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This paper presents a probabilistic framework for road geometry estimation using a millimetre wave radar. It aims at estimating the geometry of roads without assuming any particular infrastructure such as lane marks. It provides also the vehicle location with respect to the edges of the road. This system employs a radar sensor in view of its robustness to weather conditions such as fog, dust, rain and snow. The proposed approach is robust to noisy measurements since the radar target locations are modelled as Gaussian distributions. These observations are integrated into a Kalman Particle filter to estimate the posterior distribution of the parameters that best describe the geometry of the road. Experimental results using data acquired on a highway road are presented. The effectiveness of the proposed approach is demonstrated by a qualitative analysis of the results.

This paper presents a new technique for sparsification of the information matrix of a multi-dimensional Gaussian distribution. We call this technique Conservative Sparsification (CS) and show that it produces estimates which are consistent with respect to an optimal filter. This technique was applied to the Simultaneous Localisation and Mapping (SLAM) problem, and compared with two existing sparsification approaches; the Sparse Extended Information Filter (SEIF) and the Data Discarding Sparse Extended Information Filter (DDSEIF). Simulation demonstrates that CS is a consistent approach and provides a tighter upper bound than existing conservative methods.

This paper presents a distributed algorithm for performing joint localisation of a team of robots. The mobile robots have heterogeneous sensing capabilities, with some having high quality inertial and exteroceptive sensing, while others have only low quality sensing or none at all. By sharing information, a combined estimate of all robot poses is obtained. Inter-robot range-bearing measurements provide the mechanism for transferring pose information from well-localised vehicles to those less capable. In our proposed formulation, high frequency egocentric data (e.g., odometry, IMU, GPS) is fused locally on each platform. This is the distributed part of the algorithm. Inter-robot measurements, and accompanying state estimates, are communicated to a central server, which generates an optimal minimum mean-squared estimate of all robot poses. This server is easily duplicated for full redundant decentralisation. Communication and computation are efficient due to the sparseness properties of the information-form Gaussian representation. A team of three indoor mobile robots equipped with lasers, odometry and inertial sensing provides experimental verification of the algorithms effectiveness in combining location information.

Novel lazy Lauritzen-Spiegelhalter (LS), lazy Hugin and lazy Shafer-Shenoy (SS) algorithms are devised for Gaussian Bayesian networks (BNs). In the lazy algorithms, the clique potentials and separator potentials are kept in combinable decomposed form instead of combined to be a single valuation in conventional junction tree algorithms. By employing decomposed form potentials, the independence relations between variables are explored online and the directed graph information is utilized in the message calculations. In the proposed algorithms, a consistent junction tree with the evidence entered can be obtained by a single round of message passing. The moments form parametrization of Gaussian distributions allows the deterministic relationships between variables. Preliminary analysis shows that the lazy LS algorithm and the lazy Hugin algorithm are more computationally efficient than the lazy SS algorithm, especially when there are multiple items of evidence to be incorporated.

