AMADA-Analysis of Multidimensional Astronomical Datasets

We present AMADA, an interactive web application to analyse multidimensional datasets. The user uploads a simple ASCII file and AMADA performs a number of exploratory analysis together with contemporary visualizations diagnostics. The package performs a hierarchical clustering in the parameter space, and the user can choose among linear, monotonic or non-linear correlation analysis. AMADA provides a number of clustering visualization diagnostics such as heatmaps, dendrograms, chord diagrams, and graphs. In addition, AMADA has the option to run a standard or robust principal components analysis, displaying the results as polar bar plots. The code is written in R and the web interface was created using the Shiny framework. AMADA source-code is freely available at https://goo.gl/KeSPue, and the shiny-app at http://goo.gl/UTnU7I.Comment: Accepted for publication in Astronomy & Computin

Intermittent predictive control of an inverted pendulum

Intermittent predictive pole-placement control is successfully applied to the constrained-state control of a prestabilised experimental inverted pendulum

Symbolic Control and Planning of Robotic Motion [Grand Challenges of Robotics]

Mobile robots are complex systems that combine mechanical elements such as wheels and gears, electromechanical devices such as motors, clutches and brakes, digital circuits such as processors and smart sensors, and software programs such as embedded controllers. They have mechanical constraints (e.g., a car-like robot cannot move sideways), limited energy resources, and computation, sensing, and communication capabilities. They operate in environments cluttered with possibly moving and shape changing obstacles, and their objectives can change over time, such as in the case of appearing and disappearing targets. Robot motion planning and control is the problem of automatic construction of robot control strategies from task specifications given in high-level, human-like language. The challenge in this area is the development of computationally efficient frameworks allowing for systematic, provably correct, control design accommodating both the robot constraints and the complexity of the environment, while at the same time allowing for expressive task specifications

Mass spectrometric methods and bioinformatics tools for accurate identification of MicroRNA biomarkers

MicroRNA (miRNA) are a class of endogenous non-protein-coding RNA of ~19-25 nucleotides long that post-transcriptionally regulate protein expression by targeting messenger RNAs for cleavage or translational repression. MiRNAs have been implicated in the initiation and progression of 160+ human diseases. Unique miRNA differential expression signatures can be used as a basis of discriminating against the presence or absence of human diseases. MiRNAs are therefore a promising and emerging class of disease biomarkers and therapeutic targets; however, the accurate detection of a specific miRNA has continued to be a challenging issue. Recently, mass spectrometry (MS) has seen remarkable technological advancements making it an attractive alternative to the conventional molecular biology miRNA characterization techniques. This study consistently documents the development of various analytical techniques aimed at characterization of miRNAs. The current literature in the field of miRNA is covered in chapter one. In chapter two, two new MS based concepts for detection of miRNA are introduced; a) the miRNA is captured using a specific complementary DNA probe, eluted and digested with specific endonuclease. The digested miRNA fragments are measured by MS resulting in a peak pattern that is dependent on the miRNA sequence i.e. an intrinsic mass signature and b) a unique mass signature is created by incorporating extra nucleotide(s) to the 3' end of miRNA and the extended miRNA is measured by using MS. The molecular mass of the extended miRNA, which is defined as extended mass signature, is expected to be different from the other miRNA within the same sample. These two approaches can improve the accuracy on qualitative MS identification of specific miRNA. To better understand miRNA function however, it is important to elucidate the nucleotide sequence of the miRNA. Chapter three of this study introduces a novel MS based assay for the sequencing of miRNA through chemical hydrolysis. In this study, by taking advantage of the mixing between a miRNA sample and an acidic MALDI matrix prior to the MALDI-TOF MS measurements, a unique yet simple and relatively cost-effective approach to generate miRNA sequencing ladders was developed. By using this method, 100% sequence coverage and accuracy in the sequencing of selected miRNAs were achieved. When many samples are involved, the data generated from miRNA measurements can be complex and manual data processing is tedious and challenging, as such, the spectral interpretation of mass spectrometric data can quickly turn out to be the bottleneck in miRNA analysis. The success of MS as a tool for analysis of miRNA will therefore strongly depend on the development of relevant computational software with the ability to properly interpret and analyze the large data. To meet this need, chapter four of this work explains the development of MicroRNA MultiTool, a computational software for the rapid interpretation of MS data containing human miRNA. Users can directly enter data obtained from mass spectrometric measurement in order to obtain the identify of miRNA, highly reducing the time needed to process data. The development of such analytical and bioinformatics tools will provide scientists with the opportunity to better understand miRNA functions and will be influential in propelling the breakthroughs of miRNA in clinical diagnostics and therapeutic fields

Optimal path planning for surveillance with temporal-logic constraints

In this paper we present a method for automatically generating optimal robot paths satisfying high-level mission specifications. The motion of the robot in the environment is modeled as a weighted transition system. The mission is specified by an arbitrary linear temporal-logic (LTL) formula over propositions satisfied at the regions of a partitioned environment. The mission specification contains an optimizing proposition, which must be repeatedly satisfied. The cost function that we seek to minimize is the maximum time between satisfying instances of the optimizing proposition. For every environment model, and for every formula, our method computes a robot path that minimizes the cost function. The problem is motivated by applications in robotic monitoring and data-gathering. In this setting, the optimizing proposition is satisfied at all locations where data can be uploaded, and the LTL formula specifies a complex data-collection mission. Our method utilizes Büchi automata to produce an automaton (which can be thought of as a graph) whose runs satisfy the temporal-logic specification. We then present a graph algorithm that computes a run corresponding to the optimal robot path. We present an implementation for a robot performing data collection in a road-network platform.This material is based upon work supported in part by ONR-MURI (award N00014-09-1-1051), ARO (award W911NF-09-1-0088), and Masaryk University (grant numbers LH11065 and GD102/09/H042), and other funding sources (AFOSR YIP FA9550-09-1-0209, NSF CNS-1035588, NSF CNS-0834260). (N00014-09-1-1051 - ONR-MURI; W911NF-09-1-0088 - ARO; LH11065 - Masaryk University; GD102/09/H042 - Masaryk University; FA9550-09-1-0209 - AFOSR YIP; CNS-1035588 - NSF; CNS-0834260 - NSF