Clinical significance of signal changes in the quadratus femoris muscle on MR

'Objectives: 'To evaluate the clinical significance of quadratus femoris muscle signal changes (QFMC) on MRI. 'Methods: '204 consecutive bilateral MRI hip examinations (132 female, 72 male) were reviewed in retrospect for QFMC. Inclusion imaging parameters were edema or atrophy of the quadratus femoris muscle. The presence or absence of symptoms and additional ipsilateral and/or contralateral imaging findings were used to differentiate between isolated symptomatic, co-incidental and asymptomatic QFMC. 'Results: '24 (11.8%) patients and 30 (7.3%) hips demonstrated QFMC. Atrophy was present in 5 symptomatic hips. Female to male ratio was 23:1. Isolated symptomatic QFMC: 4 hips (13.3%), 1 bilateral. Clinical symptoms in this group were non-specific greater trochanter pain and stiffness of the hip. Co-incidental QFMC: 19 symptomatic hips, ipsilateral associated findings present in 18 hips (94.7%) and contralateral additional findings present in 8 hips (42.1%). Asymptomatic QFMC: 7 hips (23.3%), ipsilateral associated asymptomatic findings in 5 hips (71.4%) and contralateral associated symptomatic findings in 6 hips (85.7%). Edema around the greater trochanter and hamstring insertions were the most frequent associated findings. 'Conclusion: 'In this study, most cases of QFMC were co-incidental or asymptomatic. In isolated symptomatic QFMC clinical complaints were non-specific. Atrophy was found only in the symptomatic hips

Time evolution for dynamic probabilistic tensors in hierarchical tucker decomposition form

Many real world applications of target tracking and state estimation are non-linear filtering problems and can therefore not be solved by closed-form analytical solutions. In the recent past, tensor based approaches have become increasingly popular due to very effective decomposition algorithms, which allow the representation of discretized, high-dimensional data in compressed form. In this paper, a solution of the prediction step for a Bayesian filter is proposed, where the probability density function (pdf) is approximated by a tensor in Hierarchical Tucker Decomposition. It is shown, that the computation of the predicted pdf is about five times faster than the previously proposed Canonical Polyadic Decomposition format

A classify-while-track approach using dynamical tensors

Since a lot of nuclear material (e.g. from hospitals or power plants) is out of the control of the authorities, chances are high that terrorist groups will be able to own such material and mix it with explosives in a “dirty bomb” to intensify the scaring effect of an attack. In this paper, a fusion framework based on a tensor with dynamic changing dimensions is presented to solve the association problem of a nuclear source in an open space scenario. Since the decay process of a nuclear source is a random process itself, a likelihood is derived to integrate the distances of all persons to the sensors, the Poisson nature of such a decay, and additive white white noise in the sensing process. As a closed form solution is intractable in general, a Poisson approximation and the saddle point method is proposed. The approach is evaluated in an experimental setup using different scenarios which are motivated from typical situations in a railway station

On a CPD Decomposition of a Multi-Variate Gaussian

Tensor decomposition based sensor data fusion is a novel field of numerical solutions to the Bayesian filtering problem. Due to the exponential growth of high dimensional tensors, this approach has not got much attention in the past. This has changed with the rise of efficient decomposition algorithms such as the 'Canonical Polyadic Decomposition' (CPD), which allow a compact representation of the precise, discretized information in the state space. As solutions of the prediction-filtering cycle were developed, it usually is assumed that a decomposition of the likelihood or the initial prior is available. In this paper, we propose a numerical method to compute the CPD form of a multivariate Gaussian, either a likelihood or a prior, in terms of an analytical solution in combination with the Taylor approximation of the pointwise tensor exponential

Distributed Kalman filter fusion at arbitrary instants of time

Track-to-track fusion aims at combining locally preprocessed information of individual sensors optimally, i.e. in a way that is equivalent to fusing all measurements of all sensors directly. It is well known that this can be achieved if the local sensor tracks produced at all individual scan times are available in the fusion center. Full-rate communication in this sense, however, is impractical in certain applications. In [1] W. Koch thus proposes a distributed Kalman-type processing scheme, which provides optimal track-to-track fusion results at arbitrarily chosen instants of time by communicating and combining the local sensor 'tracks' referring to this time. However, this scheme needs an exchange of sensor data for the track prediction and retrodiction, if the number of sensors exceeds two. Therefore, we present an improvement, which extends the algorithm to arbitrary sensor count

