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Jensen, J. M.

Publications and source records attributed to Jensen, J. M..

Deep-Focusing Time-Distance Helioseismology

Much progress has been made by measuring the travel times of solar acoustic waves from a central surface location to points at equal arc distance away. Depth information is obtained from the range of arc distances examined, with the larger distances revealing the deeper layers. This method we will call surface-focusing, as the common point, or focus, is at the surface. To obtain a clearer picture of the subsurface region, it would, no doubt, be better to focus on points below the surface. Our first attempt to do this used the ray theory to pick surface location pairs that would focus on a particular subsurface point. This is not the ideal procedure, as Born approximation kernels suggest that this focus should have zero sensitivity to sound speed inhomogeneities. However, the sensitivity is concentrated below the surface in a much better way than the old surface-focusing method, and so we expect the deep-focusing method to be more sensitive. A large sunspot group was studied by both methods. Inversions based on both methods will be compared.

Duvall, T. L., Jr.↗

The VLSI design of an error-trellis syndrome decoder for certain convolutional codes

A recursive algorithm using the error-trellis decoding technique is developed to decode convolutional codes (CCs). An example, illustrating the very large scale integration (VLSI) architecture of such a decode, is given for a dual-K CC. It is demonstrated that such a decoder can be realized readily on a single chip with metal-nitride-oxide-semiconductor technology.

Reed, I. S.↗

The VLSI design of error-trellis syndrome decoding for convolutional codes

A recursive algorithm using the error-trellis decoding technique is developed to decode convolutional codes (CCs). An example, illustrating the very large scale integration (VLSI) architecture of such a decode, is given for a dual-K CC. It is demonstrated that such a decoder can be realized readily on a single chip with metal-nitride-oxide-semiconductor technology.

Reed, I. S.↗