Browsing by Author "Kumar, R."

Now showing 1 - 20 of 60

4-(Morpholin-4-yl)-3-(trifluoro-meth-yl)-benzonitrile
(2011) Fun, H.-K.; Asik, S.I.J.; Kumar, R.; Isloor, A.M.; Shivananda, K.N.
In the title benzonitrile compound, C 12H 11F 3N 2O, an intra-molecular C - H?F hydrogen bond generates an S(7) ring motif. The trifluoro-methyl group is disordered over two orientations with a refined occupancy ratio of 0.549 (16):0.451 (16). The morpholine ring adopts a chair conformation. The benzene ring and mean plane of the morpholine ring make a dihedral angle of 58.04 (10)° with each other. In the crystal, mol-ecules are connected by inter-molecular C - H?F and C - H?O inter-actions to form R 2 2(8) ring motifs. These inter-actions also link the mol-ecules into chains parallel to the [10[ direction. © Fun et al. 2011.
A 73% PAE, Highly Gain Inverse Class-F Power Amplifier for S-Band Applications
(Springer Science and Business Media Deutschland GmbH, 2021) Naik, J.D.; Gorre, P.; Kumar, R.; Kumar, S.; Song, H.
This paper proposes a continuous-mode inverse Class F power amplifier (PA) achieving wide bandwidth, high output power, and high efficiency. This work includes transmission line-based output/input matching networks and single-ended topology. The main focus of the work is to achieve a high gain with wide bandwidth. The proposed structure incorporates a termination of even and odd harmonics to deliver voltage and current waveform isolation with minimal matching network (MN) design complexities. The analyses simulated in Keysight Technologies Advanced Design System (ADS), which results in a wideband PA design. The results are quantified by using high power-added efficiency (PAE) and output power. PAE of 72.6% and output power more than 41Â dBm obtained over wide bandwidth 2â€“4.2Â GHz at âˆ’3Â dB gain compression. The proposed PA could overcome the traditional performance and utilize for green communication. Â© 2021, Springer Nature Singapore Pte Ltd.
A 8–12 GHz, 44.3 dBm RF output class FF?1 DPA using quad-mode coupled technique for new configurable front-end 5G transmitters
(Springer, 2021) Kumar, R.; Dwari, S.; Kumar Kanaujia, B.K.; Kumar, S.; Song, H.
This paper presents a high-efficiency Class FF - 1 DPA using the quad-mode coupled technique for new configurable front-end 5G transmitters. The proposed DPA consists of carrier PA, main PA, input–output matching network and hybrid power network (HPN). The HPN includes a quad-mode coupled technique which is four-section U-shaped transmission line. The HPN is used for even–odd mode impedance analysis to ensures the high-selectivity of output power and achieve a wideband response in the presence of harmonic control conditions. The optimum harmonic impedance is analyzed for the desired band to achieve high output power and efficiency. The DPA circuit is fabricated by using 0.25 µm GaN HEMT on silicon nitride monolithic microwave integrated circuit die process. At maximum output power level of 44.3 dBm, the delivered power-added efficiency (PAE) of 64.3–67.3% and drain efficiency (DE) of 71.7–73.7% at even–odd mode operation are achieved with a gain of 13.0–14.3 dB. For the output power level of 39.045 dBm corresponding to 9 dB output back-off (OBO), the drain efficiency lies between 55–62% with 73% fractional bandwidth. All the demonstrated transmission parameters are working in the band of 8–12 GHz. The size of the chip is 2.8 × 1.9 mm2 and it occupies less die area as compared to the existing DPAs. © 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
A high efficiency on-chip reconfigurable Doherty power amplifier for LTE communication cells
(John Wiley and Sons Inc. P.O.Box 18667 Newark NJ 07191-8667, 2018) Kumar, R.; Kanuajia, B.K.; Dwari, S.; Kumar, S.; Song, H.
