The purpose of this paper is to provide an operative way of achieving high data rates for Impulse Radio (IR) transmission based systems. Because applications targeted for Ultra Wide Band (UWB) are low-cost, we especially focus on simple transceiver design. To that effect, we present an original demodulation scheme adapted to a multi-band On-Off Keying (OOK) modulation.The receiver's novelty consists in considering only the benefit from approximative delay spread and energy level to relax channel estimation constraints. The optimum demodulation turns out to be a non-trivial energetic threshold comparison whose precise theoretical computation admits an original analytical solution proving its feasibility. The knowledge of the available energy as well as the noise level required to set the threshold is easily obtained by appropriate estimators. Finally, in addition to simple hardware architectures, numerical results show the efficiency of these techniques.

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A scalable large vocabulary, speaker independent speech recognition system is being developed using Hidden Markov Models (HMMs) for acoustic modeling and a Weighted Finite State Transducer (WFST) to compile sentence, word, and phoneme models. The system comprises a software backend search and an FPGA-based Gaussian calculation which are covered here. In this paper, we present an efficient pipelined design implemented both as an embedded peripheral and as a scalable, parallel hardware accelerator. Both architectures have been implemented on an Alpha Data XRC-5T1, reconfigurable computer housing a Virtex 5 SX95T FPGA. The core has been tested and is capable of calculating a full set of Gaussian results from 3825 acoustic models in 9.03 ms which coupled with a backend search of 5000 words has provided an accuracy of over 80%. Parallel implementations have been designed with up to 32 cores and have been successfully implemented with a clock frequency of 133 MHz.

An FPGA-based custom core which computes the Gaussian calculation portion of a Hidden Markov Model (HMM) based speech recognition system, is presented. The work is part of the development of a custom embedded system which will provide speaker independend, large vocabulary continuos speech recognition and is currently presented as a hardware/software codesign. By de-coupling the Gaussian calculation from the backend search, calculation of Gaussian results is performed with minimal communication between backend search software and an FPGA based Gaussian core. Several implementations have been investigated in order to minimize memory bandwidth and FPGA resource requirements and are presented. The system has been implemented using an Alpha Data XCR-5T1, reconfigurable computer housing a Virtex 5 SX95T FPGA and has achieved better than real-time performance at 133MHz. The core has been tested and is capable of calculating a full set of Gaussian results from 3825 acoustic models in 5.3ms which coupled with a backend search of 5000 words has provided over 80% accuracy.

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