Browsing by Author "Kumar, A."

Now showing 1 - 20 of 176

3D Estimation and visualization of motion in a multicamera network for sports
(2011) Kumar, A.; Chavan, P.S.; Sharatchandra, V.K.; Sumam, David S.; Kelly, P.; O'Connor, N.E.
In this work, we develop image processing and computer vision techniques for visually tracking a tennis ball, in 3D, on a court instrumented with multiple low-cost IP cameras. The technique first obtains 2D ball tracking data from each camera view using 2D object tracking methods. Next, an automatic feature-based video synchronization method is applied. This technique uses the extracted 2D ball information from two or more camera views, plus camera calibration information. In order to find 3D trajectory, the temporal 3D locations of the ball is estimated using triangulation of correspondent 2D locations obtained from automatically synchronized videos. Furthermore, in order to improve the continuity of the tracked 3D ball during times when no two cameras have overlapping views of the ball location, we incorporate a physics-based trajectory model into the system. The resultant 3D ball tracks are then visualized in a virtual 3D graphical environment. Finally, we quantify the accuracy of our system in terms of reprojection error. � 2011 IEEE.
3D Estimation and visualization of motion in a multicamera network for sports
(2011) Kumar, A.; Chavan, P.S.; Sharatchandra, V.K.; Sumam David, S.S.; Kelly, P.; Oâ€™Connor, N.E.
In this work, we develop image processing and computer vision techniques for visually tracking a tennis ball, in 3D, on a court instrumented with multiple low-cost IP cameras. The technique first obtains 2D ball tracking data from each camera view using 2D object tracking methods. Next, an automatic feature-based video synchronization method is applied. This technique uses the extracted 2D ball information from two or more camera views, plus camera calibration information. In order to find 3D trajectory, the temporal 3D locations of the ball is estimated using triangulation of correspondent 2D locations obtained from automatically synchronized videos. Furthermore, in order to improve the continuity of the tracked 3D ball during times when no two cameras have overlapping views of the ball location, we incorporate a physics-based trajectory model into the system. The resultant 3D ball tracks are then visualized in a virtual 3D graphical environment. Finally, we quantify the accuracy of our system in terms of reprojection error. Â© 2011 IEEE.
A New Combinatorial Design Based Data En-Route Filtering Scheme for Wireless Sensor Networks
(Institute of Electrical and Electronics Engineers Inc., 2019) Kumar, A.; Pais, A.R.
Wireless sensor networks are susceptible to report fabrication attacks, where adversary can use compromised nodes to flood the network with false reports. En-route filtering is a mechanism of dropping bogus/false reports while they are being forwarded towards the sink. Majority of the proposed en-route filtering schemes are probabilistic, where the originality of forwarded reports is checked with fixed probability by intermediate nodes. Thus, false reports can travel multiple hops before being dropped in probabilistic en-route filtering schemes. Few deterministic based en-route filtering schemes have also been proposed, but all such schemes need to send the reports through fixed paths. To overcome the above mentioned limitations of existing en-route filtering schemes, we propose a novel deterministic enroute filtering scheme. In the proposed scheme, secret keys are allocated to sensor nodes based on combinatorial design. Such design ensures direct communication between any two nodes without adding more key storage overhead. We provide in-depth analysis for the proposed scheme. The proposed scheme significantly outperforms existing schemes in terms of expected filtering position of false reports and is more buoyant to selective forwarding and report disruption attacks. Our scheme also performs neck-To-neck with existing schemes in terms of protocol overheads. Â© 2018 IEEE.
A new combinatorial design based key pre-distribution scheme for wireless sensor networks
(Springer Verlag service@springer.de, 2019) Kumar, A.; Pais, A.R.
In this paper we present a new Combinatorial Design based Key Pre-Distribution scheme (CD-KPD). For the scheme, the network region is divided into cells of equal size and each cell has two types of sensor nodes namely, normal sensor nodes and cluster heads. Within a particular cell, normal sensor nodes can communicate with each other directly and cluster heads are used for inter-cell communication. To ensure secure communication we use CD-KPD to assign keys to all the sensor nodes including cluster heads. We further modify CD-KPD to propose Combinatorial Design based Reduced Key Pre-Distribution scheme (CD-RKPD) by reducing the number of keys stored in each cluster head. The CD-RKPD was need of the hour when we consider to limit the inter-cell communication of each cell within its Lee sphere region. We give in-detail analysis of both the proposed schemes. We measure the resiliency of both proposed schemes by calculating fraction of links disrupted and fraction of cells disconnected when few sensor nodes are compromised in the network. We found that CD-KPD and CD-RKPD outperforms (Ruj and Roy in ACM Trans Sens Netw 6(1):4, 2009) by 59 and 6.5% respectively in terms of Global Resiliency and 5 and 9.7% respectively in terms of fraction of cell disconnected in the network. Further, we found that both our proposed schemes achieves high resiliency than majority of existing schemes. © 2018, Springer-Verlag GmbH Germany, part of Springer Nature.
