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Development of Brain Tumor Detection and Feature Extraction through Deep Learning Approach
No full textAs the body\u27s central control system, the human brain is susceptible to a wide variety of disorders, including tumors characterized by abnormal cell growth. It is imperative to detect these tumors as early as possible to plan effective treatment and improve patient outcomes. By using contemporary medical imaging methods, this research seeks to improve the accuracy and efficiency of brain tumor detection through the careful preprocessing and analysis of images, particularly Magnetic Resonance Imaging (MRI) [1]. To provide context for the subsequent research efforts, the challenges inherent in brain tumor detection are discussed comprehensively, including segmentation accuracy, small lesion detection, and variability in imaging data [2]. A sophisticated image processing algorithm tailored for detecting brain tumors is being developed and implemented. Pre-processing techniques such as noise reduction, intensity normalization, contrast enhancement, and spatial registration are meticulously applied to enhance medical images quality and consistency. As a result of these pre-processing steps, automated detection algorithms are optimized, artifacts are mitigated, and interpretation of the imaging data is improved. The main aim of this research is to formulate a framework for processing MRI images in order to detect brain tumors early and accurately.
Consequently, the development of automated computer-aided diagnosis systems aims to enhance and segment brain scans to locate tumor regions with greater accuracy. With the introduction of a dual-condition diffusion coefficient, this chapter introduces a new anisotropic diffusion filter capable of adapting better to both healthy and pathological tumor areas. It is a dual-condition diffusion coefficient that permits dynamic adaptation of the diffusion process within tumor regions to the local image characteristic in order to introduce flexibility into the anisotropic diffusion process. A selection of contrast enhancement with edges conserved for the most part within areas of tumors that have large gradient intensity and very complex structures; hence, it achieves proper segmentation. This line drawing is trusted for the tumor outline because this process, being diffusion, is sensitive to these complex features at boundaries that the targeted methodology satisfied.
A full pipeline that surpasses state-of-the-art approaches is achieved through the combination of anisotropic diffusion filter enhancements and morphology-driven segmentation. By demonstrating enhanced results, the integrated framework contributes to developing effective systems that assist radiologists in detecting tumors and intervening in time. It is critical to pay attention to the minute details present in MR brain images during diagnosing tumor classification problems, which are most often overlooked in existing tumor detection methods. It is also important to note that a limited amount of annotated ground truth data is available. Hence, it is proposed that a new version of YOLOv5 be developed to improve brain tumor detection efficiency in MRI [3]. Furthermore, a modified cuckoo search algorithm is employed to achieve faster convergence and more accurate results, allowing the optimization algorithm to optimize the performance of the proposed network. From its conventional methods, the modified Cuckoo Search Algorithm inherits some of the important advantages. Firstly, the highly enhanced search capability leads to the better convergence rate and overall solution accuracy.
Furthermore, some new modifications greatly enhance the algorithm efficiency in running, such as variable step optimization, optimized Levy flight patterns, and the fact that it\u27s less prone to straying off due to local minima in strength toward global optima. The greatly improved exploration and exploitation abilities of the algorithm ensure better completeness and effectiveness of the search in problems terrain that is typical of high-dimensional and complex problem domains, as happens in medical image analysis. Several key metrics are examined as part of the segmentation accuracy assessment, including Mean Squared Error (MSE), Structural Similarity Index (SSIM), Feature Similarity Index (FSIM), Peak Signal-to-Noise Ratio (PSNR), and CPU Time. YOLOv8-MM framework is next presented to highlight its innovative features, including separating the Deep Convolutional Neural Network (DCNN) into two parts that enhance information transfer.
As part of this study, Modified Fuzzy C-means Clustering (MFCC) is also introduced as a technique for MRI image segmentation. The latter is representative of Pareto optimization, balancing the accuracy and smoothness of tumor-segmented areas against other competing objectives for increased segmentation performance. cluster centre refinement discusses modifications to the fuzzy C-means clustering method aimed at improving its performance with regard to intrinsic ambiguity in medical images, through minimization of an objective function by an iterative process of cluster center refinement. Also, the improvement here in Pareto optimisation is the best possible compromise between these goals. Thus, Pareto optimisation provides the means for better accuracy in brain tumor identification for MRI images by allowing more efficient and correct segmentation, particularly while outlining complex borders of tumours.
