Abstract

India Meteorological Department (IMD) has started block-level level agromet advisory (AA) service from the year 2015 and is currently operating in a few blocks of each state across India. In a block-level AA service, on every Tuesday and Friday, AA is being prepared for each block based on the block-level Medium Range weather Forecast (MRF). In this paper, we propose a framework to improve the preparation of block- level AA by modeling a weather situation as “Category-based Weather Condition (CWC)” and exploiting both “temporal reuse” and “spatial reuse” of AA based on the similarity among CWCs. The weather data analysis for 12 blocks of Telangana by considering the phenophase-specific CWCs of Rice crop showed that there is a scope to improve the efficiency of block-level AA bulletin preparation process by exploiting reuse

Abstract

here are certain dynamics which although are non-Markovian but never show information backflow. We show that if two such dynamical maps are considered in a scenario where the order of application of these two dynamical maps are not definite, then the effective channel can manifest information backflow. In particular, we use quantum SWITCH to activate such channels. In contrast, those channels can never be activated even if we use many copy of such channels in series or parallel action. We then investigate the actual cause and dynamics behind the quantum SWITCH experiment and find out that after the action of quantum SWITCH both the CP (Complete Positive)- divisiblity and P (Positive)- divisibility of the channel breaks down, triggering the information backflow. Our investigations shed new light on the fundamental cause of the advantages of quantum SWITCH

Abstract

Directions of arrival (DOA) estimation or localization of sources is an important problem in many applications for which numerous algorithms have been proposed. Most lo- calization methods use block-level processing that combines multiple data snapshots to estimate DOA within a block. The DOAs are assumed to be constant within the block duration. However, these assumptions are often violated due to source motion. In this paper, we propose a signal model that cap- tures the linear variations in DOA within a block. We applied conventional beamforming (CBF) algorithm to this model to estimate linear DOA trajectories. Further, we formulate the proposed signal model as a block sparse model and sub- sequently derive sparse Bayesian learning (SBL) algorithm. Our simulation results show that this linear parametric DOA model and corresponding algorithms capture the DOA tra- jectories for moving sources more accurately than traditional signal models and methods.

Abstract

This work proposes a tractable estimation of the maximum a posteriori (MAP) threshold of various families of sparse-graph code ensembles, by using an approximation for the extended belief propagation generalized extrinsic information transfer (EBP-GEXIT) function, first proposed by M ́easson et al. We consider the transmission over non-binary complex-input additive white Gaussian noise channel and extend the existing results to obtain an expression for the GEXIT function. We estimate the MAP threshold by applying the Maxwell construction to the obtained approximate EBP-GEXIT charts for various families of low-density parity-check (LDPC), generalized LDPC, doubly generalized LDPC, and serially concatenated turbo codes (SC-TC). When codewords of SC-TC are modulated using Gray mapping, we also explore where the spatially-coupled belief propagation (BP) threshold is located with respect to the previously computed MAP threshold. Numerical results indicate that the BP threshold of the spatially-coupled SC-TC does saturate to the MAP threshold obtained via EBP-GEXIT chart.

Abstract

The quantum speed limit indicates the maximal evolution speed of the quantum system. In this work, we determine speed limits on the informational measures, namely the von Neumann entropy, maximal information, and coherence of quantum systems evolving under dynamical processes. These speed limits ascertain the fundamental limitations on the evolution time required by the quantum systems for the changes in their informational measures. Erasing of quantum information to reset the memory for future use is crucial for quantum computing devices. We use the speed limit on the maximal information to obtain the minimum time required to erase the information of quantum systems via some quantum processes of interest.

Abstract

The normal foveal anatomy consists of the extrusion of the plexiform layers, a formed foveal pit, cone photoreceptor outer segment (OS) lengthening, and outer nuclear layer (ONL) widening relative to the parafoveal OS and ONL.1 Foveal development begins with the centrifugal displacement of the inner retinal layers, followed by the centripetal migration of the cone photoreceptors toward the incipient fovea and finally cone photoreceptor specialization (PRS) (Fig 1A–E ).2 3 , 4 Foveal pit formation begins midgestation and continues postnatally characterized by pit deepening and widening. Simultaneously, outer retinal specialization occurs at a rapid rate in the first 2 years of life, but ONL widening continues up to the age of 13 years.4 Lengthening of the OS layer represents cone PRS and reflects peak cone photoreceptor density.5 The maturation of the human fovea is a complex process and is critical for visual function.4 Many genes are involved in the development of the retina. Pathogenic variants in these genes often cause disruption to the foveal developmental process, resulting in foveal hypoplasia (FH), which describes the underdevelopment of the fovea.Foveal hypoplasia is characterized by the continuation of inner retinal layers posterior to the foveola, and progressive loss of these foveal elements is represented by increasing grades of FH. Three key foveal development stages form the basis of the Leicester Grading System for FH, developed by Thomas et al1 (Fig 1F–H). The Leicester Grading System is divided into 4 grades of typical FH (grades 1–4) and 1 grade of atypical FH, which is associated with photoreceptor degeneration. The Leicester Grading System can be applied to a diverse range of genetic disorders, including albinism, achromatopsia, and those caused by pathogenic variants in PAX6, SLC38A8, FRMD7, and AHR. In addition to providing insight into the degree of foveal development, identifying the grade of FH has diagnostic and prognostic implications.1 ,7 Rufai et al7 reported that identifying the grade of FH can predict future visual acuity (VA) in preverbal children with nystagmus. The introduction of OCT has revolutionized ophthalmic diagnosis with the ability to promptly visualize retinal morphology in a high-resolution, noninvasive manner.8 ,9 Furthermore, the advent of handheld OCT has facilitated the foveal examination of pediatric patients.10 OCT is now available in most ophthalmology clinics and subsequently has rapidly become part of routine ophthalmic assessment.8 Thus, the necessary technology to identify foveal developmental abnormalities is now accessible in most centers. The phenotypic spectrum of albinism has been reported, including the variance in degree of arrested retinal development.11 ,12 Furthermore, high grades of FH (grades 3 and 4) have recently been consistently associated with SLC38A8 variants13 ; however, to date, the full spectrum of FH linked with variants of known genes involved in retinal development has not been investigated. Thus, it is unclear whether variants of certain genes known to be related to FH and nystagmus are associated with a more underdeveloped foveal morphology. We performed a comparative study to investigate and characterize the genotypic and phenotypic spectrum of FH in albinism, achromatopsia, PAX6, SLC38A8, FRMD7, and AHR variants.

