Classification of Chest Diseases using Wavelet Transforms and Transfer Learning

Chest X-ray scan is a most often used modality by radiologists to diagnose many chest related diseases in their initial stages. The proposed system aids the radiologists in making decision about the diseases found in the scans more efficiently. Our system combines the techniques of image processing for feature enhancement and deep learning for classification among diseases. We have used the ChestX-ray14 database in order to train our deep learning model on the 14 different labeled diseases found in it. The proposed research shows the significant improvement in the results by using wavelet transforms as pre-processing technique.Comment: 8 pages, 4 figures, Presented in International Conference On Medical Imaging And Computer-Aided Diagnosis (MICAD 2020), proceeding will be published with Springer in their "Lecture Notes in Electrical Engineering (LNEE)" (ISSN: 1876-1100

Order-Based Representation in Random Networks of Cortical Neurons

The wide range of time scales involved in neural excitability and synaptic transmission might lead to ongoing change in the temporal structure of responses to recurring stimulus presentations on a trial-to-trial basis. This is probably the most severe biophysical constraint on putative time-based primitives of stimulus representation in neuronal networks. Here we show that in spontaneously developing large-scale random networks of cortical neurons in vitro the order in which neurons are recruited following each stimulus is a naturally emerging representation primitive that is invariant to significant temporal changes in spike times. With a relatively small number of randomly sampled neurons, the information about stimulus position is fully retrievable from the recruitment order. The effective connectivity that makes order-based representation invariant to time warping is characterized by the existence of stations through which activity is required to pass in order to propagate further into the network. This study uncovers a simple invariant in a noisy biological network in vitro; its applicability under in vivo constraints remains to be seen

Hybrid Islanding Detection in Microgrid with Multiple Connection Points to Smart Grids Using Fuzzy-Neural Network

© 1969-2012 IEEE. This paper presents a new hybrid islanding detection approach for microgrids (MGs) with multiple connection points to smart grids (SGs) which is based on the probability of islanding (PoI) calculated at the SG side and sent to the central control for microgrid (CCMG). The PoI values are determined using a combination of passive, active, and communication islanding detection approaches based on the utility signals measured at the SGs sides which are processed by discrete wavelet transform using an artificial neural network (ANN). If {\text{PoI}}-{{\rm{ANN}}} is larger than the threshold value (indicating high possibility of islanding) then a more accurate approach based on fuzzy network is used to recompute it ({\text{PoI}}-{{\rm{FUZZY}}}) where the fuzzy parameters are determined by an adaptive neuro-fuzzy inference system. In the proposed technique, an active islanding is only performed when PoI is high and the amplitudes of the disturb signals are proportional to {\text{PoI}}-{{\rm{FUZZY}}}. Furthermore, if the PoI is not correctly received by CCMG, two auxiliary tests will be performed in the MG side to detect islanding. These tests include an intentional passive islanding detection in a short preset time and an active islanding detection with disturb signals proportional to the calculated PoI. Detailed simulations are performed and analyzed to evaluate the performance of the proposed method

Diabetic macular edema: it is more than just VEGF [version 1; referees: 2 approved]

Diabetic macular edema is a serious visual complication of diabetic retinopathy. This article reviews the history of previous and current therapies, including laser therapy, anti-vascular endothelial growth factor agents, and corticosteroids, that have been used to treat this condition. In addition, it proposes new ways to use them in combination in order to decrease treatment burden and potentially address other causes besides vascular endothelial growth factor for diabetic macular edema

Self-aligned graphene on silicon substrates as ultimate metal replacement for nanodevices

We have pioneered a novel approach to the synthesis of high-quality and highly uniform few-layer graphene on silicon wafers, based on solid source growth from epitaxial 3C-SiC films [1,2]. The achievement of transfer-free bilayer graphene directly on silicon wafers, with high adhesion, at temperatures compatible with conventional semiconductor processing, and showing record- low sheet resistances, makes this approach an ideal route for metal replacement method for nanodevices with ultimate scalability fabricated at the wafer –level