In situ observations on deformation behavior and stretching-induced failure of fine pitch stretchable interconnect

Electronic devices capable of performing in extreme mechanical conditions such as stretching, bending, or twisting will improve biomedical and wearable systems. The required capabilities cannot be achieved with conventional building geometries, because of structural rigidity and lack of mechanical stretchability. In this article, a zigzag-patterned structure representing a stretchable interconnect is presented as a promising type of building block. In situ experimental observations on the deformed interconnect are correlated with numerical analysis, providing an understanding of the deformation and failure mechanisms. The experimental results demonstrate that the zigzag-patterned interconnect enables stretchability up to 60% without rupture. This stretchability is accommodated by in-plane rotation of arms and out-of-plane deformation of crests. Numerical analysis shows that the dominating failure cause is interfacial in-plane shear stress. The plastic strain concentration at the arms close to the crests, obtained by numerical simulation, agrees well with the failure location observed in the experiment

State and trait characteristics of anterior insula time-varying functional connectivity.

The human anterior insula (aINS) is a topographically organized brain region, in which ventral portions contribute to socio-emotional function through limbic and autonomic connections, whereas the dorsal aINS contributes to cognitive processes through frontal and parietal connections. Open questions remain, however, regarding how aINS connectivity varies over time. We implemented a novel approach combining seed-to-whole-brain sliding-window functional connectivity MRI and k-means clustering to assess time-varying functional connectivity of aINS subregions. We studied three independent large samples of healthy participants and longitudinal datasets to assess inter- and intra-subject stability, and related aINS time-varying functional connectivity profiles to dispositional empathy. We identified four robust aINS time-varying functional connectivity modes that displayed both "state" and "trait" characteristics: while modes featuring connectivity to sensory regions were modulated by eye closure, modes featuring connectivity to higher cognitive and emotional processing regions were stable over time and related to empathy measures

Structured Random Linear Codes (SRLC): Bridging the Gap between Block and Convolutional Codes

Several types of AL-FEC (Application-Level FEC) codes for the Packet Erasure Channel exist. Random Linear Codes (RLC), where redundancy packets consist of random linear combinations of source packets over a certain finite field, are a simple yet efficient coding technique, for instance massively used for Network Coding applications. However the price to pay is a high encoding and decoding complexity, especially when working on $GF(2^8)$, which seriously limits the number of packets in the encoding window. On the opposite, structured block codes have been designed for situations where the set of source packets is known in advance, for instance with file transfer applications. Here the encoding and decoding complexity is controlled, even for huge block sizes, thanks to the sparse nature of the code and advanced decoding techniques that exploit this sparseness (e.g., Structured Gaussian Elimination). But their design also prevents their use in convolutional use-cases featuring an encoding window that slides over a continuous set of incoming packets. In this work we try to bridge the gap between these two code classes, bringing some structure to RLC codes in order to enlarge the use-cases where they can be efficiently used: in convolutional mode (as any RLC code), but also in block mode with either tiny, medium or large block sizes. We also demonstrate how to design compact signaling for these codes (for encoder/decoder synchronization), which is an essential practical aspect.Comment: 7 pages, 12 figure

Online Bearing Remaining Useful Life Prediction Based on a Novel Degradation Indicator and Convolutional Neural Networks

In industrial applications, nearly half the failures of motors are caused by the degradation of rolling element bearings (REBs). Therefore, accurately estimating the remaining useful life (RUL) for REBs are of crucial importance to ensure the reliability and safety of mechanical systems. To tackle this challenge, model-based approaches are often limited by the complexity of mathematical modeling. Conventional data-driven approaches, on the other hand, require massive efforts to extract the degradation features and construct health index. In this paper, a novel online data-driven framework is proposed to exploit the adoption of deep convolutional neural networks (CNN) in predicting the RUL of bearings. More concretely, the raw vibrations of training bearings are first processed using the Hilbert-Huang transform (HHT) and a novel nonlinear degradation indicator is constructed as the label for learning. The CNN is then employed to identify the hidden pattern between the extracted degradation indicator and the vibration of training bearings, which makes it possible to estimate the degradation of the test bearings automatically. Finally, testing bearings' RULs are predicted by using a $\epsilon$-support vector regression model. The superior performance of the proposed RUL estimation framework, compared with the state-of-the-art approaches, is demonstrated through the experimental results. The generality of the proposed CNN model is also validated by transferring to bearings undergoing different operating conditions

