Mobility based energy efficient and multi-sink algorithms for consumer home networks

With the fast development of the Internet, wireless communications and semiconductor devices, home networking has received significant attention. Consumer products can collect and transmit various types of data in the home environment. Typical consumer sensors are often equipped with tiny, irreplaceable batteries and it therefore of the utmost importance to design energy efficient algorithms to prolong the home network lifetime and reduce devices going to landfill. Sink mobility is an important technique to improve home network performance including energy consumption, lifetime and end-to-end delay. Also, it can largely mitigate the hot spots near the sink node. The selection of optimal moving trajectory for sink node(s) is an NP-hard problem jointly optimizing routing algorithms with the mobile sink moving strategy is a significant and challenging research issue. The influence of multiple static sink nodes on energy consumption under different scale networks is first studied and an Energy-efficient Multi-sink Clustering Algorithm (EMCA) is proposed and tested. Then, the influence of mobile sink velocity, position and number on network performance is studied and a Mobile-sink based Energy-efficient Clustering Algorithm (MECA) is proposed. Simulation results validate the performance of the proposed two algorithms which can be deployed in a consumer home network environment.

IMILAST: a community effort to intercompare extratropical cyclone detection and tracking algorithms

The variability of results from different automated methods of detection and tracking of extratropical cyclones is assessed in order to identify uncertainties related to the choice of method. Fifteen international teams applied their own algorithms to the same dataset—the period 1989–2009 of interim European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERAInterim) data. This experiment is part of the community project Intercomparison of Mid Latitude Storm Diagnostics (IMILAST; see www.proclim.ch/imilast/index.html). The spread of results for cyclone frequency, intensity, life cycle, and track location is presented to illustrate the impact of using different methods. Globally, methods agree well for geographical distribution in large oceanic regions, interannual variability of cyclone numbers, geographical patterns of strong trends, and distribution shape for many life cycle characteristics. In contrast, the largest disparities exist for the total numbers of cyclones, the detection of weak cyclones, and distribution in some densely populated regions. Consistency between methods is better for strong cyclones than for shallow ones. Two case studies of relatively large, intense cyclones reveal that the identification of the most intense part of the life cycle of these events is robust between methods, but considerable differences exist during the development and the dissolution phases.

Graph-based algorithms for comparison and prediction of household-level energy use profiles

We present an efficient graph-based algorithm for quantifying the similarity of household-level energy use profiles, using a notion of similarity that allows for small time–shifts when comparing profiles. Experimental results on a real smart meter data set demonstrate that in cases of practical interest our technique is far faster than the existing method for computing the same similarity measure. Having a fast algorithm for measuring profile similarity improves the efficiency of tasks such as clustering of customers and cross-validation of forecasting methods using historical data. Furthermore, we apply a generalisation of our algorithm to produce substantially better household-level energy use forecasts from historical smart meter data.

Progress in the genetics of common obesity and type 2 diabetes

The prevalence of obesity and diabetes, which are heritable traits that arise from the interactions of multiple genes and lifestyle factors, continues to rise worldwide, causing serious health problems and imposing a substantial economic burden on societies. For the past 15 years, candidate gene and genome-wide linkage studies have been the main genetic epidemiological approaches to identify genetic loci for obesity and diabetes, yet progress has been slow and success limited. The genome-wide association approach, which has become available in recent years, has dramatically changed the pace of gene discoveries. Genome-wide association is a hypothesis-generating approach that aims to identify new loci associated with the disease or trait of interest. So far, three waves of large-scale genome-wide association studies have identified 19 loci for common obesity and 18 for common type 2 diabetes. Although the combined contribution of these loci to the variation in obesity and diabetes risk is small and their predictive value is typically low, these recently identified loci are set to substantially improve our insights into the pathophysiology of obesity and diabetes. This will require integration of genetic epidemiological methods with functional genomics and proteomics. However, the use of these novel insights for genetic screening and personalised treatment lies some way off in the future.

