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Introduction: The novel corona virus (COVID-19), which began in Wuhan, China, in December 2019, has been declared pandemic by the World Health Organization (WHO) on 11th March 2021. Till now, even when nations are in race for developing vaccine against COVID-19, diagnosing the cases and isolating them still remain the only way to contain the disease spread. Hence, effective triage of the suspected COVID 19 patients, becomes an effective tool in preventing disease spread, especially in developing nations. Simple hematological parameters like leukocyte counts, platelet counts, hemoglobin and parameters derived from complete blood counts namely NLR, derived NLR, PLR and liver function tests can be studied and analysed to differentiate COVID 19 patients from other non-COVID 19 Severe Acute Respiratory Illness (SARI) patients. These are the investigations at first point of contact with the patient and are relatively cost effective, keeping in mind the population of developing nations. Patients and Methods: Retrospective case control study conducted over a period of one month including 286 consecutive Severe Acute Respiratory Illness(SARI) patients admitted in a tertiary care hospital in Delhi. The patients were divided into COVID-19 positive and COVID-19 negative groups based on their results of reverse transcriptase-polymerase chain reaction (RT-PCR) for COVID-19. The demographic data, and routine hematological and biochemical parameters including derived parameters like Neutrophil lymphocyte ratio (NLR), derived NLR ratio, Platelet lymphocyte ratio (PLR) and Prognostic nutrition index (PNI) were analyzed and compared between the two groups using SPSS version 23.0. A p value of

Despite the prevalence of wireless connectivity in urban areas around the globe, there remain numerous and diverse situations where connectivity is insufficient or unavailable. To address this, we introduce mobile wireless infrastructure on demand, a system of UAVs that can be rapidly deployed to establish an ad-hoc wireless network. This network has the capability of reconfiguring itself dynamically to satisfy and maintain the required quality of communication. The system optimizes the positions of the UAVs and the routing of data flows throughout the network to achieve this quality of service (QoS). By these means, task agents using the network simply request a desired QoS, and the system adapts accordingly while allowing them to move freely. We have validated this system both in simulation and in real-world experiments. The results demonstrate that our system effectively offers mobile wireless infrastructure on demand, extending the operational range of task agents and supporting complex mobility patterns, all while ensuring connectivity and being resilient to agent failures.

Simulators play a critical role in aerial robotics both in and out of the classroom. We present RotorPy, a simulation environment written entirely in Python intentionally designed to be a lightweight and accessible tool for robotics students and researchers alike to probe concepts in estimation, planning, and control for aerial robots. RotorPy simulates the 6-DoF dynamics of a multirotor robot including aerodynamic wrenches, obstacles, actuator dynamics and saturation, realistic sensors, and wind models. This work describes the modeling choices for RotorPy, benchmark testing against real data, and a case study using the simulator to design and evaluate a model-based wind estimator., Appearing as a contributed paper in "The Role of Robotics Simulators for Unmanned Aerial Vehicles" workshop at the 2023 International Conference on Robotics and Automation (ICRA). See more at https://imrclab.github.io/workshop-uav-sims-icra2023/

The sixth-generation networks envision the terahertz (THz) band as one of the key enabling technologies because of its ultrawide bandwidth. To combat the severe attenuation, the THz wireless systems employ large arrays, wherein the near-field beam-split (NB) severely degrades the accuracy of channel acquisition. Contrary to prior works that examine only either narrowband beamforming or far-field models, we estimate the wideband THz channel via an NB-aware orthogonal matching pursuit (NBA-OMP) approach. We design an NBA dictionary of near-field steering vectors by exploiting the corresponding angular and range deviation. Our OMP algorithm accounts for this deviation thereby ipso facto mitigating the effect of NB. Numerical experiments demonstrate the effectiveness of the proposed channel estimation technique for wideband THz systems., Accepted Paper in 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Optimal allocation of shared resources is key to deliver the promise of jointly operating radar and communications systems. In this paper, unlike prior works which examine synergistic access to resources in colocated joint radar-communications or among identical systems, we investigate this problem for a distributed system comprising heterogeneous radars and multi-tier communications. In particular, we focus on resource allocation in the context of multi-target tracking (MTT) while maintaining stable communication connections. By simultaneously allocating the available power, dwell time and shared bandwidth, we improve the MTT performance under a Bayesian tracking framework and guarantee the communications throughput. Our alternating allocation of heterogeneous resources (ANCHOR) approach solves the resulting nonconvex problem based on the alternating optimization method that monotonically improves the Bayesian Cram\'er-Rao bound. Numerical experiments demonstrate that ANCHOR significant improves the tracking error over two baseline allocations and stability under different target scenarios and radar-communications network distributions.

