Fuzzy Tuned PID Controller for Envisioned Agricultural Manipulator

Machine Intelligence Research - The implementation of image-based phenotyping systems has become an important aspect of crop and plant science research which has shown tremendous growth over the...     

A Continuous Relaxation of the Constrained $$\ell _2-\ell _0$$ ℓ 2 - ℓ 0 Problem

Journal of Mathematical Imaging and Vision - We focus on the minimization of the least square loss function under a k-sparse constraint encoded by a $$\ell _0$$ pseudo-norm. This is a non-convex,...     

Highly accelerated intracranial 4D flow MRI: evaluation of healthy volunteers and patients with intracranial aneurysms

Objectives

To evaluate an accelerated 4D flow MRI method that provides high temporal resolution in a clinically feasible acquisition time for intracranial velocity imaging.

Materials and methods

Accelerated 4D flow MRI was developed by using a pseudo-random variable-density Cartesian undersampling strategy (CIRCUS) with the combination of k-t, parallel imaging and compressed sensing image reconstruction techniques (k-t SPARSE-SENSE). Four-dimensional flow data were acquired on five healthy volunteers and eight patients with intracranial aneurysms using CIRCUS (acceleration factor of R = 4, termed CIRCUS4) and GRAPPA (R = 2, termed GRAPPA2) as the reference method. Images with three times higher temporal resolution (R = 12, CIRCUS12) were also reconstructed from the same acquisition as CIRCUS4. Qualitative and quantitative image assessment was performed on the images acquired with different methods, and complex flow patterns in the aneurysms were identified and compared.

Results

Four-dimensional flow MRI with CIRCUS was achieved in 5 min and allowed further improved temporal resolution of <30 ms. Volunteer studies showed similar qualitative and quantitative evaluation obtained with the proposed approach compared to the reference (overall image scores: GRAPPA2 3.2 ± 0.6; CIRCUS4 3.1 ± 0.7; CIRCUS12 3.3 ± 0.4; difference of the peak velocities: ?3.83 ± 7.72 cm/s between CIRCUS4 and GRAPPA2, ?1.72 ± 8.41 cm/s between CIRCUS12 and GRAPPA2). In patients with intracranial aneurysms, the higher temporal resolution improved capturing of the flow features in intracranial aneurysms (pathline visualization scores: GRAPPA2 2.2 ± 0.2; CIRCUS4 2.5 ± 0.5; CIRCUS12 2.7 ± 0.6).

Conclusion

The proposed rapid 4D flow MRI with a high temporal resolution is a promising tool for evaluating intracranial aneurysms in a clinically feasible acquisition time.

     

Comment on ‘Fault tolerance analysis for stochastic systems using switching diffusion processes’ by Yang,Jiang and Cocquempot

Results are given in Yang, Jiang and Cocquempot (Yang, H., Jiang, B., and Cocquempot, V. (2009), ‘Fault Tolerance Analysis for Stochastic Systems using Switching Diffusion Processes’, International Journal of Control, 82, 1516–1525) regarding the overall stability of switched diffusion processes based on stability properties of separate processes combined through stochastic switching. This article argues two main results to be empty, in that the presented hypotheses are logically inconsistent.     

Accelerating difficulty estimation for conformal regression forests

The conformal prediction framework allows for specifying the probability of making incorrect predictions by a user-provided confidence level. In addition to a learning algorithm, the framework requires a real-valued function, called nonconformity measure, to be specified. The nonconformity measure does not affect the error rate, but the resulting efficiency, i.e., the size of output prediction regions, may vary substantially. A recent large-scale empirical evaluation of conformal regression approaches showed that using random forests as the learning algorithm together with a nonconformity measure based on out-of-bag errors normalized using a nearest-neighbor-based difficulty estimate, resulted in state-of-the-art performance with respect to efficiency. However, the nearest-neighbor procedure incurs a significant computational cost. In this study, a more straightforward nonconformity measure is investigated, where the difficulty estimate employed for normalization is based on the variance of the predictions made by the trees in a forest. A large-scale empirical evaluation is presented, showing that both the nearest-neighbor-based and the variance-based measures significantly outperform a standard (non-normalized) nonconformity measure, while no significant difference in efficiency between the two normalized approaches is observed. The evaluation moreover shows that the computational cost of the variance-based measure is several orders of magnitude lower than when employing the nearest-neighbor-based nonconformity measure. The use of out-of-bag instances for calibration does, however, result in nonconformity scores that are distributed differently from those obtained from test instances, questioning the validity of the approach. An adjustment of the variance-based measure is presented, which is shown to be valid and also to have a significant positive effect on the efficiency. For conformal regression forests, the variance-based nonconformity measure is hence a computationally efficient and theoretically well-founded alternative to the nearest-neighbor procedure.     

Textures and spin waves in rotating superfluid3H3-B

The spin wave modes in rotating superfluid³He-B have been calculated for a texture of cylindrical symmetry. The textural configuration is determined by minimizing the appropriate free energy and solving the resulting Euler-Lagrange equations for then vector. The texture determines the effective potential entering the spin wave equation. The potential deviates significantly from a harmonic oscillator under realistic experimental conditions, giving rise to shifts of the oscillator frequencies. The agreement with the recent experimental observations of Ikkalaet al. is excellent at all temperatures studied.     

Recycled concrete and fly ash make calcium silicate bricks

It has been shown that calcium hydroxide from hydrated cement reacts with fly ash and siliceous particles in the crusher fines of recycled concrete to give calcium silicate products similar to sand-lime bricks when autoclaved. Such products achieved compressive strengths up to 12.5 MPa.     

Risk assessment for fat derivatives in case of contamination with BSE

Prion diseases are not only of outstanding scientific interest but have also enormous economic impact. In particular, the human food and animal feed industry and even oleochemical manufacturing processes are afflicted. In the oleochemical industry, bovine edible tallow is widely used as raw material for the production of fatty acids, glycerol, and their derivatives. Although there is no evidence that tallow has been a causal factor for BSE nor that infectivity partitions preferentially with tallow, the potential risk associated with tallow‐derived products has to be evaluated in the light of the specific production process. To the present day, no experimental data under technically relevant conditions are available on the safety of fatty acids, glycerol, and their derivatives in the case of a hypothetical contamination of tallow with prions. A risk assessment calculation is provided here based on quantitative data for the degradation of the pathological prion protein as well as the inactivation of prion infectivity. It can be concluded that the industrial conditions of the basic oleochemical process of hydrolytic fat splitting constitute an effective means for reducing the risk of TSE contamination to an acceptable minimum. All industrial tallow‐derived products can be regarded as safe, independently of their origin.     

Learning object,grasping and manipulation activities using hierarchical HMMs

This article presents a probabilistic algorithm for representing and learning complex manipulation activities performed by humans in everyday life. The work builds on the multi-level Hierarchical Hidden Markov Model (HHMM) framework which allows decomposition of longer-term complex manipulation activities into layers of abstraction whereby the building blocks can be represented by simpler action modules called action primitives. This way, human task knowledge can be synthesised in a compact, effective representation suitable, for instance, to be subsequently transferred to a robot for imitation. The main contribution is the use of a robust framework capable of dealing with the uncertainty or incomplete data inherent to these activities, and the ability to represent behaviours at multiple levels of abstraction for enhanced task generalisation. Activity data from 3D video sequencing of human manipulation of different objects handled in everyday life is used for evaluation. A comparison with a mixed generative-discriminative hybrid model HHMM/SVM (support vector machine) is also presented to add rigour in highlighting the benefit of the proposed approach against comparable state of the art techniques.