Indian Institute of Technology Tirupati

The Visual Object Tracking challenge VOT2019 is the seventh annual tracker benchmarking activity organized by the VOT initiative. Results of 81 trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in the recent years. The evaluation included the standard VOT and other popular methodologies for short-term tracking analysis as well as the standard VOT methodology for long-term tracking analysis. The VOT2019 challenge was composed of five challenges focusing on different tracking domains: (i) VOTST2019 challenge focused on short-term tracking in RGB, (ii) VOT-RT2019 challenge focused on "real-time" shortterm tracking in RGB, (iii) VOT-LT2019 focused on longterm tracking namely coping with target disappearance and reappearance. Two new challenges have been introduced: (iv) VOT-RGBT2019 challenge focused on short-term tracking in RGB and thermal imagery and (v) VOT-RGBD2019 challenge focused on long-term tracking in RGB and depth imagery. The VOT-ST2019, VOT-RT2019 and VOT-LT2019 datasets were refreshed while new datasets were introduced for VOT-RGBT2019 and VOT-RGBD2019. The VOT toolkit has been updated to support both standard shortterm, long-term tracking and tracking with multi-channel imagery. Performance of the tested trackers typically by far exceeds standard baselines. The source code for most of the trackers is publicly available from the VOT page. The dataset, the evaluation kit and the results are publicly available at the challenge website.

Abstract It is well known that three challenges of hydrogen economy, that is, production, storage, and transportation or application put tremendous stress on scientific community for the past several decades. Based on several investigations, reported in literature, it is observed that the storage of hydrogen in solid form is more suitable option to overcome the challenges like its storage and transportation. In this form, hydrogen can be stored by absorption (metal hydrides and complex hydrides) and adsorption (carbon materials). Compared to absorption, adsorption of hydrogen on carbon materials is observed to be more favorable in terms of storage capacity. Taking in to account of these facts, in this short review, an overview on hydrogen adsorption on activated carbon and different allotropes of carbon like graphite, carbon nanotubes, and carbon nanofibers is presented. Synthesis processes of all the carbon materials are discussed in brief along with their hydrogen storage capacities at different operating conditions, and thermodynamic properties and reaction kinetics. In addition, different methods to improve hydrogen storage capacities of carbon materials are presented in detail. Finally, comparison is made between different carbon materials to estimate the amount of hydrogen that can be stored and retract practically. The experimentally measured maximum hydrogen storage capacity of activate carbon, graphite, single‐walled nanotubes, multiwalled nanotubes, and carbon nanofibers at room temperature are 5.5 wt%, 4.48 wt%, 4.5 wt%, 6.3 wt%, and 6.5 wt%, respectively.

The rapid developments in the Internet of Medical Things (IoMT) help the smart healthcare systems to deliver more sophisticated real-time services. At the same time, IoMT also raises many privacy and security issues. Also, the heterogeneous nature of these devices makes it challenging to develop a common security standard solution. Furthermore, the existing cloud-centric IoMT healthcare systems depend on cloud computing for electrical health records (EHR) and medical services, which is not suggestible for a decentralized IoMT healthcare systems. In this article, we have proposed a blockchain-based novel architecture that provides a decentralized EHR and smart-contract-based service automation without compromising with the system security and privacy. In this architecture, we have introduced the hybrid computing paradigm with the blockchain-based distributed data storage system to overcome blockchain-based cloud-centric IoMT healthcare system drawbacks, such as high latency, high storage cost, and single point of failure. A decentralized selective ring-based access control mechanism is introduced along with device authentication and patient records anonymity algorithms to improve the proposed system's security capabilities. We have evaluated the latency and cost effectiveness of data sharing on the proposed system using Blockchain. Also, we conducted a logical system analysis, which reveals that our architecture-based security and privacy mechanisms are capable of fulfilling the requirements of decentralized IoMT smart healthcare systems. Experimental analysis proves that our fortified-chain-based H-CPS needs insignificant storage and has a response time in the order of milliseconds as compared to traditional centralized H-CPS while providing decentralized automated access control, security, and privacy.

