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Economical way of Processing bio-char/activated Carbon from the wasteland weed Calotropis Gigantea and its Characterizations for Possible Applications
No full textThere is a huge demand of energy in the whole world. Scientists and researchers are exploring various renewable biomass sources which are novel and can produce different forms of energy in addition to help mitigating environmental climate change, soil infertility and waste mismanagement problem. In order to cater these issues related to environment, soil and energy; biochar /activated carbon were processed through economical ways using renewable wasteland weed biomass resources in this work. In the present work, biomass of the different portions i.e. stem, leaf and flower of wasteland weed Calotropis Gigantea were carbonized individually between the temperature range 200°C to 900°C and percentage yield was estimated. Calotropis Gigantea bio-char obtained were characterised by yield, proximate analysis, CHNS analysis, bomb calorimetry analysis, Thermal Gravimetry Analysis (TGA) and Differential Thermal Analysis (DTA), X-Ray Diffraction (XRD), Raman spectroscopy, Scanning Electron Microscopy (SEM), Energy Dispersive Spectroscopy (EDS), Fourier Transform Infra-Red (FTIR) Spectroscopy, Field Emission Scanning Electron Microscopy (FESEM), (Brunauer Emmett-Teller) BET surface area and pore size analysis. Presence of Carbon, Hydrogen, Nitrogen and Sulphur were confirmed from CHNS analysis. Based on the calorific value of bio-char obtained, it is found that it can be utilized as solid fuel. Thermal stability from TGA/DTA analysis leads to stability of bio-chars in soil. Presence of crystalline phase in biochar confirmed from XRD analysis. Further, with increase in carbonization temperature, cellulose crystalline peak of raw biomass was lost and formations of turbostratic carbon crystallites were confirmed from XRD analysis. Also, with increasing carbonization temperature, disordered band and graphitic band pattern of carbon became distinguishable as observed from RAMAN spectroscopy analysis. The bio-char surface is found to be porous based on SEM analysis. Presence of carbon and other elements in biochar is confirmed from EDS analysis. Surface functional groups found from FTIR analysis are helpful for adsorption of ions. Presence of lots of different dimension pores were also confirmed from FESEM analysis and thus this biochar can be used for adsorption purposes in different applications. Due to large pore size, BET surface area of the biochar was found less. The property of biochar depends on the composition, the type of biomass and the parameter of carbonizations. The temperature at which carbonization was done has huge influence on the characteristics and yield of the char. Based on the characterization analysis of carbonaceous residue (bio-char) obtained, it can be confirmed that use of such bio-chars can be potentially utilized for adsorption of ions and provide spaces for nutrients and water retention, increase soil fertility, act as source to sequester carbon in soil, mitigate climate change and reduce greenhouse gas emissions, helps in soil amendment for agricultural & environmental protection uses. There is huge demand of hierarchical porous activated carbon for use in energy storage devices and is a front-line area of research due to its versatile applications. Many attempts are being made to economise the process parameters to minimize the cost of production of activated carbon. Activated carbon (AC) from the wasteland biomass of Calotropis Gigantea stem was produced using chemical activating agent bleaching powder (CaOCl2) in the ratio of 0.5:1 and 1:1 of chemical and biomass at different activation temperatures of 400 oC, 600 oC and 900 oC in normal atmospheric conditions. Characterizations like XRD, FTIR, Raman Spectroscopy, FESEM, BET surface area and pore size analysis, HRTEM were done to find its suitability for application in Lithium/Sodium (Li/Na) ion batteries. Presence of graphitic structure was confirmed from XRD analysis. Functional groups found from FTIR analysis are active adsorption sites. Raman spectroscopy ordered graphitic structure is prerequisite for electrochemical performance. The highly porous activated carbon surface observed from vii FESEM analysis was further confirmed to have both mesopores and also micropores having appropriate surface area through BET surface area analysis. Highly porous activated carbon and crystalline graphitic structure found from HRTEM analysis makes it useful as an anode material for Li /Na ion batteries which are storage devices with high-energy density, long cycle life, safe operating and shelf life. Stem activated carbon made at 0.5:1 chemical impregnation had shown better properties than that of activated carbon made at 1:1 chemical impregnation. Therefore, in case of leaf and flower, chemical activation was done with bleaching powder (CaOCl2) at 0.5:1 (chemical:biomass) impregnation ratio at temperatures 400 oC, 600 oC and 900 oC in normal atmospheric conditions and were further characterized. Activated carbon