Air‐ground Matching: Appearance‐based GPS‐denied Urban Localization of Micro Aerial Vehicles

In this paper, we address the problem of globally localizing and tracking the pose of a camera‐equipped micro aerial vehicle (MAV) flying in urban streets at low altitudes without GPS. An image‐based global positioning system is introduced to localize the MAV with respect to the surrounding buildings. We propose a novel air‐ground image‐matching algorithm to search the airborne image of the MAV within a ground‐level, geotagged image database. Based on the detected matching image features, we infer the global position of the MAV by back‐projecting the corresponding image points onto a cadastral three‐dimensional city model. Furthermore, we describe an algorithm to track the position of the flying vehicle over several frames and to correct the accumulated drift of the visual odometry whenever a good match is detected between the airborne and the ground‐level images. The proposed approach is tested on a 2 km trajectory with a small quadrocopter flying in the streets of Zurich. Our vision‐based global localization can robustly handle extreme changes in viewpoint, illumination, perceptual aliasing, and over‐season variations, thus outperforming conventional visual place‐recognition approaches. The dataset is made publicly available to the research community. To the best of our knowledge, this is the first work that studies and demonstrates global localization and position tracking of a drone in urban streets with a single onboard camera.     

QoS aggregation for service orchestrations based on workflow pattern rules and MCDM method: evaluation at design time and runtime

Service consumers satisfaction is considered today as one of the main concern to be ensured by service providers, especially with the spread of concurrency and the increase of functionally equivalent services. This satisfaction is closely related to quality of service (QoS) perceived by service consumers. In this context, we propose an approach to determine the satisfaction degree corresponding to the QoS of service-based applications, with regard to service consumers’ QoS expectations. Our approach is based on a preference model, which is built only on the basis of service consumer’s provided information. This preference model is also based on the 2-additive Choquet operator that takes into account preferential dependencies. In this paper, we target both design time and runtime aggregation of QoS of service-based applications.     

Note: a high transmission Faraday optical isolator in the 9.2 μm range

We have fabricated and characterized an n-doped InSb Faraday isolator in the mid-IR range (9.2 μm). A high isolation ratio (31(2) dB) and low insertion loss (1.9(3) dB) are obtained. Temperature dependance is analyzed. Further possible improvements are discussed, including the realization of a two-stage isolator. A similar design can be used to cover a wide wavelength range (λ ~ 7.5-30 μm).     

Multi-objective optimization with an adaptive resonance theory-based estimation of distribution algorithm

The introduction of learning to the search mechanisms of optimization algorithms has been nominated as one of the viable approaches when dealing with complex optimization problems, in particular with multi-objective ones. One of the forms of carrying out this hybridization process is by using multi-objective optimization estimation of distribution algorithms (MOEDAs). However, it has been pointed out that current MOEDAs have an intrinsic shortcoming in their model-building algorithms that hamper their performance. In this work, we put forward the argument that error-based learning, the class of learning most commonly used in MOEDAs is responsible for current MOEDA underachievement. We present adaptive resonance theory (ART) as a suitable learning paradigm alternative and present a novel algorithm called multi-objective ART-based EDA (MARTEDA) that uses a Gaussian ART neural network for model-building and a hypervolume-based selector as described for the HypE algorithm. In order to assert the improvement obtained by combining two cutting-edge approaches to optimization an extensive set of experiments are carried out. These experiments also test the scalability of MARTEDA as the number of objective functions increases.     

Discrete‐time output feedback for Markov jump systems with uncertain transition probabilities

This article addresses the output feedback control for discrete‐time Markov jump linear systems. With fully known transition probability, sufficient conditions for an internal model based controller design are obtained. For the case where the transition probabilities are uncertain and belong to a convex polytope with known vertices, we provide a sufficient LMI condition that guarantees the norm of the closed‐loop system is below a prescribed level. That condition can be improved through an iterative procedure. Additionally, we are able to deal with the case of cluster availability of the Markov mode, provided that some system matrices do not vary within a given cluster, an assumption that is suitable to deal with packet dropout models for networked control systems. A numerical example shows the applicability of the design and compares it with previous results. Copyright © 2012 John Wiley & Sons, Ltd.     

Image Compression and Video Segmentation Using Hierarchical Self-Organization

Both image compression based on color quantization and image segmentation are two typical tasks in the field of image processing. Several techniques based on splitting algorithms or cluster analyses have been proposed in the literature. Self-organizing maps have been also applied to these problems, although with some limitations due to the fixed network architecture and the lack of representation in hierarchical relations among data. In this paper, both problems are addressed using growing hierarchical self-organizing models. An advantage of these models is due to the hierarchical architecture, which is more flexible in the adaptation process to input data, reflecting inherent hierarchical relations among data. Comparative results are provided for image compression and image segmentation. Experimental results show that the proposed approach is promising for image processing, and the powerful of the hierarchical information provided by the proposed model.     

