MScMS-II: an innovative IR-based indoor coordinate measuring system for large-scale metrology applications

According to the current great interest concerning large-scale metrology applications in many different fields of manufacturing industry, technologies and techniques for dimensional measurement have recently shown a substantial improvement. Ease-of-use, logistic and economic issues, as well as metrological performance are assuming a more and more important role among system requirements. This paper describes the architecture and the working principles of a novel infrared (IR) optical-based system, designed to perform low-cost and easy indoor coordinate measurements of large-size objects. The system consists of a distributed network-based layout, whose modularity allows fitting differently sized and shaped working volumes by adequately increasing the number of sensing units. Differently from existing spatially distributed metrological instruments, the remote sensor devices are intended to provide embedded data elaboration capabilities, in order to share the overall computational load. The overall system functionalities, including distributed layout configuration, network self-calibration, 3D point localization, and measurement data elaboration, are discussed. A preliminary metrological characterization of system performance, based on experimental testing, is also presente

Mixed marker-based/marker-less visual odometry system for mobile robots

When moving in generic indoor environments, robotic platforms generally rely solely on information provided by onboard sensors to determine their position and orientation. However, the lack of absolute references often leads to the introduction of severe drifts in estimates computed, making autonomous operations really hard to accomplish. This paper proposes a solution to alleviate the impact of the above issues by combining two vision‐based pose estimation techniques working on relative and absolute coordinate systems, respectively. In particular, the unknown ground features in the images that are captured by the vertical camera of a mobile platform are processed by a vision‐based odometry algorithm, which is capable of estimating the relative frame‐to‐frame movements. Then, errors accumulated in the above step are corrected using artificial markers displaced at known positions in the environment. The markers are framed from time to time, which allows the robot to maintain the drifts bounded by additionally providing it with the navigation commands needed for autonomous flight. Accuracy and robustness of the designed technique are demonstrated using an off‐the‐shelf quadrotor via extensive experimental test

A fast and robust hand-driven 3D mouse

The development of new interaction paradigms requires a natural interaction. This means that people should be able to interact with technology with the same models used to interact with everyday real life, that is through gestures, expressions, voice. Following this idea, in this paper we propose a non intrusive vision based tracking system able to capture hand motion and simple hand gestures. The proposed device allows to use the hand as a "natural" 3D mouse, where the forefinger tip or the palm centre are used to identify a 3D marker and the hand gesture can be used to simulate the mouse buttons. The approach is based on a monoscopic tracking algorithm which is computationally fast and robust against noise and cluttered backgrounds. Two image streams are processed in parallel exploiting multi-core architectures, and their results are combined to obtain a constrained stereoscopic problem. The system has been implemented and thoroughly tested in an experimental environment where the 3D hand mouse has been used to interact with objects in a virtual reality application. We also provide results about the performances of the tracker, which demonstrate precision and robustness of the proposed syste

Motion Artifact Reduction in Breast Dynamic Infrared Imaging

Dynamic infrared imaging is a promising technique in breast oncology. In this study a QWIP infrared camera is used to acquire a sequence of consecutive thermal images of the patient's breast for 10 s. Information on the local blood perfusion is obtained from the spectral analysis of the time series at each image pixel. Due to respiratory and motion artifacts, the direct comparison of the temperature values that a pixel assumes along the sequence becomes difficult. In fact, the small temperature changes due to blood perfusion, of the order of 10-50 mK, which constitute the signal of interest in the time domain, are superimposed onto large temperature fluctuations due to the subject's motion, which represent noise. To improve the time series signal-to-noise ratio, and, as a consequence, enhance the specificity and sensitivity of the dynamic infrared examination, it is important to realign the thermal images of the acquisition sequence thus reducing motion artifacts. In a previous study we demonstrated that a registration algorithm based on fiducial points is suitable to both clinical applications and research, when associated with a proper set of skin markers. In this paper, we quantitatively evaluate the performance of different marker sets by means of a model that allows for estimating the signal-to-noise ratio increment due to registration, and we conclude that a 12-marker set is a good compromise between motion artifact reduction and the time required to prepare the patien