MFCC can provide a robust foundation for accurate tumor geometry identification through iterative and Pareto optimization [4]. This chapter concludes with a discussion of the experimental setup, dataset characteristics, and performance evaluation methods, as well as rigorous experiments demonstrating the proposed methodology\u27s robustness in detecting, segmenting, and classifying brain tumors utilizing publicly available datasets. Hence, developing a computer vision model with suitable feature extraction provides accurate brain tumor detection from MRI images
Power Allocation for Next Generation IoT Systems using CNOMA
Get PDFThe global Pandemic/COVID-19 keeps people connected through wireless commu- nication. The tremendous growth of mobile data traffic and an increase in massive IoT connectivity among different devices leads to a scarcity of wireless resources. One of the advancing technologies for the 5G Beyond communication network, Non-Orthogonal Multiple Access (NOMA), is used for M2M, V2V, and connecting low-power IoT devices. NOMA outperforms OMA schemes such as FDMA, TDMA, CDMA, and OFDMA of 1G, 2G, 3G, and 4G in terms of data rate, latency, and spectral efficiency. NOMA exploits the multiplexing of signals in the power domain with the same frequency/time, and the interfered signal can be decoded at the receiver using Successive Interference Cancellation (SIC).
An indicator of Quality of Service (QoS), the Fairness Index ranges from 0 to 1 and gauges how evenly resources are distributed among users connected to a given system. Jain’s Fairness Index (JFI) of NOMA is higher than that of OMA for variable transmit power. Two NOMA users have a JFI of 0.85, whereas that of OMA users have a JFI of 0.6. With the NOMA systems, this guarantees the QoS and massive connection. After developing the NOMA system-level design with SystemVue, results are attained. SIC is considered perfect in most of the work, but it is not so in real-time scenarios. The performance of NOMA integrated with MIMO systems under imperfect SIC is analyzed. The Bit-Error Rate (BER) and Capacity of the suggested system are impacted by the SIC residual term. The analysis of the effect of the power allocation coefficient on the system’s performance highlights the importance of proper power distribution.
The MIMO-NOMA system can use co-operative methods to boost system reliability. The use of many Radio Frequency (RF) chains increases the power consumption and complexity of detection for MIMO-NOMA implementation. This further leads to Inter-Channel Interference (ICI) and Inter Antenna Synchronization (IAS). Implementing Index Modulation (IM) methods, such as Spatial Modulation (SM), in the NOMA system, provides a potential solution to these problems. The UR-SM-CNOMA system, which stands for User Relaying Cooperative NOMA with SM, is suggested. A recursive algorithm for maximizing the sum capacity of UR-CNOMA to obtain the optimal power allocation factor.
The simulation compares the proposed system to the established User Relaying Cooperative NOMA (UR-CNOMA) system using the analytically determined results, ensuring the effectiveness of the proposed method. Simulation results of the sum capacity of UR-SM-CNOMA for different spatial locations of near user are depicted, and conditions for the site of near user and far user are obtained to outperform SM-OMA. The existing MIMO antennas are not well exploited, and the suggested system only supports a limited number of users. An upgraded IM method known as Fully Generalized Spatial Modulation is used in the proposed three-user C-NOMA (FGSMN) system to address these problems.
Maximum Sum Capacity (MSC) and recursive algorithms are two recommended power allocation ways to improve the optimal power allocation. The suggested system’s optimal power allocation enhances the sum and achievable capacity. In addition to per- forming a computational complexity analysis, it is found that the FGSMN system’s Jain’s Fairness Index (JFI) is superior. The sum capacity of FGSMN is calculated as 573 Mbps/ for a bandwidth of 100 at a transmit power of 5 . The beamforming and power allocation simulation results for the NOMA system for M2M applications are provided
Analysis of Retrial Queueing Systems with Working Vacations and Breakdown Services
No full textThe Chapter One titled “Introduction and Preliminaries” deals with the basic definition of queues; characteristics of the queueing theory. It also presents the motivation for this thesis. The literature survey in the area of retrial queue with different phases of service, priority arrivals, feedback, breakdowns, repairs, working vacations and working breakdown service models together with their applications are analyzed.
