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In this paper, the development of a low cost fuel cell inverter system is detailed. The approach consists of a three-terminal push-pull dc-dc converter to boost the fuel cell voltage (48 V) to [+ or -]200 VDC. A four switch [insulated gate bipolar transistor (IGBT)] inverter is employed to produce 120-V/240-V, 60-Hz ac outputs. High performance, easy manufacturability, lower component count, safety and cost are addressed. Protection and diagnostic features form an important part of the design. Another highlight of the proposed design is the control strategy, which allows the inverter to adapt to the requirements of the load as well as the power source (fuel cell). A unique aspect of the design is the use of the TMS320LF2407 DSP to control the inverter. Two sets of lead-acid batteries are provided on the high voltage dc bus to supply sudden load demands. Efficient and smooth control of the power drawn from the fuel cell and the high voltage battery is achieved by controlling the front end dc-dc converter in current mode. The paper details extensive experimental results of the proposed design on Department of Energy (DoE) National Energy Technology Laboratory (NETL) fuel cell. Index Terms--Current mode, dc-dc converter, fuel cell.

This paper discusses a real-time digital signal processor (DSP)-based hierarchical neural network classifier capable of classifying both analog and digital modulation signals. A high-performance DSP processor, namely the TMS320C6701, is utilized to implement different kinds of classifiers including a hierarchical neural network classifier. A total of 31 statistical signal features are extracted and used to classify 11 modulation signals plus white noise. The modulation signals include CW, AM, FM, SSB, FSK2, FSK4, PSK2, PSK4, OOK, QAM16, and QAM32. A classification hierarchy is introduced and the genetic algorithm is employed to obtain the most effective set of features at each level of the hierarchy. The classification results and the number of operations on the DSP processor indicate the effectiveness of the introduced hierarchical neural network classifier in terms of both classification rate and processing time. Index Terms--Digital signal processor (DSP) implementation, hierarchical neural network classifier, modulation signal classification.

Targets of interest in video acquired from imaging infrared sensors often exhibit profound appearance variations due to a variety of factors, including complex target maneuvers, ego-motion of the sensor platform, background clutter, etc., making it difficult to maintain a reliable detection process and track lock over extended time periods. Two key issues in overcoming this problem are how to represent the target and how to learn its appearance online. In this paper, we adopt a recent appearance model that estimates the pixel intensity histograms as well as the distribution of local standard deviations in both the foreground and background regions for robust target representation. Appearance learning is then cast as an adaptive Kalman filtering problem where the process and measurement noise variances are both unknown. We formulate this problem using both covariance matching and, for the first time in a visual tracking application, the recent autocovariance least-squares (ALS) method. Although convergence of the ALS algorithm is guaranteed only for the case of globally wide sense stationary process and measurement noises, we demonstrate for the first time that the technique can often be applied with great effectiveness under the much weaker assumption of piecewise stationarity. The performance advantages of the ALS method relative to the classical covariance matching are illustrated by means of simulated stationary and nonstationary systems. Against real data, our results show that the ALS-based algorithm outperforms the covariance matching as well as the traditional histogram similarity-based methods, achieving sub-pixel tracking accuracy against the well-known AMCOM closure sequences and the recent SENSIAC automatic target recognition dataset. [ABSTRACT FROM AUTHOR]

The U.S. weather radar network is currently being upgraded with dual-polarization capability. Weather radar polarimetry is an interdisciplinary area of engineering and meteorology. This paper presents efficient ways to learn weather radar polarimetry through several basic and practical topics. These topics include: 1) hydrometeor scattering model and calculation of radar variables; 2) characteristics of polarimetric radar measurements and radar echo classification; and 3) rain microphysics retrieval. The supporting files, data, codes, and results are provided on the Web site xlink:type="simple" xmlns:xlink="http://www.w3.org/1999/xlink"http://arrc.ou.edu/~guzhang/ Polarimetry/main.html. The study modules provide hands-on experience in the area of weather radar polarimetry and related fields. [ABSTRACT FROM PUBLISHER]

This paper discusses a real-time digital signal processor (DSP)-based hierarchical neural network classifier capable of classifying both analog and digital modulation signals. A high-performance DSP processor, namely the TMS320C6701, is utilized to implement different kinds of classifiers including a hierarchical neural network classifier. A total of 31 statistical signal features are extracted and used to classify 11 modulation signals plus white noise. The modulation signals include CW, AM, FM, SSB, FSK2, FSK4, PSK2, PgK4, 00K, QAM16, and QAM32. A classification hierarchy is introduced and the genetic algorithm is employed to obtain the most effective set of features at each level of the hierarchy. The classification results and the number of operations on the DSP processor indicate the effectiveness of the introduced hierarchical neural network classifier in terms of both classification rate and processing time. [ABSTRACT FROM AUTHOR]

This is a comment article on the publication "Techniques and Architectures for Hazard-Free Semi-Parallel Decoding of LDPC Codes" Rovini et al. (2009).We mention that there has been similar work reported in the literature before, and the previous work has not been cited correctly, for example Gunnam et al. (2006, 2007). This brief note serves to clarify these issues. [ABSTRACT FROM AUTHOR]