We demonstrate how to solve constraint satisfaction problems (CSPs) with DNA computing. Assuming that DNA operations can be faulty, we estimate error probability of our algorithm. We show that for any k-CSP, there is a polynomial-time DNA algorithm with bounded probability of error. Thus, k-CSPs belong to a DNA analogue of BPP.

Optimal power allocation for multiple-input multiple-output radar waveform design subject to combined peak and sum power constraints using two different criteria is addressed in this paper. The first one is by maximizing the mutual information between the random target impulse response and the reflected waveforms, and the second one is by minimizing the mean square error in estimating the targe...

Packet data broadcast systems require error free delivery of packet data without retransmission requests. Turbo codes can be used to achieve very low packet error rates. To achieve this low error rate with a very small gap from the system capacity, several design considerations need to be made. We need to use higher constraintlength codes (increasing complexity exponentially) with larger block ...

Spectral matching algorithms can be used for the identification of unknown spectra based on a measure of similarity with one or more known spectra. Two popular spectral matching algorithms use different error metrics and constraints to determine the existence of a spectral match. Multiple endmember spectral mixture analysis (MESMA) is a linear mixing model that uses a root mean square error (RM...

The weighted constraint satisfaction problem (WCSP) framework is a soft constraint framework which can model many real life optimization or overconstrained problems. While there are many local consistency notions available to speed up WCSP solving, in this paper, we investigate how to effectively combine and channel mutually redundant WCSP models to increase constraint propagation. This success...

In this paper, we propose novel structured language modeling methods for code mixing speech recognition by incorporating a well-known syntactic constraint for switching code, namely the Functional Head Constraint (FHC). Code mixing data is not abundantly available for training language models. Our proposed methods successfully alleviate this core problem for code mixing speech recognition by us...

This work is devoted to constraint solving motivated by the debugging of constraint logic programs a la GNU-Prolog. The paper focuses only on the constraints. In this framework, constraint solving amounts to domain reduction. A computation is formalized by a chaotic iteration. The computed result is described as a closure. This model is well suited to the design of debugging notions and tools, ...

We investigate methods of coding for a channel subject to a large dead-time constraint, i.e., a constraint on the minimum spacing between transmitted pulses, with the deep-space optical channel as the motivating example. Several constrained codes designed to satisfy the dead-time constraint are considered and compared on the basis of throughput, complexity, and decoded error rate. The performan...

We propose a parameter shrinkage adaptation framework to estimate models with only a limited set of adaptation data to improve accuracy for automatic speech recognition, by regularizing an objective function with a sum of parameterwise power q constraint. For the first attempt, we formulate ridge maximum likelihood linear regression (MLLR) and ridge constraint MLLR (CMLLR) with an element-wise ...

We present a compiler that automates the task of converting high-level code to constraint sets of the form accepted by the Ginger and Zaatar protocols for verified computation. Performing the conversion from high-level code to constraints by hand is prone to human error and therefore not practical for large computations. This paper evaluates the performance of the compiler and the effectiveness...