Robust and Efficient Self-Adaptive Position Tracking in Wireless Embedded Systems

© 2015 IEEE.Apart from static deployments, sensor nodes in Wireless Sensor Networks (WSNs) are unaware of their location information. In order to estimate their actual or relative positions with respect to other nodes, they are required to self-localize themselves by collecting information from their environment. However, due to the high dynamism and the noise introduced by the WSN environment, self-localization procedures are not straightforward and they may require quite sophisticated algorithmic techniques to satisfy precision requirements of the WSN applications. Among the self-localization procedures in the literature, the ones based upon the technique of trilateration are easy to implement and efficient in terms of resource requirements. On the other hand, their performance is fragile against environmental dynamics. Besides, even though multilateration based procedures are reported to be more robust, their practicability in WSNs seems questionable due to their high resource requirements. In this paper, our objective is to develop a practical self-localization procedure for WSNs that puts away the fragility against noisy ranging measurements in an efficient manner. To that end, we take a different approach to self-localization procedure and treat it as a search process during which sensor nodes find their relative positions without knowing the actual correct values. We present a novel trilateration-based self-localization procedure by exploiting a robust and efficient search technique, named Adaptive Value Tracking (AVT), that finds and tracks a dynamic searched value in a given search space through successive feedbacks. We evaluate this procedure on a real test bed setup and show that our approach to self-localization is efficient, robust to environmental dynamics and adaptive in the sense of reacting to position changes

Jack vertex operators and realization of Jack functions

We give an iterative method to realize general Jack functions from Jack functions of rectangular shapes. We first show some cases of Stanley's conjecture on positivity of the Littlewood-Richardson coefficients, and then use this method to give a new realization of Jack functions. We also show in general that vectors of products of Jack vertex operators form a basis of symmetric functions. In particular this gives a new proof of linear independence for the rectangular and marked rectangular Jack vertex operators. Thirdly a generalized Frobenius formula for Jack functions was given and was used to give new evaluation of Dyson integrals and even powers of Vandermonde determinant.Comment: Expanded versio

Modern insulation materials for warming of walls

Biodiversity hotspots understandably attract considerable conservation attention. However, deserts are rarely viewed as conservation priority areas, due to their relatively low productivity, yet these systems are home to unique species, adapted to harsh and highly variable environments. While global attention has been focused on hotspots, the world's largest tropical desert, the Sahara, has suffered a catastrophic decline in megafauna. Of 14 large vertebrates that have historically occurred in the region, four are now extinct in the wild, including the iconic scimitar-horned oryx (Oryx dammah). The majority has disappeared from more than 90% of their Saharan range, including addax (Addax nasomaculatus), dama gazelle (Nanger dama) and Saharan cheetah (Acinonyx jubatus hecki) - all now on the brink of extinction. Greater conservation support and scientific attention for the region might have helped to avert these catastrophic declines. The Sahara serves as an example of a wider historical neglect of deserts and the human communities who depend on them. The scientific community can make an important contribution to conservation in deserts by establishing baseline information on biodiversity and developing new approaches to sustainable management of desert species and ecosystems. Such approaches must accommodate mobility of both people and wildlife so that they can use resources most efficiently in the face of low and unpredictable rainfall. This is needed to enable governments to deliver on their commitments to halt further degradation of deserts and to improve their status for both biodiversity conservation and human well-being. Only by so-doing will deserts be able to support resilient ecosystems and communities that are best able to adapt to climate change. © 2013 John Wiley & Sons Ltd

The Photodetector Array Camera and Spectrometer (PACS) on the Herschel Space Observatory

The Photodetector Array Camera and Spectrometer (PACS) is one of the three science instruments on ESA's far infrared and submillimetre observatory. It employs two Ge:Ga photoconductor arrays (stressed and unstressed) with 16x25 pixels, each, and two filled silicon bolometer arrays with 16x32 and 32x64 pixels, respectively, to perform integral-field spectroscopy and imaging photometry in the 60-210\mu\ m wavelength regime. In photometry mode, it simultaneously images two bands, 60-85\mu\ m or 85-125\mu\m and 125-210\mu\ m, over a field of view of ~1.75'x3.5', with close to Nyquist beam sampling in each band. In spectroscopy mode, it images a field of 47"x47", resolved into 5x5 pixels, with an instantaneous spectral coverage of ~1500km/s and a spectral resolution of ~175km/s. We summarise the design of the instrument, describe observing modes, calibration, and data analysis methods, and present our current assessment of the in-orbit performance of the instrument based on the Performance Verification tests. PACS is fully operational, and the achieved performance is close to or better than the pre-launch predictions