from raw stem was prepared by using Potassium Carbonate (K2CO3) as chemical activating agent in the impregnation ratio of (0.5:1, 1:1 and 2:1) at 400 oC, 600 oC, 750 oC and 900 oC carbonization temperatures using normal atmosphere (NA) and in the impregnation ratio of (0.5:1, 1:1 and 2:1) at 600 oC, 750 oC carbonization temperatures in inert atmosphere (IA) of nitrogen at 100 ml/min. Further the effect of carbonization temperature and impregnation ratio of K2CO3 on the properties of activated carbons prepared under normal atmosphere and inert atmosphere were characterized and compared. While XRD analysis confirmed the presence of both disordered amorphous carbon humps and graphitic crystallite peaks. Ordered graphitic band increases with increase of impregnation but decreases with increase in carbonization temperature as studied from RAMAN spectroscopic analysis. Presences of functional groups found from FTIR analysis were more prominent in case of AC made in NA than that in IA. From FESEM study it was found that the micro porosity increases with increase of impregnation ratio and increase of activation temperature. But the porosity level attained at 400 oC carbonization temperature using NA was similar to that of obtained at 600 oC carbonization temperature using IA. BET surface area at 750 oC at chemical impregnation ratio 1:1 under NA was recorded highest (567.61 m2/g) containing both micropores and mesopores. The presence of micro and mesopores were further confirmed from HRTEM study. From the above analysis it can be stated that, NA activated carbons are preferable for different adsorption related applications and can be suitable for diversities of applications like batteries, fuel cells and supercapacitors. Therefore, CR2032 lithium coin-cell battery was fabricated using anode made out of activated carbon produced in this work and the electrochemical testing like cyclic performance, rate performance, Galvanostatic charge discharge (GCS), Cyclic-voltammetry (CV) and Electrochemical Impedance Spectroscopy (EIS) study of CR2032 coin-cells were carried out and found that the produced activated carbon is suitable to be used as anode material for Li ion batteries. Hence in the present study, highly porous activated carbon was processed by economical way of processing from the wasteland weed Calotropis Gigantea and was found suitable for application in energy storage devices as an alternative to non-renewable sources to meet future energy demands
Occurrence and Suppression of Limit Cycle Oscillations in Coupled DC-DC Converters with Constant Power Loads
No full textIn recent times, DC distribution power systems (DPSs) consisting of a network interconnection of controlled switching power converters are becoming increasingly common in various practical applications; such as, in VLSI mainframe computers' power supplies, telecommunication systems, electric cars, aircraft, DC power generation and distribution systems, and so on. Advantages of these DC DPSs are the power interfacing flexibility due to reduced size and weight, highly efficient energy conversion, simpler implementation of power source paralleling, easy incorporation of DC-type renewable resources, and the ability to satisfy a variety of control objectives. However, major problems for such DC distribution is the potential stability degradation that can occur when switching converters are connected to a common DC bus. This destabilizing effect can happen because of the negative impedance property of the load-side converter and hence leads to the undesirable large-scale oscillations (called as limit cycle oscillation) in the systems. This thesis explores the cause of occurrence of such limit cycle oscillations and their suppression in couple cascaded DC-DC converters systems using the concepts of nonlinear dynamics and bifurcation theory, in particular, the amplitude death (AD) phenomena. The AD is a well-known mathematical phenomenon characterizing the coupling induced stabilization of several interconnected nonlinear oscillatory systems. It has been discussed that AD-based solutions can be used for the stabilization of DC DPSs under various coupling schemes. These have been demonstrated here through numerical simulations and experimental validations. Numerical results reveal that if heterogeneity ( e.g., the system parameters are mismatched) is introduced in coupled oscillatory systems, the AD can happen. However, in some situations, when internal parameters of the systems are not accessible to the user, AD can only happen if an instantaneous delay is introduced. It has been found that adding a dynamical coupling - where coupling link has its dynamical properties - is a replacement of delay circuits, and that can lead the coupled identical and nonidentical oscillating systems to steady-state equilibrium point through AD phenomena.To do so, the stability of the equilibrium solution is analyzed for coupled converters systems using the averaged differential equations near a supercritical Hopf bifurcation. Death regions are identified for asymptotic stability under different coupling conditions. It is shown that the largest eigenvalue obtained from XPPAUTO completely characterizes the effect of connection configuration on the stability of diffusively coupled identical and nonidentical systems. In particular, all identical systems have no death regions regardless of the type of couplings. Furthermore, identical converters systems with delay, dynamical or relay coupling, or nonidentical systems with any types coupling provide, respectively, upper and lower bounds for the parametric stability regions. The results further characterize the different coupling configurations as the mechanism for the death of coupled oscillators near Hopf bifurcation. Also, some generalizations are given for converters networks with LRC-type coupling