In Chapter Two “Analysis of an M/G/1 Retrial Queueing System with Priority Customers Under J number of Working Vacations” elaborates on a single server retrial queueing system with priority arrivals under J number of working vacations. If an arriving priority customer finds the server free, the customer begins his service immediately. While the server is working with an ordinary customer, the arriving priority customer will interrupt the service time of the ordinary customer and the server begins its service immediately. If an arriving ordinary customer finds the server busy or on working vacation, the arrivals join a pool of blocked customers called an orbit in accordance with FCFS discipline.
As soon as the orbit becomes empty at regular service completion instant, the server takes at most J number of working vacations until at least one customer is received in the orbit when the server returns from a working vacation. During the working vacation period, the server serves at a lower speed service rate (μv\u3c μb). The steady state probability generating function of the orbit size and system size is obtained using supplementary variable technique and also obtained some analytic expressions for various performance measures such as system state probabilities, mean orbit size, and mean system size of this model. Numerical illustrations are analyzed to identify the effect of system parameters.
In Chapter Three “Performance Analysis of an M/G/1 retrial G-queue with Feedback under Working Breakdown Services” discusses a new type of retrial queueing model with feedback and working breakdown services has been discussed. The regular busy server may become defective due to disasters (negative customers) at any point of time. The negative customers arrive only at the service time of positive customer and will remove positive customer from the service. At a failure instant, the main server will be sent to repair and the repair period immediately begins. During the repair period, the server provides service at a low speed (working breakdown period). The steady state probability generating function for system size and orbit size are obtained using supplementary variable technique. Some analytical expressions for various performance measures such as system state probabilities, mean orbit size, mean system size of this model and some important special cases are derived. Finally, some numerical examples are presented to study the impact of system parameters.
In Chapter Four titled “Analysis of Retrial queue with Different Classes of Customers under Working Vacation Schedule” describes a working vacation queueing model with three different classes of customers: regular, priority, and disaster. The regular server serves all arriving customers, whereas the optional reservice is only provided to those who request it. The Bernoulli Working Vacation (BWV) schedule is considered. In WV time, the server serves at a slower rate. The Generating Functions (GF) technique is used to determine the system capacity of various server states. Different system performances, reliability indices, and cost optimization values are numerically shown. For the current Covid19 pandemic situation, the motivation for this approach is presented in telephonic communication system.
Chapter Five examines a new class of working vacation queueing models that contain regular (original) and retrial waiting queues. Upon arrival, a customer either starts their service instantly if the server is available, or they join the regular queue if the server is occupied. When it is empty, the server departs the system to take a Working Vacation (WV). The server provides services more slowly during the WV period. If the server is on vacation, new customers join the retrial queue (orbit). The Supplementary Variable Technique (SVT) examines the steady state Probability Generating Functions (PGF) of queue size for different server states. Several system performances are numerically displayed by including system state probabilities, mean busy cycles, mean queue lengths, sensitivity analysis, and cost optimization values. The motivation for this model in a pandemic situation is to analyze new healthcare service systems and reflect the characteristics of patient services
Multi Base Station Energy Efficient Cluster-aware Routing for Wireless Sensor Networks with Realtime Data Backup
No full textWireless Sensor Networks (WSNs) is created, stemming from their applications in distinct areas. This research focuses on implementing an efficient clustering and routing protocols to maximize the lifespan of the WSN by proposing a novel method known as the Energy Efficient Cluster-aware Routing Protocol (EECR). The proposed method comprises of three steps: cluster formation, cluster head (CH) selection, and multi-hop data transmission. The factors needed are residual energy, the minimum distance to the base station (BS), and the minimum Load Count as given in the Energy and Distance CH selection algorithm. The shortest pathway is estimated by the Energy Route Request Adhoc On demand Distance Vector (ERRAODV) algorithm.
