We describe a set of programs for circular convolution and prime length fast Fourier transforms (FFT's) that are relatively short, possess great structure, share many computational procedures, and cover a large va...
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We describe a set of programs for circular convolution and prime length fast Fourier transforms (FFT's) that are relatively short, possess great structure, share many computational procedures, and cover a large variety of lengths, The programs make clear the structure of the algorithms and clearly enumerate independent computational branches that can be performed in parallel, Moreover, each of these independent operations is made up of a sequence of suboperations that can be implemented as vector/parallel operations, This is in contrast with previously existing programs for prime length FFT's: They consist of straight line code, no code is shared between them, and they cannot be easily adapted for vector/parallel implementations. We have also developed a program that automatically generates these programs for prime length FFT's, This code-generating program requires information only about a set of modules for computing cyclotomic convolutions.
We consider the problem of nonadaptive noiseless group testing of N items of which K are defective. We describe four detection algorithms, the COMP algorithm of Chan et al., two new algorithms, DD and SCOMP, which req...
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We consider the problem of nonadaptive noiseless group testing of N items of which K are defective. We describe four detection algorithms, the COMP algorithm of Chan et al., two new algorithms, DD and SCOMP, which require stronger evidence to declare an item defective, and an essentially optimal but computationally difficult algorithm called SSS. We consider an important class of designs for the group testing problem, namely those in which the test structure is given via a Bernoulli random process. In this class of Bernoulli designs, by considering the asymptotic rate of these algorithms, we show that DD outperforms COMP, that DD is essentially optimal in regimes where K >= root N, and that no algorithm can perform as well as the best nonrandom adaptive algorithms when K > N-0.35. In simulations, we see that DD and SCOMP far outperform COMP, with SCOMP very close to the optimal SSS, especially in cases with larger K.
We introduce a framework for synthesis of behavioral models in which design information is represented using an automaton model. This model offers the advantage of supporting different constraints (e.g., timing, resou...
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We introduce a framework for synthesis of behavioral models in which design information is represented using an automaton model. This model offers the advantage of supporting different constraints (e.g., timing, resource, synchronization, etc.) with a uniform formalism. The set of all feasible execution traces (schedules) is constructed and traversed using efficient BDD-based implicit state-traversal techniques. As an application example of this formalism, we present a novel scheduling/control-generation algorithm under environmental constraints where both the design and constraints are represented using automata. We present an algorithm that generates a minimum-latency schedule and a control unit representation. This approach is able to exploit degrees of freedom among interacting components of a multimodule system during scheduling, and is well suited for system-level design, where component encapsulation and interfacing are important.
A new optimal window is presented in this paper. It can provide the best tradeoff between the peak sidelobe level and the total sidelobe energy. Necessary and sufficient conditions are established for the optimal wind...
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A new optimal window is presented in this paper. It can provide the best tradeoff between the peak sidelobe level and the total sidelobe energy. Necessary and sufficient conditions are established for the optimal window. A designalgorithm is presented and examples are discussed. The examples demonstrate that the new window is capable of much better performance than previously available windows, such as the Hamming window. Relationships between the maximum error and the mean-squared error are explored for optimization problems. It is concluded that minimax and least squares approximations are both fundamentally inefficient. This conclusion is supported by experimental evidence and mathematical analyses.
A hardware description language should be simple, expressive, orthogonal, easy to read, and extendable. It should have associated efficient, user-friendly tools that produce good results, and it should have functional...
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A hardware description language should be simple, expressive, orthogonal, easy to read, and extendable. It should have associated efficient, user-friendly tools that produce good results, and it should have functional, structural and layout semantics. Zeus satisfies most of these requirements and therefore its features should be included in any HDL proposed as a standard. Zeus and its recent developments-its close integration with Modula-2 and its implementation in the context of a metaprogramming environment-are presented here. Two example circuits, a comparator circuit and a music generation circuit, illustrate some essential features of Zeus. A number of CAD tools that will use Zeus are under development, and Zeus has been benchmarked with over 40 design examples.
In the analysis of electromyographic (EMG) signals during dynamic movement, we have proposed an estimation algorithm for the time-varying parameters of an autoregressive model. The parameters correspond to less biased...