A new hybrid key pre-distribution scheme for wireless sensor networks
(Springer New York LLC barbara.b.bertram@gsk.com, 2019) Kumar, A.; Pais, A.R.
This article presents a novel hybrid key pre-distribution scheme based on combinatorial design keys and pair-wise keys. For the presented scheme, the deployment zone is cleft into equal-sized cells. We use the combinatorial design based keys to secure intra-cell communication, which helps to maintain low key storage overhead in the network. For inter-cell communication, each cell maintain multiple associations with all the other cells within communication range and these associations are secured with pair-wise keys. This helps to ensure high resiliency against compromised sensor nodes in the network. We provide in-depth analysis for the presented scheme. We measure the resiliency of the presented scheme by calculating fraction of links effected and fraction of nodes disconnected when adversary compromises some sensor nodes in the network. We find that the presented scheme has high resiliency than majority of existing schemes. Our presented scheme also has low storage overhead than existing schemes. © 2018, Springer Science+Business Media, LLC, part of Springer Nature.
A partial key pre-distribution based en-route filtering scheme for wireless sensor networks
(Springer Science and Business Media Deutschland GmbH, 2021) Kumar, A.; Bansal, N.; Pais, A.R.
Compromised sensor nodes can be used to inject false reports (bogus reports) in wireless ssensor networks (WSNs). This can cause the sink to take wrong decisions. En-route filtering is a method to detect and filter false reports from WSNs. Most of the existing en-route filtering schemes use probabilistic approaches to filter false reports from the network, where filtering of false reports is based on a fixed probability. Thus false reports can travel multiple hops before being dropped. In this article we seek to overcome limitations of the existing schemes and reduce the overall key storage overhead in the cluster heads. In this article we propose a combinatorial design based partial en-route filtering scheme (CD-PEFS) which filters the fabricated reports deterministically. CD-PEFS reduces the energy requirements in the network by early detection and elimination of the false reports. Adoption of combinatorial design based keys get rid of shared key discovery phase from the network. This considerably reduces the communication overhead in the network. We carried out a detailed analysis of CD-PEFS against an increasing number of compromised sensor nodes in the network. We found that our scheme performs better than existing schemes in terms of filtering efficiency while maintaining low key storage overhead in the network. Further the performance of CD-PEFS is at par with existing schemes in terms of other protocol overheads. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature.
A review of various materials for additive manufacturing: Recent trends and processing issues
(Elsevier Editora Ltda, 2022) Srivastava, M.; Rathee, S.; Patel, V.; Kumar, A.; Koppad, P.G.
Tremendous growth has been witnessed in the field of additive manufacturing (AM) technology over the last few decades. It offers a plethora of applications and is already being utilized in almost every sphere of life. Owing to inherent differences between each AM technique, newer fields of research consistently emerge and demand attention. Also, the innovative applications of AM open up newer challenges and thus avenues for focused attention. One such avenue is AM materials. Raw material plays an important role in determining the properties of fabricated part. The type and form of raw material largely depend on the type of AM fabricators. There is a restriction on material compatibility with most of the established AM techniques. This review aims to provide an overview of various aspects of AM materials highlighting the progress made especially over the past two decades. © 2022 The Author(s).
A review of various materials for additive manufacturing: Recent trends and processing issues
(Elsevier Editora Ltda, 2022) Srivastava, M.; Rathee, S.; Patel, V.; Kumar, A.; Koppad, P.G.
Tremendous growth has been witnessed in the field of additive manufacturing (AM) technology over the last few decades. It offers a plethora of applications and is already being utilized in almost every sphere of life. Owing to inherent differences between each AM technique, newer fields of research consistently emerge and demand attention. Also, the innovative applications of AM open up newer challenges and thus avenues for focused attention. One such avenue is AM materials. Raw material plays an important role in determining the properties of fabricated part. The type and form of raw material largely depend on the type of AM fabricators. There is a restriction on material compatibility with most of the established AM techniques. This review aims to provide an overview of various aspects of AM materials highlighting the progress made especially over the past two decades. Â© 2022 The Author(s).