In Multi Base Station Energy Efficient Cluster-aware Routing (MBS-EECR) algorithm is developed to overcome the problem that occurs due to the relay transmission over a period of time, if all the clusters nearer to the BS reach the maximum threshold value of load count, then the network doesn\u27t permit the data transmission further to take an alternate route of data packets. The result is data loss, and its always reduces the network performance. In the multi-base station load balancing mechanism (MBSLBM), an algorithm is used to dynamically assign the work load to other base stations.
Cloud preserving real time data efficiently, since local servers cannot maintain the immense volumes of data. The middleware named as sensor cloud gateway (SCG) compress the data from WSN, it procures better compression. The proposed Novel lossless Advanced Neighborhood Indexing Scheme (ANIS) compresses the data efficiently, which minimizes data size with storage requisites.
The ANIS technique introduced here amends the compression ratio to 80.73% from 78.31%, which is obtained from an existing New Lossless Neighborhood Indexing scheme (NIS) algorithm. The proposed algorithms are achieved the objective of maximizing the life span, reducing energy consumption and identify the node failure, and then handling the data communication problem in WSNs
Identification of Delamination Defect in Fibre Reinforced Polymer Composite Tapered Plate based on Vibration Measurement
No full textFibre-reinforced polymer (FRP) composite plates have been widely used in various engineering applications due to their high strength-to-weight ratio, corrosion resistance, and durability. Despite their many benefits, Delamination is a separation of the layers in composite material and can occur due to various factors such as impact, fatigue, and manufacturing defects. Identifying delamination in composite structures is crucial for ensuring safety and reliability. In this study, model-dependent vibration methods, which are an effective non-destructive technique, were utilised for delamination identification. The primary objective is to investigate the dynamic behaviour of delaminated thickness-tapered plates and develop a reliable methodology for delamination identification.
Initially, this research aims to address the effect of delamination and form a surrogate model to solve the forward problem in tapered composite plates. Initially, the design points for delamination resistance during the design of the sharp plate were considered. The numerical model incorporates the layerwise theory (LWT) and the constrained mode delamination approach. The eigenfrequency analysis was solved using the Finite Element Method. The accuracy of the numerical model is correlated with experimental modal analysis measurements. The surrogate model is developed using the Response Surface Methodology (RSM), Multi-output Artificial Neural Networks (ANN), and single-output ANN model.
The surrogate model predicts the natural frequency shift of the delaminated tapered plate. Two algorithms, such as the Surrogate Assisted Real-coded Genetic Algorithm (SARGA) and direct measurement using ANN, are employed for solving inverse problems to identify the delamination\u27s size, in-plane location, and interface. The literature shows limited research has been conducted to identify damages in tapered laminated composites with ply drop-off using the damage identification methods (Modal Curvature).
Therefore, this study aims to address these gaps by experimentally analysing the damage detection capabilities on a glass fibre-reinforced polymer (GFRP) composite tapered plate under varying severity of delamination. In this study, the modal curvature and its derivative parameters, such as the Damage Index (DI) based on Mode Shape Curvature (MSC), Gapped Smoothing Method (GSM), Mode Shape Curvature Square (MSCS), and Gapped Smoothing Method Square (GSMS), as well as the Curvature Damage Factor (CDF) based on MSC, MSCS, GSM, and GSMS
Temporal Dynamics of Coastal Sediment Characteristics: A Comparative Study on Pre- and Post-2004 Tsunami Event Along the Nagoor - Poompuhar Stretch, Tamil Nadu
No full textTsunamis represent some of the most devastating natural events for coastal regions, as exemplified by the 26th December 2004 Great Indonesian Earthquake triggered a massive tsunami across the Bay of Bengal and Indian Ocean, causing extensive destruction in various countries, including India. This study addresses a significant gap in the literature by focusing on the detailed sedimentological aspects of coastal dynamics. Specifically, it examines the pre-tsunami (December 2004), post-tsunami (January 2005), and recent (December 2022) sediment characteristics along a 35 km stretch of the Coromandel coast from Poompuhar to Nagoor.
