ABSTRACT
This article proposes a novel discrete event-triggered scheme (DETS) for the synchronization of delayed neural networks (NNs) using the dynamic output-feedback controller (DOFC). The proposed DETS uses both the current and past samples to determine the next trigger, unlike the traditional event-triggered scheme (ETS) that uses only the current sample. The proposed DETS is employed in a dual setup for two network channels to significantly reduce redundant data transmission. A DOFC is designed to achieve the synchronization of the NNs. Stability criteria of the synchronisation error system are derived based on the Lyapunov-Krasovskii functional method, and the co-design of the DOFC and DETS parameters are accomplished using the Cone-complementarity linearization (CCL) approach. The effectiveness and advantages of the proposed method are illustrated considering an example of the chaotic system.
ABSTRACT
The current study concentrates on the event-based controller design for nonlinear networked systems characterized by the interval type-2 (IT2) fuzzy model. An innovative sum-based discrete event-triggered mechanism (SDETM), whose triggering condition includes several previous measurement samples, is proposed. Compared to the traditional event-triggered mechanism (ETM), the novel SDETM requires less network resources consumption. A dynamic output feedback controller (DOFC) is designed to achieve stability of the system. A novel stability criteria is established via the Lyapunov-Krasovskii functional method with the prescribed H∞ performance. Co-design of the DOFC and SDETM parameters is carried out using the cone complimentarity linearization (CCL) algorithm. The effectiveness of the proposed method is demonstrated with two practical cases.
ABSTRACT
The present study proposes a new approach to automated screening of Clinically Significant Macular Edema (CSME) and addresses two major challenges associated with such screenings, i.e., exudate segmentation and imbalanced datasets. The proposed approach replaces the conventional exudate segmentation based feature extraction by combining a pre-trained deep neural network with meta-heuristic feature selection. A feature space over-sampling technique is being used to overcome the effects of skewed datasets and the screening is accomplished by a k-NN based classifier. The role of each data-processing step (e.g., class balancing, feature selection) and the effects of limiting the region of interest to fovea on the classification performance are critically analyzed. Finally, the selection and implication of operating points on Receiver Operating Characteristic curve are discussed. The results of this study convincingly demonstrate that by following these fundamental practices of machine learning, a basic k-NN based classifier could effectively accomplish the CSME screening.
Subject(s)
Diabetic Retinopathy , Macular Edema , Algorithms , Exudates and Transudates , Humans , Machine Learning , Macular Edema/diagnostic imaging , Neural Networks, Computer , ROC CurveABSTRACT
BACKGROUND: Anterior talofibular ligament (ATFL) is considered as the weakest ankle ligament that is most prone to injuries. Ultrasound imaging with its portable, non-invasive and non-ionizing radiation nature is increasingly being used for ATFL diagnosis. However, diagnosis of ATFL injuries requires its segmentation from ultrasound images that is a challenging task due to the existence of homogeneous intensity regions, homogeneous textures and low contrast regions in ultrasound images. To address these issues, this research has developed an efficient ATFL segmentation framework that would contribute to accurate and efficient diagnosis of ATFL injuries for clinical evaluation. METHODS: The developed framework comprises of five computational steps to segment the ATFL ligament region. Initially, region of interest is selected from the original image, which is followed by the adaptive histogram equalization to enhance the contrast level of the ultrasound image. The enhanced contrast image is further optimized by the particle swarm optimization algorithm. Thereafter, the optimized image is processed by the Chan-Vese method to extract the ATFL region through curve evolution; then the resultant image smoothed by morphological operation. The algorithm is tested on 25 subjects' datasets and the corresponding performance metrics are evaluated to demonstrate its clinical applicability. RESULTS: The performance of the developed framework is evaluated based on various measurement metrics. It was found that estimated computational performance of the developed framework is 12 times faster than existing Chan-Vese method. Furthermore, the developed framework yielded the average sensitivity of 98.3 %, specificity of 96.6 % and accuracy of 96.8 % as compared to the manual segmentation. In addition, the obtained distance using Hausdorff is 14.2 pixels and similarity index by Jaccard is 91 %, which are indicating the enhanced performance whilst segmented area of ATFL region obtained from five normal (average Pixels-16,345.09), five tear (average Pixels-14,940.96) and five thickened (average Pixels-12,179.20) subjects' datasets show good performance of developed framework to be used in clinical practices. CONCLUSIONS: On the basis of obtained results, the developed framework is computationally more efficient and more accurate with lowest rate of coefficient of variation (less than 5 %) that indicates the highest clinical significance of this research in the assessment of ATFL injuries.
