7940 research outputs found
Sort by
Intelligent Trajectory Planning and Navigational Analysis of Wheeled Mobile Robot in Cluttered Workspace
No full textPath planning and navigation analysis of wheeled mobile robots have received a significant role in the field of robotics by researchers in the past few decades. The research proposed by the researchers previously related to mobile robotics mainly considered three aspects such as localization, map building and path-planning. These aspects played an important role in finding out the feasible navigational path and it is also responsible for smooth or safe navigation in an environment. By keeping all these aspects in mind related to the solution of the trajectory planning problems, the trajectory planning and navigation control algorithms are presented in this proposed work. The proposed algorithms (BNN, DAYINDI AI, AGSA-DAYINDI AI and PSO-DAYINDI AI) can learn from the search space and configure themselves with the help of sensor modules. Based on this principle, the robot generates a collision-free path by avoiding obstacles from the source to the target. The primary objective of this research work is to design and develop smart computational intelligence techniques that addresses the online navigation problems as well as solves the problems using its learning feature. In this work, the path planning problems are addressed for unknown and messy environments using developed AI techniques. Two individual computational intelligence methodologies have been developed based on the Behaviour Based Neural Network (BNN) and DAYINDI AI algorithm. Also, hybrid methodologies have been developed by integrating the AGSA with DAYINDI AI and PSO with DAYINDI AI, to solve the mobile robot navigation problems. The performances of the techniques are examined individually, through simulation and real-time experiments. During the comparative study, good agreements (average deviation is less than 6%) have been found between simulation and real-time experiments. It has been noticed that AGSA-DAYINDI (deviation is less than 6%) and PSO-DAYINDI (deviation is less than 5.5%) hybrid controllers execute better results as compared to BNN (deviation is less than 6%) and DAYINDI AI (deviation is less than 6.5%) controllers. The navigational results obtained from the developed techniques are validated by comparing with the results of existing navigational techniques such as Fuzzy logic, Neuro-Fuzzy, Behavior-based Fuzzy, Heuristic approach, Heterogeneous ACO and SACOdm techniques. In comparison studies, it is found that the proposed methodologies generate better navigational results as compared to above existing techniques
Creep, Tensile, Wear and Corrosion Behaviour of SiC Nanoparticles Reinforced Squeeze-Cast AZ91-Ca-Sb Magnesium Alloy
Get PDFIn the present thesis, the influence of SiC nanoparticles additions on the creep, tensile, wear, and corrosion behaviour of the AZ91-2.0Ca-0.3Sb (wt.%) alloy fabricated by squeeze-casting has been investigated. For comparison, these properties are also investigated in the AZ91- 2.0Ca-0.3Sb alloy without nanoparticles addition. The nominal compositions of the fabricated alloy and nanocomposites are AZ91-2.0Ca-0.3Sb (AZXY9120), AZ91-2.0Ca-0.3Sb-0.5SiCnp (NC1), AZ91-2.0Ca-0.3Sb-1.0SiCnp (NC2), and AZ91-2.0Ca-0.3Sb-2.0SiCnp (NC3) (wt.%). A detailed microstructural characterization of the alloy and nanocomposites was carried out. An impression creep testing setup was employed for carrying out the creep tests in the stress and temperature range of 300-480 MPa and 448-523 K, respectively. The alloy and nanocomposites were tensile tested at ambient and elevated temperatures of 298, 423, and 473 K. The dry sliding wear tests were conducted employing a pin-on-disc setup at normal loads of 10, 20, 30 and 40 N at a sliding velocity of 1.2 m/s for a sliding distance of 1000 m. The corrosion responses of the alloy and nanocomposites in a 0.1 M NaCl solution at ambient temperature and pH 7.0 were evaluated by immersion, hydrogen evolution, and potentiodynamic polarization scan. The results indicate that the α-Mg, β-Mg17Al12, Al2Ca and Ca2Sb phases are present in both the alloy and nanocomposites. The additions of SiC nanoparticles refine the grain size, reduce the volume fraction of the β-Mg17Al12 phase, and increase the amount of Al2Ca phase, which is more pronounced with an increase in the nanoparticle content. All the nanocomposites exhibit superior creep resistance than the unreinforced alloy. The best creep resistance is obtained in the NC3 nanocomposite. The values of stress exponents and the activation energies are in the range of 4.5 to 6.2, and 101.9 ± 2.5 to 115.5 ± 3.2 kJ/mol suggesting the governing creep mechanism for the alloy and nanocomposites is dislocation climb controlled by pipe diffusion. The post creep microstructural observation confirms that the β-Mg17Al12 phase in the alloy is rigorously fragmented, and aligns in the direction of material flow, which deteriorates its creep resistance. In contrast, the presence of Al2Ca