Fabrication of Flexible Superconducting Wiring with High Current-Carrying Capacity Indium Interconnects.

The X-ray integral field unit (X-IFU) is a cryogenic spectrometer for the Advanced Telescope for High ENergy Astrophysics (ATHENA). ATHENA is a planned next-generation space-based X-ray observatory with capabilities that surpass the spectral resolution of prior missions. Proposed device designs contain up to 3840 transition edge sensors, each acting as an individual pixel on the detector, presenting a unique challenge for wiring superconducting leads in the focal plane assembly. In prototypes that require direct wiring, the edges of X-IFU focal plane have hosted aluminum wirebonding pads; however, indium (In) 'bumps' deposited on an interface layer such as molybdenum nitride (MoN) can instead be used as an array of superconducting interconnects. We investigated bumped MoN:In structures with different process cleans and layer thicknesses. Measurements of the resistive transitions showed variation of transition temperature T c as a function of bias and generally differed from the expected bulk T c of In (3.41 K). Observed resistance of the In bump structures at temperatures below the MoN transition (at 8.0 K) also depended on the varied parameters. For our proposed X-IFU geometry (10 µm of In mated to a 1-µm In bump), we measured a minimum T c of 3.14 K at a bias current of 3 mA and a normal resistance of 0.59 mΩ per interconnect. We also investigated the design and fabrication of superconducting niobium (Nb) microstrip atop flexible polyimide. We present a process for integrating In bumps with the flexible Nb leads to enable high-density wiring for the ATHENA X-IFU focal plane.

A Finite Element Model Approach to Determine the Influence of Electrode Design and Muscle Architecture on Myoelectric Signal Properties.

INTRODUCTION: Surface electromyography (sEMG) is the measurement of the electrical activity of the skeletal muscle tissue detected at the skin's surface. Typically, a bipolar electrode configuration is used. Most muscles have pennate and/or curved fibres, meaning it is not always feasible to align the bipolar electrodes along the fibres direction. Hence, there is a need to explore how different electrode designs can affect sEMG measurements. METHOD: A three layer finite element (skin, fat, muscle) muscle model was used to explore different electrode designs. The implemented model used as source signal an experimentally recorded intramuscular EMG taken from the biceps brachii muscle of one healthy male. A wavelet based intensity analysis of the simulated sEMG signal was performed to analyze the power of the signal in the time and frequency domain. RESULTS: The model showed muscle tissue causing a bandwidth reduction (to 20-92- Hz). The inter-electrode distance (IED) and the electrode orientation relative to the fibres affected the total power but not the frequency filtering response. The effect of significant misalignment between the electrodes and the fibres (60°-90°) could be reduced by increasing the IED (25-30 mm), which attenuates signal cancellation. When modelling pennated fibres, the muscle tissue started to act as a low pass filter. The effect of different IED seems to be enhanced in the pennated model, while the filtering response is changed considerably only when the electrodes are close to the signal termination within the model. For pennation angle greater than 20°, more than 50% of the source signal was attenuated, which can be compensated by increasing the IED to 25 mm. CONCLUSION: Differences in tissue filtering properties, shown in our model, indicates that different electrode designs should be considered for muscle with different geometric properties (i.e. pennated muscles).

Effects of high-pass and low-pass spatial filtering on face identification.

If face images are degraded by block averaging, there is a nonlinear decline in recognition accuracy as block size increases, suggesting that identification requires a critical minimum range of object spatial frequencies. The identification of faces was measured with equivalent Fourier low-pass filtering and block averaging preserving the same information and with high-pass transformations. In Experiment 1, accuracy declined and response time increased in a significant nonlinear manner in all cases as the spatial-frequency range was reduced. However, it did so at a faster rate for the quantized and high-passed images. A second experiment controlled for the differences in the contrast of the high-pass faces and found a reduced but significant and nonlinear decline in performance as the spatial-frequency range was reduced. These data suggest that face identification is preferentially supported by a band of spatial frequencies of approximately 8-16 cycles per face; contrast or line-based explanations were found to be inadequate. The data are discussed in terms of current models of face identification.

Spatial content and spatial quantisation effects in face recognition.

It has recently become apparent that if face images are degraded by spatial quantisation, or block averaging, there is a nonlinear acceleration of the decline in accuracy of recognition as block size increases. This suggests recognition requires a critical minimum range of object spatial frequencies. Two experiments were performed to clarify the phenomenon. In experiment 1, the speed and accuracy of recognition for six frontoparallel photographs of faces were measured. After familiarisation training sessions, the images were shown for 100 ms with 11, 21, and 42 pixels per face, horizontally measured. Transformations calculated to remove the same range of spatial frequencies were performed by means of quantisation, a Fourier low-pass filter, and Gaussian blurring. Although accuracy declined and speed increased in a significant, nonlinear manner in all cases as the image quality was reduced, it did so at a faster rate for the quantised images. In experiment 2, faces rated as being typical were shown at 9, 12, 23, and 45 pixels per face and with appropriate Fourier low-pass versions. The nonlinear decline was confirmed and it was shown that it could not be attributed to a ceiling effect. A further condition allowed quantised and Fourier low-pass conditions to be compared with an unstructured-noise condition of equal strength to that of the quantised images. These gave comparable, but slightly less impaired, recognition than the quantised images. It can be inferred from these results that the removal of a critical range of at least 8-16 cycles per face of information explains the step decline in recognition seen with quantised images. However, the decline found with quantised images is reinforced by internal masking from pixelisation.