Pen needles (PN) for subcutaneous insulin therapy have become smaller; 5 mm PNs are now the shortest in use. We evaluated the safety, efficacy and patient ratings of a new 4 mm x 32 gauge (G) PN.Subjects with type 1 and type 2 diabetes and HbA1c 5.5% to 9.5% participated in a randomized non-inferiority cross-over trial, at four U.S. centers. Subjects used 4 mm x 32G PNs and either 5 mm x 31G PNs (4/5 mm) or 8 mm x 31G PNs (4/8 mm) in two, 3-week treatment periods; order of needle use was controlled. Subjects were either 'low dose' or 'regular dose' users (highest single insulin doseor= 20 units and 21-40 units, respectively). Percent absolute change in serum fructosamine (% |Delta Fru|) was the primary endpoint; unexplained, severe hypo- or hyperglycemia was a secondary measure. Leakage at injection sites and pain measured by visual analog scale were tertiary measures. Equivalent glycemic control was defined á priori as % |Delta Fru| (including 95% CI) within 20%; 40 subjects per subgroup provides 90% power at alpha = 0.05.The study was registered on clinicaltrials.gov (identifier: NCT00928057).Of 173 subjects randomized, 168 completed the study, and 163 were included in the fructosamine analyses--83 and 80 in the 4/5 mm and 4/8 mm groups, respectively. Subjects were 56% male, mean 52.6 yrs, 63% type 2. Baseline HbA1c = 7.5 +/- 1.0% and fructosamine 301 +/- 55.1 micromol/L. Mean % |Delta Fru| was 4.9% (95% CI 3.8, 6.0) and 5.5% (4.5, 6.4), respectively, for the 4/5 mm and 4/8 mm groups, meeting glycemic equivalence criteria; results were similar in both dose groups. The median |Delta Fru| was 11.0 micromol/L (8.0, 13.0) and 13.5 micromol/L (9.8, 18.0) for the 4/5 mm and 4/8 mm groups, respectively. Unexplained, severe hypo- and hyperglycemic episodes were infrequent and not different between PNs. The 4 mm PN was rated significantly less painful and preferred by approximately 2/3 of subjects (p0.01). All three PNs had similar reported injection site leakage.The study was of relatively short duration, in adults in the U.S. Further trials in other patients (e.g., GLP-1 users, pediatrics, obese) should be performed.The 4 mm x 32G PN provided equivalent glycemic control compared to 31G, 5 mm and 8 mm PNs with reduced pain, no difference in insulin leakage and was preferred by patients.

Background: The purpose of this study was to compare the accuracy of measurements obtained from the DexCom™ SEVEN® system with Yellow Springs Instrument (YSI) laboratory measurements of venous blood glucose. Methods: Seventy-two subjects with insulin-requiring diabetes, aged 18–71, were enrolled in a multicenter, prospective single-arm study. All participants wore the SEVEN continuous glucose monitoring (CGM) system for one, 7-day wear period. Calibration with capillary finger stick measurements was performed 2 hours after sensor insertion and once every 12 hours thereafter. A subset of subjects (28) wore two systems simultaneously to assess precision. All subjects participated in one, 10-hour in-clinic session on day 1, 4, or 7 of the study to compare CGM measurements against a laboratory method (YSI analyzer) using venous measurements taken once every 20 minutes. Carbohydrate consumption and insulin dosing were adjusted in order to obtain a broad range of glucose values. Results: Comparison of CGM measurements with the laboratory reference method ( n = 2318) gave mean and median absolute relative differences (ARDs) of 16.7 and 13.2%, respectively. The percentage was 70.4% in the clinically accurate Clarke error grid A zone and 27.5% in the benign error B zone. Performance of the SEVEN system was consistent over time with mean and median ARD lowest on day 7 as compared to YSI (13.3 and 10.2%, respectively). Average sensor time lag was 5 minutes. Conclusions: Measurements of the DexCom SEVEN system were found to be consistent and accurate compared with venous measurements made using a laboratory reference method over 7 days of wear.

In this paper we address the problem of closing the loop from perception to action selection for unmanned ground vehicles, with a focus on navigating slopes. A new non-parametric learning technique is presented to generate a mobility representation where the maximum feasible speed is used as a criterion to classify the world. The inputs to the algorithm are terrain gradients derived from an elevation map and past observations of wheel slip. It is argued that such a representation can aid in path planning with improved selection of vehicle heading and velocity in off-road slopes. In addition, an information theoretic test is proposed to validate a chosen proprioceptive representation (such as slip) for mobility map generation. Results of mobility map generation and its benefits to path planning are shown.

Continuous glucose monitoring (CGM) is a new tool that has recently become available to people with diabetes. Properly designing and conducting trials in order to answer important questions regarding the clinical usefulness of CGM is very difficult and fraught with many confounding variables and unique challenges. Initial clinical trials using older technology using retrospective data analysis have not shown unequivocal benefit; however, more recent data using real-time CGM suggest significant improvements in important clinical outcomes such as hemoglobin A1c, time spent in the hypo- and hyperglycemic range, glucose variability, quality of life, and perceived value to physicians and patients. Additional variables such as age of the subject and duration of sensor use have important implications in defining the benefits of CGM in a heterogeneous population with diabetes. This review will present the most relevant recent literature and comment on the methodological difficulties that have made this field challenging.