Eight stations were selected for sampling: Poompuhar, Chinnankudi, Kuttyandiyur, Chandrapadi, Kottucherimedu, Karaikal Beach, Vadakku Vanjiyur North, and Nagoor Beach. The study area is characterized by various geomorphic features, including deltas, flood plains, and paleochannels, with principal rivers such as the Cauvery, Nandalar, and Arasalar emptying into the Bay of Bengal. Surface sediment samples were collected at 5 km intervals along the coast and at 5 m intervals from dune to low tide within each location, resulting in a total of 97 samples.
These samples were analyzed for grain size metrics, including mean, standard deviation, skewness, and kurtosis, using the R software package G2Sd. Granulometric and granule trends, along with heavy mineral analysis, were conducted to discern textural characteristics, sediment shape, and variations in mineral content. The study employed bivariate graphs, linear discriminant function analysis, C-M diagrams, and Tractive current plots to elucidate depositional environments and sediment transport dynamics. The results revealed significant temporal changes in sediment characteristics. Pre-tsunami sediments were finer, especially in high tide areas, while post-tsunami sediments showed a marked coarsening.
Recent sediments indicate a return to finer conditions, resembling pre-tsunami states, except in highly erosive areas like Poompuhar, Chandrapadi, and Nagoor. The study also noted shifts in sediment sources, with a decline in riverine and tidal sands and an increase in aeolian and marine deposits. Variations in heavy minerals, particularly the resurgence of certain placer deposits in recent years, highlight the complex interplay of geological processes shaping coastal evolution. This research provides critical insights for coastal management and disaster risk reduction
Experimental Investigations On Mechanical Wear And Corrosion Behaviour Of Sintered Atomet 4601 Based Hybrid Alloys
No full textPowder metallurgy enables the development of new materials to manufacture products for applications requiring unique properties. The present research initially investigates the mechanical and tribological behaviour of powder metallurgy alloys made from ATOMET 4601 prealloyed powder and varying amounts of elemental carbon (0, 0.5, and 1.0 wt%) as reinforcement. ATOMET 4601 is a high-strength, water-atomized prealloyed powder capable of being compressed to higher densities, with applications in producing near-net or net-shaped parts for the industrial field.
Mechanical behaviour studies on sinter-forged ATOMET 4601, ATOMET 4601+0.5C, and ATOMET 4601+1.0C alloy steels revealed increased hardness, tensile strength, and impact strength, with ATOMET 4601+1.0C having the highest strength. Fracture analysis of ATOMET 4601, ATOMET 4601+0.5C, and ATOMET 4601+1.0C tensile test specimens revealed ductile, quasi-static, and brittle fractures, respectively.
Subsequently, dry sliding wear studies were conducted on the above alloys which address the effect of load (15 to 50 N) and speed (300 to 1200 rpm) on wear behaviour using a pin-on-disc tribometer. ATOMET 4601+0.5C and ATOMET 4601+1.0C have exhibited a lesser wear rate due to their higher hardness and strength. A soft ferritic–pearlite phase matrix and lesser hardness could have caused a higher wear rate in the base alloy. Carbide and oxide formation caused a lower wear rate in both carbon-reinforced alloys. Both load and speed significantly affected the coefficient of friction in all three alloys.
Based on the mechanical and tribological test results, the research focus was narrowed down to develop and study the performance of ATOMET 4601+0.35C (Hybrid alloy 1) and ATOMET 4601+0.35C+0.25Mn+0.1Si+0.9Cr (Hybrid alloy 2) which can replace products made from conventional medium carbon steel (AISI 1035) and EN24 wrought steel respectively. The sintered preforms are considered to study the formability parameters like true lateral strain, true height strain, relative density, and hardness. The research on workability revealed that the hybrid alloy 2 test specimens have undergone lesser densification and deformation due to the work hardening mechanism caused by the addition of alloying elements.
