Abstract

This article presents ‘Teledrive’, a telepresence robotic system with embodied AI features that empowers an operator to navigate the telerobot in any unknown remote place with minimal human intervention. We conceive Teledrive in the context of democratizing remote ‘care-giving’ for elderly citizens as well as for isolated patients, affected by contagious diseases. In particular, this paper focuses on the problem of navigating to a rough target area (like ‘bedroom’ or ‘kitchen’) rather than pre-specified point destinations. This ushers in a unique ‘AreaGoal’ based navigation feature, which has not been explored in depth in the contemporary solutions. Further, we describe an edge computing-based software system built on a WebRTC-based communication framework to realize the aforementioned scheme through an easy-to-use speech-based human-robot interaction. Moreover, to enhance the ease of operation for the remote caregiver, we incorporate a ‘person following’ feature, whereby a robot follows a person on the move in its premises as directed by the operator. Moreover, the system presented is loosely coupled with specific robot hardware, unlike the existing solutions. We have evaluated the efficacy of the proposed system through baseline experiments, user study, and real-life deployment.

Abstract

Cooling towers are commonly used in commercial, industrial, and hospital buildings for Heating, Ventilating, and Air Conditioning requirements. In general, they are mounted on roof of buildings and designed as Non-Structural Elements (NSE). Further, for minimum transfer of vibrations during strong earthquake shaking to the supporting buildings, vibration isolators are used. While experimental test results provide realistic seismic demand estimates, numerical analysis help predict demands reasonably well if numerical modelling and nonlinear analysis are carried out using realistic assumptions of structure and its behaviour. This study is an attempt in this direction, considers a cooling tower from past shake table test, and presents numerical modelling and pertaining seismic response investigations. For the numerical study, complete coupled modelling (cooling tower and the building modelled together) is carried out in commercial software SAP2000, and decoupled modelling (cooling tower only) in ABAQUS. And, nonlinear time history analysis is performed to estimate component amplification factor, peak shear force, peak axial force, maximum relative displacements, and roof acceleration response history. Several outcomes were closely matched with the experimental results. However, significant variation is observed in some of the seismic demands, which can be attributed to assumptions made in numerical modelling.

Abstract

This paper presents a meticulous and well structured approach to annotating a corpus of Hindi spoken data. We deployed 4 annotators to augment the spoken section of the EMILLE Hindi corpus by marking the various linguistic phenomena observed in spoken data. Then we analyzed various phonological (sound deletion), morphological (code-mixing and reduplication) and syntactic phenomena (case markers and ambiguity), not attested in written data. Code mixing and switching constitute the majority of the phenomena we annotated, followed by orthographic errors related to symbols in the Devanagiri script. In terms of divergences from written form of Hindi, case marker usage, missing auxiliary verbs and agreement patterns are markedly distinct for spoken Hindi. The annotators also assigned a quality rating to each sentence in the corpus. Our analysis of the quality ratings revealed that most of the sentences in the spoken data corpus are of moderate to high quality. Female speakers produced a greater percentage of high quality sentences compared to their male counterparts. While previous efforts in corpus annotation have been largely focused on creating resources for engineering applications, we illustrate the utility of our dataset for scientific hypothesis testing. Inspired from the Surprisal Theory of language comprehension (Hale, 2001; Levy, 2008), we validate the hypothesis that sentences with high values of lexical surprisal are rated low in terms of quality by native speakers, even when controlling for sentence length and word frequencies in a sentence.

Abstract

Breast cancer remains a significant global health concern, and machine learning algorithms and computer-aided detection systems have shown great promise in enhancing the accuracy and efficiency of mammography image analysis. However, there is a critical need for large, benchmark datasets for training deep learning models for breast cancer detection. In this work we developed Mammo-Bench, a large-scale benchmark dataset of mammography images, by collating data from seven well-curated resources, viz., INbreast, Mini-DDSM, KAU-BCMD, CMMD, CDD-CESM, DMID, and RSNA Screening Dataset. To ensure consistency across images from diverse sources while preserving clinically relevant features, all the images underwent a preprocessing pipeline that includes breast segmentation, pectoral muscle removal, and intelligent cropping. The dataset consists of 71,844 high-quality mammographic images from 26,500 patients across 8 countries and is one of the largest open-source mammography databases to the best of our knowledge. To show the utility of Mammo-Bench, ResNet101 architecture was used for classifying the images into Normal, Benign and Malignant classes. Performance of ResNet101 was evaluated on the proposed dataset and the results compared with a few member datasets and an external dataset, VinDr-Mammo. We show that training on the larger, proposed benchmark dataset is more reliable compared to when trained on other smaller datasets. An accuracy of 78.8% (with data augmentation of the minority classes) and 77.8% (without data augmentation) was achieved on the proposed benchmark dataset, compared to the other datasets for which the accuracy varied from 25 – 69%. Most striking was the improved prediction of the minority classes using the Mammo-Bench. These results establish baseline performance and demonstrate Mammo-Bench's utility as a comprehensive resource for developing and evaluating mammography analysis systems.

