Crystal violet is a basic dye that is widely used by various industries, such as textiles and paints. These industries discharge their effluents, contaminated with crystal violet, into water streams, and these effluents have an adverse effect on aquatic organisms, the environment, and human health. Crystal violet is a basic dye that is widely used by various industries, such as textiles and paints. These industries discharge their effluents, contaminated with crystal violet, into water streams, and these effluents have an adverse effect on aquatic organisms, the environment, and human health. Hence, this paper is directed at studying the removal of crystal violet using environmentally friendly, cost-effective adsorbent materials such as raw fava bean (RFP-H3F), and chemically activated carbon (H3F) in comparison to commercial activated carbon (CAC).Various characterization techniques are applied, such as XRD, FT-IR,and SEM analyses. Then, the process of optimizing is shown through some preliminary experiments and a Response Surface Methodology (RSM) experiment to find the best conditions for removing crystal violet efficiently. Results revealed that the raw fava bean peels and the commercial activated carbon have the maximum removal efficiency of 95 %, and 83 % respectively, after 180 min of contact time. It is hypothesized that raw fava bean peels (RFP) and chemically activated carbon using phosphoric acid RFP-H3F will exhibit comparable efficiency in removing crystal violet when compared to commercial activated carbon (CAC). Various characterization techniques, including X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FT-IR),and scanning electron microscopy (SEM), are applied to analyze the properties of the adsorbent materials. Afterwards, the optimization process is displayed through some preliminary experiments followed by a Response Surface Methodology (RSM) experiment to obtain the optimum conditions, which achieve high crystal violet removal efficiency. The results demonstrate that both raw fava bean peels and commercial activated carbon exhibit significant removal efficiencies, with raw fava bean peels achieving a maximum removal efficiency of 95 % and commercial activated carbon achieving 83 %. © 2023 The Authors
Reconfigurable hardware implementation of K-nearest neighbor algorithm on FPGA
Nowadays, Machine Learning is commonly integrated into most daily life applications in various fields. The K Nearest Neighbor (KNN), which is a robust Machine Learning algorithm, is traditionally used in classification tasks for its simplicity and training-less nature. Hardware accelerators such as FPGAs and ASICs are greatly needed to meet the increased requirements of performance for these applications. It is well known that ASICs are non-programmable and only fabricated once with high expenses, this makes the fabrication of a complete chip for a specific classification problem inefficient. As a better alternative to this challenge, in this work, a reconfigurable hardware architecture of the KNN algorithm is proposed where the employed dataset, the algorithm parameters, and the distance metric used to evaluate the nearest neighbors are all updatable after fabrication, in the ASIC case, or after programming, in the FPGA case. The architecture is also made flexible to accommodate different memory requirements and allow variable arithmetic type and precision selection. Both parameters can be adjusted before fabrication to account only for the expected memory requirement and the fixed point precision required or floating point arithmetic if needed. The proposed architecture is realized on the Genesys 2 board based on Xilinx’s Kintex-7 FPGA. The results obtained from the experiment are consistent with those obtained from the simulation and software analysis. The proposed realization reaches a frequency of up to around 110 MHz and a power consumption of less than 0.4 watts © 2023 Elsevier GmbH
Software and hardware realizations for different designs of chaos-based secret image sharing systems
Secret image sharing (SIS) conveys a secret image to mutually suspicious receivers by sending meaningless shares to the participants, and all shares must be present to recover the secret. This paper proposes and compares three systems for secret sharing, where a visual cryptography system is designed with a fast recovery scheme as the backbone for all systems. Then, an SIS system is introduced for sharing any type of image, where it improves security using the Lorenz chaotic system as the source of randomness and the generalized Arnold transform as a permutation module. The second SIS system further enhances security and robustness by utilizing SHA-256 and RSA cryptosystem. The presented architectures are implemented on a field programmable gate array (FPGA) to enhance computational efficiency and facilitate real-time processing. Detailed experimental results and comparisons between the software and hardware realizations are presented. Security analysis and comparisons with related literature are also introduced with good results, including statistical tests, differential attack measures, robustness tests against noise and crop attacks, key sensitivity tests, and performance analysis. © The Author(s) 2024.
Hardware realization of a secure and enhanced s-box based speech encryption engine
This paper presents a secure and efficient substitution box (s-box) for speech encryption applications. The proposed s-box data changes every clock cycle to swap the input signal with different data, where it generated based on a new algorithm and a memristor chaotic system. Bifurcation diagrams for all memristor chaotic system parameters are introduced to stand for the chaotic range of each parameter. Moreover, the effect of each component inside the proposed encryption system is studied, and the security of the system is validated through perceptual and statistical tests. The size of the encryption key is 175 bits to meet the global standards for the optimum encryption key width (> 128). MATLAB software is used to calculate entropy, MSE, and correlation coefficient. Both chaotic circuit and encryption/decryption schemes are designed using Verilog HDL and simulated by Xilinx ISE 14.7. Xilinx Virtex 5 FPGA kit is used to realize the proposed algorithm with a throughput 0.536 of Gbit/s. The cryptosystem is tested using two different speech files to examine its efficiency. © 2020, Springer Science+Business Media, LLC, part of Springer Nature.
