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.

Abstract

The demand for contactless heartbeat monitoring systems is rapidly increasing, particularly in healthcare settings where minimizing physical contact is crucial. Conventional methods often require subjects to remain stationary to ensure accurate measurements, which restricts their use in dynamic or realworld scenarios. In this paper, we present a novel system that utilizes Texas Instruments’ 77 GHz AWR1843BOOST FMCW radar technology to circumvent this constraint. By mitigating the effects of motion artifacts, the system enables accurate heartbeat detection even when the subject is in motion. Implemented on the PYNQ-Z2 system-on-chip (SOC) platform, the system achieves an accuracy of 82.78%, providing a more robust and portable embedded solution for real-time, non-invasive heart monitoring in a dynamic environments.

Abstract

In this paper, we propose an FPGA based system for non-contact monitoring of respiration rates (RR) using a mmWave frequency modulated continuous wave (FMCW) radar. The novel FPGA based hardware architecture presented in this paper classifies normal and abnormal RR with a Support Vector Machine (SVM) model using mmWave radar-based sensing. SVM with various kernels are evaluated in order to identify the most effective approach for respiratory rate (RR) classification. SVM with a quadratic polynomial kernel shows a high classification accuracy of 95%. While the accuracy was comparable to other kernels, the quadratic polynomial kernel achieved a substantial reduction in the number of support vectors and other parameters. This optimisation results in lower resource utilisation and power consumption, making it highly efficient for FPGA implementa- tion.

Abstract

This letter presents the development of a real-time object detection system using frequency modulated continuous wave millimeter-wave (mmWave) radar signals and the python productivity for zynq ultrascale + mpsocs (PYNQ-ZU) field-programmable gate array (FPGA) board, which is widely used in advanced driving assistance system 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 3-D point cloud images into 2-D representations made object detection more accurate. Following the use of filtration techniques, we extracted features from the filtered 2-D image using the visual geometry group (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, TV filter, VGG 16 Model, and LR to highly increase object detection accuracy over existing methods.