A technique for fabricating novel infrared (IR) lenses can enable a reduction in the size and weight of IR imaging optics through the use of layered glass structures. These structures can range from having a few thick glass layers, mimicking cemented doublets and triplets, to having many thin glass layers approximating graded index (GRIN) lenses. The effectiveness of these structures relies on having materials with diversity in refractive index (large Delta n) and dispersion and similar thermo-viscous behavior (common glass transition temperature, Delta Tg = 10 degrees C). A library of 13 chalcogenide glasses with broad IR transmission (NIR through LWIR bands) was developed to satisfy these criteria. The lens fabrication methodology, including glass design and synthesis, sheet fabrication, preform making, lens molding and surface finishing are presented

A double-base number system (DBNS) has recently been introduced and investigated [1] [2] [3]. This system has been shown to have some interesting and potentially far-reaching applications in digital filtering, encryption, digital electronics, and image enhancement. In this paper we present a new concept of generating parametric number representations by fusing systems such as DBNS using multiplication and addition operations. We introduce Fibonacci like (p,q)-sequences and determine their efficiency in representing data. We develop an algorithm to test the sparsity of fused number representation systems and explore the dual relationship between sparsity and memory. We also consider the applications of these representations in data compression and barcoding. Simulation results are presented to demonstrate the performance of the new class of systems. A comparison with commonly used doublebase number systems is also presented.

A multi-channel, agile, computationally enhanced camera based on the PANOPTES architecture is presented. Details of camera operational concepts are outlined. Preliminary image acquisition results and an example of super-resolution enhancement of captured data are given.

Counting classes are classes of languages defined in terms of the number of accepting computations of non-deterministic polynomial-time Turing machines. Well known examples of counting classes are NP, co-NP, ⊕P, and PP. Every counting class consists of languages in P#P[1], the class of languages computable in polynomial time using a single call to an oracle capable of determining the number of accepting paths of an NP machine. We perform an in-depth investigation of counting classes defined in terms of thresholds and moduli. We show that the computational power of a threshold machine is a monotone function of the threshold. Then we show that the class MODZkP is at least as large as FewP. Finally, we improve a result of Cai and Hemachandra by showing that recognizing languages in the class Few is as easy as distinguishing uniquely satisfiable formulas from unsatisfiable formulas (or detecting unique solutions, as in [21]).

We propose the first single bound that supersedes Gallager's (1978) upper bound on the redundancy of binary Huffman codes with the given largest source symbol probability ranging from 0 to 0.5. We define the linear logarithm and linear logarithm entropy. We find the maximal difference between the linear and the ordinary logarithms. We prove that the “redundancy” of a binary Huffmann code with respect to the linear logarithm entropy is no more than the largest source symbol probability. We therefore establish a better upper bound than Gallager's on the redundancy of binary Huffman codes

We prove that the maximum data expansion of Huffman coding is at most 0.83485 bits per symbol, improving on the previous best known bound of 1.268 bits per symbol. The bound is very close to the 0.8 bits per symbol conjectured by Cheng et al. (1995)

We investigate a hidden Markov channel model which is a special case of Fritchman (1967) model with attractive features. We obtain a closed form of the channel capacity in terms of the transition matrix coefficients, derive statistics of the channel, discuss how to evaluate the block error control code performance, with or without interleaver, based on the channel model. We also show how to parameterize the model parameters efficiently. The complexity of parameterizing the model grows only linearly with the number of parameters. This model is promising in modeling bursty communication channels and storage channels

Abstract MIPS is a new single chip VLSIprocessor,architecture. it attempts to achievehigh,performance,with the use of a simplified instruction set, similar to those found in microengines. The processor is a fast pipelined engine without pipeline interlocks. Software solutions to severaltraditional hardware problems, such as providing pipeline interlocks, are used. . Key Words and Phrases: Instruction set design, VLSI, computer architecture, pipelining,

MIPS is a new single chip VLSI microprocessor. It attempts to achieve high performance with the use of a simplified instruction set, similar to those found in microengines. The processor is a fast pipelined engine without pipeline interlocks. Software solutions to several traditional hardware problems, such as providing pipeline interlocks, are used.

Most new computer architectures are concerned with maximizing performance by providing suitable instruction sets for compiled code and providing support for systems functions. We argue that the most effective design methodology must make simultaneous tradeoffs across all three areas: hardware, software support, and systems support. Recent trends lean towards extensive hardware support for both the compiler and operating systems software. However, consideration of all possible design tradeoffs may often lead to less hardware support. Several examples of this approach are presented, including: omission of condition codes, word-addressed machines, and imposing pipeline interlocks in software. The specifics and performance of these approaches are examined with respect to the MIPS processor.

