Physical layer of modern communication systems involves several floating point computations that require higher numerical fidelity and dynamic range for achieving the maximum data rate. Posit number format is a promis...
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ISBN:
(纸本)9781728128610
Physical layer of modern communication systems involves several floating point computations that require higher numerical fidelity and dynamic range for achieving the maximum data rate. Posit number format is a promising alternative for floating point computation as it provides a better dynamic range and numerical fidelity for the same number of bits used for representation. However, the Posit number format has not yet been thoroughly studied in the context of signalprocessingalgorithms in the physical layer. In addition, a configurable Posit arithmetic hardware is essential for adapting to the ever changing communication standards and to configure the dynamic range according to algorithmic demands. The main contributions in this paper are: 1) Performance analysis of common signalprocessingalgorithms using the Posit number format in comparison with the IEEE Standard for Single Precision Floating Point arithmetic (IEEE 754). 2) A novel reconfigurable hardware accelerator for Posit arithmetic operations and comparison with the state-ofthe-art Posit and IEEE Floating Point arithmetic architectures. Although our proposed architecture consumes over 3x energy and area compared to IEEE Single Precision arithmetic, our results show that using the Posit number format for physical layer algorithms results in significant performance gain. We achieved over 15 dB and 25 dB gain for FFT and matrix multiplication algorithms. In addition, compared to state-of-the-art Posit arithmetic architecture, our proposed architecture resulted in over 2x speedup in operating frequency and 35% savings in energy consumption.
This paper addresses the problem of designing signals for general group representations subject to constraints which are formulated as convex sets in the Hilbert space of the group states. In particular, the paper con...
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This paper addresses the problem of designing signals for general group representations subject to constraints which are formulated as convex sets in the Hilbert space of the group states. In particular, the paper considers irreducible representations in an infinite dimensional Hilbert space and derives an iterative procedure for proceeding from an arbitrary element of the Hilbert space to a state of the group subject to a priori imposed constraints with closed convex range. As examples, the paper focusses on narrowband and wideband radar ambiguity synthesis.
This paper presents the application of the Linear Sequential Array (LSA) retiming approach, developed for conventional digit-recurrence algorithms, to on-line multiplication. The result is a modular and fast pipelined...
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This paper presents the application of the Linear Sequential Array (LSA) retiming approach, developed for conventional digit-recurrence algorithms, to on-line multiplication. The result is a modular and fast pipelined structure which due to a small constant fan-out and cycle time independent of precision is suitable for FPGA implementation. First we present the basics of on-line multiplication, and determine data dependencies according to the LSA design methodology. Based on these dependencies we redesign the traditional on-line multiplier to obtain the LSA structure. Since in DSP applications one of the multiplier operands is fixed for a long sequence of operations, we briefly present a parallel-serial multiplication unit that receives one of the operands in parallel and the other operand in Most-Significant-Digit-First format. Performance and area results are provided for the LSA on-line multiplier design and then compared with the conventional on-line design, using xilinx FPGAs as the target technology.
This conference proceedings contains 41 papers arranged in five sections. Section 1 reviews ongoing research in optical processing including recent results in optical correlator, optical computer photorefractive, and ...
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ISBN:
(纸本)0819403474
This conference proceedings contains 41 papers arranged in five sections. Section 1 reviews ongoing research in optical processing including recent results in optical correlator, optical computer photorefractive, and spatial light-modulating device research. Section 2 deals with new developments in filter design, particularly phase-only filters and experimental filter implementations. Section 3, covering optical processing is devoted primarily to new developments in 2D-optical signalprocessing systems for spectral analysis and range/doppler signalprocessing. Section 4 concerns acousto-optic spectrum analyzer systems and components. Section 5 deals with optical computing and presents various algorithms and architectures for computing.
Linear Algebra (i. e. , the algebra of vector spaces) provides widely used mathematical tools and concepts which are today considered for implementation in special computer architectures. It seems that so many signal ...
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ISBN:
(纸本)0892524669
Linear Algebra (i. e. , the algebra of vector spaces) provides widely used mathematical tools and concepts which are today considered for implementation in special computer architectures. It seems that so many signalprocessing problems can be expressed and, more importantly, implemented efficiently as a sequence of vector and matrix operations, that a signalprocessing system with a capability for high speed linear algebra is necessary if the more advancedsignalprocessingalgorithms are to be implemented to operate in real time. This paper supports the notion that linear algebra is a sound basis for important signalprocessing system implementations and suggests that multilinear algebra (i. e. , the algebra of vector, bivector, trivector, etc. spaces) offers an even broader set of signalprocessing tools. Examples and ideas from direction finding and time series analysis are discussed.
