Concrete is the most widely used engineered material in the world with more than 10 billion tons produced annually. Unfortunately, with that scale comes a significant burden in terms of energy, water, and release of greenhouse gases and other pollutants; indeed 8% of worldwide carbon emissions are attributed to the production of cement, a key ingredient in concrete. As such, there is interest in creating concrete formulas that minimize this environmental burden, while satisfying engineering performance requirements including compressive strength. Specifically for computing, concrete is a major ingredient in the construction of data centers.

In software-as-a-service applications provisioned through cloud computing, locally cached data are often modified with updates from new versions. In some cases, with each edit, one may want to preserve both the original and new versions. In this paper, we focus on cases in which only the latest version must be preserved. Furthermore, it is desirable for the data to not only be compressed but to also be easily modified during updates, since representing information and modifying the representation both incur cost. We examine whether it is possible to have both compression efficiency and ease of alteration, in order to promote codeword reuse. In other words, we study the feasibility of a malleable and efficient coding scheme. The tradeoff between compression efficiency and malleability cost-the difficulty of synchronizing compressed versions-is measured as the length of a reused prefix portion. The region of achievable rates and malleability is found. Drawing from prior work on common information problems, we show that efficient data compression may not be the best engineering design principle when storing software-as-a-service data. In the general case, goals of efficiency and malleability are fundamentally in conflict.

In cloud computing, storage area networks, and remote backup storage, stored data is modified with updates from new versions. It is desirable for the data to not only be compressed but to also be easily modified during updates, since representing information and modifying the representation are both expensive. A malleable coding scheme considers both compression efficiency and ease of alteration, promoting codeword reuse. We examine the trade-off between compression efficiency and malleability cost-the difficulty of synchronizing compressed versions-measured as the length of a reused prefix portion. Through a coding theorem, the region of achievable rates and malleability is expressed as a single-letter optimization. Relationships to common information problems are also described.

A malleable coding scheme considers not only representation length but also ease of representation update, thereby encouraging some form of recycling to convert an old codeword into a new one. We examine the trade-off between compression efficiency and malleability cost, measured with a string edit distance that introduces a metric topology to the representation domain. We characterize the achievable rates and malleability as the solution of a subgraph isomorphism problem.

Recently the sampling theory for certain parametric signals based on rate of innovation has been extended to all sampling kernels that satisfy the Strang-Fix conditions, thus including many attractive choices with finite support. We propose a new sampling scheme in which samples are taken simultaneously at the outputs of multiple channels. This new scheme is closely related to previously known cases, but provides a successive approximation property that can be used for detecting undermodeling. We also draw connections to splines and multi-scale sampling of signals.

In this work, we consider the problem of channel estimation by using the recently developed theory for sampling of signals with a finite rate of innovation. We show a framework which allows for lower than Nyquist rate sampling applicable for timing and channel estimation of both narrowband and wideband channels. In certain cases we demonstrate performance exceeding that of algorithms using Nyquist rate sampling while working at lower sampling rates, thus saving power and computational complexity.

Connexions is a new approach to authoring, teaching, and learning that aims to fully exploit modern information technology. Available free of charge to anyone under open-content and open-source licenses, Connexions offers custom-tailored, current course material, is adaptable to a wide range of learning styles, and encourages students to explore the links among courses and disciplines. In contrast to the traditional process of textbook writing and publishing, Connexions fosters world-wide, cross-institution communities of authors, instructors, and students, who collaboratively and dynamically fashion “modules” from which courses are constructed. We believe the ideas and philosophy embodied by Connexions have the potential to change the very nature of textbook writing and publishing, producing a dynamic, interconnected educational environment that is pedagogically sound, both time and cost efficient, and fun. This paper overviews the philosophy and technology behind Connexions and describes a nascent community developing material for DSP education.

Many communication systems are bandwidth-expanding: the transmitted signal occupies a bandwidth larger than the symbol rate. The sampling theorems of Kotelnikov, Shannon, Nyquist et al. discussed by Unser (see Proceedings of the IEEE, vol. 88, no.4, p.569-87, 2000) shows that in order to represent a bandlimited signal, it is necessary to sample at what is popularly referred to as the Shannon or Nyquist rate. However, in many systems, the required sampling rate is very high and expensive to implement. We show that it is possible to get suboptimal performance by sampling close to the symbol rate of the signal, using well-studied algorithmic components. This work is based on previous results on sampling for some classes of non-bandlimited signals. We extend these sampling results to the case when there is noise. In our exposition, we use ultra wideband (UWB) signals as an example of how our framework can be applied.

We consider the problem of efficiently transmitting sets of spatially correlated observations in a distributed sensor network without requiring inter-node communication to exploit the correlation. Specifically, we provide a construction for quantizer design given a training set, and a distributed compression scheme to efficiently relay the quantized observations to a central decoder.

This paper examines some of the complexity issues arising in the real time implementation of the symbol timing subsystem of a narrowband wireless modem. The modem prototype has been designed for 4 kHz channels in the 220-222 MHz land mobile band. In an effort to achieve high bandwidth efficiency, the modem architecture employs transmitter diversity, pilot symbol assisted modulation, and trellis coded modulation. An experimental, non-real time system has been implemented and extensively field tested demonstrating bandwidth efficiencies in excess of 3 bits per second per Hz. The work on this project is focused on the real-time implementation of the baseband receiver functions using the Texas Instruments C54/spl times/ fixed point digital signal processor. Here we report on some of the performance/complexity tradeoffs present in the design of a DSP implementation of the digital filter and square symbol timing recovery algorithm.