Nt1330 Unit 5 Final Report

Activity 1 presents the challenge of detecting vehicles in front of a stereo camera and determining the distance to them using stereo data. The team accomplished this by creating a disparity map from the two rectified images and then plotting a point cloud based on this data. A cascaded object detector is then used to get regions of interest (ROIs) where vehicles are in frame. These ROIs are then used to extract the depth of the vehicle in the point cloud. The result is a distance to the identified vehicle.

Many HUD requirements are met with multiple e-mails requesting a change or an exception. Every response from the city explaining why we cannot make an exception initiates multiple e-mails explaining why we should make this client and exception. This is so time consuming.

Partitioning strategy: The hierarchical partitioning of data into a set of directories – The placement and replication properties of directories is

As discussed, please work with Richard to upgrade Option C to quantity 16 servers with SFP+ (2 10Gb connectivity and 1GB for iDRaC per node) and 16 MD1400. Also, please verify configuration. Lastly, we need a quote from the networking team (Nexus, SFP+, and cabling).

With the development of the act, laws and regulations addressing issues associated with the prevention, responds and payment of oil pollution were put in place. Such laws and regulations mandated new requirements for companies and their associated personnel involved in the shipment or extraction of oil, such as prevention and response plans, routine documentation and licensing renewal, and evidence of personnel’s competency and knowledge. The act also revised the staffing standards of all foreign shipment vessels, thereby requiring all foreign vessels to meet U.S standards to gain entry into U.S territory. Additionally, these prevention laws and regulations required new standards for shipment vessels and routine inspections. Due to the extreme

1) Decompress the APK packages of old version and the new one of an application.

Mission-creep is when a certain organization is assigned a task and then the organization expands their agenda past the main goal of the original task. There is a desire of the organization to essentially keep going after their initial task has already been completed. An example of this is when the US Department of Agriculture wanted submachine guns for its law enforcers, for this bureaucracy does not and should not have a need for them. Another example would be when the Department of Homeland Security created fusion centers after 9/11 to monitor potential terrorist communication, but these fusions pried into other crimes aside from terrorism. Mission-creep is a potential problem for keeping bureaucracies focused because they are going beyond what is relevant when there are other things to be accomplished. Mission-creep in bureaucracies is essentially like a person who recently cut their lawn to keep it looking nice, but then in turn wanted to keep going and mow the rest of the neighborhood’s lawns as well to make the whole neighborhood look nice.

It was a pleasure talking to you last night! Below is a recap of our phone conversation, please review and advise me with any further comments you may have:

More than 2.6 million of the top 10 million websites on the web are powered by WordPress. It's no wonder hackers are so interested in WP websites. WordPress does a good job of issuing patches and monitoring vulnerabilities, but with so many third-party themes and plugins out there, your WP website may still be at risk.

As one of the most popular and widely used data mining techniques, cluster analysis is mainly divided into hierarchical clustering and partitional clustering, which are carried out in a supervised or unsupervised way to separate data into different groups based on similar characteristics. Both the hierarchical and partitional clustering have advantages and drawbacks. Especially, the efficiency and accuracy are the primary challenges that clustering analysis has to face. For example, the most efficient algorithm of hierarchical clustering is complete-linkage clustering in some special cases, the complexity of which is Ο (n2). Therefore, the hierarchical clustering usually leads to too slow efficiency for large data

For measuring the quality of clusters four criteria have been used. The first three criteria are designed so as to measure the quality of cluster sets at different levels of granularity. Ideally it’s needed to generate partitions that have compact, well separated clusters. Hence, the criteria used presently combine the two measures to return a value that indicates the quality of the partition thus the value returned is minimized when the partition is judged to consist of compact well separated clusters with different criteria judging different partition as the best one. The last criterion is based on time efficiency.

Partitioning methods: This algorithm groups dataset into q clusters, where q is a predefined parameter.

Clustering is a fundamental approach in data mining and its aim is to organize data into distinct groups to identify intrinsic hidden patterns of data. In other words, clustering methods divide a set of instances into several groups without any prior knowledge using the similarity of objects in which patterns in the same group have more similarities to each other than patterns in different groups. It has been successfully applied in various fields such as image processing (Wu & Leahy, 1993) cybersecurity (Kozma, Rosa, & Piazentin, 2013), pattern recognition (Haghtalab, Xanthopoulos, & Madani, 2015), bioinformatics(C. Xu & Su, 2015), protein analysis (de Andrades, Dorn, Farenzena, & Lamb, 2013), microarray analysis (Castellanos-Garzón,

We might have created segments that are not meaningfully different from each other. The segmentation should produce segments that are similar characteristics within the group and different characteristics across groups.

The final result is a tree like structure referred as Dendrogram, which shows the way the clusters are related. User can specify a distance or number of clusters to view the dataset in disjoint groups. In this way, the user can get rid of a cluster that does not serve any purpose as per his expertise. In this case, we used MVA (Multivariate data analysis) node in optimization package: modeFRONTIER (ESTECO, 2015) and other statistical software IBM SPSS (IBMSPSS, 2015) for HCA analysis.