Here, non-coherent detectors for quadrature-multiplexed continuous phase modulation (QM-CPM) signals are developed (Fonseka et al., 2008). QM-CPM signals are more spectrally efficient than CPM signals, and multiamplitude QM-CPM signals can be constructed that outperform QAM. The non-coherent detectors view a QM-CPM signal as having composite states that represent the information modulated both onto the in-phase and quadrature-phase carriers. Viterbi decoding is used to jointly detect the quadrature-multiplexed signal components. Optimal sequence-based quadrature-matched filter (QMF) detectors are presented for use with QM-CPM detection and a QM-MSK detector is developed in detail. Differential phase and envelope (DPE) detectors are also presented which require less computation and are able to outperform the QMF-type detectors when the unknown carrier phase varies (phase jitter, phase hits etc.) within the sequence length used in the sequence-based QMF detectors. In the absence of these phase fluctuation effects, as the sequence length used in the QMF detectors increases, the performance of the QMF detectors approaches the performance of optimal non-coherent detection. The DPE-type detectors perform within about 2.5 dB of optimal coherent detection.

This paper introduces a family of bandwidth efficient modulation schemes called quadrature multiplexed continuous phase modulation (QM-CPM). QM-CPM can be thought of as an alternative to smooth pulse-shaped quadrature amplitude modulation (QAM). Like pulse-shaped QAM, QM-CPM modulates smooth continuous signals onto the in-phase (I) and the quadrature-phase (Q) channels, but these signals are derived from CPM signals instead of being filtered to attain their desired smoothness and spectral properties. QM-MSK (QMminimum shift keying) is developed and is shown to double the bits/Hz over MSK while maintaining MSK's normalized minimum distance of d2min= 2.0. Classes of M2-ary QMCPFSK (QM-continuous phase frequency shift keying) signals are derived from constant envelope M-ary CPFSK signals to construct bandwidth efficient modulation schemes which are comparable to and offer advantages over pulse-shaped QAM.

A family of adaptive filtering algorithms for processing signals which have energy concentrated in a relatively small number of component subspaces in the spectral domain is introduced. The approach is based on transform domain signal decomposition and linear least squares filtering of the selected subset of transform domain signal components. The derivation is based on the linear least squares adaptive filtering framework introduced in previous work (1997). Fast convergence and computational efficiency are the main characteristics of the resulting algorithms. The method is applied to the problem of adaptive line enhancement comb filtering and DFT is used as a transform method. It is also shown that the resulting adaptive structure is capable of handling the case of non-coinciding frequencies. The performance of the algorithm is evaluated through a series of simulation experiments.

The subspace-based reduced rank and polynomial order (RRPO) methods estimate a reduced order linear prediction polynomial whose roots are the desired "signal roots". In this paper, we describe how to extend the RRPO methods to include constraints involving known signal information. Simulation results indicate that by incorporating known signal information such as source direction angle, the estimation of unknown source directions can be significantly improved, especially when the unknown source is weak, closely spaced and highly coherent with the known source.

We combine spherical subspace (SS) and eigen based updating methods with the affine projection (AP) method to produce a new family of fast SS-AP algorithms that offers additional tradeoffs between computation and adaptive filtering performance. Moreover, the implementation of SS-AP is less complicated than the fast RLS based AP algorithms. For certain applications, e.g., echo cancellation and equalization in digital subscriber loop (DSL) transceivers, SS-AP offers performance that is comparable to AP, but at computational costs that are less than the fast AP algorithms.

We formulate a block processing objective function for the constant modulus algorithm (CMA) and present a nonlinear optimization strategy for its rapid minimization. The resulting algorithm is called the block Shanno constant modulus algorithm (BSCMA), which is applied in the simulation to equalize multipath fading channel models with a multi-antenna system. The results show that the algorithm can converge considerably faster than the steepest descent based CMA and the RLSCMA configured similarly.

Reduced rank linear predictive frequency and direction-of-arrival (DOA) estimation algorithms use the singular value decomposition (SVD) to produce a noise-cleaned linear prediction vector. These algorithms then root this vector to obtain a subset of roots, whose angles contain the desired frequency or DOA information. The roots closest to the unit circle are deemed to be the "signal roots". The rest of the roots are "extraneous". The extraneous roots are expensive to calculate. Further, a search must be done to discern the signal roots from the extraneous roots. Here, we present a reduced polynomial order linear prediction method that simplifies the rooting computation for applications where high-speed processing is critical.

We perform an analysis of constrained and unconstrained MUSIC demonstrating that (asymptotically) improved subspace estimates always result from the use of constraints, and (asymptotically) the variance of constrained MUSIC is less than that of unconstrained MUSIC under either high coherence, large numbers of sensors, or high SNR conditions. As part of this analysis, we study the effects of coherence on MUSIC and derive best/worst case coherences in terms of the variance of MUSIC. We also demonstrate that those conditions where the variance of MUSIC is predicted to be less than that of constrained MUSIC generally correspond to conditions where MUSIC is in breakdown (and constrained MUSIC is not). So, unconstrained MUSIC does not achieve its predicted advantage in those cases.

Speech recognition systems are increasingly utilized in various applications like telephone services where a user places a call by uttering the digits or the name of the person. One of the main problems in this application is the segmentation of the input utterance into speech and nonspeech portions. Current approaches typically suffer from two problems. They either incorporate noise as a part of the word to be enrolled or falsely classify a portion of a word as noise. As a result, recognition performance suffers. The authors present another approach to automatically segment continuous speech and create speaker dependent models. To verify the hypothesis, they use a database of 30 speakers whose speech has been recorded over the public switched telephone network. With this database, they benchmark their algorithm against a state of the art approach and show a 4/spl times/ reduction in the error rate of the recognition system.>

Describes and analyzes the Hybrid Array Ring Processor (HARP) architecture. The HARP is an application specific architecture built around a host processor, shared memory, and a set of memory mapped processing cells that are connected both into an open backplane and a bidirectional systolic ring. The architecture is analyzed through detailed simulation of a system implementation based on the Texas Instruments TMS34082 floating point RISC. A bus controller is designed that provides a tightly coupled DMA function that accelerates systolic communication and supports new interleaved transparent communications and reduced overhead message passing. The architecture is benchmarked with the matrix multiplication, FFT, QRD, and SVD algorithms.>

Numerically stable closed form expressions for the solution of the total least squares (TLS) problem with linear equality constraints (LCTLS) are derived. A constrained subspace linear predictive frequency estimation technique called LCTLS-linear predictive (LCTLS-LP) is proposed. The method is suited for scenarios where the presence of a known frequency component makes it difficult to resolve a closely spaced component.>

A matrix method for the design and analysis of systolic array algorithms is presented. The method starts by representing a nested loop algorithm as a set of matrices and an expression function. This representation is then mathematically integrated into the algorithm transformation technique of Moldavan (1982) and Fortes (1984). Using this framework, data position, distribution, velocity, and collision information can be computed along with processor element memory contents, all as functions of the systolic temporal vector. The systolic array synthesis equations are then derived. Their solution is a transformation matrix that generates a systolic algorithm with prespecified properties in terms of algorithm flow. Finally, the method is shown to be able to generate systolic algorithms for nonuniform recurrent algorithms such as the LU. Cholesky and QR decompositions.>