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

Asymptotics of Nonlinear LSE Precoders with Applications to Transmit Antenna Selection

This paper studies the large-system performance of Least Square Error (LSE) precoders which~minimize~the~input-output distortion over an arbitrary support subject to a general penalty function. The asymptotics are determined via the replica method in a general form which encloses the Replica Symmetric (RS) and Replica Symmetry Breaking (RSB) ans\"atze. As a result, the "marginal decoupling property" of LSE precoders for $b$-steps of RSB is derived. The generality of the studied setup enables us to address special cases in which the number of active transmit antennas are constrained. Our numerical investigations depict that the computationally efficient forms of LSE precoders based on "$\ell_1$-norm" minimization perform close to the cases with "zero-norm" penalty function which have a considerable improvements compared to the random antenna selection. For the case with BPSK signals and restricted number of active antennas, the results show that RS fails to predict the performance while the RSB ansatz is consistent with theoretical bounds.Comment: 5 pages; 2 figures; to be presented at ISIT 201

Enhancing Integrated Communication Network Service Using a Queueing Model

This paper describes a strategy for efficiently constructing the demand servicing process of an Integrated Communication Network (ICN). Performance analysis, service problems, and relief action of ICNs are provided. End-to-end statistical performance parameters are first used to measure network compliance over a given fixed period. If any of the performance objectives are not satisfied, a servicing function determines the corrective action required to maintain service quality. The advantage of this network model is its efficiency and flexibility in handling a variety of services and applications. Enhanced network service and ICN traffic problems are solved using adaptive queuing models

Numerical study on flow separation control over NACA0015 aerofoil using electromagnetic fields

AbstractIn this study, a flow solver was developed based on the governing RANS equations of compressible flows and was further extended to include the effects of electromagnetic forces namely Lorentz forces. Lorentz forces may be added as a source term in the governing fluid flow equations. Numerical studies were carried out for NACA0015 aerofoil at high angles of incidences from 15° to 30° and compared with some available cases of experimental and incompressible numerical solutions. The hydrodynamics performance was improved using a magnetic momentum coefficient of up to 0.048. The size of flow separation zone was decreased or completely eliminated by increasing this coefficient. The overall drag was not changed considerably, however the overall lift was increased up to 80 percent at stall angles