In the current period, each and every process in different sections of diligence similar as manufacturing, processing, packaging sections, etc. are being revolutionized with automated systems, due to certain downsides in the homemade system. The end of the system is to reduce the downsides of the homemade system similar as low delicacy, low thickness, high charges, etc. The proposed system feathers and counts objects grounded on their colors and the collected data is transferred to the pall. In this system Node MCU is being used as control unit and transmits the entire process.TCS3200 color detector is used to sort out the objects and are being directed with the help of servo motor.

Differential flatness enables efficient planning and control for underactuated robotic systems, but we lack a systematic and practical means of identifying a flat output (or determining whether one exists) for an arbitrary robotic system. In this work, we leverage recent results elucidating the role of symmetry in constructing flat outputs for free-flying robotic systems. Using the tools of Riemannian geometry, Lie group theory, and differential forms, we cast the search for a globally valid, equivariant flat output as an optimization problem. An approximate transcription of this continuum formulation to a quadratic program is performed, and its solutions for two example systems achieve precise agreement with the known closed-form flat outputs. Our results point towards a systematic, automated approach to numerically identify geometric flat outputs directly from the system model, particularly useful when complexity renders pen and paper analysis intractable., To appear as a contributed paper in the "Geometric Representations" workshop at the 2023 International Conference on Robotics and Automation (ICRA)

Extensive monitoring of PM2.5 is critical for understanding changes in local air quality due to policy measures. With the emergence of low-cost air quality sensor networks, high spatio-temporal measurements of air quality are now possible. However, the sensitivity, noise, and accuracy of field data from such networks are not fully understood. In this study, we use frequency analysis of a two-year data record of PM2.5 from both the EPA and Purple Air (PA), a low-cost sensor network, to identify the contribution of individual periodic sources to local air quality in Chicago. We find that sources with time periods of 4, 8, 12, and 24 hours have significant but varying relative contributions to the data for both networks. Further analysis reveals that the 8- and 12-hour sources are traffic-related and photochemistry-driven, respectively, and that the contribution of both these sources is significantly lower in the PA data than in the EPA data. We also use a correction model that accounts for the contribution of relative humidity and temperature, and we observe that the PA temporal components can be made to match those of the EPA over the medium- and long-term but not over the short-term. Thus, standard approaches to improve the accuracy of low-cost sensor network data will not result in unbiased measurements. The strong source dependence of low-cost sensor network measurements demands exceptional care in the analysis of ambient data from these networks, particularly when used to evaluate and drive air quality policies.

The aim of present paper is to introduce the concept of neutrosophic 2−norm space ( briefly abbreviated as N −2−NS) and study statistical summability in these spaces. We construct examples to demonstrate that statistical convergence is stronger method than usual convergence. Finally, we define statistically Cauchy sequence, statistical completeness and obtain the Cauchy convergence criteria.

This work proposes a dynamic and adversarial resource allocation problem in a graph environment, which is referred to as the dynamic Defender-Attacker Blotto (dDAB) game. A team of defender robots is tasked to ensure numerical advantage at every node in the graph against a team of attacker robots. The engagement is formulated as a discrete-time dynamic game, where the two teams reallocate their robots in sequence and each robot can move at most one hop at each time step. The game terminates with the attacker's victory if any node has more attacker robots than defender robots. Our goal is to identify the necessary and sufficient number of defender robots to guarantee defense. Through a reachability analysis, we first solve the problem for the case where the attacker team stays as a single group. The results are then generalized to the case where the attacker team can freely split and merge into subteams. Crucially, our analysis indicates that there is no incentive for the attacker team to split, which significantly reduces the search space for the attacker's winning strategies and also enables us to design defender counter-strategies using superposition. We also present an efficient numerical algorithm to identify the necessary and sufficient number of defender robots to defend a given graph. Finally, we present illustrative examples to verify the efficacy of the proposed framework.

