ArcFVS:An integration of a Geographic Information System and Forest Vegetation Simulator

This paper presents the design and implementation of an integrated application program called ArcFVS that links the Forest Vegetation Simulator (FVS) and a Geographic Information System (GIS) to realize spatial selection of input files and graphic display of modeling output. Data for testing and running the model came from the U.S. Forest Service's Forest Inventory and Analysis (FIA) Database and were also collected in field surveys in north-central Indiana. ArcFVS 1.0 is designed using the ArcGIS software from the Environmental Systems Research Institute (ESRI) and the Visual Basic for Applications (VBA) programming environment to manipulate ESRI's ArcObjects. The resulting product offers custom functions as commands in a new menu or as tools on a new toolbar. They are used to: select spatially or by attribute the forest plots to be projected by FVS, create the FVS input files for the selected plots and display in a geospatial environment different types of FVS output (text output files, tables with variables of interest and visualization image files). Advantages of ArcFVS 1.0 include the new GIS capabilities, enhanced format translation functions and the standardized programming environment.     

Thin film based semi-active resonant marker design for low profile interventional cardiovascular MRI devices

Objectives

A new microfabrication method to produce low profile radio frequency (RF) resonant markers on catheter shafts was developed. A semi-active RF resonant marker incorporating a solenoid and a plate capacitor was constructed on the distal shaft of a 5 Fr guiding catheter. The resulting device can be used for interventional cardiovascular MRI procedures.

Materials and methods

Unlike current semi-active device visualization techniques that require rigid and bulky analog circuit components (capacitor and solenoid), we fabricated a low profile RF resonant marker directly on guiding the catheter surface by thin film metal deposition and electroplating processes using a modified physical vapor deposition system.

Results

The increase of the overall device profile thickness caused by the semi-active RF resonant marker (130 µm thick) was lowered by a factor of 4.6 compared with using the thinnest commercial non-magnetic and rigid circuit components (600 µm thick). Moreover, adequate visibility performance of the RF resonant marker in different orientations and overall RF safety were confirmed through in vitro experiments under MRI successfully.

Conclusion

The developed RF resonant marker on a clinical grade 5 Fr guiding catheter will enable several interventional congenital heart disease treatment procedures under MRI.

     

Advances in Lupus Nephritis Pathogenesis: From Bench to Bedside

Systemic lupus erythematosus (SLE) is the prototype of autoimmune disorders caused by a loss of tolerance to endogenous nuclear antigens triggering an aberrant autoimmune response targeting various tissues. Lupus nephritis (LN), a major cause of morbidity and mortality in patients with SLE, affects up to 60% of patients. The recent insights into the genetic and molecular basis of SLE and LN paved the way for newer therapies to be developed for these patients. Apart from the traditional B-cell-centered view of this disease pathogenesis, acknowledging that multiple extrarenal and intrarenal pathways contribute to kidney-specific autoimmunity and injury may help refine the individual therapeutic and prognostic characterization of such patients. Accordingly, the formerly induction-maintenance treatment strategy was recently challenged with the exciting results obtained from the trials that evaluated add-on therapy with voclosporin, belimumab, or Obinutuzumab. The scope of this review is to provide an insight into the current knowledge of LN pathogenesis and future therapeutic strategies.     

Automatic recognition of gait-related health problems in the elderly using machine learning

This paper proposes a system for the early automatic recognition of health problems that manifest themselves in distinctive form of gait. Purpose of the system is to prolong the autonomous living of the elderly at home. When the system identifies a health problem, it automatically notifies a physician and provides an explanation of the automatic diagnosis. The gait of the elderly user is captured using a motion-capture system, which consists of body-worn tags and wall-mounted sensors. The positions of the tags are acquired by the sensors and the resulting time series of position coordinates are analyzed with machine-learning algorithms in order to recognize a specific health problem. Novel semantic features based on medical knowledge for training a machine-learning classifier are proposed in this paper. The classifier classifies the user’s gait into: 1) normal, 2) with hemiplegia, 3) with Parkinson’s disease, 4) with pain in the back and 5) with pain in the leg. The studies of 1) the feasibility of automatic recognition and 2) the impact of tag placement and noise level on the accuracy of the recognition of health problems are presented. The experimental results of the first study (12 tags, no noise) showed that the k-nearest neighbors and neural network algorithms achieved classification accuracies of 100%. The experimental results of the second study showed that classification accuracy of over 99% is achievable using several machine-learning algorithms and 8 or more tags with up to 15 mm standard deviation of noise. The results show that the proposed approach achieves high classification accuracy and can be used as a guide for further studies in the increasingly important area of Ambient Assisted Living. Since the system uses semantic features and an artificial-intelligence approach to interpret the health state, provides a natural explanation of the hypothesis and is embedded in the domestic environment of the elderly person; it is an example of the semantic ambient media for Ambient Assisted Living.     

