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

No Results Found

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.

Abstract

Modern transceiver integrated circuits used in wireless communication systems seek low power wide frequency range receiver front-ends (RxFE) to support multiple communication bands. For this, RxFE leveraging passive N-path mixer first approach are becoming progressively popular due to their low power nature. However, N-path RxFE requires N precise phases of a local oscillator (LO) to control mixer operation, which becomes challenging as the frequency of operation increases (> 10 GHz). In this paper, we give useful insights and present design considerations for the faithful generation of different non- overlapping phases of LO to achieve wide band RF input match. We also present the design and post-layout simulation results of a 4-path mixer based 8-20 GHz RxFE with programmable gain of 10 to 29 dB in TSMC 65 nm CMOS technology. In post-layout simulations, the proposed design achieves an IIP3 of > -10 dB, S11 of < -12 dB and consumes total power of 66 mW from 1.2 V supply.

Abstract

Respiratory diseases contribute to a majority of deaths worldwide every year. Diseases such as asthma, bronchitis and pneumonia also adversely impact a person’s social and economic conditions. They can seriously threaten their health if left undiagnosed and untreated. Techniques such as auscultation are used in the diagnosis of most respiratory diseases. However, using such techniques requires an experienced physician and the diagnosis is subjective. To overcome these challenges, in this work, a portable handheld system has been proposed and a proof of concept implemented to detect and classify respiratory diseases automatically through the use of convolutional neural networks (CNN) running on mobile platforms. Mel spectrograms are generated from the audio signals and fed to the CNN for classification. This work makes use of the publicly available HF Lung dataset. The classification accuracy achieved by the current implementation using a Raspberry Pi for processing is 80.55% with a sensitivity of 95.65% and specificity of 98.80% on the HF Lung dataset.

Abstract

Recently, there has been a major shift in advanced driver assistance systems (ADAS) from the camera and LiDAR-oriented systems to mmWave radar-based systems. Such high-frequency band radars currently using frequency modulated continuous wave (FMCW) technique require programmable dividers with large divide ratios and fine frequency resolution to obtain high-frequency chirps with sufficiently large bandwidth. This paper presents a low-power multi-modulus programmable frequency divider for a frequency synthesizer operating in the 19.25-20.25 GHz frequency band from a reference frequency of 40 MHz. The divider consumes 14 mW of power and is capable of producing divide ratios in the range of 481.25-506.25 with a fine resolution of 1 MHz. The divider has a phase noise of −146 dBc/Hz at an offset of 2 kHz.

Abstract

Recent developments in advanced driver assistance systems (ADAS) have raised the demands of mmWave radars in 24 GHz and 77 GHz band. For higher accuracy and precision, frequency modulated continuous wave (FMCW) technique has become very popular for mmWave radars, which requires low phase noise and high bandwidth chirp frequency synthesizers. In this work, we present analysis and design of a low phase noise, transformer tank based mmWave voltage controlled oscillator (VCO) near 20 GHz for multiplier based 77 GHz FMCW chirp synthesizer. The proposed VCO design is implemented in 65 nm CMOS technology. Post layout simulations show that the proposed VCO exhibits a figure-of-merit is 192 dBc/Hz and phase noise of -117.98 dBc/Hz at 1 MHz offset while operating at 18.94 GHz and achieves a tuning range of 1.42 GHz (18.94-20.36 GHz), while consuming a power of 13.78 mW from 1.1 V supply.

Abstract

This work introduces an ultra low power gm-boosted Colpitts voltage controlled oscillator (VCO) with a transformer based tank operating near 20 GHz. The proposed VCO is implemented in TSMC 65 nm CMOS technology. Through postlayout simulations, it is demonstrated that the proposed VCO exhibits a remarkable figure-of-merit of 196.26 dBc/Hz and achieves a phase noise of -111.21 dBc/Hz at a 1 MHz offset while operating at 19.6 GHz. Additionally, the VCO provides a tuning range of 1.7 GHz (18 - 19.7 GHz) and consumes only 1.2 mW of power from a 1 V supply.