Rapid advances in designing cognitive and counter-adversarial systems have motivated the development of inverse Bayesian filters. In this setting, a cognitive `adversary' tracks its target of interest via a stochastic framework such as a Kalman filter (KF). The target or `defender' then employs another inverse stochastic filter to infer the forward filter estimates of the defender computed by the adversary. For linear systems, inverse Kalman filter (I-KF) has been recently shown to be effective in these counter-adversarial applications. In the paper, contrary to prior works, we focus on non-linear system dynamics and formulate the inverse unscented KF (I-UKF) to estimate the defender's state with reduced linearization errors. We then generalize this framework to an unknown system model by proposing reproducing kernel Hilbert space-based UKF (RKHS-UKF) to learn the system dynamics and estimate the state based on its observations. Our theoretical analyses to guarantee the stochastic stability of I-UKF and RKHS-UKF in the mean-squared sense shows that, provided the forward filters are stable, the inverse filters are also stable under mild system-level conditions. Our numerical experiments for several different applications demonstrate the state estimation performance of the proposed filters using recursive Cram\'{e}r-Rao lower bound as a benchmark., Comment: 18 pages, 2 figures. arXiv admin note: text overlap with arXiv:2210.00359

Joint radar-communications (JRC) has emerged as a promising technology for efficiently using the limited electromagnetic spectrum. In JRC applications such as secure military receivers, often the radar and communications signals are overlaid in the received signal. In these passive listening outposts, the signals and channels of both radar and communications are unknown to the receiver. The ill-posed problem of recovering all signal and channel parameters from the overlaid signal is terms as dual-blind deconvolution (DBD). In this work, we investigate a more challenging version of DBD with a multi-antenna receiver. We model the radar and communications channels with a few (sparse) continuous-valued parameters such as time delays, Doppler velocities, and directions-of-arrival (DoAs). To solve this highly ill-posed DBD, we propose to minimize the sum of multivariate atomic norms (SoMAN) that depends on the unknown parameters. To this end, we devise an exact semidefinite program using theories of positive hyperoctant trigonometric polynomials (PhTP). Our theoretical analyses show that the minimum number of samples and antennas required for perfect recovery is logarithmically dependent on the maximum of the number of radar targets and communications paths rather than their sum. We show that our approach is easily generalized to include several practical issues such as gain/phase errors and additive noise. Numerical experiments show the exact parameter recovery for different JRC, 40 pages, 6 figures. arXiv admin note: text overlap with arXiv:2208.04381

Seismic signals are ground vibrations used to detect seismic events. However, seismic signal captured from sensors is distorted signal contains noise and makes actual event detection difficult. In most cases, external noise such as manmade or any heavy vehicle vibration always overlaps with the seismic reflections over time. The presence of noise in the seismic signal makes it difficult to determine the magnitude at which the seismic events have occurred. The aim of our study is to process the signals received from seismic sensor and identify it as seismic events signal and non-seismic events signal based on the magnitude. The authors propose a robust noise suppression method using bandpass filter, IIR Wiener filter and event detection using recursive Short-Term Average (STA)/Long Term Average (LTA) and Carl Short Term Average (STA)/Long Term Average (LTA). The proposed study determines reference magnitude to distinguish seismic and non-seismic activity. The projected study is based on the analysis of seismic signal received from single sensor and sensor networks (SN) and determines the magnitude to distinguish seismic and nonseismic events and time of an actual earthquake event. The experimental dataset is a broadband seismic signal from BSVK and CUKG station sensors located at Basavakalyan, Karnataka, and the Central University of Karnataka respectively. The proposed approach helps to extract the information about preseismic event, actual seismic event, post-seismic event activities and identify the abnormal pattern that supports to detect heearth’s activities before the actual seismic event.

Integrated sensing and communications (ISAC) is a spectrum-sharing paradigm that allows different users to jointly utilize and access the crowded electromagnetic spectrum. In this context, intelligent reflecting surfaces (IRSs) have lately emerged as an enabler for non-line-of-sight (NLoS) ISAC. Prior IRS-aided ISAC studies assume passive surfaces and rely on the continuous-valued phase-shift model. In practice, the phase-shifts are quantized. Moreover, recent research has shown substantial performance benefits with active IRS. In this paper, we include these characteristics in our IRS-aided ISAC model to maximize the receive radar and communications signal-to-noise ratios (SNR) subjected to a unimodular IRS phase-shift vector and power budget. The resulting optimization is a highly non-convex unimodular quartic optimization problem. We tackle this problem via a bi-quadratic transformation to split the design into two quadratic sub-problems that are solved using the power iteration method. The proposed approach employs the M-ary unimodular sequence design via relaxed power method-like iteration (MaRLI) to design the quantized phase-shifts. Numerical experiments employ continuous-valued phase shifts as a benchmark and demonstrate that our active-IRS-aided ISAC design with MaRLI converges to a higher value of SNR with an increase in the number of IRS quantization bits.

