Faculty Publications
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Item NucMM Dataset: 3D Neuronal Nuclei Instance Segmentation at Sub-Cubic Millimeter Scale(Springer Science and Business Media Deutschland GmbH, 2021) Lin, Z.; Wei, D.; Petkova, M.D.; Wu, Y.; Ahmed, Z.; K, K.S.; Zou, S.; Wendt, N.; Boulanger-Weill, J.; Wang, X.; Dhanyasi, N.; Arganda-Carreras, I.; Engert, F.; Lichtman, J.; Pfister, H.Segmenting 3D cell nuclei from microscopy image volumes is critical for biological and clinical analysis, enabling the study of cellular expression patterns and cell lineages. However, current datasets for neuronal nuclei usually contain volumes smaller than 10- 3 mm3 with fewer than 500 instances per volume, unable to reveal the complexity in large brain regions and restrict the investigation of neuronal structures. In this paper, we have pushed the task forward to the sub-cubic millimeter scale and curated the NucMM dataset with two fully annotated volumes: one 0.1 mm3 electron microscopy (EM) volume containing nearly the entire zebrafish brain with around 170,000 nuclei; and one 0.25 mm3 micro-CT (uCT) volume containing part of a mouse visual cortex with about 7,000 nuclei. With two imaging modalities and significantly increased volume size and instance numbers, we discover a great diversity of neuronal nuclei in appearance and density, introducing new challenges to the field. We also perform a statistical analysis to illustrate those challenges quantitatively. To tackle the challenges, we propose a novel hybrid-representation learning model that combines the merits of foreground mask, contour map, and signed distance transform to produce high-quality 3D masks. The benchmark comparisons on the NucMM dataset show that our proposed method significantly outperforms state-of-the-art nuclei segmentation approaches. Code and data are available at https://connectomics-bazaar.github.io/proj/nucMM/index.html. © 2021, Springer Nature Switzerland AG.Item Computer-aided diagnosis system for tissue characterization of brain tumor on magnetic resonance images(Springer London, 2015) Arakeri, M.P.; Guddeti, G.R.M.The manual analysis of brain tumor on magnetic resonance (MR) images is time-consuming and subjective. Thus, to avoid human errors in brain tumor diagnosis, this paper presents an automatic and accurate computer-aided diagnosis (CAD) system based on ensemble classifier for the characterization of brain tumors on MR images as benign or malignant. Brain tumor tissue was automatically extracted from MR images by the proposed segmentation technique. A tumor is represented by extracting its texture, shape, and boundary features. The most significant features are selected by using information gain-based feature ranking and independent component analysis techniques. Next, these features are used to train the ensemble classifier consisting of support vector machine, artificial neural network, and k-nearest neighbor classifiers to characterize the tumor. Experiments were carried out on a dataset consisting of T1-weighted post-contrast and T2-weighted MR images of 550 patients. The developed CAD system was tested using the leave-one-out method. The experimental results showed that the proposed segmentation technique achieves good agreement with the gold standard and the ensemble classifier is highly effective in the diagnosis of brain tumor with an accuracy of 99.09 % (sensitivity 100 % and specificity 98.21 %). Thus, the proposed system can assist radiologists in an accurate diagnosis of brain tumors. © 2013, Springer-Verlag London.Item An Integrated Approach of CNT Front-end Amplifier towards Spikes Monitoring for Neuro-prosthetic Diagnosis(SpringerOpen, 2018) Kumar, S.; Kim, B.-S.; Song, H.The future neuro-prosthetic devices would be required spikes data monitoring through sub-nanoscale transistors that enables to neuroscientists and clinicals for scalable, wireless and implantable applications. This research investigates the spikes monitoring through integrated CNT front-end amplifier for neuro-prosthetic diagnosis. The proposed carbon nanotube-based architecture consists of front-end amplifier (FEA), integrate fire neuron and pseudo resistor technique that observed high electrical performance through neural activity. A pseudo resistor technique ensures large input impedance for integrated FEA by compensating the input leakage current. While carbon nanotube based FEA provides low-voltage operation with directly impacts on the power consumption and also give detector size that demonstrates fidelity of the neural signals. The observed neural activity shows amplitude of spiking in terms of action potential up to 80 ?V while local field potentials up to 40 mV by using proposed architecture. This fully integrated architecture is implemented in Analog cadence virtuoso using design kit of CNT process. The fabricated chip consumes less power consumption of 2 ?W under the supply voltage of 0.7 V. The experimental and simulated results of the integrated FEA achieves 60 G? of input impedance and input referred noise of 8.5 nv/Hzover the wide bandwidth. Moreover, measured gain of the amplifier achieves 75 dB midband from range of 1 KHz to 35 KHz. The proposed research provides refreshing neural recording data through nanotube integrated circuit and which could be beneficial for the next generation neuroscientists. © 2018, The Korean BioChip Society and Springer-Verlag GmbH Germany, part of Springer Nature.Item An effective feature extraction with deep neural network architecture for protein-secondary-structure prediction(Springer, 