A Review on Mechanical Properties of Natural Fibre Reinforced PLA Composites
(Bentham Science Publishers, 2023) Kumar Sinha, A.K.; Rao, K.R.; Soni, V.K.; Chandrakar, R.; Sharma, H.K.; Kumar, A.
Presently, scientists and researchers are in an endless quest to develop green, recyclable, and eco-friendly materials. Natural fibre reinforced polymer composites became popular among materialists due to their lightweight, high strength-to-weight ratio, and biodegradability. However, all-natural fibre reinforced polymer composites are not biodegradable. Polymer matrices like poly-lactic acid (PLA) and poly-butylene succinate (PBS) are biodegradable, whereas epoxy, polypropylene, and polystyrene are non-biodegradable polymer matrices. Besides biodeg-radability, PLA has been known for its excellent physical and mechanical properties. This review emphasises the mechanical properties (tensile, flexural, and impact strengths) of natural fibre-reinforced PLA composites. Factors affecting the mechanical properties of PLA composites are also discussed. It also unveils research gaps from the previous literature, which shows that limited studies are reported based on modeling and prediction of mechanical properties of hybrid PLA composites reinforcing natural fibres like abaca, aloe vera, and bamboo fibres. Â© 2023 Bentham Science Publishers.
A Review on Mechanical Properties of Natural Fibre Reinforced PLA Composites
(Bentham Science Publishers, 2023) Kumar Sinha, A.K.; Rao, K.R.; Soni, V.K.; Chandrakar, R.; Sharma, H.K.; Kumar, A.
Presently, scientists and researchers are in an endless quest to develop green, recyclable, and eco-friendly materials. Natural fibre reinforced polymer composites became popular among materialists due to their lightweight, high strength-to-weight ratio, and biodegradability. However, all-natural fibre reinforced polymer composites are not biodegradable. Polymer matrices like poly-lactic acid (PLA) and poly-butylene succinate (PBS) are biodegradable, whereas epoxy, polypropylene, and polystyrene are non-biodegradable polymer matrices. Besides biodeg-radability, PLA has been known for its excellent physical and mechanical properties. This review emphasises the mechanical properties (tensile, flexural, and impact strengths) of natural fibre-reinforced PLA composites. Factors affecting the mechanical properties of PLA composites are also discussed. It also unveils research gaps from the previous literature, which shows that limited studies are reported based on modeling and prediction of mechanical properties of hybrid PLA composites reinforcing natural fibres like abaca, aloe vera, and bamboo fibres. © 2023 Bentham Science Publishers.
A robust method for nuclei segmentation of HE stained histopathology images
(Institute of Electrical and Electronics Engineers Inc., 2020) Lal, S.; Desouza, R.; Maneesh, M.; Kanfade, A.; Kumar, A.; Perayil, G.; Alabhya, K.; Chanchal, A.K.; Kini, J.
Segmentation of histopathology images is an initial and vital step for image understanding. To increase the throughput and to maintain high accuracy, we have to go for an automatic image segmentation method. Here, a robust method for segmentation of cell nuclei in Hematoxylin and Eosin (HE) stained histopathology images is proposed. The proposed segmentation step consists of an initial pre-processing step containing adaptive colour de-convolution and a succession of morphological operations, followed by multilevel thresholding and post-processing steps. Minimum region size is the one parameter which is necessary for this method and set according to the resolution of histopathology image. The proposed nuclei segmentation method does not require any assumptions or prior information about cell morphology. Hence, proposed method applies to the analysis of a wide range of tissues such as liver, kidney, breast, gastric mucosa, and bone marrow and HE stained liver histopathology images from the Hospital. Results yield that proposed nuclei segmentation provides better results in terms of quantitatively and qualitatively on two datasets. Â© 2020 IEEE.
Accurate detection of congestive heart failure using electrocardiomatrix technique
(Springer, 2022) Sharma, K.; Mohan Rao, B.M.; Marwaha, P.; Kumar, A.