In this paper, a high efficiency on-chip reconfigurable Doherty power amplifier (DPA) with proposed topology is proposed for LTE or 4G communication cells. The proposed DPA consists of input driver topology, hybrid coupler, asymmetric amplifiers, and 1:1 balun filtered network. The proposed input driver circuit provides wide amplified signal operation within range of 2.3GHz to 6GHz with flat gain of 33 dB. The amplified signal is unsteadily divided into two paths toward the carrier and the power amplifier by 900 hybrid couplers and demonstrates 27.6 dB and 28.3 dB of gain along with 83.2% and 84.5% of power added efficiency at average output power of 40 dBm. The high efficiency and almost flatness in gain stability of proposed DPA providing better solution in order to overcome the interference and the broadband issues for LTE communication cells. The balun-filtered network is employed for combined the two outputs of carrier and peak amplifiers that provides more uniform desired band of operation in the frequency responses. The proposed DPA circuit are implemented and optimized by using advanced design RF simulator platform. The fabricated chip is made by using 0.13 ?m GaN HEMT on Si-Nitride monolithic microwave integrated circuit die process. The fabricated chip of DPA provides 85% of PAE with 28 dB gain which are made close agreement with simulation results. The size of chip is 2.8*1.2mm2 which occupies less die area as compared to existing DPAs. © 2018 Wiley Periodicals, Inc.
A novel dataset and efficient deep learning framework for automated grading of renal cell carcinoma from kidney histopathology images
(Nature Research, 2023) Chanchal, A.K.; Lal, S.; Kumar, R.; Kwak, J.T.; Kini, J.
Trends of kidney cancer cases worldwide are expected to increase persistently and this inspires the modification of the traditional diagnosis system to respond to future challenges. Renal Cell Carcinoma (RCC) is the most common kidney cancer and responsible for 80–85% of all renal tumors. This study proposed a robust and computationally efficient fully automated Renal Cell Carcinoma Grading Network (RCCGNet) from kidney histopathology images. The proposed RCCGNet contains a shared channel residual (SCR) block which allows the network to learn feature maps associated with different versions of the input with two parallel paths. The SCR block shares the information between two different layers and operates the shared data separately by providing beneficial supplements to each other. As a part of this study, we also introduced a new dataset for the grading of RCC with five different grades. We obtained 722 Hematoxylin & Eosin (H &E) stained slides of different patients and associated grades from the Department of Pathology, Kasturba Medical College (KMC), Mangalore, India. We performed comparable experiments which include deep learning models trained from scratch as well as transfer learning techniques using pre-trained weights of the ImageNet. To show the proposed model is generalized and independent of the dataset, we experimented with one additional well-established data called BreakHis dataset for eight class-classification. The experimental result shows that proposed RCCGNet is superior in comparison with the eight most recent classification methods on the proposed dataset as well as BreakHis dataset in terms of prediction accuracy and computational complexity. © 2023, The Author(s).
Ageist Spider Monkey Optimization algorithm
(Elsevier B.V., 2016) Sharma, A.; Sharma, A.; Panigrahi, B.K.; Kiran, D.; Kumar, R.
Swarm Intelligence (SI) is quite popular in the field of numerical optimization and has enormous scope for research. A number of algorithms based on decentralized and self-organized swarm behavior of natural as well as artificial systems have been proposed and developed in last few years. Spider Monkey Optimization (SMO) algorithm, inspired by the intelligent behavior of spider monkeys, is one such recently proposed algorithm. The algorithm along with some of its variants has proved to be very successful and efficient. A spider monkey group consists of members from every age group. The agility and swiftness of the spider monkeys differ on the basis of their age groups. This paper proposes a new variant of SMO algorithm termed as Ageist Spider Monkey Optimization (ASMO) algorithm which seems more practical in biological terms and works on the basis of age difference present in spider monkey population. Experiments on different benchmark functions with different parameters and settings have been carried out and the variant with the best suited settings is proposed. This variant of SMO has enhanced the performance of its original version. Also, ASMO has performed better in comparison to some of the recent advanced algorithms. © 2016
An integrated cascode DE power amplifier for RF calibration system towards measurement of bio-sensor applications
(John Wiley and Sons Inc. P.O.Box 18667 Newark NJ 07191-8667, 2019) Kumar, R.; Kumar Kanaujia, B.K.; Dwari, S.; Kumar, S.; Song, H.