phase network and the SiC nanoparticles increase the dislocation pile-ups and dislocation tangling resulting in superior creep resistance of all the nanocomposites. All the nanocomposites illustrate greater yield strength (YS) and ultimate tensile strength (UTS) in contrast to the alloy at all the temperatures employed. The YS, UTS and elastic modulus of both the alloy and nanocomposites decline, whereas the work to fracture increases with a rise in temperature. Among the nanocomposites, the NC3 demonstrates the best tensile properties. All the nanocomposites display superior strain hardening response than the alloy, and the maximum strain hardening is perceived in the NC3 nanocomposite. The improved tensile properties of the nanocomposites are ascribed to the reduced grain size, the increase in dislocation density owing to CTE mismatch between the alloy and the SiC nanoparticles, the Orowan strengthening as well as the presence of a relatively higher amount of Al2Ca phase in the nanocomposites. The contributions to the improvement of strength of the nanocomposites in decreasing order of their influence are the Orowan strengthening, the strengthening due to CTE mismatch, and the Hall-Petch strengthening. The fracture surfaces of the tensile specimens tested at 298 K confirm the presence of transgranular cleavage fracture which remains unchanged at 473 K as well. The wear rate is lower for all the nanocomposites compared to the alloy. All the nanocomposites demonstrate the lower specific wear rates than the alloy. Among the nanocomposites, the NC3 exhibits the best tribological performance. The values of the coefficient of friction are lower for the nanocomposites than the alloy. The abrasion, adhesion, oxidation, and delamination are the dominant wear mechanisms. The 3D topography depicts that the addition of nanoparticles to the alloy results in the reduced surface roughness during the wear tests, confirming the superior wear behaviour of the nanocomposites compared to the alloy. All the nanocomposites demonstrate a superior corrosion resistance than the alloy, and the NC3 nanocomposite exhibits the highest corrosion resistance. The improved corrosion performance of the nanocomposites is attributed to the decrease in the potential difference between α-Mg and β-Mg17Al12 phases, reduced quantity of β-Mg17Al12 phase, and an increased amount of Al2Ca phase following the SiC nanoparticles additions. To conclude, all the nanocomposites display the superior creep, tensile, wear and corrosion response compared to the alloy. Among the nanocomposites, the NC3 nanocomposite illustrates the best creep, tensile, wear and corrosion performance. Therefore, the use of the nanocomposites would be more beneficial than the alloy
Impact of Changing Climate on North Indian Ocean Cyclonic Disturbances and Associated Meteorological Features
Get PDFCyclonic disturbances (CDs) have a significant impact on human life, properties, and the environment. Therefore, it has been an interesting research area among researchers all over the world. Fundamental and advanced processes associated with CDs are studied by most of the researchers to understand better and thereby predict the genesis and evolution of CDs. However, the meteorological, climatological, and landfalling features associated with CDs over a particular ocean basin are quite significant to be emphasized too. This thesis provides an overview of the climatology of these fascinating storms formed over the North Indian Ocean (NIO) basin and associated rainfall, meteorological and landfalling characteristics, and other environmental features. For this purpose, TC best track data provided by India Meteorological Department (IMD) and Joint Typhoon Warning Center (JTWC), rainfall product from IMD, several parameters like sea surface temperature (SST), air temperature, surface-level relative humidity (RH), mid-tropospheric relative humidity (RH500), surface-level wind (SW), and potential evaporation factor (PEF) from International Comprehensive Ocean-Atmosphere Data Set (ICOADS), National Oceanic and Atmospheric Administration (NOAA), and Hadley centre and inter-annual oscillation indices from NOAA, the United States and Bureau of Meteorology, Australia are used.
Prior to using the IMD TC best track data, the reliability of the same, and improvement by the implementation of satellite technology is discussed. The analysis indicates an improvement in the IMD best track data over the years in terms of quality, availability, and the frequency of genesis, intensity, and landfall etc. The determination of location and proper track of the CDs over NIO has improved during the satellite era, and the information related to the frequency of looping, southward moving, and recurving CDs is improvised. The percentage of the systems crossed or grazed the coast in the NIO, Bay of Bengal (BOB), and Arabian Sea (AS) basin/sub-basin is ~100% for both pre-satellite and satellite era. There has been almost robust data availability from 1961 onward with the advent of satellite technology.
