Toward a unified PNT, Part 1: Complexity and context: Key challenges of multisensor positioning

The next generation of navigation and positioning systems must provide greater accuracy and reliability in a range of challenging environments to meet the needs of a variety of mission-critical applications. No single navigation technology is robust enough to meet these requirements on its own, so a multisensor solution is required. Known environmental features, such as signs, buildings, terrain height variation, and magnetic anomalies, may or may not be available for positioning. The system could be stationary, carried by a pedestrian, or on any type of land, sea, or air vehicle. Furthermore, for many applications, the environment and host behavior are subject to change. A multi-sensor solution is thus required. The expert knowledge problem is compounded by the fact that different modules in an integrated navigation system are often supplied by different organizations, who may be reluctant to share necessary design information if this is considered to be intellectual property that must be protected

Pathophysiology, diagnosis and treatment of tachycardiomyopathy.

Tachycardiomyopathies (TCMP) are an important cause of left ventricular (LV) dysfunction that should be recognised by physicians as they are potentially reversible and have a significant impact on morbidity and prognosis. They are classically defined as the reversible impairment of ventricular function induced by persistent arrhythmia. However, it is becoming increasingly evident that they can be induced by atrial and ventricular ectopy promoting dyssynchrony and indeed the term ‘arrhythmia-induced cardiomyopathy’ is emerging to describe the phenomenon.1 2 A more current proposed definition highlights aetiology: ‘Atrial and/or ventricular dysfunction—secondary to rapid and/or asynchronous/irregular myocardial contraction, partially or completely reversed after treatment of the causative arrhythmia’ 3 (figure 1). Two categories of the condition exist: the arrhythmia is the only reason for ventricular dysfunction (arrhythmia-induced), and another where the arrhythmia exacerbates ventricular dysfunction and/or worsens heart failure (HF) in a patient with concomitant heart disease (arrhythmia-mediated).4 The exclusion of underlying structural heart disease can be challenging as current imaging techniques, for example, MRI cannot easily identify diffuse fibrosis which may itself be primary or secondary to the effects of arrhythmia promoting ventricular wall dyskinesis and stretch or valvular regurgitation

The Limits of In-run Calibration of MEMS and the Effect of New Techniques

Inertial sensors can significantly increase the robustness of an integrated navigation system by bridging gaps in the coverage of other positioning technologies, such as GNSS or Wi-Fi positioning [1]. A full set of chip-scale MEMS accelerometers and gyros can now be bought for less than $10, potentially opening up a wide range of new applications. However, these sensors require calibration before they can be used for navigation[2]. Higher quality inertial sensors may be calibrated “in-run” using Kalman filter-based estimation as part of their integration with GNSS or other position-fixing techniques. However, this approach can fail when applied to sensors with larger errors which break the Kalman filter due to the linearity and small-angle approximations within its system model not being valid. Possible solutions include: replacing the Kalman filter with a non-linear estimation algorithm, a pre-calibration procedure and smart array [3]. But these all have costs in terms of user effort, equipment or processing load. This paper makes two key contributions to knowledge. Firstly, it determines the maximum tolerable sensor errors for any in-run calibration technique using a basic Kalman filter by developing clear criteria for filter failure and performing Monte-Carlo simulations for a range of different sensor specifications. Secondly, it assesses the extent to which pre-calibration and smart array techniques enable Kalman filter-based in-run calibration to be applied to lower-quality sensors. Armed with this knowledge of the Kalman filter’s limits, the community can avoid both the unnecessary design complexity and computational power consumption caused by over-engineering the filter and the poor navigation performance that arises from an inadequate filter. By establishing realistic limits, one can determine whether real sensors are suitable for in-run calibration with simple characterization tests, rather than having to perform time-consuming empirical testing

Syncope in a young man: Role of Purkinje fibres in idiopathic ventricular fibrillation

A young man suffered cardiac arrests with polymorphic ventricular tachycardia (PVT) and ventricular fibrillation (VF) triggered by ventricular premature contractions (PVCs). The arrhythmia was resistant to anti-arrhythmics, so after ICD implantation he underwent successful ablation of the triggering VE beat, which was pace-mapped to the left posterior hemi-fascicle. We review the evidence for the role of the Purkinje network in the initiation and maintenance of PVT and VF, postulating a channelopathy as a possible underlying cause, and provide recommendations for PVC ablation

