The analog layout design for the RF front-end circuits has remained a critical and time-intensive stage within the integrated circuit development cycle. Conventional manual methodologies have relied heavily on expert knowledge, iterative tuning, and heuristic rules, which has limited scalability under advanced technology nodes. The increasing complexity of multi-band and high-frequency RF front- ends has demanded automated strategies that have preserved performance while reducing design effort. Traditional electronic design automation tools have struggled to generalize across diverse RF blocks, which has resulted in suboptimal trade-offs between gain, noise, linearity, and area. Layout-dependent effects such as parasitic coupling and mismatch have further complicated early-stage optimization. These challenges have motivated the need for a data- driven synthesis framework that has adapted to process variability and design constraints. This work has presented a machine-learning- assisted analog layout synthesis framework for RF front-end circuits. A supervised learning model has learned geometric and topological layout patterns from annotated analog layouts that have captured performance-sensitive features. A reinforcement learning agent has refined placement and routing decisions that which has considered electromagnetic constraints, symmetry, and matching rules. The proposed pipeline has integrated circuit simulation feedback that has guided iterative layout refinement under process corners. Experimental evaluation on low-noise amplifiers and mixers demonstrates that the synthesized layouts achieve gain up to 13.4 dB, noise figure as low as 1.4 dB, linearity of -17.0 dBm, layout area of 1165 µm², and parasitic capacitance of 20 fF, outperforming existing template-based, optimization-driven, and reinforcement learning placement methods. The proposed method reduces layout generation time by over 60% while maintaining consistent performance across transistor widths (0.16–0.24 µm) and lengths (0.32–0.36 µm), indicating strong generalization and suitability for next-generation RF front-end designs.

The growing prevalence of air pollution poses significant risks to human health and ecological stability. Conventional air quality monitoring systems, while accurate, are expensive and geographically limited, restricting their deployment in large-scale sensing networks. Recent advancements in Complementary Metal-Oxide Semiconductor (CMOS) sensor technologies offer a promising pathway for developing cost-effective and miniaturized air monitoring platforms. However, these sensors often face limitations in calibration stability, data drift, and environmental noise interference, which compromise the reliability of pollutant concentration measurements. The major challenge lies in enhancing the accuracy and spatial scalability of low-cost CMOS-based air pollution sensors. Traditional machine learning models fail to capture the complex spatial-temporal dependencies between sensing nodes and environmental factors such as humidity, temperature, and wind dispersion patterns. This study proposes a Graph Neural Network (GNN)-enhanced environmental sensing framework that integrates CMOS-based gas and particulate matter sensors with a distributed graph learning model. The GNN architecture models inter-node relationships and spatial correlations across sensor networks, allowing real-time inference and adaptive recalibration. Data collected from multiple low-cost sensor nodes were processed through graph convolutional layers to estimate pollutant levels (PM2.5, NO2, CO, and O3) with high precision. The system was implemented on a resource-efficient embedded platform to ensure scalability and low energy consumption. The proposed framework demonstrates high predictive accuracy, achieving a Mean Absolute Error (MAE) of 3.2 µg/m³ for PM2.5, Root Mean Squared Error (RMSE) of 4.2, and R² of 0.93, significantly outperforming Random Forest, CNN regression, and Graph Attention Network baselines. The Calibration Drift Reduction (CDR) reached 42%, validating the effectiveness of adaptive recalibration. Computational efficiency remained within 30 ms per node, ensuring feasibility for real-time, large-scale deployment. The results confirm that moderate graph correlation weights (0.4–0.5) and EMA smoothing coefficient of 0.7 provide optimal performance, which shows the robustness, reliability, and scalability of the proposed GNN-enhanced CMOS sensor network for urban air quality monitoring.