Out-of-sequence processing of cluttered sensor data using multiple evolution models

In target tracking applications, the full information on the kinematic target states accumulated over a certain time window up to the present time is contained in the joint probability density function of these state vectors, given the time series of all sensor data. In [1] the structure of this Accumulated State Density (ASD) has been revealed. Furthermore, ASDs enable us to process Out-of-Sequence (OoS) measurements in a neat and straightforward way. This paper presents an algorithm for the processing of OoS measurements in situations with more relaxed assumptions. On the one hand, sensors often return ambiguous measurement data. Then, measurement association methodologies as the Multi-Hypothesis Tracker (MHT) are required. On the other hand, the evolution model in use might not be unique. The well-known approach to this challenge is the Interacting Multiple Model (IMM) filter. In this paper, an IMM/MHT extension to the ASD paradigm is discussed, tested by simulation, and evaluated

On the superposition principle of linear Gaussian estimation - a physical analogy

In this paper a new interpretation of linear estimation in the context of classical mechanics is presented. In this context, Accumulated State Densities can be interpreted as the Lagrange function of a “least action” principle that provides the expectation vectors for filtering and retrodiction as a solution. The superposition principle, which states that the solution of this algorithm is a linear combination of the initial value and the measurements, is a consequence of the fact that the measurements are independent conditioned on the accumulated state and that the “action” functionals are mutually independent. Two example applications are shown, which save some computation when the estimate of the complete trajectory becomes important

Gaussian Mixture Based Target Tracking Combining Bearing-Only Measurements and Contextual Information

Gaussian mixtures (GM) provide a flexible and numerically robust means for the treatment of nonlinearities as well as for the integration of context knowledge into target tracking algorithms. Contextual information lead to constraints on the target state which can be incorporated in the time prediction step of a tracking filter (model of the target dynamics) as well as in the measurement update step in terms of a constraint likelihood function. In this paper, we present examples for each possibility: road-map assisted target tracking and integration of terrain map data for target localization. The algorithms are applied to the problem of airborne passive emitter localization and demonstrate enhanced tracking and localization precision for moving and for stationary ground based emitters

Counter drones: Tensor decompostion-based data fusion and systems design aspects

Unmanned Aerial Systems UAS, i.e. drones, revolutionize the market for mobility based services and enable more efficient defence and security operations. Also this rapidly developing technology, however, proves to be Janus-faced. Despite their unquestionable benefits, UAS increasingly pose serious safety and security threats. Detection, tracking, and classification of small and highly agile drones, however, is one of the most challenging surveillance tasks. Only a properly designed suite of heterogeneous and mutually complementary sensor provides the required sensor data. On this basis, the key technology proves to advanced multiple sensor data fusion that provides situational awareness and the key information for assigning appropriate counter measures. We in particular focus on a novel approach and highly promising approach which has the potential of a paradigm shift in sensor data fusion: tensor decomposition based multiple sensor tracking filters. This new methodology for fusion engines is able to efficiently represent the full informational content of advanced sensors and sophisticated dynamic models for drone motion. Powerful multilinear decomposition methods for tensors are drastically reducing the computational efforts for producing high-quality tracks for dim and agile drones. Moreover, the deterministic performance characteristics of tensor decomposition based fusion have beneficial implications for systems design aspects. These advanced algorithms of multiple sensor data fusion play a key role in designing counter drone systems. In the context of C5ISR systems (Command, Control, Communications, Computer, Cyber, Intelligence, Surveillance and Reconnaissance), the technological challenges can be met, but require close cooperation between the military and police forces, research institutes and the relevant industries. In the protection of stationary equipment and mobile units in urban or open terrain, the integration of drone detection / tracking / classification in decision support systems is crucial

State dependent mode transition probabilities

A multiple model filter similar to the IMM filter is developed for tracking of maneuvering targets. The mode transition probabilities are modeled as dependent on the state. This allows using information about the mode of a target that is contained in the state. Thus, better estimates of the mode can be obtained. Convergence of the mode estimates occurs more quickly. As an application, choosing acceleration dependent mode transitions in a scenario using constant velocity motion and coordinated turn motion is discussed