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.
Different Approximation Techniques For A FOPID Feedback Control of a DC Motor
DC motors are commonly employed in many industrial applications due to their various advantages. This study aims to compare the response of the Oustaloup-Recursive-Approximation (ORA) and El-Khazali’s approximation method in controlling a DC motor with a FOPID controller. The two employed methods are used to design the FOPID and approximate. For various fractional orders, many behaviours are presented. A simulation comparison between these methods is performed regarding overshoot, settling time and rise time. © 2022 IEEE.
On Fractional-order Capacitive Wireless Power Transfer System
Wireless power transfer is becoming an increasingly viable solution for the electrical powering of various electronic gadgets. However, precise outputs are not guaranteed with integer systems, so fractional-order capacitors are vital. This paper studies a four-plate fractional capacitive power transfer system by varying six orders of capacitors between the plates along with the load resistance. A mathematical model based on a 4× 4 mutual fractional capacitance matrix is established for equidistantly placed four identical metal plates. Moreover, the chosen circuit topology is identified and analyzed based on the proposed model. © 2022 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.
Valorization of Agricultural and Marine Waste for Fabrication of Bio-Adsorbent Sheets
Industrial wastewater often contains considerable amounts of toxic pollutants that would endanger public health and the environment. In developing countries, these toxins are often discharged into natural ecosystems without pretreatment as it requires costly treatment processes, which causes long-term harmful socioeconomic impacts. Employing wastewater treatment plants using physical, biological, and chemical methods to clean the wastewater is considered by many nations the answer to the environmental crises. The treated water could be used for targeting the irrigation systems in its majority, as it is biologically acceptable for that specific use, which economizes the use of freshwater sources for municipal use specifically. This study presents a novel method for fabricating an efficient adsorbent sheet for wastewater treatment. The sheets are fabricated by combining sugarcane bagasse pulp as a scaffold with commercial, naturally activated carbon and bimetallic-prepared adsorbents. Fava beans and algae biomass are utilized in the production of activated carbon because of their high carbon contents, availability, and low cost. The prepared composite sheets are synthesized and investigated for several pollutants’ removal such as methyl orange, crystal violet dyes, and chromium heavy metals. These pollutants are selected due to the high discharge amount and toxic effect on aquatic life. FT-IR and SEM analyses are used to characterize the samples. To determine the mechanism of adsorption, the intra-particle diffuse, pseudo-first-order, and pseudo-second-order kinetic models are used to test the experimental data. All the prepared sheets can retain the pollutants, with the best removal efficiency of 96.24% for methyl orange adsorption onto the bio-composite mixed sheet. For methyl orange, the error values and correlation coefficient R2of 0.971 and 0.951 shows that the Temkin isotherm and pseudo-first-order kinetic model, respectively, are capable of providing the highest goodness of fit for the experimental data. The results of the isotherms and kinetics parameter sets provided valuable proof that the adsorption of methyl orange onto the bio-composite sheet is an endothermic phenomenon involving both chemical and physical adsorption. © World Environmental and Water Resources Congress 2023.All rights reserved
Generic Hardware Realization of K Nearest Neighbors on FPGA
K Nearest Neighbors (KNN) algorithm is a straight-forward yet powerful Machine Learning (ML) tool widely used in classification, clustering, and regression applications. In this work, KNN is applied, with three distance metrics, to classify different datasets, experimentally testing each distance metric effect on the classification performance. A static K is applied for the whole dataset optimally chosen based on a 5-fold cross-validation. A reconfigurable hardware realization on field programmable gate array (FPGA) of each distance metric applying selection sort algorithm is proposed. The FPGA realization reaches a throughput up to 4.44 Gbit/sec while only occupying 1% of the Genesys 2 Kintex-7 board area. The algorithm managed to classify all the tested datasets with above 90% accuracy. © 2022 IEEE.
Registerless Multiplierless YCoCg-R and YCoCg Color Space Converters Hardware Implementation
Multimedia data, e.g., images and videos, are widely used over the internet and on computers. Image processing applications require color space conversion to be able to deal with these types of data more efficiently. This paper investigates three color space conversions and proposes simplified combinational hardware designs and FPGA realizations for RGB to YCoCg-R and YCoCg color spaces encoders and decoders and compares them to their sequential counterparts. The proposed hardware design for the encoders and decoders uses only adders and subtractors without any registers or multipliers. The proposed YCoCg-R converter exhibits better resources utilization compared to implementing the design using shift registers, where it uses 56.3% and 72.1% less LUTs and FFs, respectively. Similarly for the YCoCg color space, the combinational design used 48.1% less LUTs and 67.8% less FFs than its sequential counterpart. © 2022 IEEE.
Do the Bio-impedance Models Exhibit Pinched Hysteresis?
Recently, pinched hysteresis has been found in the electrical modelling of regular plant tissues. Usually, the biological tissues are characterized in the frequency domain using bio-impedance analyzers without investigating the time domain, which would show the pinched hysteresis. In this paper, the current-voltage analysis of some of the widely known electrical bio-impedance models is studied. The investigated models are the single dispersion Cole-impedance model, the double dispersion Cole-impedance model and the fractional-order simplified Hayden model to prove that these models can not exhibit pinched hysteresis. It is proved mathematically in this paper that there are no pinch-off points that would exist in these models. These results are verified with numerical simulations of three different plants: tomato, carrot and banana, concluding that the bioimpedance modelling needs a nonlinear element to model the pinched hysteresis in the current-voltage behaviour of these tissues. © 2020 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.