Toward the improvement of image-guided interventions for minimally invasive surgery: three factors that affect performance

OBJECTIVES: The objectives were to measure the impact of specific features of imaging devices on tasks relevant to minimally invasive surgery (MIS) and to investigate cognitive and perceptual factors in such tasks. BACKGROUND: Although image-guided interventions used in MIS provide benefits for patients, they pose drawbacks for surgeons, including degraded depth perception and reduced field of view (FOV). It is important to identify design factors that affect performance. METHOD: In two navigation experiments, observers fed a borescope through an object until it reached a target. Task completion time and object shape judgments were measured. In a motion perception experiment, observers reported the direction of a line that moved behind an aperture. A motion illusion associated with reduced FOV was measured. RESULTS: Navigation through an object was faster when a preview of the object's exterior was provided. Judgments about the object's shape were more accurate with a preview (compared with none) and with active viewing (compared with passive viewing). The motion illusion decreased with a rectangular or rotating octagonal viewing aperture (compared with circular). CONCLUSIONS: Navigation performance may be enhanced when surgeons develop a mental model of the surgical environment, when surgeons (rather than assistants) control the camera, and when the shape of the image is designed to reduce visual illusions. APPLICATION: Unintentional contact between surgical tools and healthy tissues may be reduced during MIS when (a) visual aids permit surgeons to maintain a mental model of the surgical environment, (b) images are bound by noncircular apertures, and (c) surgeons manually control the camera.     

Early warning prediction system for high inflation: an elitist neuro-genetic network model for the Indian economy

In this paper, we propose elitist genetic algorithms–based artificial neural network (ANN) model for setting up an early warning system for occurrence of high inflation. The proposed warning system uses values of an appropriate set of economic fundamental variables as input and builds an ANN model for quantifying the possibility of high inflation within a fixed period of time window. Elitism-based generational genetic algorithm is used for optimizing the architecture of the ANN model. We empirically evaluate the proposed neuro-genetic approach to identify the class of leading economic indicators and build an early warning signalling system of an occurrence of high inflation (overall and component inflations) using the data from the Indian economy. We further compare the results of the proposed approach with the commonly used data-driven signals approach. In the empirical studies, we observe promising performance of the proposed neuro-genetic warning system, which is capable of generating accurate early warning signals of an impending high inflation.

     

Improved arsenic doping in metalorganic chemical vapor deposition of HgCdTe andin situ growth of high performance long wavelength infrared photodiodes

Controlled and effective p-type doping is a key ingredient forin situ growth of high performance HgCdTe photodiode detectors. In this paper, we present a detailed study of p-type doping with two arsenic precursors in metalorganic chemical vapor deposition (MOCVD) of HgCdTe. Doping results from a new precursortris-dimethylaminoarsenic (DMAAs), are reported and compared to those obtained from tertiarybutylarsine (TBAs). Excellent doping control has been achieved using both precursors in the concentration range of 3 × 1015-5 × 10¹⁷ cm⁻³ which is sufficient for a wide variety of devices. Arsenic incorporation efficiency for the same growth temperature and partial pressure is found to be higher with DMAAs than with TBAs. For doping levels up to 1 × 10₁₇ cm⁻³, the alloy composition is not significantly affected by DMAAs. However, at higher doping levels, an increase in the x-value is observed, possibly as a result of surface adduct formation of DMAAs dissociative products with dimethylcadmium. The activation of the arsenic as acceptors is found to be in the 152–50% range for films grown with DMAAs following a stoichiometric anneal. However, a site transfer anneal increases the acceptor activation to near 100%. Detailed temperature dependent Hall measurements and modeling calculations show that two shallow acceptor levels are involved with ionization energies of 11.9 and 3.2 meV. Overall, the data indicate that DMAAs results in more classically behaved acceptor doping. This is most likely because DMAAs has a more favorable surface dissociation chemistry than TBAs. Long wavelength infrared photodiode arrays were fabricated on P-on-n heterojunctions, grownin situ with iodine doping from ethyl iodide and arsenic from DMAAs on near lattice matched CdZnTe (100) substrates. At 77K, for photodiodes with 10.1 and 11.1 (im cutoff wavelengths, the average (for 100 elements 60 × 60 μm² in size) zero-bias resistance-area product, R₀A are 434 and 130 ohm-cm², respectively. Quantum efficiencies are ≥50% at 77K. These are the highest R₀A data reported for MOCVDin situ grown photodiodes and are comparable to state-of-the-art LPE grown photodiodes processed and tested under identical conditions.     