The paper investigates a technique for computing conservative data fusion for gaussian mixture model (GMM) in decentralized networks with any topology. The main advantage of conservative solutions is that they do not deteriorate the performance of a sensor network in presence of any kind of correlations. The paper exploits normalize geometric mean for computing conservative data fusion. It computes normalized geometric mean by Newton generalized binomial theorem and Monte Carlo technique. It is shown that the solution by Newton's generalized binomial theorem exhibits divergence and numerical instability. On the other hand, Monte Carlo technique offers conservative solution. The tradeoffs are that it requires considerable computational time and is expensive as large numbers of samples are required to get statistical accuracy.

This paper presents HybridSLAM: an approach to SLAM which combines the strengths and avoids the weaknesses of two popular mapping strategies: FastSLAM and EKF-SLAM. FastSLAM is used as a front-end, producing local maps which are periodically fused into an EKF-SLAM back-end. The use of FastSLAM locally avoids linearisation of the vehicle model and provides a high level of robustness to clutter and ambiguous data association. The use of EKFSLAM globally allows uncertainty to be remembered over long vehicle trajectories, avoiding FastSLAM’s tendency to become over-confident. Extensive trials in randomly-generated simulated environments show that HybridSLAM significantly out-performs either pure approach. The advantages of HybridSLAM are most pronounced in cluttered environments where either pure approach encounters serious difficulty. In addition, the HybridSLAM algorithm is experimentally validated in a real urban environment.

For many practical probability density representations such as for the widely used Gaussian mixture densities, an analytic evaluation of the differential entropy is not possible and thus, approximate calculations are inevitable. For this purpose, the first contribution of this paper deals with a novel entropy approximation method for Gaussian mixture random vectors, which is based on a component-wise Taylor-series expansion of the logarithm of a Gaussian mixture and on a splitting method of Gaussian mixture components. The employed order of the Taylor-series expansion and the number of components used for splitting allows balancing between accuracy and computational demand. The second contribution is the determination of meaningful and efficiently to calculate lower and upper bounds of the entropy, which can be also used for approximation purposes. In addition, a refinement method for the more important upper bound is proposed in order to approach the true entropy value.

Decentralised estimation of heterogeneous sensors is performed on an outdoor network. Attributes such as position, appearance, and identity represented by non-Gaussian distributions are used in in the fusion process. It is shown here that real-time decentralised data fusion of non-Gaussian estimates can be used to build rich environmental maps. Human operators are also used as additional sensors in the network to complement robotic information.

This paper presents the development and demonstration of non-Gaussian, decentralised state estimation using an outdoor sensor network consisting of an autonomous air vehicle, a manual ground vehicle, and two human operators. The location and appearance of landmarks were estimated using bearing only observations from monocular cameras. We show that inclusion of visual and identity information aids validation gating for data association when geometric information alone cannot discriminate individual landmarks. The combination of geometric, appearance, and identity information provided a common description (or map) of natural features for each of the nodes in the network. We also show the final map from the live demonstration which includes position estimates and classification labels of the observed features.

This paper presents a new generalisation of simultaneous localisation and mapping (SLAM). SLAM implementations based on extended Kalman filter (EKF) data fusion have traditionally relied on simple geometric models for defining landmarks. This limits EKF-SLAM to environments suited to such models and tends to discard much potentially useful data. The approach presented in this paper is a marriage of EKF-SLAM with scan correlation. Instead of geometric models, landmarks are defined by templates composed of raw sensed data, and scan correlation is shown to produce landmark observations compatible with the standard EKF-SLAM framework. The resulting Scan-SLAM combines the general applicability of scan correlation with the established advantages of an EKF implementation: recursive data fusion that produces a convergent map of landmarks and maintains an estimate of uncertainties and correlations. Experimental results are presented which validate the algorithm.

Although full 3D navigation and mapping is recognized as one of the most important challenges for autonomous navigation, the lack of robust sensors, providing 3D information in real time, has burdened the progress in this direction. This paper presents our ongoing work towards the deployment of an integrated sensing system for 3D mapping in outdoor environments. We first describe a 3D data acquisition architecture based on a standard 2D laser. Techniques for registering scans using a scan matching procedure and for estimating the errors are then introduced. We finally present results showing the performance of the proposed architecture in real outdoor environments by means of the integration of the 3D scans with dead reckoning and inertial measurement unit (IMU) information.

