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Register a new userTen Quick Tips for Using a Raspberry Pi
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Added by
Cameron Mura
Publication date
2018
fields
Informatics Engineering
and research's language is
English
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Much of biology (and, indeed, all of science) is becoming increasingly computational. We tend to think of this in regards to algorithmic approaches and software tools, as well as increased computing power. There has also been a shift towards slicker, packaged solutions--which mirrors everyday life, from smart phones to smart homes. As a result, its all too easy to be detached from the fundamental elements that power these changes, and to see solutions as black boxes. The major goal of this piece is to use the example of the Raspberry Pi--a small, general-purpose computer--as the central component in a highly developed ecosystem that brings together elements like external hardware, sensors and controllers, state-of-the-art programming practices, and basic electronics and physics, all in an approachable and useful way. External devices and inputs are easily connected to the Pi, and it can, in turn, control attached devices very simply. So whether you want to use it to manage laboratory equipment, sample the environment, teach bioinformatics, control your home security or make a model lunar lander, its all built from the same basic principles. To quote Richard Feynman, What I cannot create, I do not understand.
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Machine learning is a modern approach to problem-solving and task automation. In particular, machine learning is concerned with the development and applications of algorithms that can recognize patterns in data and use them for predictive modeling. Artificial neural networks are a particular class of machine learning algorithms and models that evolved into what is now described as deep learning. Given the computational advances made in the last decade, deep learning can now be applied to massive data sets and in innumerable contexts. Therefore, deep learning has become its own subfield of machine learning. In the context of biological research, it has been increasingly used to derive novel insights from high-dimensional biological data. To make the biological applications of deep learning more accessible to scientists who have some experience with machine learning, we solicited input from a community of researchers with varied biological and deep learning interests. These individuals collaboratively contributed to this manuscripts writing using the GitHub version control platform and the Manubot manuscript generation toolset. The goal was to articulate a practical, accessible, and concise set of guidelines and suggestions to follow when using deep learning. In the course of our discussions, several themes became clear: the importance of understanding and applying machine learning fundamentals as a baseline for utilizing deep learning, the necessity for extensive model comparisons with careful evaluation, and the need for critical thought in interpreting results generated by deep learning, among others.
We identified and computed the horizontal wavelengths of atmospheric gravity waves in clouds using a visible camera installed on a window of the Columbus module of the International Space Station (ISS) and controlled by a Raspberry Pi computer. The experiment was designed in the context of the Astro Pi challenge, a project run by ESA in collaboration with the Raspberry Pi Foundation, where students are allowed the opportunity to write a code to be executed at the ISS. A code was developed to maximize the probability of capturing images of clouds while the ISS is orbiting the Earth. Several constraints had to be fulfilled such as the experiment duration limit (3 hours) and the maximum data size (3 gigabytes). After receiving the data from the ISS, small-scale gravity waves were observed in different regions in the northern hemisphere with horizontal wavelengths in the range of 1.0 to 4.7 km.
We present a novel solution to automated beam alignment optimization. This device is based on a Raspberry Pi computer, stepper motors, commercial optomechanics and electronic devices, and the open source machine learning algorithm M-LOOP. We provide schematic drawings for the custom hardware necessary to operate the device and discuss diagnostic techniques to determine the performance. The beam auto-aligning device has been used to improve the alignment of a laser beam into a single-mode optical fiber from manually optimized fiber alignment with an iteration time of typically 20~minutes. We present example data of one such measurement to illustrate device performance.
Decades of social science research identified ten fundamental dimensions that provide the conceptual building blocks to describe the nature of human relationships. Yet, it is not clear to what extent these concepts are expressed in everyday language and what role they have in shaping observable dynamics of social interactions. After annotating conversational text through crowdsourcing, we trained NLP tools to detect the presence of these types of interaction from conversations, and applied them to 160M messages written by geo-referenced Reddit users, 290k emails from the Enron corpus and 300k lines of dialogue from movie scripts. We show that social dimensions can be predicted purely from conversations with an AUC up to 0.98, and that the combination of the predicted dimensions suggests both the types of relationships people entertain (conflict vs. support) and the types of real-world communities (wealthy vs. deprived) they shape.
Electricity is an essential comfort to support our daily activities. With the competitive increase and energy costs by the industry, new values and opportunities for delivering electricity to customers are produced. One of these new opportunities is electric vehicles. With the arrival of electric vehicles, various challenges and opportunities are being presented in the electric power system worldwide. For example, under the traditional electric power billing scheme, electric power has to be consumed where it is needed so that end-users could not charge their electric vehicles at different points (e.g. a relatives house) if this the correct user is not billed (this due to the high consumption of electrical energy that makes it expensive). To achieve electric mobility, they must solve new challenges, such as the smart metering of energy consumption and the cybersecurity of these measurements. The present work shows a study of the different smart metering technologies that use blockchain and other security mechanisms to achieve e-mobility.
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