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Recent studies show the impact of genetic algorithms (GA) in the design of evolutionary finite impulse response (FIR) filters. Studies have shown hardware and software method of GA implementation for design. Hardware method improves speed due to parallelism, pipelining and the absence of the function calls compared to software implementation. But area constraint was the main issue of hardware implementation. Therefore, this paper illustrates a hardware–software co-design concept to implement an Adaptive GA processor (AGAP) for FIR filter design. The architecture of AGAP uses adaptive crossover and mutation probabilities to speed up the convergence of the GA process. The AGAP architecture was implemented using Verilog Hardware Description Language (HDL) and instantiated as a custom intellectual property (IP) core to the soft-core MicroBlaze processor of Spartan 6 (XC6SLX45-3CSG324I) FPGA. The MicroBlaze processor controls the AGAP IP core and other interfaces using Embedded C programs. The experiment demonstrated a significant 134% improvement in speed over hardware implementation but with a marginal increase in area. The complete evaluation and evolution of the filter coefficients were executed on a single FPGA. The system on chip (SoC) concept enables a robust and flexible system.

Battery Management System (BMS) functions to monitor individual cell in a battery pack and its crucial task is to maintain stability throughout the battery pack. The BMS is responsible for maintaining the safety of the battery as well as not to harm the user or environment. The parameters that are to be monitored in a battery are Voltage, Current and Temperature. With the collected data, BMS carefully monitors the charging–discharging behavior of the battery particularly in the Lithium-ion (Li-ion) batteries in which charging and discharging behavior are completely different. This paper proposes a real-time IOT connected deep learning algorithm for estimation of State-of-Charge (SoC) of Li-ion batteries. This paper provides unique objectives and congruence between model-based conventional methods and state-of-the-art deep learning algorithm, specifically Feed Forward Neural Network (FNN) which is nonRecurrent. This paper also highlights the advantages of Internet-of-Things (IoT) connected deep learning algorithm for estimation of State-of-Charge of Li-ion batteries in Hybrid Electric Vehicles (HEVs) and Electric Vehicles (EVs). The major advantage of the proposed method is that the Artificial Intelligence (AI)-based techniques aim to bring the estimation error less than 2% at a low cost and time without the model of the battery, at par with conventional method of Extended Kalman Filter (EKF) which is the best ever practical estimation theory. Another advantage of the proposed method is that in an abnormal condition (i.e., Unsafe Temperature) the IF This Then That (IFTTT) IoT mobile application interfaced with BMS through ThingSpeak cloud, sends a notification alert to the battery expert or to the user prior to an emergency. Finally, the real-time data of the battery parameters are collected through ThingSpeak cloud platform for future research and analysis.

We propose a safety-oriented design process for IP-based safety-critical system-on-chip (SoC). The proposed safety process can facilitate the measurement of the robustness based on the safety-related metrics and scales of failure-induced risks in a system that can be employed to locate the critical components for protection to effectively diminish the influence of failures on the system. The risk reduction phase is activated to enhance the robustness of critical components identified by vulnerability analysis if the measured robustness is insufficient. An SoC-level safety design platform was built on the SystemC Synopsys Platform Architect MCO to demonstrate the core idea of the safety process. The safety-oriented design process for an ARM-embedded SoC modeled at the TLM level was conducted to demonstrate the feasibility of our safety approach.