Based on the annual SST anomaly trend, the period of study (from 1891 onward) is divided into pre-warming (PWP; during 1880–1946) and current warming (CWP; 1947 onward) with negative and positive anomaly (trend) respectively. The Mann-Kendall test and Sen’s slope estimation indicates a decreasing trend in annual CD (total storms) and CS+SCS (cyclones and severe cyclones) frequency during CWP for NIO region and particularly BOB at 95% confidence level. However, the CD and CS+SCS frequencies were increasing during the PWP. CD activity over southern and northern BOB is decreasing sharply during CWP. The southern sector of BOB hosts mostly severe systems (intensity >48 kts) and middle sector, tropical cyclones (intensity ≥ 34 kts). CD activity over the eastern sector of AS shows considerable enhancement during CWP. Increasing SST, SW, RH500, and PEF are helpful in the formation of intensified storms during CWP. The activities during PWP were reversed compared to that of CWP. A significant temperature anomaly difference between atmosphere and ocean also perceived to play a key role in modulating the enhanced intensity of TCs during CWP. The SST range of 27.5 to 29.5 °C and the supportive flow field is helping to enhance the middle and upper tropospheric moisture content; eventually, resulting in increased SST, PEF, and RH through a possible feedback mechanism. The trend for vertical wind shear is decreasing, supporting a higher rate of intensification of depressions to severe ones.
The impact of the warming climate on landfall activity reveals that Bangladesh (BD), Andhra Pradesh (AP), and Tamil Nadu (TN) are more vulnerable to severe cyclones formed over BOB during the CWP. Among western coastal states, Gujarat (GJ) is prone to SCS, and Arabian Peninsula countries are vulnerable to CS formed over the AS during the current warming climate as well. During CWP, BD and Arakan are more vulnerable to CD landfall in the pre-monsoon season, whereas in post-monsoon months, AP, TN, and BD are more prone coastal areas of BOB. The enhanced genesis over the southern and middle sector of BOB is mainly responsible for more landfall over AP, TN, and BD. The seasonal analysis of change in genesis location of CDs during PWP and CWP over BOB and AS agrees well with the landfall point of CDs. Also, changes in wind direction from NW to N-NW and increased meridional SST over BOB found to be encouraging the landfall activity near AP and TN coasts. The W-SW and zonally distributed SST supports landfall over Gujarat. There is less impact of change in genesis location over AS landfalling CDs. The destruction potential of CDs in terms of accumulated cyclone energy (ACE) during recent years is observed to be increasing as the rate of intensification of CDs has increased over NIO.
Every year, CDs cause destruction along the coastal areas of the world basins by pouring heavy rainfall, which causes floods and landslides. By using high-quality daily rainfall data, the contribution of rainfall by NIO CDs over India was also investigated.
Among eastern coastal states, the accumulated rainfall is higher over AP, TN, Odisha (OD), and southern West Bengal (WB) during pre-monsoon season. Among western coastal states, Karnataka (KA) and Kerala (KL) suffer maximum rainfall from CDs. During the post-monsoon season, coastal AP, TN, OD, KA, and coastal KL received higher accumulated rainfall. Gujarat received ~70%, and both AP and TN received up to 20-30% of rainfall by CDs during pre-monsoon months. In most of the states, the overall rainfall contribution by CDs is observed to have a decreasing trend during both seasons. Owing to the stable rainfall trend along with decreasing CD frequency during the post-monsoon season, the results indicate an increased amount of rainfall contribution by CDs during the season. CDs contribute a considerable amount of rainfall to central and northern India during the post-monsoon season.
Further, the rainfall contribution by CDs during El-Nino southern Oscillation (ENSO), Indian Ocean Dipole (IOD), and Madden-Julian Oscillation (MJO) under the impact of the warming climate emphasized for both NIO TC sessions. The accumulated rainfall observed to be high over AP and OD during the events considered in pre-monsoon season except for the La-Nina event where rainfall is high over GJ. During the post-monsoon season, the accumulated rainfall is high over AP, OD, and TN. In terms of annual variations, the annual CD days are low; however, the annual average rainfall is high for MJO periods during pre-monsoon season. La-Nina periods contributed second highest annual average CD rainfall during pre-monsoon season. For the post-monsoon season, negative IOD and La-Nina contributed higher CD rainfall, and the maximum CD days also observed to be higher. A significant amount of CD attributed rainfall is observed during a positive IOD event, and the same is also confirmed by the principal component analysis. The WRF regional analyses showed that CD rainfall in both seasons is relatively lower than the observations, but the spatial distribution is reasonably well predicted.