The integrated cascode DE power amplifier for RF calibration system toward measurement of bio-sensor applications is presented in this paper. The proposed architecture includes cascode class-D and class-E amplifier stages that could provide better calibration accuracy in terms of wide bandwidth, power efficiency, high gain, minimum group delay, and lowest calibration system. The achieved high performance of proposed amplifier overcomes conventional measurement issues toward bio-sensor application. The inductive ?-shape matching network drives RF input to class-D stage and provides wide bandwidth of operation. While class-E stage with T-shape matching network maintains stable gain and high efficiency in desired band of operation. The performance of the CMOS proposed amplifier is executed in RF ADS simulator along with fabricated chip using commercial TSMC 65 nm manufacturing process. The simulated and measured data achieves Ku band (12 GHz to 18 GHz) with almost flat gain of 30 dB. The DE amplifier provides an output and saturated power of 17 dBm with highest power efficiency of 45%. The measured calibration factor at maximum resonant frequency of 13.5 GHz achieves best value of less than 2 dB within input power range of ?50 dBm to 0 dBm. The lowest calibration factor provides best accuracy along with the other parameters and could be beneficial toward bio-sensor measurement in the various applications. The calculated area of the fabricated chip is as 0.45*0.45mm2 where class-E consuming area of 38% and class-D of 44%. The fabricated chip consumes less power consumption of 3.2 mW under power supply of 1 V. © 2018 Wiley Periodicals, Inc.
Antifouling and performance enhancement of polysulfone ultrafiltration membranes using CaCO3 nanoparticles
(2013) Nair, A.K.; Isloor, A.M.; Kumar, R.; A.F., A.F.
Calcium carbonate nanoparticles were synthesized from calcium nitrate via chemical precipitation method. The nanoparticles were characterized using scanning electron microscope (SEM), Attenuated total reflectance infra red (ATR-IR) spectrum and by X-ray diffraction (XRD). These nanoparticles were used as additive for polysulfone (PSf) ultrafiltration membrane along with polyethylene glycol (PEG) as pore forming agent. The PSf hybrid membranes were characterized by ATR-IR, XRD, and SEM studies. ATR-IR and XRD results indicated the successful incorporation of the nanoparticles in the membranes. Cross sectional images of the membranes along with the elemental mapping of calcium on the membrane surface were assessed using SEM. Hydrophilicity of the membranes was evaluated in terms of contact angle measurements. The permeability of the membranes was determined by measuring the pure water flux (PWF). Membranes were also subjected to antifouling studies using bovine serum albumin (BSA) as the standard protein for rejection. The membranes showed better permeability and antifouling property with the increased addition of CaCO3 nanoparticles. © 2013 Elsevier B.V.
AutoCov22: A Customized Deep Learning Framework for COVID-19 Detection
(Springer, 2023) Bhowmik, B.; Varna, S.; Kumar, A.; Kumar, R.
The novel coronavirus disease 2019 (COVID-19) spill has spread rapidly and appeared as a pandemic affecting global public health. Due to the severe challenges faced with the increase of suspected cases, more testing methods are explored. These methods, however, have several disadvantages, such as test complexity and associated problems—sensitivity, reproducibility, and specificity. Hence, many of them need help to achieve satisfactory performance. Motivated by these shortcomings, this work proposes a custom deep neural network framework named “AutoCov22” that detects COVID-19 by exploiting medical images—chest X-ray and CT-scan. First, multiple neural networks extract deep features from the input medical images, including popularly used VGG16, ResNet50, DenseNet121, and InceptionResNetV2. Then, the extracted features are fed to different machine-learning techniques to identify COVID-19 cases. One objective of this work is to quicken COVID-19 detection. Another goal is to reduce the number of falsely detected cases by a significant margin. Comprehensive simulation results achieve a classification accuracy of 99.74%, a precision of 99.69%, and a recall of 98.80% on exercising chest X-ray images. Extended experiment results in accuracy, precision, and recall up to 87.18%, 84.98%, and 85.66%, respectively, in processing CT-scan images. Thus, the AutoCov22 approach demonstrates a promising and plausible best solution over several methods in the state-of-the-art. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.