The coronavirus disease (COVID-19) created enormous pressure across the globe due to an increasing number of COVID-19 infected cases. All the governments’ primary focus is to save humanity from this pandemic problem, and they have lockdown almost the entire nation to stop the spread of infection. This lockdown resulted in a considerable impact on the global as well as a local economy that will take a long time to perform with business as usual scenario. However, improvement in the air quality of the cities across the globe has emerged as a key benefit of this lockdown. Therefore, this study aims to assess the overall impact of social and travel lockdown in five megacities of India; Delhi, Mumbai, Chennai, Kolkata, and Bangalore. The study evaluated the spatiotemporal variations in five criteria pollutants over two time periods, i.e., March–April 2019 and March–April 2020 and 10th–20th March 2020 (before lockdown) and 25th March to 6th April 2020 (during lockdown). The results highlighted a statistically significant decline in all the pollutants in all the megacities except for ozone. It was observed that the concentration of PM2.5, PM10, NO2 and CO declined by ~41% (66–39 µg m–3), ~52% (153–73 µg m–3), ~51% (39–19 µg m–3) and ~28% (0.9–0.65 mg m–3) during the lockdown phase in comparison to the before lockdown in Delhi, respectively. Similar decline in pollutant concentration was observed in other megacities as well. Further, the study conducted an expert survey to identify the possible challenges India might face after lockdown is over. All the experts said that reviving the economy will be a big challenge for the government, and it may result in some tradeoff while managing the air quality in the near future due to scarcity of funds, etc.

Phase unwrapping is a crucial signal processing problem in several applications that aims to restore original phase from the wrapped phase. In this letter, we propose a novel framework for unwrapping the phase using deep fully convolutional neural network termed as PhaseNet. We reformulate the problem definition of directly obtaining continuous original phase as obtaining the wrap-count (integer jump of 2 π) at each pixel by semantic segmentation and this is accomplished through a suitable deep learning framework. The proposed architecture consists of an encoder network, a corresponding decoder network followed by a pixel-wise classification layer. The relationship between the absolute phase and the wrap-count is leveraged in generating abundant simulated data of several random shapes. This deliberates the network on learning continuity in wrapped phase maps rather than specific patterns in the training data. We compare the proposed framework with the widely adapted quality-guided phase unwrapping algorithm and also with the well-known MATLAB's unwrap function for varying noise levels. The proposed framework is found to be robust to noise and computationally fast. The results obtained highlight that deep convolutional neural network can indeed be effectively applied for phase unwrapping, and the proposed framework will hopefully pave the way for the development of a new set of deep learning based phase unwrapping methods.

Phase unwrapping is an ill-posed classical problem in many practical applications of significance such as 3D profiling through fringe projection, synthetic aperture radar and magnetic resonance imaging. Conventional phase unwrapping techniques estimate the phase either by integrating through the confined path (referred to as path-following methods) or by minimizing the energy function between the wrapped phase and the approximated true phase (referred to as minimum-norm approaches). However, these conventional methods have some critical challenges like error accumulation and high computational time and often fail under low SNR conditions. To address these problems, this paper proposes a novel deep learning framework for unwrapping the phase and is referred to as “PhaseNet 2.0”. The phase unwrapping problem is formulated as a dense classification problem and a fully convolutional DenseNet based neural network is trained to predict the wrap-count at each pixel from the wrapped phase maps. To train this network, we simulate arbitrary shapes and propose new loss function that integrates the residues by minimizing the difference of gradients and also uses L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> loss to overcome class imbalance problem. The proposed method, unlike our previous approach PhaseNet, does not require post-processing, highly robust to noise, accurately unwraps the phase even at the severe noise level of -5 dB, and can unwrap the phase maps even at relatively high dynamic ranges. Simulation results from the proposed framework are compared with different classes of existing phase unwrapping methods for varying SNR values and discontinuity, and these evaluations demonstrate the advantages of the proposed framework. We also demonstrate the generality of the proposed method on 3D reconstruction of synthetic CAD models that have diverse structures and finer geometric variations. Finally, the proposed method is applied to real-data for 3D profiling of objects using fringe projection technique and digital holographic interferometry. The proposed framework achieves significant improvements over existing methods while being highly efficient with interactive frame-rates on modern GPUs.