Microstructures of the cold upset test specimens are correlated with the densification behaviour. Further, the study investigated the corrosion behaviour of sintered hybrid preforms with different densities in an 18% HCl corrosive solution for 25, 50, 75 and 100 hours. Results showed that hybrid alloy 2 had a lesser corrosion rate than hybrid alloy 1 due to the presence of different alloying elements. The XRD, optical, and SEM images of corroded surfaces of the test specimens were correlated with corrosion mechanisms.
After understanding the better strength and corrosion resistance of hybrid alloy 2, the wear test was conducted for hybrid alloy 2. Dry sliding wear studies on sintered hybrid alloy 2 revealed its lowest wear rate among all the alloys. Coefficient of friction is higher than the base alloy and lesser than ATOMET 4601+1.0C at the lowest load conditions. Finally, empirical relations were developed, and model adequacy was checked using ANOVA. Thus, the research provides insights to assist the powder metallurgy designer in developing the high performance materials for functional parts in engineering industries
Design and Implementation of Non-isolated High Gain DC-DC Converter for Photovoltaic Application
Get PDFFossil fuel-based power plants emit 25% of greenhouse gases and 40% of carbon emissions globally. Therefore, several countries focus on deploying RES-based electricity production (such as solar and wind). As per the International Energy Agency report, solar PV might be the lowest-cost option for generating electricity in the future. Solar energy is highly emission-free, but it is still a key challenge in procuring the maximum amount of energy from solar PV due to its non–linear characteristics. Therefore, a DC-DC converter must integrate with solar PV to increase the output voltage level. Many existing topologies exist in the literature, but the utilization of device count or operating duty cycle is higher to attain the required voltage gain.
So, emerging topologies are still required to improve performance parameters based on different applications. Therefore, the main objective of this research work is to design a high-gain quadratic-based DC-DC power converter for solar PV applications. The first specific aim of the research objective focuses on the design of a novel nonisolated high gain quadratic-based DC-DC converter topology. Three different proposed converters are designed focusing on the improvement in size, voltage gain, number of components, voltage stress, etc. The first proposed converter (P-I) combines a novel voltage multiplier unit (VMC) (4C, 2L, 2D) with a modified quadratic boost converter to attain a voltage gain of 10.75 at a 60% duty cycle.
Although the converter attains higher voltage gain, the total component count (TCC-20) is higher. So, a proposed converter-II (P-II) is developed to focus on reducing the components, thereby increasing power density. It utilizes 16 components to attain a voltage gain of 9.75 at a 60% duty cycle, providing an efficiency of 91% with a power density of 1.03 kW/L. Even though the proposed converter has improved power density and voltage gain, the efficiency of the converter is lower. Therefore, an attempt is made to design a proposed converter-III (P-III) focusing on improving the efficiency and power density with less TCC. The proposed converter-III can provide 93% efficiency by utilizing 12 components with a power density of 1.36 kW/L. The operation of the proposed converters is validated through the laboratory-based experimental prototype.
The second specific aim of the research objectives focuses on the stability and reliability analysis of the proposed converter-III. The stability analysis is performed for the proposed converter-III using a PID controller in closed-loop conditions. The validation of the PID closed-loop controller is verified using MATLAB-Simulink with a wide range of variations in the different parameters, such as input voltage, load value, and reference voltage values. The simulated results show that the proposed converter with a PID controller adapts well to dynamic changes. Reliability analysis is performed using the military handbook (MILHDBK- 217F) to predict the failure rate of individual components in the proposed converter- III. It is found that the failure rate of the switch is higher than that of any other component in the proposed converter-III.
The third specific aim of the research objectives focuses on the integration of the proposed converter-III with solar PV for procuring maximum power. The whale optimization algorithm effectively finds global peaks under partial shading conditions, but the exploration process of theWOA is lacking due to the randomness of the initial search process. Therefore, a modified whale optimization algorithm (MWOA) is proposed to identify the initial search process to reduce the exploration time. It would reduce the convergence time by providing an optimized duty cycle under varying irradiation conditions. The system is examined with three different patterns in MATLAB Simulink Environment such as pattern 1 (1000W/m2), pattern 2 (1000W/m2 and 600W/m2) and pattern 3 (1000W/m2,600W/m2 and 800W/m2). The simulated results show that the MWOA performs well with the proposed converter-III during the partial shading conditions
Design and Development of Contactless Capacitive Coupled Electrodes for Cardiovascular Signal Acquisition
Get PDFIn biosignal acquisition and physiological monitoring, developing advanced electrode technologies is crucial in enhancing signal quality, minimizing interference, and improving overall reliability. One such innovative approach is the utilization of capacitive-coupled electrodes, a cutting-edge solution that addresses some of the challenges associated with traditional electrodes in biosignal recording.
