User-friendly Programming Interface Research Articles

Towards a ROS2-based software architecture for service robots

This paper presents a scalable software architecture based on robot operating system 2 (ROS2) for service robots. ROS2 supports the data distribution service (DDS) protocol that provides benefits such as real-time operation and security and performance enhancements. However, ROS2 still lacks task management capabilities, essential for practical robotic applications consisting of multiple threads and processes. Moreover, integrating new devices into ROS2 requires additional development effort to create specific drivers for specific devices. The proposed software architecture addresses these drawbacks and provides a simple and user-friendly programming interface for easier integrating of various devices and existing ROS2 applications. Moreover, it is designed using python with multi-processing to avoid issues related to the python global interrupt lock (GIL). To verify the developed software architecture, an application for a custom-made service robot called the SeoulTech service robot (SSR) is implemented on a Jetson Xavier NX board with various features, such as ROS2 navigation and SLAM, text-to-speech (TTS), speech recognition, and face recognition.

ST4ML: Machine Learning Oriented Spatio-Temporal Data Processing at Scale

Data scientists and researchers utilize enormous spatio-temporal data and build machine learning models to solve practical problems in diverse domains including intelligent transportation, urban planning, epidemic prediction, and many more. Extracting application-specific features from big spatio-temporal data poses system requirements of heterogeneous data support, efficient and scalable computing over spatial and temporal dimensions, as well as a user-friendly programming interface. This paper presents ST4ML, a distributed spatio-temporal data processing system to support scalable machine-learning-oriented applications. We propose a three-stage pipelining computing framework, namely "selection-conversion-extraction" to abstract the distributed computing flow and implement it based on Apache Spark. To the best of our knowledge, ST4ML is the first of its kind to realize our design considerations. Extensive experiments with real-world datasets evidence that ST4ML outperforms straightforward extensions of existing ST data processing systems by up to an order of magnitude. ST4ML is open-sourced at https://github.com/Panrong/st4ml.

Neural Parallel Engine: A toolbox for massively parallel neural signal processing

BackgroundLarge-scale neural recordings provide detailed information on neuronal activities and can help elicit the underlying neural mechanisms of the brain. However, the computational burden is also formidable when we try to process the huge data stream generated by such recordings. New methodIn this study, we report the development of Neural Parallel Engine (NPE), a toolbox for massively parallel neural signal processing on graphical processing units (GPUs). It offers a selection of the most commonly used routines in neural signal processing such as spike detection and spike sorting, including advanced algorithms such as exponential-component-power-component (EC-PC) spike detection and binary pursuit spike sorting. We also propose a new method for detecting peaks in parallel through a parallel compact operation. ResultsOur toolbox is able to offer a 5× to 110× speedup compared with its CPU counterparts depending on the algorithms. A user-friendly MATLAB interface is provided to allow easy integration of the toolbox into existing workflows. Comparison with existing methodsPrevious efforts on GPU neural signal processing only focus on a few rudimentary algorithms, are not well-optimized and often do not provide a user-friendly programming interface to fit into existing workflows. There is a strong need for a comprehensive toolbox for massively parallel neural signal processing. ConclusionsA new toolbox for massively parallel neural signal processing has been created. It can offer significant speedup in processing signals from large-scale recordings up to thousands of channels.

Development of a fully automated system for delivering odors in an MRI environment

We describe the development and evaluation of a computer-controlled system for delivering odors in a magnetic resonance imaging (MRI) environment. The system allows a timely presentation of different odors in synchrony with MRI sequences and participant's inspiration phase. The rise/fall time of odor deliverance has been optimized to generate prompt and strong stimulations. Equipped with a user-friendly programming interface, the system can be used reliably in a wide range of experimental paradigms. We have paid particular attention to developing a portable system that is relatively easy, rapid, and inexpensive to replicate. The equipment has been tested in a 3-Tesla MRI in a boxcar paradigm, in which stimulation conditions alternated with rest periods (no stimulation). The experiment demonstrated the good functioning of the device and its efficiency in producing the expected activation in the olfactory cortex; it also revealed some methodological and technical aspects to be improved.

Incremental Learning of Tasks From User Demonstrations, Past Experiences, and Vocal Comments

Since many years the robotics community is envisioning robot assistants sharing the same environment with humans. It became obvious that they have to interact with humans and should adapt to individual user needs. Especially the high variety of tasks robot assistants will be facing requires a highly adaptive and user-friendly programming interface. One possible solution to this programming problem is the learning-by-demonstration paradigm, where the robot is supposed to observe the execution of a task, acquire task knowledge, and reproduce it. In this paper, a system to record, interpret, and reason over demonstrations of household tasks is presented. The focus is on the model-based representation of manipulation tasks, which serves as a basis for incremental reasoning over the acquired task knowledge. The aim of the reasoning is to condense and interconnect the data, resulting in more general task knowledge. A measure for the assessment of information content of task features is introduced. This measure for the relevance of certain features relies both on general background knowledge as well as task-specific knowledge gathered from the user demonstrations. Beside the autonomous information estimation of features, speech comments during the execution, pointing out the relevance of features are considered as well. The results of the incremental growth of the task knowledge when more task demonstrations become available and their fusion with relevance information gained from speech comments is demonstrated within the task of laying a table.

Analysis of common k-mers for whole genome sequences using SSB-tree.

As sequenced genomes become larger and sequencing process becomes faster, there is a need to develop a tool to analyze sequences in the whole genomic scale. However, on-memory algorithms such as suffix tree and suffix array are not applicable to the analysis of whole genome sequence set, since the size of individual whole genome ranges from several million base pairs to hundreds billion base pairs. In order to effectively manipulate the huge sequence data, it is necessary to use the indexed data structure for external memory. In this paper, we introduce a workbench called SequeX for the analysis and visualization of whole genome sequences using SSB-tree (Static SB-tree). It consists of two parts: the analysis query subsystem and the visualization subsystem. The query subsystem supports various transactions such as pattern matching, k-occurrence, and k-mer analysis. The visualization subsystem helps biologists to easily understand whole genome structure and feature by sequence viewer, annotation viewer, CGR (Chaos Game Representation) viewer, and k-mer viewer. The system also supports a user-friendly programming interface based on Java script for batch processing and the extension for a specific purpose of a user. SequeX can be used to identify conserved genes or sequences by the analysis of the common k-mers and annotation. We analyze the common k-mer for 72 microbial genomes announced by Entrez, and find an interesting biological fact that the longest common k-mer for 72 sequences is 11-mer, and only 11 such sequences exist. Finally we note that many common k-mers occur in conserved region such as CDS, rRNA, and tRNA.