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Neutral hydrogen (HI) $21$-cm intensity mapping (IM) is a promising probe of the large-scale structures in the Universe. However, a few orders of magnitude brighter foregrounds obscure the IM signal. Here we use the Tapered Gridded Estimator (TGE) to estimate the multi-frequency angular power spectrum (MAPS) $C_{\ell}(\Delta\nu)$ from a $24.4\,\rm{MHz}$ bandwidth uGMRT Band $3$ data at $432.8\,\rm{MHz}$. In $C_{\ell}(\Delta\nu)$ foregrounds remain correlated across the entire $\Delta\nu$ range, whereas the $21$-cm signal is localized within $\Delta\nu\le[\Delta \nu]$ (typically $0.5-1\,\rm{MHz}$). Assuming the range $\Delta\nu>[\Delta \nu]$ to have minimal $21$-cm signal, we use $C_{\ell}(\Delta\nu)$ in this range to model the foregrounds. This foreground model is extrapolated to $\Delta\nu\leq[\Delta \nu]$, and subtracted from the measured $C_{\ell}(\Delta\nu)$. The residual $[C_{\ell}(\Delta\nu)]_{\rm res}$ in the range $\Delta\nu\le[\Delta\nu]$ is used to constrain the $21$-cm signal, compensating for the signal loss from foreground subtraction. $[C_{\ell}(\Delta\nu)]_{\rm{res}}$ is found to be noise-dominated without any trace of foregrounds. Using $[C_{\ell}(\Delta\nu)]_{\rm res}$ we constrain the $21$-cm brightness temperature fluctuations $\Delta^2(k)$, and obtain the $2\sigma$ upper limit $\Delta_{\rm UL}^2(k)\leq(18.07)^2\,\rm{mK^2}$ at $k=0.247\,\rm{Mpc}^{-1}$. We further obtain the $2\sigma$ upper limit $ [\Omega_{{\rm HI}}b_{{\rm HI}}]_{\rm UL}\leq0.022$ where $\Omega_{{\rm HI}}$ and $b_{{\rm HI}}$ are the comoving HI density and bias parameters respectively. Although the upper limit is nearly $10$ times larger than the expected $21$-cm signal, it is $3$ times tighter over previous works using foreground avoidance on the same data., Comment: Accepted for publication in MNRAS. 16 pages (including Appendix), 8 figures (plus 8 in Appendix), 5 Tables. In version 2, the HI symbol is changed in the arxiv abstract; there are no changes in the manuscript

There has been a significant increase in the adoption of technology in cricket recently. This trend has created the problem of duplicate work being done in similar computer vision-based research works. Our research tries to solve one of these problems by segmenting ball deliveries in a cricket broadcast using deep learning models, MobileNet and YOLO, thus enabling researchers to use our work as a dataset for their research. The output from our research can be used by cricket coaches and players to analyze ball deliveries which are played during the match. This paper presents an approach to segment and extract video shots in which only the ball is being delivered. The video shots are a series of continuous frames that make up the whole scene of the video. Object detection models are applied to reach a high level of accuracy in terms of correctly extracting video shots. The proof of concept for building large datasets of video shots for ball deliveries is proposed which paves the way for further processing on those shots for the extraction of semantics. Ball tracking in these video shots is also done using a separate RetinaNet model as a sample of the usefulness of the proposed dataset. The position on the cricket pitch where the ball lands is also extracted by tracking the ball along the y-axis. The video shot is then classified as a full-pitched, good-length or short-pitched delivery.

We introduce a non-homogeneous version of the generalized counting process (GCP), namely, the non-homogeneous generalized counting process (NGCP). We time-change the NGCP by an independent inverse stable subordinator to obtain its fractional version, and call it as the non-homogeneous generalized fractional counting process (NGFCP). A generalization of the NGFCP is obtained by time-changing the NGCP with an independent inverse subordinator. We derive the system of governing differential-integral equations for the marginal distributions of the increments of NGCP, NGFCP and its generalization. Then, we consider the GCP time-changed by a multistable subordinator and obtain its L\'evy measure, associated Bern\v{s}tein function and distribution of the first passage times. The GCP and its fractional version, that is, the generalized fractional counting process when time-changed by a L\'evy subordinator are known as the time-changed generalized counting process-I (TCGCP-I) and the time-changed generalized fractional counting process-I (TCGFCP-I), respectively. We obtain the distribution of first passage times and related governing equations for the TCGCP-I. An application of the TCGCP-I to ruin theory is discussed. We obtain the conditional distribution of the $k$th order statistic from a sample whose size is modelled by a particular case of TCGFCP-I, namely, the time fractional negative binomial process. Later, we consider a fractional version of the TCGCP-I and obtain the system of differential equations that governs its state probabilities. Its mean, variance, covariance, {\it etc.} are obtained and using which its long-range dependence property is established. Some results for its two particular cases are obtained.

