LeviSense: a platform for the multisensory integration in levitating food and insights into its effect on flavour perception

Eating is one of the most multisensory experiences in everyday life. All of our five senses (i.e. taste, smell, vision, hearing and touch) are involved, even if we are not aware of it. However, while multisensory integration has been well studied in psychology, there is not a single platform for testing systematically the effects of different stimuli. This lack of platform results in unresolved design challenges for the design of taste-based immersive experiences. Here, we present LeviSense: the first system designed for multisensory integration in gustatory experiences based on levitated food. Our system enables the systematic exploration of different sensory effects on eating experiences. It also opens up new opportunities for other professionals (e.g., molecular gastronomy chefs) looking for innovative taste-delivery platforms. We describe the design process behind LeviSense and conduct two experiments to test a subset of the crossmodal combinations (i.e., taste and vision, taste and smell). Our results show how different lighting and smell conditions affect the perceived taste intensity, pleasantness, and satisfaction. We discuss how LeviSense creates a new technical, creative, and expressive possibilities in a series of emerging design spaces within Human-Food Interaction

Avian Retinal Photoreceptor Detection and Classification using Convolutional Neural Networks

Avian retinas contain special light filtering cones called photoreceptors. These photoreceptors help filter out specific wavelengths of light, giving birds a good range of distinction between colors. There are five distinct types of photoreceptors: red, yellow, transparent, colorless and principle. A specific photoreceptor can be identified by an organelle called an oil droplet. Dectecting and classifying the oil droplets is currently done by hand which can be a time consuming process. Using computer vision detecting and classifying the photoreceptors can be done automatically. The recent introduction of deep learning in computer vision has revolutionized automatic classification, producing classification results identical to what a human could do. Using deep learning the human element can be eliminated from oil droplet detection and classification. It can take days for a human to process and entire retina, but using deep learning a computer can perform the same take in a matter of minutes

Computational imaging and automated identification for aqueous environments

Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution June 2011Sampling the vast volumes of the ocean requires tools capable of observing from a distance while retaining detail necessary for biology and ecology, ideal for optical methods. Algorithms that work with existing SeaBED AUV imagery are developed, including habitat classi fication with bag-of-words models and multi-stage boosting for rock sh detection. Methods for extracting images of sh from videos of longline operations are demonstrated. A prototype digital holographic imaging device is designed and tested for quantitative in situ microscale imaging. Theory to support the device is developed, including particle noise and the effects of motion. A Wigner-domain model provides optimal settings and optical limits for spherical and planar holographic references. Algorithms to extract the information from real-world digital holograms are created. Focus metrics are discussed, including a novel focus detector using local Zernike moments. Two methods for estimating lateral positions of objects in holograms without reconstruction are presented by extending a summation kernel to spherical references and using a local frequency signature from a Riesz transform. A new metric for quickly estimating object depths without reconstruction is proposed and tested. An example application, quantifying oil droplet size distributions in an underwater plume, demonstrates the efficacy of the prototype and algorithms.Funding was provided by NOAA Grant #5710002014, NOAA NMFS Grant #NA17RJ1223, NSF Grant #OCE-0925284, and NOAA Grant #NA10OAR417008

Self-growing neural network architecture using crisp and fuzzy entropy

The paper briefly describes the self-growing neural network algorithm, CID2, which makes decision trees equivalent to hidden layers of a neural network. The algorithm generates a feedforward architecture using crisp and fuzzy entropy measures. The results of a real-life recognition problem of distinguishing defects in a glass ribbon and of a benchmark problem of differentiating two spirals are shown and discussed