Wastewater Treatment: Recycling, Management, and Valorization of Industrial Solid Wastes bridges the gap between the theory and applications of wastewater treatments, principles of diffusion, and the mechanism of biological and industrial treatment processes. It presents the practical applications that illustrate the treatment of several types of data, providing an overview of the characterization and treatment of wastewaters, and then examining the different biomaterials and methods for the evaluation of the treatment of biological wastewaters. Further, it considers the various types of

Energy harvesting materials and systems have become a popular study topic that is rapidly expanding. The harvesters will be used for a variety of applications, including distributed wireless sensor nodes for structural health monitoring, embedded and implanted sensor nodes for medical applications, recharging large system batteries, monitoring pressure in automobiles, powering unmanned vehicles, and running security systems in domestic settings. Components and devices at micro-macro sizes, spanning materials, electronics, and integration, have recently been developed. Energy harvesting has

This paper investigates two different blind watermarking systems in the frequency domain with the development of a Pseudo Random Number Generator (PRNG), based on a fractional-order chaotic system, for watermark encryption. The methodology is based on converting the cover image to the YCbCr color domain and applying two different techniques of frequency transforms, Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT), to the Y channel. Then, the encrypted watermark is embedded in the middle-frequency band and HH band coefficients for the DCT and DWT, respectively. For more

The precise identification of electrical model parameters of Li-Ion batteries is essential for efficient usage and better prediction of the battery performance. In this work, the model identification performance of two metaheuristic optimization algorithms is compared. The algorithms in comparison are the Marine Predator Algorithm (MPA) and the Partial Reinforcement Optimizer (PRO) to find the optimal model parameter values. Three fractional-order (FO) electrical equivalent circuit models (ECMs) of Li-Ion batteries with different levels of complexity are used to fit the electrochemical

Area and power consumption are the main challenges in Network on Chip (NoC). Indeed, First Input First Output (FIFO) memory is the key element in NoC. Increasing the FIFO depth, produces an increas in the performance of NoC but at the cost of area and power consumption. This paper proposes a new hybrid CMOS-Memristor based FIFO architecture that consumes low power and has a small size compared to the conventional CMOS-based FIFOs. The predicted area is approximately equal to the half of that wasted in conventional FIFOs. The implementation of FIFO controller module is implemented using HDL

Digital Image Processing (DIP) is a growing field for various applications, such as autonomous vehicles and video surveillance. To improve the performance of DIP systems, image processing algorithms are implemented in hardware rather than software. The idea here is primarily to get a faster system than software imaging or other alternative hardware. Field-programmable gate arrays (FPGAs) have the advantages of parallel processing, low cost, and low power consumption. These semiconductor devices contain many logic blocks that can be programmed to perform everything from basic digital gate-level

Water contamination with colours and heavy metals from textile effluents has harmed the ecology and food chain, with mutagenic and carcinogenic effects on human health. As a result, removing these harmful chemicals is critical for the environment and human health. Various standard physicochemical and biological treatment technologies are used; however, there are still some difficulties. Adsorption is described as a highly successful technology for removing contaminants from textile-effluents wastewater compared to other methods. Several adsorbent materials, including nanomaterials, natural

The popularity of wearable devices has grown as they improve the quality of life in many applications. In particular, for medical devices, energy harvesters are the dominating source of energy for wearable devices. However, their power budget is limited. Thus, power-saving techniques are essential components in the whole technology stack of those devices. That is, choosing the optimal schedule for different tasks running on the wearable device can help to reduce energy consumption. This paper presents a sensor task scheduling technique for optimizing energy consumption for energy harvesting

Nano zero-valent iron (nZVI), bimetallic Nano zero-valent iron-copper (Fe 0- Cu), and fava bean activated carbon-supported with bimetallic Nano zero-valent iron-copper (AC-F e 0-Cu) were prepared and characterized by DLS, FT-IR, XRD, and SEM. The influence of the synthesized adsorbents on the adsorption and removal of soluble anionic methyl orange (M.O) dye was investigated using UV-V spectroscopy. The influence of numerous operational parameters was studied at varied pH (3–9), time intervals (15–180 min), and dye concentrations (25–1000 ppm) to establish the best removal conditions. The

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