Friedmann-Robertson-Walker metric in curvature coordinates and its applications

For the first time, we express the general Friedmann-Robertson-Walker (FRW) metric (k = +1, 0,?1) into explicit “Schwarzschild” or “Curvature” form, which is important from the viewpoint of cosmology. With this form of the FRW metric, we reconsider the old problem of embedding a Schwarzschild mass (SM) in a pre-existing FRW background from the viewpoints of both (1) the enigmatic McVittie metric, obtained in 1933 and (2) the Einstein-Straus approach (1945) of scooping out a spherical cavity in the same background. Since the exterior of the SM is, by definition, described in the Schwarzschild coordinates, for a definitive study of the Einstein-Straus approach we employ this form of the FRW metric. We find that a necessary condition for a SM to participate in the cosmic expansion is that the background fluid is dust.     

Satellite image segmentation with Shadowed C-Means

Satellite images often require segmentation in the presence of uncertainty, caused due to factors like environmental conditions, poor resolution and poor illumination. Since any subsequent image analysis depends on the quality of such segmentation, one has to obtain an efficient algorithm for the purpose. Pixel clustering is a popular way of determining the homogeneous image regions, corresponding to the different land cover types, based on their spectral properties. In this paper we map the newly developed shadowed clustering algorithm to the problem of segmenting remotely sensed images.It is observed that shadowed clustering can efficiently handle overlapping among segments while modeling uncertainty among the boundaries. Unlike rough clustering, here the choice of user-defined parameters is fully eliminated. The number of segments is automatically optimized in terms of validity indices. The algorithm is robust in the presence of outliers. The superiority of the system is demonstrated in segmenting a synthetic image, along with land cover types from the Indian Remote Sensing (IRS) images of the cities of Mumbai and Kolkata and the SPOT image around Kolkata. The algorithm is found to efficiently and accurately extract the different homogeneous regions in the presence of uncertainty. The results are analyzed both qualitatively and quantitatively.     

State-dependent routing on symmetric loss networks with trunkreservations. I

The aggregated-least-busy-alternative (ALBA), a distributed, state-dependent, dynamic routing strategy for circuit-switched loss networks is discussed. The networks considered are symmetric and fully connected. The offered calls form Poisson streams, and routes have at most two links. In ALBA(K), the states of each link are lumped into K (K⩾2) aggregates, and the route of each call is determined by local information on the aggregate states of the links of the alternate routes at the time of the call's arrival. The last aggregate is always the set of states reserved for direct traffic. A fixed-point model for ALBA(K) for general K is presented. The particular case of ALBA in which there is no aggregation is least busy alternative (LBA); ALBA(2) represents the other extreme of aggregation. Simulation and analytic results for LBA are compared. An asymptotic scaling based on the fixed-point models is also discussed. It is shown that there is a dichotomy in network behavior: if the offered traffic is below a threshold, then the network loss probability decreases exponentially with increasing network size, and above the threshold, performance is poor     

Effective bandwidth of general Markovian traffic sources andadmission control of high speed networks

A prime instrument for controlling congestion in a high-speed network is admission control, which limits calls and guarantees a grade of service determined by delay and loss probability in the multiplexer. It is shown that for general Markovian traffic sources it is possible to assign a notional effective bandwidth to each source that is an explicitly identified, simply computed quantity with provably correct properties in the natural asymptotic regime of small loss probabilities. It is the maximal real eigenvalue of a matrix that is directly obtained from the source characteristics and the admission criterion, and for several sources it is simply additive. Both fluid and point process models are considered. Numerical results show that the acceptance set for heterogeneous classes of sources is closely approximated and conservatively bounded by the set obtained from the effective bandwidth approximation. The bandwidth-reducing properties of the leaky bucket regulator are exhibited numerically     

Modeling subjectively perceived annoyance of H.264/AVC video as a function of perceived artifact strength

This paper is concerned with the subjective perception of video coding artifacts in H.264/AVC encoded and decoded video. Our objective is to model the perceived annoyance of such low bit rate video sequences as a function of perceived artifact strength. We introduce a new method for determining this function and apply it to the data from two psychophysical experiments. Both experiments produced numerical judgments of the subjectively perceived annoyance of artifact combinations and the perceived strength of the respective coding artifacts. Our method produces a discrete multi-dimensional representation of the relationships in the data from which the function relating annoyance to artifact strength is derived. The method is applied to the data of the first experiment and the resulting function is shown to describe the data from the second experiment as well.