Applying model-driven engineering to the development of Rich Internet Applications for Business Intelligence

Business Intelligence (BI) applications have been gradually ported to the Web in search of a global platform for the consumption and publication of data and services. On the Internet, apart from techniques for data/knowledge management, BI Web applications need interfaces with a high level of interoperability (similar to the traditional desktop interfaces) for the visualisation of data/knowledge. In some cases, this has been provided by Rich Internet Applications (RIA). The development of these BI RIAs is a process traditionally performed manually and, given the complexity of the final application, it is a process which might be prone to errors. The application of model-driven engineering techniques can reduce the cost of development and maintenance (in terms of time and resources) of these applications, as they demonstrated by other types of Web applications. In the light of these issues, the paper introduces the S^m4RIA-B methodology, i.e., a model-driven methodology for the development of RIA as BI Web applications. In order to overcome the limitations of RIA regarding knowledge management from the Web, this paper also presents a new RIA platform for BI, called RI@BI, which extends the functionalities of traditional RIAs by means of Semantic Web technologies and B2B techniques. Finally, we evaluate the whole approach on a case study—the development of a social network site for an enterprise project manager.     

One-Way Functions Using Algorithmic and Classical Information Theories

We prove several results relating injective one-way functions, time-bounded conditional Kolmogorov complexity, and time-bounded conditional entropy. First we establish a connection between injective, strong and weak one-way functions and the expected value of the polynomial time-bounded Kolmogorov complexity, denoted here by?E(K ^t (x|f(x))). These results are in both directions. More precisely, conditions on?E(K ^t (x|f(x))) that imply that?f is a weak one-way function, and properties of?E(K ^t (x|f(x))) that are implied by the fact that?f is a strong one-way function. In particular, we prove a separation result: based on the concept of time-bounded Kolmogorov complexity, we find an interval in which every function?f is a necessarily weak but not a strong one-way function. Then we propose an individual approach to injective one-way functions based on Kolmogorov complexity, defining Kolmogorov one-way functions and prove some relationships between the new proposal and the classical definition of one-way functions, showing that a Kolmogorov one-way function is also a deterministic one-way function. A relationship between Kolmogorov one-way functions and the conjecture of polynomial time symmetry of information is also proved. Finally, we relate?E(K ^t (x|f(x))) and two forms of time-bounded entropy, the unpredictable entropy?H ^unp, in which ??one-wayness?? of a function can be easily expressed, and the Yao⁺ entropy, a measure based on compression/decompression schema in which only the decompressor is restricted to be time-bounded.     

Young's modulus repeatability assessment using cycling compression loading on cancellous bone

For various applications, precision of the Young's modulus of cancellous bone specimens is needed. However, measurement variability is rarely given. The aim of this study was to assess the Young's modulus repeatability using a uniaxial cyclic compression protocol on embedded specimens of human cancellous bone. Twelve specimens from 12 human calcanei were considered. The specimens were first defatted and then 1 or 2 mm at the ends were embedded in an epoxy resin. The compression experiment consists in applying 20 compressive cycles between 0.2 per cent and 0.6 per cent strain with a 2 Hz loading frequency. The coefficient of variation of the current protocol was found to be 1.2 percent. This protocol showed variability similar to the end-cap technique (considered as a reference). It can be applied on porous specimen (especially human bone) and requires minimal bone length to limit end-artifact variability. The current method could be applied in association with noninvasive measurements (such as ultrasound) with full compatibility. This possibility opens the way for bone damage follow-up based on Young's modulus monitoring.     

Dual-color fluorescence lifetime correlation spectroscopy to quantify protein-protein interactions in live cell

Dual-color fluorescence correlation spectroscopy is an interesting method to quantify protein interaction in living cells. But, when performing these experiments, one must compensate for a known spectral bleed through artifact that corrupts cross-correlation data. In this article, problems with crosstalk were overcome with an approach based on fluorescence lifetime correlation spectroscopy (FLCS). We show that FLCS applied to dual-color EGFP and mCherry cross-correlation allows the determination of protein-protein interactions in living cells without the need of spectral bleed through calibration. The methodology was validated by using EGFP-mCherry tandem in comparison with coexpressed EGFP and mCherry in live cell. The dual-color FLCS experimental procedure where the different laser intensities do not have to be controlled during experiment is really very helpful to study quantitatively protein interactions in live sample.