Adaptive Perception, State Estimation, and Navigation Methods for Mobile Robots

In this cumulative habilitation, publications with focus on robotic perception, self-localization, tracking, navigation, and human-machine interfaces have been selected. While some of the publications present research on a PR2 household robot in the Robotics Learning Lab of the University of California Berkeley on vision and machine learning tasks, most of the publications present research results while working at the AutoNOMOS-Labs at Freie Universität Berlin, with focus on control, planning and object tracking for the autonomous vehicles "MadeInGermany" and "e-Instein"

Air Force Institute of Technology Research Report 2016

This Research Report presents the FY16 research statistics and contributions of the Graduate School of Engineering and Management (EN) at AFIT. AFIT research interests and faculty expertise cover a broad spectrum of technical areas related to USAF needs, as reflected by the range of topics addressed in the faculty and student publications listed in this report. In most cases, the research work reported herein is directly sponsored by one or more USAF or DOD agencies. AFIT welcomes the opportunity to conduct research on additional topics of interest to the USAF, DOD, and other federal organizations when adequate manpower and financial resources are available and/or provided by a sponsor. In addition, AFIT provides research collaboration and technology transfer benefits to the public through Cooperative Research and Development Agreements (CRADAs)

Achievements, open problems and challenges for search based software testing

Search Based Software Testing (SBST) formulates testing as an optimisation problem, which can be attacked using computational search techniques from the field of Search Based Software Engineering (SBSE). We present an analysis of the SBST research agenda, focusing on the open problems and challenges of testing non-functional properties, in particular a topic we call 'Search Based Energy Testing' (SBET), Multi-objective SBST and SBST for Test Strategy Identification. We conclude with a vision of FIFIVERIFY tools, which would automatically find faults, fix them and verify the fixes. We explain why we think such FIFIVERIFY tools constitute an exciting challenge for the SBSE community that already could be within its reach

Crime prediction and monitoring in Porto, Portugal, using machine learning, spatial and text analytics

Crimes are a common societal concern impacting quality of life and economic growth. Despite the global decrease in crime statistics, specific types of crime and feelings of insecurity, have often increased, leading safety and security agencies with the need to apply novel approaches and advanced systems to better predict and prevent occurrences. The use of geospatial technologies, combined with data mining and machine learning techniques allows for significant advances in the criminology of place. In this study, official police data from Porto, in Portugal, between 2016 and 2018, was georeferenced and treated using spatial analysis methods, which allowed the identification of spatial patterns and relevant hotspots. Then, machine learning processes were applied for space-time pattern mining. Using lasso regression analysis, significance for crime variables were found, with random forest and decision tree supporting the important variable selection. Lastly, tweets related to insecurity were collected and topic modeling and sentiment analysis was performed. Together, these methods assist interpretation of patterns, prediction and ultimately, performance of both police and planning professionals

Spatial Applications of Topological Data Analysis: Cities, Snowflakes, Random Structures, and Spiders Spinning Under the Influence

Spatial networks are ubiquitous in social, geographical, physical, and biological applications. To understand the large-scale structure of networks, it is important to develop methods that allow one to directly probe the effects of space on structure and dynamics. Historically, algebraic topology has provided one framework for rigorously and quantitatively describing the global structure of a space, and recent advances in topological data analysis (TDA) have given scholars a new lens for analyzing network data. In this paper, we study a variety of spatial networks -- including both synthetic and natural ones -- using novel topological methods that we recently developed for analyzing spatial networks. We demonstrate that our methods are able to capture meaningful quantities, with specifics that depend on context, in spatial networks and thereby provide useful insights into the structure of those networks, including a novel approach for characterizing them based on their topological structures. We illustrate these ideas with examples of synthetic networks and dynamics on them, street networks in cities, snowflakes, and webs spun by spiders under the influence of various psychotropic substances.Comment: revised versio