Various foot complications can be easily avoided by continuous monitoring of foot plantar temperature and pressure. In this work, the design, characterization, and implementation of a Fiber Bragg Gratings (FBG) based smart insole capable of simultaneously measuring plantar pressure and temperature has been reported. The instrumented insole was tested and verified during static and gait exercises. The paper also provides a comparison of the developed optoelectronic-based solution with a commercially available and widely used plantar pressure measurement and analysis system and a lab-made, electronic sensor-based smart insole that can measure both plantar temperature and pressure. It was shown that even though the commercially available expensive system is very robust and highly precise due to many sensing units on the insole, the developed insole with a smaller number of sensors can provide both plantar temperature and pressure with reasonable precision while displaying both foot pressure and temperature maps, and gait cycle plots in real-time. The FBG-based solution is comfortable and safer than the other systems being compared. Thus, confirming the effectiveness of the proposed solution as an addition to the research area for detecting foot complications non-invasively using foot insoles.

A complete smart insole solution that continuously monitors the foot plantar pressure and temperature can detect foot complications early and that too from the convenience of the user home. Widespread health complications such as Diabetic Mellitus need continuous foot complication monitoring to avoid severe complications. With that motivation, this paper provides a detailed design of a wearable insole using popular off-the-shelf sensors to monitor foot plantar pressure and temperature. The design provides details of which temperature and pressure to be used, circuit configuration for characterizing the sensors, and the considerations for developing a compact Printed Circuit board design using appropriate microcontrollers and communication protocol. The system also provides details of how the foot pressure and temperature data from the subjects using the sensors can be transferred wirelessly using a low-power consuming communication protocol to a central device where the data will be recorded. The investigation can help in developing a low-cost, feasible and portable foot monitoring system for patients by facilitating real-time, home monitoring of foot condition using Gait Cycle or Foot Pressure patterns and temperature heterogeneity between two feet. The proposed system will work in real-time., Comment: 29 page, 30 figures

Fast and accurate detection of the disease can significantly help in reducing the strain on the healthcare facility of any country to reduce the mortality during any pandemic. The goal of this work is to create a multimodal system using a novel machine learning framework that uses both Chest X-ray (CXR) images and clinical data to predict severity in COVID-19 patients. In addition, the study presents a nomogram-based scoring technique for predicting the likelihood of death in high-risk patients. This study uses 25 biomarkers and CXR images in predicting the risk in 930 COVID-19 patients admitted during the first wave of COVID-19 (March-June 2020) in Italy. The proposed multimodal stacking technique produced the precision, sensitivity, and F1-score, of 89.03%, 90.44%, and 89.03%, respectively to identify low or high-risk patients. This multimodal approach improved the accuracy by 6% in comparison to the CXR image or clinical data alone. Finally, nomogram scoring system using multivariate logistic regression -- was used to stratify the mortality risk among the high-risk patients identified in the first stage. Lactate Dehydrogenase (LDH), O2 percentage, White Blood Cells (WBC) Count, Age, and C-reactive protein (CRP) were identified as useful predictor using random forest feature selection model. Five predictors parameters and a CXR image based nomogram score was developed for quantifying the probability of death and categorizing them into two risk groups: survived (<50%), and death (>=50%), respectively. The multi-modal technique was able to predict the death probability of high-risk patients with an F1 score of 92.88 %. The area under the curves for the development and validation cohorts are 0.981 and 0.939, respectively., Comment: 25 pages, 8 Tables, 10 Figures

In this paper, we introduce and study fractional versions of three compound Poisson processes, namely, the Bell-Touchard process, the Poisson-logarithmic process and the generalized P\'olya-Aeppli process. It is shown that these processes are limiting cases of a recently introduced process by Di Crescenzo et al. (2016), namely, the generalized counting process. We obtain the mean, variance, covariance, long-range dependence property etc. for these processes. Also, we obtain several equivalent forms of the one-dimensional distribution of fractional versions of these processes.

The electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) signals, highly non-stationary in nature, greatly suffers from motion artifacts while recorded using wearable sensors. This paper proposes two robust methods: i) Wavelet packet decomposition (WPD), and ii) WPD in combination with canonical correlation analysis (WPD-CCA), for motion artifact correction from single-channel EEG and fNIRS signals. The efficacy of these proposed techniques is tested using a benchmark dataset and the performance of the proposed methods is measured using two well-established performance matrices: i) Difference in the signal to noise ratio ({\Delta}SNR) and ii) Percentage reduction in motion artifacts ({\eta}). The proposed WPD-based single-stage motion artifacts correction technique produces the highest average {\Delta}SNR (29.44 dB) when db2 wavelet packet is incorporated whereas the greatest average {\eta} (53.48%) is obtained using db1 wavelet packet for all the available 23 EEG recordings. Our proposed two-stage motion artifacts correction technique i.e. the WPD-CCA method utilizing db1 wavelet packet has shown the best denoising performance producing an average {\Delta}SNR and {\eta} values of 30.76 dB and 59.51%, respectively for all the EEG recordings. On the other hand, the two-stage motion artifacts removal technique i.e. WPD-CCA has produced the best average {\Delta}SNR (16.55 dB, utilizing db1 wavelet packet) and largest average {\eta} (41.40%, using fk8 wavelet packet). The highest average {\Delta}SNR and {\eta} using single-stage artifacts removal techniques (WPD) are found as 16.11 dB and 26.40%, respectively for all the fNIRS signals using fk4 wavelet packet. In both EEG and fNIRS modalities, the percentage reduction in motion artifacts increases by 11.28% and 56.82%, respectively when two-stage WPD-CCA techniques are employed., Comment: 25 pages, 10 figures and 2 tables

Continuous long-term monitoring of electrocardiography (ECG) signals is crucial for the early detection of cardiac abnormalities such as arrhythmia. Non-clinical ECG recordings acquired by Holter and wearable ECG sensors often suffer from severe artifacts such as baseline wander, signal cuts, motion artifacts, variations on QRS amplitude, noise, and other interferences. Usually, a set of such artifacts occur on the same ECG signal with varying severity and duration, and this makes an accurate diagnosis by machines or medical doctors extremely difficult. Despite numerous studies that have attempted ECG denoising, they naturally fail to restore the actual ECG signal corrupted with such artifacts due to their simple and naive noise model. In this study, we propose a novel approach for blind ECG restoration using cycle-consistent generative adversarial networks (Cycle-GANs) where the quality of the signal can be improved to a clinical level ECG regardless of the type and severity of the artifacts corrupting the signal. To further boost the restoration performance, we propose 1D operational Cycle-GANs with the generative neuron model. The proposed approach has been evaluated extensively using one of the largest benchmark ECG datasets from the China Physiological Signal Challenge (CPSC-2020) with more than one million beats. Besides the quantitative and qualitative evaluations, a group of cardiologists performed medical evaluations to validate the quality and usability of the restored ECG, especially for an accurate arrhythmia diagnosis., Comment: 16 pages, 10 figures, journal article submission

Raman spectroscopy provides a vibrational profile of the molecules and thus can be used to uniquely identify different kind of materials. This sort of fingerprinting molecules has thus led to widespread application of Raman spectrum in various fields like medical dignostics, forensics, mineralogy, bacteriology and virology etc. Despite the recent rise in Raman spectra data volume, there has not been any significant effort in developing generalized machine learning methods for Raman spectra analysis. We examine, experiment and evaluate existing methods and conjecture that neither current sequential models nor traditional machine learning models are satisfactorily sufficient to analyze Raman spectra. Both has their perks and pitfalls, therefore we attempt to mix the best of both worlds and propose a novel network architecture RamanNet. RamanNet is immune to invariance property in CNN and at the same time better than traditional machine learning models for the inclusion of sparse connectivity. Our experiments on 4 public datasets demonstrate superior performance over the much complex state-of-the-art methods and thus RamanNet has the potential to become the defacto standard in Raman spectra data analysis

Cardiovascular diseases are the most common causes of death around the world. To detect and treat heart-related diseases, continuous Blood Pressure (BP) monitoring along with many other parameters are required. Several invasive and non-invasive methods have been developed for this purpose. Most existing methods used in the hospitals for continuous monitoring of BP are invasive. On the contrary, cuff-based BP monitoring methods, which can predict Systolic Blood Pressure (SBP) and Diastolic Blood Pressure (DBP), cannot be used for continuous monitoring. Several studies attempted to predict BP from non-invasively collectible signals such as Photoplethysmogram (PPG) and Electrocardiogram (ECG), which can be used for continuous monitoring. In this study, we explored the applicability of autoencoders in predicting BP from PPG and ECG signals. The investigation was carried out on 12,000 instances of 942 patients of the MIMIC-II dataset and it was found that a very shallow, one-dimensional autoencoder can extract the relevant features to predict the SBP and DBP with the state-of-the-art performance on a very large dataset. Independent test set from a portion of the MIMIC-II dataset provides an MAE of 2.333 and 0.713 for SBP and DBP, respectively. On an external dataset of forty subjects, the model trained on the MIMIC-II dataset, provides an MAE of 2.728 and 1.166 for SBP and DBP, respectively. For both the cases, the results met British Hypertension Society (BHS) Grade A and surpassed the studies from the current literature., Comment: 22 pages, Figure 8, Table 13