From the study carried out in this thesis, it is quite evident that the warming climate has an adverse impact on CD genesis over NIO. The decrease in the number of CDs with an increased rate of intensification in the changing climate scenario poses a threat to society. And, the increased rainfall by CDs during post-monsoon is also causing massive destruction in recent years
A Study on Development of Path loss Modelling Schemes and Coverage Analysis for a Broadband Wireless Access Network
No full textWorldwide interoperability for Microwave Access (WiMAX) recognized as a 4G wireless broadband access technology enables ubiquitous delivery of services such as voice, data and multimedia for fixed and mobile users. High data rate, simple network architecture, ease of installation, low cost, reliability etc., are the major advantages of this technology. Accurate planning as well as the proper deployment of this network is quite challenging and also link budget analysis is a prerequisite. Moreover precise path loss (PL) models are essential for coverage analysis study and accurate estimation of outage probability such that a trade-off between transmission power and adjacent frequency blocks interference can be possible. Hence frequency band has a major significance on the dimension and planning of the wireless network. So in consideration of above factors, this research study attempts on development of some empirical path loss models based on regression analysis for specific 2.63GHz and 2.65GHz WiMAX networks deployed in LOS and NLOS environments of nearby rural and suburban locations respectively. Several measurement campaigns are carried out for collecting appropriate received signal strength indicator (RSSI) as well as carrier to interference plus noise ratio (CINR) data in such network environments through experimental setups. Further, the link budget analysis and statistical characterization of proposed path loss are undertaken and compared with standard models. In order to enhance the accuracy of the predicted path loss models, the outlier removal algorithm is combined with regression technique. The efficiency of the proposed empirical path loss models is compared with the standard path loss models on the basis of goodness of fit parameters. Further, the coverage analysis of the WiMAX network employed in the considered measurement scenario is studied using the proposed path loss models. It is observed that due to overall path loss, shadowing and fading effects, the received signal strength at the cell edge is far more inferior compared to the threshold signal to noise ratio (SNR) required for establishing a reliable communication link. As the application of cooperative relaying are already being established by various researchers, this research work has suggested an improvised WiMAX framework incorporating cooperative relaying schemes. The standard amplify-forward (AF) relay protocol is incorporated to ensure the coverage extension for such network. Further, the impact of varying base station (BS) transmit power and height of receiver on cell coverage area are
discussed in detail. It has been observed that though the multirelay architecture improves the performance of wireless network by exploiting the diversity gain and has improved SNR at receiver, it suffers some key limitations like increased computational complexities at the BS, the requirement of precise time and frequency synchronization etc. So, this research work has further studied various effective relay selection algorithms in an IEEE 802.16j mobile multihop relay (MMR) WiMAX framework which outperform the existing schemes on the basis of symbol error rate (SER) and channel capacity. Detail analysis of both AF and decode forward (DF) relaying are carried out along with the standard diversity combining techniques such as maximal ratio combining (MRC) and selection combining (SC) etc. Further, this research work proposes modified relay selection schemes such as threshold based max-min and threshold based harmonic mean of SNR which have been employed for various combinations of relay-diversity combining techniques (i.e. AF-SC, AF-MRC, DF-SC and DF-MRC). The performance of this network utilizing the proposed relay selection algorithms is analysed for these hybrid schemes. In addition, the impacts of varying the relay locations on the performance metrics are demonstrated through MATLAB based simulation study. Reliable communication with peak data rate in high mobility scenarios such as high speed railway (HSR) and vehicles are expected to be the major challenges for upcoming 5G wireless network. So, this research work has attempted to verify the suitability of this broadband access technology for HSR communication with enhanced network coverage in diverse terrain scenarios. It is observed through performance analysis that the proposed relay reinforced WiMAX network has the potential to be utilized for improving the proficiency of HSR communication service
Development of Facial Expression Recognition System Using Machine Learning Techniques
No full textEmotion can be defined as a vital part of the physiological process, which can be described as a stimulus to any changes based on the events occurring around a person. It can be revealed through various channels, such as the variation in the body gestures, postures, speed and pitch of the tone, and most significantly through facial expressions. The face is the most dynamic and visible part of the body, thereby allowing efficient and easy transmission of knowledge about the emotions. Automated Facial Expression Recognition (FER) is a challenging task and has been an active topic of research in the domain of pattern recognition and computer vision due to its wide applications in natural Human-computer Interaction (HCI), behavioral science, clinical practice, telecommunication, animations, video games, security, surveillance, marketing and advertisement. In these applications, robust emotional awareness is a significant point to accomplish the assistive task at best. However, to make our day-to-day life more comfortable, smooth, and exciting, there is a great necessity for the