Role of Destination Brand Engagement in Enhancing Tourist Satisfaction and Promoting Destination Advocacy
No full textIn recent years, destination brand engagement (DBE) has gained substantial attention from academia and practitioners and emerged as a concept of strategic importance in destination brand management. Particularly, developing an in-depth understanding of tourists’ engagement levels with the tourism attractions remains crucial in successfully executing destination management and marketing strategies. Because engagement transcends commitment and involvement, facilitating a proactive relationship between tourists and the destination. Although DBE has become a norm in the tourism industry, it suffers from conceptual consensus and rigorous measurement of its related constructs. This study addresses the apparent research gaps based on extensive review of relevant literature and tests transformative learning theory-informed DBE. Hence, proposing an integrated DBE framework, including its three key antecedents (destination authenticity, destination attachment, and destination experience) developed from both “destination-led” and “tourist-centered” perspectives, besides DBE’s associated consequences, namely, tourist satisfaction and destination advocacy. In addition, the mediating effect of tourist satisfaction between the association of DBE and destination advocacy was examined. As tourists’ satisfaction studies lack insights into DBE's direct and indirect effect. This study focused on the Indian tourism industry, specifically tourist destination, to attain its aims and objectives. A self-administered questionnaire survey was employed to capture visitors’ responses, and sample profiling was done through convenience and purposive sampling techniques. Descriptive analysis was undertaken to ensure data normality and sample appropriateness of the gathered responses. Afterward, the proposed framework was analyzed and validated through factor analysis and structural equation modeling. The findings were discussed, conforming to transformative learning theory by relating it to DBE and its modeled constructs. The results indicate that destination authenticity, attachment, and experience positively influence DBE. Moreover, destination satisfaction emerges as a mediator by supporting the positive indirect relationship between DBE and destination advocacy. Additionally, this study prioritizes the tourists’ preferences and perceptions of the destination, thereby assist destination managers in strategy formulation to ensure tourists’ high-level loyalty transcending purchases. Finally, Implications for academia and practice are suggested, which the tourism organizations may utilize to foster DBE and sustain the tourist-destination relationships
Cognitive State Classification using Multi-modal Features
No full textRecently, classification of cognitive states has received considerable attention from several disciplines such as psychology, cognitive science and medical engineering, etc. The multi-modal features extracted from gait and EEG data play an important role for analysing and understanding different types of cognitive states. The existing gait analysis systems are very expensive with the utilisation of high-end gold standard cameras namely Qualisys, OptiTrack, Vicon etc. A low-cost experimental setup for gait analysis is proposed in this thesis. This thesis provides a unique and innovative method for classification of cognitive state using multi-modal features. The dynamics of human gait is studied with anatomical knowledge of the human body for understanding the cognitive states. We apply different vision-based and sensor-based approaches for classifying cognitive states using multi-modal features. Camera calibration is an important step for measuring an instrument’s accuracy with its parameters. A multi-Kinect setup is created due to the limitation of single Kinect measurement range for capturing complete movements of a person. We apply fusion techniques for acquiring synchronized data captured from multiple calibrated Kinects. Two fusion methods namely Kalman filtering and a modified Set-Membership filtering are compared for estimating states of discrete time linear systems. Both the fusion techniques are tested on overground and treadmill data. The outcomes are validated with the gold standard cameras. The proposed Set-Membership filtering approach is compared quantitatively