Two new approaches to spatial interpolation with inherent sidelobe suppression for imaging riometers

Absorption images as obtained by imaging riometers such as IRIS are usually created by interpolating between absorption values for individual beams. For IRIS, the locations of the beam centres serve as grid points for subsequent linear interpolation. Although generally producing good results, the fact that the actual shape of the imaging beams is not considered, potentially introduces errors and can lead to misinterpretations. In this paper, two alternative interpolation methods are introduced. Method A is based on measuring the similarity between simulated reception of individual point sources and actually received data. Method B uses a mathematical model of the sky brightness distribution parametrised by the received data. All interpolation methods are applied to power data, as opposed to absorption data, in order to avoid any errors that might be introduced by intermediate processing steps, especially QDC (quiet-day curve) generation. We apply all methods to synthetically generated test data as well as to three exemplary real datasets which are also compared to a calculated sky brightness distribution obtained from a skymap

Lip Reading Sentences in the Wild

The goal of this work is to recognise phrases and sentences being spoken by a talking face, with or without the audio. Unlike previous works that have focussed on recognising a limited number of words or phrases, we tackle lip reading as an open-world problem - unconstrained natural language sentences, and in the wild videos. Our key contributions are: (1) a 'Watch, Listen, Attend and Spell' (WLAS) network that learns to transcribe videos of mouth motion to characters; (2) a curriculum learning strategy to accelerate training and to reduce overfitting; (3) a 'Lip Reading Sentences' (LRS) dataset for visual speech recognition, consisting of over 100,000 natural sentences from British television. The WLAS model trained on the LRS dataset surpasses the performance of all previous work on standard lip reading benchmark datasets, often by a significant margin. This lip reading performance beats a professional lip reader on videos from BBC television, and we also demonstrate that visual information helps to improve speech recognition performance even when the audio is available

Systematic Error-Correcting Codes for Rank Modulation

The rank-modulation scheme has been recently proposed for efficiently storing data in nonvolatile memories. Error-correcting codes are essential for rank modulation, however, existing results have been limited. In this work we explore a new approach, \emph{systematic error-correcting codes for rank modulation}. Systematic codes have the benefits of enabling efficient information retrieval and potentially supporting more efficient encoding and decoding procedures. We study systematic codes for rank modulation under Kendall's $\tau$-metric as well as under the $\ell_\infty$-metric. In Kendall's $\tau$-metric we present $[k+2,k,3]$-systematic codes for correcting one error, which have optimal rates, unless systematic perfect codes exist. We also study the design of multi-error-correcting codes, and provide two explicit constructions, one resulting in $[n+1,k+1,2t+2]$ systematic codes with redundancy at most $2t+1$. We use non-constructive arguments to show the existence of $[n,k,n-k]$-systematic codes for general parameters. Furthermore, we prove that for rank modulation, systematic codes achieve the same capacity as general error-correcting codes. Finally, in the $\ell_\infty$-metric we construct two $[n,k,d]$ systematic multi-error-correcting codes, the first for the case of $d=O(1)$, and the second for $d=\Theta(n)$. In the latter case, the codes have the same asymptotic rate as the best codes currently known in this metric

Dusty space plasma diagnosis using temporal behavior of polar mesospheric summer echoes during active modification

The objective of this paper is to study the effect of different plasma and dust parameters on Polar Mesospheric Summer Echoes (PMSE) temporal behavior after turn-on and turn-off of radio wave heating and to use these responses to diagnose the properties of the dust layer. The threshold radar frequency and dust parameters for the enhancement or suppression of radar echoes after radio wave heating turn-on are investigated for measured mesospheric plasma parameters. The effect of parameters such as the electron temperature enhancement during heating, dust density, dust charge polarity, ion-neutral collision frequency, electron density and dust radius on the temporal evolution of electron irregularities associated with PMSE are investigated. The possible diagnostic information for various charged dust and background plasma quantities using the temporal behavior of backscattered radar power in active experiments is discussed. The computational results are used to make predictions for PMSE active modification experiments at 7.9, 56, 139, 224 and 930MHz corresponding to existing radar facilities. Data from a 2009 VHF (224 MHz) experiment at EISCAT is compared with the computational model to obtain dust parameters in the PMSE

Combined EISCAT radar and optical multispectral and tomographic observations of black aurora

Black auroras are recognized as spatially well-defined regions within a uniform diffuse auroral background where the optical emission is significantly reduced. Black auroras typically appear post-magnetic midnight and during the substorm recovery phase, but not exclusively so. We report on the first combined multimonochromatic optical imaging, bistatic white-light TV recordings and incoherent scatter radar observations of black aurora by EISCAT of the phenomenon. From the relatively larger reduction in luminosity at 4278 Å than at 8446 Å we show that nonsheared black auroras are most probably not caused by downward directed electrical fields at low altitude. From the observations, we determine this by relating the height and intensity of the black aurora to precipitating particle energy within the surrounding background diffuse aurora. The observations are more consistent with an energy selective loss cone. Hence the mechanism causing black aurora is most probably active in the magnetosphere rather than close to Earth

Generation of speech training data for special speech recognition tasks

Speech recognition is widely used as a voice-user interface in several settings, e.g., interactive voice response, virtual personal assistants, transcription, translation applications, etc. Although speech recognition technology has advanced far enough to be useful to a sizeable number of human speakers, there are still populations that cannot take full advantage of speech recognition. For example, people with impaired speech, speakers of rare languages or dialects, with strong accents, etc. have difficulty using an application that uses speech recognition. The reason for such user difficulty is that there is insufficient data to train an automatic speech recognizer to recognize such relatively rare speech. This disclosure describes techniques for creating a large training set out of a small set of speech samples. Acoustic and linguistic features peculiar to a class of speakers are extracted out of a small set of their speech samples, with their consent and permission. These features presented as constraints to a speech synthesizer in order to generate a larger training set