development of a real-time, improved and robust FER system. This research is focused towards the improvement of the FER system in all its vital stages including, facial image acquisition, facial feature extraction, feature reduction, and classification. The key stages of the FER framework are feature extraction and classification. The existing literature reveals that most of the feature extraction techniques suffer from various issues like occlusion, illumination, face shape variation, rotation, translation, etc. To address these challenges, the first research contribution is focused on improving the recognition performance with the use of a powerful feature extractor called Histogram of Oriented Gradients (HOG). The performance of the proposed scheme is also tested using an important variant of HOG, namely Pyramid HOG (PHOG). According to the earlier investigations, features based on frequency domain techniques have outperformed the spacial domain methods. Therefore, a frequency domain based image transform specifically Stationary Wavelet Transform (SWT) is utilized as a feature extractor in the second contribution. SWT addresses the translation issues of the standard wavelet transform. In the subsequent contribution, we have integrated SWT coefficients and texture features to obtain improved performance. To preserve the capabilities of both frequency and spatial domain features, PHOG has been applied over SWT sub-bands to retrieve the prominent facial features. A combined Principal Component Analysis and Linear Discriminant Analysis (PCA+LDA) method is applied to generate a set of reduced and discriminant features. Further, a multi-scale and multi-directional feature extraction technique called Ripplet Transform Type II (RT-II) is explored. This method not only represents the image along various scales and directions but also represent the image with better edge information. The classification is carried out using Least Squares SVM (LS-SVM) with Radial Basis Function (RBF) kernel which offers several advantages such as low computational cost and local minima avoidance, and provides a solution to a convex optimization problem. The existing FER systems employ the traditional grid-search method to fine-tune the parameters of the classifiers, which is a time consuming task. To mitigate this issue, recent meta-heuristic approaches like Jaya Optimization (JO), and Whale Optimization (WO) are explored to fine-tune classifier parameters which prompts to low computational cost and better generalization performance. In the last contribution, the Variational Mode Decomposition (VMD) is used for feature extraction. For classification, a hybrid classifier is proposed by combining WO with Kernel Extreme Learning Machine (KELM). The WO algorithm is employed to find the optimal parameter of KELM with RBF kernel. The efficacy of different proposed methods is evaluated using two benchmark facial expression datasets, namely Extended Cohn-Kanade (CK+) and Japanese Female Facial Expressions (JAFFE). The simulation results are compared with the current state-of-the-art methods and the results demonstrate the superiority of the proposed schemes. The VMD and KELM based approach has been discovered to be the most precise and effective method for recognizing facial expressions and hence can be utilized to detect emotions in real-time
A Detailed Investigation on The Development of Biomolecule Based Multifaceted Therapeutic Nanoformulations and Their Cytotoxic Behaviour in Multiple Cancer Cells
No full textIn the present thesis, we have developed protein-based multiple therapeutic nanoformulations and evaluated their anticancer efficacy in multiple cancer cells as well as other therapeutic potentials such as antimicrobial, antioxidant, and anti-inflammatory activity. In the first strategy, we prepared hybrid supramolecular nanoassembly (SNLYZ-BLA) of two proteins, bovine α-lactalbumin, and hen egg-white lysozyme to evaluate their cytotoxic effect in multiple cancer cells including breast cancer (MCF-7 and MDAMB231), cervical (HeLa), osteosarcoma (MG 63) and myeloma (B16F10) cells. Both the proteins have already been reported to possess anticancer activity in either native form (lysozyme) or the conjugate state with oleic acid (α-lactalbumin, such as BAMLET or HAMLET). However, their potential as a drug delivery agent and the subsequent anticancer potential has not been much explored. Moreover, we also investigated the anticancer activity of two independent nanoassembly of pure lysozyme (SNLYZ) as well as non-anticancer protein, casein (NSCS), and their curcumin (anticancer phytochemical agent) loaded formulations. Both SNLYZ-BLA and SNLYZ demonstrated exceptional drug (curcumin) loading (SNLYZ-BLA-Cur and SNLYZ-Cur) ability and exhibited excellent cytotoxic effect in both forms (without and with the drug-loaded state) in multiple cancer cells. We further demonstrated significant (>80%) drug release ability of the formulations under low pH conditions. Moreover, after the drug release, the anticancer activity of both reconstituted SNLYZ-BLA and SNLYZ (their drug released state) was investigated and found almost the same as that of freshly prepared. All these supramolecular protein nanoassembly (SNLYZ-BLA and SNLYZ) showed excellent stability under wide pH and temperature. In the second strategy, metal-based nanoparticles such as zinc oxide (ZnONPCS) and silver-gold alloy (AgAuNP) nanoparticles were synthesized via biogenic synthesis method using casein as capping and reducing agent to improve their biocompatibility. Further, curcumin, a phytochemical, was loaded (ZnONPCS-Cur and AgAuNP-Cur) on both nanoparticles, and their anticancer potential was investigated. Results revealed an excellent cytotoxic effect of the formulation and drug delivery system in multiple cancer types. We also demonstrated that both ZnONPCS and ZnONPCS-Cur were biodegradable under acidic conditions, thus making them more applicable as a drug delivery system and therapeutic agent in cancer than AgAuNP or AgAuNP-Cur. Further, we also performed folate based targeting for all the above formulations, which revealed their enhanced cytotoxic effect