Chaotic neural network quantization and its robustness against adversarial attacks
Achieving robustness against adversarial attacks while maintaining high accuracy remains a critical challenge in neural networks. Parameter quantization is one of the main approaches used to compress deep neural networks to have less inference time and less storage memory size. However, quantization causes severe degradation in accuracy and consequently in model robustness. This work investigates the efficacy of stochastic quantization to enhance robustness and accuracy. Noise injection during quantization is explored to understand the impact of noise types and magnitudes on model performance. A comprehensive comparison between different applying scenarios for stochastic quantization and different noise types and magnitudes was implemented in this paper. Compared to the baseline deterministic quantization, chaotic quantization achieves a comparable accuracy, however, it achieves up to a 43% increase in accuracy against various attack scenarios. This highlights stochastic quantization as a promising defense mechanism. In addition, there is a crucial role played by the choice of noise type and magnitude in stochastic quantization. Lorenz and Henon noise distributions in stochastic quantization outperform traditional uniform and Gaussian noise in defending against attacks. A transferability analysis was discussed to understand the generalizability and effectiveness of the proposed stochastic quantization techniques. A cross-validation definition was newly evaluated in this scope to analyse the model’s stability and robustness against attacks. The study outperformed a quantization network technique and improved the model’s robustness and stability against adversarial attacks using chaotic quantization instead of deterministic quantization or even instead of stochastic quantization using traditional noise. © 2024 Elsevier B.V.
Hardware Accelerator of Fractional-Order Operator Based on Phase Optimized Filters With Applications
Hardware accelerators outperform CPUs in terms of performance by parallelizing the algorithm architecture and using the device’s programmable resources. FPGA is a type of hardware accelerator that excels not only in performance but also in energy efficiency. So, it provides a suitable platform for implementing complicated fractional-order systems. This paper proposes a novel phase-based optimization method to implement fractional operators using FIR and IIR filters. We also compare five fractional operator implementation methods on FPGA regarding resource utilization, execution time, power, and accuracy. These methods and the proposed one are evaluated in terms of power consumption, delay, and resources to assist the designer in determining the most suitable implementation method for the given application. The proposed method has a lower phase error of 14.7% in the case of derivative operation and a lower phase error of 18.83% in the case of integration compared to the literature. In addition, the proposed methods decreased the consumed power and area by more than three times compared to the fixed-window GL fractional operator. The proposed approach implements Heaviside’s inductor-terminated lossy line. In addition, it is employed as an edge detection kernel to demonstrate its effectiveness in image processing applications. © 2023 IEEE.
Optimization of Double fractional-order Image Enhancement System
Image enhancement is a vital process that serves as a tool for improving the quality of a lot of real-life applications. Fractional calculus can be utilized in enhancing images using fractional order kernels, adding more controllability to the system, due to the flexible choice of the fractional order parameter, which adds extra degrees of freedom. The proposed system merges two fractional order kernels which helps in image enhancement techniques, and the contribution of this work is based on the study of how to optimize this process. The optimization of the two fractional kernels was done using the neural network optimization algorithm (NNA) to utilize the best order for the two kernels. In this paper, three fractional kernels are studied to highlight the performance of image enhancement using fractional kernels against different metrics. Furthermore, three different combinations of two kernels are combined and studied to enhance the metrics score by utilizing two different fractional orders for each kernel. Various optimization algorithms are used to obtain the optimum fractional order for both single and combined kernels. Using the constrained NNA, the evaluation metrics of the image enhancement show a 33% increase in measure of enhancement metric (EME), 21% increase in contrast, and 4% increase in average gradient compared to the best-achieved metrics by the literature while keeping the similarity metric above 0.75. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.
Chaotic Dynamics and FPGA Implementation of a Fractional-Order Chaotic System with Time Delay
This article proposes a numerical solution approach and Field Programmable Gate Array implementation of a delayed fractional-order system. The proposed method is amenable to a sufficiently efficient hardware realization. The system’s numerical solution and hardware realization have two requirements. First, the delay terms are implemented by employing LookUp Tables to keep the already required delayed samples in the dynamical equations. Second, the fractional derivative is numerically approximated using Grünwald-Letnikov approximation with a memory window size, L, according to the short memory principle such that it balances between accuracy and efficiency. Bifurcation diagrams and spectral entropy validate the chaotic behaviour of the system for commensurate and incommensurate orders. Additionally, the dynamic behaviour of the system is studied versus the delay parameter, ?, and the window size, L. The system is realized on Nexys 4 Artix-7 FPGA XC7A100T with throughput 1.2 Gbit/s and hardware resources utilization 15% from the total LookUp Tables and 4% from the slice registers. Oscilloscope experimental results verify the numerical solution of the delayed fractional-order system. The amenability to digital hardware realization, which is experimentally validated in this article, is added to the system’s advantages and encourages its utilization in future digital applications such as chaos control and synchronization and chaos-based communication applications. © 2020 IEEE.
