Bendtsenbisgaard8500

The semantic gap can be bridged by the learned feature-label correlation. Finally, extensive experimental results on several benchmarks under four domains are presented to show the effectiveness of the proposed framework.Quantitative assessment of retinal layer thickness in spectral domain-optical coherence tomography (SD-OCT) images is vital for clinicians to determine the degree of ophthalmic lesions. However, due to the complex retinal tissues, high-level speckle noises and low intensity constraint, how to accurately recognize the retinal layer structure still remains a challenge. To overcome this problem, this paper proposes an adaptive-guided-coupling-probability level set method for retinal layer segmentation in SD-OCT images. Specifically, based on Bayes's theorem, each voxel probability representation is composed of two probability terms in our method. The first term is constructed as neighborhood Gaussian fitting distribution to characterize intensity information for each intra-retinal layer. The second one is boundary probability map generated by combining anatomical priors and adaptive thickness information to ensure surfaces evolve within a proper range. Then, the voxel probability representation is introduced into the proposed segmentation framework based on coupling probability level set to detect layer boundaries. A total of 1792 retinal B-scan images from 4 SD-OCT cubes in healthy eyes, 5 cubes in abnormal eyes with central serous chorioretinaopathy and 5 SD-OCT cubes in abnormal eyes with age-related macular disease are used to evaluate the proposed method. The experiment demonstrates that the segmentation results obtained by the proposed method have a good consistency with ground truth, and the proposed method outperforms six methods in the layer segmentation of uneven retinal SD-OCT images.This article presents new theoretical results on global exponential synchronization of nonlinear coupled delayed memristive neural networks with reaction-diffusion terms and Dirichlet boundary conditions. First, a state-dependent memristive neural network model is introduced in terms of coupled partial differential equations. Next, two control schemes are introduced distributed state feedback pinning control and distributed impulsive pinning control. A salient feature of these two pinning control schemes is that only partial information on the neighbors of pinned nodes is needed. By utilizing the Lyapunov stability theorem and Divergence theorem, sufficient criteria are derived to ascertain the global exponential synchronization of coupled neural networks via the two pining control schemes. Finally, two illustrative examples are elaborated to substantiate the theoretical results and demonstrate the advantages and disadvantages of the two control schemes.Nowadays, tactile surfaces, such as smartphones, provide haptic feedback to signify that a task has been performed correctly or more generally to enrich the interaction. However, this haptic feedback induces vibrations in the surface that propagate to the whole surface, reverberate and attenuate, thus making multi-finger interaction, with different feedbacks, difficult. Recently, the Inverse Filter Method has been proposed control the propagation of these vibrations, and thus enable to product localized multitouch on a glass surface. This way, a user can put several fingers on a tactile surface and yet feel stimuli independently on his/her different fingers. This paper continues this work and demonstrates that a localized multitouch haptic feedback can be delivered in real time using a capacitive screen. To achieve this, this paper presents the two necessary steps a calibration step and an interpolation calculation in order to save calculation and learning time. learn more Furthermore, the paper describes the performance of the device through a study on the behaviour of the screen subjected to the Inverse Filter Method, indicating the movement of the whole screen and the voltage requirement for any haptic feedback.The primary objective of this paper is to build classification models and strategies to identify breathing sound anomalies (wheeze, crackle) for automated diagnosis of respiratory and pulmonary diseases. In this work we propose a deep CNN-RNN model that classifies respiratory sounds based on Mel-spectrograms. We also implement a patient specific model tuning strategy that first screens respiratory patients and then builds patient specific classification models using limited patient data for reliable anomaly detection. Moreover, we devise a local log quantization strategy for model weights to reduce the memory footprint for deployment in memory constrained systems such as wearable devices. The proposed hybrid CNN-RNN model achieves a score of 66.31% on four-class classification of breathing cycles for ICBHI'17 scientific challenge respiratory sound database. When the model is re-trained with patient specific data, it produces a score of 71.81% for leave-one-out validation. The proposed weight quantization technique achieves ≍ 4× reduction in total memory cost without loss of performance. The main contribution of the paper is as follows Firstly, the proposed model is able to achieve state of the art score on the ICBHI'17 dataset. Secondly, deep learning models are shown to successfully learn domain specific knowledge when pre-trained with breathing data and produce significantly superior performance compared to generalized models. Finally, local log quantization of trained weights is shown to be able to reduce the memory requirement significantly. This type of patient-specific re-training strategy can be very useful in developing reliable long-term automated patient monitoring systems particularly in wearable healthcare solutions.Wheat is an important cereal crop grown worldwide but it's yield is severely affected by various biotic and abiotic stresses. SNAREs are key regulators of vesicle trafficking and are present in abundance in higher plant species suggesting their prominence in growth and development. Novel Plant SNAREs (NPSN) are found exclusively in plants. Hence, a comprehensive analysis of these two gene families in wheat genome was accomplished in this study. We report here 27 SNAREs and eight NPSN genes. These genes and their respective proteins were investigated for gene structure, physiochemical properties, domain and motif architecture, phylogeny, chromosomal localization and possible interactions. Phylogenetic and motif analysis confirmed SNARE domain in all the proteins. Functional annotation revealed participation in biological process like vesicle fusion, exocytosis, protein targeting to vacuole and SNAP receptor activity. At subcellular level, SNAREs were localized in multiple organelles whereas NPSN proteins were localized in cytoplasm where they regulate vesicle fusion.