Parallel processing and expert systems

Whether it be monitoring the thermal subsystem of Space Station Freedom, or controlling the navigation of the autonomous rover on Mars, NASA missions in the 1990s cannot enjoy an increased level of autonomy without the efficient implementation of expert systems. Merely increasing the computational speed of uniprocessors may not be able to guarantee that real-time demands are met for larger systems. Speedup via parallel processing must be pursued alongside the optimization of sequential implementations. Prototypes of parallel expert systems have been built at universities and industrial laboratories in the U.S. and Japan. The state-of-the-art research in progress related to parallel execution of expert systems is surveyed. The survey discusses multiprocessors for expert systems, parallel languages for symbolic computations, and mapping expert systems to multiprocessors. Results to date indicate that the parallelism achieved for these systems is small. The main reasons are (1) the body of knowledge applicable in any given situation and the amount of computation executed by each rule firing are small, (2) dividing the problem solving process into relatively independent partitions is difficult, and (3) implementation decisions that enable expert systems to be incrementally refined hamper compile-time optimization. In order to obtain greater speedups, data parallelism and application parallelism must be exploited

Introducing SPeDE : high-throughput dereplication and accurate determination of microbial diversity from matrix-assisted laser desorption-ionization time of flight mass spectrometry data

The isolation of microorganisms from microbial community samples often yields a large number of conspecific isolates. Increasing the diversity covered by an isolate collection entails the implementation of methods and protocols to minimize the number of redundant isolates. Matrix-assisted laser desorption-ionization time-of-flight (MALDI-TOF) mass spectrometry methods are ideally suited to this dereplication problem because of their low cost and high throughput. However, the available software tools are cumbersome and rely either on the prior development of reference databases or on global similarity analyses, which are inconvenient and offer low taxonomic resolution. We introduce SPeDE, a user-friendly spectral data analysis tool for the dereplication of MALDI-TOF mass spectra. Rather than relying on global similarity approaches to classify spectra, SPeDE determines the number of unique spectral features by a mix of global and local peak comparisons. This approach allows the identification of a set of nonredundant spectra linked to operational isolation units. We evaluated SPeDE on a data set of 5,228 spectra representing 167 bacterial strains belonging to 132 genera across six phyla and on a data set of 312 spectra of 78 strains measured before and after lyophilization and subculturing. SPeDE was able to dereplicate with high efficiency by identifying redundant spectra while retrieving reference spectra for all strains in a sample. SPeDE can identify distinguishing features between spectra, and its performance exceeds that of established methods in speed and precision. SPeDE is open source under the MIT license and is available from https://github.com/LM-UGent/SPeDE. IMPORTANCE Estimation of the operational isolation units present in a MALDI-TOF mass spectral data set involves an essential dereplication step to identify redundant spectra in a rapid manner and without sacrificing biological resolution. We describe SPeDE, a new algorithm which facilitates culture-dependent clinical or environmental studies. SPeDE enables the rapid analysis and dereplication of isolates, a critical feature when long-term storage of cultures is limited or not feasible. We show that SPeDE can efficiently identify sets of similar spectra at the level of the species or strain, exceeding the taxonomic resolution of other methods. The high-throughput capacity, speed, and low cost of MALDI-TOF mass spectrometry and SPeDE dereplication over traditional gene marker-based sequencing approaches should facilitate adoption of the culturomics approach to bacterial isolation campaigns

Recovering complete and draft population genomes from metagenome datasets.

Assembly of metagenomic sequence data into microbial genomes is of fundamental value to improving our understanding of microbial ecology and metabolism by elucidating the functional potential of hard-to-culture microorganisms. Here, we provide a synthesis of available methods to bin metagenomic contigs into species-level groups and highlight how genetic diversity, sequencing depth, and coverage influence binning success. Despite the computational cost on application to deeply sequenced complex metagenomes (e.g., soil), covarying patterns of contig coverage across multiple datasets significantly improves the binning process. We also discuss and compare current genome validation methods and reveal how these methods tackle the problem of chimeric genome bins i.e., sequences from multiple species. Finally, we explore how population genome assembly can be used to uncover biogeographic trends and to characterize the effect of in situ functional constraints on the genome-wide evolution