in cancer cells. The physical and storage stability of all the above formulations were investigated in terms of their commercial potential and was found stable. We also investigated the antioxidant, antimicrobial, and anti inflammatory activity of all the formulations loaded with curcumin. While both SNLYZ-BLA-Cur and SNLYZ-Cur exhibited excellent antibacterial, antioxidant and anti inflammatory activity, the anti-inflammatory activity of SNLYZ-Cur was predominantly due to loaded curcumin. ZnONPCS exhibited excellent antimicrobial activity and photocatalysis of toxic industrial dyes. However, NSCS and AgAuNP loaded with curcumin demonstrated antioxidant activity and anti inflammatory activity due to loaded curcumin, while AgAuNP also showed antibacterial activity due to the presence of nanosilver (Ag). Further, it was also found that the solubility and stability of the poorly soluble drug, curcumin, were significantly increased when loaded on various nanostructure, thereby increased its bioavailability, thus improved their therapeutic use. All the formulations were observed to be biocompatible to healthy human cells (HaCaT cells) and erythrocytes. Finally, we validated the potential of SNLYZ-BLA as a drug delivery system using doxorubicin, a well-known chemotherapeutic agent. We found the excellent drug loading capacity of SNLYZ-BLA for DOX (48.2 mg/g) and its pH-responsive in vitro drug release up to 74 % at pH 5.0, which indicates its potential as an effective drug delivery system. Further, when SNLYZ-BLA-DOXwas applied to cancer cells, it demonstrated better cytotoxicity than when applied alone (DOX and SNLYZ-BLA). More importantly, the undesired cytotoxicity of DOX was reduced to the healthy cells after loading to protein nanoassembly (SNLYZ-BLA).From the overall study, we observed that among all the nanoformulations we investigated, both SNLYZ-BLA and ZnONPCS demonstrated excellent anticancer potential in vitro in multiple types of cancer cells as a therapeutic agent as well as a drug delivery system. However, considering that the hybrid supramolecular nanoassembly (SNLYZ-BLA) is composed of pure biomolecules (proteins), and both proteins were reported to have a little/ignorable immunogenic response, this fact might make them as a potential therapeutic candidate for the
treatment of multiple cancers along with its excellent antibacterial, antioxidant and anti-inflammatory activity
Grid-Tied Photovoltaic System under Non-ideal Source Voltage with Battery Energy Storage
No full textA Photo-Voltaic (PV) generator can be considered as a DC power supplier, AC source of power using a stand-alone inverter, or a grid-connected inverter. Unfortunately, the PV generator fails to produce power at its full capacity during low light condition or night, which force the whole system to be removed from the grid. Moreover, the frequent parallel operation and break-up actions make the control of the system more difficult. To overcome these difficulties multi-functional inverters along with Battery Energy System (BESS) can be used. Multi-function highlights the uninterrupted utilization of the inverter even when there is no light or at night. It can compensate for reactive power and harmonics of the connected load, ensuring unit power factor operation of the grid under distorted source voltage. Whereas, during strong sunlight, it can deliver active power to the grid and compensate for the reactive power of the load simultaneously. Grid integration of photovoltaic systems through various configurations is developed and performance is analyzed in this thesis to address the improvement using BESS. The photovoltaic inverter is a crucial element of a grid-connected DC system for the flow of power from the PV to a utility grid. There lies an opportunity to utilize the PV inverter for some other electrical issues such as load reactive power and harmonic compensation, apart from sending the power to the utility grid. This multi-functional application offers an opportunity as well as a challenge to utilize the PV inverter to achieve the above-cited objectives simultaneously in a coordinated manner. The coordinated regulation of active and reactive power, as well as harmonic compensation, can be successfully carried out using power theories such as Instantaneous Reactive Power (IRP) theory or using Instantaneous Active and Reactive Current Component (id−iq) methods. Under non-ideal source voltage, which includes distortion, voltage sag, and voltage swell phenomenon, the traditional methods do not yield the desired results. The variations in magnitude, unbalanced three-phase voltage waveform, and distorted source voltage waveform both for single-phase and three-phase are the frequently encountered conditions for which it can be termed as non-ideal source voltage. The proposed thesis work formulates the appropriate configurations to address these power quality issues under special circumstances with grid-connected solar PV and Battery Energy Storage Systems under non-ideal mains voltage
Weathering Characteristics in the Mahanadi River Basin, India
No full textThe processes of weathering and erosion are the most important natural events that reshape the surface of the earth and regulate geochemical cycling of elements. The study of processes involved in rock weathering, sediment formation and subsequent transfer of weathered materials by the river to the ocean is essential for understanding different earth surface processes. The transport of sediments by rivers to the oceans represents an important link between the terrestrial and marine ecosystem. Chemical weathering of rocks releases soluble products and produce solid residues, which control the land-ocean-atmospheric fluxes and earth’s climate. The geochemistry of river sediments gives information about the provenance characteristics and characteristics of weathering and erosion in a basin. Therefore, this thesis aims to study the spatio-temporal variation of the sediment discharge and erosion rate, geochemistry of water and sediments of the Mahanadi river basin, one of the biggest rivers in India to understand the weathering characteristics of the basin.