Backscatter communication has emerged as a key enabling technology for ultra-low-power and batteryless wireless platforms, especially in the context of the Internet of Things (IoT) and pervasive sensing systems. Traditional designs often treat the antenna and RF circuitry independently, leading to suboptimal performance due to impedance mismatches, low energy harvesting efficiency, and poor communication reliability. Flexible materials and novel antenna structures have gained attention for wearable, implantable, and conformal applications but face challenges in maintaining performance consistency under mechanical deformation. This paper proposes a co-design methodology for flexible RF/antenna systems to optimize energy harvesting and communication efficiency in wirelessly powered backscatter communication platforms. The method integrates the antenna and RF front-end design to ensure impedance matching, maximize power transfer, and enable flexible operation. A meandered dipole antenna integrated with a Schottky-diode-based rectifier is designed using flexible polyimide substrate. The design ensures mechanical flexibility while achieving high RF-DC conversion efficiency. Simulations using CST Microwave Studio and co-simulation with Keysight ADS validate the antenna-rectifier co-design. Experimental results show up to 52% RF-DC conversion efficiency at 915 MHz under +5 dBm input power. Backscatter communication using On-Off Keying (OOK) achieves a range of 6 meters with minimal bit error rate under ambient powering conditions. The co-design improves energy harvesting by 19.4% and communication range by 27% compared to non-co-designed setups.

Modern portable electronic devices demand compact, efficient, and adaptive antennas to support multiple wireless standards and ensure consistent connectivity. Traditional antennas are limited by fixed structural properties and bandwidth constraints. To address this, we propose a novel AI-enabled compact metamaterial antenna integrated with a dynamic reconfiguration mechanism tailored for smart portable electronics. The antenna utilizes a planar metamaterial substrate with tunable unit cells controlled by an artificial intelligence (AI) model—specifically a lightweight reinforcement learning (RL) algorithm—to optimize operational parameters based on environmental feedback. The method enables real-time reconfiguration of frequency, radiation pattern, and gain characteristics. Simulations were conducted using CST Microwave Studio, and a hardware prototype was validated through an anechoic chamber. Results demonstrate that the proposed antenna achieves multiband operation from 2.4 GHz to 6 GHz, 50% size reduction compared to traditional antennas, and adaptive beam steering with <1 µs reconfiguration latency. This intelligent design ensures enhanced signal quality, power efficiency, and seamless interoperability in dynamic mobile environments.

Recent advances in implantable medical devices demand seamless, efficient, and miniaturized solutions for organ-specific wireless communication and power transfer. The integration of antenna systems into bioelectronics offers a transformative path to realizing robust biotelemetry and energy harvesting capabilities. Traditional antenna designs are hindered by biological loading effects, size constraints, and inconsistent power transfer across varying tissue types. This work presents a novel Integrated Antenna System (IAS) tailored for miniaturized organ-specific bioelectronics, designed to operate efficiently within heterogeneous tissue environments. The proposed system combines metamaterial-inspired miniaturization with substrate- integrated antennas, optimized through electromagnetic simulations to support dual functionality: robust biotelemetry and wireless power transfer (WPT). A multi-band design approach is employed to ensure compatibility with Medical Implant Communication Service (MICS) and Industrial, Scientific, and Medical (ISM) bands, crucial for real- time data transfer and sustained operation. Extensive simulations using CST Microwave Studio and HFSS validate the electromagnetic behavior within heterogeneous anatomical models (brain, heart, and liver tissues). Results indicate enhanced power transfer efficiency (> 65%) and stable radiation performance with minimal tissue heating (SAR < 1.6 W/kg). In-vitro and in-vivo prototypes show consistent impedance matching and link reliability, with telemetry range exceeding 10 cm and power transfer above 5 mW, sufficient for organ- specific bioelectronic function.

This article presents a real-time, cloud-integrated cardiac monitoring system developed using a portable ECG device that communicates via HTTPS and REST API protocols. The raw ECG signals are transmitted in JSON format to a secure cloud server, where Symlet4 wavelet transform is employed to denoise the signals in real time. This process enables the accurate extraction of key cardiac features, including HRV, QRS complex, RR interval, QT interval, PR interval, ST segment, P wave, and T wave. These features are processed and stored for subsequent analysis. Arrhythmia classification is initially performed using rule-based clinical logic derived from these parameters, while a structured dataset is concurrently generated to support the development and training of machine learning models for future diagnostic applications. Additionally, HRV data is visualized in real time through a responsive frontend interface, facilitating remote cardiac health monitoring by healthcare professionals. The system was validated using ECG recordings from 98 patients of varying ages to assess performance, reliability, and scalability across diverse clinical and home care scenarios. This article highlights a novel implementation of wavelet-based ECG signal filtering integrated with cloud computing within a complete IoT-based healthcare architecture.