The genetics of diabetes mellitus

Genes play an important role in the development of diabetes mellitus. Putative susceptibility genes could be the key to the development of diabetes. Type 1 diabetes mellitus is one of the most common chronic diseases of childhood. A combination of genetic and environmental factors is most likely the cause of Type 1 diabetes. The pathogenetic sequence leading to the selective autoimmune destruction of islet beta-cells and development of Type 1 diabetes involves genetic factors, environmental factors, immune regulation and chemical mediators. Unlike Type 1 diabetes mellitus, Type 2 diabetes is often considered a polygenic disorder with multiple genes located on different chromosomes being associated with this condition. This is further complicated by numerous environmental factors which also contribute to the clinical manifestation of the disorder in genetically predisposed persons. Only a minority of cases of type 2 diabetes are caused by single gene defects such as maturity onset diabetes of the young (MODY), syndrome of insulin resistance (insulin receptor defect) and maternally inherited diabetes and deafness (mitochondrial gene defect). Although Type 2 diabetes mellitus appears in almost epidemic proportions our knowledge of the mechanism of this disease is limited. More information about insulin secretion and action and the genetic variability of the various factors involved will contribute to better understanding and classification of this group of diseases. This article discusses the results of various genetic studies on diabetes with special reference to Indian population.

Sea surface temperature climate change initiative: alternative image classification algorithms for sea-ice affected oceans

We present a Bayesian image classification scheme for discriminating cloud, clear and sea-ice observations at high latitudes to improve identification of areas of clear-sky over ice-free ocean for SST retrieval. We validate the image classification against a manually classified dataset using Advanced Along Track Scanning Radiometer (AATSR) data. A three way classification scheme using a near-infrared textural feature improves classifier accuracy by 9.9 % over the nadir only version of the cloud clearing used in the ATSR Reprocessing for Climate (ARC) project in high latitude regions. The three way classification gives similar numbers of cloud and ice scenes misclassified as clear but significantly more clear-sky cases are correctly identified (89.9 % compared with 65 % for ARC). We also demonstrate the poetential of a Bayesian image classifier including information from the 0.6 micron channel to be used in sea-ice extent and ice surface temperature retrieval with 77.7 % of ice scenes correctly identified and an overall classifier accuracy of 96 %.

Fast identification algorithms for Gaussian process model

A class identification algorithms is introduced for Gaussian process(GP)models.The fundamental approach is to propose a new kernel function which leads to a covariance matrix with low rank,a property that is consequently exploited for computational efficiency for both model parameter estimation and model predictions.The objective of either maximizing the marginal likelihood or the Kullback–Leibler (K–L) divergence between the estimated output probability density function(pdf)and the true pdf has been used as respective cost functions.For each cost function,an efficient coordinate descent algorithm is proposed to estimate the kernel parameters using a one dimensional derivative free search, and noise variance using a fast gradient descent algorithm. Numerical examples are included to demonstrate the effectiveness of the new identification approaches.

Evaluation of seven European aerosol optical depth retrieval algorithms for climate analysis

Satellite data are increasingly used to provide observation-based estimates of the effects of aerosols on climate. The Aerosol-cci project, part of the European Space Agency's Climate Change Initiative (CCI), was designed to provide essential climate variables for aerosols from satellite data. Eight algorithms, developed for the retrieval of aerosol properties using data from AATSR (4), MERIS (3) and POLDER, were evaluated to determine their suitability for climate studies. The primary result from each of these algorithms is the aerosol optical depth (AOD) at several wavelengths, together with the Ångström exponent (AE) which describes the spectral variation of the AOD for a given wavelength pair. Other aerosol parameters which are possibly retrieved from satellite observations are not considered in this paper. The AOD and AE (AE only for Level 2) were evaluated against independent collocated observations from the ground-based AERONET sun photometer network and against “reference” satellite data provided by MODIS and MISR. Tools used for the evaluation were developed for daily products as produced by the retrieval with a spatial resolution of 10 × 10 km2 (Level 2) and daily or monthly aggregates (Level 3). These tools include statistics for L2 and L3 products compared with AERONET, as well as scoring based on spatial and temporal correlations. In this paper we describe their use in a round robin (RR) evaluation of four months of data, one month for each season in 2008. The amount of data was restricted to only four months because of the large effort made to improve the algorithms, and to evaluate the improvement and current status, before larger data sets will be processed. Evaluation criteria are discussed. Results presented show the current status of the European aerosol algorithms in comparison to both AERONET and MODIS and MISR data. The comparison leads to a preliminary conclusion that the scores are similar, including those for the references, but the coverage of AATSR needs to be enhanced and further improvements are possible for most algorithms. None of the algorithms, including the references, outperforms all others everywhere. AATSR data can be used for the retrieval of AOD and AE over land and ocean. PARASOL and one of the MERIS algorithms have been evaluated over ocean only and both algorithms provide good results.

