Sensor Data Integrity : Multi-Sensor Perception for Unmanned Ground Vehicles

This document describes large, accurately calibrated and time-synchronised datasets, gathered in controlled environmental conditions, using an unmanned ground vehicle equipped with a wide variety of sensors. These sensors include: multiple laser scanners, a millimetre wave radar scanner, a colour camera and an infra-red camera. Full details of the sensors are given, as well as the calibration parameters needed to locate them with respect to each other and to the platform. This report also specifies the format and content of the data, and the conditions in which the data have been gathered. The data collection was made in two different situations of the vehicle: static and dynamic. The static tests consisted of sensing a fixed ’reference’ terrain, containing simple known objects, from a motionless vehicle. For the dynamic tests, data were acquired from a moving vehicle in various environments, mainly rural, including an open area, a semi-urban zone and a natural area with different types of vegetation. For both categories, data have been gathered in controlled environmental conditions, which included the presence of dust, smoke and rain. Most of the environments involved were static, except for a few specific datasets which involve the presence of a walking pedestrian. Finally, this document presents illustrations of the effects of adverse environmental conditions on sensor data, as a first step towards reliability and integrity in autonomous perceptual systems.

The Marulan data sets: multi-sensor perception in a natural environment with challenging conditions

In this paper we present large, accurately calibrated and time-synchronized data sets, gathered outdoors in controlled and variable environmental conditions, using an unmanned ground vehicle (UGV), equipped with a wide variety of sensors. These include four 2D laser scanners, a radar scanner, a color camera and an infrared camera. It provides a full description of the system used for data collection and the types of environments and conditions in which these data sets have been gathered, which include the presence of airborne dust, smoke and rain.

Laser-camera data discrepancies and reliable perception in outdoor robotics

This work aims to promote integrity in autonomous perceptual systems, with a focus on outdoor unmanned ground vehicles equipped with a camera and a 2D laser range finder. A method to check for inconsistencies between the data provided by these two heterogeneous sensors is proposed and discussed. First, uncertainties in the estimated transformation between the laser and camera frames are evaluated and propagated up to the projection of the laser points onto the image. Then, for each pair of laser scan-camera image acquired, the information at corners of the laser scan is compared with the content of the image, resulting in a likelihood of correspondence. The result of this process is then used to validate segments of the laser scan that are found to be consistent with the image, while inconsistent segments are rejected. Experimental results illustrate how this technique can improve the reliability of perception in challenging environmental conditions, such as in the presence of airborne dust.

A hybrid genetic algorithm for the energy-efficient virtual machine placement problem in data centers

Server consolidation using virtualization technology has become an important technology to improve the energy efficiency of data centers. Virtual machine placement is the key in the server consolidation technology. In the past few years, many approaches to the virtual machine placement have been proposed. However, existing virtual machine placement approaches consider the energy consumption by physical machines only, but do not consider the energy consumption in communication network, in a data center. However, the energy consumption in the communication network in a data center is not trivial, and therefore should be considered in the virtual machine placement. In our preliminary research, we have proposed a genetic algorithm for a new virtual machine placement problem that considers the energy consumption in both physical machines and the communication network in a data center. Aiming at improving the performance and efficiency of the genetic algorithm, this paper presents a hybrid genetic algorithm for the energy-efficient virtual machine placement problem. Experimental results show that the hybrid genetic algorithm significantly outperforms the original genetic algorithm, and that the hybrid genetic algorithm is scalable.

Meta-analysis of individual patient data from randomized trials of chemotherapy plus cetuximab as first-line treatment for advanced non-small cell lung cancer

OBJECTIVES: Four randomized phase II/III trials investigated the addition of cetuximab to platinum-based, first-line chemotherapy in patients with advanced non-small cell lung cancer (NSCLC). A meta-analysis was performed to examine the benefit/risk ratio for the addition of cetuximab to chemotherapy. MATERIALS AND METHODS: The meta-analysis included individual patient efficacy data from 2018 patients and individual patient safety data from 1970 patients comprising respectively the combined intention-to-treat and safety populations of the four trials. The effect of adding cetuximab to chemotherapy was measured by hazard ratios (HRs) obtained using a Cox proportional hazards model and odds ratios calculated by logistic regression. Survival rates at 1 year were calculated. All applied models were stratified by trial. Tests on heterogeneity of treatment effects across the trials and sensitivity analyses were performed for all endpoints. RESULTS: The meta-analysis demonstrated that the addition of cetuximab to chemotherapy significantly improved overall survival (HR 0.88, p=0.009, median 10.3 vs 9.4 months), progression-free survival (HR 0.90, p=0.045, median 4.7 vs 4.5 months) and response (odds ratio 1.46, p<0.001, overall response rate 32.2% vs 24.4%) compared with chemotherapy alone. The safety profile of chemotherapy plus cetuximab in the meta-analysis population was confirmed as manageable. Neither trials nor patient subgroups defined by key baseline characteristics showed significant heterogeneity for any endpoint. CONCLUSION: The addition of cetuximab to platinum-based, first-line chemotherapy for advanced NSCLC significantly improved outcome for all efficacy endpoints with an acceptable safety profile, indicating a favorable benefit/risk ratio.