Ceria-Terbium-based electrospun nanofiber catalysts for soot oxidation activity and its kinetics
(Taiwan Institute of Chemical Engineers, 2024) Patil, S.S.; Kumar, R.; Prasad Dasari, H.P.
Background: Ceria-based materials have an excellent potential to be catalysts for catalytic three-way converters in the automobile industry. Developing Ceria-based nanofiber catalysts can be a significant approach for further exploring the application of these materials in automobile industries. Methods: In this study, Ag, Cu, or Co doped Ceria–Terbium nanofibers were synthesized using the electrospinning technique. The obtained nanofiber catalysts were characterized using FE-SEM, XRD, FT-Raman Spectroscopy, and BET-BJH analysis and tested for soot oxidation activity and its kinetics. Significant findings: FE-SEM examination reveals that the obtained nanofibers have a diameter ranging from around 100 to 600 nm. CeTbCo nanofibers exhibited a reduced particle size and enhanced pore formation. The XRD investigation revealed that all the nanofibers displayed a face-centered fluorite structure of CeO2. In Raman spectroscopy analysis, CeTbCo nanofibes showed the emergence of a secondary Co3O4 phase. The CeTbCo nanofiber catalyst showed better SBET (specific surface area) (66 m2/g) and average pore size (12.08 nm) and total pore volume (0.223 cc/g)), better soot oxidation activity (T50 = 347 ℃) than other nanofiber catalysts. The CeTbCo nanofiber catalyst exhibited an activation energy of 132 kJ mol−1 and a pre-exponential factor (ln (A)) of 25.63 min−1. © 2024 Taiwan Institute of Chemical Engineers
Chitosan and its derivatives as potential materials for membrane technology
(CRC Press, 2015) Kumar, R.; Isloor, A.M.
Chitosan (CS), a biomaterial obtained via alkaline N-deacetylation of chitin, has recently attracted much attention from scientists across the globe. After cellulose, it is the second highest naturally occurring polymer on earth. It shows many excellent biological properties such as nontoxicity, biodegradability, antimicrobial activity, and immunological activity. As a membrane material, it has got excellent film-forming nature and hydrophilic in nature. Although the polymer backbone consists of hydrophilic functional groups, CS is normally insoluble in water and most of the common organic solvents. Chemical modification of CS is the best method to enhance its solubility at neutral pH or in organic solvents. So the obtained derivatives have got vast applications in the biomedical field as well as membrane technology. © 2015 by Taylor & Francis Group, LLC.
Comparative study of ocean wave spectrum using ENVISAT SAR data and wave rider buoy data
(2006) Pai, B, J.; Kumar, R.; Sarkar, A.; Hegde, A.V.; Dwarakish, G.S.
A comparative study of ENVISAT ASAR data and corresponding wave rider buoy data has been attempted. An algorithm has been developed to retrieve Ocean Wave Spectrum from SAR data. The resulting spectrum is compared with the wave rider buoy measured wave spectrum. To compute the 2-D image spectrum from multi-look SAR data, various corrections to the original SAR data has been applied. Thereafter, Modulation Transfer Function has been computed and utilized to convert image spectrum to the Ocean Wave Spectrum. This final ocean wave height spectrum is used to estimate the ocean wave spectral parameters and has been compared with the in-situ measurements and model derived wave spectrum. An attempt has also been made to process the Single Look Complex (SLC) data to reduce the speckle noise in the SAR data using Fast Fourier Transform (FFT).