A New Approach to Better Low-Cost MEMS IMU Performance Using Sensor Arrays

Over the past decade and a half, the combination of low-cost, lightweight micro-electro-mechanical sensors (MEMS) technology and multisensor integration has enabled inertial sensors to be deployed over a much wider range of navigation applications [1]. Examples include pedestrian dead-reckoning using step detection technology [2, 3], aiding of GNSS signal tracking during jamming [4, 5], and simultaneous localisation and mapping (SLAM) using radio signals [6]. However, for best performance, a MEMS inertial measurement unit (IMU) must be calibrated in the laboratory prior to use, which increases the cost by more than $1000 per unit. In this paper, we examine and present a range of techniques which use an array of inexpensive MEMS sensors to improve the performance of a MEMS IMU without requiring a full calibration prior to use. As the cost of calibration of a high-performance MEMS IMU far outweighs the cost of the hardware, there is considerable scope to improve the performance by adding additional sensors, before the cost of the IMU reaches that of a laboratory calibrated equivalent. Combining MEMS IMUs in an array has been studied before. However, the most common approach was simply to take an average of the input of several identical sensors [7]. If the sensor errors were independent, this could be expected to improve performance by a factor of root-n where, n is the number of IMUs combined. In this paper more sophisticated techniques are investigated that use knowledge of the sensor characteristics to obtain better performance. Three different properties of MEMS sensors may potentially be exploited: 1) The common-mode errors of different sensors of the same design; 2) The different characteristics of in-plane and out-of-plane sensors; and 3) The complementary properties of MEMS sensors with different dynamic ranges. In [8], it is shown that different individual sensors of the same design exhibit similar bias variation with temperature and that improved accuracy may be obtained by differencing the outputs of two gyroscopes mounted with their sensitive axes in opposing directions. Here, this approach will be independently verified and the performance benefits assessed with a range of different MEMS accelerometers and gyros, including Bosch BMA180 accelerometers, Analogue Devices ADXL345 accelerometers, ST Microtronics L3G4200D gyroscopes. Preliminary indications are that there is considerable common bias variation with temperature for the in-plane sensors of L3G4200D gyroscopes, and some common mode behaviour for the low-cost accelerometers. The second idea presented is exploiting the differences between the in-plane and out-of-plane axis outputs of single-chip inertial sensor triads, to improve the performance of an array-based IMU. Early experiment s point to considerable differences between the two which could markedly affect navigation performance. Both accelerometer and gyro triads can exhibit smaller errors from the in-plane sensors than from the out-of-plane sensors. Therefore, experiments were conducted using mutually-perpendicular arrays of accelerometer and gyro triads to determine whether better performance could be obtained using only the in-plane sensors. The third idea is to combine the outputs of MEMS sensors with different dynamic ranges to exploit the lower noise exhibited by some lower-dynamic-range sensors compared to their higher-dynamic-range counterparts. The sensor outputs are thus weighted according to the platform dynamics. That is, predominantly using the high-precision sensor when dynamics are low and using the full-range sensor when the dynamics are high. Several versions of this weighted signal combination will be presented and compared. Early indications are that there can be a significant benefit in this approach for some sensor designs, but not others. Finally, this paper will also examine the efficacy of a once-only static calibration on purchase, performed by the user instead of the supplier, for improving navigation performance. It is essential for a user-performed calibration that the physical movements required of the sensor are very simple and easily understood and completed, even if the underlying method is complex. To this end data, recorded on different days from an array of MEMS sensors within a precisely manufactured rapid prototyped ‘calibration cube’, will be analysed. These measurements are taken at precisely orthogonal angles of the cubes six faces, and allow the scale factor errors, biases and axes alignments of the accelerometers to be determined. The computed calibration corrections over several days will be compared to enable the efficacy of the one-time calibration technique to be assessed. The development of a full calibration routine will be the subject of future research. In summary, this paper will present several new methods for utilising the output of an array of low-cost sensors to improve the performance of a MEMS IMU, and also expands on methods proposed in existing research. As uncalibrated MEMS IMUs are of low performance there is a great potential for new applications if the performance can be improved closer to the level of those which are factory calibrated. / References [1] Groves, P. D., Principles of GNSS, inertial, and multi-sensor integrated navigation systems, Second Edition, Artech House, 2013. [2] Gustafson, D., J. Dowdle, and K. Flueckiger, “A Deeply Integrated Adaptive GPS-Based Navigator with Extended Range Code Tracking,” Proc. IEEE PLANS 2000. [3] Groves, P. D., C. J. Mather and A. A. Macaulay, “Demonstration of Non-Coherent Deep INS/GPS Integration for Optimized Signal to Noise Performance,” Proc. ION GNSS 2007. [4] Ma, Y., W. Soehren, W. Hawkinson, and J. Syrstad, "An Enhanced Prototype Personal Inertial Navigation System," Proc. ION GNSS 2012. [5] Groves, P. D., et al., “Inertial Navigation Versus Pedestrian Dead Reckoning: Optimizing the Integration,” Proc. ION GNSS 2007. [6] Faragher, R. M., C. Sarno, and M. Newman, “Opportunistic Radio SLAM for Indoor Navigation using Smartphone Sensors,” Proc. IEEE/ION PLANS 2012. [7] Bancroft, J. B., and G. Lachapelle, “Data fusion algorithms for multiple inertial measurement units,” Sensors, Vol. 11, No. 7, 2011, pp. 6771-6798. [8] Yuksel, Y., N. El-Sheimy, N., and A. Noureldin, “Error modelling and characterization of environmental effects for low cost inertial MEMS units,” Proc. IEEE/ION PLANS 2010

Organische Düngung intensiv genutzten Dauergrünlandes im Vergleich mit Mineraldüngung - Ergebnisse eines 22 jährigen Versuches auf Wiese und Mähweide.

