DeepASL: Enabling Ubiquitous and Non-Intrusive Word and Sentence-Level Sign Language Translation

There is an undeniable communication barrier between deaf people and people with normal hearing ability. Although innovations in sign language translation technology aim to tear down this communication barrier, the majority of existing sign language translation systems are either intrusive or constrained by resolution or ambient lighting conditions. Moreover, these existing systems can only perform single-sign ASL translation rather than sentence-level translation, making them much less useful in daily-life communication scenarios. In this work, we fill this critical gap by presenting DeepASL, a transformative deep learning-based sign language translation technology that enables ubiquitous and non-intrusive American Sign Language (ASL) translation at both word and sentence levels. DeepASL uses infrared light as its sensing mechanism to non-intrusively capture the ASL signs. It incorporates a novel hierarchical bidirectional deep recurrent neural network (HB-RNN) and a probabilistic framework based on Connectionist Temporal Classification (CTC) for word-level and sentence-level ASL translation respectively. To evaluate its performance, we have collected 7,306 samples from 11 participants, covering 56 commonly used ASL words and 100 ASL sentences. DeepASL achieves an average 94.5% word-level translation accuracy and an average 8.2% word error rate on translating unseen ASL sentences. Given its promising performance, we believe DeepASL represents a significant step towards breaking the communication barrier between deaf people and hearing majority, and thus has the significant potential to fundamentally change deaf people's lives

Making Sense of the Meaning of Color in Industrial Design

Color is key to the understanding of our world. We experience, interact and understand our specific environment through color. Color provides information such as attractiveness, value, functionality, honesty, safety, danger and so on. The meaning and function of products can be communicated, emphasized or hidden only with the use of color. The understanding of color though, is different from civilization to civilization and from one culture to another. The primary goal of this thesis is to provide help and guidance for designers who want to better understand how to make purposeful color choices in their work. This thesis is not providing examples like in a color harmony book but is looking at the meaningful application of color in the field of design in a holistic approach

Deep multiband surface photometry on star forming galaxies: I. A sample of 24 blue compact galaxies

[Abridged] We present deep optical and near-infrared UBVRIHKs imaging data for 24 blue compact galaxies (BCGs). The sample contains luminous dwarf and intermediate-mass BCGs which are predominantly metal-poor, although a few have near-solar metallicities. We have analyzed isophotal and elliptical integration surface brightness and color profiles, extremely deep (mu_B<29 mag arcsec^{-2}) contour maps and RGB images for each galaxy in the sample. The colors are compared to different spectral evolutionary models. We detect extremely extended low surface brightness (LSB) components dominant beyond the Holmberg radius as well as optical bridges between companion galaxies at the mu_V~28th mag arcsec^{-2} isophotal level. The central surface brightness mu_0 and scale length h_r are derived from two radial ranges typically assumed to be dominated by the underlying host galaxy. We find that mu_0 and h_r of the BCGs host deviate from those of dwarf ellipticals (dE) and dwarf irregulars (dI) solely due to a strong burst contribution to the surface brightness profile almost down to the Holmberg radius. Structural parameters obtained from a fainter region, mu_B=26-28 mag arcsec^{-2}, are consistent with those of true LSB galaxies for the starbursting BCGs in our sample, and with dEs and dIs for the BCGs with less vigorous star formation.Comment: 61 pages, 45 figures, submitte