Capacitive-based sensors have become prominent in physiological signal measurement over the past decade. The electric field from the external affects both the human body and the electrode. The primary aim while designing the capacitive electrode is to maximize the signal-to-noise ratio. Hence, we introduce shielding and guarding techniques to improve the signal-to-noise ratio. In this work, we proposed a Capacitive Electrode (CE) with optimal Shielding and Guarding design (SGD). The copper was coated on the base material.
Then, gold was coated over the copper to prevent oxidation and improve conductivity. The base material used was FR4. Three different designs were proposed. The performance of each design was evaluated using the Finite Element Method (FEM). On comparing all the electrophysical parameters of the three designs, the design: ”The shielding was placed around the sensing plate, and the guarding was formed as a concentric around the sensing plate” was shown as the CE with optimal SGD, which enables higher performance.
The electrostatic potential of the optimal design is -1e-3 V, which is higher than the electrostatic potential of CE designs. The proposed electrode was applied for the ECG application. The optimal electrodes (OE) were placed in the lead II and V1 V2 lead positions. The comparison of the OE and other electrode at the lead II position (electrostatic potential is -1e-3 V) and chest lead v1 and v2 positions (electrostatic potential is -1e-4 V) is performed, and the results show that the lead II position is the OE lead position. The OE and electrode position to fetch the ECG signal were more accurate with less noise than other designs.
The proposed capacitive electrode with optimal Shielding and Guarding design can be used to fetch other bio-signals. When the proposed electrode was applied to fetch the ECG signal, we obtained the optimal signal-to-noise ratio of the ECG signal of order. The signal sensed in the capacitive sensors has to be classified for the abnormality. However, the raw data obtained from these sensors often requires advanced processing techniques for accurate analysis and classification of ECG patterns. Detecting cardiac disorders is difficult due to several contributing factors linked with patients and diagnostic materials. It required high computational complexity and a feasible method.
Many classification techniques have been proposed in the literature. This work focused on the novel methodology for classifying seven arrhythmic abnormalities of ECG signal. A novel Deep Convolution Neural Network method called RaNet has been developed. Then, transfer learning has been diligent in the RaNet for fast computation and classification. The training has been performed with a 12-lead ECG dataset from the physionet (unbalanced dataset).
In our method, the testing dataset need not be a twelve-lead dataset. We used ECG signals taken from single-lead, two-lead, trio-lead or six-lead. The efficiency of this method is analyzed by comparing its accuracy with that of other neural networks in the literature.
The network performance is evaluated by comparative analysis between different architectures in transfer learning. The proposed model RaNet with transfer learning reaches the accuracy of 98.44% with an F1 score of 98.98%.
The precision and recall values are 98.77% and 99.21%, respectively. The proposed method is also compared with other 2D image-based CNN methods, and it has been proved that the proposed method is faster and more efficient than other methods for classifying ECG abnormalities. The major advantage of the proposed method is that it reduces the computation time and has higher accuracy. The capacitive sensor monitoring system in this work is designed as one lead system.