In a 22-year-old experiment in Southwest Germany, the effects of different fertilization systems (organic and mineral fertilizers) on permanent grassland were investigated. The effects were investigated under cutting and mown pasture with two grazing periods per year. The experiment had 8 fertilizer treatments and 3 replications, the size of field plots were 25 m2 in area. Dry matter (DM) yields and mineral contents in soil and forage (P, K) were measured. The botanical composition was investigated each second year. Maximum DM yields were obtained by mineral NPK fertilization and a treatment called ‘alternating fertilizer’, with yearly alternating use of farmyard manure, liquid manure and mineral NPK. The application of composted farmyard manure reduced DM yields. The additional application of stone-meal and metallurgical lime to slurry did not increase the effects of untreated slurry on yield. Fertilization with slurry increased the proportions of grasses, whereas farmyard manure increased forbs. The proportion of legumes was increased by PK and by fertilization with slurry with lime

Context Detection, Categorization and Connectivity for Advanced Adaptive Integrated Navigation

Context is the environment that a navigation system operates in and the behaviour of its host vehicle or user. The type and quality of signals and environmental features available for positioning varies with the environment. For example, GNSS provides high-quality positioning in open environments, low-quality positioning in dense urban environments and no solution at all deep indoors. The behaviour of the host vehicle (or pedestrian) is also important. For example, pedestrian, car and train navigation all require different map-matching techniques, different motion constraints to limit inertial navigation error growth, and different dynamic models in a navigation filter [1]. A navigation system design should therefore be matched to its context. However, the context can change, particularly for devices, such as smartphones, which move between indoor and outdoor environments and can be stationary, on a pedestrian, or in a vehicle. For best performance, a navigation system should therefore be able to detect its operating context and adapt accordingly; this is context-adaptive positioning [1]. Previous work on context-adaptive navigation and positioning has focused on individual subsystems. For example, there has been substantial research into determining the motion type and sensor location for pedestrian dead reckoning using step detection [2-4]. Researchers have also begun to investigate context-adaptive (or cognitive) GNSS [5-7]. However, this paper considers context adaptation across an integrated navigation system as a whole. The paper addresses three aspects of context-adaptive integrated navigation: context detection, context categorization and context connectivity. It presents experimental results showing how GNSS C/N0 measurements, frequency-domain MEMS inertial sensor measurements and Wi-Fi signal availability could be used to detect both the environmental and behavioural contexts. It then looks at how context information could be shared across the different components of an integrated navigation system. Finally, the concept of context connectivity is introduced to improve the reliability of context detection. GNSS C/N0 measurement distributions, obtained using a smartphone, and Wi-Fi reception data collected over a range of indoor, urban and open environments will be compared to identify suitable features from which the environmental context may be derived. In an open environment, strong GNSS signals will be received from all directions. In an urban environment, fewer strong signals will be received and only from certain directions. Inside a building, nearly all GNSS signals will be much weaker than outside. Wi-Fi signals essentially vary with the environment in the opposite way to GNSS. Indoors, more access points (APs) can be received at higher signal strengths and there is greater variation in RSS. In urban environments, large numbers of APs can still be received, but at lower signal strengths [6]. Finally, in open environments, few APs, if any, will be received. Behavioural context is studied using an IMU. Although an Xsens MEMS IMU is used in this study, smartphone inertial sensors are also suitable. Pedestrian, car and train data has been collected under a range of different motion types and will be compared to identify context-dependent features. Early indications are that, as well as detecting motion, it is also possible to distinguish nominally-stationary IMUs that are placed in a car, on a person or on a table from the frequency spectra of the sensor measurements. The exchange of context information between subsystems in an integrated navigation system requires agreement on the definitions of those contexts. As different subsystems are often supplied by different organisations, it is desirable to standardize the context definitions across the whole navigation and positioning community. This paper therefore proposes a framework upon which a “context dictionary” could be constructed. Environmental and behavioural contexts are categorized separately and a hierarchy of attributes is proposed to enable some subsystems to work with highly specific context categories and others to work with broader categories. Finally, the concept of context connectivity is introduced. This is analogous to the road link connectivity used in map matching [8]. As context detection involves the matching of measurement data to stored context profiles, there will always be occurrences of false or ambiguous context identification. However, these may be minimized by using the fact that it is only practical to transition directly between certain pairs of contexts. For example, it is not normally possible to move directly from an airborne to an indoor environment as an aircraft must land first. Thus, the air and land contexts are connected, as are the land and indoor contexts, but the air and indoor contexts are not. Thus, by only permitting contexts that are connected to the previous context, false and ambiguous context detection is reduced. Robustness may be further enhanced by considering location-dependent connectivity. For example, people normally board and leave trains at stations and fixed-wing aircraft typically require an airstrip to take off and land. / References [1] Groves, P. D., Principles of GNSS, inertial, and multi-sensor integrated navigation systems, Second Edition, Artech House, 2013. [2] Park, C. G., et al., “Adaptive Step Length Estimation with Awareness of Sensor Equipped Location for PNS,” Proc. ION GNSS 2007. [3] Frank, K., et al., “Reliable Real-Time Recognition of Motion Related Human Activities Using MEMS Inertial Sensors,” Proc. ION GNSS 2010. [4] Pei, L., et al., “Using Motion-Awareness for the 3D Indoor Personal Navigation on a Smartphone,” Proc. ION GNSS 2011. [5] Lin, T., C. O’Driscoll, and G. Lachapelle, “Development of a Context-Aware Vector-Based High-Sensitivity GNSS Software Receiver,” Proc. ION ITM 2011. [6] Shafiee, M., K., O’Keefe, and G. Lachapelle, “Context-aware Adaptive Extended Kalman Filtering Using Wi-Fi Signals for GPS Navigation,” Proc. ION GNSS 2011. [7] Shivaramaiah, N. C., and A. G. Dempster, “Cognitive GNSS Receiver Design: Concept and Challenges,” Proc. ION GNSS 2011. [8] Quddus, M. A., High Integrity Map Matching Algorithms for Advanced Transport Telematics Applications, PhD Thesis, Imperial College London, 2006

