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Remote Sensing Assessment of Ionospheric Total Electron Content Over Thanjavur GPS Station Geomagnetic 2.91 N 152.22 E
No full textA Global Positioning System (GPS) station for ionosphere studies was established during December 2019 in Thanjavur (Geographic 10.72◦ E, 79.01◦ N, Geomagnetic 2.91◦ N, 152.22◦ E). The study involved analysing Total Electron Content (TEC) data and correcting biases to estimate Slant Total Electron Content (STEC). STEC was then converted to Vertical Total Electron Content (VTEC), useful for space weather, navigation, surveying, and environmental monitoring.
During the course of our study, this Thanjavur station witnessed a Annular Solar eclipse of Dec-26, 2019. The Solar eclipses can impact ionospheric conditions, causing VTEC depletion. This study shows how the near-earth environment changes during the solar eclipse. The empirical model is helpful for the VTEC variations. The climate models such as NeQuick2 and International Reference Ionosphere for Plasma sphere (IRI-Plas) models captured seasonal variations effectively. The study assessed climate models performance at this equatorial location, with NeQuick2 having a low Root Mean Square Error (RMSE) of 2 TECU, making it a potential choice. Machine Learning (ML) models are applied for filling the missing data.
A Bi-directional Long Short Term Model (Bi-LSTM) model significantly outperformed conventional Long Short Term Model (LSTM), reducing Mean Absolute Error (MAE) by 28%, Mean Square Error (MSE) by 48%, and RMSE by 24%. It successfully predicted VTEC during equinox and solstice periods with low errors and a good fit. There is a proposal that aimed to enhance spoofing detection in navigation systems by using ionospheric signature like STEC,
Amplitude (S4), and phase ( σφ) scintillation, increasing reliability and security for future civilian navigation systems, especially in vehicles. In summary, this research contributes valuable insights into VTEC prediction during quiet geomagnetic condition, equinox, solstice and the potential use of ionospheric signature to enhance spoofing detection in Global Navigation Satellite System (GNSS)/GPS applications for surveying/navigation. We applied a TEC for the electro dynamical coupling between Sporadic E layer (ES) and F region of the ionosphere as a case study for the future plan to our GPS station. Our finding implies that the electro dynamical coupling between the layer and F region could play an important role in growing Medium Scale Traveling Ionosphere Disturbances (MSTID) and this coupling phenomena depends the trend of Solar activity
Improving Fatigue Characteristics of Cold Formed Low Carbon Steel
No full textEN353 is a low carbon alloy steel, predominantly used in the manufacturing of heavy-duty gears, shafts, and pinions, especially crown wheel and pinions. It is usually carburized, hardened, and tempered to produce a hard, wear-resistant case. Heat- treated EN353 steel exhibits enhancement in ductility, toughness, strength, and hardness as the internal stresses get relieved in the material. Spheroidizing of medium and high carbon steel is a method of prolonged heating at a temperature below the eutectoid temperature, where pearlite, the lowest energy arrangement of steel, gets converted to ferrite and cementite.
The graphite content of steel assumes a spheroidal shape, and after prolonged heating the pearlite layers are broken down, and spherical lumps of cementite or spheroidite are found. The structures in spheroidite are much larger than those of pearlite and are spaced further apart, making the steel extremely ductile. However, for low-carbon steels, which are soft and ductile in nature, pre-heat treatment enhances the workability. In this work, EN353 steel billets which are used as preforms for the cold forming process are heat treated to possess properties that meet the service demands through microstructure evolution.
Hot compression test is carried out over a broad processing spectrum and the influence of the processing parameters on flow stress is assessed. Using the experimental data, the safe and unsafe processing domains for EN353 steel is identified using Dynamic Material Modeling technique which needs to be integrated with the ideal processing parameters. An Artificial Neural Network model and a mathematical model are developed to predict the flow behavior of the steel in the preferred processing spectrum. Also, the effect of heat treatment on the material’s fatigue behaviour is studied using low cycle fatigue test.
