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

The full-wave rectifier is an essential step for extracting energy from a piezoelectric source. Yet, the inherent capacitance of the piezoelement significantly is considered a limitation of the efficiency of extraction. To address this issue, the bias-flip rectifier can be used. However, this rectifier needs large inductor and precise tuning. The large inductor increases the overall volume of the system which is inefficient. This paper address the problems with the traditional bias-flip rectifier by introducing an enhanced multi-step bias-flip rectifier to achieve a high voltage-flip

The Internet of Things (IoT) is a global network of interconnected computing, sensing, and networking devices that can exchange data and information via various network protocols. It can connect numerous smart devices thanks to recent advances in wired, wireless, and hybrid technologies. Lightweight IoT protocols can compensate for IoT devices with restricted hardware characteristics in terms of storage, Central Processing Unit (CPU), energy, etc. Hence, it is critical to identify the optimal communication protocol for system architects. This necessitates an evaluation of next-generation

There is an increasing interest in energy harvesting for wearable biomedical devices. This requires power conservation and management to ensure long-term and steady operation. Hence, task scheduling algorithms will be used throughout this work to provide a reliable solution to minimize energy consumption while considering the system operation constraints. This study proposes a novel power-aware task scheduler to manage system operations. For example, we used the scheduler to handle system operations, including heart rate and temperature sensors. Two optimization techniques have been used to

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

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

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

Power management and energy conservation are crucial for medical wearable devices that rely on energy harvesting. These devices operate under strict power budgets and require prolonged and stable operation. To achieve this, Energy-aware task scheduling is proposed as a solution to minimize energy consumption while ensuring the continued operational capabilities of the device. our paper presents a task scheduling method using the Flower Pollination Algorithm (FPA). The proposed task scheduling focuses on managing the activity of key components such as the heart rate sensor, temperature sensor