A new extension to the way in which the Bidirectional Associative Memory (BAM) algorithms are implemented is
presented here. We will show that by utilizing the singular value decomposition (SVD) and integrating principles of
independent component analysis (ICA) into the nullspace (NS) we have created a novel approach to mitigating
spurious attractors. We demonstrate this with two applications. The first application utilizes a one-layer
association while the second application is modeled after the several hierarchal associations of ventral pathways.
The first application will detail the way in which we manage the associations in terms of matrices. The second
application will take what we have learned from the first example and apply it to a cascade of a convolutional
neural network (CNN) and perceptron this being our signal processing model of the ventral pathways, i.e., visual
A description of the design parameters for a scaled RF environment is presented. This scaled RF environment was developed for purposes of simulating and investigating multipath phenomena in urban environments. A number of experiments were conducted with this scaled urban environment including a series of tests with eight spatially distributed receivers and one transmitter. Details with regard to the instrumentation system along with the measurement philosophy are provided. The primary focus of this paper is a detailed treatment of data analysis and exploitation techniques for the multipath data generated by this scaled RF environment. A portion of the material on multipath data analysis and exploitation is focused on developing techniques for identifying a optimum placement of receiver pairs for purposes of maximizing information content on a embedded target. In other words, data from the eight distributed receiver locations are analyzed and techniques are presented that allow for the selection of receiver pairs that provide the most information on targets that are embedded within the multipath environment. The last section of the paper discusses visualization and pseudo-imaging techniques for targets embedded in multipath environments.
This paper addresses the problem of phase cycle-slip (FM click noise) elimination. For analyses and application demonstration, the signal of interest is a commercial FM transmission, received and sampled for subsequent demodulation as typical in software defined radios. There are two parts to this paper. The first part will investigate the advantages of using data fitting to repair the time series in the neighbourhood of a detected click. Previous papers have considered time series which have neighbourhoods in which only one point was considered as a click, and hence, only one point needed to be repaired. We will consider the more difficult and practical case where there is a frequency modulated signal passed through a band-pass filter and a (software/digital) FM limiter discriminator used for demodulation. This receive system has the effect of causing click distortion over multiple samples, making the repairing that much more difficult. The methods of forward- backward linear prediction (FBLP or Wiener filtering), least squares polynomial fitting (LSPOLY), and twin Tukey window (TTW) filtering are discussed. The results are shown empirically, and will show that the TTW technique outperforms the FBLP and LSPOLY techniques for the presented application.
The second part of this paper will discuss potential techniques to discern samples which are clicks, from samples which are normal yet click-like. We will consider the combination of autocorrelation, kurtosis, 4th order moment, and spectral characteristics, to form a threshold detection level to identify clicks.
This paper extends simulation and target detection results from an investigation entitled "Self-Training Algorithms for Ultra-wideband SAR Target Detection" that was conducted last year and presented at the 2003 SPIE Aerosense Conference on "Algorithms for Synthetic Aperture Radar Imagery." Under this approach, simulated SAR impulse clutter data was generated by modulating a tophat model for the SAR video phase history with K-distributed data models. Targets were synthesized and "instanced" within the SAR image via the application of a dihedral model to represent broadside targets. For this paper, these models are extended and generalized by developing a set of models that approximate major scattering mechanisms due to terrain relief and approximate major scattering mechanisms due to scattering from off-angle targets. Off-angle targets are difficult to detect at typical ultra-wideband radar frequencies and are denoted as "diffuse scatterers." Potential approaches for detecting synthetic off-angle targets that demonstrate this type of "diffuse scattering" are developed and described in the algorithms and results section of the paper. A preliminary set of analysis outputs are presented with synthetic data from the resulting simulation testbed.