This paper presents a decentralised particle filtering algorithm that enables multiple vehicles to jointly track 3D features under limited communication bandwidth. This algorithm, applied within a decentralised data fusion (DDF) framework, deals with correlated estimation errors due to common past information when fusing two discrete particle sets. Our solution is to transform the particles into Gaussian mixture models (GMMs) for communication and fusion. Not only can decentralised fusion be approximated by GMMs, but this representation also provides summaries of the particle set. Less bandwidth per communication step is required to communicate a GMM than the particle set itself hence conversion to GMMs for communication is an advantage. Real airborne data is used to demonstrate the accuracy of our decentralised particle filtering algorithm for airborne tracking and mapping.

This paper presents an analysis of the extended Kalman filter formulation of simultaneous localisation and mapping (EKF-SLAM). We show that the algorithm produces very optimistic estimates once the "true" uncertainty in vehicle heading exceeds a limit. This failure is subtle and cannot, in general, be detected without ground-truth, although a very inconsistent filter may exhibit observable symptoms, such as disproportionately large jumps in the vehicle pose update. Conventional solutions - adding stabilising noise, using an iterated EKF or unscented filter, etc., - do not improve the situation. However, if "small" heading uncertainty is maintained, EKF-SLAM exhibits consistent behaviour over an extended time-period. Although the uncertainty estimate slowly becomes optimistic, inconsistency can be mitigated indefinitely by applying tactics such as batch updates or stabilising noise. The manageable degradation of small heading variance SLAM indicates the efficacy of submap methods for large-scale maps

In this paper we investigate the use of Exponential Mixture Densities (EMDs) as suboptimal update rules for distributed data fusion. We show that EMDs have a pointwise bound "from below" on the minimum value of the probability distribution. However, the distributions are not bounded from above and thus can be interpreted as a fusion operation.

This paper presents two solutions for performing decentralised particle filtering in view of non-linear, non-Gaussian tracking in sensor networks. The issue is that no known methods exist to deal with correlated estimation errors due to common past information between two discrete particle sets. The first method transforms the particles to a Gaussian Mixture Model, the second approximates the set by a Parzen density estimate. Both of these representations accommodate consistent fusion and maintain accurate summaries of the particles. Requiring less bandwidth than particle representations, transformations to GMMs or Parzen representations for communication provide an added advantage. The accuracy in which the algorithms summarise the particle set, fusion methods and bandwidth requirements of each representation will be compared. Our results show that whilst less GMM components are required to summarise the sample statistics, the decentralised fusion solution using Parzen representations yields a more accurate result.

This paper presents an analysis of FastSLAM - a Rao-Blackwellised particle filter formulation of simultaneous localisation and mapping. It shows that the algorithm degenerates with time, regardless of the number of particles used or the density of landmarks within the environment, and would always produce optimistic estimates of uncertainty in the long-term. In essence, FastSLAM behaves like a non-optimal local search algorithm; in the short-term it may produce consistent uncertainty estimates but, in the long-term, it is unable to adequately explore the state-space to be a reasonable Bayesian estimator. However, the number of particles and landmarks does affect the accuracy of the estimated mean and, given sufficient particles, FastSLAM can produce good non-stochastic estimates in practice. FastSLAM also has several practical advantages, particularly with regard to data association, and would probably work well in combination with other versions of stochastic SLAM, such as EKF-based SLAM

This paper develops a general theory of validation gating for non-linear non-Gaussian mod- els. Validation gates are used in target tracking to cull very unlikely measurement-to-track associa- tions, before remaining association ambiguities are handled by a more comprehensive (and expensive) data association scheme. The essential property of a gate is to accept a high percentage of correct associ- ations, thus maximising track accuracy, but provide a su±ciently tight bound to minimise the number of ambiguous associations. For linear Gaussian systems, the ellipsoidal vali- dation gate is standard, and possesses the statistical property whereby a given threshold will accept a cer- tain percentage of true associations. This property does not hold for non-linear non-Gaussian models. As a system departs from linear-Gaussian, the ellip- soid gate tends to reject a higher than expected pro- portion of correct associations and permit an excess of false ones. In this paper, the concept of the ellip- soidal gate is extended to permit correct statistics for the non-linear non-Gaussian case. The new gate is demonstrated by a bearing-only tracking example.