with state-of-the-art techniques. Another study is done to determine the accuracy and reliability of gait features. A novel approach for human detection and tracking is proposed which involves gait feature learning principles from depth and RGB videos. We apply various machine and deep learning modes on depth-based features. The feasibility study of gait signatures is performed using various statistical methods as well for validation with benchmark dataset. A novel event driven environment is created for analysis of cognitive states, using external stimuli through capturing EEG data with 14-channel Emotiv neuro-headset. We extract Gammatone Cepstrum Coefficients (GTCC) features from ambulatory EEG signals. Higher feature importance analysis-based scores are obtained for GTCC features indicating their discriminative ability. Various classification models are employed to achieve promising accuracy on proposed features. The entire approach is validated with benchmark SEED-IV dataset as well. Understanding the human psychology and classification of the cognitive states via multi-modal features is a new area of research in the field of cognitive science. A novel approach using multi-modal feature analysis is proposed for classification of cognitive states. The advantage of a multi-modal system is to provide adequate and diversified data to ensure data reliability for classification. Initially, the association between cognitive states and types of gait is estimated using Pearson’s Correlation Coefficient, Analysis of variance (ANOVA) and Support Vector Machine (SVM) classifier. We propose temporal and non-temporal Bayesian network-based probabilistic models for estimating cognitive states. We use different techniques such as Gaussian Mixture Modelling-Expectation Maximization (GMM-EM), k-Nearest Neighbors (k-NN) and Principal Component Analysis (PCA) to calculate the input probabilities for the Dynamic Bayesian Network (DBN) model. Furthermore, we use deep learning classification models such as Gated Recurrent Units (GRU) and Convolutional Neural Networks (CNN) for classifying the cognitive states. Standard statistical tests and comparative analysis with state-of-the-art research are performed on acquired dataset to validate the experimental results
Effective Unsupervised Learning Techniques for Outlier Detection
No full textUnsupervised learning approaches are widely used in outlier detection domain as training data is not required for decision making process. Unsupervised approaches based on clustering, distance, density have been observed to be effective for identifying outlier instances over the last several years. Finding the structure of clusters is the main objective of the clustering-based approach. Distance-based and density-based unsupervised approaches focus on finding outliers. However, they are not very effective to find outliers in varying density datasets. This thesis addresses the issues of finding outliers in varying density datasets. An effective and efficient approach termed as Reversed Density Peak for Outlier Detection (RDPOD) which is a two phase method is proposed. In the first stage, widely used K-means clustering technique is utilized for grouping the instances of a dataset into a number of clusters. The characteristics of each instance such as density and relative distance with higher density points are exploited to detect the probable outlier instances from each obtained cluster. Finally, genuine outliers are detected based on proposed outlier factors of probable outlier instances. Recently, outlier detection using deep learning models (specially Autoencoder and Generative Adversarial Network (GAN) ) has drawn attention of researchers. Autoencoder based models obtain the abnormal instances based on the reconstruction error. However, reconstruction procedure of autoencoder based models may be contaminated in the presence of anomalous instances in dataset. Therefore, effectiveness of the model may be significantly deteriorated because of sensitivity to abnormal instances. To address the issue of reconstruction error, Self Organizing Map (SOM) and Autoencoder are exploited in another proposed outlier detection approach. Main aim of the introduced approach is to get the autoencoder learnt over only ‘normal points’. In the proposed technique, the Self Organizing Map (SOM) is intelligently utilized as clustering approach for identifying the probable outliers from each cluster and exclude them temporarily to obtain only ‘normal points’. Finally, learnt model is applied over whole dataset to find outliers instances. Generally, normal samples are very large in number compared to abnormal samples in a dataset. Generative Adversarial Networks can be exploited to achieve a balance between normal and abnormal samples. Existing outlier detection methods using GAN follow a distribution in generating fake samples. However, abnormal samples in the original data may not be from the assumed distribution. To address this issue, a method named Generative Adversarial Learning for Outlier Detection (GALOD) which generates fake samples based on the identified probable outliers is introduced in this thesis. There are three phases in our proposed model. First phase is dimensionality reduction (feature extraction) phase, where autoencoder is applied for reducing the number of features. In second phase, having applied spectral clustering, density information is utilized to find probable outlier instances considered as noisy instances. Finally, generative adversarial network (GAN) is exploited on each cluster for detecting outlier instances. Experimental results on synthetic as well as real world datasets validate the effectiveness of proposed outlier detection techniques
