IR Thermometer with Automatic Emissivity Correction

The paper describes the design and implementation of an infrared (IR) thermometer with automatic emissivity correction. The temperature measurement is carried out by the simple digital thermopile sensor MLX90614. The emissivity correction is based on benefits of diffuse reflecting materials and it uses an IR laser diode in conjunction with a selective amplifier. Moreover, the paper includes the design of the control interface with a graphics LCD. Furthermore, this paper describes the power supply unit with a Li-ion cell controlled by basic integrated circuits

Modelling and analysis of FSO ground-to-train communications for straight and curved tracks

In this work, a free space optical (FSO) link for the ground-to-train communications is proposed. Analytical analysis is carried out for the case of the straight as well as curved rail tracks. We show that the transmitter divergence angle, the transmit power and the size of the concentration lens needs to increase for the curved section of the rail track compared to the straight track. We derive the analytical expression (11) for the received power level based on the link geometry for the cases of straight and curved tracks. The received power variation is compared for two cases showing a similar dynamic range. In the worst case scenario when the radius of curvature is 120 m, the transmit power at the optical base station (OBS) needs to increase by over 2 dB when the concentration lens radius is increased by 5 times. Analyses also show that received power increases with the radius of curvature. Finally, results are compared with the existing straight track model

AutoRapper - Automatic Alignment of Speech with a Rhythm

Tato práce popisuje návrh a implementaci aplikace, která automaticky převádí vstupní řeč na rap. Tento proces je založen na zarovnání řeči s rytmem, které je dosaženo pomocí rozpoznávání fonémù, slabikování a časové modifikáce řeči. Další funkce, jako je hudební podklad a vokální efekt jsou přidány za účelem přiblížení se ke skutečnému rapu. Výsledná aplikace je dostupná jako webová služba pro uživatele.This thesis describes a design and implementation of an application that automatically converts the input speech recording into a rap. This process is based on alignment of speech with a rhythm, which is achieved by phoneme recognition, syllabification and time-scale modification. The external features such as beat and vocal effect are added in order to make the resulting signal as close as possible to the real rap. The resulting application runs as a web service available to the users.

On the Evaluation of Semantic Phenomena in Neural Machine Translation Using Natural Language Inference

We propose a process for investigating the extent to which sentence representations arising from neural machine translation (NMT) systems encode distinct semantic phenomena. We use these representations as features to train a natural language inference (NLI) classifier based on datasets recast from existing semantic annotations. In applying this process to a representative NMT system, we find its encoder appears most suited to supporting inferences at the syntax-semantics interface, as compared to anaphora resolution requiring world-knowledge. We conclude with a discussion on the merits and potential deficiencies of the existing process, and how it may be improved and extended as a broader framework for evaluating semantic coverage.Comment: To be presented at NAACL 2018 - 11 page

Hypothesis Only Baselines in Natural Language Inference

We propose a hypothesis only baseline for diagnosing Natural Language Inference (NLI). Especially when an NLI dataset assumes inference is occurring based purely on the relationship between a context and a hypothesis, it follows that assessing entailment relations while ignoring the provided context is a degenerate solution. Yet, through experiments on ten distinct NLI datasets, we find that this approach, which we refer to as a hypothesis-only model, is able to significantly outperform a majority class baseline across a number of NLI datasets. Our analysis suggests that statistical irregularities may allow a model to perform NLI in some datasets beyond what should be achievable without access to the context.Comment: Accepted at *SEM 2018 as long paper. 12 page