@inproceedings {430,
	title = {A parameterizable SIMD stream processor},
	booktitle = {18th Canadian Conference on Electrical and Computer Engineering},
	year = {2005},
	abstract = {<p>Stream processing is a data processing paradigm in which long sequences of homogeneous data records are passed through one or more computational kernels to produce sequences of processed output data. Applications that fit this model include polygon rendering (computer graphics), matrix multiplication (scientific computing), 2D convolution (media processing), and data encryption (security). Computers that exploit stream computations process data faster than conventional microcomputers because they utilize a memory system and an execution model that increases on-chip bandwidth and delivers high throughput. We have designed a general-purpose, parameterizable, SIMD stream processor that operates on IEEE single-precision floating point data. The system is implemented in VHDL, and consists of a configurable FPU, execution unit array, and memory interface. The FPU supports pipelined operations for multiplication, addition, division, and square root. The data width is configurable. The execution array operates in lock-step with an instruction controller, which issues 32-bit instructions to the execution array. To exploit stream parallelism, the number of execution units as well as the number of interleaved threads is specified as a parameter at compilation time. The memory system allows all execution units to access one element of data from memory in every clock cycle. All memory accesses also pass through a routing network to support conditional reads and writes of stream data. Functional and timing simulations have been performed using a variety of benchmark programs. The system has also been synthesized into an Altera FPGA to verify resource utilization</p>
},
	keywords = {2D convolution, Altera FPGA, clock cycle, Computer Graphics, configurable FPU, data encryption, data processing, execution unit array, floating point arithmetic, hardware description languages, IEEE single-precision floating point data, instruction controller, matrix multiplication, media processing, memory interface, memory system, on-chip bandwidth, parallel processing, polygon rendering, resource utilization, routing network, scientific computing, security, SIMD, stream processing, VHDL},
	doi = {http://dx.doi.org/10.1109/CCECE.2005.1557051},
	author = {A Munshi and W Bishop and A Wong and S Braganza and A Clinton and M McCool}
}
@article {418,
	title = {Statistical processing of large image sequences},
	journal = {IEEE Transactions on Image Processing},
	volume = {14},
	year = {2005},
	pages = {80 - 93},
	abstract = {<p>The dynamic estimation of large-scale stochastic image sequences, as frequently encountered in remote sensing, is important in a variety of scientific applications. However, the size of such images makes conventional dynamic estimation methods, for example, the Kalman and related filters, impractical. We present an approach that emulates the Kalman filter, but with considerably reduced computational and storage requirements. Our approach is illustrated in the context of a 512 times; 512 image sequence of ocean surface temperature. The static estimation step, the primary contribution here, uses a mixture of stationary models to accurately mimic the effect of a nonstationary prior, simplifying both computational complexity and modeling. Our approach provides an efficient, stable, positive-definite model which is consistent with the given correlation structure. Thus, the methods of this paper may find application in modeling and single-frame estimation.</p>
},
	keywords = {Algorithms, Artificial Intelligence, Automated, Biological, Cluster Analysis, computational complexity, Computer Graphics, Computer Simulation, Computer-Assisted, correlation methods, correlation structure, dynamic estimation method, image enhancement, image interpretation, image sequences, Imaging, Information Storage and Retrieval, Kalman filter, Kalman filters, large-scale stochastic image sequence estimation, Models, Movement, Numerical Analysis, ocean surface temperature, Pattern Recognition, Remote Sensing, Reproducibility of Results, Sensitivity and Specificity, Signal Processing, single-frame estimation, static estimation step, Statistical, statistical analysis, statistical image processing, stochastic processes, Subtraction Technique, Three-Dimensional, Video Recording},
	issn = {1057-7149},
	doi = {http://dx.doi.org/10.1109/TIP.2004.838703},
	author = {F M. Khellah and P Fieguth and M J. Murray and M R. Allen}
}