Congestive Heart Failures (CHFs) are prevalent, expensive, and deadly, causing damage or overload to the pumping power of the heart muscles. These leads to severe medical issues amongst humans and contribute to a greater death risk of numerous diseases at a later stage. We need accurate and less difficult techniques to detect these problems in our world with a growing population which will prevent many diseases and reduce deaths. In this work, we have developed a technique to diagnose CHF using the Electrocardiomatrix (ECM) technique. The 1-D ECG signals are transformed to a colourful 3D matrix to diagnose CHF. The detection of CHF using ECM are then compared with annotated CHF Electrocardiogram (ECG) signals manually. It has been found that ECM is able to detect the affected CHF duration from the ECG signals. Also, the ECM provides the reduction in both false positive and false negative which in turn improves the detection accuracy. The performance of the proposed approach has been tested on BIDMC CHF database. The proposed method achieved an accuracy of 97.6%, sensitivity of 98.0%, specificity of 97.0%, precision of 99.4%, and F1-Score of 98.3%. From this study, it has been revealed that the ECM technique allows the accurate, intuitive, and efficient detection of CHF and using ECM practitioners can diagnose the CHF without sacrificing the accuracy. © 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
Accurate Estimation forÂ Stability ofÂ Slope andÂ Partition OverÂ Old Underground Coal Workings Using Regression-Based Algorithms
(Springer Science and Business Media Deutschland GmbH, 2022) Dorthi, K.; Kumar, A.; Ram Chandar, K.R.
Numerical modeling simulation has found to be best solution for predicting slope and partition stability over old underground coal workings. But it has taken huge time to complete a single simulation model. In this regard, machine learning-based framework is used to predict the stability of old galleries. A case study is taken up in opencast mine and simulation is carried out using numerical model and machine learning-based framework. Framework has shown an overall accuracy of 94â€“95% for different slope and partition stability. Framework shows a speedup of 2366 Ã— against numerical simulator. Â© 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Accurate Router Level Estimation of Network-on-Chip Architectures using Learning Algorithms
(2019) Kumar, A.; Talawar, B.
The problem of intra-communication between the Intellectual Properties(IPs) due to the rise in the amount of cores on single chips in System-on-Chip(SoC). Network-on-Chips(NoCs) has emerged as a reliable on-chip communication framework for Chip Multiprocessors and SoCs. Estimating NoC power and performance in the early stages has become crucial. We employ Machine Learning(ML) approaches to estimate architecture-level on-chip router models and performance. Experiments were carried out with distinct topology sizes with various virtual channels, injection rates, and traffic patterns. Booksim and Orion simulators are used to validate the results. Approximately 6% to 8% prediction error and a minimum speedup of 1500 � to 2000 � were shown in the framework. � 2019 IEEE.
Accurate Router Level Estimation of Network-on-Chip Architectures using Learning Algorithms
(Institute of Electrical and Electronics Engineers Inc., 2019) Kumar, A.; Talawar, B.
The problem of intra-communication between the Intellectual Properties(IPs) due to the rise in the amount of cores on single chips in System-on-Chip(SoC). Network-on-Chips(NoCs) has emerged as a reliable on-chip communication framework for Chip Multiprocessors and SoCs. Estimating NoC power and performance in the early stages has become crucial. We employ Machine Learning(ML) approaches to estimate architecture-level on-chip router models and performance. Experiments were carried out with distinct topology sizes with various virtual channels, injection rates, and traffic patterns. Booksim and Orion simulators are used to validate the results. Approximately 6% to 8% prediction error and a minimum speedup of 1500 Ã— to 2000 Ã— were shown in the framework. Â© 2019 IEEE.
Acoustic response behavior of porous 3D graphene foam plate
(Elsevier Ltd, 2020) Kumar, A.; Gunasekaran, V.; Mailan Chinnapandi, L.B.M.; Jeyaraj, J.
Sound radiation and sound transmission loss (STL) behavior of porous 3D graphene (3D-GrF) foam plate are presented. Two variable refined plate theory which includes both transverse bending and shear stresses is used to model the plate and Navier's solution is used to calculate the vibration responses while Rayleigh integral is used to analyze the acoustic response. Variation in free vibration frequencies with the nature of porosity distribution is significant for the 3D-GrF plates having higher porosity co-efficient. The natural frequency of the 3D-GrF plate with more porosity around the center and less porosity at the outer surfaces is high. However, resonant amplitudes of the responses and STL of the plates are controlled by both the nature of the porosity distribution pattern and porosity co-efficient. In general, STL of the plate with less porosity around the center and high porosity at the extreme surfaces is high compared to the other cases. © 2020 Elsevier Ltd
Adaptive conductance function based improved diffusion filtering and bi-dimensional empirical mode decomposition based image denoising
(Springer, 2023) Gupta, H.; Singh, H.; Kumar, A.; Vishwakarma, A.
