Performance of band-limited baseband synchronous CDMA using orthogonal Independent Component Analysis (ICA) spreading sequences is investigated. The orthogonal ICA sequences have an orthogonality condition in a synchronous CDMA like the Walsh-Hadamard sequences. Furthermore, these have useful correlation properties like the Gold sequences. These sequences are obtained easily by using the ICA which is one of the brain-style signal processing algorithms. In this study, the ICA is used not as a separator for received signal but as a generator of spreading sequences. The performance of the band-limited synchronous CDMA using the orthogonal ICA sequences is compared with the one using the Walsh-Hadamard sequences. For limiting bandwidth, a Root Raised Cosine filter (RRC) is used. We investigate means and variances of correlation outputs after passing the RRC filter and the Bit Error Rates (BERs) of the system in additive white Gaussian noise channel by numerical simulations. It is found that the BER in the band-limited system using the orthogonal ICA sequences is much lower than the one using the Walsh-Hadamard sequences statistically.

This paper presents a new method for reconstruction of trigonometric polynomials, a specific class of bandlimited signals, from a number of integrated values of input signals. It is applied in signal reconstruction, spectral estimation, system identification, as well as in other important signal processing problems. The proposed method of processing can be used for precise rms measurements of periodic signal (or power and energy) based on the presented signal reconstruction. Based on the value of the integral of the original input (analogue) signal, with a known frequency spectrum but unknown amplitudes and phases, a reconstruction of its basic parameters is done by the means of derived analytical and summarized expressions. Subsequent calculation of all relevant indicators related to the monitoring and processing of ac voltage and current signals is provided in this manner. Computer simulation demonstrating the precision of these algorithms. We investigate the errors related to the signal reconstruction, and provide an error bound around the reconstructed time domain waveform.

In various applications of signals transmission and processing, there is always a possibility of loss of samples. The iterative algorithm of Papoulis-Gerchberg (PG) is famous for solving the band-limited lost samples recovery problem. Two problems are known in this domain: (1) a band-limited signal practically is difficult to be obtained and (2) the convergence rate is too slow. By inserting a subtraction by a polynomial in the PG algorithm, using boundary-matched concept, a significant increase in performance and speed of its convergence has been achieved. In this paper, we propose an efficient approach to restore lost samples by adding a preprocess which meets the periodic boundary conditions of Fast Fourier transform in the iteration method. The accuracy of lost samples reconstruction by using the PG algorithm can be greatly improved with the aid of mapping the input data sequence of satisfying the boundary conditions. Further, we also developed another approach that force the signal to meet a new critical boundary conditions in Fourier domain that make the parameters of the preprocessing can be easily obtained. The simulation indicates that the mean square error (MSE) of the recovery and the convergence rate with the preprocess concept is better and faster than the one without preprocess concept. Our both proposed approaches can also be applied to other cases of signal restoration, which allow Cadzow's iterative processing method, with more convenience and flexibility.

This paper proves a general sampling theorem, which is an extension of Shannon's classical theorem. Let o be a closed subspace of square integrable functions and call o a signal space. The main aim of this paper is giving a necessary and sufficient condition for unique existence of the sampling basis {Sn}o without band-limited assumption. Using the general sampling theorem we rigorously discuss a frequency domain treatment and a general signal space spanned by translations of a single function. Many known sampling theorems in signal spaces, which have applications for multiresolution analysis in wavelets theory are corollaries of the general sampling theorem.

The evolution of Multiple-Valued Logic (MVL) circuits has been inexorably tied to the rapid technological changes induced by evolving needs and emerging developments in computing methodologies. Unfortunately for MVL, the numbers of designers of technologies and circuits whose lives are dedicated to the improvement of binary techniques, are large and overwhelming. Correspondingly, technological developments in MVL typically await the appearance of a problem or technique in the larger binary world to motivate and/or make possible some new advance. Such opportunities are inevitably quite transient since each such problem is simultaneously attacked by many others of a more conventional bent, and, as well, each technological change begets yet another, quickly. It is in the sensing of this reality that the present paper is written. Correspondingly, its thrust is two-fold: One target is the possibility of encouraging a leap ahead through modest technological projection. The other is the possibility of identifying application areas that already exist in this unbalanced competition, but which are specially suited to multiple-valued solutions. For example, it has been clear for decades that one such area is that of arithmetic. Correspondingly, we in MVL must strive quickly to concentrate our efforts on applications that exploit such demonstrable strengths. Some such applications are includes here; others are visible historically, many probably remain to be found: Search on!