It was observed that people with prediabetes had greater performance fatigability of the knee extensors due to contractile mechanisms compared with controls, although less performance fatigability than that of people with Type 2 Diabetes Mellitus. The aim of this study was to compare muscle fatigue in normal and prediabetic individuals. A comparative cross- sectional study was conducted in adults between 24-60 years of age.A total of 100 age matched normal and 100 prediabetic individuals were selected for the study. Fatigability was calculated as a function of work done by the pleximeter finger of the dominant hand. The work done was categorized broadly into four groups viz. 0-2 kg.m, 2.1-4 kg.m, 4.1-6 kg.m and 6.1 and above. Fatigue test was performed using Mosso’s Ergograph. The study was conducted at Rajarshi Dashrath Autonomous State Medical College, Ayodhya. Overall, average work done by prediabetic individuals was lesser (0-2 kg.m=23%, 2.1-4 kg.m=58%, 4.1-6= 17% and 6.1 and above=2%) as compared to normal individuals (0-2 kg.m= 20%, 2.1-4 kg.m= 48%, 4.1-6= 27% and 6.1 and above= 5%).T

As India’s population is growing so is its water demand and corresponding water contamination. Water is a very essential element in formation of life and is an essential requirement for living organisms to thrive. Therefore, it is very important that the drinking water available must be free of any contamination and proper measures be taken in that direction. Faecal contamination poses crucial threat to people having an impaired immune system and can be life threatening. Microbiological contamination causes serious health issues in human being. In case microbiological contamination found in water, it means that water quality is very poor and this type of water causes Gastrointestinal, Urinary tract infection, dysentery, diarrhoea and typhoid disease. Keywords: Faecal coliform, Pathogenic Bacteria, Song River, Dehradun, Water Quality.

Hand hygiene is important to reduce the spread of microbes in clinical settings. Hand sanitisers that last longer may be beneficial.Longevity of hand sanitisation products on fingers and hands may be important to help reduce microbial transmission. The current study evaluated the persistence of disinfection of three hand sanitisers.Initially the minimum inhibitory concentrations of the hand sanitisers were determined using strains ofThe minimum inhibitory concentration of an alcohol-based sanitiser (AS) was 10%, for the capric acid-based (CS) sanitiser was 70%, and for the quaternary ammonium-based (QS) sanitiser was 10%. AS significantly reduced the number of microbes on fingers 10 minutes after hand washing (18.2 cfu/mL) compared to CS (59.7 cfu/mL; p 0.0001) or QS (64.5 cfu/mL; p 0.0001). Twenty minutes after use, microbes on fingers after AS (23 cfu/mL) or CS (16.7 cfu/mL) were significantly reduced compared to QS (72.2 cfu/mL; p 0.0001) and the numbers on fingers after CS was significantly less than after AS (p = 0.002). Four hours after use of any hand sanitiser, the number of microbes increased to near baseline levels. The reduction in bacterial numbers was not affected by the use of neutralisers in agar (48 ± 28% reduction with, 47 ± 49% reduction without; p = 0.876).Hand sanitisers containing capric acid or alcohol out-performed one containing quaternary ammonium in the clinical trial and may help reduce the spread of microbes.

The intelligent reflecting surface (IRS) alters the behavior of wireless media and, consequently, has potential to improve the performance and reliability of wireless systems such as communications and radar remote sensing. Recently, integrated sensing and communications (ISAC) has been widely studied as a means to efficiently utilize spectrum and thereby save cost and power. This article investigates the role of IRS in the future ISAC paradigms. While there is a rich heritage of recent research into IRS-assisted communications, the IRS-assisted radars and ISAC remain relatively unexamined. We discuss the putative advantages of IRS deployment, such as coverage extension, interference suppression, and enhanced parameter estimation, for both communications and radar. We introduce possible IRS-assisted ISAC scenarios with common and dedicated surfaces. The article provides an overview of related signal processing techniques and the design challenges, such as wireless channel acquisition, waveform design, and security., Comment: Accepted paper in IEEE Network Magazine

COVID-19 greatly affected the lives of adolescents through restrictions such as less playtime, more screen time, and limited interaction with peers. In this study, we assessed the impact of the COVID-19 pandemic on the dietary and physical activity-related behavior of school students aged 10–16 years. This cross-sectional study was conducted with adolescents recruited from seven randomly selected private schools in Delhi, India, during 2021. A self-administered web-based survey was conducted to evaluate the behavior of the participants before and during the pandemic. Of the 512 students (53% males) who participated in the survey, 39% gained weight during the COVID-19 pandemic. There was a significant increase in the number of meals per day (p = 0.005) and a reduction in physical activity (p = 0.00) compared to the situation before the pandemic. The percentage of students who played indoor board and computer games increased from 13% to 46%. Students’s gender (p = 0.007) and parents’ education (mother: p = 0.003; father: p = 0.025) were significantly associated with physical activity during the pandemic. Higher socioeconomic status was significantly associated with consumption of more than two meals per day. The students who had working fathers with advanced/professional degrees were three times more likely [AOR 3.24, 95% CI (0.91–11.53)] to be physically active and eat a minimum of three major meals per day [AOR 3.21, 95% CI (1.77–5.81)] during the pandemic compared to those whose fathers were unemployed. This study highlighted the need for innovative strategies for adolescents and parents to adopt and practice a healthy lifestyle, especially during public health crises, such as the COVID-19 pandemic.

