On adaptive HMM state estimation

In this paper new online adaptive hidden Markov model (HMM) state estimation schemes are developed, based on extended least squares (ELS) concepts and recursive prediction error (RPE) methods. The best of the new schemes exploit the idempotent nature of Markov chains and work with a least squares prediction error index, using a posterior estimates, more suited to Markov models then traditionally used in identification of linear systems.

Adaptive estimation of hidden semi-Markov chains with parameterised transition probabilities and exponential decaying states

This paper develops maximum likelihood (ML) estimation schemes for finite-state semi-Markov chains in white Gaussian noise. We assume that the semi-Markov chain is characterised by transition probabilities of known parametric from with unknown parameters. We reformulate this hidden semi-Markov model (HSM) problem in the scalar case as a two-vector homogeneous hidden Markov model (HMM) problem in which the state consist of the signal augmented by the time to last transition. With this reformulation we apply the expectation Maximumisation (EM ) algorithm to obtain ML estimates of the transition probabilities parameters, Markov state levels and noise variance. To demonstrate our proposed schemes, motivated by neuro-biological applications, we use a damped sinusoidal parameterised function for the transition probabilities.

Optimal HMM processing of differentially phase modulated communication systems

This paper investigates demodulation of differentially phase modulated signals DPMS using optimal HMM filters. The optimal HMM filter presented in the paper is computationally of order N3 per time instant, where N is the number of message symbols. Previously, optimal HMM filters have been of computational order N4 per time instant. Also, suboptimal HMM filters have be proposed of computation order N2 per time instant. The approach presented in this paper uses two coupled HMM filters and exploits knowledge of ...

Reduced complexity on-line estimation of hidden Markov model parameters

In this paper we propose and study low complexity algorithms for on-line estimation of hidden Markov model (HMM) parameters. The estimates approach the true model parameters as the measurement noise approaches zero, but otherwise give improved estimates, albeit with bias. On a nite data set in the high noise case, the bias may not be signi cantly more severe than for a higher complexity asymptotically optimal scheme. Our algorithms require O(N3) calculations per time instant, where N is the number of states. Previous algorithms based on earlier hidden Markov model signal processing methods, including the expectation-maximumisation (EM) algorithm require O(N4) calculations per time instant.

Faster parameter estimation using risk-sensitive filters

In this paper, we propose a risk-sensitive approach to parameter estimation for hidden Markov models (HMMs). The parameter estimation approach considered exploits estimation of various functions of the state, based on model estimates. We propose certain practical suboptimal risk-sensitive filters to estimate the various functions of the state during transients, rather than optimal risk-neutral filters as in earlier studies. The estimates are asymptotically optimal, if asymptotically risk neutral, and can give significantly improved transient performance, which is a very desirable objective for certain engineering applications. To demonstrate the improvement in estimation simulation studies are presented that compare parameter estimation based on risk-sensitive filters with estimation based on risk-neutral filters.

On-line estimation of Allan variance parameters

A new online method is presented for estimation of the angular random walk and rate random walk coefficients of IMU (inertial measurement unit) gyros and accelerometers. The online method proposes a state space model and proposes parameter estimators for quantities previously measured from off-line data techniques such as the Allan variance graph. Allan variance graphs have large off-line computational effort and data storage requirements. The technique proposed here requires no data storage and computational effort of O(100) calculations per data sample.

Optimal HMM filtering and decision feedback equalisation for differential encoded transmission systems

In this paper conditional hidden Markov model (HMM) filters and conditional Kalman filters (KF) are coupled together to improve demodulation of differential encoded signals in noisy fading channels. We present an indicator matrix representation for differential encoded signals and the optimal HMM filter for demodulation. The filter requires O(N3) calculations per time iteration, where N is the number of message symbols. Decision feedback equalisation is investigated via coupling the optimal HMM filter for estimating the message, conditioned on estimates of the channel parameters, and a KF for estimating the channel states, conditioned on soft information message estimates. The particular differential encoding scheme examined in this paper is differential phase shift keying. However, the techniques developed can be extended to other forms of differential modulation. The channel model we use allows for multiplicative channel distortions and additive white Gaussian noise. Simulation studies are also presented.

Looming aircraft threats : shape-based passive ranging of aircraft from monocular vision

This paper proposes new techniques for aircraft shape estimation, passive ranging, and shape-adaptive hidden Markov model filtering which are suitable for a monocular vision-based non-cooperative collision avoidance system. Vision-based passive ranging is an important missing technology that could play a significant role in resolving the sense-and-avoid problem in un-manned aerial vehicles (UAVs); a barrier hindering the wider adoption of UAVs for civilian applications. The feasibility of the pro- posed shape estimation, passive ranging and shape-adaptive filtering techniques is evaluated on flight test data.

Short-data recursive HMM parameter estimation for rapid vision-based aircraft heading estimation

Rapid recursive estimation of hidden Markov Model (HMM) parameters is important in applications that place an emphasis on the early availability of reasonable estimates (e.g. for change detection) rather than the provision of longer-term asymptotic properties (such as convergence, convergence rate, and consistency). In the context of vision- based aircraft (image-plane) heading estimation, this paper suggests and evaluates the short-data estimation properties of 3 recursive HMM parameter estimation techniques (a recursive maximum likelihood estimator, an online EM HMM estimator, and a relative entropy based estimator). On both simulated and real data, our studies illustrate the feasibility of rapid recursive heading estimation, but also demonstrate the need for careful step-size design of HMM recursive estimation techniques when these techniques are intended for use in applications where short-data behaviour is paramount.