Catalytic reaction between PVA-capped AgNPs and hydrogen peroxide, and the corresponding LSPR optical absorbance spectra as a function of time.

Resource economy constitutes one of the key challenges for researchers and practitioners in academia and industries, in terms of rising demand for sustainable and green synthetic methodology. To achieve ideal levels of resource economy in molecular syntheses, novel avenues are required, which include, but are not limited to the use of naturally abundant, renewable feedstocks, solvents, metal catalysts, energy, and redox reagents. In this context, electrosyntheses create the unique possibility to replace stoichiometric amounts of oxidizing or reducing reagents as well as electron transfer events by electric current. Particularly, the merger of Earth-abundant 3d metal catalysis and electrooxidation has recently been recognized as an increasingly viable strategy to forge challenging C-C and C-heteroatom bonds for complex organic molecules in a sustainable fashion under mild reaction conditions. In this review, we highlight the key developments in 3d metallaelectrocatalysis in the context of resource economy in molecular syntheses until February 2020.

Some of the most prominent and promising catalysts in organic synthesis for the requisite construction of C–C and C–N bonds are palladium (Pd) catalysts, which play a pivotal role in pharmaceutical and medicinal chemistry.

Distributed generator (DG) resources are small scale electric power generating plants that can provide power to homes, businesses or industrial facilities in distribution systems. Power loss reductions, voltage profile improvement and increasing reliability are some advantages of DG units. The above benefits can be achieved by optimal placement of DGs. Whale optimization algorithm (WOA), a novel metaheuristic algorithm, is used to determine the optimal DG size. WOA is modeled based on the unique hunting behavior of humpback whales. The WOA is evaluated on IEEE 15, 33, 69 and 85-bus test systems. WOA was compared with different types of DGs and other evolutionary algorithms. When compared with voltage sensitivity index method, WOA and index vector methods gives better results. From the analysis best results have been achieved from type III DG operating at 0.9 pf.

High Resolution Image Download MS PowerPoint Slide Core–shell ZIF-8@ZIF-67- and ZIF-67@ZIF-8-based zeolitic imidazolate frameworks (ZIFs) were synthesized solvothermally using a seed-mediated methodology. Transmission electron microscopy–energy-dispersive X-ray spectrometry, line scan, elemental mapping, X-ray photoelectron spectroscopy, and inductively coupled plasma-atomic emission spectroscopy analyses were performed to confirm the formation of a core–shell structure with the controlled Co/Zn elemental composition of ∼0.50 for both the core–shell ZIFs. The synthesized core–shell ZIF-8@ZIF-67 and ZIF-67@ZIF-8 frameworks conferred enhanced H 2 (2.03 and 1.69 wt %) storage properties at 77 K and 1 bar, which are ca. 41 and 18%, respectively, higher than that of the parent ZIF-8. Notably, the distinctly remarkable H 2 storage properties shown by both the core–shell ZIFs over the bimetallic Co/Zn-ZIF and the physical mixture of ZIF-8 and ZIF-67 clearly evidenced their unique structural properties (confinement of porosity) and elemental heterogeneity due to the core–shell morphology of the outperforming core–shell ZIFs. Moreover, H 2 adsorption isotherm data of these frameworks are best fitted with the Langmuir model ( R 2 ≥ 0.9999). Along with the remarkably enhanced H 2 storage capacities, the core–shell ZIFs also displayed an improved CO 2 capture behavior. Hence, we demonstrated here that the controlled structural features endorsed by the rationally designed porous materials may find high potential in H 2 storage applications.

