Design of a Low-pass Filter from Fractional Chebyshev Polynomials

This paper introduces a novel magnitude approximation for the fractional-order Chebyshev low-pass filter. The proposed magnitude response is constructed from the fractional Chebyshev polynomials originating from the series solution of fractional-order Chebyshev differential equation. The transfer function of the fractional-order Sallen-Key biquad is used as a prototype for the approximation. To identify the coefficients of the Sallen-Key topology, the flower pollination algorithm (FPA) is used to minimize an objective function representing the sum of relative magnitude error. The optimization problem is executed in MATLAB, and stable solutions are chosen for the implementation. Two different cases are investigated corresponding to filter orders 1.8 and 2.7. LT-Spice is used for circuit simulations, and the Valsa approach is used for fractional-order capacitor approximation. The original magnitude response is compared with the optimized one and the circuit simulation results, and this comparison shows a magnitude error less than 2%. © 2021 IEEE.

Double Fractional-order Masks Image Enhancement

Image enhancement is better achieved when fractional-order masks are used rather than integer-order ones, this is due to the flexibility of fractional-order parameters control. This paper proposes a combination of fractional-order masks to be used in parallel as double filters system structure to improve image enhancement rather than using a single-stage filter. Various performance metrics are used in this work to evaluate the proposed system, such as Information Entropy (IE), Average Gradient (AG), Structural Similarity Index Metric (SSIM) and Peak Signal to Noise Ratio (PSNR). Based on visual as well as numerical results, it is found that the combination of two double masks is superior to the single fractional-order system in terms of enhancing texture and edges. © 2021 IEEE.

Time-domain Li-ion Battery Modeling under Staircase Charging and Discharging

Parameter identification of Li-ion battery models is important for efficiently charge and discharge the most widely used energy storage devices. In this work, we propose a simplified battery model with a parameter identification method for time-domain charging and discharging. Staircase PotentioElectrochemical Impedance Spectroscopy technique (SPEIS) is chosen to characterize the batteries during charging and discharging cycles at different voltage steps values. Marine Predator Algorithm (MPA) is used to identify the proposed model parameters on two commercial Li-ion coin-shaped batteries. The proposed model shows very good matching with the experiments with absolute current error less than 10 4. Hence, the proposed model can be used for real-time applications to predict the battery’s behavior under different operating conditions. © 2021 IEEE.

MPPT for a Partially Shaded PV System Using Accelerated Particle Swarms

MPPT is developed to get the most power out from photovoltaic (PV) modules in various conditions, including changing weather and partial shading (PS). The partial shade of a PV system is a significant issue. PV systems’ power characteristics are so complicated under PS that there are a variety of MPPs. Traditional MPPT methods may become stuck in Local MPPs(LMPPs) instead of Global MPPs (GMPP). The GMPP can be tracked fast and correctly using accelerated particle swarm optimization (APSO). By comparing the employed algorithm to the traditional ones, simulation results validate the optimization performance. © 2021 IEEE.

Comparison of Different Implementation Methods of Fractional-Order Derivative/Integral

Implementing a fractional-order operator requires many resources to acquire an accurate response compared to the theoretical response. In this paper, three implementation methods of digital fractional-order operators are exploited. The three implementation methods are based on FIR, IIR, and lattice wave digital filters. The three methods are implemented using different optimization algorithms to optimize the choice of the coefficients of the three filters. This optimization is done to approximate the frequency response of an ideal fractional operator. This comparison aims to determine each implementation method’s accuracy and resource usage level to decide which method is better for different systems. © 2021 IEEE.

Vulnerable Road Users Detection and Tracking using YOLOv4 and Deep SORT

Over the years, The detection and tracking of Vulnerable Road Users (VRUs) have become one of the most critical features of self-driving car components. Because of its processing efficiency and better detection algorithms, tracking-by-detection appears to be the best paradigm. In this paper, a detection-based tracking approach is presented for Multiple VRU Tracking of video from an inside-vehicle camera in real-time. YOLOv4 scans every frame to detect VRUs first, then Simple Online and Realtime Tracking with a Deep Association Metric (Deep SORT) algorithm, which is customized for multiple VRU tracking, is applied. The results of our experiments on both the Joint Attention in Autonomous Driving (JAAD) and Multiple Object Tracking (MOT) datasets exhibit competitive performance. © 2021 IEEE.

A Comparative Study of Different Chaotic Systems in Path Planning for Surveillance Applications

This paper compares the performance of four different chaotic systems in path planning for surveillance applications. The four investigated systems are Lorenz, Arneodo, Liu, and Chen. While the Lorenz system was employed in a similar application before, Arneodo, Liu, and Chen systems are newly introduced in this paper. A bounded-grid chaotic path planner is proposed based on the mirror mapping technique, which keeps the robot bounded in the terrain and prevents it from going outside. The effect of using different state variables of each chaotic system to control the motion angle of the robot is discussed and shown to have a significant impact on the robot’s performance. The obtained trajectory and several performance metrics show promising results of the chaotic path planner for the four systems. © 2021 IEEE.