Automated algorithms for multilayer thin film filter design using metamaterials

Currently, infrared filters for astronomical telescopes and satellite radiometers are based on multilayer thin film stacks of alternating high and low refractive index materials. However, the choice of suitable layer materials is limited and this places limitations on the filter performance that can be achieved. The ability to design materials with arbitrary refractive index allows for filter performance to be greatly increased but also increases the complexity of design. Here a differential algorithm was used as a method for optimised design of filters with arbitrary refractive indices, and then materials are designed to these specifications as mono-materials with sub wavelength structures using Bruggeman’s effective material approximation (EMA).

Control methodologies: Peak reduction algorithms for DNO owned storage devices on the Low Voltage network

The Distribution Network Operators (DNOs) role is becoming more difficult as electric vehicles and electric heating penetrate the network, increasing the demand. As a result it becomes harder for the distribution networks infrastructure to remain within its operating constraints. Energy storage is a potential alternative to conventional network reinforcement such as upgrading cables and transformers. The research presented here in this paper shows that due to the volatile nature of the LV network, the control approach used for energy storage has a significant impact on performance. This paper presents and compares control methodologies for energy storage where the objective is to get the greatest possible peak demand reduction across the day from a pre-specified storage device. The results presented show the benefits and detriments of specific types of control on a storage device connected to a single phase of an LV network, using aggregated demand profiles based on real smart meter data from individual homes. The research demonstrates an important relationship between how predictable an aggregation is and the best control methodology required to achieve the objective.

Fractional order and non-linear system identification algorithms for biomedical applications

We discuss the modelling of dielectric responses of amorphous biological samples. Such samples are commonly encountered in impedance spectroscopy studies as well as in UV, IR, optical and THz transient spectroscopy experiments and in pump-probe studies. In many occasions, the samples may display quenched absorption bands. A systems identification framework may be developed to provide parsimonious representations of such responses. To achieve this, it is appropriate to augment the standard models found in the identification literature to incorporate fractional order dynamics. Extensions of models using the forward shift operator, state space models as well as their non-linear Hammerstein-Wiener counterpart models are highlighted. We also discuss the need to extend the theory of electromagnetically excited networks which can account for fractional order behaviour in the non-linear regime by incorporating nonlinear elements to account for the observed non-linearities. The proposed approach leads to the development of a range of new chemometrics tools for biomedical data analysis and classification.

Standardized evaluation of algorithms for computer-aided diagnosis of dementia based on structural MRI: The CADDementia challenge

Algorithms for computer-aided diagnosis of dementia based on structural MRI have demonstrated high performance in the literature, but are difficult to compare as different data sets and methodology were used for evaluation. In addition, it is unclear how the algorithms would perform on previously unseen data, and thus, how they would perform in clinical practice when there is no real opportunity to adapt the algorithm to the data at hand. To address these comparability, generalizability and clinical applicability issues, we organized a grand challenge that aimed to objectively compare algorithms based on a clinically representative multi-center data set. Using clinical practice as the starting point, the goal was to reproduce the clinical diagnosis. Therefore, we evaluated algorithms for multi-class classification of three diagnostic groups: patients with probable Alzheimer's disease, patients with mild cognitive impairment and healthy controls. The diagnosis based on clinical criteria was used as reference standard, as it was the best available reference despite its known limitations. For evaluation, a previously unseen test set was used consisting of 354 T1-weighted MRI scans with the diagnoses blinded. Fifteen research teams participated with a total of 29 algorithms. The algorithms were trained on a small training set (n = 30) and optionally on data from other sources (e.g., the Alzheimer's Disease Neuroimaging Initiative, the Australian Imaging Biomarkers and Lifestyle flagship study of aging). The best performing algorithm yielded an accuracy of 63.0% and an area under the receiver-operating-characteristic curve (AUC) of 78.8%. In general, the best performances were achieved using feature extraction based on voxel-based morphometry or a combination of features that included volume, cortical thickness, shape and intensity. The challenge is open for new submissions via the web-based framework: http://caddementia.grand-challenge.org.

Rapid preconditioning of data for accelerating convex hull algorithms

Given a dataset of two-dimensional points in the plane with integer coordinates, the method proposed reduces a set of n points down to a set of s points s ≤ n, such that the convex hull on the set of s points is the same as the convex hull of the original set of n points. The method is O(n). It helps any convex hull algorithm run faster. The empirical analysis of a practical case shows a percentage reduction in points of over 98%, that is reflected as a faster computation with a speedup factor of at least 4.