Architectural, Performance Exploration and Security Augmentation of Network on Chip
No full textThe ever-increasing demand for portable and smart computing brought electronics design to a new paradigm, where a large number of electronic components can be integrated into a single chip to create a System on Chip (SoC). SoC integrates different cores, such as multi-processor, memory, input, and output peripheral cores, into a single chip. SoCs traditionally employ shared bus architecture for communicating between different cores. However, this provides certain limitations, such as reduced bandwidth along with unscalable architecture. To deal with these challenges and to increase communication efficiency between high-density components, Network on Chip (NoC) has been proposed as a viable solution. NoC is a subset of SoC that comprises different nodes connected via routers. Network interfaces (NIs) are used to connect core to routers in a network. The various parts of NoCs are routers, NIs, links, switches, buffers, allocators, and virtual channels. The communication between the cores occurs by sending the packets through the network. A routing algorithm is used to transfer messages from the source node to the destination node in a NoC. It plays a vital role in deciding the performance of the NoC. Finding an optimal solution to the shortest path between the source and the destination is always an active area for research. Hence, this work uses the Ant colony optimization (ACO) algorithm to propose a new routing algorithm that can be used to predict the shortest path between the source node and the destination node without compromising the performance of NoC. The results show the proposed algorithm reduces the latency of the NoC by 20 % in comparison to standard XY and Odd-even routing algorithms. Moreover, various architectural aspects of NoC design are considered. FPGA implementation of a 3x3 mesh NoC is done, and the parametrized resource evaluation is carried out. The throughput of an 8-bit flit width NoC is found to be 308.433 Mbps. The parameterized results will help the researcher to use this knowledge as a base work for designing NoC. Being a scalable and reliable commutation infrastructure, NoC is designed through various design stages that may lead to weaknesses in the system. Hence, the security aspect of NoC must be taken care of in the early stage of the SoC design. Being a communication architecture, NoC can handle security attacks and disallow undesired transactions by notifying components designed for security violations. Four types of Hardware Trojan(HT) called head Trojan, tail Trojan, address Trojan, and quantity Trojan, are designed and inserted in a 4x4 mesh NoC. As a result, the performance of NoC is degraded. A bit shuffling method has been proposed to mitigate the effect of Trojan on the performance of NoC. The results show packet drop has been reduced, and around 80 % of packets reached the destination address by mitigating the effect due to address Trojan. The throughput and latency of the NoC is increased by 70 %. Further Simulation results show that the proposed mitigation scheme is useful in limiting the malicious effect of hardware Trojans. Thus, an effort has been made to address the routing, architectural, and security challenges of NoC design. A three-tier solution is proposed to safeguard the data and resources of NoC from HT. The proposed Trojan cognizant routing algorithm (TCRA) limits the HTs to a single router. Another method to trick and find the HTs is data shuffling with Trojan detectability. The proposed method reduces the hazards the Trojan poses, such as data leakage, performance degradation, denial of service, and live locking of data packets,at the cost of a little extra delay and hardware
Transient and Total Settlement Estimation of Shallow Strip Footing Subjected to Eccentrically Inclined Static Load and Cyclic Load on Granular Soil
