expression for the signal-to-noise ratio is derived, and it is shown to be independent of the input signal strength. Simulation results confirm the signal-to-noise ratio performance and indicate considerable improvement in the dynamic range of the modulator compared to earlier structures.

Analysis of deteriorating inventory/production systems using a linear quadratic regulator

We consider an inventory-production system where items deteriorate at a constant rate. The objective is to develop an optimal production policy that minimizes the cost associated with inventory and production rate. The inventory problem is first modeled as a linear optimal control problem. Then linear quadratic regulator (LQR) technique is applied to the control problem in order to determine the optimal production policy. Examples are solved for three different demand functions. Sensitivity analysis is then conducted to study the effect of changing the cost parameters on the objective function.

Adaptive predictive power control for the uplink channel in DS-CDMA cellular systems

In this paper, we analyze the conventional closed-loop power-control system. We explain that the system behaves essentially as a companded delta modulator and then derive an expression for the power-control error in terms of the channel fading, which suggests methods for reducing the error variance. This is achieved by using a prediction technique for estimating the channel-power fading profile. The prediction module is combined with several proposed schemes for closed-loop power control. The resulting architectures are shown to result in improved performance in simulations.

Location of banking automatic teller machines based on convolution

In this paper, the problem of determining the optimum number and locations of banking automatic teller machines (ATMs) is considered. The objective is to minimize the total number of ATMs to cover all customer demands within a given geographical area. First, a mathematical model of this optimization problem is formulated. A novel heuristic algorithm with unique features is then developed to efficiently solve this problem. Finally, simulation results show the effectiveness of this algorithm in solving the ATM placement problem.

A stable adaptive structure for delta modulation with improved performance

We propose and study an adaptive delta modulator that has improved SNR performance and robustness in tracking highly varying signals. The step-size adaptation used in this modulator is based on information about the absolute value of the quantizer input. The modulator is shown to be free of zero-input limit cycles and is BIBO stable.

An adaptive structure for sigma delta modulation with improved dynamic range

This work proposes a novel structure for adaptive sigma delta modulation that leads to considerable improvement in the dynamic range of the modulator. The quantizer step-size is adapted based on estimates of the input signal to the quantizer block rather than on estimates of the input signal to the modulator itself, as is common in current schemes. The proposed structure can be implemented rather directly by means of analog switches. Theoretical and simulation results show considerable improvement in SNR performance, especially for small amplitude signals, over existing adaptive sigma delta modulators.

Neural regulator design

Design of a neural-net-based regulator for nonlinear plants is considered. Both state and output feedback regulators with deterministic and stochastic disturbances have been investigated. A Multilayered Feedforward Neural Network (MFNN) has been employed as the nonlinear controller. The training of the MFNN utilizes the recently developed concept of Block Partial Derivatives (BPDs).

Logarithmic quantization in the least mean squares algorithm

In this paper, we introduce a framework for adaptive filtering techniques with simplified recursion. The simplification is mainly carried out by rounding the full-precision error information of the recursion to their closest power-of-two values. A new method for power-of-two quantization is proposed in this study. The method uses companded delta modulation structure to perform the quantization. The proposed structure shows a performance that is comparable to that of full precision adaptive filters. Convergence analysis of this structure is included and closed-form expressions for the error statistics are derived. Furthermore, an efficient method for implementing the new structure is presented where only simple shift and loop operations are required.

Stability analysis of an adaptive structure for sigma delta modulation

In previous work, we have proposed an adaptive sigma delta modulator with improved dynamic range. The modulator adapts the step-size of the quantizer from estimates of the quantizer input. In this paper, we conduct a stability analysis of the new modulator.

SNR performance of an adaptive sigma delta modulator

In a previous study, we proposed an adaptive sigma delta modulator with an improved dynamic range. The modulator adapts the step size of the quantizer from estimates of the quantizer input instead of the modulator input. In this study, we conduct an error variance analysis of the new modulator and derive expression for the SNR. The derived expression shows that the SNR is independent of the input signal strength, which supports the simulation results.