计量经济分析 = Econometric analysis

In general, Micropower Impulse Radar (MIR) depends on Ultra-Wideband (UWB) transmission systems. UWB technology can supply innovative new systems and products that have an obvious value for radar and communications uses. Important applications include bridge-deck inspection systems, ground penetrating radar, mine detection, and precise distance resolution for such things as liquid level measurement. Most of these UWB inspection and measurement methods have some unique qualities, which need to be pursued. Therefore, in considering changes to Part 15 the FCC needs to take into account the unique features of UWB technology. MIR is applicable to two general types of UWB systems: radar systems and communications systems. Currently LLNL and its licensees are focusing on radar or radar type systems. LLNL is evaluating MIR for specialized communication systems. MIR is a relatively low power technology. Therefore, MIR systems seem to have a low potential for causing harmful interference to other users of the spectrum since the transmitted signal is spread over a wide bandwidth, which results in a relatively low spectral power density.

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Response to FCC 98-208 notice of inquiry in the matter of revision of part 15 of the commission's rules regarding ultra-wideband transmission systems

This chapter contains sections titled: Introduction Overview of Multicarrier CDMA Systems Channel Model Performance of MC-CDMA System Performance of Overlapping Multicarrier DS-CDMA Systems Performance of Multicarrier DS-CDMA-I Systems Performance of AMC DS-CDMA Systems Performance of SFH/MC DS-CDMA Systems Chapter Summary and Conclusion 
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Overview of Multicarrier CDMA

Optimal State Estimation

Among algorithms for comparing the areas under two or more correlated receiver operating characteristic (ROC) curves, DeLong's algorithm is perhaps the most widely used one due to its simplicity of implementation in practice. Unfortunately, however, the time complexity of DeLong's algorithm is of quadratic order (the product of sample sizes), thus making it time-consuming and impractical when the sample sizes are large. Based on an equivalent relationship between the Heaviside function and mid-ranks of samples, we improve DeLong's algorithm by reducing the order of time complexity from quadratic down to linearithmic (the product of sample size and its logarithm). Monte Carlo simulations verify the computational efficiency of our algorithmic findings in this work.

Fast Implementation of DeLong’s Algorithm for Comparing the Areas Under Correlated Receiver Operating Characteristic Curves

In this paper, reverse converters for two recently proposed four-moduli sets {2n - 1,2n,2n + 1,2n+1 - 1} and {2n - 1, 2n, 2n + 1, 2n+1 + 1} are described. The reverse conversion in the three-moduli set {2n - 1,2n,2n + 1} has been optimized in literature. Hence, the proposed converters are based on two new moduli sets {(2n(22n-1)),2n+1-1} and {(2n(22n-1)), 2n+1+1} and use mixed radix conversion. The resulting designs do not require any ROM. Both are similar in their architecture except that the converter for the moduli set {2n - 1, 2n, 2n + 1, 2n+1 + 1} is slightly complicated due to the difficulty in performing reduction modulo (2n+1+1) as compared with modulo (2n+1-1). The proposed conversion techniques are compared with earlier realizations described in literature with regard to conversion time as well as area requirements.

RNS-to-Binary Converters for Two Four-Moduli Sets $\{2^{n}-1,2^{n},2^{n}+1,2^{{n}+1}-1\}$ and $\{2^{n}-1,2^{n},2^{n}+1,2^{{n}+1}+1\}$

This paper considers the problem of tracking multiple acoustic sources using a single acoustic vector sensor (AVS). Firstly, a particle filtering (PF) approach is developed to track the direction of arrivals of fixed and known number of sources. Secondly, a more realistic tracking scenario which assumes that the number of acoustic sources is unknown and time-varying is considered. A random finite set (RFS) framework is employed to characterize the randomness of the state process, i.e., the dynamics of source motion and the number of active sources, as well as the measurement process. As deriving a closed-form solution for the multi-source probability density is difficult, a particle filtering approach is employed to arrive at a computationally tractable approximation of the RFS densities. The proposed RFS-PF algorithm is able to simultaneously detect and track multiple sources. Simulations under different tracking scenarios demonstrate the ability of the proposed approaches in tracking multiple acoustic sources.

Particle Filtering Approaches for Multiple Acoustic Source Detection and 2-D Direction of Arrival Estimation Using a Single Acoustic Vector Sensor

A residue number system to binary converter that converts numbers in the moduli set 2n+1,2n,2n-1 is described. This moduli set is an extension of the more popular set, 2/sup n/+1, 2/sup n/, 2/sup n/-1. A low-complexity implementation using some properties of modular arithmetic is proposed. The converter does not use any explicit modulo operation in the evaluation as is normally done in the Chinese remainder theorem. >

An RNS to binary converter in 2n+1, 2n, 2n-1 moduli set

The vertical common subexpression elimination (CSE) method proposed by Jang et al. does not guarantee hardware reduction over the conventional horizontal CSE method in practical linear phase finite impulse response (LPFIR) filter implementations. A method to implement FIR filters with a minimum number of adders by efficiently combining horizontal and vertical common subexpressions is proposed.

FIR filter implementation by efficient sharing of horizontal and vertical common subexpressions

NonGaussian impulsive noises distort the source signal and cause problems for direction of arrival (DOA) estimation of an acoustic source. In this paper, a Bayesian framework and its particle filtering (PF) implementation for DOA tracking in the presence of complex symmetric alpha-stable noise process are developed. A constant velocity model is employed to model the source dynamics, and spatial spectra are exploited to formulate a pseudo likelihood of particles. Since the second-order statistics of alpha-stable processes do not exist, the fractional lower order moment matrix of the received data is used to replace the covariance matrix in calculating the spatial spectra. The noise usually spreads and distorts the mainlobe of the likelihood function and the particles cannot be weighted accurately. Hence, the likelihood function is exponentially weighted to emphasize the particles in a high likelihood area and thus enhance the resampling efficiency. The performance of the proposed tracking algorithm is extensively studied under simulated alpha-stable noise environments. The results show that the proposed algorithm significantly outperforms the existing PF tracking approach and the traditional localization approaches in DOA estimation.

A.B. Premkumar

Papers

RNS-to-Binary Converters for Two Four-Moduli Sets $\{2^{n}-1,2^{n},2^{n}+1,2^{{n}+1}-1\}$ and $\{2^{n}-1,2^{n},2^{n}+1,2^{{n}+1}+1\}$

Particle Filtering Approaches for Multiple Acoustic Source Detection and 2-D Direction of Arrival Estimation Using a Single Acoustic Vector Sensor

An RNS to binary converter in 2n+1, 2n, 2n-1 moduli set

FIR filter implementation by efficient sharing of horizontal and vertical common subexpressions

Particle Filtering for Acoustic Source Tracking in Impulsive Noise With Alpha-Stable Process