Fluorescence monitoring of capilarry electrophoresis separation in a lab-on-a-chip with monolithically integrated waveguides

Femtosecond-laser-written optical waveguides were monolithically integrated into a commercial lab-on-a-chip to intersect a microfluidic channel. Laser excitation through these waveguides confines the excitation window to a width of 12 μm, enabling high-spatial-resolution monitoring of different fluorescent analytes, during their migration/separation in the microfluidic channel by capillary electrophoresis. Wavelength-selective monitoring of the on-chip separation of fluorescent dyes is implemented as a proof-of-principle. We envision well-controlled microfluidic plug formation, waveguide excitation, and a low limit of detection to enable monitoring of extremely small quantities with high spatial resolution

Raw Multi-Channel Audio Source Separation using Multi-Resolution Convolutional Auto-Encoders

Supervised multi-channel audio source separation requires extracting useful spectral, temporal, and spatial features from the mixed signals. The success of many existing systems is therefore largely dependent on the choice of features used for training. In this work, we introduce a novel multi-channel, multi-resolution convolutional auto-encoder neural network that works on raw time-domain signals to determine appropriate multi-resolution features for separating the singing-voice from stereo music. Our experimental results show that the proposed method can achieve multi-channel audio source separation without the need for hand-crafted features or any pre- or post-processing

A Type-Theoretic Approach to Structural Resolution

Structural resolution (or S-resolution) is a newly proposed alternative to SLD-resolution that allows a systematic separation of derivations into term-matching and unification steps. Productive logic programs are those for which term-matching reduction on any query must terminate. For productive programs with coinductive meaning, finite term-rewriting reductions can be seen as measures of observation in an infinite derivation. Ability of handling corecursion in a productive way is an attractive computational feature of S-resolution. In this paper, we make first steps towards a better conceptual understanding of operational properties of S-resolution as compared to SLD-resolution. To this aim, we propose a type system for the analysis of both SLD-resolution and S-resolution. We formulate S-resolution and SLD-resolution as reduction systems, and show their soundness relative to the type system. One of the central methods of this paper is realizability transformation, which makes logic programs productive and non-overlapping. We show that S-resolution and SLD-resolution are only equivalent for programs with these two properties.Comment: LOPSTR 201

Image Reconstruction with a LaBr3-based Rotational Modulator

A rotational modulator (RM) gamma-ray imager is capable of obtaining significantly better angular resolution than the fundamental geometric resolution defined by the ratio of detector diameter to mask-detector separation. An RM imager consisting of a single grid of absorbing slats rotating ahead of an array of a small number of position-insensitive detectors has the advantage of fewer detector elements (i.e., detector plane pixels) than required by a coded aperture imaging system with comparable angular resolution. The RM therefore offers the possibility of a major reduction in instrument complexity, cost, and power. A novel image reconstruction technique makes it possible to deconvolve the raw images, remove sidelobes, reduce the effects of noise, and provide resolving power a factor of 6 - 8 times better than the geometric resolution. A 19-channel prototype RM developed in our laboratory at Louisiana State University features 13.8 deg full-angle field of view, 1.9 deg geometric angular resolution, and the capability of resolving sources to within 35' separation. We describe the technique, demonstrate the measured performance of the prototype instrument, and describe the prospects for applying the technique to either a high-sensitivity standoff gamma-ray imaging detector or a satellite- or balloon-borne gamma-ray astronomy telescope.Comment: submitted to Nuclear Instrument & Methods, special edition: SORMA 2010 on June 16, 201

Fault-tolerant and finite-error localization for point emitters within the diffraction limit

We implement an estimator for determining the separation between two incoherent point sources. This estimator relies on image inversion interferometry and when used with the appropriate data analytics, it yields an estimate of the separation with finite-error, even when the sources come arbitrarily close together. The experimental results show that the technique has a good tolerance to noise and misalignment, making it an interesting consideration for high resolution instruments

TasNet: time-domain audio separation network for real-time, single-channel speech separation

Robust speech processing in multi-talker environments requires effective speech separation. Recent deep learning systems have made significant progress toward solving this problem, yet it remains challenging particularly in real-time, short latency applications. Most methods attempt to construct a mask for each source in time-frequency representation of the mixture signal which is not necessarily an optimal representation for speech separation. In addition, time-frequency decomposition results in inherent problems such as phase/magnitude decoupling and long time window which is required to achieve sufficient frequency resolution. We propose Time-domain Audio Separation Network (TasNet) to overcome these limitations. We directly model the signal in the time-domain using an encoder-decoder framework and perform the source separation on nonnegative encoder outputs. This method removes the frequency decomposition step and reduces the separation problem to estimation of source masks on encoder outputs which is then synthesized by the decoder. Our system outperforms the current state-of-the-art causal and noncausal speech separation algorithms, reduces the computational cost of speech separation, and significantly reduces the minimum required latency of the output. This makes TasNet suitable for applications where low-power, real-time implementation is desirable such as in hearable and telecommunication devices.Comment: Camera ready version for ICASSP 2018, Calgary, Canad

On the estimation of the current density in space plasmas: multi versus single-point techniques

Thanks to multi-spacecraft mission, it has recently been possible to directly estimate the current density in space plasmas, by using magnetic field time series from four satellites flying in a quasi perfect tetrahedron configuration. The technique developed, commonly called 'curlometer' permits a good estimation of the current density when the magnetic field time series vary linearly in space. This approximation is generally valid for small spacecraft separation. The recent space missions Cluster and Magnetospheric Multiscale (MMS) have provided high resolution measurements with inter-spacecraft separation up to 100 km and 10 km, respectively. The former scale corresponds to the proton gyroradius/ion skin depth in 'typical' solar wind conditions, while the latter to sub-proton scale. However, some works have highlighted an underestimation of the current density via the curlometer technique with respect to the current computed directly from the velocity distribution functions, measured at sub-proton scales resolution with MMS. In this paper we explore the limit of the curlometer technique studying synthetic data sets associated to a cluster of four artificial satellites allowed to fly in a static turbulent field, spanning a wide range of relative separation. This study tries to address the relative importance of measuring plasma moments at very high resolution from a single spacecraft with respect to the multi-spacecraft missions in the current density evaluation