Let A be a language chosen randomly by tossing a fair coin for each string x to determine whether x belongs to A . With probability 1, each of the relativized classes ${\textbf{LOGSPACE}}^A $, ${\bf P}^A $, ${\bf NP}^A $, ${\bf PP}^A $, and ${\textbf{PSPACE}}^A $ is properly contained in the next. Also, ${\bf NP}^A \ne {\text{co-}} {\bf NP}^A $ with probability 1. By contrast, with probability 1 the class ${\bf P}^A $ coincides with the class ${\bf BPP}^A $ of languages recognized by probabilistic oracle machines with error probability uniformly bounded below $\tfrac{1}{2}$. ${\bf NP}^A $ is shown, with probability 1, to contain a ${\bf P}^A $-immune set, i.e., a set having no infinite subset in ${\bf P}^A $. The relationship of ${\bf P}^A $-immunity to p -sparseness and ${\bf NP}^A $-completeness is briefly discussed: ${\bf P}^A $-immune sets in ${\bf NP}^A $ can be sparse or moderately dense, but not co-sparse. Relativization with respect to a random length-preserving permutation $\pi $, instead of a random oracle A , yields analogous results and in addition the proper containment, with probability 1, of ${\bf P}^\pi $ in ${\bf NP}^\pi \cap {\text{co-}}{\bf NP}^\pi $, which we have been unable to decide for a simple random oracle. Most of these results are shown by straightforward counting arguments, applied to oracle-dependent languages designed not to be recognizable without a large number of oracle calls. It is conjectured that all $p^A $-invariant statements that are true with probability 1 of subrecursive language classes uniformly relativized to a random oracle are also true in the unrelativized case.

Let A be a language chosen randomly by tossing a fair coin for each string x to determine whether x belongs to A. With probability 1, each of the relativized classes ${\textbf{LOGSPACE}}^A $, ${\bf P}^A $, ${\bf NP}^A $, ${\bf PP}^A $, and ${\textbf{PSPACE}}^A $ is properly contained in the next. Also, ${\bf NP}^A \ne {\text{co-}} {\bf NP}^A $ with probability 1. By contrast, with probability 1 the class ${\bf P}^A $ coincides with the class ${\bf BPP}^A $ of languages recognized by probabilistic oracle machines with error probability uniformly bounded below $\tfrac{1}{2}$. ${\bf NP}^A $ is shown, with probability 1, to contain a ${\bf P}^A $-immune set, i.e., a set having no infinite subset in ${\bf P}^A $. The relationship of ${\bf P}^A $-immunity to p-sparseness and ${\bf NP}^A $-completeness is briefly discussed: ${\bf P}^A $-immune sets in ${\bf NP}^A $ can be sparse or moderately dense, but not co-sparse. Relativization with respect to a random length-preserving permutation $\pi $, instead of a random oracle A, yields analogous results and in addition the proper containment, with probability 1, of ${\bf P}^\pi $ in ${\bf NP}^\pi \cap {\text{co-}}{\bf NP}^\pi $, which we have been unable to decide for a simple random oracle. Most of these results are shown by straightforward counting arguments, applied to oracle-dependent languages designed not to be recognizable without a large number of oracle calls. It is conjectured that all $p^A $-invariant statements that are true with probability 1 of subrecursive language classes uniformly relativized to a random oracle are also true in the unrelativized case.

Let A be a language chosen randomly by tossing a fair coin for each string x to determine whether x belongs to A. With probability 1, each of the relativized classes ${\textbf{LOGSPACE}}^A $, ${\bf P}^A $, ${\bf NP}^A $, ${\bf PP}^A $, and ${\textbf{PSPACE}}^A $ is properly contained in the next. Also, ${\bf NP}^A \ne {\text{co-}} {\bf NP}^A $ with probability 1. By contrast, with probability 1 the class ${\bf P}^A $ coincides with the class ${\bf BPP}^A $ of languages recognized by probabilistic oracle machines with error probability uniformly bounded below $\tfrac{1}{2}$. ${\bf NP}^A $ is shown, with probability 1, to contain a ${\bf P}^A $-immune set, i.e., a set having no infinite subset in ${\bf P}^A $. The relationship of ${\bf P}^A $-immunity to p-sparseness and ${\bf NP}^A $-completeness is briefly discussed: ${\bf P}^A $-immune sets in ${\bf NP}^A $ can be sparse or moderately dense, but not co-sparse. Relativization with respect to a random length-preserving permutation $\pi $, instead of a rand...

The tape requirements of probabilistic and deterministic Turing machine transducers are polynomially related.

A probabilistic Turing machine is a Turing machine with the ability to make decisions based on the outcomes of unbiased coin tosses. The partial function computed by a probabilistic machine is defined by assigning to each input the output which occurs with probability greater than $\frac{1}{2}$. With this definition, only partial recursive functions are probabilistically computable. The run time and tape of probabilistic machines are defined. A palindrome-like language is described that can be recognized faster by one-tape probabilistic Turing machines than by one-tape deterministic Turing machines. It is shown that every nondeterministic machine can be simulated in the same space by a probabilistic machine with small error probability. Several classes of languages recognized probabilistically in polynomial time are defined and compared with $NP$.