Soft tissue human thigh and buttock finite element model to simulate vehicle seat cushion indentation

Accurate modelling of automotive occupant posture is strongly related to the mechanical interaction between human body soft tissue and flexible seat components. This paper presents a finite-element study simulating the deflection of seat cushion foam and supportive seat structures, as well as human buttock and thigh soft tissue when seated. The thigh-buttock surface shell model was based on 95th percentile male subject scan data and made of two layers, covering thin to moderate thigh and buttock proportions. To replicate the effects of skin and fat, the neoprene rubber layer was modelled as a hyperelastic material with viscoelastic behaviour. The analytical seat model is based on a Ford production seat. The result of the finite-element indentation simulation is compared to a previous simulation of an indentation with a hard shell human model of equal geometry, and to the physical indentation result. We conclude that SAE composite buttock form and human-seat indentation of a suspended seat cushion can be validly simulated.

Towards strongly consistent online HMM parameter estimation using one-step Kerridge inaccuracy

In this paper, we propose a novel online hidden Markov model (HMM) parameter estimator based on the new information-theoretic concept of one-step Kerridge inaccuracy (OKI). Under several regulatory conditions, we establish a convergence result (and some limited strong consistency results) for our proposed online OKI-based parameter estimator. In simulation studies, we illustrate the global convergence behaviour of our proposed estimator and provide a counter-example illustrating the local convergence of other popular HMM parameter estimators.

Genome-wide meta-analysis identifies 56 bone mineral density loci and reveals 14 loci associated with risk of fracture

Bone mineral density (BMD) is the most widely used predictor of fracture risk. We performed the largest meta-analysis to date on lumbar spine and femoral neck BMD, including 17 genome-wide association studies and 32,961 individuals of European and east Asian ancestry. We tested the top BMD-associated markers for replication in 50,933 independent subjects and for association with risk of low-trauma fracture in 31,016 individuals with a history of fracture (cases) and 102,444 controls. We identified 56 loci (32 new) associated with BMD at genome-wide significance (P < 5 × 10−8). Several of these factors cluster within the RANK-RANKL-OPG, mesenchymal stem cell differentiation, endochondral ossification and Wnt signaling pathways. However, we also discovered loci that were localized to genes not known to have a role in bone biology. Fourteen BMD-associated loci were also associated with fracture risk (P < 5 × 10−4, Bonferroni corrected), of which six reached P < 5 × 10−8, including at 18p11.21 (FAM210A), 7q21.3 (SLC25A13), 11q13.2 (LRP5), 4q22.1 (MEPE), 2p16.2 (SPTBN1) and 10q21.1 (DKK1). These findings shed light on the genetic architecture and pathophysiological mechanisms underlying BMD variation and fracture susceptibility.

The Role of the Law in Communicating Patient Safety

This chapter considers the role of the law in communicating patient safety. Downie, Lahey, Ford, et al’s (2006) preventing, knowing and responding theoretical framework is adopted to classify the different elements of patient safety law. Rather than setting out all relevant patient safety laws in detail, this chapter highlights key legal strategies which are employed to: prevent the occurrence of patient safety incidents (preventing); support the discovery and open discussion of patient safety incidents when they do occur (knowing),; and guide responses after they occur (responding) (Downie, Lahey, Ford, et al 2006). The law is increasingly being invoked to facilitate open discussion of and communication surrounding patient safety. After highlighting some legal strategies used to communicate patient safety, two practice examples are presented. The practice examples highlight different aspects of patient safety law and are indicative of communication issues commonly faced in practice. The first practice example focuses on the role of the Ccoroner in communicating patient safety. This example highlights the investigative role of the law in relation to patient safety (knowing). It also showcases the preventing responding and preventing elements in respect of the significant number of communication errors that can occur in a multi-disciplinary, networked health system. The main focus of the second practice example is responding example illustrates how the law responds to health service providers’ and professionals’ miscommunication (and subsequent incidents) during treatment, however it also touches upon knowing and preventing.

Shelf life of minimally processed honeydew, kiwifruit, papaya, pineapple and cantaloupe

Shelf life of minimally processed (peeled, deseeded, and diced) honeydew melon, kiwifruit, papaya, pineapple, and cantaloupe stored at 4°C was studied. Sensory assessments were carried out at 3-day intervals by highly trained panels until the end of shelf life. Microbiological counts were made immediately after dicing fruit and at the end of shelf life. Results indicated that both the length of shelf life and type of spoilage were related to fruit species. Minimally processed fruit had longer shelf life at 4°C than at temp. recommended for whole fruit when these were >4°C. Spoilage of 4°C-stored kiwifruit, papaya, and pineapple pieces was found to be not as a consequence of microbial growth

Changes in sensory quality of sterile cantaloupe dice stored in controlled atmospheres.

Diced cantaloupe flesh that was microbiologically sterile was prepared in order to study the physiological deterioration of fruit when stored under a range of controlled atmospheres at 4.5°C. Sterile fruit pieces were prepared by boiling whole melons for 3 min, then dicing aseptically. Storage atmospheres were in continuous flow and contained from 0 to 26% CO2 and 3.5 to 17% O2. Sensory assessments were carried out by a highly trained panel at 14-day intervals. Products that were acceptable for up to 28 days were obtained when the following 3 treatments were used: 6% CO2 and 6% O2; 9.5% CO2 and 3.5% O2; and 15% CO2 and 6% O2. Overall, treatment with 0, 19.5 or 26% CO2 (irrespective of O2 concn.) caused significant deterioration in sensory properties.