Optimisation of Mobile Communication Networks - OMCO NET

The mini conference “Optimisation of Mobile Communication Networks” focuses on advanced methods for search and optimisation applied to wireless communication networks. It is sponsored by Research & Enterprise Fund Southampton Solent University. The conference strives to widen knowledge on advanced search methods capable of optimisation of wireless communications networks. The aim is to provide a forum for exchange of recent knowledge, new ideas and trends in this progressive and challenging area. The conference will popularise new successful approaches on resolving hard tasks such as minimisation of transmit power, cooperative and optimal routing

Brain image clustering by wavelet energy and CBSSO optimization algorithm

Previously, the diagnosis of brain abnormality was significantly important in the saving of social and hospital resources. Wavelet energy is known as an effective feature detection which has great efficiency in different utilities. This paper suggests a new method based on wavelet energy to automatically classify magnetic resonance imaging (MRI) brain images into two groups (normal and abnormal), utilizing support vector machine (SVM) classification based on chaotic binary shark smell optimization (CBSSO) to optimize the SVM weights. The results of the suggested CBSSO-based KSVM are compared favorably to several other methods in terms of better sensitivity and authenticity. The proposed CAD system can additionally be utilized to categorize the images with various pathological conditions, types, and illness modes

Learning spatio-temporal representations for action recognition: A genetic programming approach

Extracting discriminative and robust features from video sequences is the first and most critical step in human action recognition. In this paper, instead of using handcrafted features, we automatically learn spatio-temporal motion features for action recognition. This is achieved via an evolutionary method, i.e., genetic programming (GP), which evolves the motion feature descriptor on a population of primitive 3D operators (e.g., 3D-Gabor and wavelet). In this way, the scale and shift invariant features can be effectively extracted from both color and optical flow sequences. We intend to learn data adaptive descriptors for different datasets with multiple layers, which makes fully use of the knowledge to mimic the physical structure of the human visual cortex for action recognition and simultaneously reduce the GP searching space to effectively accelerate the convergence of optimal solutions. In our evolutionary architecture, the average cross-validation classification error, which is calculated by an support-vector-machine classifier on the training set, is adopted as the evaluation criterion for the GP fitness function. After the entire evolution procedure finishes, the best-so-far solution selected by GP is regarded as the (near-)optimal action descriptor obtained. The GP-evolving feature extraction method is evaluated on four popular action datasets, namely KTH, HMDB51, UCF YouTube, and Hollywood2. Experimental results show that our method significantly outperforms other types of features, either hand-designed or machine-learned

Impact of eye fundus image preprocessing on key objects segmentation for glaucoma identification

The pathological changes in the eye fundus image, especially around Optic Disc (OD) and Optic Cup (OC) may indicate eye diseases such as glaucoma. Therefore, accurate OD and OC segmentation is essential. The variety in images caused by different eye fundus cameras makes the complexity for the existing deep learning (DL) networks in OD and OC segmentation. In most research cases, experiments were conducted on individual data sets only and the results were obtained for that specific data sample. Our future goal is to develop a DL method that segments OD and OC in any kind of eye fundus image but the application of the mixed training data strategy is in the initiation stage and the image preprocessing is not discussed. Therefore, the aim of this paper is to evaluate the mage preprocessing impact on OD and OC segmentation in different eye fundus images aligned by size. We adopted a mixed training data strategy by combining images of DRISHTI-GS, REFUGE, and RIM-ONE datasets, and applied image resizing incorporating various interpolation methods, namely bilinear, nearest neighbor, and bicubic for image resolution alignment. The impact of image preprocessing on OD and OC segmentation was evaluated using three convolutional neural networks Attention U-Net, Residual Attention U-Net (RAUNET), and U-Net++. The experimental results show that the most accurate segmentation is achieved by resizing images to a size of 512 x 512 px and applying bicubic interpolation. The highest Dice of 0.979 for OD and 0.877 for OC are achieved on  RISHTI-GS test dataset, 0.973 for OD and 0.874 for OC on the REFUGE test dataset, 0.977 for OD and 0:855 for OC on RIM-ONE test dataset. Anova and Levene’s tests with statistically significant evidence at α = 0.05 show that the chosen size in image resizing has impact on the OD and OC segmentation results, meanwhile, the interpolation method does influent OC segmentation only