Health big data analytics : current perspectives, challenges and potential solutions

Modern health information systems can generate several exabytes of patient data, the so called "Health Big Data", per year. Many health managers and experts believe that with the data, it is possible to easily discover useful knowledge to improve health policies, increase patient safety and eliminate redundancies and unnecessary costs. The objective of this paper is to discuss the characteristics of Health Big Data as well as the challenges and solutions for health Big Data Analytics (BDA) – the process of extracting knowledge from sets of Health Big Data – and to design and evaluate a pipelined framework for use as a guideline/reference in health BDA.

Oscar Pistorius and the death of Reeva Steenkamp : mapping public opinions on Twitter using frame analysis and data mining

This paper uses innovative content analysis techniques to map how the death of Oscar Pistorius' girlfriend, Reeva Steenkamp, was framed on Twitter conversations. Around 1.5 million posts from a two-week timeframe are analyzed with a combination of syntactic and semantic methods. This analysis is grounded in the frame analysis perspective and is different than sentiment analysis. Instead of looking for explicit evaluations, such as “he is guilty” or “he is innocent”, we showcase through the results how opinions can be identified by complex articulations of more implicit symbolic devices such as examples and metaphors repeatedly mentioned. Different frames are adopted by users as more information about the case is revealed: from a more episodic one, highly used in the very beginning, to more systemic approaches, highlighting the association of the event with urban violence, gun control issues, and violence against women. A detailed timeline of the discussions is provided.

Analysis of traffic injury severity in Dhaka, Bangladesh

This research identifies roadway, traffic, and environmental factors that influence the injury severity of road traffic crashes in Dhaka. Dhaka provides a rather unusual driving risk environment to study, since virtually anyone can obtain a drivers’ license and very little traffic enforcement and fines are given when drivers violate traffic rules. To examine this city with presumed heightened crash severity risk, police reported crash data from 2007 to 2011 containing about 2714 road traffic crashes were collected. The injury severity of traffic crashes—recorded as either fatal, serious injury, or property damage only—were modeled using an ordered Probit model. Significant factors increasing the probability of fatal injuries include crashes along highways (65%), absence of a road divider (80%), crashes during night time (54%), and vehicle-pedestrian collisions (367%); whereas two-way traffic configuration (21%), and traffic police controlled schemes (41%) decrease the probability of fatalities. Both similarities and differences of the findings between crash risk in Dhaka and developed countries are discussed in policy relevant terms.

High-load transit line passenger transmission and productiveness efficiencies

Performance of urban transit systems may be quantified and assessed using transit capacity and productive capacity in planning, design and operational management activities. Bunker (4) defines important productive performance measures of an individual transit service and transit line, which are extended in this paper to quantify efficiency and operating fashion of transit services and lines. Comparison of a hypothetical bus line’s operation during a morning peak hour and daytime hour demonstrates the usefulness of productiveness efficiency and passenger transmission efficiency, passenger churn and average proportion line length traveled to the operator in understanding their services’ and lines’ productive performance, operating characteristics, and quality of service. Productiveness efficiency can flag potential pass-up activity under high load conditions, as well as ineffective resource deployment. Proportion line length traveled can directly measure operating fashion. These measures can be used to compare between lines/routes and, within a given line, various operating scenarios and time horizons to target improvements. The next research stage is investigating within-line variation using smart card passenger data and field observation of pass-ups. Insights will be used to further develop practical guidance to operators.

Data innovation and open access [Blog]

After nearly fifteen years of the open access (OA) movement and its hard-fought struggle for a more open scholarly communication system, publishers are realizing that business models can be both open and profitable. Making journal articles available on an OA license is becoming an accepted strategy for maximizing the value of content to both research communities and the businesses that serve them. The first blog in this two-part series celebrating Data Innovation Day looks at the role that data-innovation is playing in the shift to open access for journal articles.

