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.
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
Enhanced removal of crystal violet using rawfava bean peels, its chemically activated carbon compared with commercial activated carbon
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
DISH: Digital image steganography using stochastic-computing with high-capacity
Stochastic computing is a relatively new approach to computing that has gained interest in recent years due to its potential for low-power and high-noise environments. It is a method of computing that uses probability to represent and manipulate data, therefore it has applications in areas such as signal processing, machine learning, and cryptography. Stochastic steganography involves hiding a message within a cover image using a statistical model. Unlike traditional steganography techniques that use deterministic algorithms to embed the message, stochastic steganography uses a probabilistic approach to hide the message in a way that makes it difficult for an adversary to detect. Due to this error robustness and large bit streams stochastic computing, they are well suited for high capacity and secure image steganography. In this paper, as per the authors’ best knowledge, image steganography using stochastic computing based on linear feedback shift register (LFSR) is proposed for the first time. In the proposed technique, the cover image is converted to stochastic representation instead of the binary one, and then a secret image is embedded in it. The resulting stego image has a high PSNR value transmitted with no visual trace of the hidden image. The final results are stego image with PSNR starting from 30 dB and a maximum payload up to 40 bits per pixel (bpp) with an effective payload up to 28 bpp. The proposed method achieves high security and high capability of the number of stored bits in each pixel. Thus, the proposed method can prove a vital solution for high capacity and secure image steganography, which can then be extended to other types of steganography. © 2024, The Author(s).
A Study on Fractional Power-Law Applications and Approximations
The frequency response of the fractional-order power-law filter can be approximated by different techniques, which eventually affect the expected performance. Fractional-order control systems introduce many benefits for applications like compensators to achieve robust frequency and additional degrees of freedom in the tuning process. This paper is a comparative study of five of these approximation techniques. The comparison focuses on their magnitude error, phase error, and implementation complexity. The techniques under study are the Carlson, continued fraction expansion (CFE), Padé, Charef, and MATLAB curve-fitting tool approximations. Based on this comparison, the recommended approximation techniques are the curve-fitting MATLAB tool and the continued fraction expansion (CFE). As an application, a low-pass power-law filter is realized on a field-programmable analog array (FPAA) using two techniques, namely the curve-fitting tool and the CFE. The experiment aligns with and validates the numerical results. © 2024 by the authors.
Soft robotic grippers: A review on technologies, materials, and applications
The growing need for manipulators capable of handling delicate objects with care and coexisting safely with humans has brought soft robots to the forefront as a practical and cost-effective solution. In this context, this paper aims to explore soft grippers, a unique and versatile subset of soft robots. It provides an overview of various soft grasping techniques and materials, highlighting their respective advantages and limitations, along with showcasing several designed and tested models. As medicine and agriculture are acknowledged as pivotal domains required for basic human survival, this paper explores the potential applications of soft robotic grippers in these respective fields. Additionally, it further investigates how soft grippers can contribute to reducing cost and enhancing production efficiency while addressing practical relevant solutions. Considering the escalating environmental threats, particularly in oceans and coral reefs, the paper examines the potential of soft grasping underwater to mitigate these challenges, considered as crucial for conserving the fisheries industry and pertinent economic fields. Lastly, it outlines the current challenges and future prospects of soft grippers, emphasizing the importance of overcoming obstacles through finding solutions such as using bioinspiration to create effective technical solutions and highlighting the importance of commercialization. © 2024 Elsevier B.V.
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.
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.
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.
Crystal violet removal using algae-based activated carbon and its composites with bimetallic Fe0-Cu
The textile industry is considered a source of pollution because of the discharge of dye wastewater. The dye wastewater effluent has a significant impact on the aquatic environment. According to the World Bank, textile dyeing, and treatment contribute 17 to 20% of the pollution of water. This paper aims to prepare the bimetallic nano zero-valent iron-copper (Fe0-Cu), algae-activated carbon, and their composites (AC-Fe0-Cu), which are employed as adsorbents. In this paper, Synthetic adsorbents are prepared and examined for the adsorption and removal of soluble cationic crystal violet (CV) dye. The influence of synthetic adsorbents on the adsorption and removal of soluble cationic crystal violet (CV) dye is investigated using UV-V spectroscopy at different pH (3-10), time intervals (15-180) min, and initial dye concentrations (50-500 ppm). Raw algae exhibit an impressive 96.64% removal efficiency under the following conditions: pH 7, contact time of 180 min, rotational speed of 120 rpm, temperature range of 25 °C-30 °C, concentration of 300 ppm in the CV dye solution, and a dose of 4 g l?1 of raw algae adsorbent. The best removal efficiencies of Raw algae Fe0-Cu, and H3PO4 chemical AC-Fe0-Cu are 97.61 % and 97.46 %, respectively, at pH = 7, contact time = 150 min, rotational speed = 120 rpm, T = (25-30) °C, concentration = 75 ppm of CV dye solution, and 1.5 g l?1 doses of raw algae F e0-Cu adsorbent and 1 g l?1 dose of H3PO4 chemical AC-Fe0-Cu adsorbent. The maximum amounts (q max) of Bi-RA and RA adsorbed for the adsorption process of CV are 85.92 mg g?1 and 1388 mg g?1, respectively. The Bi-H3A-AC model, optimized using PSO, demonstrates superior performance, with the highest adsorption capacity estimated at 83.51 mg g?1. However, the Langmuir model predicts a maximum adsorption capacity (q e ) of 275.6 mg g?1 for the CV adsorption process when utilizing Bi-H3A-AC. Kinetic and isothermal models are used to fit the data of time and concentration experiments. DLS, zeta potential, FT-IR, XRD, and SEM are used to characterize the prepared materials. Response surface methodology (RSM) is used to model the removal efficiency and then turned into a numerical optimization approach to determine the ideal conditions for improving removal efficiency. An artificial neural network (ANN) is also used to model the removal efficiency. © 2024 The Author(s). Published by IOP Publishing Ltd.