Competitive Assessments for HAP Delivery of Mobile Services in Emerging Countries

In recent years, network deployment based on High Altitude Platforms (HAPs) has gained momentum through several initiatives where air vehicles and telecommunications payloads have been adapted and refined, resulting in more efficient and less expensive platforms. In this paper, we study HAP as an alternative or complementary fast-evolving technology to provide mobile services in rural areas of emerging countries, where business models need to be carefully tailored to the reality of their related markets. In these large areas with low user density, mobile services uptake is likely to be slowed by a service profitability which is in turn limited by a relatively low average revenue per user. Through three architectures enabling different business roles and using different terrestrial, HAP and satellite backhaul solutions, we devise how to use in an efficient and profitable fashion these multi-purpose aerial platforms, in complement to existing access and backhauling satellite or terrestrial technologies

Integer Echo State Networks: Hyperdimensional Reservoir Computing

We propose an approximation of Echo State Networks (ESN) that can be efficiently implemented on digital hardware based on the mathematics of hyperdimensional computing. The reservoir of the proposed Integer Echo State Network (intESN) is a vector containing only n-bits integers (where n<8 is normally sufficient for a satisfactory performance). The recurrent matrix multiplication is replaced with an efficient cyclic shift operation. The intESN architecture is verified with typical tasks in reservoir computing: memorizing of a sequence of inputs; classifying time-series; learning dynamic processes. Such an architecture results in dramatic improvements in memory footprint and computational efficiency, with minimal performance loss.Comment: 10 pages, 10 figures, 1 tabl

Systolic VLSI chip for implementing orthogonal transforms, A

Includes bibliographical references.This paper describes the design of a systolic VLSI chip for the implementation of signal processing algorithms that may be decomposed into a product of simple real rotations. These include orthogonal transformations. Applications of this chip include projections, discrete Fourier and cosine transforms, and geometrical transformations. Large transforms may be computed by "tiling" together many chips for increased throughput. A CMOS VLSI chip containing 138 000 transistors in a 5x3 array of rotators has been designed, fabricated, and tested. The chip has a 32-MHz clock and performs real rotations at a rate of 30 MHz. The systolic nature of the chip makes use of fully synchronous bit-serial interconnect and a very regular structure at the rotator and bit levels. A distributed arithmetic scheme is used to implement the matrix-vector multiplication of the rotation.This work was supported by Ball Aerospace, Boulder, CO, and by the Office of Naval Research, Electronics Branch, Arlington, VA, under Contract ONR 85-K-0693

Mixed-signal CNN array chips for image processing

Due to their local connectivity and wide functional capabilities, cellular nonlinear networks (CNN) are excellent candidates for the implementation of image processing algorithms using VLSI analog parallel arrays. However, the design of general purpose, programmable CNN chips with dimensions required for practical applications raises many challenging problems to analog designers. This is basically due to the fact that large silicon area means large development cost, large spatial deviations of design parameters and low production yield. CNN designers must face different issues to keep reasonable enough accuracy level and production yield together with reasonably low development cost in their design of large CNN chips. This paper outlines some of these major issues and their solutions

Simulation of cell movement through evolving environment: a fictitious domain approach

A numerical method for simulating the movement of unicellular organisms which respond to chemical signals is presented. Cells are modelled as objects of finite size while the extracellular space is described by reaction-diffusion partial differential equations. This modular simulation allows the implementation of different models at the different scales encountered in cell biology and couples them in one single framework. The global computational cost is contained thanks to the use of the fictitious domain method for finite elements, allowing the efficient solve of partial differential equations in moving domains. Finally, a mixed formulation is adopted in order to better monitor the flux of chemicals, specifically at the interface between the cells and the extracellular domain

Advances in quantum machine learning

Here we discuss advances in the field of quantum machine learning. The following document offers a hybrid discussion; both reviewing the field as it is currently, and suggesting directions for further research. We include both algorithms and experimental implementations in the discussion. The field's outlook is generally positive, showing significant promise. However, we believe there are appreciable hurdles to overcome before one can claim that it is a primary application of quantum computation.Comment: 38 pages, 17 Figure