If you conduct a scientific experiment or undertake a piece of research, you’ll usually need to write up a corresponding project or lab report, to summarize the objective of your task, the methods you followed, the results you obtained, and the conclusions you drew from your work. Here we provide a sample of great templates for producing such reports, which include layout guidelines to help guide you through the process.
Optimization is a crucial step in the development context of algorithms, where depending on the purpose, different levels of optimization can be applied. Thus, the Network Dijkstra algorithm has been choosen in order to perform the compilation and execution with some levels of optimization from the GCC, measuring its execution time, number of cycles and instructions. In the present work, it is also discussed how the front-end and middle-end analyzes are performed in GCC.
This report gives an overview of the various machine learning algorithms implemented to detect certain comments that may appear insulting to another participant on a social networking platform. Feature selection was performed using n-grams, and the WEKA machine learning toolkit was used to build supervised learning clasifiers, that provided an accuracy of 82% on the test dataset. The dataset was obtained from the popular data science competition portal, Kaggle.
Physics being an experimental science, we sought to learn how to prepare a lab and perform as a team accounting for errors and uncertainties and to reduce them. We gathered values for volume using Micrometer, gathered information on acceleration, velocity, and created a histogram using a PASCO motion sensor. A jumping experiment was also performed with a human and the motion sensor. Our main goal was to test the effects of human error and eliminating mechanical error.
This paper implements Simultaneous Localization and Mapping (SLAM) technique to construct a map of a given environment. A Real Time Appearance Based Mapping (RTAB-Map) approach was taken for accomplishing this task. Initially, a 2d occupancy grid and 3d octomap was created from a provided simulated environment. Next, a personal simulated environment was created for mapping as well. In this appearance based method, a process called Loop Closure is used to determine whether a robot has seen a location before or not. In this paper, it is seen that RTAB-Map is optimized for large scale and long term SLAM by using multiple strategies to allow for loop closure to be done in real time and the results depict that it can be an excellent solution for SLAM to develop robots that can map an environment in both 2d and 3d.
After the fixture was sealed within the bell jar, the Margherita 2 underwent bakeout and a test seal on a one-inch tile. Following this small success, a larger project of sealing a larger tile during cesiation launched in the final week of the author's summer in the LAPPD Lab. Central to the ability to control these processes is the ability to monitor temperatures, pressures, and RGA plots as well as adjust heating inputs accurately and timely. This report documents the heating procedures, basic sealing cycle, and plotting past events or in real-time with the Margherita 2 infrastructure.
Computer algorithms that are written with the intent to keep data private are used in every day cryptography. These algorithms may exhibit execution time behaviour which is dependant on secret information that is not known to an outsider. When carefully analysed, this dependency may leak information that can be used to gain unintended access to private data, effectively nullifying the use of such algorithms. This threat poses a vital risk to the field of computer cryptography, and analysis should be done in attempt to eradicate this potential threat from any algorithms in modern day use.
In this paper, attacks are orchestrated against several algorithms that have previously been used in cryptography, resulting in the successful retrieval of secret data within a manageable time-scale.
Accurate prognosis and prediction of a patient's current disease state is critical in an ICU. The use of vast amounts of digital medical information can help in predicting the best course of action for the diagnosis and treatment of patients. The proposed technique investigates the strength of using a combination of latent variable models (latent dirichlet allocation) and structured data to transform the information streams into potentially actionable knowledge. In this project, I use Apache Spark to predict mortality among ICU patients so that it can be used as an acuity surrogate to help physicians identify the patients in need of immediate care.