Also, the detection and transmission of 12-lead ECG data for anomaly detection proves time-consuming. Hence, in this work, we developed a CNN algorithm to reconstruct the 12-lead ECG model into a 2-lead ECG model. Initially, a data set with 23 abnormalities is considered and the best 2-lead combination for each of the most commonly occurring anomalies from all the available combinations using the K-mean clustering algorithm. This is done by detecting any deviation from the ideal PQRST ECG
Investigation On Production, Kinetic Modelling, Downstream Processing, And Characterization Of Microbial Dextran Using Sucrose-Rich Alternate Feedstocks
Get PDFMicrobial exopolysaccharides (EPS) synthesized by microorganisms have a wide range of applications from thickener agents in the field of food to formulations in the field of pharmaceuticals. Owing to their rheological, structural, and physicochemical characteristics in addition to its biodegradability, low antigenicity, antioxidant activities, etc., several EPS have gained research focus in recent years. Dextran is one such example of EPS, which is synthesized by lactic acid bacteria (LAB). However, its production is limited to utilization of sucrose medium with a yield of 0.3-0.4 g dextran/ g sucrose. Hence, this thesis work focuses on dextran production using sucrose-rich alternate feedstocks and its optimization.
Sugarcane juice (SOJ) and sugarcane molasses (SCM) are enriched sources of vitamins, minerals and carbohydrates that proves to be an effective medium for LAB growth and dextran synthesis. The presence of melanoidins, negatively charged heteropolymer, in SCM greatly affects the production and recovery of dextran from fermentation broth. Therefore, SCM pretreatment with activated charcoal (TCM) resolves this issue. After conventional optimization, Leuconostoc mesenteroides produced a maximum titer of 44.5 ± 0.5 g/L and 42.0 ± 2.2 g/L dextran on SOJ and TCM media respectively. However, only 0.29-0.4 g/g of dextran could be obtained. So, further optimization by statistical method (Central-Composite Design) improved the dextran titer to 78.1 ± 1.9 g/L (SOJ) and 60.0 ± 2.0 g/L (TCM), resulting in a 9.8-fold and 9.0-fold increase on SOJ and TCM media, respectively when compared with unoptimized media.
The produced dextran yield was also compared with that of the optimized sucrose medium (94.5 ± 2.0 g/L). In the case of sucrose medium, around 9.5-fold increase in production has been observed. Also, the yield was improved to 0.47 ± 0.01, 0.39 ± 0.01, and 0.4 ± 0.01 g/g in sucrose, SOJ and TCM media, respectively. The characterization studies such as NMR, FTIR, and GPC, of the produced polymer confirmed it to be dextran. With the help of rheology study, the dextran solutions were found to be pseudoplastic (n = 0.89 for dextran from SOJ media, and n = 0.62 for dextran from TCM media at 10 % w/v) that can affect fermentation performances such as mass transfer rate, mixing, aeration and downstream processing.
On the basis of economic yield and molecular mass, dextran production using TCM media proved to be cost-effective. Hence, further understanding about kinetic parameters for dextran production on TCM media was studied using various mathematical models. A maximum specific growth rate of 0.35 h-1 was obtained using logistic equation. From Leudeking-Piret model, dextran production was found to be predominantly growth associated with a specific productivity of 3.79 g/(g.h).
Interestingly, from the literature survey, it was observed that the functional properties of dextran is usually affected by its recovery conditions, aeration, agitation, etc. So, in this study, dextran production in a 3L fermenter was carried out under previously optimized conditions to study the influence of aeration and agitation on the dextran titer, its molecular mass and broth rheology.
It was found that the aeration did not significantly affect dextran titer but on increasing impeller speed (50 to 150 rpm), not just the titer (55 to 60 g/L), but also its molecular mass improved from 2780.6 kDa to 3930.6 kDa. Apart from fermentation conditions, recovery of dextran also plays an important role on the overall cost as well as functional properties of dextran. By optimizing the downstream processing, the yield of dextran was found to improve from 0.4 g/g to 0.47 g/g on using 1:4 v/v solvent supernatant ratio of ethanol.
In addition, DES was also able to successfully precipitate 70.0 ± 2.5 g/L of dextran at a ratio of 1:2 v/v supernatant to solvent. The obtained dextran was efficiently utilized in the bioremediation and biorefinery processes. For industrial production of dextran, large throughput is necessary which can be achieved by transferring the optimized conditions to large-scale fermenters and by proposing certain scale-up strategies. During scale-up, based on constant P/V strategy, an agitation of 54 rpm was estimated for efficient dextran production in a 2000 L working volume