The well-known uncertainty principle is often invoked in signalprocessing. It is also often considered to have the same implications in signal analysis as does the uncertainty principle in quantum mechanics. The unce...
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ISBN:
(纸本)0819406945
The well-known uncertainty principle is often invoked in signalprocessing. It is also often considered to have the same implications in signal analysis as does the uncertainty principle in quantum mechanics. The uncertainty principle is often incorrectly interpreted to mean that one cannot locate the time-frequency coordinates of a signal with arbitrarily good precision, since, in quantum mechanics, one cannot determine the position and momentum of a particle with arbitrarily good precision. Renyi information of the third order is used to provide an information measure on time-frequency distributions. The results suggest that even though this new measure tracks time-bandwidth results for two Gabor log-ons separated in time and/or frequency, the information measure is more general and provides a quantitative assessment of the number of resolvable components in a time frequency representation. As such, the information measure may be useful as a tool in the design and evaluation of time-frequency distributions.
We introduce the use of multidimensional logarithmic number system (MDLNS) as a generalization of the classical 1-D logarithmic number system (LNS) and analyze its use in DSP applications. The major drawback of the LN...
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We introduce the use of multidimensional logarithmic number system (MDLNS) as a generalization of the classical 1-D logarithmic number system (LNS) and analyze its use in DSP applications. The major drawback of the LNS is the requirement to use very large ROM arrays in implementing the additions and subtraction and it limits its use to low-precision applications. MDLNS allows exponential reduction of the size of the ROMs used without affecting the speed of the computational process: moreover, the calculations over different bases and digits are completely independent, which makes this particular representation perfectly suitable for massively parallel DSP architectures. The use of more than one base has at least two extra advantages. Firstly, the proposed architecture allows us to obtain the final result straightforwardly in binary form, thus, there is no need of the exponential amplifier, used in the known LNS architectures. Secondly, the second base can be optimized in accordance to the specific digital filter characteristics. This leads to dramatic reduction of the exponents used and, consequently, to large area savings. We offer many examples showing the computational advantages of the proposed approach.
Current bilinear time-frequency representations apply a fixed kernel to smooth the Wigner distribution. However, the choice of a fixed kernel limits the class of signals that can be analyzed effectively. This paper pr...
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Current bilinear time-frequency representations apply a fixed kernel to smooth the Wigner distribution. However, the choice of a fixed kernel limits the class of signals that can be analyzed effectively. This paper presents optimality criteria for the design of signal-dependent kernels that suppress cross-components while passing as much auto-component energy as possible, irrespective of the form of the signal. A fast algorithm for the optimal kernel solution makes the procedure competitive computationally with fixed kernel methods. Examples demonstrate the superior performance of the optimal kernel for a frequency modulated signal.
Autocorrelation and spectra of linear random processes can be can be expressed in terms of cumulants and polyspectra, respectively. The insensitivity of the latter to additive Gaussian noise of unknown covariance, is ...
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Autocorrelation and spectra of linear random processes can be can be expressed in terms of cumulants and polyspectra, respectively. The insensitivity of the latter to additive Gaussian noise of unknown covariance, is exploited in this paper to develop spectral estimators of deterministic and linear non-Gaussian signals using polyspectra. In the time-domain, windowed projections of third-order cumulants are shown to yield consistent estimators of the autocorrelation sequence. Both batch and recursive algorithms are derived. In the frequency-domain, a Fourier-slice solution and a least-squares approach are described for performing spectral analysis through windowed bi-periodograms. Asymptotic variance expressions of the time- and frequency-domain estimators are also presented. Two-dimensional extensions are indicated, and potential applications are discussed. Simulations are provided to illustrate the performance of the proposed algorithms and compare them with conventional approaches.
The scale dependent wavelet transform can be augmented by a rotation dependent version as well as other generalizations. Tomographic analysis and line segment transforms are special cases of rotation dependent wavelet...
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The scale dependent wavelet transform can be augmented by a rotation dependent version as well as other generalizations. Tomographic analysis and line segment transforms are special cases of rotation dependent wavelet analysis. Other cases suggested by biological analogy9 are a rotation dependent edge segment transform (using edge segments rather than line segments) and a binocular rotation dependent wavelet transform that introduces depth information into the reconstructed image. Applications to robot vision and synthetic aperture radar appear particularly promising.
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