Abstract

This paper presents a constant gm, 1.2 V operational transconductance amplifier (OTA) designed in TSMC 65 nm CMOS technology. The proposed OTA achieves a constantgm characteristic over a full rail-to-rail input/output range by employing a constant gm rail-to-rail input stage, a folded cascode summing stage, and a quiescent current controlled Class-AB output stage. The input stage comprises complementary NMOS and PMOS differential pairs connected in parallel to achieve rail-to-rail input operation. Conventional parallel input stages suffer from significant gm variation with input common-mode voltage, leading to changes in small and large signal parameters such as open-loop gain, slew rate and unity-gain bandwidth (UGB). In this work, a current-injection mechanism dynamically adjusts the active input pair’s bias, maintaining a nearly constant transconductance across the entire common-mode range. Postlayout simulation shows that the proposed OTA achieves less than 4.8% gm variation across the entire input range while maintaining uniform large signal characteristics. Performance metrics include a DC gain of 80–87 dB, UGB of 9.72 MHz, phase margin of 64°, CMRR of 97.9 dB, and a power consumption of 260 μW when driving a 100 pF capacitive load.

Abstract

Respiratory diseases such as asthma, bronchitis, and pneumonia remain a significant global health concern, particularly in settings where access to trained clinicians and advanced diagnostic tools is limited. This work presents an edge-based AI platform for real-time classification of respiratory conditions using lung sound analysis. The system captures respiratory audio via a microphone and processes it locally on a PYNQ-ZU FPGA platform using a lightweight Convolutional Neural Network (CNN) trained on Mel spectrogram representations. Evaluated on the publicly available ICBHI2017 dataset, the model achieved an accuracy of 96%, with a sensitivity of 97.06% and specificity of 94.12%. These results demonstrate the potential of portable, AI-driven auscultation tools to provide accessible and consistent respiratory diagnostics in resource-constrained environments.

Abstract

Frequency Modulated Continuous Wave (FMCW) radars are emerging a reliable solution for object classification, having consistent performance under varying environmental conditions, unlike traditional Lidar and camera-based approaches. This paper presents an end-to-end object classification pipeline using point cloud data from an FMCW radar, deployed on the Qualcomm Innovators Development Kit (QIDK), demonstrating its suitability for embedded, Machine Learning and real-time interfaces. A PCA+SVM based classifier is used in the proposed system to enable efficient on-device inference with lower computational overhead. Experimental evaluation on a custom-made dataset containing over 11,000 samples shows an accuracy of 96.23%, with an inference latency of less than 25.9 milliseconds and a model size of 69.91 MB, demonstrating the suitability of the proposed approach for real-time, on-device object classification in smart mobility applications and intelligent sensing platforms.

Abstract

Respiratory diseases such as asthma, bronchitis, and pneumonia remain a leading cause of mortality worldwide, significantly affecting both individual health and socioeconomic conditions. Early and accurate diagnosis is essential to mitigate these effects, yet traditional diagnostic techniques such as auscultation are subjective and rely on the expertise of trained physicians. To address these challenges and improve diagnostic accessibility, this work proposes a fully portable and low-cost system for the automated detection and classification of respiratory diseases. Lung sounds are captured using a microphone interfaced with a microcontroller. These recordings are then transmitted to an Edge hardware platform, where a Convolutional Neural Network (CNN) model processes Mel spectrogram representations of the audio for classification. The system leverages the publicly available HF Lung dataset for training and evaluation. The proposed system achieved a classification accuracy of 80.5%, with a sensitivity of 95.65% and a specificity of 98.80%. This portable approach demonstrates the potential for real-time, AI-powered respiratory diagnostics in remote or resource-limited settings, bridging the gap between clinical expertise and everyday health monitoring.

Abstract

Frequency Modulated Continuous Wave (FMCW) radars, known for their millimeter-level accuracy, are increasingly used in healthcare for non-contact monitoring of vital signs like Heart Rate (HR) and Breath Rate (BR). HR and BR are estimated using frequency domain analysis of the extracted respiration and cardiac activity from Radar Data. The focus of our work is to extract the waveforms of chest vibrations due to respiration and cardiac activity, for which higher time resolution is required. This work utilizes a signal processing technique known as Zero Frequency Filtering (ZFF) on the FMCW Radar Data. ZFF is known to have the best time resolution. The processing pipeline is implemented on the PYNQ-ZU platform to ensure increased portability. Experimental results show that the proposed system achieves an accuracy of over 95.26% for HR estimation and over 98.47% for BR estimation, with a latency of less than 2 ms.