No full textAnalysis of response of shallow foundations due to eccentrically inclined static load is a problem domain of specific interest. With increase in number of structures in the vicinity of industrial areas, shallow foundations, in addition to static loads, are often influenced by machine induced cyclic loads. An analysis of established theories indicates that the former problem i.e. the static analysis has been dealt separately from the later one i.e. cyclic analysis. The bearing capacity of shallow foundations under eccentrically inclined static load was studied by Meyerhof (1953), Meyerhof (1963), Hansen (1970), Patra et al. (2012), Sahu et al. (2016), Pham et al. (2020). The settlement under cyclic load was observed by Raymond and Komos (1978). A similar methodology was adopted by Das et al. (1995), Tafreshi et al. (2011), Fatah et al. (2019). It is observed that “the incorporation of load eccentricity as well as load inclination is limited to static cases only”. Also it is key to note that “Incorporation of influence of cyclic load is limited to centric vertical cases only”. The objective of this work is to fill this gap by using both the concepts, in order to observe the foundation settlement in more practical situations. In order to achieve this objective, a numerical model for a strip footing is developed based on Beam on Nonlinear Winkler Foundation (BNWF) model. The initial results are validated with existing experimental results reported by Patra et al. (2012). Then, the study is extended for cyclic analysis problem. The model is developed for three relative densities (Dr) of 35%, 51% and 69% respectively. The embedment ratio Df/B is varied from 0 to 1. The eccentricity ratio i.e. e/B is varied from 0 to 0.15 in 0.05 increment. The value of angle of load inclination with the vertical (α) is varied from 0 to 15 in 5 increment. The intensity of cyclic load (qd(max)) is taken as 5%, 10% and 13% of the ultimate bearing capacity (qu). The footing settlement due to allowable static load and cyclic load is observed. The allowable static load is calculated as the ultimate load divided by a factor of safety. Then settlement of the foundation due to first load cycle and due to 106 load cycles are analyzed separately. The settlement of the footing as a response to first load cycle is termed as transient response which is the immediate response of the foundation to the change in loading state. The settlement due to 106 cycles is the long term response of the foundation. During the analysis of long term response, the settlement pattern is observed and a key phenomenon is noticed, i.e. the settlement becomes constant at a particular value of number of load cycles. While studying the long term response, the effect of minor viii variations in the frequency of loading is also considered. In total, 1728 models are simulated for transient settlement response and 5184 models are simulated for long term response. Based on the dataset obtained from the numerical analysis, artificial intelligence techniques are applied to analyze the generated dataset. These artificial intelligence techniques are Levenberg Marquardt Neural Network (LMNN), Bayesian Regularization Neural Network (BRNN), Support Vector Machine (SVM), Multivariate Adaptive Regression Splines (MARS), Adaptive Neuro Fuzzy Interface System (ANFIS) and Multi Gene Genetic Programming (MGGP). The outcomes of the above mentioned procedures indicate that, the settlement of the foundation is largely influenced by the amount of the static load already applied on the foundation. It is also noted that a minor variation of frequency of loading is not significant in determining the settlement of the footing
Experimental Studies on the Reactive Extraction of Different Carboxylic Acids
No full textIn modern society, organic acids are used in food, agriculture, medicine, pharmaceuticals and other fields. Caproic acid is used for manufacturing artificial flavors, elastic, colors, pharmaceutical products, etc. Effective uses of n-butyric acid are notable for cancer treatment, for the production of alternative energy sources such as butanol and in the food and pharmaceutical industries. Iso-butyric acid acts as the precursor to produce methacrylic acid used to manufacture transparent acrylic glass, water bottles, kitchen and housewares, and medical equipment. Valeric acid has wide use in pharmaceutical and solvent industries and as an intermediate in the production of perfumes, fungicides, plasticizers and lubricants, and is also used as the monomer in polymer industries. Major uses of propionic acid can be found as an artificial food flavoring agent in food industries, fungicides, herbicides and plasticizers in pharmaceutical industries.
Due to the quick depletion of petroleum crude soon, the fermentation of biomass will be the major source of carboxylic acid. The produced acids in the broth are present at a very low concentration level. Reactive extraction can be an effective technology for recovering the carboxylic acids from the fermenter broth due to the integration of the fermentation and separation processes together. In the current work, an attempt has been made to identify the microorganism friendly reactive extractant and diluent pairs for different carboxylic acid systems. The performances of the natural diluents were compared with the petrochemical-based diluents. The study conferred the effect of temperature in the range of 298K-318K and also analyzed the effect of the initial concentrations of the selected carboxylic acids and the reactive extractant. Important performance determining parameters, distribution coefficient, loading ratio, extraction efficiency etc. as well as thermodynamic data like enthalpy and entropy changes were estimated.
