A secure transmission application of the State Controlled Cellular Neural Network (SC–CNN)-based circuit is presented. Since the SC–CNN-based circuit has feedback connections between the cells, it is potentially very suitable for realizing a chaotic masking system with feedback algorithm. So, we have constructed a chaotic masking system with feedback using the SC–CNN-based circuit. PSpice simulation experiments verify that the proposed SC–CNN-based secure communication system exhibits a good performance for a wide range of amplitude and spectral characteristics of the information signal.

In this study experimental results of State Controlled Cellular Neural Network (SC-CNN)-based chaotic masking system are presented. By means of this study, the chaotic masking system with feedback algorithm in SC-CNN-based circuit is experimentally proved.

Survey on diabetes is one of the popular fields of biomedical signal processing. In this paper, a closed-loop system which utilizes modified Stolwijk-Hardy glucose insulin interaction model is considered. The modified model was derived by adding an exogenous insulin infusion term. Two control algorithms are used for exogenous insulin infusion: a Mamdani type fuzzy logic controller (FLC), and a fuzzy-PID controller. Simulations are performed to assess control function in terms of keeping desired steady state plasma glucose level (0.81 mg/ml) against to exogenous glucose input. Simulation results are notable and significant in terms of controlling blood glucose level (BGL). The control algorithms that applied to the model are firstly proposed, therefore this study is made a contribution to the literature.

It is expected that electric vehicles (EVs) will be important part of smart gird, not only in form of load but also as distributed energy source in Vehicle to Grid (V2G) system. As increase of EVs integration, V2G contributes to improve flexibility, reliability and stability of grid by providing ancillary services. These services, however, could accelerate degradation of battery whose price is almost half of EV. Thus, battery degradation cost must be considered while scheduling of EV charging. In this paper, a battery degradation cost model of EV lithium-ion batteries was incorporated in the optimal charging schedule of 400 EVs. EVs are located to 33 bus system in order to consider network losses in calculations. Heuristic algorithms such as Genetic Algorithm (GA), Differential Evolution (DE), Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC) are used for solving the associated optimization problem. The objective function aims to maximize user profit under dynamic pricing. Also, distribution system and EVs constraints are considered during optimization. The numerical results illustrate that each of the used heuristic algorithms able to mitigate peak loads and improve voltage levels. GA presents the most profitable charging scheduling in terms of EV owners.

An improved realization of mixed-mode chaotic circuit which has both autonomous and nonautonomous chaotic dynamics is proposed. Central to this study is inductorless realization of mixed-mode chaotic circuit using FTFN-based inductance simulator. FTFN-based topology used in this realization enables the simulation of ideal floating and grounded inductance. This modification provides an alternative solution to the integration problem of not only mixed-mode chaotic circuit but also other chaotic circuits in the literature using CMOS VLSI technologies. In addition to this major improvement, CFOA-based nonlinear resistor was used in the new realization of mixed-mode chaotic circuit. The usage of CFOA-based nonlinear resistor in the circuit's structure reduces the component count and provides buffered and isolated output.