Neural Network Based Detection of Driver’s Drowsiness

<div>The primary purpose of this paper was to propose a way to alert sleepy drivers in the act of driving. Most of the traditional methods to detect drowsiness are based on behavioral aspects while some are intrusive and may distract drivers, while some require expensive sensors/hardware. Therefore, in this paper, driver’s drowsiness detection system is developed and implemented to aid drowsy drivers from falling asleep and to prevent accidents. The system takes images from the device as input. Using these image templates, the trained model starts execution and predicts/classifies whether the face of the person in the image is drowsy or alert. The proposed model is able to achieve accuracy of 99.93% using CNN on trained image dataset.</div>

Neural Network Based Detection of Driver’s Drowsiness

<div>The primary purpose of this paper was to propose a way to alert sleepy drivers in the act of driving. Most of the traditional methods to detect drowsiness are based on behavioral aspects while some are intrusive and may distract drivers, while some require expensive sensors/hardware. Therefore, in this paper, driver’s drowsiness detection system is developed and implemented to aid drowsy drivers from falling asleep and to prevent accidents. The system takes images from the device as input. Using these image templates, the trained model starts execution and predicts/classifies whether the face of the person in the image is drowsy or alert. The proposed model is able to achieve accuracy of 99.93% using CNN on trained image dataset.</div>

Temporal Stacking of Cross-Correlation for Glacier Offset Tracking

&lt;p&gt;Offset tracking is one of the most widely applied methods for measuring glacier flow velocities using remote sensing data. It uses the pair-wise cross-correlation of images acquired at two different times to detect offsets between image templates of a certain size. Despite the simplicity and reliability of the method, accurate estimations of glacier velocities are limited by the accountability of features and the noise, e.g. radar speckles in synthetic aperture radar (SAR) images. One way of gaining robust estimations is to increase the size of image templates, but the resolution of obtained velocity field is inevitably depreciate. Furthermore, for templates that only contain extremely weak features with respect to the noise, increasing the size of templates is not helpful as the noise is boosted more than the features.&lt;/p&gt;&lt;p&gt;To overcome these issues, we propose a temporal stacking algorithm that first averages a time series of local cross-correlation functions calculated from a series of consecutive image pairs, and then estimates the averaged velocity from the stacked cross-correlation functions. Assuming the flow velocity of a glacier is constant during a certain time span (e.g. a season), the offsets between consecutive image pairs in the time series ought to be equal. Therefore, the cross-correlation functions can be considered as a time series of signals that record the identical offsets and thus are temporally coherent. Hence, we can temporally stack the signals to enhance the signal-to-noise ratio (SNR) of cross-correlation functions and better estimate offsets from the stacked cross-correlation functions.&amp;#160;&lt;/p&gt;&lt;p&gt;The proposed algorithm is assessed by mapping the flow velocity of the Aletsch Glacier using a time series of about 10 SAR images acquired by TanDEM-X in 2017 with constant revisit time of 11 days. The results show that temporal stacking of cross-correlation functions significantly enhances the spatial coverage and resolution of the obtained velocity fields compared to standard offset tracking using only pair-wise cross-correlation functions. This algorithm promotes the ability of mapping glacier velocities to a new extent with larger spatial coverage and higher spatial resolution, and provides a new perspective of measuring glacier velocities through exploiting the emerging time series data from recent high resolution space-born imaging sensors.&lt;/p&gt;

Binary Image Recognition Utilizing Computer Generated Templates

This paper covers a method of taking images of physical parts which are then preprocessed and compared against CAD generated templates. A pseudo milling operation was performed on discretized points along CAD generated mill paths to create binary image templates. The computer-generated images were then tested against one another as a preliminarily sorting technique. This was done to reduce the number of sorting approaches used, by selecting the most reliable and discerning ones, and discarding the others. To apply the selected sorting methods for comparing CAD generated images and the images of physical parts, a translational and scaling normalization technique was implemented. Rotational variation occurs while scanning physical parts and it was addressed using two different techniques: first by determination of best rotation based on modified-Hausdorff distance (MHD); and second by comparing against all CAD based images for all template rotations. The proposed approach for automated sorting of physical parts was demonstrated by categorizing multiple geometries.

Migraine with aura attacks overriding dreams

While the anatomical pathways involved in migraine with aura are near to be completely known, those involved in dreaming remain less studied. We aimed to describe two cases of migraine with visual aura, whose migraine auras would override their dreams, and to discuss the pathways involved in visual aura and in dreams. The following hypothesis can derive from the above mentioned cases: a) visual aura projects over hippocampal and parahippocampal image templates where they fuse in an unique image; b) memory banks use the extrastriatal occipital cortex to form they image templates, fusion with visual aura occurring during this process; c) both process occur independently, but fuse within visual association areas. The description of case reports like the two above is important to understand the aura phenomenon and the dreaming process and to reinforce the cortical role for these manifestations.