Comparative study of ocean wave spectrum using ENVISAT SAR data and wave rider buoy data
(2006) Pai, J.; Kumar, R.; Sarkar, A.; Hegde, A.V.; Dwarakish, G.S.
A comparative study of ENVISAT ASAR data and corresponding wave rider buoy data has been attempted. An algorithm has been developed to retrieve Ocean Wave Spectrum from SAR data. The resulting spectrum is compared with the wave rider buoy measured wave spectrum. To compute the 2-D image spectrum from multi-look SAR data, various corrections to the original SAR data has been applied. Thereafter, Modulation Transfer Function has been computed and utilized to convert image spectrum to the Ocean Wave Spectrum. This final ocean wave height spectrum is used to estimate the ocean wave spectral parameters and has been compared with the in-situ measurements and model derived wave spectrum. An attempt has also been made to process the Single Look Complex (SLC) data to reduce the speckle noise in the SAR data using Fast Fourier Transform (FFT).
Deep Learning based framework for dynamic Detection and Mitigation of ARP Spoofing attacks
(Institute of Electrical and Electronics Engineers Inc., 2023) Puram, H.; Kumar, R.; Chandavarkar, B.R.
Address Resolution Protocol (ARP) is a protocol that links the IP address of a network node to the Media Access Control (MAC) address of another node for communication. An attack known as ARP spoofing affects a network's data-link layer and permits malicious access to network data. The sending device can be tricked, and potentially valuable data can be stolen, by connecting the attacker's MAC address to the IP address of the receiving device. Several approaches exist today to detect ARP attacks accurately and efficiently but have drawbacks in various aspects such as speed of detection, accuracy, dynamicity, and scalability. To overcome these issues, we propose DL-ARP, a novel dynamic framework based on an XGBoost Classifier followed by a CNN-LSTM architecture. This technique can identify and mitigate ARP spoofing assaults in real-time by collecting packets of data as they are received. The model automatically categorizes them and creates entry cache logs in the process. This paper aims to show the effectiveness and the potential of the suggested methodology for real-time ARP spoofing detection and prevention, this study also assess the performance of the proposed methodology in comparison to other existing methods. Â© 2023 IEEE.
Deep Learning for Odor Prediction on Aroma-Chemical Blends
(American Chemical Society, 2025) Sisson, L.; Barsainyan, A.A.; Sharma, M.; Kumar, R.
The application of deep-learning techniques to aroma chemicals has resulted in models that surpass those of human experts in predicting olfactory qualities. However, public research in this field has been limited to predicting the qualities of individual molecules, whereas in industry, perfumers and food scientists are often more concerned with blends of multiple molecules. In this paper, we apply both established and novel approaches to a data set we compiled, which consists of labeled pairs of molecules. We present graph neural network models that accurately predict the olfactory qualities emerging from blends of aroma chemicals along with an analysis of how variations in model architecture can significantly impact predictive performance. © 2025 The Authors. Published by American Chemical Society.
Dense Sense: a novel approach utilizing electron density augmented machine learning paradigm to understand the complex odour landscape
(Royal Society of Chemistry, 2025) Saha, P.; Sharma, M.; Balaji, S.; Barsainyan, A.A.; Kumar, R.; Steuber, V.; Schmuker, M.
Olfaction is a complex process where multiple nasal receptors interact to detect specific odorant molecules. Elucidating structure–activity-relationships for odorants and their receptors remains difficult since crystallization of the odor receptors is an extremely difficult process. Therefore, ligand-based approaches that leverage machine learning remain the state of the art for predicting odorant properties for molecules, such as the graph neural network approach used by Lee et al. In this paper we explored how information from quantum mechanics (QM) could synergistically improve the results obtained with the graph neural network. Our findings underscore the possibility of this methodology in predicting odor perception directly from QM data, offering a novel approach in the machine learning space to understand olfaction. This journal is © The Royal Society of Chemistry, 2025
Design of series, Fi=Fi-1+Fi-3 for the denominators (1, 2,6) of switched capacitor converter
(2017) Subburaj, V.; Jena, D.; Kumar, R.; Deshmukh, A.V.; Nayak, B.; Bansal, H.