Air Force Institute of Technology Research Report 2017

This Research Report presents the FY18 research statistics and contributions of the Graduate School of Engineering and Management (EN) at AFIT. AFIT research interests and faculty expertise cover a broad spectrum of technical areas related to USAF needs, as reflected by the range of topics addressed in the faculty and student publications listed in this report. In most cases, the research work reported herein is directly sponsored by one or more USAF or DOD agencies. AFIT welcomes the opportunity to conduct research on additional topics of interest to the USAF, DOD, and other federal organizations when adequate manpower and financial resources are available and/or provided by a sponsor. In addition, AFIT provides research collaboration and technology transfer benefits to the public through Cooperative Research and Development Agreements (CRADAs)

Forecasting Network Traffic: A Survey and Tutorial with Open-Source Comparative Evaluation

This paper presents a review of the literature on network traffic prediction, while also serving as a tutorial to the topic. We examine works based on autoregressive moving average models, like ARMA, ARIMA and SARIMA, as well as works based on Artifical Neural Networks approaches, such as RNN, LSTM, GRU, and CNN. In all cases, we provide a complete and self-contained presentation of the mathematical foundations of each technique, which allows the reader to get a full understanding of the operation of the different proposed methods. Further, we perform numerical experiments based on real data sets, which allows comparing the various approaches directly in terms of fitting quality and computational costs. We make our code publicly available, so that readers can readily access a wide range of forecasting tools, and possibly use them as benchmarks for more advanced solutions

Features extraction scheme for behavioral biometric authentication in touchscreen mobile devices

Common authentication mechanisms in mobile devices such as passwords and Personal Identification Number have failed to keep up with the rapid pace of challenges associated with the use of ubiquitous devices over the Internet, since they can easily be lost or stolen. Thus, it is important to develop authentication mechanisms that can be adapted to such an environment. Biometric-based person recognition is a good alternative to overcome the difficulties of password and token approaches, since biometrics cannot be easily stolen or forgotten. An important characteristic of biometric authentication is that there is an explicit connection with the user's identity, since biometrics rely entirely on behavioral and physiological characteristics of human being. There are a variety of biometric authentication options that have emerged so far, all of which can be used on a mobile phone. These options include but are not limited to, face recognition via camera, fingerprint, voice recognition, keystroke and gesture recognition via touch screen. Touch gesture behavioural biometrics are commonly used as an alternative solution to existing traditional biometric mechanism. However, current touch gesture authentication schemes are fraught with authentication accuracy problems. In fact, the extracted features used in some researches on touch gesture schemes are limited to speed, time, position, finger size and finger pressure. However, extracting a few touch features from individual touches is not enough to accurately distinguish various users. In this research, behavioural features are extracted from recorded touch screen data and a discriminative classifier is trained on these extracted features for authentication. While the user performs the gesture, the touch screen sensor is leveraged on and twelve of the user‘s finger touch features are extracted. Eighty four different users participated in this research work, each user drew six gesture with a total of 504 instances. The extracted touch gesture features are normalised by scaling the values so that they fall within a small specified range. Thereafter, five different Feature Selection Algorithm were used to choose the most significant features subset. Six different machine learning classifiers were used to classify each instance in the data set into one of the predefined set of classes. Results from experiments conducted in the proposed touch gesture behavioral biometrics scheme achieved an average False Reject Rate (FRR) of 7.84%, average False Accept Rate (FAR) of 1%, average Equal Error Rate (EER) of 4.02% and authentication accuracy of 91.67%,. The comparative results showed that the proposed scheme outperforms other existing touch gesture authentication schemes in terms of FAR, EER and authentication accuracy by 1.67%, 6.74% and 4.65% respectively. The results of this research affirm that user authentication through gestures is promising, highly viable and can be used for mobile devices

Measuring the direction of innovation: frontier tools in unassisted machine learning

NSF SciSIP grant, SES-1564368. - National Science FoundationOthe