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In the analysis of electromyographic (EMG) signals during dynamic movement, we have proposed an estimation algorithm for the time-varying parameters of an autoregressive model. The parameters correspond to less biased time-varying reflection coefficients. We determined the less biased estimation using a locally quasi-stationary model and named these parameters "k parameters." We estimated k parameters up to the fifth order for the surface EMG signals of a masseter muscle during rapid open-close movement of the lower jaw, a ballistic contraction, and fatigue. According to the results, the time courses of the k parameters displayed remarkable properties. In order to study the behavior of k parameters physiologically, we produced a muscle-structured simulation model based on anatomical and physiological data. The simulation results suggested that the behavior of the third parameter is related to the number of active motor units (MU's) at the shallow layer of a muscle. The detailed recruitment mechanism in terms of the MU's types has not yet been solved. Although further study is required, the parametric analysis using k parameters offers a new perspective for evaluation of muscle dynamics during several movements.
This paper describes results from an ongoing project concerned with recognizing objects in complex scene domains, especially in the domain that includes the natural outdoor world. Traditional machine recognition parad...
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This paper describes results from an ongoing project concerned with recognizing objects in complex scene domains, especially in the domain that includes the natural outdoor world. Traditional machine recognition paradigms assume either 1) that all objects of interest are definable by a relatively small number of explicit shape models or 2) that all objects of interest have characteristic, locally measurable features. The failure of both assumptions in a complex domain such as the natural outdoor world has a dramatic impact on the form of an acceptable architecture for an object recognition system. In our work, we make the use of contextual information a central issue, and explicitly design a system to identify and use context as an integral part of recognition. In so doing, we provide a new paradigm for visual recognition that eliminates the traditional dependence on stored geometric models and universal image partitioning algorithms. This paradigm combines the results of many simple procedures that analyze monochrome, color, stereo, or 3-D range images. By interpreting their results along with relevant contextual knowledge, a reliable recognition result is achieved, even in the face of imperfect visual procedures. Initial experimentation with the system on ground-level outdoor imagery has already demonstrated competence beyond what we believe is attainable with other existing vision systems.
We present an improved algorithm for an established filter design problem, the design of an FIR filter that best approximates, in the complex Chebyshev sense, a desired complex-valued frequency response. The algorithm...
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We present an improved algorithm for an established filter design problem, the design of an FIR filter that best approximates, in the complex Chebyshev sense, a desired complex-valued frequency response. The algorithm is a variant of the simplex algorithm of linear programming, which has an interpretation as an implicit multiple exchange. It is iterative, robust, and exhibits good convergence speed. Global optimum convergence is guaranteed. Both complex and real-valued impulse responses can be designed with it;the design of complex coefficient filters is new. An example is given for each case. The design of noncausal filters is new. In addition to these new applications, we conjecture that this new algorithm may have important advantages over existing techniques, with respect to the maximum filter length possible, speed and stability of convergence, accuracy, and memory requirements. The ability to design long filters is among the more significant improvements over previous work. Filters of length 1000 have been designed with the new method.
A method is presented whereby, given a mathematical description of a function, a conic spline approximating the plot of the function is produced. Conic arcs were selected as the primitive curves because there are simp...
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A method is presented whereby, given a mathematical description of a function, a conic spline approximating the plot of the function is produced. Conic arcs were selected as the primitive curves because there are simple incremental plotting algorithms for conics already included in some device drivers, and there are simple algorithms for local approximations by conics. A split-and-merge algorithm for choosing the knots adaptively, according to shape analysis of the original function based on its first-order derivatives, is introduced
In almost all analyses of the least-mean-square (LMS) adaptive filter, it is assumed that the filter coefficients are statistically independent of the input data currently in filter memory, an assumption that is incor...
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In almost all analyses of the least-mean-square (LMS) adaptive filter, it is assumed that the filter coefficients are statistically independent of the input data currently in filter memory, an assumption that is incorrect for shift-input data, In this paper, we present a method for deriving a set of linear update equations that can be used to predict the exact statistical behavior of a finite-impulse-response (FIR) LMS adaptive filter operating upon finite-time correlated input data, Using our method, we can derive exact bounds upon the LMS step size to guarantee mean and-mean-square convergence, Our equation-deriving procedure is recursive and algorithmic, and we describe a program written in the MAPLE symbolic-manipulation software package that automates the derivation for arbitrarily-long adaptive filters operating on input data with stationary statistics, Using our analysis, we present a search algorithm that determines the exact step size mean-square stability bound for a given filter length and input correlation statistics, Extensive computer simulations indicate that the exact analysis is more accurate than previous analyses in predicting adaptation behavior, Our results also indicate that the exact step size bound is much more stringent than the bound predicted by the independence assumption analysis for correlated input data.
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