The aim of this paper is to demonstrate the validity of using Gaussian mixture models (GMM) for representing probabilistic distributions in a decentralised data fusion (DDF) framework. GMMs are a powerful and compact stochastic representation allowing efficient communication of feature properties in large scale decentralised sensor networks. It will be shown that GMMs provide a basis for analytical solutions to the update and prediction operations for general Bayesian filtering. Furthermore, a variant on the covariance intersect algorithm for Gaussian mixtures will be presented ensuring a conservative update for the fusion of correlated information between two nodes in the network. In addition, purely visual sensory data will be used to show that decentralised data fusion and tracking of non-Gaussian states observed by multiple autonomous vehicles is feasible.

Simultaneous Localisation And Mapping (SLAM) is a stochastic map building method which permits consistent robot navigation without requiring an a priori map. The map is built incrementally as the robot observes the environment with its on-board sensors and, at the same time, is used to localise the robot. Typically, SLAM has been performed using range-bearing sensors, but the development of a SLAM implementation using only bearing measurements is desirable as it permits the use of sensors such as CCD cameras, which are small, reliable and cheap. However, bearing-only SLAM is hindered by the feature initialisation problem, where the estimated location of a new map landmark cannot be determined from a single measurement, and combined information from multiple measurements may be ill-conditioned. This paper presents a solution to the feature initialisation problem called constrained initialisation, which defers the use of sensor information until initialisation becomes well-conditioned. Measurements may be used out-of-sequence and all the available information can be incorporated without inconsistency. Furthermore, this method operates within the conventional extended Kalman filter (EKF) framework of the SLAM algorithm.

Smaller gauge, shorter needles have been shown to be as safe and effective for insulin delivery as longer needles in many patients. However, in obese patients with diabetes, results have been inconsistent with regard to the impact of needle length on leakage of injectate.A single-blind, randomized, two-period, crossover study compared injections with 5 mm needles to 8 mm needles regarding leakage, pain, bleeding, and bruising at abdominal injection sites in obese patients with diabetes using 20- and 60-unit (U) volume equivalent injections of sterile insulin diluent.Fifty-six patients (54% male; mean age 56 years; mean body mass index of 36 kg/m(2)) with type 1 (n = 13) or type 2 (n = 43) diabetes participated. Median leakage (U) was similar for both needles [0.04 (5 mm/20 U) vs 0.02 (8 mm/20 U), P = .32; and 0.04 (5 mm/60 U) vs 0.02 (8 mm/60 U), P = .48]. Pain scores (mean) were similar [1.27 (5 mm/20 U) vs 1.14 (8 mm/20 U), P = .75, and 1.68 (5 mm/60 U) vs 0.95 (8 mm/60 U, P = .21)]. The proportion of injections with bleeding [10.8% (5 mm/20 U) vs 5.83% (8 mm/20 U), P = .23, and 4.92% (5 mm/60 U) vs 6.56% (8 mm/60 U), P = .73] and the proportion of patients with bruising [8.11% (5 mm/20 U) vs 10.81% (8 mm/20 U), p = .56, and 21.05% (5 mm/60 U) vs 26.32% (8 mm/60 U), p = .65] at injection sites were similar. Mean bruise size (mm) [0.73 (5 mm/20 U] vs 2.68 (8 mm/20 U), P = .23; and 1.11 (5 mm/60 U) vs 4.21 (8 mm/60 U), P = .08] at injection sites was similar.This study supports the suitability of the 5 mm needle for the injection of insulin in obese patients with diabetes.