The trend analysis study is conducted in the time series data (1981-2010) of water flux sediment discharge and rainfall (1990-2010) of the Mahanadi river to study the sediment load variation. The trend test result represents that the sediment load delivered from the Mahanadi river to the global ocean has decreased sharply at the rate of 0.42×106 tons/year between 1981 and 2010. Water discharge and rainfall in the basin show no significant decreasing trend except at only one tributary. The decline in sediment discharge from the basin to the Bay of Bengal is mainly due to the increase in the number of dams, which shows the increase from 70 to 253 during the period of 1980 to 2010. Over the past 30 years, the Mahanadi river discharges about 48.01±20 km3 of water and 14.52±12.7×106 tons of sediment annually to the Bay of Bengal whereas the mean erosional rate is 238±116 tons/km2/year. Based on the current data (2001–2010), sediment flux and water discharge to the ocean are 11.02±5×106 tons/year and 50.91±16 km3/year respectively; and ranking Mahanadi river second in terms of water discharge and sediment flux to the ocean among the peninsular rivers in India. The sediment load in the basin is mostly influenced by the variation in water discharge and relief.
The results of hydrogeochemical study of the Mahanadi river basin reveal that the dissolved loads in the basin are dominantly controlled by rock weathering particularly chemical weathering of silicates and carbonates. The TDS in the basin is higher than the global average. The estimated chemical weathering rates based on the forward model are 44.94 tons/km2/year in monsoon and 2.45 tons/km2/year in pre-monsoon with annual average chemical weathering rate of 23.69 tons/km2/year. The contributions of silicate weathering rates in the basin are 32.15 and 1.55 tons/km2/year during monsoon and pre-monsoon period respectively. The estimated CO2 consumption rate associated with chemical weathering in the basin is 13.3×105 mol/km/year during monsoon and 0.66×105 mol/km/year during pre-monsoon period with an average annual rate of 6.91×105 mol/km/year which is higher than the global average and most Indian rivers. The net rate of CO2 consumption by silicate weathering is estimated to be approximately 4.78×105 mol/km/year. It is observed that the runoff and lithology are the major factors influencing chemical weathering in the basin.
The geochemistry of sediment shows that they are mainly derived from felsic source rocks with a minor contribution from mafic and carbonate rocks. This is also evidenced from the ratio of Al2O3/TiO2, higher content of K2O and Rb along with the abundance of quartz, feldspar and variable quantity of dolomite and calcite in the sediments. Higher values of (La/Lu)N and LREE/HREE ratios suggest the presence of acidic source rock in the basin. The majority of sediments are chemically similar to arkose and litharenite sandstone. The values of the chemical index of alteration (CIA), index of compositional variability (ICV) and the ratio of Rb/Sr indicate most of the sediments are compositionally immature and undergone weak weathering. Positive Ce anomaly, as well as the ratios of Ni/Co and V/Cr, suggests oxidising environments of deposition. The sediments are deposited on a passive continental marginal setting with semi-arid to semi-humid climatic condition
Robust and Adaptive Control Algorithms for Autonomous Underwater Vehicle
No full textThe vastness of the ocean on earth provides limitless potential for exploration. This has sparked an interest in researchers to design and control underwater vehicles as a means to explore and use marine life more productively. Autonomous Underwater Vehicles (AUVs) nd a lot of applications including defence, pipeline survey and mine survey. However, the AUV dynamics is uncertain due to parametric variation, ocean current and wave disturbances. Hence, in order to address the motion control problem of an AUV, it is necessary to design robust and adaptive controllers. These motion control algorithms necessitate mathematical representation of AUV which comprises of hydrodynamic damping, Coriolis terms, mass and inertia terms etc. To obtain dynamics of an AUV, a system identication technique using Extreme Learning Machine (ELM) structure is considered in this work for the identifying the dynamics of AUV.
This thesis focuses on designing four robust and adaptive control algorithms namely, H1 model predictive controller (MPC), Fast Quadratic MPC (FQMPC), nonlinear model predictive controller (NMPC) and explicit model predictive control (EMPC) for accomplishing ecient path following control of an AUV. In order to verify the ef-cacies of the proposed control algorithms, both simulation and experimentation were pursued.
A prototype torpedo-shaped mono-hull AUV consisting of a single board computer along with Arduino microcontroller and with a thruster placed at rear end is designed in the laboratory. The proposed robust and adaptive control algorithms are implemented using the computational unit of the prototype AUV to achieve desired path following control of an AUV using python programming language.
The thesis rst focuses on the identication of the unknown dynamics of the AUV is identied by using an ridge regression based ELM model using the experimental input data (i.e. rudder orientation) and output data (i.e. yaw rate and sway velocity) from a prototype AUV designed in the laboratory. In order to increase the converge rate of the ELM model during training, the hidden layer parameters are optimized using modied Particle Swarm Optimization (PSO) algorithm. The problem of path following can be approached by parameterizing the desired path using Serret{Frenet(SF) frame. Using the desired path and kinematic equations of AUV, a Lyapunov based backstepping controller is designed to obtain the velocity references for the dynamics of AUV. This is subsequently used to design a dynamic H1 MPC to track the above velocity references. This H1 MPC algorithm is then simulated in MATLAB followed by real-time experimentation on a prototype AUV developed in our laboratory.
