Fast time-invariant implementations of Gaussian signal detectors

A new implementation is presented for the optimum likelihood ratio detector for stationary Gaussian signals in white Gaussian noise that uses only two causal time-invariant filters. This solution also has the advantage that fast algorithms based on the Levinson and Chandrasekhar equations can he used for the determination of these time-invariant filters. By using a notion of "closeness to stationarity,' there is a natural extension of the above results for nonstationary signal processes.     

Optimal estimation for discrete time jump processes

Optimum estimates of nonobservable random variables or random processes which influence the rate functions of a discrete time jump process (D) are derived. The approach used is based on the {em a posteriori} probability of a nonobservable event expressed in terms of the {em a priori} probability of that event and of the sample function probability of the DTJP. Thus a general representation is obtained for optimum estimates, and recursive equations are derived for minimum mean-squared error (MMSE) estimates. In general, MMSE estimates are nonlinear functions of the observations. The problem of estimating the rate of a DTJP when the rate is a random variable with a beta probability density function and the jump amplitudes are binomially distributed is considered. It is shown that the MMSE estimates ale linear. The class of beta density functions is rather rich and explains why there are insignificant differences between optimum unconstrained and linear MMSE estimates in a variety of problems.     

Linear periodic systems and multirate filter design

We present a systematic procedure for the design of filters intended for multirate systems. This procedure Is motivated by viewing the equiripple design of filters in linear time-invariant systems as a process of obtaining optimum minimax filters for a class of bounded energy input signals. The philosophy of designing optimum minimax filters for classes of input signals is extended to multirate systems, which are not time-invariant. We develop a generalized Fourier analysis appropriate for linear periodic systems and use it to derive new error criteria for multirate filter design. Using such criteria yields optimum minimax multirate filters for the input signal class. The utility of our method is demonstrated by using it to analyze several multirate systems. We give numerical results on the design of a multirate implementation of a narrowband filter and compare our work to previous work on multirate filter design. Our numerical analysis is based upon a new formulation of the design as a semi-infinite linear programming problem     

Basis arrays and successive packing for M-D interleaving

In this paper, we first present a novel concept of 2-D basis interleaving array (also referred to as basis array for short). That is, an m × m interleaved array is said to be a basis array if the shortest distance among all pairs of elements in each of the so-called m-equivalent sets within the m × m array reaches the maximum. It is shown that this maximum is given by ${\lfloor \sqrt{2m} \rfloor}$ and an m × m basis array can be constructed by using a simple cyclic translation method. The previously developed concept of successive packing is then generalized in the sense that it can be applied to any basis array to generate an interleaved array with a larger size. Except that optimality cannot be guaranteed, the concept of basis arrays and successive packing are extended to M-D cases. It is shown that for any M ?? 2, the proposed technique can spread any error burst of block size ${m_{1}^{k} \times m_{2}^{k} \times \cdots \times m_{M}^{k}}$ within an ${ m_{1}^{n} \times m_{2}^{n} \times \cdots \times m_{M}^{n}}$ array (1 ?? k ?? n?1) so effectively that the error burst can be corrected with some simple random error-correcting code (provided the error-correcting code is available). It is shown that important prior results in M-D interleaving such as the t-interleaved array based approach by Blaum et al. and the successive packing approach by Shi and Zhang now become special cases of the framework based on basis arrays and successive packing, proposed in this paper.     

Time-invariant trellis encoding of ergodic discrete-time sources with a fidelity criterion

The theory of sliding-block codes (nonlinear, time-invariant, discrete-time filters) is employed to obtain general source coding theorems for ergodic sources using time-invariant trellis coding (time-invariant decoding filter and replicating trellis). The results are coupled with the theory of universal block source codes to obtain universal trellis source coding theorems for classes of sources. It is shown for a certain class of sources that the problem of designing good trellis codes is equivalent to that of simulating general random processes by filtering digital memoryless sources.     

A Fake Process Approach to Data Compression

The problem of designing a good decoder for a timeinvariant tree-coding data compression system is equivalent to that of finding a good low rate "fake process" for the original source, where the fake is produced by a time-invariant nonlinear filtering of an independent, identically distributed sequence of uniformly distributed discrete random variables and "goodness" is measured by thebar{rho}-distance between the fake and the original source. Several simple ad hoc techniques for obtaining such fake processes are introduced and shown by simulation to provide an improvement of typically 1-2 dB over optimum quantization, delta modulation, and predictive quantization for one-bit per symbol compression of Gaussian memoryless, autoregressive, and moving average sources. In addition, the fake process viewpoint provides a new intuitive explanation of why delta modulation and predictive quantization work as well as they do on Gaussian autoregressive sources.     

