A three-dimensional iterative scheme for an electromagneticinductive applicator

An efficient iterative method for solving quasi-static electromagnetic field problems is presented. The electromagnetic field is generated by an inductive applicator and is represented as a superposition of two constituents, viz. a primary field in absence of the tissue configuration and a secondary field generated by the presence of the tissue. Then, for the secondary field a quasi-static approximation is employed. In the quasi-static field equations a relaxation function is introduced, such that the resulting equations can be solved iteratively. For a realistic three-dimensional model of a human hand numerical results are presented.     

A VLSI priority packet queue with inheritance and overwrite

Reliable priority-based flow-control is essential for real-time applications involving hard-deadlines. However, the use of first-in-first-out (FIFO) queues in such systems introduces priority inversion resulting in unbounded transmission delays. For this reason, a priority transmission queue is critical for multimedia and mission-critical systems. Yet very few priority queue implementations exist. This paper presents the design of a novel VLSI priority packet queue (PPQ), implemented in 1.2 μm CMOS technology. It achieves fast operation by manipulating its contents in terms of packet segments, rather than individual words. Similar to paged memory, this new segmented architecture greatly reduces implementation cost by reusing segments and avoiding storage area fragmentation. By distributing the computationally intensive priority comparison operation over the access time for an entire segment, the PPQ achieves 96% of the speed of a high-speed packet FIFO. The PPQ can either perform priority inheritance or overwrite lower priority packets to minimize the impact of queue overflow. In addition, it is suitable as a general computer network interface buffer, since it robustly handles asynchronous read and write clocks of greatly disparate frequencies. Our initial implementation achieves well over twice the speed of a nonpipelined approach with minimal additional overhead. Furthermore, scaling this design to larger capacities and more priority levels results in an even greater improvement in speed over conventional approaches     

Extended definition TV over an analog FM satellite channel

The performance of several extended definition TV (EDTV) formats are compared in terms of signal-to-noise ratio (SNR) operation in an analog FM satellite channel. The formats considered are the National Television System Committee (NTSC), extended NTSC (ENTSC), multiplexed analog components (MAC), and extend MAC (EMAC). These formats are compared in terms of SNR above and below the FM threshold. Analytically derived curves which show the luminance and chrominance SNR as a function of horizontal resolution for the above-threshold operation are presented. The effect of group delay on the composite video formats is determined for an actual satellite channel. The below-threshold performances for NTSC and MAC are compared in the luminance channel     

A Maximum Entropy Approach for Collaborative Filtering

Collaborative filtering (CF) involves predicting the preferences of a user for a set of items given partial knowledge of the user's preferences for other items, while leveraging a database of profiles for other users. CF has applications e.g. in predicting Web sites a person will visit and in recommending products. Fundamentally, CF is a pattern recognition task, but a formidable one, often involving a huge feature space, a large data set, and many missing features. Even more daunting is the fact that a CF inference engine must be capable of predicting any (user-selected) items, given any available set of partial knowledge on the user's other preferences. In other words, the model must be designed to solve any of a huge (combinatoric) set of possible inference tasks. CF techniques include memory-based, classification-based, and statistical modelling approaches. Among these, modelling approaches scale best with large data sets and are the most adept at handling missing features. The disadvantage of these methods lies in the statistical assumptions (e.g. feature independence), which may be unjustified. To address this shortcoming we propose a new model-based CF method, based on the maximum entropy principle. For the MS Web application, the new method is demonstrated to outperform a number of CF approaches, including naive Bayes and latent variable (cluster) models, support vector machines (SVMs), and the (Pearson) correlation method.     

Extended fundamental frequency analysis of the LCC resonant converter

Fundamental frequency techniques are used to analyze the series-parallel resonant converter under heavy load conditions, both with a continuous, but distorted parallel capacitor voltage waveform, and with a discontinuous capacitor voltage waveform. The analysis is validated with results from an experimental prototype. The application of the technique to the parallel-loaded L-C resonant converter is also considered.