The heat treatment cycle is designed to integrate the preform preparation and the cold forming process for heat treated EN353 steel. The heat-treated steel became softer with improved ductility and toughness without considerable grain coarsening. Grain sizes of the steel after heat treatment were found to be ranging from ASTM number 5 to ASTM number 8 (i.e. average grain diameter of 0.022 mm to 0.062 mm). The fatigue life of EN353 steel has improved significantly (increased by 50%) but at the expense of its strength. The ideal high temperature processing domains for the EN353 steel were identified to optimize the preform preparation for the orbital cold forming process
Investigation on Strength Durability and Crack Healing Efficiency of Natural Fiber Reinforced Bacterial Concrete under Various Exposures
Get PDFConcrete is the most broadly utilized construction material due to its availability and cost. It is more susceptible to cracks, allowing chemical substances and water to enter and deteriorate the materials over time, as well as affecting the durability properties of structures. In recent years, the use of bacteria and natural fibers in concrete has not only reduced cracks but also enhanced its strength and durability.
Microbial-induced carbonate precipitation (MICP) is highly acceptable in concrete to improve rheological and crack repair properties. In the current research isolated five bacteria from various locations and subjected to basic tests such as urease enzyme activity, CaCO3 production, and pH growth. The bacteria Bacillus paramycoides was isolated from concrete efflorescence and showed good results in basic tests. It was used in concrete with hybrid combinations of coir, flax, and jute, and the results were studied. The bacteria (self-healing agent) was added to natural fiber reinforced concrete using direct addition (DA) and immobilization (IM) techniques.
Compared to control and DA, IM rendered improved values in strength, durability and crack healing properties. The strength results of IM specimens showed improved compressive strength by 25.95%, compressive strength regain by 61.45%, split tensile strength by 45.65%, flexural strength by 28% and impact strength by 444.92%.
The IM samples under durability tests, showed 85.41% reduction in sorptivity, 2% strength loss in chloride exposures, and 2% strength loss in sulphate exposure. The average rate of crack healing (internal and external) for pre-cracked cube samples (crack range - 0.3 to 1.2 mm) after 28 days of full-wet curing, wet-dry curing, normal soil curing, and marine soil curing was 87.85%, 96.18%, 79.86%, and 49.66%, respectively. The corrosion level of reinforced steel bars under chloride curing was assessed, using gravimetric analysis.
At 270 days, the corrosion level of embedded bars from bacterial samples (DA and IM) showed a range of 0.07–0.23%. The microstructure study was carried using Scanning electron microscopy (SEM), X-ray diffractometers (XRD), and Energy dispersive spectroscopy (EDS). It was concluded that the presence of aragonite, calcite and vaterite (polymorphs of CaCO3) in bacterial samples is due to the biological activity of Bacillus paramycoides with a natural fiber-reinforced cementitious system
Sustainable Valorization of Woody and Herbaceous Biomass for Energy and Environmental Applications
Get PDFPyrolysis is a thermochemical conversion method to produce gas and liquid fuels from lignocellulosic biomass. This process involves the thermal decomposition of organic compounds without oxygen. The vapors formed are rapidly condensed to yield a liquid product called bio-oil and a solid product called biochar.
In the present study, Sesame indicum (SI) (herbaceous agro residue) and Prosopis julifora (PJ) (woody biomass weed) were chosen as feedstocks for the pyrolysis process and the respective solid and liquid products were collected and characterized. The proportion of biomass pyrolysis products is significantly influenced by biomass composition and process conditions such as temperature, heating rate, and residence time. The kinetic models for the thermal decomposition of SI and PJ biomass were calculated using the isoconversional models, and energy and exergy analysis was performed.
A comparison between these systems, such as thermal decomposition characteristics and kinetic behavior, could help identify the ideal operating conditions for both energy and environmental applications. Biochar obtained from PJ exhibits a highly porous morphology with a wide range of surface area.
Moreover, the surface chemistry of biochar can be altered by introducing several chemical functional groups using physical and chemical activations. We have investigated the effect of physical and various chemical activations of PJ biochar, which can be exploited for energy and environmental applications. The zinc chloride (ZnCl2) activated biochar was utilized for the adsorptive removal of ciprofloxacin, an emerging contaminant in batch and continuous mode.
Activated carbon prepared using treatment with potassium hydroxide (KOH) from PJ Biochar is utilized for supercapacitor electrodes and has high specific capacitance and excellent performance. On the other hand, SI pyrolysis produced bio-oil with a high amount of water content. The bio-oil aqueous phase (BOAP) obtained from SI pyrolysis showed that it could be a promising candidate for fighting hospital-associated infections (HAIs).