The present paper introduces a focus stacking-based approach for automated quantitative detection of Plasmodium falciparum malaria from blood smear. For the detection, a custom designed convolutional neural network (CNN) operating on focus stack of images is used. The cell counting problem is addressed as the segmentation problem and we propose a 2-level segmentation strategy. Use of CNN operating on focus stack for the detection of malaria is first of its kind, and it not only improved the detection accuracy (both in terms of sensitivity [97.06%] and specificity [98.50%]) but also favored the processing on cell patches and avoided the need for hand-engineered features. The slide images are acquired with a custom-built portable slide scanner made from low-cost, off-the-shelf components and is suitable for point-of-care diagnostics. The proposed approach of employing sophisticated algorithmic processing together with inexpensive instrumentation can potentially benefit clinicians to enable malaria diagnosis.

Autoencoder (AE) is a deep neural network that has been widely utilized in process industry owing to its superior abilities of feature extraction and data reconstruction. Recently, assuming the latent variables to be random variables, a probabilistic variant of it called variational autoencoder (VAE) has achieved a major success in different applications. In this article, we develop two novel submodels based on deep VAEs (DVAE), which are further utilized to establish a soft sensor framework. By the use of our first submodel known as supervised DVAE (SDVAE), the distribution information of latent features can be obtained. This is used as a prior of the second submodel known as the modified unsupervised DVAE (MUDVAE). Then, a new soft sensor framework can be constructed by combing the encoder of SDVAE with the decoder of MUDVAE. Since our designed VAE has superior ability in data reconstruction, it also works well under the missing data situation which is common in process industries due to sensor failures. Thus, we extend the proposed soft sensor framework to handle the missing data situation. The effectiveness of our proposed soft sensor frameworks is finally demonstrated via an industrial polymerization dataset.

Distributed generator (DG) resources are the emerging micro-generating technologies such as fuel cells, micro turbines, IC engines. They also make use of renewable energy sources such as PV arrays and wind turbines. DG units have low emission rates and are environment friendly and economical. Power loss reductions, voltage profile improvement and increasing reliability are some advantages of DG units. The above benefits can be achieved by optimal placement of DGs. Optimal DG locations are obtained from power loss index method. A novel meta heuristic algorithm called whale optimization algorithm (WOA) is used to determine the optimal DG-unit's size in this paper. WOA modeled based on the unique hunting behavior of humpback whales. The WOA algorithm is tested on IEEE 15-bus, 33-bus, 69-bus, 85-bus and 118-bus test systems. The results obtained by the proposed WOA algorithm was compared with different types of DGs and other evolutionary algorithms. When compared with other algorithms the WOA algorithm gives better results. From the analysis best results have been achieved from type III DG operating at 0.9 pf. Keywords: Whale optimization algorithm, Power loss index method, Distributed generation placement, Radial distribution system, Loss reduction

The world has witnessed several incidents of epidemics and pandemics since the beginning of human existence. The gruesome effects of microbial threats create considerable repercussions on the healthcare systems. The continually evolving nature of causative viruses due to mutation or re-assortment sometimes makes existing medicines and vaccines inactive. As a rapid response to such outbreaks, much emphasis has been placed on personal protective equipment (PPE), especially face mask, to prevent infectious diseases from airborne pathogens. Wearing face masks in public reduce disease transmission and creates a sense of community solidarity in collectively fighting the pandemic. However, excessive use of single-use polymer-based face masks can pose a significant challenge to the environment and is increasingly evident in the ongoing COVID-19 pandemic. On the contrary, face masks with inherent antimicrobial properties can help in real-time deactivation of microorganisms enabling multiple-use and reduces secondary infections. Given the advantages, several efforts are made incorporating natural and synthetic antimicrobial agents (AMA) to produce face mask with enhanced safety, and the literature about such efforts are summarised. The review also discusses the literature concerning the current and future market potential and environmental impacts of face masks. Among the AMA tested, metal and metal-oxide based materials are more popular and relatively matured technology. However, the repeated use of such a face mask may pose a danger to the user and environment due to leaching/detachment of nanoparticles. So careful consideration is required to select AMA and their incorporation methods to reduce their leaching and environmental impacts. Also, systematic studies are required to establish short-term and long-term benefits.