Copyright © 2021 The Authors. Directly solar radiation absorbing nanofluids have the potential to absorb a wide spectrum of solar radiation and displace selectively coated metallic receivers in solar thermal collectors. Parameters including nanoparticle concentration, synthesis and storage conditions, can influence their long-term usage. In this study, 60 min was found to be optimal sonication duration to synthesise a uniform suspension of nanofluid containing amorphous-carbon nanoparticles and ethylene glycol as base fluid. Nanoparticle concentration can be used to tune extinction coefficient of nanofluid in the range of 75–400 m−1 for wavelength range of 320–1000 nm. Long-term stability and high temperature studies showed a time and temperature dependent increase in transmittance of nanofluid which is restored by 5 min of stirring. Computational modelling highlighted the role of incident intensity, nanoparticle concentration as well as inlet flow rate on receiver exit temperature. A ray-optics model employing weather data for Delhi (India) can predict the optical efficiency of an Asymmetric Compound Parabolic Concentrator solar collector. This combined approach can enable to predict the flow rate required to achieve a desired supply temperature at target locations. This rational framework combining experimental and computational approaches can be used to identify design parameters relevant for application of nanofluids in thermal collectors. UK-India Education and Research Initiative (UKIERI) and Department of Science and Technology (DST), Government of India, for the COSTARMSW project through the grant agreement IND/CONT/GA/18–19/16.

An intelligent reflecting surface (IRS) consists of passive reflective elements capable of altering impinging waveforms. The IRS-aided radar systems have recently been shown to improve detection and estimation performance by exploiting the target information collected via non-line-of-sight paths. However, the waveform design problem for an IRS-aided radar has remained relatively unexplored. In this paper, we consider a multi-IRS-aided orthogonal frequency-division multiplexing (OFDM) radar and study the theoretically achievable accuracy of target detection. In addition, we jointly design the OFDM signal and IRS phase-shifts to optimize the target detection performance via an alternating optimization approach. To this end, we formulate the IRS phase-shift design problem as a unimodular bi-quadratic program which is tackled by a computationally cost-effective approach of power-method-like iterations. Numerical experiments illustrate that our proposed joint design of IRS phase-shifts and OFDM code improves the detection performance in comparison with conventional OFDM radar.

Advancements in wireless communication and the increased accessibility to low-cost sensing and data processing IoT technologies have increased the research and development of urban monitoring systems. Most smart city research projects rely on deploying proprietary IoT testbeds for indoor and outdoor data collection. Such testbeds typically rely on a three-tier architecture composed of the Endpoint, the Edge, and the Cloud. Managing the system's operation whilst considering the security and privacy challenges that emerge, such as data privacy controls, network security, and security updates on the devices, is challenging. This work presents a systematic study of the challenges of developing, deploying and managing urban monitoring testbeds, as experienced in a series of urban monitoring research projects, followed by an analysis of the relevant literature. By identifying the challenges in the various projects and organising them under the V-model development lifecycle levels, we provide a reference guide for future projects. Understanding the challenges early on will facilitate current and future smart-cities IoT research projects to reduce implementation time and deliver secure and resilient testbeds., 25 pages, under review

There has been a tonne of study on use of ML for speech processing applications, particularly voice recognition, over the past few decades. This study suggested an innovative method in speech signal processing and segmentation relied upon deep learning configurations. As input, this speech signal has been accumulated from the crime scene and this signal has been pre-processed using using K-means clustering (K-means C)for cluster the fragments of the input speech signal and process them for noise removal and signal artifacts removal. Here the segmentation is carried out for processed signal using Kernel based deep belief networks (KDBN). Experimental results demonstrate that proposed method outperforms the input speech signal based on both weighted accuracy (WA) and unweighted accuracy (UA).

