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Wildbook: Crowdsourcing, computer vision, and data science for conservation

Photographs, taken by field scientists, tourists, automated cameras, and incidental photographers, are the most abundant source of data on wildlife today. Wildbook is an autonomous computational system that starts from massive collections of images and, by detecting various species of animals and identifying individuals, combined with sophisticated data management, turns them into high resolution information database, enabling scientific inquiry, conservation, and citizen science. We have built Wildbooks for whales (flukebook.org), sharks (whaleshark.org), two species of zebras (Grevy's and plains), and several others. In January 2016, Wildbook enabled the first ever full species (the endangered Grevy's zebra) census using photographs taken by ordinary citizens in Kenya. The resulting numbers are now the official species census used by IUCN Red List: http://www.iucnredlist.org/details/7950/0. In 2016, Wildbook partnered up with WWF to build Wildbook for Sea Turtles, Internet of Turtles (IoT), as well as systems for seals and lynx. Most recently, we have demonstrated that we can now use publicly available social media images to count and track wild animals. In this paper we present and discuss both the impact and challenges that the use of crowdsourced images can have on wildlife conservation.Comment: Presented at the Data For Good Exchange 201

Animal Wildlife Population Estimation Using Social Media Images

While tracking of wildlife populations is critical for protecting endangered species, the operational and financial burdens of large-scale censuses sharply limit their use. A promising solution to tracking such populations is to turn to an opportunistic form of citizen science: mining publicly available social media photos of animals. Estimation of wildlife population using such a solution is not straight-forward because of complexities in patterns of sharing due to biases inherent in social media data. In this paper, we aim to explicitly capture the biases of social media photo posts of wildlife. Specifically, we build a learning model that can predict the likelihood that any given safari photo will be shared. In our experiments, we find the classifiers showed promising prediction accuracies and other metrics. It is evident from the results that we can train classifiers that can model preferences in sharing safari photos on social media very accurately. Furthermore, we investigate the predictive features to explore the feasibility of embedding this approach in a population estimation application

An Instruction Set Architecture Based Code Compression Scheme for Embedded Processors

We propose a general purpose code compression scheme for embedded systems, based on the instruction set architecture and report results on the Intel StrongARM, a low-cost, low-power RISC architecture and TI TMS320C62x, a widely used VLIW architecture. Fast decompression techniques are explored to improve the decompression overhead of the compression scheme. Compression ratios ranging from 68% to 75% were obtained for TMS320C62x and 69% to 78% for the StrongARM processor. The basic idea of the compression scheme is to divide the instructions into different logical classes and to build multiple dictionaries for them. The size and the number of multiple dictionaries are fixed for a given processor and are determined by the partitioning algorithm which works over the instruction set architecture supplied as input. Frequently occurring unique instruction segments are inserted into the dictionaries and the instructions are encoded as pointers to the respective entries. An opcode, which helps in fast decompression, is attached to an instruction segment to identify its logical class and the dictionary to be accessed

INCREASED MOTORBIKE DRIVING RANGE BY ALTERNATOR CRANKSHAFT COUPLING AND DUAL DRIVE

As modern culture and technology continue to develop, the growing presence of global warming and irreversible climate change draws increasing amounts of concern from the world’s population. Countries around the world are working to drastically optimize the use of fossil fuels, reduce CO2 emissions as well as other harmful environmental pollutants by advancing existing vehicular technology. By incorporating alternative energy drive-trains into vehicles that also use combustion engines, they allow for a slightly cleaner mode of transportation. The paper investigates the effect of externally loading the engine and hence shifting the fuel consumption curve to leanest air fuel ratio to obtain a better driving range. The mechanical design for this test was done in CATIA. The bike is powered by a single cylinder, 4 Stroke air-cooled 125cc conventional petrol engine and a 360W/15A geared DC Motor (Jack Erjavec and Jeff Arias, 2012). The bike’s design is chopper in nature with a high front rake angle. Motor gets its power from the batteries and drives the vehicle when the engine’s cut off, controlled by an electronic control unit. Our system enables approximately a 40 % increase in the bike’s overall driving range, lesser air and noise pollution, enhanced comfort in city drive at low speeds and greater co-generative efficiency. Keywords