Nonparametric polyspectral estimators for kth-order (almost)cyclostationary processes

Second- and higher-order almost cyclostationary processes are random signals with almost periodically time-varying statistics. The class includes stationary and cyclostationary processes as well as many real-life signals of interest. Cyclic and time-varying cumulants and polyspectra are defined for discrete-time real kth-order cyclostationary processes, and their interrelationships are explored. Smoothed polyperiodograms are proposed for cyclic polyspectral estimation and are shown to be consistent and asymptotically normal. Asymptotic covariance expressions are derived along with their computable forms. Higher than second-order cyclic cumulants and polyspectra convey time-varying phase information and are theoretically insensitive to any stationary (for nonzero cycles) as well as additive cyclostationary Gaussian noise (for all cycles)     

A class of high-precision multiplier-free FIR filter realizationswith periodically time-varying coefficients

Proposes a class of high-precision, multiplier-free realizations for FIR filters. These realizations use upsampling and downsampling in conjunction with a periodically time-varying system to achieve time-invariant, multiplier-free FIR filter operation. Nonbinary encoding schemes are used for obtaining the filter coefficients, which are periodically time-varying (PTV), i.e., they vary in a periodic fashion. Each target filter coefficient is directly mapped into a set of PTV coefficients so that the realizations are easy to obtain. The values of the PTV coefficients are restricted to either the ternary set {±1, 0} or the quinary set {±2, ±1, 0} so that the realizations can be implemented with only add/subtract and one-bit shift (for the quinary case) operations. A few shift-and-add operations are also needed at the beginning and the end of the structure. Coefficient precisions (in bits) of these realizations are given and they are sufficiently high for most applications. Advantages of the proposed realizations accrue at the expense of a higher clock rate     

On the cyclostationary statistics of ultra-wideband signals in the presence of timing and frequency jitter

Cyclostationarity is an inherent characteristic of many man-made communication signals, which, if properly recognized, can be exploited for performing various signal-processing tasks. Determining the cyclostationary characteristics of a signal of interest is the first step in the design of signal processing systems exploiting this cyclostationary behaviour. This paper investigates the cyclostationary statistics of various signalling schemes employed in ultra-wideband (UWB) communication systems. Analytical expressions are derived for the cyclic autocorrelation and spectral correlation density functions in the presence of random timing and frequency jitter, which are characterized by discrete-time stationary random processes with known distribution functions.     

Application of the mutual information principle to spectral density estimation

The power spectrum of a stationary Gaussian random process is estimated when partial knowledge of the autocorrelation function is available {em a priori}. Particular attention is paid to the case when the {em a priori} knowledge is not precise, i.e., when there are errors in the measurements, perhaps due to the presence of noise. In the special case when the {em a priori} knowledge consists ofnpoints of the autocorrelation function, Burg's method of picking the spectrum which maximizes the entropy of the Gaussian process has been recently extended by Newman to account for a weighted average error in the estimates of the correlation function points. A new method is suggested here that uses the mutual information principle (MIP) of Tzannes and Noonan. The firstnpoints of the correlation function (obtained with errors) are used to derive an approximate spectrum by Burg's or any other method. This spectrum, as well as the error constraints involved, is then used to arrive at the underlying spectrum in the framework of the MIP approach.     

Improving an algorithm for factoring polynomials over a finite field and constructing large irreducible polynomials

Letf(x)in {bf F}_{q}[X]be a polynomial with simple roots to be factored. The so-called Berlekamp subalgebraBspanned over{bf F}_{q}by the idempotents ofA={bf F}_{q}[X]/(f(X))is considered. An exponential technique introduced earlier is based upon taking elements from B at random and enables us to obtain idempotents and, from that, the factors of f(X). This algorithm is speeded up in three ways. The concept of a separating subset of B is introduced and the McEliece operator mapping A onto B is used to construct a small separating set. {em Factoring} subsets of{bf F}_{q}were defined and investigated previously. The algorithm and these subsets are used together with a process introduced by F. J. McWilliams for the rapid construction of primitive idempotents. Finally, an algorithm is introduced for constructing irreducible polynomials of{bf F}_{q}[X]of degreed, for large values ofd, in which the most expensive operation is the Euclidian algorithm applied to two polynomials of degree2d.     

