K. L. N. College of Engineering

The various industrial sectors stand to gain significant advantages from the implementation of the Industrial Internet of Things (IIoT). Furthermore, given restricted channel materials, it is challenging to make the globe’s most effective choice in distributed wireless sensor networks (DWSN). Industrial sensor networks are susceptible to complicated industrial circumstances which are susceptible to an integration of various signal patterns. The complex interference from multiple signals can significantly lower the effectiveness of the classification of signals on industrial equipment, requiring a significant process of training that characteristics can be obtained. Furthermore, generalized envelope squared spectrum (GESS) is supplied for increased reconstruction of transmission signals. In this study, we propose a machine learning-based model an adaptive modulation-based simplified deep convolutional neural network (AM-SDCNN) for classifying signal nodes DWSN. Frequency reduction and sampling conditioning are implemented to receive signals from nodes, enabling the generation of intelligent signal representations. The aggregation center gets the output of the signal categorization process carried out by every single sensor node over a network. The simulation demonstrates that the suggested AM-SDCNN technique has beneficial performance in signal classification. Particularly, the suggested technique does not need transmitting signals from nodes to the aggregation center, which can preserve the privacy of industrial information.

Global freshwater systems face compounding pressure from population growth, industrial pollution, climate variability, and emerging contaminants, driving parallel innovation across water quality assessment and water treatment technology. This paper reviews recent advances in both domains, synthesizing the machine-learning-and-Internet-of-Things (IoT) literature on real-time water quality monitoring and prediction with the materials-science literature on membrane-based desalination, multifunctional filtration, and emerging-contaminant removal. Particular attention is given to the specific technical mechanisms, IoT sensor networks, machine-learning-based parameter prediction, electrochemical heavy-metal sensing, and advanced membrane and nanomaterial treatment processes, that the reviewed studies identify as driving measurable improvements in monitoring accuracy and treatment efficiency. Comparative tables map water quality parameters to the sensing and machine-learning methods used to monitor them, cross-reference treatment technologies against the specific contaminant classes each addresses, and set reported performance metrics, detection limits, classification accuracy, and monitoring accuracy gains, against the technological approach that produced them. The review finds that IoT-integrated machine-learning monitoring systems now achieve substantially higher measurement accuracy than traditional manual methods, while membrane, nanomaterial, and hybrid treatment architectures are increasingly capable of addressing previously intractable emerging contaminants such as per- and polyfluoroalkyl substances (PFAS), but that regional infrastructure constraints, particularly in low-resource settings, and the sustainability of treatment by-products remain significant unresolved barriers to universal safe water supply.

As the scaling limitations of conventional CMOS technology increasingly affect power efficiency and performance, alternative nano-scale computing paradigms have emerged as promising solutions. Quantum-dot Cellular Automata (QCA) is a post-CMOS technology that represents binary information through electron polarization rather than current flow, enabling ultra-low power dissipation and high-speed operation. In parallel, Vedic multiplication based on the Urdhva Tiryagbhyam sutra offers an efficient arithmetic technique by enabling parallel generation of partial products. The Vedic multiplication algorithm is systematically translated into logical expressions and implemented using QCA majority gates and inverters with appropriate clocking schemes. The proposed architecture exploits the inherent parallelism of the Vedic sutra to reduce computational delay and circuit complexity. The design is modeled and simulated using QCA Designer to verify functional correctness. Simulation results demonstrate that the proposed QCA-based Vedic multiplier achieves improved speed and reduced structural complexity compared to conventional CMOS-based multiplier architectures. Due to its low-power operation and parallel processing capability, the proposed design is well suited for high-speed arithmetic units in future nano-scale and energy-efficient computing systems.

Electric Vehicles (EVs) require stable auxiliary power supplies to operate electronic subsystems such as controllers, sensors, communication modules, and display units. However, fluctuations in the EV battery voltage due to load variations may affect the reliable operation of these sensitive electronic components. To overcome this issue, a digitally controlled DC–DC voltage regulation system is proposed. In this project, a PWM-based closed-loop buck converter is implemented using an Arduino UNO. The system is designed to operate with an EV battery as the primary DC input source. A discrete buck converter consisting of a MOSFET, inductor, diode, and capacitor is used as the power stage to step down and regulate the voltage. The output voltage is sensed through a voltage divider circuit and fed to the Arduino ADC for feedback control. The system can generate selectable output voltages such as 3.3V, 5V, and 12V, which are commonly used in EV electronic subsystems. For demonstration, components such as LED indicators and a buzzer are connected as loads representing devices operating at these voltage levels. The output voltage can also be modified through software programming, making the system flexible and user configurable for different voltage requirements. Based on the selected reference voltage, the Arduino adjusts the PWM duty cycle to maintain the required output. The regulated voltage is displayed on an LCD for real-time monitoring. The proposed system demonstrates a flexible, user-efficient, and digitally controlled auxiliary voltage regulation system for EV applications.

Scaling CMOS technology is being more threatening and therefore limited due to power dissipation, leakages and physical consideration at the nano-scale. A promising post-CMOS computing model that can be used to achieve ultra-low-power computation through encoding binary information using electron polarization, and not current flow, is Quantum-Dot Cellular Automata (QDCA). In this paper, the QDCA circuit design algorithm discussed is an energy-efficient algorithm that targets optimization of the majority logic designs and reduction of the unwanted interactions of cells. The suggested method reinvents Boolean logic based on majority algebra and utilizes layout-level optimization to minimize the number of cells, circuit area, latency and energy dissipation. It has been shown through simulation that the proposed designs can provide scalings in cell count (about 27-30 percent), layout area (more than 30 percent) reduction, latency (approximately 32) reduction, and energy dissipation (nearly 35 percent) reduction over conventional QDCA implementations. These advances underscore the usefulness of the suggested methodology towards creating scalable, low-power nano-scale digital circuits that can be used in the next-generation computing systems.