2021) Jayasimha, A.; Mudambi, R.; Pavan, P.; Lokaksha, B.M.; Bankapur, S.; Patil, N.With the increased importance of proteins in day-to-day life, it is imperative to know the protein functions. Deciphering protein structure elucidates protein functions. Experimental approaches for protein-structure analysis are expensive and time-consuming, and require high dexterity. Thus, finding a viable computational approach is vital. Due to the high complexity of predicting protein structure (tertiary structure) directly, research in this field aims at the protein-secondary-structure prediction which is directly related to its tertiary structure. This research aims at exploring a plethora of features, namely position-specific scoring matrices, hidden Markov model alignment matrices, and physicochemical properties, that carry rich information required to predict the secondary structure. Furthermore, it aims at exploring a suitable combination of the features which could capture diverse information about the protein secondary structure. Finally, a cascaded convolutional neural network and bidirectional long short-term memory architecture is fit on the models, and two evaluation metrics, namely, Q8 score and segment overlap score, are benchmarked on various datasets. Our proposed model trained on data of CB6133 dataset and tested on CB513 dataset beats the benchmark models by a minimum of 2.9%. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature.Item Airborne Kite Tether Force Estimation and Experimental Validation Using Analytical and Machine Learning Models for Coastal Regions(MDPI, 2022) Castelino, R.V.; Kashyap, Y.; Kosmopoulos, P.Wind power can significantly contribute to the transition from fossil fuels to renewable energies. Airborne Wind Energy (AWE) technology is one of the approaches to tapping the power of high-altitude wind. The main purpose of a ground-based kite power system is to estimate the tether force for autonomous operations. The tether force of a particular kite depends on the wind velocity and the kite’s orientation to the wind vector in the figure-eight trajectory. In this paper, we present an experimental measurement of the pulling force of an Airush Lithium 12 (Formula presented.) kite with a constant tether length of 24 m in a coastal region. We obtain the position and orientation data of the kite from the sensors mounted on the kite. The flight dynamics of the kite are studied using multiple field tests under steady and turbulent wind conditions. We propose a physical model (PM) using Artificial Neural Network (ANN) and Long Short-Term Memory (LSTM) deep neural network algorithms to estimate the tether force in the experimental validation. The performance study using the root mean square error (RMSE) method shows that the LSTM model performs better, with overall error values of 126 N and 168 N under steady and turbulent wind conditions. © 2022 by the authors.Item Solar Irradiation Prediction Hybrid Framework Using Regularized Convolutional BiLSTM-Based Autoencoder Approach(Institute of Electrical and Electronics Engineers Inc., 2023) Chiranjeevi, M.; Karlamangal, S.; Moger, T.; Jena, D.Solar irradiance prediction is an essential subject in renewable energy generation. Prediction enhances the planning and management of solar installations and provides several economic benefits to energy companies. Solar irradiation, being highly volatile and unpredictable makes the forecasting task complex and difficult. To address the shortcomings of the traditional approaches, this research developed a hybrid resilient architecture for an enhanced solar irradiation forecast by employing a long short-term memory (LSTM) autoencoder, convolutional neural network (CNN), and the Bi-directional Long Short Term Memory (BiLSTM) model with grid search optimization. The suggested hybrid technique is comprised of two parts: feature encoding and dimensionality reduction using an LSTM autoencoder, followed by a regularized convolutional BiLSTM. The encoder is tasked with extracting the key features in order to deduce the input into a compact latent representation. The decoder network then predicts solar irradiance by analyzing the encoded representation's attributes. The experiments are conducted on three publicly available data sets collected from Desert Knowledge Australia Solar Centre (DKASC), National Solar Radiation Database (NSRDB), and Hawaii Space Exploration Analog and Simulation (HI-SEAS) Habitat. The analysis of univariate and multivariate-multi step ahead forecasting performed independently and it is compared with the conventional approaches. Several benchmark forecasting models and three performance metrics are utilized to validate the hybrid approach's prediction performance. The results show that the proposed architecture outperforms benchmark models in accuracy. © 2013 IEEE.Item WideCaps: a wide attention-based capsule network for image classification(Springer Science and Business Media Deutschland GmbH, 2023) Pawan, S.J.; Sharma, R.; Reddy, H.; Vani, M.; Rajan, J.The capsule network is a distinct and promising segment of the neural network family that has drawn attention due to its unique ability to maintain equivariance by preserving spatial relationships among the features. The capsule network has attained unprecedented success in image classification with datasets such as MNIST and affNIST by encoding the characteristic features into capsules and building a parse-tree structure. However, on datasets involving complex foreground and background regions, such as CIFAR-10 and CIFAR-100, the performance of the capsule network is suboptimal due to its naive data routing policy and incompetence