Journal Articles
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/19884
Browse
22 results
Search Results
Item A continuous flow microreactor as a practical tool for the synthesis of active pharmaceutical ingredients(Institution of Chemical Engineers, 2024) Bhaskar, K.; Rashed, M.; Bhat, K.; Lee, J.; Kim, K.-H.; Buruga, K.Continuous flow processing has become a key technology to maximize the capabilities of chemical syntheses. Numerous new tactics have been devised to synthesize various chemicals including active pharmaceutical ingredients (APIs) via a continuous flow system using microreactors. This review aims to illustrate the holistic system approach and diverse applications of continuous flow microreactors for the synthesis of APIs (with respect to type, design, and fabrication). This review also highlights the applicability of continuous flow microreactors in the synthesis of APIs in contrast to conventional batch-type methods along with a discussion of their merits and demerits. Overall, this review is expected to offer valuable insights into the utility of continuous flow microreactor technology for the upscaled production of commercially feasible APIs. © 2024 The Institution of Chemical EngineersItem ZigBee based Real Time System for Environmental Parameters Monitoring in Model Mine: An Experimental Study(World Researchers Associations, 2025) Naik Anil, S.; Kumar, R.S.; Raj, M.G.The global mining industry, known for extracting valuable minerals like coal, iron ore, gold, silver, copper and zinc, primarily operates through underground mining. These underground mine environments are influenced by various environmental factors including toxic and flammable gases, increased carbon dioxide (CO2) levels and decreased oxygen (O2) concentrations. These factors significantly impact the productivity and safety of mine workers, making monitoring harmful gases crucial. Various methods exist to measure gas concentrations and respond appropriately when these levels exceed safe thresholds. However, each method has its own set of limitations. In underground mines, wireless monitoring systems are essential to monitor environmental parameters in real time. This study focuses on the assessment of ZigBee-based wireless techniques for their application in underground mines. The developed ZigBee wireless communication system was tested and validated at the surface level and in a model mine laboratory at the Mining Engineering Department of the National Institute of Technology Karnataka (NITK), Surathkal, India. This laboratory replicates an underground mine setting. The ZigBee-based system is designed to wirelessly monitor environmental parameters. Experiment results suggest that the ZigBee network is suitable for real-time monitoring of environmental conditions in underground mines. Further details regarding the functioning, effectiveness and potential applications of the ZigBee-based system in underground mining are discussed in the study. © 2025, World Researchers Associations. All rights reserved.Item Automatic identification of diabetic maculopathy stages using fundus images(2009) Nayak, J.; Subbanna Bhat, P.S.; Acharya, R.Diabetes mellitus is a major cause of visual impairment and blindness. Twenty years after the onset of diabetes, almost all patients with type 1 diabetes and over 60% of patients with type 2 diabetes will have some degree of retinopathy. Prolonged diabetes retinopathy leads to maculopathy, which impairs the normal vision depending on the severity of damage of the macula. This paper presents a computer-based intelligent system for the identification of clinically significant maculopathy, non-clinically significant maculopathy and normal fundus eye images. Features are extracted from these raw fundus images which are then fed to the classifier. Our protocol uses feed-forward architecture in an artificial neural network classifier for classification of different stages. Three different kinds of eye disease conditions were tested in 350 subjects. We demonstrated a sensitivity of more than 95% for these classifiers with a specificity of 100%, and results are very promising. Our systems are ready to run clinically on large amounts of datasets. © 2009 Informa Healthcare USA, Inc.Item Accurate lumen diameter measurement in curved vessels in carotid ultrasound: an iterative scale-space and spatial transformation approach(Springer Verlag service@springer.de, 2017) Krishna Kumar, P.; Araki, T.; Rajan, J.; Saba, L.; Lavra, F.; Ikeda, N.; Sharma, A.M.; Shafique, S.; Nicolaïdes, A.; Laird, J.R.; Gupta, A.; Suri, J.S.Monitoring of cerebrovascular diseases via carotid ultrasound has started to become a routine. The measurement of image-based lumen diameter (LD) or inter-adventitial diameter (IAD) is a promising approach for quantification of the degree of stenosis. The manual measurements of LD/IAD are not reliable, subjective and slow. The curvature associated with the vessels along with non-uniformity in the plaque growth poses further challenges. This study uses a novel and generalized approach for automated LD and IAD measurement based on a combination of spatial transformation and scale-space. In this iterative procedure, the scale-space is first used to get the lumen axis which is then used with spatial image transformation paradigm to get a transformed image. The scale-space is then reapplied to retrieve the lumen region and boundary in the transformed framework. Then, inverse transformation is applied to display the results in original image framework. Two hundred and two patients’ left and right common carotid artery (404 carotid images) B-mode ultrasound images were retrospectively analyzed. The validation of our algorithm has done against the two manual expert tracings. The coefficient of correlation between the two manual tracings for LD was 0.98 (p < 0.0001) and 0.99 (p < 0.0001), respectively. The precision of merit between the manual expert tracings and the automated system was 97.7 and 98.7%, respectively. The experimental analysis demonstrated superior performance of the proposed method over conventional approaches. Several statistical tests demonstrated the stability and reliability of the automated system. © 2016, International Federation for Medical and Biological Engineering.Item A benchmark study of automated intra-retinal cyst segmentation algorithms using optical coherence tomography B-scans(Elsevier Ireland Ltd, 2018) Girish, G.N.; Anima, V.A.; Kothari, A.R.; Sudeep, P.V.; Roychowdhury, S.; Rajan, J.