This study aims to find the effect of Customer Satisfaction on the Repurchase Intension and the Word-of-Mouth Advertising in Online Shopping Behavior. The exploratory research design is used for the exploration of new facts, new knowledge, and new information on online shopping trends. The descriptive research design is used to test the hypothesis. A total of 339 respondents are selected for the study of Customer Satisfaction Towards Repurchase Intension and the Word-of-Mouth Advertising in Online Shopping Behavior. The significance level is set at 5% in this study. The correlation and regression analysis are used for the cause-and- effect relationship between independent and dependent variables. The line and pie chart are also used for the pictorial presentation of the data. The SPSS 23.0 version software is used for the data analysis. The findings concluded that there is significant effect of Customer Satisfaction on the Repurchase Intension and the Word-of-Mouth Advertising in Online Shopping Behavior.

In this work, we study the problem of decentralized multi-agent perimeter defense that asks for computing actions for defenders with local perceptions and communications to maximize the capture of intruders. One major challenge for practical implementations is to make perimeter defense strategies scalable for large-scale problem instances. To this end, we leverage graph neural networks (GNNs) to develop an imitation learning framework that learns a mapping from defenders' local perceptions and their communication graph to their actions. The proposed GNN-based learning network is trained by imitating a centralized expert algorithm such that the learned actions are close to that generated by the expert algorithm. We demonstrate that our proposed network performs closer to the expert algorithm and is superior to other baseline algorithms by capturing more intruders. Our GNN-based network is trained at a small scale and can be generalized to large-scale cases. We run perimeter defense games in scenarios with different team sizes and configurations to demonstrate the performance of the learned network., Comment: 20 pages, 10 figures. Published in Frontiers in Control Engineering 2023. arXiv admin note: substantial text overlap with arXiv:2211.01757

Recent developments in counter-adversarial system research have led to the development of inverse stochastic filters that are employed by a defender to infer the information its adversary may have learned. Prior works addressed this inverse cognition problem by proposing inverse Kalman filter (I-KF) and inverse extended KF (I-EKF), respectively, for linear and non-linear Gaussian state-space models. However, in practice, many counter-adversarial settings involve highly non-linear system models, wherein EKF's linearization often fails. In this paper, we consider the efficient numerical integration techniques to address such nonlinearities and, to this end, develop inverse cubature KF (I-CKF) and inverse quadrature KF (I-QKF). We derive the stochastic stability conditions for the proposed filters in the exponential-mean-squared-boundedness sense. Numerical experiments demonstrate the estimation accuracy of our I-CKF and I-QKF with the recursive Cram\'{e}r-Rao lower bound as a benchmark., Comment: 8 pages, 1 figure

Entity linking is an important step towards constructing knowledge graphs that facilitate advanced question answering over scientific documents, including the retrieval of relevant information included in tables within these documents. This paper introduces a general-purpose system for linking entities to items in the Wikidata knowledge base. It describes how we adapt this system for linking domain-specific entities, especially for those entities embedded within tables drawn from COVID-19-related scientific literature. We describe the setup of an efficient offline instance of the system that enables our entity-linking approach to be more feasible in practice. As part of a broader approach to infer the semantic meaning of scientific tables, we leverage the structural and semantic characteristics of the tables to improve overall entity linking performance.

To enable non-line-of-sight (NLoS) sensing and communications, dual-function radar-communications (DFRC) systems have recently proposed employing reconfigurable intelligent surface (RIS) as a reflector in wireless media. However, in the dense environment and higher frequencies, severe propagation and attenuation losses are a hindrance for RIS-aided DFRC systems to utilize wideband processing. To this end, we propose equipping the transceivers with the reconfigurable holographic surface (RHS) that, different from RIS, is a metasurface with an embedded connected feed deployed at the transceiver for greater control of the radiation amplitude. This surface is crucial for designing compact low-cost wideband wireless systems, wherein ultra-massive antenna arrays are required to compensate for the losses incurred by severe attenuation and diffraction. We consider a novel wideband DFRC system equipped with an RHS at the transceiver and a RIS reflector in the channel. We jointly design the digital, holographic, and passive beamformers to maximize the radar signal-to-interference-plus-noise ratio (SINR) while ensuring the communications SINR among all users. The resulting nonconvex optimization problem involves maximin objective, constant modulus, and difference of convex constraints. We develop an alternating maximization method to decouple and iteratively solve these subproblems. Numerical experiments demonstrate that the proposed method achieves better radar performance than non-RIS, random-RHS, and randomly configured RIS-aided DFRC systems.