Lower bounds on lifetime of ultra wide band wireless sensor networks

The asymptotic lower bounds on the lifetime of time hopping impulse radio ultra wide band (TH-IR UWB) wireless sensor networks are derived using percolation theory arguments. It is shown that for static dense TH-IR UWB wireless sensor network, which sensor nodes are distributed in a square of unit area according to a Poisson point process of intensity n, the lower bound on the lifetime is \( \Upomega \left( {\left( {{{\sqrt n } \mathord{\left/ {\vphantom {{\sqrt n } {\log \sqrt n }}} \right. \kern-\nulldelimiterspace} {\log \sqrt n }}} \right)^{\alpha - 2} } \right) \), where α > 2 is the path loss exponent, thus dense TH-IR UWB wireless sensor network is fit to be employed in large-scale network. For static extended TH-IR UWB wireless sensor network which sensor nodes are distributed in a square \( \left[ {0,\sqrt n } \right] \times \left[ {0,\sqrt n } \right] \) according to a Poisson point process of unit intensity, the lower bound on the lifetime is \( \Upomega \left( {{{\left( {\log \sqrt n } \right)^{2 - \alpha } } \mathord{\left/ {\vphantom {{\left( {\log \sqrt n } \right)^{2 - \alpha } } n}} \right. \kern-\nulldelimiterspace} n}} \right) \), therefore large-scale extended network will lead to shorten network lifetime. The results also indicate that the lower bound on the lifetime in the ideal case is longer than that of a static network by a factor of \( n^{1/2} \left( {\log \sqrt n } \right)^{\alpha - 4} \). Hence mobility of sensor nodes can improve network lifetime.     

Equalizers for multiple input/multiple output channels and PAMsystems with cyclostationary input sequences

The author studies minimum mean square error (MMSE) linear and decision feedback (DF) equalisers for multiple input/multiple output (MIMO) communication systems with intersymbol interference (ISI) and wide-sense stationary (WSS) inputs. To derive these equalizers, one works in the D-transform domain and uses prediction theory results. Partial-response MMSE equalizers are also found. As an application, the author considers a pulse amplitude modulation (PAM) communication system with ISI and cyclostationary inputs. The MMSE linear and DF equalizers are determined by studying an equivalent MIMO system. The resulting filters are expressed in compact matrix notation and are time-invariant, whereas the corresponding single input/single output filters are periodically time-invariant. The author also considers MMSE equalizers for a wide-sense stationary process by introducing a `random phase'. To aid in the performance evaluation of various equalizers, the author derives their mean square errors     

On a class of linear time-varying filters

This paper is devoted to a special class of linear time-varying filters, members of which are commonly employed in practice. It is shown that some of these filters share a number of desirable properties with linear stationary systems, the most important of which is the preservation of wide sense stationarity of stochastic inputs. An application to the problem of transmitting a continuous signal over a sampled data channel is included. Some results coincide with those for a multichannel system with simultaneous optimum stationary presampling and postsampling filters.     

Transient analysis of electrical and linear charge preset on the input stage of surface CCD's

Transient numerical analysis of electrical signal injection into the surface charge-coupled device (CCD) is given regarding the potential equilibration method. The analysis is composed of the charging and discharging processes of signal charges, characterized by time intervals TCand TDof the respective input pulses. A concept of "setting time TS" explains how the mode of injection converts from the potential equilibration to dynamic injection, and determines an optimum TC. A concept of "residual effect of excess charges" is discussed in relation to sampling effect. The procedure to determine an optimum TDis also proposed. The optimum input timing is divided into two instances in accordance with the degree of the gate lengthL; L > < 5-8µm. For the instances (L > 5-8µm) the timing condition is further split into two cases, depending on the biased potential differenceV_{G1}-V_{BL}. The above conclusions can be applied to setting input pulses and the structure of CCD inputs designed for analog signal processing.     

