Letter to nephew, 11/14/1881

Three page handwritten letter to nephew, Daniel Avery Whedon, from Daniel D. Whedon. Dated 11/14/1881.

Letter to nephew, 01/13/1868

January 13 1868 letter from Daniel A. Whedon to nephew.

Letter to nephew, 12/11/1868

Letter from Daniel Whedon to nephew, Daneil Avery Whedon. Dated December 11 1868.

Letter to nephew, June 7, 1881

Handwritten letter dated June 7, 1881, to nephew, Daniel Avery Whedon.

Letter to nephew, 10/10/1867

Daniel Avery Whedon's four page letter to nephew, dated October 10, 1867.

Letter to Daniel A. Whedon. New York. April 13, 1867

Handwritten 1867 letter from Daniel D. Whedon to his nephew, Daniel A. Whedon, requesting books.

Letter to Daniel Avery Whedon. Brooklyn, NY. Nov. 22, 1865.

Handwritten 1865 handwritten letter from Daniel D. Whedon to Daniel A. Whedon, his nephew, regarding slavery in relation to the Church as well as the Christian Union.

Letter to D. A. Whedon. Pittsfield. July 12, 1844.

Letter from Daniel Denison Whedon to Daniel A. Whedon, his nephew.

Letter to nephew, August 9 1867

Hanwritten letter from Daniel Denison Whedon to nephew. Dated 08/09/1867.

Letter to nephew, 02/06/1868

Letter from Daniel Whedon to nephew, Daniel Avery Whedon. Dated February, 6, 1868.

Letter to V.V. Haynes, 04/02/1878

Two page letter from Daniel D. Whedon to V.V. Haynes. Dated March 2, 1878.

Letter to Gershom Cox, dated January 29, 1844

This is a letter from Phoebe Palmer and her husband written on January 29, 1844 to Gershom Cox.

Automated Placement of Cameras in a Floorplan to Satisfy Task-Specific Constraints

In many multi-camera vision systems the effect of camera locations on the task-specific quality of service is ignored. Researchers in Computational Geometry have proposed elegant solutions for some sensor location problem classes. Unfortunately, these solutions utilize unrealistic assumptions about the cameras' capabilities that make these algorithms unsuitable for many real-world computer vision applications: unlimited field of view, infinite depth of field, and/or infinite servo precision and speed. In this paper, the general camera placement problem is first defined with assumptions that are more consistent with the capabilities of real-world cameras. The region to be observed by cameras may be volumetric, static or dynamic, and may include holes that are caused, for instance, by columns or furniture in a room that can occlude potential camera views. A subclass of this general problem can be formulated in terms of planar regions that are typical of building floorplans. Given a floorplan to be observed, the problem is then to efficiently compute a camera layout such that certain task-specific constraints are met. A solution to this problem is obtained via binary optimization over a discrete problem space. In preliminary experiments the performance of the resulting system is demonstrated with different real floorplans.

Automated Camera Layout to Satisfy Task-Specific and Floorplan-Specific Coverage Requirements

In many multi-camera vision systems the effect of camera locations on the task-specific quality of service is ignored. Researchers in Computational Geometry have proposed elegant solutions for some sensor location problem classes. Unfortunately, these solutions utilize unrealistic assumptions about the cameras' capabilities that make these algorithms unsuitable for many real-world computer vision applications: unlimited field of view, infinite depth of field, and/or infinite servo precision and speed. In this paper, the general camera placement problem is first defined with assumptions that are more consistent with the capabilities of real-world cameras. The region to be observed by cameras may be volumetric, static or dynamic, and may include holes that are caused, for instance, by columns or furniture in a room that can occlude potential camera views. A subclass of this general problem can be formulated in terms of planar regions that are typical of building floorplans. Given a floorplan to be observed, the problem is then to efficiently compute a camera layout such that certain task-specific constraints are met. A solution to this problem is obtained via binary optimization over a discrete problem space. In experiments the performance of the resulting system is demonstrated with different real floorplans.

Task Assignment in a Distributed System: Improving Performance by Unbalancing Load

We consider the problem of task assignment in a distributed system (such as a distributed Web server) in which task sizes are drawn from a heavy-tailed distribution. Many task assignment algorithms are based on the heuristic that balancing the load at the server hosts will result in optimal performance. We show this conventional wisdom is less true when the task size distribution is heavy-tailed (as is the case for Web file sizes). We introduce a new task assignment policy, called Size Interval Task Assignment with Variable Load (SITA-V). SITA-V purposely operates the server hosts at different loads, and directs smaller tasks to the lighter-loaded hosts. The result is that SITA-V provably decreases the mean task slowdown by significant factors (up to 1000 or more) where the more heavy-tailed the workload, the greater the improvement factor. We evaluate the tradeoff between improvement in slowdown and increase in waiting time in a system using SITA-V, and show conditions under which SITA-V represents a particularly appealing policy. We conclude with a discussion of the use of SITA-V in a distributed Web server, and show that it is attractive because it has a simple implementation which requires no communication from the server hosts back to the task router.