(Background and objectives) Retinal cysts are formed by accumulation of fluid in the retina caused by leakages from inflammation or vitreous fractures. Analysis of the retinal cystic spaces holds significance in detection and treatment of several ocular diseases like age-related macular degeneration, diabetic macular edema etc. Thus, segmentation of intra-retinal cysts and quantification of cystic spaces are vital for retinal pathology and severity detection. In the recent years, automated segmentation of intra-retinal cysts using optical coherence tomography B-scans has gained significant importance in the field of retinal image analysis. The objective of this paper is to compare different intra-retinal cyst segmentation algorithms for comparative analysis and benchmarking purposes. (Methods) In this work, we employ a modular approach for standardizing the different segmentation algorithms. Further, we analyze the variations in automated cyst segmentation performances and method scalability across image acquisition systems by using the publicly available cyst segmentation challenge dataset (OPTIMA cyst segmentation challenge). (Results) Several key automated methods are comparatively analyzed using quantitative and qualitative experiments. Our analysis demonstrates the significance of variations in signal-to-noise ratio (SNR), retinal layer morphology and post-processing steps on the automated cyst segmentation processes. (Conclusion) This benchmarking study provides insights towards the scalability of automated processes across vendor-specific imaging modalities to provide guidance for retinal pathology diagnostics and treatment processes. © 2017 Elsevier B.V.Item Design and performance analysis of quantitative feedback theory based automated robust controller: An application to uncertain autonomous wind power system(AIMS Press, 2018) Gudimindla, G.; Manjunatha, S.K.Use of a robust controller for handling the operational uncertainties has become imperative in real time. This paper presents a modified fitness function based automated robust controller with the aid of quantitative feedback theory (QFT) using Genetic algorithm (GA). A controller exhibiting the desired decreasing modular plot and descending phase response is devised. The addition of arctangent function as one of the fitness function term is the proposed modification that facilitates in capturing the ideal controller characteristics. The proposed controller is applied to extract maximum power from a permanent magnet synchronous generator based autonomous wind power system. The step by step design guidelines for the automated QFT robust controller is deliberated in detail. The performance evaluation is carried out for step change and stochastically varying wind speed. Finally, benchmarking of the proposed controller against those available in the literature is accomplished through extensive simulations and it will be shown that the maximum power extraction along with least electromagnetic torque oscillations are achieved with the proposed fitness function based automated QFT controller. © 2018 the Author(s).Item A fully-automated system for identification and classification of subsolid nodules in lung computed tomographic scans(Elsevier Ltd, 2019) Savitha, G.; Padikkal, P.A fully-automated computer-aided detection (CAD) system is being proposed in this paper for identification and classification of subsolid lung nodules present in Computed Tomography(CT) scans. The system consists of two stages. The first stage aims at detecting locations of the nodules, while the second one classifies the same into the solid and subsolid category. The system performs segmentation of the region of interest (ROI) and extraction of relevant features from the segmented ROI. Graylevel covariance matrix (GLCM) is being used to extract the Feature vectors. Principle component analysis (PCA) algorithm is used to select significant features in the feature space formed by the vectors. The nodule localization is performed using support vector machine, fuzzy C-means, and random forest classification algorithms. The identified nodules are further grouped into solid and subsolid nodules by extracting histogram of gradient (HoG) features adopting K-means and support vector machine (SVM) classifiers. A large number of annotated images from the widely available benchmark database is tested to validate the results. Efficiency and reliability of the system are evaluated visually and numerically using the relevant quantitative measures. The developed CAD system is found to identify subsolid nodules with a high percentage of accuracy. © 2019 Elsevier LtdItem A Multi-Protocol Home Automation System Using Smart Gateway(Springer, 2021) Chaudhary, S.K.; Yousuff, S.; Meghana, N.P.; Ashwin, T.S.; Guddeti, R.M.R.Smart Home is one of the most established applications of the Internet of Things. Almost every equipment we use in our daily life—appliances, electric lights, electrical outlets, heating, and cooling systems-connected to a remotely controllable network, giving the user’s ability to remotely control and