This review exclusively elaborates the unnoticed vision into the design, fabrication, mechanism, and investigation of fascinating Ni(OH) ₂ -based supercapacitors in an asymmetric fashion.

concentration were also developed for three kitchen categories for both TCS and ICS. Thus, the current study concludes that usage of ICS coupled with efficient designing of the kitchen can improve the overall IAQ of the household along with immense health benefits. Overall, the study emphasized the need of more comprehensive studies to fully assess the association of household air pollution (HAP) and health of individual in the rural settings by considering the toxicity of PM.

This paper mainly focused on the impact of distributed generation (DG) placement on distribution system. The integration of DG is transforming the traditional radial distribution system into a multi-source system. Distributed generation is a term that refers to the production of electricity near the consumption place. The effects of distributed generation are short circuit levels are increased, load losses change, reliability change and voltage profiles change along the network. The above advantages can be accomplished by ideal position and sizing of DG units. The ideal positions are obtained from index vector method. Ant Lion Optimization (ALO), a novel meta heuristic algorithm is used to determine the optimal DG size. ALO is modeled based on the unique hunting behavior of ant lions. The ALO algorithm is evaluated on IEEE 15, 33, 69 and 85-bus test systems. ALO algorithm was compared with different types of DG units and other evolutionary algorithms. When compared with other algorithms the ALO algorithm gives better results. From the analysis best results have been achieved from type III DG operating at 0.9 pf.

BACKGROUND: Little if any cutaneous production of vitamin D3 occurs at latitudes above and below 35° N and 35° S during the winter months. It was postulated that those residing in tropics synthesize enough vitamin D3 year round. Several studies have documented the effect of latitude, season and time of the day on the cutaneous production of vitamin D3 in an ampoule model. Studies from India have shown high prevalence of vitamin D deficiency despite abundant sunshine. METHODS: We studied the influence of season and time of the day on synthesis of previtamin D3 in an ampoule model in Tirupati, (latitude 13.40° N and longitude 77.2° E) south India, between May 2007 to August 2008. Sealed borosilicate glass ampoules containing 50 μg of 7-DHC in 1 ml of methanol were exposed to sunlight hourly from 8 a.m. until 4 p.m. The percent conversion of 7-DHC to previtamin D3 and its photoproducts and the percent of previtamin D3 and vitamin D3 formed was estimated and related to solar zenith angle. RESULTS: The percent conversion of 7-DHC to previtamin D3 and its photoproducts and formation of previtamin D3 and vitamin D3 was maximal between 11 a.m. to 2 p.m. of the day during the entire year (median 11.5% and 10.2% respectively at 12.30 p.m.). CONCLUSIONS: Therefore at this latitude exposure to sunlight between the hours of 11 a.m. and 2 p.m. will promote vitamin D production in the skin year round.

Copyright © 2018 The Authors. Dynamics of puffing and micro-explosion phenomena occurring in ternary fuel emulsion droplets under high temperature environment were explored using high speed backlight imaging technique. A single droplet composed of diesel-biodiesel-ethanol emulsion was placed at the tip of a 75 µm gauge thermocouple and introduced rapidly into a furnace maintained at 500 °C. Several interesting features such as oscillation of suspended droplets, physical transformations occurring within the droplet, vapour expulsion, puffing, micro-explosion, sheet formation, perforations, growth of perforations, sheet disintegration and rotation of secondary droplets were observed. High resolution image analysis revealed separation of emulsion components within the core of the suspended droplet, which appeared either as a single nucleus or multiple nuclei. Two distinct types of micro-explosion were identified. For droplets encountering a single nucleus at the core resulted in a stronger vapour expulsion followed by intense micro-explosion. For droplets having multiple nuclei at the core resulted in a weaker vapour expulsion and slower growth of droplet prior to micro-explosion. Both types of micro-explosion process resulted in a number of child droplets. For the case of strong vapour expulsion nearly 80% of its child droplets have their sizes distributed within 150 μm compared to 60% for weaker vapour expulsion. The child droplets that were generated from the primary events of both puffing and micro-explosion cascaded further into secondary and tertiary events of puffing and micro-explosion in freely suspended environment.