Reliable road object detection is crucial for self-driving cars and intelligent transportation systems. However, vision-only techniques fail in low-visibility conditions such as fog, rain, or nightfall, whereas deep radar-based models frequently suffer from high latency and energy consumption. To overcome these restrictions, we propose a millimeter-wave (mmWave) radar-based object identification system based on a PYNQ-ZU (Field-Programmable Gate Array) FPGA that achieves a compromise between accuracy, efficiency, and real-time performance. Our proposed pipeline transforms 3D radar point clouds to 2D Top-View (TV) representations and uses the Spectral Graph Wavelet Transform (SGWT) to extract discriminative spatialtemporal features with little computational cost. A Random Forest (RF) classifier achieves 97% accuracy across seven object classes with an end-to-end latency of 61 ms. When it comes to power performance, the FPGA implementation uses 119.32 mW at idle, 268.48 mW on average, and 357.97 mW at its peak, demonstrating its energy efficiency under dynamic workloads. Compared with deep learning alternatives like Tiny Convolutional Neural Network (CNN) (73.60 MB, 4493 mW, 449 ms), the model size is 6.47 MB, which is substantially smaller and more power-efficient. By overcoming the limitations of vision-only systems (poor visibility) and deep radar models (high latency, energy, and memory), the proposed SGWT + RF framework enables real-time, multi-class detection in resource-constrained environments—offering a practical and robust solution for autonomous navigation and traffic monitoring applications.

This paper presents an innovative approach to respiration-rate or breath-rate (BR) measurement using Texas Instruments’ 77 GHz AWR1843BOOST FMCW radar, addressing challenges posed by motion artifacts and external interference. The system employs the Fast Fourier Transform (FFT) for range estimation, followed by phase extraction and unwrapping for accurate respiratory signal capture. Beyond traditional bandpass filtering (BF), advanced signal processing methods, viz. Wavelet Denoising (WD), Savitzky-Golay Filtering (SGF), and Kalman Filtering (KF) are comparatively analyzed. SGF reduces root mean square error (RMSE) in BR by 40%, excelling at noise reduction at short ranges. WD lowers RMSE by 30% at longer ranges, while KF offers the best computational efficiency. These techniques improve non-contact BR measurement in the presence of motion artifacts.

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 implementation.

This paper presents a novel approach to the design of Delta-Sigma Modulators (DSMs) for Fractional-N Phase Locked Loop (PLL) Frequency Synthesizers by incorporating an approximate adder characterized by near-normal error distribution. Traditional Frequency Synthesizers utilize DSMs that rely on precise adders and Pseudo-Random Binary Sequence (PRBS) generators to provide modulus inputs to multi-modulus frequency dividers for achieving Fractional-N Frequency Synthesis. The inclusion of PRBS is critical in introducing randomness in the DSM’s output, thereby mitigating frequency spurs in the output of the synthesizer. In contrast, the proposed DSM design leverages an approximate adder with near-normal error distribution to introduce the required randomness, eliminating the dependency on PRBS. This innovative design leads to substantial improvements in power efficiency, area, processing speed and improves the performance of the synthesizer. The proposed design demonstrates a reduction of approximately 44.7% in power consumption and 39.73% in area compared to conventional DSMs combined with PRBS generators when implemented in 65nm TSMC technology. The proposed design achieves comparable accuracy in output frequency, maintains similar Signal-to-Noise Ratio (SNR) and Effective Number of Bits (ENOB), and shows improvement in Spurious-Free Dynamic Range (SFDR).

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 real-world 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.

This paper presents the design and implementation of a hardware system for real-time object detection and range estimation utilizing Frequency-Modulated Continuous Wave (FMCW) millimeter wave radar signals, which are commonly used in Advance Driver Assistance Systems (ADAS) and robotic applications. The proposed system utilizes the Fast Fourier Transform (FFT) algorithm to process the FMCW radar signal for estimating the range of an object. For developing a resource-efficient and low latency range-estimation hardware, this paper also presents a comparative analysis for the implementation of three popular FFT architectures (iterative Radix- 2, iterative Radix-22, and pipelined Radix-2) on FPGA. Based on the presented analysis the lowest latency range-estimation architecture has been chosen for FFT implementation and a system prototype is developed as a proof-of-concept by integrating the commercially available Texas Instruments’ 77 GHz AWR1642BOOST FMCW radar module with a FPGA to host the chosen FFT architecture. Measurement results of the proposed system hardware are also presented in this paper, which shows > 99.21% range-estimation accuracy.

Human posture analysis has gained a significant research interest in the recent times. It helps in many applications such as gait analysis for detecting neurological disorders, fall detection of elderly people, and continuous monitoring of severely ill patients. Camera-based vision systems are commonly employed for detecting human postures; however, they cause concerns over the subjects’ privacy. To address this challenge, we present a millimeter wave (mmWave) radar-based, truly non-contact, non-intrusive, and privacy-conscious posture detection and classification system in this research. The proposed system utilizes three-dimensional point cloud data of the subject to comprehensively classify body postures, capturing intricate real-time details. In this work, we also present a custom-designed Convolutional Neural Network (CNN) and its comparison with other models, which are conventionally used for posture classification. We also demonstrate the hardware implementation of the proposed system and present the measurement results using Texas Instruments’ IWR1843BOOST radar module. The proposed CNN model achieves an accuracy of 97.10% while classifying standing, sitting, lying and bending postures.