Extended Robust \mathcal{H}_{2} and \mathcal{H}_{\infty} Filter Design for Discrete Time-Invariant Linear Systems with Polytopic Uncertainty

This paper is concerned with the problem of robust $\mathcal{H}_{2}$ and $\mathcal{H}_{\infty}$ filter design for discrete-time linear time-invariant systems with polytopic parameter uncertainties. Less conservative robust $\mathcal{H}_{2}$ and $\mathcal{H}_{\infty}$ filter design procedures are proposed in terms of single-parameter minimization problems with linear matrix inequality constraints. To this end, we generalize the filter structures available in the literature to date in such a way that the filter’s next state is built by summing the filter’s states over several samples from the past to the present. For stability of the filtering error system, the homogeneous polynomial parameter-dependent Lyapunov functions are employed. Finally, illustrative examples are given to demonstrate the merits of the proposed methods.     

Study on Time Domain Characteristics of Memristive RLC Series Circuits

The notion of memristive system was first proposed in 2009. This concept of memory element has been extended from memristors \((\hbox {R}_{\mathrm{M}})\) to memcapacitors \((\hbox {C}_{\mathrm{M}})\) and meminductors \((\hbox {L}_{\mathrm{M}})\). Currently, the above elements are not available as off-the-shelf components. Therefore, based on the realization of a light-dependent resistor (LDR), memristor analog model, memcapacitor and meminductor analog circuit models based on \(\hbox {R}_{\mathrm{M}}-\hbox {C}_{\mathrm{M}}\) and \(\hbox {R}_{\mathrm{M}}-\hbox {L}_{\mathrm{M}}\) converters are first introduced. Then, instead of the traditional resistor, capacitor, and inductor, memristor-, memcapacitor-, and meminductor-equivalent circuits are used to determine the time domain characteristics of the RLC-mode circuits with mem-elements. These circuits are discussed in detail, and in particular, the phenomena caused by the memory characteristics of the mem-elements are studied. This research provides an important reference for further research into mem-element applications in circuit theory.     

Detection and estimation of frequency-random signals (Corresp.)

A random frequency-varying signal is approximated by a finite-parameter model. A maximum {em a posteriori} estimate of the parameter set leads to an estimation criterion, which when maximized is also the decision statistic obtained with an estimator-correlator detector. An efficient implementation of the frequency-parameter estimate (which corresponds to maximizing the criterion) is presented. The resulting estimate is compared to previous techniques found in the literature. Finally, performance bounds for the resulting detector are evaluated and compared to the asymptotically optimum low-energy coherence detector.     

Gaussian arbitrarily varying channels

The {em arbitrarily varying channel} (AVC) can be interpreted as a model of a channel jammed by an intelligent and unpredictable adversary. We investigate the asymptotic reliability of optimal random block codes on Gaussian arbitrarily varying channels (GAVC's). A GAVC is a discrete-time memoryless Gaussian channel with input power constraintP_{T}and noise powerN_{e}, which is further corrupted by an additive "jamming signal." The statistics of this signal are unknown and may be arbitrary, except that they are subject to a power constraintP_{J}. We distinguish between two types of power constraints: {em peak} and {em average.} For peak constraints on the input power and the jamming power we show that the GAVC has a random coding capacity. For the remaining cases in which either the transmitter or the jammer or both are subject to average power constraints, no capacities exist and onlylambda-capacities are found. The asymptotic error probability suffered by optimal random codes in these cases is determined. Our results suggest that if the jammer is subject only to an average power constraint, reliable communication is impossible at any positive code rate.     

Decentralized \mathcal{L}_{2}–\mathcal{L}_{\infty} Filtering for Interconnected Markovian Jump Systems with Delays

This paper deals with the problem of decentralized $\mathcal{L}_{2}$ – $\mathcal{L}_{\infty}$ filtering for a class of interconnected (or large-scale) Markovian jump systems with constant time delays. The purpose is to present delay-dependent conditions for the existence of mode-dependent decentralized filters, which guarantees that the filtering error system is stochastically stable with a prescribed $\mathcal{L}_{2}$ – $\mathcal{L}_{\infty}$ disturbance attenuation level. Such a purpose is achieved by using a mode-dependent centralized Lyapunov functional together with the so-called Jensen’s inequality. The obtained synthesis conditions are expressed in terms of linear matrix inequalities (LMIs), which leads to a convex design method for the concerned filters. An example including numerical and simulation results is provided finally to illustrate the effectiveness of the proposed design method.