Abstract

The COVID-19 pandemic has underscored the need for low cost, scalable approaches to measuring contactless vital signs, either during initial triage at a healthcare facility or virtual telemedicine visits. Remote photoplethysmography (rPPG) can accurately estimate heart rate (HR) when applied to closeup videos of healthy volunteers in well-lit laboratory settings. However, results from such highly optimized laboratory studies may not be readily translated to healthcare settings. One significant barrier to practical application of rPPG in healthcare is accurate localization of region of interest (ROI). Clinical or telemedicine visits may involve sub-optimal lighting, movement artifacts, variable camera angle, and subject distance. This paper presents an rPPG ROI selection method based on 3D facial landmarks and patient head yaw angle. We then demonstrate robustness of this ROI selection method when coupled to Plane-Orthogonal-to-Skin (POS) rPPG method when applied to videos of patients presenting to an Emergency Department for respiratory complaints. Our primary contributions are twofold: (1) a robust ROI selection framework that adapts to real-world clinical scenarios, and (2) first unrestricted rPPG dataset collected from emergency ward settings, addressing critical gaps between controlled laboratory conditions and real-world clinical environments. Our results demonstrate effectiveness of our proposed approach in improving accuracy and robustness of rPPG in a challenging clinical environment.

Abstract

Supervised machine learning methods for geological mapping via remote sensing face limitations due to the scarcity of accurately labelled training data that can be addressed by unsupervised learning, such as dimensionality reduction and clustering. Dimensionality reduction methods have the potential to play a crucial role in improving the accuracy of geological maps. Although conventional dimensionality reduction methods may struggle with nonlinear data, unsupervised deep learning models such as autoencoders can model non-linear relationships. Stacked autoencoders feature multiple interconnected layers to capture hierarchical data representations useful for remote sensing data. We present an unsupervised machine learning-based framework for processing remote sensing data using stacked autoencoders for dimensionality reduction and k-means clustering for mapping geological units. We use Landsat 8, ASTER, and Sentinel-2 datasets to evaluate the framework for geological mapping of the Mutawintji region in Western New South Wales, Australia. We also compare stacked autoencoders with principal component analysis (PCA) and canonical autoencoders. Our results reveal that the framework produces accurate and interpretable geological maps, efficiently discriminating rock units. The results reveal that the combination of stacked autoencoders with Sentinel-2 data yields the best performance accuracy when compared to other combinations. We find that stacked autoencoders enable better extraction of complex and hierarchical representation of the input data when compared to canonical autoencoders and PCA. We also find that the generated maps align with prior geological knowledge of the study area while providing novel insights into geological structures.

Abstract

Air quality estimation through sensor-based methods is widely used. Nevertheless, their frequent failures and maintenance challenges constrain the scalability of air pollution monitoring efforts. Recently, it has been demonstrated that air quality estimation can be done using image-based methods. These methods offer several advantages including ease of use, scalability, and low cost. However, the accuracy of these methods hinges significantly on the diversity and magnitude of the dataset utilized. The advancement of air quality estimation through image analysis has been limited due to the lack of available datasets. Addressing this gap, we present TRAQID - Traffic-Related Air Quality Image Dataset, a novel dataset capturing 26,678 front and rear images of traffic alongside co-located weather parameters, multiple levels of Particulate Matters (PM) and Air Quality Index (AQI) values. Spanning over multiple seasons, with over 70 hours of data collection in the twin cities of Hyderabad and Secunderabad, India, the TRAQID offers diverse day and night imagery amid unstructured traffic conditions, encompassing six AQI categories ranging from “Good” to “Severe”. State-of-the-art air quality estimation techniques, which were trained on a smaller and less-diverse dataset, showed poor results on the dataset presented in this paper. TRAQID models various uncertainty types, including seasonal changes, unstructured traffic patterns, and lighting conditions. The information from the two views (front and rear) of the traffic can be combined to improve the estimation performance in such challenging conditions. As such, the TRAQID serves as a benchmark for image-based air quality estimation tasks and AQI prediction, given its diversity and magnitude.

