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.

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.

Recently, diamond color defect based quantum sensing applications such as nitrogen-vacancy (NV) center magnetometry have emerged in CMOS technology, which use optically detected magnetic resonance (ODMR) for sensing magnetic field strengths $(|\tilde{\mathrm{B}}|)$ from different environmental physical quantities. For ODMR based sensing, CMOS quantum sensors seek an onchip 2.87 GHz microwave (MW) signal generator. Moreover, in order to sense smaller $|\tilde{\mathrm{B}}|$, these CMOS quantum sensors also require that MW signal should be swept with sufficiently small step-size near 2.87 GHz. In this work, we present a fractional-N synthesizer based 2.87 GHz MW-generator (MWG) with an extremely small programmable sweep step-size for improved sensitivity of $|\tilde{\mathrm{B}}|$ measurements in CMOS NV magnetometry. The proposed MWG is implemented in 180 nm CMOS technology and simulations were done to validate the proposed design. Post-layout simulation results show that the proposed MWG achieves a minimum sweep-step size of 50 kHz, which can be used to sense $|\tilde{\mathrm{B}}|\lt 0.9\;\mu \mathrm{T}$ and exhibits a phase noise of −114.5 dBc/Hz at an offset of 1 MHz near 2.87 GHz center frequency.

This paper presents a low-power integrated wireless telemetry system (Bio-WiTel) for healthcare applications in 401-406 MHz frequency band of medical device radiocommunication (MedRadio) spectrum. In this paper, necessary design considerations for telemetry system for short-range (upto 3 m) communication of biosignals are presented. These considerations help greatly in making important design decisions, which eventually lead to a simple, low power, robust, and reliable wireless system implementation. Transmitter (TX) and receiver (RX) of Bio-WiTel system have been fabricated in 180 nm mixed mode CMOS technology. While radiating -18 dBm output power to a 50 antenna, the packaged TX IC consumes 250 μW power in 100% on state from 1 V supply, whereas the RX IC consumes 990 μW power from 1.8 V supply with a sensitivity of -75 dBm. Measurement results show that TX fulfils the spectral mask requirement at a maximum data rate of 72 kb/s. The measured bit error rate (BER) of RX is less than for a data rate of 200 kb/s. The proposed Bio-WiTel system is tested successfully in home and hospital environments for the communication of electrocardiogram and photoplethysmogram signals at a data rate of 57.6 kb/s with a measured BER of <10 for a maximum distance of 3 m.

In this paper, we are presenting a novel programmable gain and bandwidth instrumentation amplifier (PGB-INA) for biomedical applications. By virtue of its programmable gain and bandwidth, it can measure various bio-potentials such as ECG, EMG, EOG, etc. The proposed PGB-INA also features an extremely low input referred noise (1.34 μVrms within the integration bandwidth of 50 mHz to 11 kHz) and high CMRR (84 dB). The design is fabricated in 180 nm mixed-mode CMOS technology. The PgB-INA provides a measured programmable gain and bandwidth of 30 to 40 dB and 100 Hz to 2.7 kHz, respectively. The PGB-INA occupies die area of only 150 μm × 200 μm and consumes 18.3 μΑ current from 1.8 V supply. Various bio-potentials are measured using the proposed PGB-INA such as ECG, EMG and EOG which are also presented in the paper.

A novel receiver front-end is proposed for FM/FSK modulated signal, which is transmitted from a body worn device in the MedRadio spectrum of 401–406 MHz. The front-end comprises a low noise amplifier (LNA), a zero crossing detector (ZCD) and a frequency to digital converter (FDC). The proposed architecture offers direct digitization of the input signal and is immune to LO leakage and problem of image frequencies. Designed in 180 nm mixed mode CMOS technology, the worst case post layout simulation results show a sensitivity of −80 dBm at 200 kbps for FSK modulated input with only 990 μW power consumption.