On the evolution of elastic properties during laboratory stick-slip experiments spanning the transition from slow slip to dynamic rupture

The physical mechanisms governing slow earthquakes remain unknown, as does the relationship between slow and regular earthquakes. To investigate the mechanism(s) of slow earthquakes and related quasi-dynamic modes of fault slip we performed laboratory experiments on simulated fault gouge in the double direct shear configuration. We reproduced the full spectrum of slip behavior, from slow to fast stick slip, by altering the elastic stiffness of the loading apparatus (k) to match the critical rheologic stiffness of fault gouge (kc). Our experiments show an evolution from stable sliding, when k>kc, to quasi-dynamic transients when k ~ kc, to dynamic instabilities when k<kc. To evaluate the microphysical processes of fault weakening we monitored variations of elastic properties. We find systematic changes in P wave velocity (Vp) for laboratory seismic cycles. During the coseismic stress drop, seismic velocity drops abruptly, consistent with observations on natural faults. In the preparatory phase preceding failure, we find that accelerated fault creep causes a Vp reduction for the complete spectrum of slip behaviors. Our results suggest that the mechanics of slow and fast ruptures share key features and that they can occur on same faults, depending on frictional properties. In agreement with seismic surveys on tectonic faults our data show that their state of stress can be monitored by Vp changes during the seismic cycle. The observed reduction in Vp during the earthquake preparatory phase suggests that if similar mechanisms are confirmed in nature high-resolution monitoring of fault zone properties may be a promising avenue for reliable detection of earthquake precursors

Point Source Extraction with MOPEX

MOPEX (MOsaicking and Point source EXtraction) is a package developed at the Spitzer Science Center for astronomical image processing. We report on the point source extraction capabilities of MOPEX. Point source extraction is implemented as a two step process: point source detection and profile fitting. Non-linear matched filtering of input images can be performed optionally to increase the signal-to-noise ratio and improve detection of faint point sources. Point Response Function (PRF) fitting of point sources produces the final point source list which includes the fluxes and improved positions of the point sources, along with other parameters characterizing the fit. Passive and active deblending allows for successful fitting of confused point sources. Aperture photometry can also be computed for every extracted point source for an unlimited number of aperture sizes. PRF is estimated directly from the input images. Implementation of efficient methods of background and noise estimation, and modified Simplex algorithm contribute to the computational efficiency of MOPEX. The package is implemented as a loosely connected set of perl scripts, where each script runs a number of modules written in C/C++. Input parameter setting is done through namelists, ASCII configuration files. We present applications of point source extraction to the mosaic images taken at 24 and 70 micron with the Multiband Imaging Photometer (MIPS) as part of the Spitzer extragalactic First Look Survey and to a Digital Sky Survey image. Completeness and reliability of point source extraction is computed using simulated data.Comment: 20 pages, 13 Postscript figures, accepted for publication in PAS

Structural latches for modular assembly of spacecraft and space mechanisms

Latching techniques are changing from early approaches due to the advent of berthing technology. Latch selection for a given interface may be conducted by evaluating candidate capabilities which meet functional interface requirements. A judgment criteria system is presented along with an example of its use in choosing the Rollerscrew Structural Latch (RSL) for the NASA Flat Plate Interface Prototype (FPIP). Details are given on Rollerscrew operation, design, and development difficulties. A test plan is also outlined for the RSL and FPIP

Degradation-level assessment and online prognostics for sliding chair failure on point machines

This paper presents a degradation-level assessment and failure prognostics methodology for degrading systems. The proposed methodology consists of offline and online phases. In the offline phase, different time-domain health indicators (HIs) are extracted and the best indicator of degradation is selected by filter-based methods. Then, a degradation model is defined and its parameters are estimated using the selected HI. In the online phase, the k-means clustering is utilized to detect a change(s) in the system’s health state and to trigger failure prognostics for remaining useful life (RUL) prediction. The degradation model parameters are updated as new data are available, and the RUL is predicted iteratively. The proposed methodology is implemented on point machine sliding chair degradation using in-field condition monitoring (CM) data. The results show that the methodology can be effectively used in machine degradation-level assessment and in online RUL predictions