Abstract

Object detection, semantic segmentation, and instance seg-mentation form the bases for many computer vision tasks in autonomous driving. The complexity of these tasks increases as we shift from object detection to instance segmentation. The state-of-the-art models are eval-uated on standard datasets such as pascal-voc and ms-cococ, which do not consider the dynamics of road scenes. Driving datasets such as Cityscapes and Berkeley Deep Drive (bdd) are captured in a structured environment with better road markings and fewer variations in the ap-pearance of objects and background. However, the same does not hold for Indian roads. The Indian Driving Dataset (idd) is captured in un-structured driving scenarios and is highly challenging for a model due toits diversity. This work presents a comprehensive evaluation of state-of-the-art models on object detection, semantic segmentation, and instance segmentation on-road scene datasets. We present our analyses and com- pare their quantitative and qualitative performance on structured driv-ing datasets (Cityscapes and bdd) and the unstructured driving dataset (idd); understanding the behavior on these datasets helps in addressing various practical issues and helps in creating real-life applications.

Abstract

Object detection is a key component in autonomous navigation systems that enables localization and classification of the objects in a road scene. Existing object detection methods are trained and inferred on a fixed number of known classes present in road scenes. However, in real-world or open-world road scenes, while inference, we come across unknown objects that the detection model hasn’t seen while training. Hence, we propose O pen Wo rld Object Detection on Road Scenes (ORDER) to address the aforementioned problem for road scenes. Firstly, we introduce Feature-Mix to improve the unknown object detection capabilities of an object detector. Feature-Mix widens the gap between known and unknown classes in latent feature space that helps improve the unknown object detection. Next, we identify that the road scene dataset compared to generic object dataset contains a significant proportion of small objects and has higher intra-class bounding box scale variations, making it challenging to detect the known and unknown objects. We propose a novel loss: Focal regression loss that collectively addresses the problem of small object detection and intra-class bounding box by penalizing more the small bounding boxes and dynamically changing the loss according to object size. Further, the detection of small objects is improved by curriculum learning. Finally, we present an extensive evaluation on two road scene datasets: BDD and IDD. Our experimental evaluations on BDD and IDD shows consistent improvement over the current state-of-the-art method. We believe that this work will lay the foundation for real-world object detection for road scenes.

Abstract

Reference Evapotranspiration (ET0) is one of the prominent hydrologic variables affecting water and energy balances and critical factors for crop water requirements and irrigation scheduling. evapotranspiration is a complex hydrological variable defined by various climatic variables. Various empirical formulations have been developed to estimate ET0 depending upon the availability of meteorological variables. Such empirical formulations are region-specific and are for particular climatic conditions. In this context, mathematical models have emerged as simple and readily implementable for the estimation of ET0 with measured meteorological parameters as independent variables. Such data-driven models can be valuable to predict ET0 when climate data is insufficient. The present study compared various empirical models and data-driven algorithms to predict ET0 using various climate variables. Artificial neural networks (ANN) were adopted to estimate reference ET0. Four empirical methods Penman-Monteith, Hargreaves, Turc, and Priestley-Taylor were used to estimate ET0 at a daily time scale. Dataset consists of daily meteorological data over a period of 51 years (1965–2015) for Hyderabad, the largest city of the Indian state, Telangana, with semi-arid climate. The input variables for the ANN model consist of maximum and minimum air temperatures, relative humidity, solar radiation, and wind speed. The Penman-Monteith method was considered as the standard method to compare the ANN and various empirical models of ET0. ANN model was trained and tested with climate variables as input variables and various empirical models as reference models. The most influencing climate variables on ET0 were found in the order of temperature, solar radiation, wind speed, and relative humidity based on correlation coefficients. These variables have formed as the basis to choose different datasets to train over ANN model. Validation has been carried out using the coefficient of determination (R2) which is obtained for the training (1965–2000) and testing period (2001–2015) period as 0.97 and 0.96 respectively. Temperature and radiation-based models of Turc and Priestley-Taylor methods can be used to estimate ET0 when all other climate variables are not available as they also correlate well with the Penman-Monteith method. Advancement towards artificial intelligence techniques in water resources engineering has motivated to simulate reference ET0 using limited meteorological variables to produce accurate results. Such data-driven algorithms developed based on standard empirical models can be implemented for prediction with limited climate data.