This paper proposes, the concept of generalized Fibonacci switched capacitor converter for Fi=Fi-1+Fi-3 series. The proposed converter have more efficiency and less equivalent resistance. Generalized Fibonacci switched capacitor converters (SCCs) are technologically advanced which operates on fixed conversion ratio. Different target ratios of Fibonacci series have already been carried out by the researchers to step-up and stepdown configuration. But, different target ratios of Fi=Fi-1+Fi-3 series has not yet proposed. In this paper different fractions 1/4,3/4,1/6,5/6 for SCC is proposed for both step-up and stepdown configurations. Theoretical results and simulation results are verified using PSIM. � 2016 IEEE.
Design of series, Fi=Fi-1+Fi-3 for the denominators (1, 2,6) of switched capacitor converter
(Institute of Electrical and Electronics Engineers Inc., 2017) Subburaj, V.; Jena, D.; Kumar, R.; Deshmukh, A.V.; Nayak, B.; Bansal, H.
This paper proposes, the concept of generalized Fibonacci switched capacitor converter for Fi=Fi-1+Fi-3 series. The proposed converter have more efficiency and less equivalent resistance. Generalized Fibonacci switched capacitor converters (SCCs) are technologically advanced which operates on fixed conversion ratio. Different target ratios of Fibonacci series have already been carried out by the researchers to step-up and stepdown configuration. But, different target ratios of Fi=Fi-1+Fi-3 series has not yet proposed. In this paper different fractions 1/4,3/4,1/6,5/6 for SCC is proposed for both step-up and stepdown configurations. Theoretical results and simulation results are verified using PSIM. Â© 2016 IEEE.
Determination of mixed layer depth from C-Band Synthetic Aperture Radar (SAR)
(2010) Pai, J.; Kumar, R.; Sarkar, A.; Hegde, A.V.; Dwarakish, G.S.
Oceanic internal waves are frequently observed on the continental shelf during the summer season, when the ocean is stratified. The appearance of internal wave phenomena in remote sensing images has been increasing the curiosity to observe internal wave at specific area in the world. Studies reveal that Synthetic Aperture Radar has a capability to detect internal waves. In the present study, ENVISAT Advanced Synthetic Aperture Radar (ASAR) image acquired on October 4, 2003, was used to determine Mixed Layer Depth (MLD) off Bay of Bengal of Indian Ocean region. The image showed several prominent trains of internal waves, with several wave packets in each train. The ocean was assumed to be a two layer system, and that the local semidiurnal tide is the generating force for the internal waves. By assuming that the local semidiurnal tide period is the generating source for these waves, and by measuring the distance between the wave packets, it is possible to derive the group velocity of the internal waves from Synthetic Aperture Radar (SAR) images directly. The mixed -layer depth is then derived by assuming the ocean as a two-layer finite depth model. The group velocity measured from the SAR image and the simulated group velocity by the two layer finite depth model was matched to get the mixed layer depth. The estimated mixed layer depth was 21m. This value show reasonably good agreement with the actual depth of 19.5m of in-situ ARGO buoy. © 2010 by IJI (CESER Publications).
Electric vehicle intelligent monitoring and analysis for battery
(American Institute of Physics, 2024) Saxena, A.; Binod, S.; Maurya, S.; Kapoor, O.; Singh, U.; Kumar, R.; Sharma, A.K.
This article shows the intelligent monitoring of electric vehicle for battery energy management with soft computing techniques. Thereafter different soft computing techniques like fuzzy logic controller, energy hub method, dynamic programming, DRL method, model predictive control was proposed to get the best results for the performance of EV and it is observed that fuzzy logic controller gives the best results as compared to other techniques vehicle for battery energy management. Â© 2024 Author(s).