'Explain in plain English' questions revisited : data structures problems

Recent studies have linked the ability of novice (CS1) programmers to read and explain code with their ability to write code. This study extends earlier work by asking CS2 students to explain object-oriented data structures problems that involve recursion. Results show a strong correlation between ability to explain code at an abstract level and performance on code writing and code reading test problems for these object-oriented data structures problems. The authors postulate that there is a common set of skills concerned with reasoning about programs that explains the correlation between writing code and explaining code. The authors suggest that an overly exclusive emphasis on code writing may be detrimental to learning to program. Non-code writing learning activities (e.g., reading and explaining code) are likely to improve student ability to reason about code and, by extension, improve student ability to write code. A judicious mix of code-writing and code-reading activities is recommended.

Mobility and out-of-home activities of older people living in suburban environments : ‘Because I'm a driver, I don't have a problem'

Governments are challenged by the need to ensure that ageing populations stay active and engaged as they age. Therefore, it is critical to investigate the role of mobility in older people's engagement in out-of-home activities, and to identify the experiences they have within their communities. This research investigates the use of transportation by older people and its implications for their out-of-home activities within suburban environments. The qualitative, mixed-method approach employs data collection methods which include a daily travel diary (including a questionnaire), Global Positioning System (GPS) tracking and semi-structured interviews with older people living in suburban environments in Brisbane, Australia. Results show that older people are mobile throughout the city, and their car provides them with that opportunity to access desired destinations. This ability to drive allows older people to live independently and to assist others who do not drive, particularly where transport alternatives are not as accessible. The ability to transport goods and other people is a significant advantage of the private car over other transport options. People with no access to private transportation who live in low-density environments are disadvantaged when it comes to participation within the community. Further research is needed to better understand the relationship between transportation and participation within the community environment, to assist policy makers and city and transportation planners to develop strategies for age-friendly environments within the community.

Estimation of biodiesel properties from chemical composition – An artificial neural network (ANN) approach

Biodiesel, produced from renewable feedstock represents a more sustainable source of energy and will therefore play a significant role in providing the energy requirements for transportation in the near future. Chemically, all biodiesels are fatty acid methyl esters (FAME), produced from raw vegetable oil and animal fat. However, clear differences in chemical structure are apparent from one feedstock to the next in terms of chain length, degree of unsaturation, number of double bonds and double bond configuration-which all determine the fuel properties of biodiesel. In this study, prediction models were developed to estimate kinematic viscosity of biodiesel using an Artificial Neural Network (ANN) modelling technique. While developing the model, 27 parameters based on chemical composition commonly found in biodiesel were used as the input variables and kinematic viscosity of biodiesel was used as output variable. Necessary data to develop and simulate the network were collected from more than 120 published peer reviewed papers. The Neural Networks Toolbox of MatLab R2012a software was used to train, validate and simulate the ANN model on a personal computer. The network architecture and learning algorithm were optimised following a trial and error method to obtain the best prediction of the kinematic viscosity. The predictive performance of the model was determined by calculating the coefficient of determination (R2), root mean squared (RMS) and maximum average error percentage (MAEP) between predicted and experimental results. This study found high predictive accuracy of the ANN in predicting fuel properties of biodiesel and has demonstrated the ability of the ANN model to find a meaningful relationship between biodiesel chemical composition and fuel properties. Therefore the model developed in this study can be a useful tool to accurately predict biodiesel fuel properties instead of undertaking costly and time consuming experimental tests.

A data analytics case study assessing factors affecting pavement deflection values

Road networks are a national critical infrastructure. The road assets need to be monitored and maintained efficiently as their conditions deteriorate over time. The condition of one of such assets, road pavement, plays a major role in the road network maintenance programmes. Pavement conditions depend upon many factors such as pavement types, traffic and environmental conditions. This paper presents a data analytics case study for assessing the factors affecting the pavement deflection values measured by the traffic speed deflectometer (TSD) device. The analytics process includes acquisition and integration of data from multiple sources, data pre-processing, mining useful information from them and utilising data mining outputs for knowledge deployment. Data mining techniques are able to show how TSD outputs vary in different roads, traffic and environmental conditions. The generated data mining models map the TSD outputs to some classes and define correction factors for each class.

Quantitative data collection and study validity

The quality of data collection methods selected and the integrity of the data collected are integral tot eh success of a study. This chapter focuses on data collection and study validity. After reading the chapter, readers should be able to define types of data collection methods in quantitative research; list advantages and disadvantages of each method; discuss factors related to internal and external validity; critically evaluate data collection methods and discuss the need to operationalise variables of interest for data collection.