The theoretical basis of the solution to the simultaneous localisation and map building (SLAM) problem where an autonomous vehicle starts in an unknown location in an unknown environment and then incrementally build a map of landmarks present in this environment while simultaneously using this map to compute absolute vehicle location is well understood. Although a number of SLAM implementations have appeared in the literature, the need to maintain the knowledge of the relative relationships between all the landmark location estimates contained in the map makes SLAM computationally intractable in implementations containing more than few tens of landmarks. In this paper, the theoretical basis and a practical implementation of a computationally efficient solution to SLAM is presented. The paper shows that it is indeed possible to remove a large percentage of the landmarks from the map without making the map-building process statistically inconsistent. Furthermore, it is shown that the efficiency of the SLAM can be maintained by judicious selection of landmarks, to be preserved in the map, based on their information content.

Data association is the process of relating features observed in the environment to features viewed previously or to features in a map. This paper presents a graph theoretic method that is applicable to data association problems where the features are observed via a batch process. Batch observations detect a set of features simultaneously or with sufficiently small temporal difference that, with motion compensation, the features can be represented with precise relative coordinates. This data association method is described in the context of two possible navigation applications: metric map building with simultaneous localisation, and topological map based localisation. Experimental results are presented using an indoor mobile robot with a 2D scanning laser sensor. Given two scans from different unknown locations, the features common to both scans are mapped to each other and the relative change in pose (position and orientation) of the vehicle between the two scans is obtained.

Insulin pen therapy is superior to vial and syringe with regard to more accurate insulin delivery, patient preference, adherence, and favorable health economics. In this issue of Journal of Diabetes Science and Technology, Sommavilla and Pietranera compare two generations of NovoPen, showing significant improvements in selecting and injecting the insulin dose as well as superior overall satisfaction with the newer versus older generation insulin pen among both experienced pen users and insulin-naïve patients. There are other potentially useful features that might be implemented and better-studied in future pen devices. These include insulin dose tracking, insulin error mitigation, and insulin dosing advice. Caring more effectively for the multitude of people impacted by the diabetes epidemic requires a new approach that will require “smarter,” more “connected” devices.

Insulin formulations and injection devices have improved dramatically since the first insulin injection was given in 1922. Adherence to insulin therapy, however, is estimated at 62–64 % despite research indicating that good glycemic control improves patient outcomes. The challenge is to improve the rates of adherence and to intensify or progress insulin therapy as needed. Changes in insulin delivery devices, especially innovations in needle technology in combination with education and support, have the potential to improve the comfort of insulin injections and encourage patients to adhere to their insulin regimens.

While insulin delivery technology continues to progress, its adoption in the clinic lags behind, particularly in people with type 2 diabetes. In this article the authors present their clinical perspective regarding insulin pen therapy in this population.

Combination therapy for type II (non-insulin-dependent) diabetes mellitus with insulin and sulfonylureas has been a topic of interest since the latter were introduced to clinical use. In recent years, improved understanding of potential complications associated with hyperinsulinemia has led to resurgent interest in alternatives to insulin monotherapy for patients who have failed on a regimen of diet and sulfonylureas. A plethora of clinical trials have reported on the efficacy of oral hypoglycemic agents in limiting insulin requirements while achieving glycemic control in this subset of diabetic patients. However, few studies have been appropriate in both design and duration to provide convincing results. The randomized placebo-controlled double-blind trials that have used a parallel design and study periods of up to 1 yr have uniformly investigated second-generation sulfonylureas and yielded the most reliable information. In all instances, the combination regimen achieved reduction in insulin requirement with varying evidence of a pancreatic effect as the responsible mechanism. This treatment modality is becoming more widely accepted in the effort to achieve optimal glycemic control with minimum risk to the patient.

This paper compares and constrasts three different probabilistic models - Particle representations, Parzen density estimates, and Gaussian mixture models - for non-Gaussian, non-linear feature tracking, when applied to multiple autonomous vehicles using the Decentralised Data Fusion (DDF) paradigm. These probabilistic models were chosen as they are all capable of approximating the probability distributions of an ideal Bayesian filter and have different properties with regard to computational efficiency and quality of the approximation. In order to show that each model satisfy the DDF requirements of modularity, scalability and robustness, the performance of each representation is taken from a simulation for multi-sensor bearing-only tracking. Performance is evaluated in three areas: (a) mathematical accuracy and optimality of fusion for correlated information between nodes, (b) computational efficiency and accuracy of various operations in the DDF framework and (c) bandwidth requirements for communicating the representations over a wireless network.