Rydberg-aided atomic electrometry using alkali-metal atoms is gaining increased research interest for detecting external electric fields. However, the inability of Rydberg probes to detect phase is a serious impediment to their realistic deployment. In this paper, we derive a novel phase retrieval algorithm for use in a phased array or synthetic aperture applications where only measurements of electric field intensity are possible at each spatial sample. These array configurations exist if a Rydberg atom probe is used in place of an antenna. We employ three-stage alternating projections to solve the resulting optimization problem. Our numerical experiments demonstrate the effectiveness of the proposed algorithm in terms of beamformed array output.

Recent results in one-bit sampling provide a framework for a relatively low-cost, low-power sampling, at a high rate by employing time-varying sampling threshold sequences. Another recent development in sampling theory is unlimited sampling, which is a high-resolution technique that relies on self-reset ADCs to yield an unlimited dynamic range. In this paper, we leverage the appealing attributes of the two aforementioned techniques to propose a novel \emph{un}limited \emph{o}ne-bit (UNO) sampling approach. In this framework, the information on the distance between the input signal value and the threshold are stored and utilized to accurately reconstruct the one-bit sampled signal. We then utilize this information to accurately reconstruct the signal from its one-bit samples via the randomized Kaczmarz algorithm (RKA); a strong linear feasibility solver that selects a random linear equation at each iteration. In the presence of noise, we employ the recent plug-and-play (PnP) priors technique with alternating direction method of multipliers (ADMM) to exploit integration of state-of-the-art regularizers in the reconstruction process. Numerical experiments with RKA and PnP-ADMM-based reconstruction illustrate the effectiveness of our proposed UNO, including its superior performance compared to the one-bit Sigma-Delta sampling.

Based on the power Cauchy distribution, we have purposed a new distribution called inverse power Cauchy distribution that offers greater modeling flexibility for lifetime data. Real-world data can be efficiently analyzed using the suggested model because it is analytically sound. Its density function can take on a number of different shapes, including reversed-J, symmetrical, and right-skewed. Depending on the different values of the parameters, it can adapt to different hazard forms, such as an upside-down bathtub, a monotonically increasing or decreasing curve, and others. Its moments, quantile, reliability, hazard, order statistics with density function, moment generating function, and entropy are all given with various explicit forms. The observed information matrix is created when the new model’s parameters are calculated through maximum likelihood technique. A simulation study is conducted to investigate the behaviour of maximum likelihood estimators. The proposed model gets a superior fit compared to certain well-known distributions, according to the test of goodness-of-fit we conducted. The significance of the purposed distribution is demonstrated empirically using two real-world data sets.

Accurate and robust state estimation is critical for autonomous navigation of robot teams. This task is especially challenging for large groups of size, weight, and power (SWAP) constrained aerial robots operating in perceptually-degraded GPS-denied environments. We can, however, actively increase the amount of perceptual information available to such robots by augmenting them with a small number of more expensive, but less resource-constrained, agents. Specifically, the latter can serve as sources of perceptual information themselves. In this paper, we study the problem of optimally positioning (and potentially navigating) a small number of more capable agents to enhance the perceptual environment for their lightweight,inexpensive, teammates that only need to rely on cameras and IMUs. We propose a numerically robust, computationally efficient approach to solve this problem via nonlinear optimization. Our method outperforms the standard approach based on the greedy algorithm, while matching the accuracy of a heuristic evolutionary scheme for global optimization at a fraction of its running time. Ultimately, we validate our solution in both photorealistic simulations and real-world experiments. In these experiments, we use lidar-based autonomous ground vehicles as the more capable agents, and vision-based aerial robots as their SWAP-constrained teammates. Our method is able to reduce drift in visual-inertial odometry by as much as 90%, and it outperforms random positioning of lidar-equipped agents by a significant margin. Furthermore, our method can be generalized to different types of robot teams with heterogeneous perception capabilities. It has a wide range of applications, such as surveying and mapping challenging dynamic environments, and enabling resilience to large-scale perturbations that can be caused by earthquakes or storms., Comment: To appear in Robotics: Science and Systems (RSS) 2023

Microorganisms are source of many industrially important enzymes, proteins, and biochemicals. They require different nutritional and culture conditions for their growth and the production of desired products. Optimization of required parameters using statistical design is an effective time-saving approach. Immobilization methods have been a powerful tool to increase the stability and reusability of biocatalysts. Different methods have been reported to modify and engineer microbes for increased productivity. Use of mutagenesis such as physical, chemical, molecular biology-based site-directed mutagenesis, and directed evolution may help to improve biocatalyst potential (activity, stability, and specificity). Co-culture strategies are also getting attention as approaches in molecular biology (construction of long metabolic pathway and cultivation of nonculturable microbes, etc.) alone are not able to solve many problems. The above techniques can lead to the development of a highly productive biocatalytic system for industrial applications.