Techno-economic and comparative life cycle assessment shows that the activated PJ biochar is a sustainable alternative to fossil fuel-derived activated carbons. Based on the results obtained from this study, both PJ and SI biomass could be efficiently utilized for both energy and environmental applications, which will be otherwise regarded as waste
Development of Monitoring Systems using Computer Vision Techniques for Precise Fish Farming
No full textFish farm is a biosystem with diversity and uncertainties demanding, an intelligent manual monitoring process, which is labor-intensive, invasive, costly, discontinuous, and persistent. The proposed work aims to develop Computer Vision (CV) -based monitoring techniques for precise fish farming to address these problems. Three major monitoring operations namely (i) Fish behavior analysis, (ii) Fish Classification (FC) and (iii) Biomass estimation have been investigated.
Fish schooling activity has been monitored using an overhead vision camera. Problems associated with fish occlusion are addressed using the weighted K-means clustering technique which provides an accurate estimation of fish school locations. Temporal variations of these fish schools are tracked using the Kalman filter (KF)-based multitarget tracking approach. Experimental results illustrate the reliability of the proposed technique to monitor fish school activity in an indoor aquaculture environment.
Multisegmented FC technique using novel fusion-based Deep Learning Network (DLN) architecture is proposed. Inspired by the fish\u27s hydrodynamic nature, convexity deficiency is determined to identify the fish head and is used to segment the fish head, scales, and body. Each segment uses an AlexNet DLN to generate inferences for FC and the inferences are fused using a naive Bayesian fusion layer. Experimental results illustrate a classification accuracy of 98.64% and 98.94% - and Brigham Young University (BYU) datasets respectively. Comparative analysis with other standard networks and ablation studies demonstrates the accuracy and robustness of the proposed fusion architecture, respectively.
DLNs-based segmental analysis technique has been proposed to determine the fish length and convert it to biomass using a calibration curve. In this work, fish segments like head, body, and tail are detected using YOLOv4 (You Look Only Once version-4) DLN. Detected segments are associated using the sequence (head-body-tail) constraint Nearest Neighborhood (NN) technique to define Completely Visible Fish (CVF).
Convex hull and oriented bounding box technique are used to determine the CVF length. Experimental results illustrate a 0.9451 mAP (mean Average Precision) for YOLOv4 and 95.4% of CVFs are detected accurately. Biomass has been estimated with an accuracy of 94.15% and 91.52% for testing and validation image sets, respectively. Integration of the proposed monitoring technique with water quality sensors and feeder systems will be the future extension
Design and Analysis of Optimal Energy Efficient Routing Protocols for Lifetime Maximization and QoS Enhancement In Wireless Body Area Networks
No full textIn recent times, Wireless Body Area Networks (WBAN) a subsection of Wireless Sensor Networks (WSN) a promising technology for the future healthcare realm with cutting-edge technologies that can assist healthcare professionals like doctors, nurses and biomedical engineers which enables one-to-one care by implementing Tele-Medicine and Tele-Health solutions. Machine Learning (ML) and Internet of Things (IoT) enabled medical big data is the future of the healthcare sector and Medical Technology-based industries leading to applications in other sectors such as fitness tracking for commercial purposes, Sportsperson health monitoring to track their day-to-day activities and wearable devices for critical and emergency care.
Recent proliferation in miniaturized microelectronics and sensor-based wireless communication and networking industries paved the way for the emergence of WBAN. Mobility in WBAN has become a major challenge in framing the network topology and achieving better network performance. To address the dynamic nature of WBAN EADC-RP protocol is introduced to analyze the network performance with different mobility rates, where a network with the highest mobility rate of 1.0 m/s has less performance when compared to the mobility rates of 0.3 m/s and 0.6 m/s. WBAN setup with different postures and a multihop routing to select a relay node to collect data from far away sensor nodes and to CCN with different locations is analyzed.
ESTEEM is a novel approach for choosing the best-fitted AN by incorporating the HMM to identify the topology variations in the network. The outcome of the proposed ESTEEM achieves increased throughput, better stability with the first dead node and extended network lifetime with the last dead node at 71% and 95.1% of total simulation time respectively. A novel approach that requires dynamic clustering with different cluster members over the course of the data transmission process. The ANFIS integrated with MGWO to optimize the energy consumption in WBAN through sensor clustering. The key objective is to enhance energy efficiency by dynamically organizing sensors into clusters based on their contextual data.