Abstract

Droughts are recognized as a natural disaster that is caused by extreme and continuous shortage of precipitation. Drought indices assist in a number of tasks, including its early warning and monitoring by computing severity levels and proclaiming the start and end of drought. Various drought indices were formulated for the forecasting and prediction of spatiotemporal drought characteristics using various hydrological variables, such as precipitation, evapotranspiration, runoff, soil moisture content, etc. Due to anthropogenic global warming and increase of temperature, evapotranspiration based drought indices have become interest in recent years in the drought assessment. This paper attempt to provide more information on drought indices which incorporates Evapotranspiration. The study used Standardized Precipitation Evapotranspiration index (SPEI), based on evapotranspiration to understand the drought variability at various time scales. This study adopted Hargreaves model to calculate Potential Evapotranspiration. SPEI index can also be used to study the wet and dry periods including Evapotranspiration along with precipitation. The study used SPEI to understand the dry and wet years over an urban semi-arid region, Hyderabad, capital and biggest city of the southern Indian state of Telangana for the year 1965 to 2015. The years 1965, 1966, 1972, 1973, 1985, 1993 and 2012 were noted as dry years with SPEI values as -1.35,-1.06,-1.43,-1.31,-1.05, -1.22 and -1.51 sequentially and year 2006 as severe wet year with SPEI value as +1.65. The characterization of dry and wet years as demonstrated in the present study will enhance the better urban water resources management.

Abstract

The nationwide lockdown was imposed over In- dia from 25 March to 31 May 2020 with varied relax- ations from phase I to phase IV to contain the spread of COVID-19. Thus, emissions from industrial and transport sectors were halted during lockdown (LD), which has re-sulted in a significant reduction of anthropogenic pollutants. The first two lockdown phases were strictly implemented (phase I and phase II) and hence were considered to be total lockdown (TLD) in this study. Satellite-based tropospheric columnar nitrogen dioxide (TCN) from the years 2015 to 2020, tropospheric columnar carbon monoxide (TCC) dur-ing 2019/20, and aerosol optical depth (AOD550) from the years 2014 to 2020 during phase I and phase II LD and pre- LD periods were investigated with observations from Aura OMI, Sentinel-5P TROPOMI, and Aqua and Terra MODIS. To quantify lockdown-induced changes in TCN, TCC, and AOD550, detailed statistical analysis was performed on de-trended data using the Student paired statistical t test. Re-sults indicate that mean TCN levels over India showed a dipof 18 % compared to the previous year and also against the 5-year mean TCN levels during the phase I lockdown, which was found to be statistically significant (p value < 0.05) against the respective period. Furthermore, drastic changes in TCN levels were observed over hotspots, namely eastern region and urban cities. For example, there was a sharp de-crease of 62 % and 54 % in TCN levels compared to 2019 and against 5-year mean TCN levels over New Delhi with a p value of 0.0002 (which is statistically significant) dur-ing total LD. The TCC levels were high in the northeast (NE) region during the phase I LD period, which is mainly attributed to the active fire counts in this region. However, lower TCC levels are observed in the same region due to the diminished fire counts during phase II. Further, AOD550 is reduced over the country by ∼ 16 % (Aqua and Terra) from the 6-year (2014–2019) mean AOD550 levels, with a signifi-cant reduction (Aqua MODIS 28 %) observed over the Indo- Gangetic Plain (IGP) region with a p value of  0.05. How-ever, an increase in AOD550 levels (25 % for Terra MODIS, 15 % for Aqua MODIS) was also observed over central India during LD compared to the preceding year and found signif-icant with a p value of 0.03. This study also reports the rate of change of TCN levels and AOD550 along with statistical metrics during the LD period.