This paper presents a new method for image denoising based on a two-dimensional empirical mode decomposition algorithm and semi-adaptive diffusion coefficient in anisotropic diffusion filter. The proposed model uses a local difference value method to compare and replace some pixels of the noisy image with a pre-processed image that has been passed through a Gaussian filter. A bi-dimensional empirical mode decomposition algorithm is then employed to decompose the noise-contaminated image into its intrinsic mode functions in which high-frequency and low-frequency noise components are removed by applying a diffusion filter. The filter has a semi-adaptive threshold in the diffusion coefficient with parameters like connectivity, conductance function, number of iterations, and gradient threshold. The semi-adaptive threshold for each diffusion is implemented by introducing gradient values in the threshold of the corrupted image. The image is then reconstructed from these denoised intrinsic mode functions. The performance of the proposed method is assessed in terms of peak signal-to-noise ratio, mean square error, and structural similarity index and is compared with the existing methodologies. The results obtained from experimentation indicate that the proposed method is efficient in both feature retention and noise suppression. © 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
Ag2Cu2O3 Nanorods as Electrocatalysts for Hydrogen Production and Overall Water Splitting
(American Chemical Society, 2025) Kumar, A.; Hegde, A.P.; Puttur, M.; Gangadharappa, L.S.; Hosakoppa, N.S.
In this research, a series of Ag2Cu2O3 nanorods as electrocatalysts were prepared with three different drying temperatures (namely, W - 50, W - 80, and W - 120), utilizing a regular coprecipitation approach. These nanorods’ surface morphology and structural attributes were thoroughly characterized using Field Emission Scanning Electron Microscopy and High-Resolution Transmission Electron Microscopy, while X-ray diffraction provided insight into their crystal structures. The compositional analysis was accomplished via X-ray photoelectron spectroscopy and Raman spectroscopy. The W - 50 catalyst exhibited the most promising electrochemical response among the synthesized samples. In the solution of 1 M KOH, at a current density of 10 mA cm-2, it demonstrated modest overpotential values and Tafel slopes of 81 and 97 mV dec-1 for the hydrogen evolution reaction (HER), whereas 409 and 140 mV dec-1 for the oxygen evolution reaction (OER). When tested with a two-electrode electrolyzer, W - 50 serving as together the anode and cathode, a trivial cell voltage of 1.9842 V was required to accomplish a current density of 100 mA cm-2, with surprising stability over 50 h of continuous operation at 200 mA cm-2 for overall water splitting. Additionally, W - 50 displayed excellent performance for HER; it necessitated an overpotential of 337 mV to accomplish an extreme current density of 800 mA cm-2. This inquiry provides precious perceptions into the importance of confined spaces within transition metal oxide-based catalysts, advancing their application in electrocatalysis. © 2025 American Chemical Society.
An Improved Air Tissue Boundary Segmentation Technique for Real Time Magnetic Resonance Imaging Video Using Segnet
(Institute of Electrical and Electronics Engineers Inc., 2019) Valliappan, C.A.; Kumar, A.; Mannem, R.; Karthik, G.R.; Ghosh, P.K.
This paper presents an improved methodology for the segmentation of the Air-Tissue boundaries (ATBs) in the upper airway of the human vocal tract using Real-Time Magnetic Resonance Imaging (rtMRI) videos. Semantic segmentation is deployed in the proposed approach using a Deep learning architecture called SegNet. The network processes an input image to produce a binary output image of the same dimensions having classified each pixel as air cavity or tissue, following which contours are predicted. A Multi-dimensional least square smoothing technique is applied to smoothen the contours. To quantify the precision of predicted contours, Dynamic Time Warping (DTW) distance is calculated between the predicted contours and the manually annotated ground truth contour. Four fold experiments are conducted with four subjects from the USC-TIMIT corpus, which demonstrates that the proposed approach achieves a lower DTW distance of 1.02 and 1.09 for the upper and lower ATB compared to the best baseline scheme. The proposed SegNet based approach has an average pixel classification accuracy of 99.3% across all the subjects with only 2 rtMRI videos (~180 frames) per subject for training. Â© 2019 IEEE.
An Improved ResNet-50 Neural Network Design for PV Panel Image Classification
(Springer Science and Business Media Deutschland GmbH, 2025) Kumar, A.; Kashyap, Y.; Sharma, A.
The increasing popularity of photovoltaic (PV) setups stems from their capacity to generate clean and cost-effective electricity. However, various factors can either totally or partially disrupt the production of PV panels. To address this challenge, this study proposes a ResNet-50 model with dynamically adjusted hyperparameters to classify real-time captured images of PV panels into efficient and non-efficient categories. The hyperparameter tuning within the ResNet-50 model is conducted across three distinct cases, revealing that the most optimal classification results are achieved with the following settings: 50 epochs, a learning rate of 0.001, and a batch size of 32. The highest weighted average metrics, including accuracy (96%), recall (96%), precision (97%), and F1-score (96%), were obtained under these settings. Â© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.