Bio-inspired adsorption sheets from waste material for anionic methyl orange dye removal
Abstract: Nano zero-valent iron (nZVI), bimetallic nano zero-valent iron-copper (Fe0–Cu), and Raw algae (sargassum dentifolium) activated carbon-supported bimetallic nano zero-valent iron-copper (AC-Fe0–Cu) are synthesized and characterized using FT-IR, XRD, and SEM. The maximum removal capacity is demonstrated by bimetallic activated carbon AC-Fe0–Cu, which is estimated at 946.5 mg/g capacity at the condition pH = 7, 30 min contact time under shaking at 120 rpm at ambient temperature, 200 ppm of M.O, and 1 g/l dose of raw algae-Fe0–Cu adsorbent. The elimination capability of the H3PO4 chemical AC-Fe0–Cu adsorbent is 991.96 mg/g under the conditions of pH = 3, 120 min contact time under shaking at 120 rpm at room temperature, 200 ppm of M.O, and 2 g/l doses of H3PO4 chemical AC-Fe0–Cu adsorbent. The Bagasse activated carbon adsorbent sheet achieves a removal capacity of 71.6 mg/g MO dye solution. Kinetic and isothermal models are used to fit the results of time and concentration experiments. The intra-particle model yields the best fit for bimetallic Fe0–Cu, AC-Fe0–Cu, H3PO4 chemical AC-Fe0–Cu and bagasse activated carbon(CH), with corrected R-Squared values of 0.9656, 0.9926, 0.964, and 0.951respectively. The isothermal results emphasize the significance of physisorption and chemisorption in concentration outcomes. Response surface methodology (RSM) and artificial neural networks (ANN) are employed to optimize the removal efficiency. RSM models the efficiency and facilitates numerical optimization, while the ANN model is optimized using the moth search algorithm (MSA) for optimal results. Highlights: 1.The Fe0–Cu composite, when combined with activated carbon from Bagasse Pulp (CH), exhibited the most effective decolorization effectiveness for anionic colours present in wastewater.2.The utilization of composites presents a promising opportunity for efficient dye removal due to its cost-effectiveness and environmentally sustainable nature. 3.The utilization of response surface approach and artificial neural network modelling improves the efficacy of removal processes and treatment techniques. © 2023, The Author(s).
Circuit realization and FPGA-based implementation of a fractional-order chaotic system for cancellable face recognition
Biometric security has been developed in recent years with the emergence of cancellable biometric concepts. The idea of the cancellable biometric traits is concerned with creating encrypted or distorted traits of the original ones to protect them from hacking techniques. So, encrypted or distorted biometric traits are stored in databases instead of the original ones. This can be accomplished through non-invertible transforms or encryption schemes. In this paper, a cancellable face recognition algorithm is introduced based on face image encryption through a fractional-order multi-scroll chaotic system. The fundamental concept is to create random keys that will be XORed with the three components of color face images (red, green, and blue) to obtain encrypted face images. These random keys are generated from the Least Significant Bits of all state variables of a proposed fractional-order multi-scroll chaotic system. Lastly, the encrypted color components of face images are combined to produce a single cancellable trait for each color face image. The results of encryption with the proposed system are full-encrypted face images that are suitable for cancellable biometric applications. The strength of the proposed system is that it is extremely sensitive to the user’s selected initial conditions. The numerical simulation of the proposed chaotic system is done with MATLAB. Phase and bifurcation diagrams are used to analyze the dynamic performance of the proposed fractional-order multi-scroll chaotic system. Furthermore, we realized the hardware circuit of the proposed chaotic system on the PSpice simulator. The proposed chaotic system can be implemented on Field Programmable Gate Arrays (FPGAs). To model our generator, we can use Verilog Hardware Description Language HDL, Xilinx ISE 14.7 and Xilinx FPGA Artix-7 XC7A100T based on Grunwald-Letnikov algorithms for mathematical analysis. The numerical simulation, the circuit simulation and the hardware experimental results confirm each other. Cancellable face recognition based on the proposed fractional-order chaotic system has been implemented on FERET, LFW, and ORL datasets, and the results are compared with those of other schemes. Some evaluation metrics containing Equal Error Rate (EER), and Area under the Receiver Operating Characteristic (AROC) curve are used to assess the cancellable biometric system. The numerical results of these metrics show EER levels close to zero and AROC values of 100%. In addition, the encryption scheme is highly efficient. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.