Adaptive noise reduction and code matching for IRIS pattern recognition system

Among all biometric modalities, iris is becoming more popular due to its high performance in recognizing or verifying individuals. Iris recognition has been used in numerous fields such as authentications at prisons, airports, banks and healthcare. Although iris recognition system has high accuracy with very low false acceptance rate, the system performance can still be affected by noise. Very low intensity value of eyelash pixels or high intensity values of eyelids and light reflection pixels cause inappropriate threshold values, and therefore, degrade the accuracy of system. To reduce the effects of noise and improve the accuracy of an iris recognition system, a robust algorithm consisting of two main components is proposed. First, an Adaptive Fuzzy Switching Noise Reduction (AFSNR) filter is proposed. This filter is able to reduce the effects of noise with different densities by employing fuzzy switching between adaptive median filter and filling method. Next, an Adaptive Weighted Shifting Hamming Distance (AWSHD) is proposed which improves the performance of iris code matching stage and level of decidability of the system. As a result, the proposed AFSNR filter with its adaptive window size successfully reduces the effects ofdifferent types of noise with different densities. By applying the proposed AWSHD, the distance corresponding to a genuine user is reduced, while the distance for impostors is increased. Consequently, the genuine user is more likely to be authenticated and the impostor is more likely to be rejected. Experimental results show that the proposed algorithm with genuine acceptance rate (GAR) of 99.98% and is accurate to enhance the performance of the iris recognition system

Pattern Recognition

Pattern recognition is a very wide research field. It involves factors as diverse as sensors, feature extraction, pattern classification, decision fusion, applications and others. The signals processed are commonly one, two or three dimensional, the processing is done in real- time or takes hours and days, some systems look for one narrow object class, others search huge databases for entries with at least a small amount of similarity. No single person can claim expertise across the whole field, which develops rapidly, updates its paradigms and comprehends several philosophical approaches. This book reflects this diversity by presenting a selection of recent developments within the area of pattern recognition and related fields. It covers theoretical advances in classification and feature extraction as well as application-oriented works. Authors of these 25 works present and advocate recent achievements of their research related to the field of pattern recognition

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

Development of Novel Scaffolds for Skeletal Muscle Tissue Engineering Applications

Tissue Engineering (TE) is emerging as an effective way of curing tissue oriented disorders through new tissue regeneration. Recently graphene oxide (GO composed of graphene oxide nanoplatelets, GOnPs) is widely being used for electronic and biomedical applications because of its favourable physicochemical properties. The present thesis work focuses on the fabrication of electrospun GO-poly (ɛ-caprolactone, PCL) and GO-poly (lactic-co-glycolic acid, PLGA) composite scaffolds for myoblast proliferation and differentiation of human cord blood derived mesenchymal stem cells (hMSCs), which is novel and challenging. The GO surface possessing different hydrophilic groups allowed it to be well dispersed in the polymer matrices for making electrospun fibrous scaffold meshes. Addition of GO in these polymers enhanced mechanical property, hydrophilicity and electrical conductivity of the GO-polymer composites. Electrical conductivity of the GO-PCL and GO-PLGA scaffolds increased by about two orders of magnitudes (from ~5x10-9 to 2.3 x10-7 S/m2) with the addition of low GO concentration (within non-toxicity limit <20 µg/ml for human cells). Such enhancement of conductivity along with nanostructural surface morphology of GO improved the biocompatibility and cell viability of the developed scaffolds. GO-polymer composites showed percolation behavior at low GO concentrations (~0.79 and 0.76wt%, respectively, for GO-PCL and GO-PLGA). High resolution TEM (HRTEM) and Raman G and D peak values indicated the presence of GO in the composite scaffold messes. In-vitro cell culture study confirmed excellent myoblast differentiation of hMSCs on these electrospun composite scaffolds. The GO-PCL composite scaffolds with suitable mechanical properties, little higher hydrophilicity as well as conductivity and dielectric constant (associated with GO surface charge) compared to those of GO-PLGA, exhibited better myoblast differentiation and promoted self-aligned myotubes formation, which were evident by cell attachment (FESEM studies), viability and proliferation (WST-8 assay), Immunohistochemical analysis etc. Moreover, IGF-1 cell signalling pathway study done on GO-PCL scaffolds also indicated superiority of the GO-PCL scaffolds for skeletal muscle tissue regeneration. It was revealed, for the first time, that GO surface charge and significant enhancement of conductivity of the GO–polymer nanocomposite scaffolds, GO-PCL scaffold in particular, might be considered as potential candidates for the myoblast differentiation of hMSCs for the next generation human skeletal muscle tissue regeneration

Mathematical model of interactions immune system with Micobacterium tuberculosis

Tuberculosis (TB) remains a public health problem in the world, because of the increasing prevalence and treatment outcomes are less satisfactory. About 3 million people die each year and an estimated one third of the world's population infected with Mycobacterium Tuberculosis (M.tb) is latent. This is apparently related to incomplete understanding of the immune system in infection M.tb. When this has been known that immune responses that play a role in controlling the development of M.tb is Macrophages, T Lymphocytes and Cytokines as mediators. However, how the interaction between the two populations and a variety of cytokines in suppressing the growth of Mycobacterium tuberculosis germ is still unclear. To be able to better understand the dynamics of infection with M tuberculosis host immune response is required of a model.One interesting study on the interaction of the immune system with M.tb mulalui mathematical model approach. Mathematical model is a good tool in understanding the dynamic behavior of a system. With the mediation of mathematical models are expected to know what variables are most responsible for suppressing the growth of Mycobacterium tuberculosis germ that can be a more appropriate approach to treatment and prevention target is to develop a vaccine. This research aims to create dynamic models of interaction between macrophages (Macrophages resting, macrophages activated and macrophages infected), T lymphocytes (CD4 + T cells and T cells CD8 +) and cytokine (IL-2, IL-4, IL-10,IL-12,IFN-dan TNF-) on TB infection in the lung. To see the changes in each variable used parameter values derived from experimental literature. With the understanding that the variable most responsible for defense against Mycobacterium tuberculosis germs, it can be used as the basis for the development of a vaccine or drug delivery targeted so hopefully will improve the management of patients with tuberculosis. Mathematical models used in building Ordinary Differential Equations (ODE) in the form of differential equation systems Non-linear first order, the equation contains the functions used in biological systems such as the Hill function, Monod function, Menten- Kinetic Function. To validate the system used 4th order Runge Kutta method with the help of software in making the program Matlab or Maple to view the behavior and the quantity of cells of each population