We consider the design of channel codes for improving the data rate and/or the reliability of communications over fading channels using multiple transmit antennas. Data is encoded by a channel code and the encoded data is split into n streams that are simultaneously transmitted using n transmit

to an address outside its fault domain. Both these software operations are portable and programming language independent. Our approach poses a tradeoff relative to hardware fault isolation: substantially faster communication between fault domains, at a cost of slightly increased execution time for distrusted

diversity-multiplexing tradeoff as achieved by more complex protocols, where coordination and distributed space–time coding for relay nodes is required, such as those proposed by Laneman and Wornell (2003). The simplicity of the technique allows for immediate implementation in existing radio hardware

. This leads to interesting visual tradeoffs in time and space, and to new video applications. These include: (i) treatment of spatial artifacts (e.g., motionblur) by increasing the temporal resolution, and (ii) combination of input sequences of different space-time resolutions (e.g., NTSC, PAL, and even high

time O(log(n/ε)). We show that there is a single approach to this fundamental problem, which both improves upon existing results and spans the spectrum of space-time tradeoffs. Given a tradeoff parameter γ, where 2 ≤ γ ≤ 1/ε, we show that there exists a data structure of space O(nγ d−1 log(1/ε

∗ (4 n) and polynomial space, or by a combination of the two in time O ∗ (4 n 2 −s) and space O ∗ (2 s) for s = n, n/2, n/4,.... Here, we show that one can improve the tradeoff to time O ∗ (T n) and space O ∗ (S n) with T S < 4 at any √ 2 < S < 2. The idea is to find a small family of “thin

instance) gives good results. Our preprocessing and side computation costs are the same as for AESA. We propose several improvements, achieving e.g. O(n 1+α) construction cost for some 0 < α < 1, and a variant using even less space. 1

Reducing instruction memory requirements by improving code density using compression techniques has been the aim of much recent work on embedded devices. Previous work has been successful in improving compression ratios with modest decompression overhead for general purpose RISC architectures

Abstract—GPUs offer drastically different performance characteristics compared to traditional multicore architectures. To explore the tradeoffs exposed by this difference, we refactor MUMmer, a widely-used, highly-engineered bioinformatics application which has both CPU- and GPU

Abstract. The technique of Schroeppel and Shamir (SICOMP, 1981) has long been the most efficient way to trade space against time for the Subset Sum problem. In the random-instance setting, however, improved tradeoffs exist. In particular, the recently discovered dissection method of Dinur et al