in extracting complex features. This paper proposes a new design strategy for capsule network architectures for efficiently dealing with complex images. The proposed method incorporates the optimal placement of the novel wide bottleneck residual block and squeeze and excitation Attention Blocks into the capsule network upheld by the modified factorized machines routing algorithm to address the defined problem. This setup allows channel interdependencies at almost no computational cost, thereby enhancing the representation ability of capsules on complex images. We extensively evaluate the performance of the proposed model on the five publicly available datasets, namely the CIFAR-10, Fashion MNIST, Brain Tumor, SVHN, and the CIFAR-100 datasets. The proposed method outperformed the top-5 capsule network-based methods on Fashion MNIST, CIFAR-10, SVHN, Brain Tumor, and gave a highly competitive performance on the CIFAR-100 datasets. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.Item Quantum-inspired hybrid algorithm for image classification and segmentation: Q-Means++ max-cut method(John Wiley and Sons Inc, 2024) Roy, S.K.; Rudra, B.Finding brain tumors is a crucial step in medical diagnosis that can have a big impact on how patients turn out. Conventional detection techniques can be laborious and demand a lot of computing power. Brain tumor detection could be made more effective and precise, thanks to the quickly developing field of quantum computing. In this article, we propose a quantum machine learning (QML)-based method for brain tumor extraction and detection based on quantum computing. To implement our strategy, we use a Hybrid Quantum-Classical Convolutional Neural Network (HQC-CNN) that has been trained using a collection of brain MRI images. Additionally, we employ Batchwise Q-Means++ Clustering for segmenting the images and a Max-cut approach with Adiabatic Quantum Computation (AQC) to extract the tumor region from the segmented MRI image. Our results highlight the strength of Quanvolutional Layer in Neural Network and reduced time complexity exponentially or quadratically in clustering and max-cut algorithms respectively and see the potential of quantum computing for improving the accuracy and speed of medical diagnosis and have implications for the future of healthcare technology. © 2024 Wiley Periodicals LLC.Item Malicious encrypted network traffic flow detection using enhanced optimal deep feature selection with DLSTM(World Scientific, 2024) Hublikar, S.; Shet, N.S.V.This paper plans to implement a novel detection model of maliciously encrypted internet protocol network flow using the deep structured concept. The major processing levels are (i) data collection, (ii) feature extraction, (iii) optimal feature selection, and (iv) detection. In the beginning, the standard dataset is taken from online databases. The deep convolutional neural network (DCNN) is introduced for the deep feature extraction process. The accurate features are chosen by the crossover decision-based krill herd algorithm (CD-KHA) which helps to minimize the training complexity of the deep structured architecture. These selected features are given to the hybridized deep learning with long short-term memory (LSTM) and deep neural network (DNN). Here, the structural design of the model is improved by the same CD-KHA. Through the comparison and analysis, the accuracy rate of the offered method shows higher performance than the other baseline approaches. © 2024 World Scientific Publishing Company.Item An iron(iii) oxide-anchored conductive polymer-graphene ternary nanocomposite decorated disposable paper electrode for non-enzymatic detection of serotonin(Royal Society of Chemistry, 2024) Prashanth, S.; Aziz, R.A.; Raghu, S.V.; Shim, Y.-B.; Prasad, K.; Vasudeva Adhikari, A.V.Serotonin, also known as 5-hydroxytryptamine (5-HT), is an important neurotransmitter that regulates many physiological processes. Both low and high concentrations of 5-HT in the body are associated with several neurological disorders. Hence, there is an urgent need to develop fast, accurate, reliable, and cost-effective disposable sensors for 5-HT detection. Herein, we report the sensing of 5-HT using a disposable paper-based electrode (PPE) modified with a ternary nanocomposite comprising poly(pyrrole) (P(py)), reduced graphene oxide (rGO), and iron oxide (Fe2O3). The sensor material was well characterized in terms of its structural, morphological, and chemical attributes using electron microscopy, spectral techniques, and electrochemical studies to prove the robust formation of the electroactive ternary nanocomposite and its suitability for 5-HT detection. The developed sensor exhibited an impressive limit of detection (LOD) of 22 nM with a wide linear range of 0.01 to 500 ?M, which falls in the recommended clinically relevant range. The analytical recovery, spike sample analysis, and interference studies with ascorbic acid (AA), uric acid (UA), and epinephrine (E) showed satisfactory results, wherein the sensor could detect simultaneously both 5-HT and dopamine (DA). The potential practical utility of the developed sensor was further assessed by quantifying the concentration of 5-HT in the brain samples of Drosophila melanogaster, a versatile genetic model organism employed for modeling different neural disorders in humans, and validated by gold-standard HPLC-UV experiments. The as-fabricated single-run disposable sensor with a ternary nanocomposite exhibits excellent stability with good reproducibility and is a promising platform for identifying clinically relevant concentrations of 5-HT. © 2024 RSC.