In this brief, conic section function neural network (CSFNN) circuitry was designed for offline signature recognition. CSFNN is a unified framework for multilayer perceptron (MLP) and radial basis function (RBF) networks to make simultaneous use of advantages of both. The CSFNN circuitry architecture was developed using a mixed mode circuit implementation. The designed circuit system is problem independent. Hence, the general purpose neural network circuit system could be applied to various pattern recognition problems with different network sizes on condition with the maximum network size of 16-16-8. In this brief, CSFNN circuitry system has been applied to two different signature recognition problems. CSFNN circuitry was trained with chip-in-the-loop learning technique in order to compensate typical analog process variations. CSFNN hardware achieved highly comparable computational performances with CSFNN software for nonlinear signature recognition problems.

Path planning (PP) is fundamental in the decision-making and control processes of computer numerical control (CNC) machines, playing a critical role in smart manufacturing research. Apart from improving optimization in PP, enhancing efficiency while decreasing CNC machine cycle time is important in manufacturing. Many methods have been offered in the literature to improve the cycle time for obtaining optimal trajectories in toolpath optimization, but these methods are mostly considered for improvements in path length or machining time in optimal PP. This study demonstrates a method for creating a smoothing path. It aims to minimize both cycle time and toolpath length, while demonstrating that the non-dominated sorting genetic algorithm (NSGA-II) is efficient in addressing the multi-objective PP problems within static situations. Pareto optimality for performance comparisons with multi-objective genetic algorithms (MOGAs) is presented in order to highlight the positive features of the non-dominant solving generated by the NSGA-II. According to the comprehensive analysis results, the optimization of the path carried out with the NSGA-II emphasizes its shorter and smoother attributes, with the optimal trajectory achieving approximately 30% and 7% reductions in path length and machining cycle time, respectively.

Lithium ion (Li-Ion) and lithium polymer (Li-Po) batteries need to be used within certain voltage/current limits. Failure to observe these limits may result in damage to the battery. In this work, we propose a low voltage battery management system (LV-BMS) that balances the processes of the battery cells in the battery pack and the activating-deactivating of cells by guaranteeing that the operation is within these limits. The system operates autonomously and provides energy from the internal battery. It has a modular structure and the software is designed to control the charging and discharging of eight battery cells at most. A STM32F103 microcontroller is used for system control. The fuzzy logic controller (FLC) is used to set the discharge voltage limit to prevent damage to the battery cells, shorten the settlement time and create a specialized design for charge control. The proposed structure enables solar panel or power supplies with different voltage values between 5 V and 8 V to be used for charging. The experimental results show there was a 42% increase in usage time and the voltage difference between the batteries was limited to a maximum of 65 mV. Moreover, the charge current settles at about 20 ms, which is a much faster response when compared to a PID controller.