ANFIS is employed to model the intricate relationship within the network, providing a flexible and adaptive system capable of learning and adjusting to the dynamic nature of the WBAN environment. MGWO inspired by social behavior in grey wolves, is utilized as a metaheuristic optimization algorithm to fine-tune the parameters of energy, distance and packet inter-arrival time. The proposed methodology involves formulating an objective function that encapsulates the energy efficiency goals of the WBAN.
An iterative MGWO optimizes the parameters and effectiveness of the hybrid approach is validated through simulations and achieves superior energy efficiency when compared to traditional optimization techniques. This research proposes Hybrid ANFIS-MGW Optimizer (HAMO) has achieved enhanced network performance and energy efficiency in WBAN. The proposed HAMO based on ANFIS and GWO for energy-efficient WBAN is 57.1%, 54.3% and 73.58% better than iM-SIMPLE, ACO-WBAN and GWO-WBAN respectively
Adaptive Solutions for Improving the Quality of Mobile User Experiences
No full textNowadays, mobile applications have drawn a lot of attention as they bring computational and storage resources close to consumers globally through high-speed networks. Applications such as the medical microscope, 2D barcode reader, environmental sensor(s), mobile security and authenticator, vehicle remote controller, and IoT-based synchronizer come pre- applications are resource-intensive, as it performs computation(s) by utilizing diverse services like location, app-tracking, networking, camera, calendar, contacts, Bluetooth, etc. for each user activity. Parallel execution of such intense services might utilize the utmost memory and CPU of the mobile device which in turn degrades the overall Quality of Experience (QoE). This ultimately ends up creating functional discrepancies during complex application execution in such mobile devices with limited computational resources. To overcome such challenges identified in mobile application user experience, intense computational processes that demand diverse application services can be offloaded to high-performance cloud cluster(s) and enhance the overall QoE.
According to the report from International Data Corporation (IDC), mobile devices are expected to generate 175 Zettabytes of data by 2025. Thereby, processing such voluminous data is both inevitable and a laborious task. Moreover, when information is accessed from a central data center, it takes longer to reach the end user. This access delay can cause a significant decline in the QoE of mobile users. Therefore, to achieve a minimal response time, caching techniques can be imposed to minimize the content delivery delay experienced by mobile users. For addressing the above-mentioned issues, this thesis aims develop adaptive algorithms for effectively utilizing the remote resources thereby improving the overall QoE of mobile users The significant contributions of this thesis are as follows:
1. Offloading intensive modules to optimum multi-site resource-rich server(s) is considered to be the conventional method for reducing the energy utilization required to execute intensive application modules on mobile devices. In order to identify highly configured nodes from a large number of available heterogeneous natured cloud nodes and to decrease the computational overhead of mobile
applications, a fuzzy logic based node classification framework is proposed. The proposed framework incorporates the Simplified Swarm Optimization technique for task integration and decomposition to lower the weighted total cost of intensive applications. The proposed framework is validated based on the following metrics such as least weighted total cost, processing time, and energy usage incurred during execution.
2. The standard protocol for offloading delay-sensitive applications to an unknown fog environment is to compute and validate each highly configured fog node(s) with a trust parameter. Hence, this work proposes a beta distribution-based algorithm for computing a trust score for each fog node. In addition to trust computation, the identified fog nodes are even made to incorporate load balancing. By imposing
parallelism in a fog environment, services are provided with faster response time. The result is validated by the latency and execution time.
3. Distributed services have to be combined and provided as a single output to the service requester. Fog node capacity directly influences the number of sub-services it can host. It is assumed that each fog device can support a single sub-service. To fully satisfy the other quality requirements of the mobile user, the system should logically identify a group of reliable fog services. The functionality of several fog services may be the same, but their QoS vary. In order to achieve the best outcomes, an algorithm is proposed in this thesis for the effective selection of the appropriate fog services for each subtask. The service composition is first modelled as a Multi-Dimensional Multi-Choice Knapsack Problem (MMKP), which is then solved using a Teaching Learning based Optimization method to identify the most reliable fog services. The proposed work is validated in terms of selecting highly trusted services with satisfied user preferences.