Abstract

Complementary Split Ring Resonators (CSRR) and Inter-Digitated Capacitors (IDC) based RF sensors have been ubiquitously optimized assuming empty cavities. How- ever, in bio-sensing applications, most of the samples are dissolved in respective solvents. As a result, designing sen-sors based on specific solvent-filled cavities is the optimal approach for future RF bio-sensors. This paper presents the application of Binary Particle Swarm Optimization (BPSO) technique for maximizing the sensitivity of CSRR and IDC sensors. BPSO optimizes the designs by cell patterning the sensing region in the presence of phosphate buffer as a solvent. An enhancement of 47.38× for the CSRR and 2.03× for the IDC in the detection of L-Lysine at frequencies 2.18 GHz and 0.72 GHz respectively has been achieved, when compared to sensitivities of their traditional counterparts. Promising results have also been observed in the detection of sucrose and glucose, making it a suitable contender for optimization of next-generation RF bio-sensors

Abstract

Greenhouse gases (GHGs) play an important role in controlling local air pollution as well as climate change. In this study, we retrieved column-averaged dry-air (X) mole fractions of carbon dioxide (CO2), methane (CH4), and carbon monoxide (CO) using a ground-based EM27/SUN Fourier transform infrared spectrometer (FTIR). The EM27/SUN spectrometers are widely in use in the COllaborative Carbon Column Observing Network (COCCON). The PROFFAST software provided by COCCON has been used to analyze the measured atmospheric solar absorption spectra. In this letter, the diurnal variation and the time series of daily averaged XCO2, XCH4, and XCO covering the period from December 2020 to May 2021 are analyzed. The maximum values of XCO2, XCH4, and XCO are observed to be 420.57 ppm, 1.93 ppm, and 170.40 ppb, respectively. Less diurnal but clear seasonal changes are observed during the study period. XCH4 and XCO from the Sentinel-5Precursor (S5P)/TROPOspheric Monitoring Instrument (TROPOMI) are compared against the EM27/SUN retrievals. The correlation coefficient for the EM27/SUN retrieved XCH4 and XCO, with the S5P/TROPOMI products, are 0.75 and 0.94, respectively

Abstract

Locomotion in Virtual Reality (VR) acts as a motion tracking unit for simulating user movements based on the Degree-of-Freedom (DOF) of the application. For effective locomotion, VR practitioners may have to transform their hardware from 3-DOF to 6-DOF. In this context, we conducted a literature review on different motion tracking methods employed in the Head-Mounted-Devices (HMD) to understand such hardware transformation to conduct locomotion in VR. Our observations led us to formulate a taxonomy of the tracking methods for locomotion in VR based on system design. Our study also captures different metrics that VR practitioners use to evaluate the hardware based on the context, performance, and significance for conducting locomotion.

Abstract

Deep learning based methods have shown great promise in achieving accurate automatic detection of Coronavirus Disease (COVID) - 19 from Chest X-Ray ( CXR) images.However, incorporating explainability in these so- lutions remains relatively less explored. We present a hi- erarchical classification approach for separating normal, non- COVID pneumonia (NCP ) and COVID cases using CXR im- ages. We demonstrate that the proposed method achieves clinically consistent explainations. We achieve this using a novel multi-scale attention architecture called Multi-scale Attention Residual Learning (MARL ) and a new loss function based on conicity for training the proposed architecture. The proposed classification strategy has two stages. The first stage uses a model derived from DenseNet to sepa- rate pneumonia cases from normal cases while the second stage uses the MARL architecture to discriminate between COVID and NCP cases. With a five-fold cross validation the proposed method achieves 93%, 96.28%, and 84.51% accu- racy respectively over three large, public datasets for nor- mal vs. NCP vs. COVID classification. This is competitive to the state-of-the-art methods. We also provide explanations in the form of GradCAM attributions, which are well aligned with expert annotations. The attributions are also seen to clearly indicate that MARL deems the peripheral regions of the lungs to be more important in the case of COVID cases while central regions are seen as more important in NCP cases. This observation matches the criteria described by radiologists in clinical literature, thereby attesting to the utility of the derived explanations.