Automotive radars at the Terahertz (THz) frequency band have the potential to be compact and lightweight while providing high (nearly-optical) angular resolution. In this paper, we propose a bistatic THz automotive radar that employs the recently proposed orthogonal chirp division multiplexing (OCDM) multi-carrier waveform. As a stand-alone communications waveform, OCDM has been investigated for robustness against interference in time-frequency selective channels. The THz-band path loss, and, hence, radar signal bandwidth, are range-dependent. We address this unique feature through a multi-carrier wideband OCDM sensing transceiver that exploits the coherence bandwidth of the THz channel. We develop an optimal scheme to combine the returns at different range/bandwidths by assigning weights based on the Cramer-Rao lower bound on the range and velocity estimates. Numerical experiments demonstrate improved target estimates using our proposed combined estimation from multiple varied-attenuation THz frequencies.

Conventional sensing applications rely on electromagnetic far-field channel models with plane wave propagation. However, recent ultra-short-range automotive radar applications at upper millimeter-wave or low terahertz (THz) frequencies envisage operation in the near-field region, where the wavefront is spherical. Unlike far-field, the near-field beampattern is dependent on both range and angle, thus requiring a different approach to waveform design. For the first time in the literature, we adopt the beampattern matching approach to design unimodular waveforms for THz automotive radars with low weighted integrated sidelobe levels (WISL). We formulate this problem as a unimodular bi-quadratic matrix program, and solve its constituent quadratic sub-problems using our cyclic power method-like iterations (CyPMLI) algorithm. Numerical experiments demonstrate that the CyPMLI approach yields the required beampattern with low autocorrelation levels.

Security research has concentrated on converting operating system audit logs into suitable graphs, such as provenance graphs, for analysis. However, provenance graphs can grow very large requiring significant computational resources beyond what is necessary for many security tasks and are not feasible for resource constrained environments, such as edge devices. To address this problem, we present the \textit{resource-interaction graph} that is built directly from the audit log. We show that the resource-interaction graph's storage requirements are significantly lower than provenance graphs using an open-source data set with two container escape attacks captured from an edge device. We use a graph autoencoder and graph clustering technique to evaluate the representation for an anomaly detection task. Both approaches are unsupervised and are thus suitable for detecting zero-day attacks. The approaches can achieve f1 scores typically over 80\% and in some cases over 90\% for the selected data set and attacks., 15 pages, 11 figures, 6 tables, for dataset see https://github.com/jpope8/container-escape-dataset, for code see https://github.com/jpope8/container-escape-analysis

Active target sensing is the task of discovering and classifying an unknown number of targets in an environment and is critical in search-and-rescue missions. This paper develops a deep reinforcement learning approach to plan informative trajectories that increase the likelihood for an uncrewed aerial vehicle (UAV) to discover missing targets. Our approach efficiently (1) explores the environment to discover new targets, (2) exploits its current belief of the target states and incorporates inaccurate sensor models for high-fidelity classification, and (3) generates dynamically feasible trajectories for an agile UAV by employing a motion primitive library. Extensive simulations on randomly generated environments show that our approach is more efficient in discovering and classifying targets than several other baselines. A unique characteristic of our approach, in contrast to heuristic informative path planning approaches, is that it is robust to varying amounts of deviations of the prior belief from the true target distribution, thereby alleviating the challenge of designing heuristics specific to the application conditions., 5 pages, 2nd Workshop on Trends and Advances in Machine Learning and Automated Reasoning for Intelligent Robots and Systems Reasoning with IEEE/RSJ International Conference on Intelligent Robots and System (IROS 2022)

Excessive antibiotic prescriptions as well as their misuse in agriculture are the main causes of antimicrobial resistance which poses a growing threat to public health. It necessitates the search for novel chemicals to combat drug resistance. Since ancient times, naturally occurring medicines have been employed and the enormous variety of bioactive chemicals found in nature has long served as an inspiration for researchers looking for possible therapeutics. Secondary metabolites from microorganisms, particularly those from actinomycetes, have made it incredibly easy to find new molecules. Different actinomycetes species account for more than 70% of naturally generated antibiotics currently used in medicine, and they also produce a variety of secondary metabolites, including pigments, enzymes, and anti-inflammatory compounds. They continue to be a crucial source of fresh chemical diversity and a crucial component of drug discovery. This review summarizes some uncommon sources of antifungal metabolites and highlights the importance of further research on these unusual habitats as a source of novel antimicrobial molecules.