Accurate decimal calculations are a fundamental requirement in accurate-operated domains such as finance and scientific research. Traditional binary arithmetic circuits, although widely used, often introduce rounding inaccuracy that can cascade in adequate errors in sensitive applications. To address this challenge, this research examines solution by designing the decimal arithmetic circuit using the CMOS-based binary-coded decimal (BCD) Adders. Unlike software-level reforms, the proposed approach embedded accuracy in circuit design itself. Decimal Adders are modeling and valid through the rhythm using 90nm CMOS technology, which ensures high loyalty in logic implementation. In addition, both Cadence virtuoso and tanner are organized to evaluate demonstration matrix using wide simulation, power efficiency and operating delays using EDA equipment. The benchmark comparison with existing architecture reveals significant reforms, especially in power consumption. This study confirms the argument that integrating the decimal arithmetic directly into silicon can greatly increase both reliability and computational accuracy in important systems.

Partial discharges (PDs) are critical early indicators of insulation degradation in high-voltage (HV) equipment. Reliable detection of PDs is essential to avoid catastrophic failures in power systems. Conventional narrowband antenna systems used for PD detection often suffer from poor sensitivity, limited frequency response, and are unable to detect weak transient signals over a broad spectrum. This work proposes a novel ultra-wideband (UWB) spiral monopole antenna tailored for enhanced sensitivity in PD detection. The design focuses on achieving a broadband response from 300 MHz to 3 GHz, allowing efficient capture of high-frequency transient PD signals. The antenna is fabricated using FR-4 substrate and validated through both simulation (using CST Microwave Studio) and experimental testing on a HV test platform with artificial PD sources. The system achieved over 90% PD detection accuracy across multiple insulation materials, outperforming conventional whip and dipole antennas in signal-to-noise ratio (SNR), bandwidth, and detection range. Results demonstrate the antenna’s high directivity, gain uniformity, and excellent transient response.

In many countries, street lighting constitutes a significant portion of the total electrical power consumption. Unfortunately, this energy is often wasted due to the unnecessary illumination of less frequented streets. To address this issue, our project introduces a smart street lighting system that optimizes energy usage by automatically turning off lights in underused areas and illuminating high-traffic streets during dark hours. This system is seamlessly connected to a mobile application, allowing users to manually control street lighting, fostering a sense of community involvement. In addition to energy efficiency, our initiative addresses waste management challenges. Overflowing trash bins have been a persistent issue, leading to environmental concerns and unsightly surroundings. To combat this problem, our waste management system alerts users when a trash can is near capacity, enabling timely action. Real-time bin status updates are sent to a user-friendly web application through the cloud, empowering residents to monitor and manage waste disposal effectively. Furthermore, we tackle safety concerns associated with gas pipeline infrastructure. Gas leaks can result in devastating human and property losses. To predict potential leaks caused by pipeline fractures, we've developed a temperature monitoring system for gas pipeline tunnels. Sensors continuously monitor temperature levels within the tunnels, and this data is transmitted to a mobile application over the cloud. In the event of a sharp temperature increase, an alarm is triggered, providing early warning to workers, and preventing potential disasters. Our integrated IoT solutions not only enhance urban sustainability but also prioritize safety and environmental consciousness. By efficiently managing street lighting, waste disposal, and gas pipeline safety, our project strives to create smarter, safer, and more sustainable cities for the benefit of all residents and the environment.

A novel slot line fed ultra compact antenna with a wide bandwidth, operating in 900MHz range is developed and discussed. Antenna offers a frequency band of operation ranging from 792 MHz to 1.05 GHz with a percentage bandwidth of 28% which is much more than FCC specified bandwidth limit for wide band antennas. Overall size of the antenna is of the order of 0.059?g × 0.059?g × 0.007?g which is very compact and this compactness obtained without any lumped RLC elements. Developed antenna structure is characterized by directional radiation pattern, good gain and moderate radiation efficiency. FDTD model and equivalent RLC model of the antenna is also developed and analyzed.