Derivation of a second-order switching surface in the boundary control of buck converters

A second-order switching surface in the boundary control of buck converters is derived in this letter. The formulated switching surface can make the overall converter exhibit better steady-state and transient behaviors than the one with a first-order switching surface. The switching surface is derived by estimating the state trajectory movement after a switching action, resulting in a high state trajectory velocity along the switching surface. This phenomenon accelerates the trajectory moving toward the target operating point. The proposed control scheme has been successfully applied to a 120-W buck converter. The large-signal performance and a comparison with the first-order switching surface have been studied.     

On the capacities of bipartite Hamiltonians and unitary gates

We consider interactions as bidirectional channels. We investigate the capacities for interaction Hamiltonians and nonlocal unitary gates to generate entanglement and transmit classical information. We give analytic expressions for the entanglement generating capacity and entanglement-assisted one-way classical communication capacity of interactions, and show that these quantities are additive, so that the asymptotic capacities equal the corresponding 1-shot capacities. We give general bounds on other capacities, discuss some examples, and conclude with some open questions.     

Classification of audio radar signals using radial basis function neural networks

Radial basis function (RBF) neural networks are used to classify real-life audio radar signals that are collected by a ground surveillance radar mounted on a tank. Currently, a human operator is required to operate the radar system to discern among signals bouncing off tanks, vehicles, planes, and so on. The objective of this project is to investigate the possibility of using a neural network to perform this target recognition task, with the aim of reducing the number of personnel required in a tank. Different signal classification methods in the neural net literature are considered. The first method employs a linear autoregressive (AR) model to extract linear features of the audio data, and then perform classification on these features, i.e, the AR coefficients. AR coefficient estimations based on least squares and higher order statistics are considered in this study. The second approach uses nonlinear predictors to model the audio data and then classifies the signals according to the prediction errors. The real-life audio radar data set used here was collected by an AN/PPS-15 ground surveillance radar and consists of 13 different target classes, which include men marching, a man walking, airplanes, a man crawling, and boats, etc. It is found that each classification method has some classes which are difficult to classify. Overall, the AR feature extraction approach is most effective and has a correct classification rate of 88% for the training data and 67% for data not used for training.     

Reliability investigation of 0.07-/spl mu/m InGaAs-InAlAs-InP HEMT MMICs with pseudomorphic In/sub 0.75/Ga/sub 0.25/As channel

The authors have investigated the reliability performance of G-band (183 GHz) monolithic microwave integrated circuit (MMIC) amplifiers fabricated using 0.07-/spl mu/m T-gate InGaAs-InAlAs-InP HEMTs with pseudomorphic In/sub 0.75/Ga/sub 0.25/As channel on 3-in wafers. Life test was performed at two temperatures (T/sub 1/ = 200 /spl deg/C and T/sub 2/ = 215 /spl deg/C), and the amplifiers were stressed at V/sub ds/ of 1 V and I/sub ds/ of 250 mA/mm in a N/sub 2/ ambient. The activation energy is as high as 1.7 eV, achieving a projected median-time-to-failure (MTTF) /spl ap/ 2 /spl times/ 10/sup 6/ h at a junction temperature of 125 /spl deg/C. MTTF was determined by 2-temperature constant current stress using /spl Delta/G/sub mp/ = -20% as the failure criteria. The difference of reliability performance between 0.07-/spl mu/m InGaAs-InAlAs-InP HEMT MMICs with pseudomorphic In/sub 0.75/Ga/sub 0.25/As channel and 0.1-/spl mu/m InGaAs-InAlAs-InP HEMT MMICs with In/sub 0.6/Ga/sub 0.4/As channel is also discussed. The achieved high-reliability result demonstrates a robust 0.07-/spl mu/m pseudomorphic InGaAs-InAlAs-InP HEMT MMICs production technology for G-band applications.     

Traumatic vertebral arterial dissection and vertebrobasilar arterial thrombosis successfully treated with endovascular thrombolysis and stenting

A case of traumatic extracranial vertebral arterial dissection leading to vertebrobasilar thrombosis and respiratory compromise requiring mechanical ventilation was managed with intraarterial thrombolysis and stenting of the vertebral intimal dissection. In contrast to similar, previously reported cases, this critically ill patient made a full recovery, returning to his job as a secondary school teacher.     

Scientific and artistic investigation of multi-dimensional fractals on the AT & T pixel machine

The pipelined architecture and parallel organization of the AT&T Pixel Machine image computer are described and demonstrated with applications for the visualization of multidimensional fractals, particularly linear fractals and quaternion/ stacked Julia sets. Techniques for pushing the Pixel Machine to its peak abilities are described and apply to more recent parallel image computers as well.     

Adaptive weighted outer-product learning associative memory

Associative-memory neural networks with adaptive weighted outer-product learning are proposed in this paper. For the correct recall of a fundamental memory (FM), a corresponding learning weight is attached and a parameter called signal-to-noise-ratio-gain (SNRG) is devised. The sufficient conditions for the learning weights and the SNRG's are derived. It is found both empirically and theoretically that the SNRG's have their own threshold values for correct recalls of the corresponding FM's. Based on the gradient-descent approach, several algorithms are constructed to adaptively find the optimal learning weights with reference to global- or local-error measure.