Abstract

How bad is my wound? How fast will the wound heal? Do I need to get hospitalized? Questions like these are critical for wound assessment, but challenging to answer. Given a wound image and patient attributes, our goal is to build models for two wound assessment tasks: (1) predicting if the patient needs hospitalization for the wound to heal, and (2) estimating wound progression, i.e., weeks to heal. The problem is challenging because wound progression and hospitalization risk depend on multiple factors that need to be inferred automatically from the given wound image. There exists no work which performs a rigorous study of wound assessment tasks considering multiple wound attributes inferred using a large dataset of wound images. We present HealTech, a two-stage wound assessment solution. The first stage predicts various wound attributes (like ulcer type, location, stage, etc.) from wound images, using deep neural networks. The second stage predicts (1) whether the wound would heal (using conventional in-house treatment) or not (needs hospitalization), and (2) the number of weeks to heal, using an evolutionary algorithm based stacked Light Gradient Boosted Machines (LGBM) model. On a large dataset of 125711 wound images, HealTech achieves a recall of 83 and a precision of 92 for wounds with the risk of hospitalization. For wounds that can be healed without hospitalization, precision and recall are as high as 99. Our wound progression model provides a mean absolute error of 3.3 weeks.

Abstract

Performing sound source separation and visual object seg- mentation jointly in naturally occurring videos is a notori- ously difficult task, especially in the absence of annotated data. In this study, we leverage the concurrency between audio and visual modalities in an attempt to solve the joint audio-visual segmentation problem in a self-supervised man- ner. Human beings interact with the physical world through a few sensory systems such as vision, auditory, movement, etc. The usefulness of the interplay of such systems lies in the con- cept of degeneracy [1]. It tells us that the cross-modal signals can educate each other without the presence of an external supervisor. In this work, we efficiently exploit this fact that learning from one modality inherently helps to find patterns in others by introducing a novel audio-visual fusion technique. Also, to the best of our knowledge, we are the first to ad- dress the partially occluded sound source segmentation task. Our study shows that the proposed model significantly out- performs existing state-of-the-art methods in both visual and audio source separation tasks

Abstract

Estimation of time delay from the received broadband signals like speech, collected at two or more spatially distributed microphones, has many applications. Methods like the cross-correlation of the signals directly and generalized crosscorrelation based methods (GCC and GCC-PHAT) have been used for several years to estimate the time delay. Performance of these methods degrades due to noise, multi-path reflections, and reverberation in a practical environment, like a live room. The estimated time delay is usually robust due to the averaging effect of the delay obtained over several frames in an utterance of a few seconds. The robustness is affected if the varying time delay of a moving speaker is desired. A smaller duration for averaging results in errors in the estimation of the time delay, and a longer duration for averaging results in loss of accuracy. Since the single frequency filtering (SFF) based analysis provides an estimation of the instantaneous time delay, it is possible to study the trade off between accuracy and robustness. This paper examines this trade-off in determining the number of stationary speakers from mixed signals and in tracking a speaker moving along a straight line path and along a circular path. The results are illustrated for actual data collected in a live room.

Abstract

Humans overcome the degradation effect of background noise on the speech by changing their vocal characteristics dynamically, which text-to-speech systems trained on clean speech cannot, re- sulting in degraded intelligibility. To improve the intelligibility of such system requires a large amount of speech samples, which is difficult to collect for various conditions like noise backgrounds and signal-to-noise ratios. This paper presents a noise dependent enhancement to text-to-speech, based on generative adversarial network training framework to generate intelligible speech in noise. The learning mechanism for the synthesizer network is inspired from the acoustic feedback humans use to nullify the effect of vari- ous background noises. The system thus trained is evaluated under cafeteria noise condition with two objective measures, which indi- cated improvement in intelligibility compared to models trained on clean speech across 3 SNRs. The proposed modification does not require any additional training data and can be applied to a variety of Deep Neura

Abstract

We propose an all-CMOS, supply independent, second-order temperature compensated, process invariant, 10.09 μA current reference circuit working on a 1.8 V power supply. The current reference is built using an adaptive regulated cascode configuration integrated with a Widlar current source. It is implemented in 180 nm UMC MPW RF process and consumes an area of 0.014 mm 2 . It has a very low line sensitivity of 0.52 %/V and a temperature coefficient of 9.94 ppm/°C over the automotive temperature range of −40°C to 125°C. The proposed current reference is used to build a high quality, power efficient stable ring oscillator circuit working at 377.9 MHz frequency to be used in IoT SoCs. The ring oscillator shows a variation of less than 1.1 % in frequency, with supply voltage over the range 1.5 V to 2 V. The invariance of frequency with supply voltage can be exploited to reduce the power consumption up to 36.93 % in the oscillator circuit.