Pitch Estimation by Block and Instantaneous Methods

Choosing a pitch estimation algorithm is not a simple task. One must balance between the accuracy and the reliability of the estimates. Two classes of methods are available. The first one, known as the block methods class, gives noise robust solutions and has an intrinsic averaging property, but is not very accurate, especially for the transition regions. The second one, known as the instantaneous (or event-based) methods class, gives very accurate estimates, but is considered to be inadequate in the presence of noise.In this paper, we present potential enhancements of the performance in pitch estimation, based on both block and instantaneous methods. In this respect we discuss mainly two algorithms: a nonlinear cepstral algorithm and a wavelet-based one. The first algorithm, due to the proposed nonlinear model, enhances the classical linear model performance related to the accuracy of the estimated pitch for the transition regions and to the robustness in the presence of noise. Concerning the second algorithm, to the inherent accuracy of the estimated pitch, we add robust estimates even in the presence of noise, based on the multiresolution properties of an improved wavelet transform. The obtained enhancements were evaluated on a hand-labeled speech database, and the improved algorithms are now being applied in our research concerning speech compression and prosody.     

Embedding new observations via sparse-coding for non-linear manifold learning

Non-linear dimensionality reduction techniques are affected by two critical aspects: (i) the design of the adjacency graphs, and (ii) the embedding of new test data—the out-of-sample problem. For the first aspect, the proposed solutions, in general, were heuristically driven. For the second aspect, the difficulty resides in finding an accurate mapping that transfers unseen data samples into an existing manifold. Past works addressing these two aspects were heavily parametric in the sense that the optimal performance is only achieved for a suitable parameter choice that should be known in advance.     

Real-time game-theoretic model predictive control for differential game of target defense

This paper evaluates experimentally a novel strategy for solving a variant of the differential game of target defense in presence of obstacles. The game is widely applied in the areas of military defense for protecting important equipment such as a ship, an aircraft, a moving vehicle, or a sensitive installation from a malicious attacker. The state-of-the-art approaches mostly employ an offline optimization strategy that is only applicable to holonomic robots. Moreover, most of the approaches could not autonomously avoid obstacles or take into account uncertainties. As a consequence, this paper presents an online optimization technique, by designing a trade-off parameter that integrates game theory with the model predictive control, which allows a nonholonomic defender to intercept the attacker while simultaneously defending the target. Simulations under different conditions as well as several indoor laboratory experiments validate the proposed approach. Moreover, performance is compared with a standard model predictive control approach.     

Adaptive rank weighted switching filter for impulsive noise removal in color images

Journal of Real-Time Image Processing - In this paper, a novel approach to the problem of impulsive noise removal in color digital images is presented. The described switching filter is based on...     

Matrix approach to deadlock-free dispatching in multi-class finite buffer flowlines

For finite-buffer manufacturing systems, the major stability issue is "deadlock," rather than "bounded-buffer-length stability." The paper introduces the concept of "system deadlock," defined rigorously in Petri net terms, and system operation with uninterrupted part-flow is characterized in terms of the absence of this condition. For a large class of finite-buffer multiclass re-entrant flowline systems, an analysis of "circular waits" yields necessary and sufficient conditions for the occurrence of "system deadlock." This allows the formulation of a maximally permissive one-step-look-ahead deadlock-avoidance control policy for dispatching jobs, while maximizing the percent utilization of resources. The result is a generalized kanban dispatching strategy, which is more general than the standard multiclass last buffer first serve (LBFS) dispatching strategies for finite buffer flowlines that typically under-utilize the resources. The problem of computational complexity associated with Petri net (PN) applications is overcome by using certain sub-matrices of the PN incidence matrix. Computationally efficient matrix techniques are given for implementing the deadlock-free dispatching policy.     

Robust gossiping with an application to consensus

We study deterministic gossiping in synchronous systems with dynamic crash failures. Each processor is initialized with an input value called rumor. In the standard gossip problem, the goal of every processor is to learn all the rumors. When processors may crash, then this goal needs to be revised, since it is possible, at a point in an execution, that certain rumors are known only to processors that have already crashed. We define gossiping to be completed, for a system with crashes, when every processor knows either the rumor of processor v or that v has already crashed, for any processor v. We design gossiping algorithms that are efficient with respect to both time and communication. Let t<n be the number of failures, where n is the number of processors. If , then one of our algorithms completes gossiping in O(log²t) time and with O(npolylogn) messages. We develop an algorithm that performs gossiping with O(n^1.77) messages and in O(log²n) time, in any execution in which at least one processor remains non-faulty. We show a trade-off between time and communication in gossiping algorithms: if the number of messages is at most O(npolylogn), then the time has to be at least . By way of application, we show that if n−t=Ω(n), then consensus can be solved in O(t) time and with O(nlog²t) messages.