This research article proposes a floating memristor emulator configuration based on n-type MOSFETs only. The proposed memristor comprises three nMOS and an extra nMOS for an external grounded capacitor. Compared to the existing literature, the proposed floating MOS memristor enables a simple design without any sophisticated design complexity. The actual fingerprint of the memristor as a pinched hysteresis loop with different frequency domains and composite characteristics as incremental and decremental are well examined using computer simulation with 90-nm CMOS technology parameters for MOSFETs. The power consumed by the proposed circuit is $2.6~\mu \text{W}$ . In addition, an experimental test using off-the-shelf components is investigated to verify the theoretical and simulated results. Moreover, the proposed nMOS-memristor emulator application is suitable for the modulation and demodulation of binary frequency-shift keying (BFSK) and Boolean logic gates.

This article explores the impact of automation on environmental sensing, focusing on advanced technologies that revolutionize data collection analysis and monitoring. The International Union of Pure and Applied Chemistry (IUPAC) defines automation as integrating hardware and software components into modern analytical systems. Advancements in electronics, computer science, and robotics drive the evolution of automated sensing systems, overcoming traditional limitations in manual data collection. Environmental sensor networks (ESNs) address challenges in weather constraints and cost considerations, providing high-quality time-series data, although issues in interoperability, calibration, communication, and longevity persist. Unmanned Aerial Systems (UASs), particularly unmanned aerial vehicles (UAVs), play an important role in environmental monitoring due to their versatility and cost-effectiveness. Despite challenges in regulatory compliance and technical limitations, UAVs offer detailed spatial and temporal information. Pollution monitoring faces challenges related to high costs and maintenance requirements, prompting the exploration of cost-efficient alternatives. Smart agriculture encounters hurdle in data integration, interoperability, device durability in adverse weather conditions, and cybersecurity threats, necessitating privacy-preserving techniques and federated learning approaches. Financial barriers, including hardware costs and ongoing maintenance, impede the widespread adoption of smart technology in agriculture. Integrating robotics, notably underwater vehicles, proves indispensable in various environmental monitoring applications, providing accurate data in challenging conditions. This review details the significant role of transfer learning and edge computing, which are integral components of robotics and wireless monitoring frameworks. These advancements aid in overcoming challenges in environmental sensing, underscoring the ongoing necessity for research and innovation to enhance monitoring solutions. Some state-of-the-art frameworks and datasets are analyzed to provide a comprehensive review on the basic steps involved in the automation of environmental sensing applications.

The design of the memristor emulator, which has found widespread of applications in analog computation, neuromorphic and biological systems, communications, etc., is much sought for. In this article, a resistorless electronically tunable grounded and floating memristor emulator by employing current-mode (CM) building blocks: operational transconductance amplifier (OTA) and voltage differencing buffered amplifier (VDBA) has been proposed. The proposed emulators can be configured as incremental and decremental operations. The robustness of the proposed emulator has been verified by performing various analyses: nonideal interpretations, process corner variations, temperature fluctuations, and nonvolatility behavior. The layout has been performed, and the theoretical fingerprint characteristics have been simulated and observed using 180-nm CMOS process parameters in the Cadence environment. The pinched hysteresis loop of the proposed memristor emulator maintains up to 5 MHz. The proposed memristor emulator uses less number of circuit components, but it maintains performance in terms of speed and operating range of frequency. Binary frequency shift keying (BFSK) has been performed using the proposed memristor to validate its functionality in the application. This memristor emulator with superior performance can be integrated into the communication system, analog computation, neuromorphic and biological systems, etc.

The memristor-based neural network configuration is a promising approach to realizing artificial neural networks (ANNs) at the hardware level. The memristors can effectively simulate the strength of synaptic connections between neurons in neural networks due to their diverse significant characteristics such as nonvolatility, nanoscale dimensions, and variable conductance. This work presents a new synaptic circuit based on memristors and Complementary Metal Oxide Semiconductor(CMOS), which can realize the adjustment of positive, negative, and zero synaptic weights using only one control signal. The relationship between synaptic weights and the duration of control signals is also explained in detail. Accordingly, Widrow–Hoff algorithm-based memristive neural network (MNN) circuits are proposed to solve the recognition of three types of character pictures. The functionality of the proposed configurations is verified using SPICE simulation.