Abstract

Stuttering constitutes a pervasive speech disorder with a global impact on individuals. Detecting stuttering in speech would aid speech pathologists in monitoring fluency over time and enhance the quality of life for those with atypical speech patterns. Presently, the available public datasets tailored for stuttering identification are confined to the utterance level, with labels ascribed to individual utterances. However, in real-time situations, a considerable proportion of individuals grappling with stuttering manifest dysfluency at the syllable level. This limitation markedly constrains the effectiveness and applicability of dysfluency detection systems. In an effort to address this lacuna in knowledge, this study introduces a novel dataset comprising audio recordings directly obtained from native Kannada-speaking individuals afflicted with the stuttering disorder at the syllable level and also provides detailed information on the dataset's creation, including patient selection criteria and clip labelling. Additionally, benchmark models trained on this distinctive dataset are presented as part of this research endeavor.

Abstract

A method and system based on machine learning to create self-adapting analog circuits adapted to change their internal components on-the-fly in response to changes in process, voltage, and temperature to re-tune the electrical characteristics back to nominal specified values is disclose. The method and system herein comprise of designing the analog circuit, generating simulation data for machine learning, creating a full query database, creating and training, using simulation results, a machine learning (ML) model of the circuit and applying the ML model to infer the required changes to internal components of the analog circuit in response to changes in P, V, and T conditions. With this method and system, evaluation of the adverse effects of PVT changes, decision on internal circuit changes, and realization of requisite design changes are performed by the computer system solely within a ML data domain

Abstract

Extracting work from a physical system is one of the cornerstones of quantum thermodynamics. The extractable work, as quantified by ergotropy, necessitates a complete description of the quantum system. This is significantly more challenging when the state of the underlying system is unknown, as quantum tomography is extremely inefficient. In this article, we analyze the number of samples of the unknown state required to extract work. With only a single copy of an unknown state, we prove that extracting any work is nearly impossible. In contrast, when multiple copies are available, we quantify the sample complexity required to estimate extractable work, establishing a scaling relationship that balances the desired accuracy with success probability. Our work develops a sample-efficient protocol to assess the utility of unknown states as quantum batteries and opens avenues for estimating thermodynamic quantities using near-term quantum computers.

Abstract

We address the problem of network connectivity inference by observing interacting time-series signals at the nodes. The evolving nodal signals depend on the connectivity structure of the network thus allowing us to successfully invert for it. Our approach is demonstrated using the example of heat diffusion dynamics. To learn the graph, we combine our knowledge of the dynamical equation and the observed graph signals. Specifically, we perform approximate numerical discretization of the dynamical equation leading to a system of linear equations in the unknown graph topology. Simulations indicate feasibility of this method for accurate network inference.

Abstract

In this article, we propose a new practical synchronization protocol for multiple Euler–Lagrange systems without structural linear-in-the-parameters (LIP) knowledge of the uncertainty and where the agents can be interconnected before control design by unknown state-dependent interconnection terms. This setting is meant to overcome two standard a priori assumptions in the literature concerning uncertainty with LIP structure and absence of interaction among agents before designing the synchronization protocol. To overcome these assumptions, we propose an adaptive distributed control mechanism having the purpose of estimating the coefficients of the resulting state-dependent uncertainty structure.

Abstract

Particulate matter (PM) is a critical air pollutant with severe health implications, yet existing stationary monitoring networks often fail to capture its complete spatial and temporal variability in urban environments. This paper presents a novel approach to city-wide air quality monitoring using low-cost sensors mounted on mobile platforms. To validate this method, a six-month field study was conducted in Hyderabad, India, deploying IoT-enabled devices on four college buses. These mobile devices capture PM concentrations, temperature, relative humidity, GPS coordinates, and vehicle speed twice daily across different parts of the city. The primary objective was to demonstrate the necessity of mobile air pollution monitoring to identify PM variability across diverse urban areas. The data revealed significant variations in PM concentrations across different parts of the city and seasons, highlighting the impact of local activities on air quality. The study examines seasonal trends, area-specific variations, and temporal patterns of PM concentrations, identifying pollution hotspots within the city. It shows how important it is to provide up-to-date, location-specific air quality information to people with pollution-related health issues.