The uncertainties may arise due to wave disturbances, model mismatch, ocean current or payload change. A Fast Quadratic Model Predictive Controller (FQMPC) based on on-line sequential ELM (OS-ELM) is proposed to handle these uncertainties. To ease the control law formulation, way points tracking is used for controlling AUV for tracking a desired path. Thus, the FQMPC is proposed for addressing the path following control problem using way-points for an AUV.
The above two controllers namely, H1 MPC and FQMPC are designed based on linear dynamic model of AUV. However, the dynamics of AUV is nonlinear, so a nonlinear model predictive controller (NMPC) is then designed using OS-ELM model of AUV. In order to increase the ecacy in the performance of the OS-ELM model, the signicant terms in the input vector of OS-ELM model are determined using Forward Regression Orthogonal Least Square (FROLS) algorithm based on Error Reduction Ration (ERR) criterion. From the simulation and experiment results, it is observed that NMPC though provides successful tracking of the desired path, it is computationally expensive due to solving the optimization problem at each sampling instant.
Subsequently, to to reduce the computational time an Explicit Model Predictive Controller (EMPC) scheme is proposed. The EMPC is designed using two steps.
Firstly, using Generalized Hermite Biehler Theorem (GHBT) all stabilizing gains of a proportional-integral (PI) controller is computed o-line with a specied gain margin (GM) and phase margin (PM) design criterion. Secondly, based on the stabilizing set of PI controller, an Explicit MPC (EMPC) is designed using look-up table. The eectiveness of the proposed EMPC is veried by conducting experiment on the prototype AUV. The experimentation is conducted at a swimming pool with dimension of 10mx5mx1.6m. From the obtained results and evaluation of performances of all the proposed controllers, it is contemplated that the EMPC based control strategy is preferable for real-time implementation to steer the AUV to achieve eective tracking performances
Analysis of Hematopoietic cells for Leukemia Detection and Classification
No full textCancer has been menacing the human race so much so that in most of the nations, it is a predominant cause of death. The word “Cancer” is a generic term being used for a large group of diseases that can affect any part of the body. Each type of cancer has its own symptoms, rate of growth, treatment, and cure. The possibility of healing a cancer patient depends on at what stage the disease has been detected; early detection increases the chances of survival.
In last two decades, Computer Aided Classification (CAC) Systems have gained quite a popular for their fast, efficient, robust, and reliable models that help in detection of a wide variety of diseases. This research work addresses the main problems associated with hematological analysis, Acute Lymphoblastic Leukemia (ALL) detection in particular. This thesis helps in the development of algorithms for the extraction of useful information for classification of pattern of tissues.
An improved watershed algorithm using marker scheme (IWAMS) is proposed to separate the grouped cells (agglomerates) present in the microscopic images. This framework intends to divide the entire image or tissue from the background by using marker scheme. A shadowed cmeans (SCM) technique is further employed to isolate the tissue structure in different regions or the cells in their parts (nucleus and cytoplasm).
It is witnessed that the textural component of a normal and an affected blood cell varies significantly due to the change of their chromatin distribution. To automatically detect abnormalities and speed up the process of detection of ALL in the Peripheral Blood Smear (PBS), efficient feature extraction methodologies have been proposed in the subsequent chapters to extract features from the nucleus and cytoplasm region. Those features are then fed to the different classifiers for obtaining the accuracy of the model.
A multiresolution analysis based feature extraction approach is developed which uses TwoDimensional Discrete Wavelet Transform. Subsequently the relevant features are
selected using a combination of PCA and Bhattacharyya distance technique. The resultant feature set is of substantially lower dimension. On application of various classifier, it is observed that Back Propagation Neural Network (BPNN) gives better classification accuracy as compared to others.
A Gray Level Cooccurrence Matrix (GLCM) based framework is proposed which is used to differentiate the spatial relationship between pixels of an image. A probabilistic principal component analysis (PPCA) approach is employed to reduce the feature set. Of all classifiers, Random Forest (RF) results in providing greater accuracy.
Another multiresolution ALL detection using TwoDimensional Stationary Wavelet Transform is developed which uses the shift invariant properties to extract the texture features from microscopic images. This scheme allows the use of principal component analysis and linear discriminant analysis for reduction of the most appropriate features. This features with Support Vector Machine (SVM) classifier outperform other classifiers concerning accuracy.
Each model is studied independently, and tests are carried out to evaluate their performances. Each scheme is validated using the available dataset ALLIDB.
Performance measures, i.e., accuracy, sensitivity, and specificity are very promising and are used to compare the efficacy of proposed automated systems with that of standard diagnostic
procedures