Embedded Vision Systems: A Review of the Literature

Over the past two decades, the use of low power Field Programmable Gate Arrays (FPGA) for the acceleration of various vision systems mainly on embedded devices have become widespread. The reconfigurable and parallel nature of the FPGA opens up new opportunities to speed-up computationally intensive vision and neural algorithms on embedded and portable devices. This paper presents a comprehensive review of embedded vision algorithms and applications over the past decade. The review will discuss vision based systems and approaches, and how they have been implemented on embedded devices. Topics covered include image acquisition, preprocessing, object detection and tracking, recognition as well as high-level classification. This is followed by an outline of the advantages and disadvantages of the various embedded implementations. Finally, an overview of the challenges in the field and future research trends are presented. This review is expected to serve as a tutorial and reference source for embedded computer vision systems

The global decline of cheetah Acinonyx jubatus and what it means for conservation

Establishing and maintaining protected areas (PAs) are key tools for biodiversity conservation. However, this approach is insufficient for many species, particularly those that are wide-ranging and sparse. The cheetah Acinonyx jubatus exemplifies such a species and faces extreme challenges to its survival. Here, we show that the global population is estimated at ∼7,100 individuals and confined to 9% of its historical distributional range. However, the majority of current range (77%) occurs outside of PAs, where the species faces multiple threats. Scenario modeling shows that, where growth rates are suppressed outside PAs, extinction rates increase rapidly as the proportion of population protected declines. Sensitivity analysis shows that growth rates within PAs have to be high if they are to compensate for declines outside. Susceptibility of cheetah to rapid decline is evidenced by recent rapid contraction in range, supporting an uplisting of the International Union for the Conservation of Nature (IUCN) Red List threat assessment to endangered. Our results are applicable to other protection-reliant species, which may be subject to systematic underestimation of threat when there is insufficient information outside PAs. Ultimately, conserving many of these species necessitates a paradigm shift in conservation toward a holistic approach that incentivizes protection and promotes sustainable human–wildlife coexistence across large multiple-use landscapes

Discrimination between two different grades of human glioma based on blood vessel infrared spectral imaging

Gliomas are brain tumours classified into four grades with increasing malignancy from I to IV. The development and the progression of malignant glioma largely depend on the tumour vascularization. Due to their tissue heterogeneity, glioma cases can be difficult to classify into a specific grade using the gold standard of histological observation, hence the need to base classification on a quantitative and reliable analytical method for accurately grading the disease. Previous works focused specifically on vascularization study by Fourier transform infrared (FTIR) spectroscopy, proving this method to be a way forward to detect biochemical changes in the tumour tissue not detectable by visual techniques. In this project, we employed FTIR imaging using a focal plane array (FPA) detector and globar source to analyse large areas of glioma tumour tissue sections via molecular fingerprinting in view of helping to define markers of the tumour grade. Unsupervised multivariate analysis (hierarchical cluster analysis and principal component analysis) of blood vessel spectral data, retrieved from the FPA images, revealed the fine structure of the borderline between two areas identified by a pathologist as grades III and IV. Spectroscopic indicators are found capable of discriminating different areas in the tumour tissue and are proposed as biomolecular markers for potential future use of grading gliomas. Graphical Abstract Infrared imaging of glioma blood vessels provides a means to revise the pathologists' line of demarcation separating grade III (GIII) from grade IV (GIV) parts

Robust and Efficient Self-Adaptive Position Tracking in Wireless Embedded Systems

© 2015 IEEE.Apart from static deployments, sensor nodes in Wireless Sensor Networks (WSNs) are unaware of their location information. In order to estimate their actual or relative positions with respect to other nodes, they are required to self-localize themselves by collecting information from their environment. However, due to the high dynamism and the noise introduced by the WSN environment, self-localization procedures are not straightforward and they may require quite sophisticated algorithmic techniques to satisfy precision requirements of the WSN applications. Among the self-localization procedures in the literature, the ones based upon the technique of trilateration are easy to implement and efficient in terms of resource requirements. On the other hand, their performance is fragile against environmental dynamics. Besides, even though multilateration based procedures are reported to be more robust, their practicability in WSNs seems questionable due to their high resource requirements. In this paper, our objective is to develop a practical self-localization procedure for WSNs that puts away the fragility against noisy ranging measurements in an efficient manner. To that end, we take a different approach to self-localization procedure and treat it as a search process during which sensor nodes find their relative positions without knowing the actual correct values. We present a novel trilateration-based self-localization procedure by exploiting a robust and efficient search technique, named Adaptive Value Tracking (AVT), that finds and tracks a dynamic searched value in a given search space through successive feedbacks. We evaluate this procedure on a real test bed setup and show that our approach to self-localization is efficient, robust to environmental dynamics and adaptive in the sense of reacting to position changes