Abstract

Crack segmentation plays a crucial role in ensuring the structural integrity and seismic safety of civil structures. However, existing crack segmentation algorithms encounter challenges in maintaining accuracy with domain shifts across datasets. To address this issue, we propose a novel deep network that employs incremental training with unsupervised domain adaptation (UDA) using adversarial learning, without a significant drop in accuracy in the source domain. Our approach leverages an encoder-decoder architecture, consisting of both domain-invariant and domain-specific parameters. The encoder learns shared crack features across all domains, ensuring robustness to domain variations. Simultaneously, the decoder's domain-specific parameters capture domain-specific features unique to each domain. By combining these components, our model achieves improved crack segmentation performance. Furthermore, we introduce BuildCrack, a new crack dataset comparable to sub-datasets of the well-established CrackSeg9K dataset in terms of image count and crack percentage. We evaluate our proposed approach against state-of-the-art UDA methods using different sub-datasets of CrackSeg9K and our custom dataset. Our experimental results demonstrate a significant improvement in crack segmentation accuracy and generalization across target domains compared to other UDA methods - specifically, an improvement of 0.65 and 2.7 mIoU on source and target domains respectively. Code, models, and dataset will be made available.

Abstract

Low Quiescent Current, Capacitor-Less LDO with Adaptively Biased Power Transistors and Load Aware Feedback Resistance

Abstract

This paper presents a sub-1V, 593 pA current reference without using resistors and amplifiers. Temperature compensation is achieved by incorporating a CTAT gate to source voltage and a PTAT drain to source voltage in an NMOS-based sub-threshold biased circuit. The proposed architecture achieves process variation of ±0.75%, which enables seamless trim-free operation. Implemented in the 90nm CMOS process, the temperature-compensated current reference achieves a temperature coefficient of 378 ppm/°C over a high-temperature range of −40 to 100°C and a line sensitivity of 0.198%/V in a supply voltage range from 0.8 to 2.5 V. Duty cycling technique is adopted in the CTAT generator to reduce the total average power consumption. The proposed circuit occupies an area of 0.074 mm 2 while consuming only 3.3 nW at a typical corner of 27 °C and 0.8 V supply.

Abstract

The prevalence of MOSFETs in diverse electronic applications highlights their significance. However, the sensitivity of MOSFET threshold voltage to process variation poses a substantial challenge in achieving consistent device performance. This paper proposes a novel approach to overcome this challenge by employing a threshold voltage (VTH) tracking circuit that tracks the variation in VTH. Our primary objective is to develop a current reference that remains invariant across process, voltage, and temperature (PVT) variations, eliminating the need for an external trimming circuit. This is achieved by the meticulous integration of VTH tracking circuit, CTAT generator, PTAT generator, and a current adder. By employing this design,a process variation (σ/µ) of 0.48%, a temperature coefficient (TC) of 210ppm/ ◦C for a temperature range of −40◦C to 120◦C, and a line sensitivity of 1.6% is achieved over a voltage range of 1.6V − 3V. The proposed circuit occupies an area of 0.013mm2 while consuming power of 56µW at a typical corner of 27◦C and a supply of 1.8V. The given circuit is implemented in a 180nm CMOS process node with a generated reference current of 61nA.

Abstract

This paper presents a new approach to efficient process monitor (PMON) design. It focuses on robust process tracking of PMOS and NMOS devices while minimizing the effects of external factors such as die temperature variations and local supply voltage changes. The proposed design method introduces a comprehensive set of PMON libraries tailored for the specific device types provided by a target technology node. Central to this method is a novel design automation routine based on Zero Temperature Coefficient (ZTC) biasing of a ring oscillator-based PMON. This work demonstrates the effectiveness and scalability of the proposed method across multiple process nodes. The generated PMON structures show a multi-fold improvement in sensitivity for process tracking compared to current state-of-the-art solutions.

Abstract

This paper presents a low-voltage, low-power PVT-invariant voltage reference with excellent line sensitivity for IoT and biomedical applications. By applying a bias current in an NMOS-based composite pair and bias voltage at the body of NMOS to get the temperature-compensated voltage reference over a wide temperature range. The proposed design implemented in a 180nm CMOS process gives an output of 141mV which is independent of process, voltage, and temperature. Without trimming, the process variation of the proposed design is 0.988% , and the temperature coefficient of the proposed voltage reference is over a wide temperature range of to . For a supply voltage ranging from 0.6V the line sensitivity of the reference is 0.0012%N.

Abstract

Designing robust performance models for modern complex digital circuits in the face of rapidly accelerating process variations is a critical yet demanding task. This paper introduces an efficient statistical performance modeling approach for VLSI digital circuits that incurs minimal computational expense. The fundamental concept involves capitalizing on knowledge gained from circuit modeling in one technology node to streamline the modeling process in another. This is achieved by merging previously established statistical models of process technology with a limited set of simulation data from a subsequent process technology through transfer learning. Comprehensive experiments conducted across diverse technology nodes demonstrate that the proposed framework is robust, precise, efficient in data usage, and computationally superior to other cutting-edge performance modeling techniques.