4. Caching popular material in a nearby fog node to reduce access time delay is the conventional method for mobile users to obtain popular content from remote servers. In order to discover popular content and an effective cache node to retain it, this work provides a framework that results in faster access time. A collaborative filtering-based recommendation system is employed to determine the popular content that should be placed. An overlay network is built over the physical fog network to identify the caching nodes in the fog environment using unique graph theory-based notions like semigraph and dominating set. Selected popular content is located in the identified nearby fog nodes to the mobile user, and it is accessible to mobile users, resulting in the reduction of access time and improvement in the QoE of mobile users. The proposed work is validated in terms of throughput and latency when accessing remote content.
The algorithms proposed in this thesis are implemented and compared with the state-ofthe- art algorithms using the generated synthetic dataset. The proposed work\u27s energy consumption, overall execution time, weighted total cost, end-to-end delay, and throughput are examined for various computationally-intensive applications. The outcomes demonstrate that the proposed algorithms are capable of executing intensive, trustworthy mobile applications with reduced energy consumption and accessing remote content with reduced latency, which in turn improves the QoE of mobile users
Development Of Hybrid Multi Criteria Decision Making Techniques For Efficient Cloud Service Selection
No full textDuring the past few decades, cloud computing became a primary driver for the next generation of digital technology due to the increase in organizational performance and profitability based on a ‘pay-as-you-use’ fashion at anytime and anywhere across the globe. Cloud computing enables various enterprises to access pooled resources (like storage, network bandwidth, software applications, processing power, etc.) over the Internet with minimal Information Technology (IT) infrastructure and capital expenditure.
Indeed, the enormous popularity of cloud computing in both academia & industry over the decade has resulted in a wide range of similar cloud services offered by numerous service providers. Even though, cloud computing is a powerful service model, researchers, organizations, and academicians are still reluctant to utilize cloud services because of its uncertain and dynamic nature in terms of availability, security, and resource elasticity.
Cloud services are often associated with uncertainty in user feedback, levels of Quality of Service (QoS), availability zones and resources, etc. Since the cloud computing environment is prominent to dynamic nature, it creates a negative impact on the quality of cloud services. The uncertainty in the context of cloud services still remains a challenging research problem to identify trustworthy cloud service providers.
Further, the Inconsistent Service Ranking (ISR) and Rank Reversal Phenomenon (RRP) are the two major concerns that lead to an inefficient cloud service selection. To address these major concerns (i.e., uncertainty, ISR, and RRP), the thesis focusses on the development of hybrid Multi-Criteria Decision-Making (MCDM) techniques to identify trustworthy service providers in cloud computing environments. The major contributions of the thesis are highlighted as,
1. The performance of cloud services that evaluate based on the subjective assessment data (user feedback) and objective assessment data (real-time monitored QoS values) are imprecise and inconsistent. To address this, an Interval-Valued Intuitionistic Fuzzy Set (IVIFS) with a hybrid weight method is integrated with an MCDM technique for the identification of trustworthy service providers in the user feedback dataset
2. To address the uncertainty and develop an accurate trust prediction model for objective assessment data, a Picture Fuzzy Set (PFS)-based MCDM approach with Naïve Bayes is designed to predict the trust values of cloud services
3. An objective weighted scheme-based MCDM with enhanced accuracy normalization technique is presented to address the Inconsistent Service Ranking (ISR) and Rank Reversal Phenomenon (RRP) issues in cloud computing environments.
The different techniques that are formulated for research work are validated against three real-world datasets namely, (1) CloudArmor-a trust feedback dataset with a set of storage service providers, (2) Quality of Web Services (QWS)-real-time monitored QoS values of cloud services that ensure the quality of cloud services, and (3) CloudHarmony-provide metrics to analyze the performance of cloud services and the service status of different CSPs. To ensure the robustness of the proposed hybrid MCDM techniques, the results are compared with the state-of-the-art MCDM approaches and sensitivity analysis
ITIHAS Vol. 24 Issue No. 1
No full textNEWSLETTER FROM SASTRA DEEMED UNIVERSITYhttps://knowledgeconnect.sastra.edu/itihas/1002/thumbnail.jp
ITIHAS Vol. 24 Issue No. 3
No full textNEWSLETTER FROM SASTRA DEEMED UNIVERSITYhttps://knowledgeconnect.sastra.edu/itihas/1004/thumbnail.jp