A (k,n)-Secret Image Sharing With Steganography Using Generalized Tent Map

Secret Image Sharing (SIS) transfers an image to mutually suspicious receivers as n meaningless shares, where k or more shares must be present to recover the secret. This paper proposes a (k, n)-SIS system for any image type using polynomial interpolation based on Lagrange polynomials, where the generated shares are of size 1/k of the secret image size. A full encryption system, consisting of substitution and permutation stages, is employed by using the generalized Tent map as a source of randomness. In addition to using a long and sensitive system key, steganography using the Least Significant Bits (LSBs) embedding technique is utilized to improve security. Detailed experimental analysis of the security, robustness and performance of the proposed system is provided, which is more comprehensive than the analyses given in other related works. Security is demonstrated using statistical tests, and robustness against noise and crop attacks is validated. © 2024 IEEE.

Battery Modeling with Mittag-Leffler Function

In various areas of life, rechargeable lithium-ion batteries are the technology of choice. Equivalent circuit models are utilized extensively in characterizing and modeling energy storage systems. In real-time applications, several generic-based battery models are created to simulate the battery’s charging and discharging behavior more accurately. In this work, we present two generic battery models based on Mittag-Leffler function using a generic Standard battery model as a reference. These models are intended to fit the continuous discharging cycles of lithium-ion, Nickel-cadmium, and Nickel-metal hydride batteries, as well as one set from the NASA randomized battery usage dataset. We formulate the parameter identification as an optimization problem, solved with Marine Predator Algorithm. The optimized models show very good matching against the measured data. © 2024 IEEE.

Novel Fast Prediction Algorithm for Advanced and High Efficiency Video Coding

This paper introduces an efficient prediction algorithm tailored for advanced and high efficiency video coding, encompassing both H.264 and H.265. The proposed approach aims at replacing the standard intra prediction methodology by employing a streamlined prediction mode, which significantly reduces computational overhead and system complexity while eliminating the requirement for mode decision. By leveraging block comparison criteria, the designed method combines neighboring blocks in a linear fashion to accurately represent the target block. Extensive comparisons are conducted with the H.264 intra prediction using various video sequences and multiple evaluation criteria. The results demonstrate substantial time savings of up to 60% compared to the H.264 standard intra prediction algorithm, with a minor peak signal-to-noise ratio drop. The proposed algorithm holds promise for enhancing real-time video processing and compression in video coding systems, offering notable efficiency gains without sacrificing predictive accuracy. © 2024 IEEE.

Carbon Nanomaterials and Their Composites as Adsorbents

Carbon nanomaterials with various nanostructures (carbon nanotubes, graphene, graphene oxide, fullerene, nano diamonds, carbon quantum dots, carbon nanofibers, graphitic carbon nitrides, and nano porous carbons) are the decade’s most distinguishing and popular materials. They have distinctive physicochemical qualities such as chemical stability, mechanical strength, hardness, thermal and electrical conductivities, and so on. Furthermore, they are easily surface functionalized and tweaked, modifying them for high-end specific applications. Carbon nanostructures’ properties and surface characteristics are determined by the synthesis method used to create them. Nanoscience and nanotechnology have the potential to create materials with unexpected functions and qualities, which are transforming all industries. Carbon nanoparticles such as fullerene, carbon nanotubes, and graphene stand out among the various kinds of nanomaterials. These nanoparticles offer a wide range of practical applications, particularly in adsorption processes. Carbon nanoparticles exhibit unique structures that could be used in the construction of extremely sensitive, selective, and effective adsorbent devices for the removal of inorganic, organic, and biological pollutants from water solutions, as well as nano sensors and drug delivery systems. In this chapter, we demonstrated the number of studies published in recent years that used carbon nanomaterials as adsorbents. Furthermore, this chapter discusses essential features of adsorption and different nanocarbon carbon composite material, such as the contrast between physical and chemical absorption. Furthermore, diverse carbon nanomaterial synthesis such as AC–FeO ?Cu and Bimetallic FeO ?Cu/algae activated carbon composites AC–Fe0 ?Cu methodologies, functionalization, and characteristics are provided and logically addressed. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

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.

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.

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).

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

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.