monitor the house, save energy without compromising on comfort and ultimately improve the quality of experience of staying in the house. We present a cost-effective system and address a major challenge that the industry faces today-Protocol Compatibility. To address the challenge, we make use of separate gateways/bridges for each network and an open-source home automation framework called OpenHAB, where each bridge links with a single master Wi-Fi gateway, providing a single window of control through an Application or a web interface for an integrated Smart Home. We integrate an elderly health monitoring device-Beehealth with OpenHAB; addressing the paramount need of a portable, accurate, and efficient health monitoring and fall detection device. We present two methods for fall detection, namely: threshold-based and Neural Network-based, with the latter resulting in 94% accuracy for fall detection. We evaluate the Smart Home devices on parameters like syncing time, battery life, recharge time, deployability, and cost. © 2020, Springer Science+Business Media, LLC, part of Springer Nature.Item Deep neural models for automated multi-task diagnostic scan management - Quality enhancement, view classification and report generation(IOP Publishing Ltd, 2022) Karthik, K.; Kamath S․, S.The detailed physiological perspectives captured by medical imaging provides actionable insights to doctors to manage comprehensive care of patients. However, the quality of such diagnostic image modalities is often affected by mismanagement of the image capturing process by poorly trained technicians and older/poorly maintained imaging equipment. Further, a patient is often subjected to scanning at different orientations to capture the frontal, lateral and sagittal views of the affected areas. Due to the large volume of diagnostic scans performed at a modern hospital, adequate documentation of such additional perspectives is mostly overlooked, which is also an essential key element of quality diagnostic systems and predictive analytics systems. Another crucial challenge affecting effective medical image data management is that the diagnostic scans are essentially stored as unstructured data, lacking a well-defined processing methodology for enabling intelligent image data management for supporting applications like similar patient retrieval, automated disease prediction etc. One solution is to incorporate automated diagnostic image descriptions of the observation/findings by leveraging computer vision and natural language processing. In this work, we present multi-task neural models capable of addressing these critical challenges. We propose ESRGAN, an image enhancement technique for improving the quality and visualization of medical chest x-ray images, thereby substantially improving the potential for accurate diagnosis, automatic detection and region-of-interest segmentation. We also propose a CNN-based model called ViewNet for predicting the view orientation of the x-ray image and generating a medical report using Xception net, thus facilitating a robust medical image management system for intelligent diagnosis applications. Experimental results are demonstrated using standard metrics like BRISQUE, PIQE and BLEU scores, indicating that the proposed models achieved excellent performance. Further, the proposed deep learning approaches enable diagnosis in a lesser time and their hybrid architecture shows significant potential for supporting many intelligent diagnosis applications. © 2021 IOP Publishing Ltd.Item Automated Molecular Subtyping of Breast Carcinoma Using Deep Learning Techniques(Institute of Electrical and Electronics Engineers Inc., 2023) Niyas, S.; Bygari, R.; Naik, R.; Viswanath, B.; Ugwekar, D.; Mathew, T.; Kavya, J.; Kini, J.R.; Rajan, J.Objective: Molecular subtyping is an important procedure for prognosis and targeted therapy of breast carcinoma, the most common type of malignancy affecting women. Immunohistochemistry (IHC) analysis is the widely accepted method for molecular subtyping. It involves the assessment of the four molecular biomarkers namely estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2), and antigen Ki67 using appropriate antibody reagents. Conventionally, these biomarkers are assessed manually by a pathologist, who finally combines individual results to identify the molecular subtype. Molecular subtyping necessitates the status of all the four biomarkers together, and to the best of our knowledge, no such automated method exists. This paper proposes a novel deep learning framework for automatic molecular subtyping of breast cancer from IHC images. Methods and procedures: A modified LadderNet architecture is proposed to segment the immunopositive elements from ER, PR, HER2, and Ki67 biomarker slides. This architecture uses long skip connections to pass encoder feature space from different semantic levels to the decoder layers, allowing concurrent learning with multi-scale features. The entire architecture is an ensemble of multiple fully convolutional neural networks, and learning pathways are chosen adaptively based on input data. The segmentation stage is followed by a post-processing stage to quantify the extent of immunopositive elements to predict the final status for each biomarker. Results: The performance of segmentation models for each IHC biomarker is evaluated qualitatively and quantitatively. Furthermore, the biomarker prediction results are also evaluated. The results obtained by our method are highly in concordance with manual assessment by pathologists. Clinical impact: Accurate automated molecular subtyping can speed up this pathology procedure, reduce pathologists' workload and associated costs, and facilitate targeted treatment to obtain better outcomes. © 2013 IEEE.
- «
- 1 (current)
- 2
- 3
- »