Abstract

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).

Abstract

Wireless body area network (WBAN) communica- tions in 400 MHz Medical Device Radio-communication (MedRa- dio) band seek wearable and implantable sensor nodes with extremely small antennas for reduced form factor. However, it is very challenging to shrink the 400 MHz antenna dimensions to sub-cm region for the conventional 50 Ω transmitters (TX) due to large wavelength. Thus there is a strong need to investigate extremely small, unconventional radiating elements with alternate TX topologies. In this work, we utilize extremely small surface mount inductors (SML) as a radiating element in non-50 Ω MedRadio band TX and present detailed fundamental analysis and models for its radiating-zone boundaries and corresponding path losses. To validate the proposed theory, simulation and measurement results of path loss of a cylindrical SML (1.2mm × 8.5mm) have been also presented, which show congruence with analytical models within ≤ 3 dB.

Abstract

Phase-unwrapping (PU) is a critical signal processing step used in fields such as radar, sonar, and laser technology. This paper presents a faster and more efficient VLSI architecture for implementing the phase-unwrapping algorithm on FPGA platforms. To evaluate the effectiveness of the proposed FPGAbased VLSI architecture for the phase-unwrapping algorithm in chest wall vibration sensing, we compared its performance with both a PYNQ board implementation and an equivalent software solution. The results are compared in terms of speed, resource utilization, and power consumption. Additionally, prototypes were developed on both the ZedBoard and PYNQ board, and the outcomes were physically verified. The proposed VLSI architecture achieves nearly 70× and 111× speed-up compared to software-based solutions and PYNQ board implementations, respectively.

Abstract

These days, autonomous vehicles are equipped with a variety of sensors that allow them to sense their environment and control several functions. In particular, if a child is left alone in a parked car, the passenger occupancy detection system can detect it. Traditionally, camera sensors have been used to detect vehicle occupancy. Cameras perform well in bright light but struggle in low light. To address these limitations, we opted to detect vehicle occupancy using millimeter-wave radar which operates accurately regardless of lighting conditions. Figure 1 shows the pipeline of the proposed algorithm. Figure 2 depicts the setup for Vehicle occupancy Detection with FMCW radar, specifically the Texas Instruments AWR1843 booster pack evaluation board, which operates in the 76-81 GHz band. Figure 3 depicts vehicle occupancy detection images.

Abstract

In this work, we introduce an innovative approach to estimate the vital signs of multiple human subjects simultaneously in a non-contact way using a Frequency Modulated Continuous Wave (FMCW) radar-based system. Traditional vital sign monitoring methods often face significant limitations, including subject discomfort with wearable devices, challenges in calibration, and the risk of infection transmission through contact measurement devices. To address these issues, this research is motivated by the need for versatile, non-contact vital monitoring solutions applicable in various critical scenarios. This work also explores the challenges of extending this capability to an arbitrary number of subjects, including hardware and theoretical limitations. Supported by rigorous experimental results and discussions, the paper illustrates the system’s potential to redefine vital sign monitoring. An FPGA-based implementation is also presented as proof of concept for a hardware-based and portable solution, improving upon previous works by offering 2.7x faster execution and 18.4% less Look-Up Table (LUT) utilization, as well as providing over 7400x acceleration compared to its software counterpart.

Abstract

Identifying correct human postures is crucial in areas like patient care in hospitals. However, the traditional vision-based methods widely used for this purpose raise privacy concerns for the subject, and the other wearable sensor-based approaches are impractical for real-world scenarios. In this paper, we propose a contactless, privacy-conscious, and memoryefficient posture classification system based on millimeter wave (mmWave) radar. This system utilizes threedimension(3D) point-cloud data captured using Texas Instrument’s IWR1843BOOST Frequency Modulated Continuous Wave (FMCW) radar module to classify the posture of the subject. Two types of datasets are extracted from this radar data: (i) image dataset derived from the isometric view of the point-cloud data, and (ii) spatial coordinates dataset also extracted from the point-cloud data. A low-computational Tiny Machine Learning (TinyML) model is employed on the datasets for efficient implementation on embedded hardware, Raspberry Pi 3 B+. The proposed model’s parameters were quantized to 8 bits (int8), which accurately classify four postures, i.e., standing, sitting, lying, and bending, with an accuracy of 98.97% for the image data. However, to make it more computationally efficient, the int8 quantized TinyML model was trained on the spatial coordinates dataset, giving an accuracy of 96.12%. This highlights the efficiency and effectiveness of our proposed lightweight model that can be deployed on edge devices for real-world applications.

Abstract

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Abstract

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.

Abstract

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-4, 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.

Abstract

Efficient and reliable non-invasive methods for micro-organism detection have significant implications in various fields such as medical diagnostics, environmental monitoring, and food safety. Existing detection techniques, such as culture-based methods, polymerase chain reaction (PCR) have limitations including time-consuming processes, expensive equipment requirements and invasive sampling procedures. These drawbacks emphasize the need for alternate detection techniques that are non-invasive, rapid, sensitive, specific, and user-friendly. For this purpose, in this research, we present a portable integrated system for micro-organism detection, which is based on electrochemical impedance spectroscopy (EIS). The proposed system includes Zinc Oxide (ZnO) nanorods based interdigitized electrode (IDE) sensor and an EIS circuit. This work also presents the considerations and methodology for the fabrication of ZnO nanorods with high-yield. To validate the efficacy of the proposed methods, experimental results through scanning electron microscope are also presented. Finally, measurement results for detection of a micro-organism (yeast) is also presented to demonstrate the utility and effectiveness of the proposed methods and system.