N-butyric acid was extracted using tri-octyl amine (TOA). In physical extraction, KD and partition coefficient, decreased in the order of decanol > sunflower oil > soybean oil, whereas dimerization constant, showed an opposite trend. In chemical extraction, the extraction efficiencies were 85-90% for decanol, 82-88% for sunflower oil and 75-86% for soybean oil. In terms of the distribution coefficient, decanol was better diluent than sunflower oil followed by soybean oil. The achieved distribution coefficient and extraction efficiency using sunflower and soybean oils were found encouraging in bio-refinery industries. The reaction equilibrium constant, (:) has the highest value at the lowest temperature (298 K) and decreased with temperature and has a value in the order of decanol > sunflower > soybean. Iso-butyric acid was extracted by using tri-octyl amine (TOA) in eight different diluents. In physical extraction, KD decreased in the order of decanol > octanol > hexanol > MIBK > toluene > petroleum ether > sunflower oil > soybean oil. In chemical extraction, the extraction efficiencies were 82-93% in decanol, 80-88% in octanol 78-87% in hexanol, 76-86% in MIBK, 75-85% in toluene, 75-83% in petroleum ether, 73-82% in sunflower oil and 72-81% in soybean oil. The significantly high distribution coefficient and extraction efficiency using sunflower and soybean oils suggested their use in bio-refinery industries. The reaction equilibrium constant, (:) has the highest value at the lowest temperature (298 K). The extraction of valeric acid (VA) using tri-butyl phosphate (TBP) as a reactive extractant was carried out. The high values of the distribution coefficient and extraction efficiency advocated for using sunflower and soybean oils in the bio-refinery industries. Sunflower oil appeared to be a better diluent than soybean oil. The higher values of (:) occurred due to the higher stability of the VA-TBP complex in sunflower oil. Caproic acid was extracted using TBP in green and non-toxic diluents: sunflower and soybean oils. Current work has suggested using sunflower and soybean oil in the biorefinery industry for recovering the acid from the fermentation unit. The reactive extraction of propionic acid was carried out using both TBP and TOA as the extractant in non-toxic soybean and rice bran oil as the diluents. In physical extraction, soybean oil (maximum of 0.44 at 298 K) exhibited slightly better extraction performance than rice bran oil (maximum of 0.41 at 298 K). Using TBP as the extract, the overall distribution coefficient and extraction efficiency were found marginally higher in soybean oil (0.78) than in rice bran oil (0.72). Using TOA, soybean oil (maximum of 0.73 at 298 K,) showed better extraction performance than rice bran oil (maximum of 0.66 at 298 K). The performance of TBP as the extractant was found little better than TOA. (:) appeared higher due to the stability of the PA-TOA complex in soybean oil than in rice bran oil
Subject Independent Vision based Hand Gesture Recognition using Convolutional Neural Networks
No full textHand gestures are one of the most important ways for humans to communicate and express their expectation. Automatic recognition of hand gestures using the computer vision technique is a popular area of research. Due to its user-friendliness and flexibility, hand gesture recognition (HGR) systems are widely utilized for human-computer interface (HCI) and human-robot interaction (HRI) technologies. HGR has shown incredible potential in many fields, including sign language interpretation, virtual reality, robot control, gaming, etc. Performance of HGR system differs substantially between subjects. In most of the literature, the HGR systems are generally implemented in a subject dependent mode. Such systems cannot work accurately in real-time as the system is user dependent. Subject independent HGR systems are suitable for real-time applications because no further training is required for new subjects to recognize hand gestures. Furthermore, the effects of illumination variations, complex backgrounds, shape of the user’s hand, etc., remain common challenges in this research area. These challenges motivate for the development of improved subject independent HGR techniques, with more informative and better feature extraction, and classification algorithms. In this regard, a deep convolutional neural network based features extraction technique is proposed for automatic recognition of hand gestures. Here, deep features from different deep CNNs are fused to represent the hand gesture image more efficiently. The derived features extract high-level information like abstract information of hand gesture image from the receptive fields of the deep CNN’s last convolutional layer. After feature fusion, principal component analysis (PCA) based dimension reduction technique is applied to eliminate the redundant, irrelevant information present in the feature vector. The reduced feature are used to recognize the hand gestures with a support vector machine (SVM) classifier. Next, a deep residual block intensity (RBI) feature extraction technique with the support of a two-stage residual CNN architecture is proposed for HGR. In this technique, a compact CNN architecture with residual learning is developed, which is represented as 2RCNN. The 2RCNN architecture effectively captures the hand gesture attributes from the raw input image, and the network’s compactness is achieved with tuning the network using optimum number of filters. Thus the proposed 2RCNN reduces the number of trainable parameters of the network. The proposed RBI features are obtained from the residual blocks of the 2RCNN architecture. The RBI features capture both the low-level information such as lines, edges, blobs of hand gesture image and