The Sensor-Angle Distribution (SAD) is a recently introduced tool representing the power arriving at each sensor as a function of angle (or spatial frequency). It can be used to characterize near-field scatter environ...
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ISBN:
(纸本)0819445584
The Sensor-Angle Distribution (SAD) is a recently introduced tool representing the power arriving at each sensor as a function of angle (or spatial frequency). It can be used to characterize near-field scatter environments. The SAD, as originally introduced, under-sampled the spatial correlation of the received signal (measured at each sensor) causing the SAD to be aliased for common source location cases. In this paper we indicate how this may be overcome. Additional results are provided showing that the SAD may be implemented as a multiple weighted subarray beamformer.
Multiple input multiple output (MIMO) communications have been a hot research area in recent years. Most literature makes the assumption that the channel information is not known at the transmitter but known perfectly...
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ISBN:
(纸本)0819445584
Multiple input multiple output (MIMO) communications have been a hot research area in recent years. Most literature makes the assumption that the channel information is not known at the transmitter but known perfectly at the receiver. We focus on the situation where both the transmitter and the receiver know the channel information. We consider a transmit diversity scheme that maximizes the signal to noise ratio at the receiver. We analyze its performance in terms of capacity, duality and asymptotic behavior. By simulation, we compare this scheme with Alamouti's transmit diversity to show the advantage of utilizing the channel side information to improve the performance of the wireless systems.
In this paper, we present implementations of a pattern recognition algorithm which uses a RBF (Radial Basis Function) neural network. Our aim is to elaborate a quite efficient system which realizes real time faces tra...
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ISBN:
(纸本)0819445584
In this paper, we present implementations of a pattern recognition algorithm which uses a RBF (Radial Basis Function) neural network. Our aim is to elaborate a quite efficient system which realizes real time faces tracking and identity verification in natural video sequences. Hardware implementations have been realized on an embedded system developed by our laboratory. This system is based on a DSP (Digital signal Processor) TMS320C6x. The optimization of implementations allow us to obtain a processing speed of 4.8 images (240 x 320 pixels) per second with a correct rate of 95% of faces tracking and identity verification.
A new feature set and decision function are proposed for classifying transient wandering-tone signals. signals are partitioned in time and modeled as having piecewise-linear instantaneous frequency and piecewise-const...
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ISBN:
(纸本)0819445584
A new feature set and decision function are proposed for classifying transient wandering-tone signals. signals are partitioned in time and modeled as having piecewise-linear instantaneous frequency and piecewise-constant amplitude. The initial frequency, chirp rate, and amplitude are estimated in each segment. The resulting sequences of estimates are used as features for classification. The decision function employs a linear Gaussian dynamical model, or hidden Gauss-Markov model (HGMM). The parameters that characterize the HGMM for each class are estimated from labeled training sequences, and the trained models are used to evaluate the class-conditional likelihoods of an unlabeled signal. The signal is assigned to the-class whose model gives the maximum conditional likelihood. Simulation experiments demonstrate perfect classification performance in a three-class forced-choice problem.
Time-frequency distributions (TFDs) of Cohen's class often dramatically reveal complex structures that are not evident in the raw signal. Standard linear filters are often not able to separate the underlying signa...
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ISBN:
(纸本)0819445584
Time-frequency distributions (TFDs) of Cohen's class often dramatically reveal complex structures that are not evident in the raw signal. Standard linear filters are often not able to separate the underlying signal from background clutter and noise. The essense of the signal can often be extracted from the TFD by evaluating strategic slices through the TFD for a series of frequencies. However, TFDs are often computationally intense compared to other methods. This paper demonstrates that quadratic filters may be designed to capture the same information as is available in the specific slices through the TFD at a considerably lower computational cost. The outputs of these filters can be combined to provide a robust impulse-like response to the chosen signal. This is particularly useful when the exact time series representation of the signal is unknown, due to variations and background clutter and noise. It is also noted that Teager's method is closely related to TFDs and are an example of a quadratic filter. Results using an ideal matched filter and the TFD motivated quadratic filter are compared to give insight into their relative responses.
This paper presents a general FIR filter architecture utilizing truncated tree multipliers for computation. The average error, maximum error, and variance of error due to truncation are derived for the proposed archit...
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ISBN:
(纸本)0819445584
This paper presents a general FIR filter architecture utilizing truncated tree multipliers for computation. The average error, maximum error, and variance of error due to truncation are derived for the proposed architecture. A novel technique that reduces the average error of the filter is presented, along with equations for computing the signal-to-noise ratio of the truncation error. A software tool written in Java is described that automatically generates structural VHDL models for specific filters based on this architecture, given parameters such as the number of taps, operand lengths, number of multipliers, and number of truncated columns. We show that a 22.5 % reduction in area can be achieved for a 24-tap filter with 16-bit operands, 4 parallel multipliers, and 12 truncated columns. For this implementation, the average reduction error is only 9.18 x 10(-5) ulps, and the reduction error SNR is only 2.4 dB less than the roundoff SNR of an equivalent filter without truncation.
In this paper, a new wave front sensor design that utilizes the benefits of image projections is described and analyzed. The projection-based wave front sensor is similar to a Shack-Hartman type wave front sensor, but...
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ISBN:
(纸本)0819445584
In this paper, a new wave front sensor design that utilizes the benefits of image projections is described and analyzed. The projection-based wave front sensor is similar to a Shack-Hartman type wave front sensor, but uses a correlation algorithm as opposed to a centroiding algorithm to estimate optical tilt. This allows the projection-based wave front sensor to estimate optical tilt parameters while guiding off of point sources and extended objects at very low signal to noise ratios. The implementation of the projection-based wave front sensor is described in detail showings important signalprocessing steps on and off of the focal plane array of the sensor. In this paper the design is tested in simulation for speed and accuracy by processing simulated astro-nomical data. These simulations demonstrate the accuracy of the projection-based wave front sensor and its superior performance to that of the traditional Shack-Hartman wave front sensor. Timing analysis is presented which shows how the collection and processing of image projections is computationally efficient and lends itself to a wave front sensor design that can produce adaptive optical control signals at speeds of up to 500 hz.
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