Whip Use by Jockeys in a Sample of Australian Thoroughbred Races—An Observational Study

The use of whips by jockeys is an issue. The current study viewed opportunistic high-speed footage of 15 race finishes frame-by-frame to examine the outcomes of arm and wrist actions (n = 350) on 40 horses viewed from the left of the field. Any actions fully or partially obscured by infrastructure or other horses were removed from the database, leaving a total of 104 non-contact sweeps and 134 strikes. For all instances of arm actions that resulted in fully visible whip strikes behind the saddle (n = 109), the outcomes noted were area struck, percentage of unpadded section making contact, whether the seam made contact and whether a visible indentation was evident on impact. We also recorded use of clockwise or counter-clockwise arm action from each jockey's whip, whether the whip was held like a tennis racquet or a ski pole, whether the hind leg on the side of the impact was in stance or swing phase and whether the jockey's arm was seen traveling above shoulder height. The goal of the study was to characterize the area struck and the visual impact of whip use at the level of the horse. We measured the ways in which both padded and unpadded sections of the whip made impact. There was evidence of at least 28 examples, in 9 horses, of breaches of the whip rules (one seam contact, 13 contacts with the head, and 14 arm actions that rose above the height of the shoulder). The whip caused a visible indentation on 83% of impacts. The unpadded section of the whip made contact on 64% of impacts. The results call into question the ability of Stewards to effectively police the rules concerning whip use and, more importantly, challenge the notion that padding the distal section of whips completely safeguards horses from any possible whip-related pain

Predictive feedback control and Fitts' law

Fitts’ law is a well established empirical formula, known for encapsulating the “speed-accuracy trade-off”. For discrete, manual movements from a starting location to a target, Fitts’ law relates movement duration to the distance moved and target size. The widespread empirical success of the formula is suggestive of underlying principles of human movement control. There have been previous attempts to relate Fitts’ law to engineering-type control hypotheses and it has been shown that the law is exactly consistent with the closed-loop step-response of a time-delayed, first-order system. Assuming only the operation of closed-loop feedback, either continuous or intermittent, this paper asks whether such feedback should be predictive or not predictive to be consistent with Fitts law. Since Fitts’ law is equivalent to a time delay separated from a first-order system, known control theory implies that the controller must be predictive. A predictive controller moves the time-delay outside the feedback loop such that the closed-loop response can be separated into a time delay and rational function whereas a non- predictive controller retains a state delay within feedback loop which is not consistent with Fitts’ law. Using sufficient parameters, a high-order non-predictive controller could approximately reproduce Fitts’ law. However, such high-order, “non-parametric” controllers are essentially empirical in nature, without physical meaning, and therefore are conceptually inferior to the predictive controller. It is a new insight that using closed-loop feedback, prediction is required to physically explain Fitts’ law. The implication is that prediction is an inherent part of the “speed-accuracy trade-off”