high-level information like abstract information of hand gesture images. The proposed technique overcomes the requirement of a separate feature reduction block due to the compact 2RCNN architecture with optimum number of filters. The above techniques are unable to distinguish the inter-class similar gesture poses of the hand gesture datasets. A compact dual-stream dense residual fusion network (DeReFNet) is proposed to solve the above issue. The DeReFNet consists of a global feature aggregation (GFA) residual stream, a spatial feature (SF) dense stream, and a feature concatenation module (FCM). The GFA residual stream is designed to extract low, mid, and high-level features from hand gesture images through the global average pooling technique. SF dense stream combines the spatial information of gesture images through feature reuse, which strengthens the network by extracting the refined local-to-global texture features of hand gesture images. Both the information of two individual streams are combined through FCM, which strengthens the proposed CNN to provide better performance of for HGR. Then, a compact deep residual network with an attention mechanism is proposed to recognize hand gestures accurately. The proposed channel attention mechanism improves the representative information of the feature maps by integrating the receptive fields of each convolutional branch over multiple scales. In addition, a cascaded approach for the combination of residual blocks with the multi-scale channel attention module is proposed to develop a deeper network that learns low-level to high-level information of input hand gesture images. Finally, a user interface system is developed based on the proposed HGR system to control a mobile robot in real time. The proposed techniques are validated on three publicly available static hand gesture datasets, such as MUGD, NUS-II, ASL-FS, and an indigenously developed dataset in the laboratory environment. Furthermore, the qualitative and quantitative analysis of the experimental results evaluated on four different datasets illustrates that the proposed techniques outperform the state-of-the-art methods reported in the literature
Development of Probiotic Finger Millet (Eleusine coracana) Milk Powder
No full textMilk and its products contain all the essential nutrients for daily nutrition. The milk is generally from an animal source. Due to the high cost involved in the upkeeping of dairy animals and various issues related to sustainable milk sourcing, the food industry is looking for alternative milk sources. Also, dairy-based milk products are less acceptable nowadays due to their association with various health issues such as high cholesterol, casein allergy, and lactose intolerance. Plant-based milk products, rich in essential nutrients, polyphenols, and phytochemicals, could be a potential solution to address these issues. As of now, soy and oat-based milk are available on the market. However, the nutritional quality and stability of these products are not satisfactory. Finger millet, an underutilized crop, could be explored as a source of milk and further value-addition. Finger millet is rich in antinutrients such as tannins and phytates that reduce the digestibility and chelates minerals. The current work was to develop a process protocol for producing probiotic finger millet milk powder. Finger millet was hydrated with ultrasound treatment to reduce the processing time and antinutrient content. Milk was extracted from the ultrasound-hydrated finger millet and then fermented with Lactiplantibacillus plantarum (MCC 2974). The fermented milk was spray-dried to encapsulate the probiotics in the finger millet milk powder. The process was optimized at every step to have reduced processing time, lesser antinutrients, maximum resistance starch, improved probiotic cell viability, and better powder quality. During hydration, an ultrasound amplitude of 66 %, treatment time of 26 min, and a grain-to-water ratio of 1:3 resulted in the best desirable parameters with a reduction in phytate and tannin contents of the finger millet by 66.98 and 62.83%, respectively. In-vitro digestion showed the presence of improved resistant starch. Increased crystallinity in XRD analysis also confirmed the presence of improved resistant starch. The fermented milk from the ultrasound hydrated finger millet showed higher cell viability (8.16±0.02 log CFU/ml), as confirmed by CLSM and FTIR analysis. The probiotic finger millet milk sample showed higher resistance in terms of cell viability (4.64±0.03 log CFU/ml) during in vitro digestion conditions. The optimum spray drying condition could be achieved at 151.50 °C inlet temperature, 29.52% maltodextrin content, and 100 ml/h feed rate. A probiotic finger millet milk powder was characterized by its various quality and morphological characteristics. Powder flow properties, phytochemical properties, and proximate composition of probiotic finger millet milk powder were in an acceptable range. SEM images of the developed powder showed higher cell viability. XRD pattern of the powder showed the amorphous structure with partial crystallinity of the particles confirming the higher shelf life. The probiotic powder stored at 25°C and 4°C exhibited 35 days and 56 days of shelf life, respectively, with the recommended probiotic viability of 6 log CFU/g. A laminated aluminium pouch with good moisture barrier properties was suitable packaging material to store probiotic powder. During moisture sorption kinetics, the Peleg model was best fitted for the sorption isotherm study. The sensorial properties of the developed probiotic finger millet milk powder showed good consumer acceptance. The cost of production of probiotic finger millet milk powder in the laboratory setup was ₹ 11.97 INR per gram