Abstract

Reliable toxic gas detection is vital for residential and industrial safety. While precise sensors are expensive, affordable ones face challenges of nonlinearity and environmental sensitivity, particularly from temperature (T) and humidity (H) effects. In this work, we present a novel predeployment calibration framework that accounts for these environmental factors on the sensor behavior, given by the resistance ratio (Rs/Ro) at constant gas concentration levels. The proposed method first refines the baseline resistance (Rs) estimation by fitting a power-law model to known gas concentrations and then applies a cubic regression model to capture the nonlinear effects of temperature and humidity on the Rs/Ro ratio. Cubic regression achieves superior accuracy (>5.8%) over lower order models while reducing over-fitting risks compared to higher order polynomials. It achieves 99.65% average accuracy, outperforming the 96.73% from standard libraries. This improved performance is particularly notable at low ppm levels, where direct Rs measurements are typically noisy and unstable. The enhanced stability and accuracy of the proposed method were validated over a continuous 90-min test period.

Abstract

Recent progress in MEMS resonant-fin-transistors (RFT) has enabled very high-Q active mode resonators, promising crystal-less monolithic clock generation for Millimeter-wave (mmWave) systems. However, there is a strong need for design of mmWave oscillators that utilize the high-Q of active-mode RFT (AM-RFT) optimally, while handling unique challenges such as resonator's low electromechanical transduction. In this brief, we develop a theory and through design and post-layout simulations in 14 nm Global Foundry (GF) process, we show the first active oscillator with AM-RFT at 30 GHz, which improves the fundamental limits of phase noise (PN) and figure-of-merit (FoM) when compared with the oscillators with conventional LC resonators. For AM-RFT with Q-factor of $sim$10k, post layout simulation results show that the proposed oscillator exhibits phase noise of less than -140 dBc/Hz and FoM greater than 228 dBc/Hz at a 1 MHz offset for 30 GHz center frequency, which are 25 dB better than the existing monolithic LC oscillators.

Abstract

This paper presents a novel Transformer (XFMR) design intended for integrated millimeter-wave (mmWave) applications. The XFMR consists of an 8-shaped primary inductor and a dumbbell-shaped secondary inductor. This structure addresses key challenges in on-chip XFMR design by improving electromagnetic interference (EMI) suppression, reducing unwanted signal coupling, and optimizing magnetic field symmetry. Additionally, the geometry enables better layout efficiency and isolation, contributing to improved performance in tightly integrated environments. To demonstrate the practical advantages of the proposed XFMR, it is implemented in a low-voltage, low-phase-noise (PN) LC VCO designed for 23.8-27.2 GHz operation. Simulation results in TSMC 65~nm CMOS technology show a tuning range of 3.4~GHz, a PN lesser than −110 dBc/Hz at 1 MHz offset, and a figure-of-merit (FoM) greater than 186 dBc/Hz, at 0.6 V supply. These results validate the effectiveness of the XFMR in enhancing VCO performance, highlighting its potential for use in high-performance mmWave design.

Abstract

This paper presents the development of a real-time object detection system using Frequency Modulated Continuous Wave(FMCW) millimeter-wave radar signals and the PYNQ-ZU FPGA Board, which is widely used in (advanced driving assistance system)ADAS and robotic applications. A hardware FPGA platform serves as a valid embedded architecture for the purpose of validating object detection and recognition applications. We used our experiment’s point cloud images to apply different machine learning models to detect these objects. Using a top-view (TV) filter to convert 3D point cloud images into 2D representations made object detection more accurate. Following the use of filtration techniques, we extracted features from the filtered 2D image using the VGG 16 model. We then assessed four machine learning models for object detection and found that the Support Vector Machine (SVM) model and Logistic Regression (LR) had better results, obtaining an accuracy of 97%. Our proposed work uses mmWave radar, Top-View Filter, VGG 16 Model, and LR to highly increase object detection accuracy over existing methods.

Abstract

A demonstration of the implementation of vehicle occupancy detection on hardware-software is shown in this paper. For the purpose of validating applications for vehicle occupancy detection, a hardware Field Programmable Gate Array (FPGA) platform, also known as PYNQ-ZU, is a feasible embedded architecture. Automatic in-car occupancy monitoring is an important technology in modern transportation, with major implications for safety, energy efficiency, and smart vehicle management. One of the primary benefits of mmWave radar is its ability to accurately detect the number and location of vehicle occupants, mmWave radar ensures robust detection under all lighting and weather conditions. In our research, the proposed approach was applied to point cloud images. Following the generation of 3D point cloud images, two filters, Top-View (TV), and Front-View (FV), were used to improve vehicle occupancy detection. These filters transformed 3D images into 2D ones. TV filter was found to be more effective than the FV filter. After filtering the 2D images, Mexican Hat Wavelet Transform (MHWT) was used to extract features from them. Four machine learning methods were then used to determine vehicle seat occupancy, with Logistic Regression (LR) and Support Vector Machine (SVM) producing the highest results, with an accuracy of 98%. In comparison to existing methods, the proposed approach, which utilizes mmWave radar, TV Filter, MHWT, FPGA (PYNQ-ZU), and LR, was determined to significantly improve the accuracy of vehicle occupancy detection.