Radish: A Cross Platform Meal Prepping App for Beginner Weightlifters

With the increasing ease of access and decreasing price of most food, obesity rates in the developing world have risen dramatically in recent years. As of March 23rd, 2019, obesity rates had reached 39.6%, a 6% increase in just 8 years. Research has shown that people with obesity have a significantly increased risk of heart disease, stroke, type 2 diabetes, and certain cancers, among other life-threatening diseases. In addition, 42% of people who begin weightlifting quit because it’s too difficult to follow a diet or workout regimen. We created Radish in an attempt to tackle these problems. Radish makes it easier for people to achieve fitness goals without having to do a large amount of diet and fitness research that generally overwhelms beginner weightlifters. Our contributions in this field are unique because we make decisions for the user so they have fewer disinhibitions from starting and continuing on their fitness journey. Our target demographic for this app are people with limited fitness experience who want to attempt to improve their health and aesthetics. We believe we’ve successfully created a strong proof of concept in the scope of this senior project. We will be continuing our work with this app after the completion of this quarter and we hope to release the app on the App Store and Google Play by the end of the year

Automatic Detection and Verification of Solar Features

YesA fast hybrid system for the automated detection and verification of active regions (plages) and filaments in solar images is presented in this paper. The system combines automated image processing with machine learning. The imaging part consists of five major stages. The solar disk is detected in the first stage, using a morphological hit-miss transform, watershed transform and Filling algorithm. An image-enhancement technique is introduced to remove the limb-darkening effect and intensity filtering is implemented followed by a modified region-growing technique to detect the regions of interest (RoI). The algorithms are tested on H- and CA II K3-line solar images that are obtained from Meudon Observatory, covering the period from July 2, 2001 till August 4, 2001. The detection algorithm is fast and it achieves false acceptance rate (FAR) error rate of 67% and false rejection rate (FRR) error rate of 3% for active regions, and FAR error rate of 19% and FRR error rate of 14% for filaments, when compared with the manually detected filaments in the synoptic maps. The detection performance is enhanced further using a neural network (NN), which is trained on statistical features extracted from the RoI and non-RoI. With the use of this combination the FAR has dropped to 2% for active regions and 4% for filaments.© 2006 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 15, 199-210, 200

L. Miletitch : Nos Ρavlikians ou Les Bulgares-Pauliciens (en bulgare)

Baxes L. L. Miletitch : Nos Ρavlikians ou Les Bulgares-Pauliciens (en bulgare). In: Échos d'Orient, tome 9, n°56, 1906. pp. 62-63

L. Miletitch : Nos Ρavlikians ou Les Bulgares-Pauliciens (en bulgare)

Baxes L. L. Miletitch : Nos Ρavlikians ou Les Bulgares-Pauliciens (en bulgare). In: Échos d'Orient, tome 9, n°56, 1906. pp. 62-63