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Lora-WAN Powered by Renewable Energy, and Its Operation with Siri / Google Assistant
LoRa WAN is a newly emerged game changing communication technology for sending small data packets of size 50 bytes or less, wirelessly over an area of up to 10 Km without the need of an internet connection. LoRa WAN has its own frequency band and the band is different for every country. This technology is now starring to boost WSN technology better than ever before. This paper aims to, power up a LoRa Enabled Device or a LoRa Gateway by using a reliable dual mode non-conventional energy resource for storage and utilization, find peak performances altering the data rate that can be achieved in a LoRa WAN Communication (using Indoor RAK Gateway), make use data compression techniques, data packet encoding/decoding, Coding Apple Shortcuts, setting up Siri and Google Assistant for voice control and future scope. 2020, Asian Research Association. All rights reserved. -
Low cost ANN based MPPT for the mismatched PV modules
Due to manufacturing dispersal, the photovoltaic (PV) panels of similar rating and manufacturer have distinctive characteristics in practical. As the maximum power point tracking (MPPT) becomes essential to optimally utilize the solar PV panel, distributed maximum power point tracking (DMPPT) is considered in this paper to follow the MPP of each panel. As the common MPP value is used in the existing DMPPT method to control all the panels, it fails to consider the uniqueness of each panel. By considering the uniqueness of each panel, the ANN based MPPT is implemented in this paper. As the ANN is trained using the actual characteristics of each panel based on the operating current, voltage and temperature, it is able to track the actual MPP. Due to the solar irradiance free MPPT, the costly pyranometer is not required in the actual PV system for MPPT. It reduces the cost of the system and also provides the interruption free tracking due to its independent nature on Voc and Isc values. Also, because of the looping free behaviour of the proposed algorithm, it is capable of following the MPP at rapidly varying condition. The proposed technique and the verified outcomes are discussed here in detail. Published under licence by IOP Publishing Ltd. -
Low cost calibrated mechanical noisemaker for hearing screening of neonates in resource constrained settings
Background & objectives: There is a need to develop an affordable and reliable tool for hearing screening of neonates in resource constrained, medically underserved areas of developing nations. This study valuates a strategy of health worker based screening of neonates using a low cost mechanical calibrated noisemaker followed up with parental monitoring of age appropriate auditory milestones for detecting severe-profound hearing impairment in infants by 6 months of age. Methods: A trained health worker under the supervision of a qualified audiologist screened 425 neonates of whom 20 had confirmed severe-profound hearing impairment. Mechanical calibrated noisemakers of 50, 60, 70 and 80 dB (A) were used to elicit the behavioural responses. The parents of screened neonates were instructed to monitor the normal language and auditory milestones till 6 months of age. This strategy was validated against the reference standard consisting of a battery of tests - namely, auditory brain stem response (ABR), otoacoustic emissions (OAE) and behavioural assessment at 2 years of age. Bayesian prevalence weighted measures of screening were calculated. Results: The sensitivity and specificity was high with least false positive referrals for 70 and 80 dB (A) noisemakers. All the noisemakers had 100 per cent negative predictive value. 70 and 80 dB (A) noisemakers had high positive likelihood ratios of 19 and 34, respectively. The probability differences for pre- and post- test positive was 43 and 58 for 70 and 80 dB (A) noisemakers, respectively. Interpretation & conclusions: In a controlled setting, health workers with primary education can be trained to use a mechanical calibrated noisemaker made of locally available material to reliably screen for severe-profound hearing loss in neonates. The monitoring of auditory responses could be done by informed parents. Multi-centre field trials of this strategy need to be carried out to examine the feasibility of community health care workers using it in resource constrained settings of developing nations to implement an effective national neonatal hearing screening programme. -
Low cost energy management for demand side integration
Numerous batteries are outfitted with a state ofcharge (SoC) demonstrating the relaxation about the charge. Building a beneficial BMS is to check while thinking about, to that amount regardless we work now not holds a reliable method in imitation of study condition state of-charge, the nearly imperative measurement concerning a battery. Perusing the relaxation about the vigour of a battery is more unpredictable than administering thin fuel as in automobiles. An electrochemical cell diminishes its greatness and the in-and-streaming coulombs are counted for SOC. The BMS together which offers commitment while charging yet releasing; that detaches the battery if the SOC is below certain percentage. Various laws like Peukerts Law for battery capacity have been employed to determine the discharge rate of the battery. Arduino Uno is used for the input parameters required for Peukerts Law and various other calculations for significant monitoring. To address the existing complicated BMS, a new approach has been provided using IoT platform and making the understanding of BMS in much similar perspective. 2018 IEEE. -
Low cost lignite derived mesoporous nanocarbon for simultaneous electrochemical detection of heavy metal IONs cadmium and lead /
Patent Number: 202241042032, Applicant: Ashlin M Raj.The present invention provides a facile, scalable, and cost-effective method for preparing mesoporous nanocarbon extracted from low grade coal, lignite that possesses an excellent electrochemical activity. The synthesized nanostructure modified electrode is used for the rapid simultaneous quantitative detection of heavy metals ions cadmium and lead in a unique triple linear detection range over the concentration ranges from 2.08 to 129 nM. -
Low temparature synthesis of non-toxic monoclinic yttrium oxide quantum dots for display and biomedical applications /
Patent Number: 202141053609, Applicant: Soorya G Nath.
Monoclinic Y203 quantum dots were synthesized at low temperature using urea as the fuel. The sample preparation was done using simple laboratory hot air oven and the synthesis temperature was maintained at 90°C throughout the experiment. Prepared samples were characterized using x ray diffraction (XRD), Raman spectroscopy, high resolution transmission electron microscopy (HRTEM), UV- Vis absorption spectroscopy and photoluminescence (PL) Studies. -
Low temperature performance evaluation of asphalt binders and mastics based on relaxation characteristics
Low temperature cracking is one of the main distresses of asphalt pavement in cold regions. Stress relaxation characteristics is critical for cracking resistance of asphalt materials, especially at low temperatures, but there are few studies on the relaxation characteristic of asphalt mastics. To evaluate the effects of relaxation characteristics of asphalt binders and mastics on its low temperature performance, beam bending relaxation test was carried out through dynamic thermomechanical analyzer at low temperatures. Relaxation rate and relaxation time were proposed to illustrate the relaxation characteristics of asphalt binders and mastics. Then, the low-temperature performance of asphalt binders and mastics was evaluated by bending beam rheometer (BBR), glass transition temperature (Tg), and single edge notch beam bending test. Finally, the correlation of relaxation characteristics with low-temperature properties was analyzed based on Pearsons correlation coefficient and Spearman rank correlation coefficient. The results show that the elasticity of asphalt mastics increases with incorporation of mineral fillers and thus the viscous deformation potential is reduced, which affects the stress relaxation capability. The low-temperature cracking performance of asphalt mastics is indeed compromised as compared with asphalt binders, and the asphalt mastics prepared with fly ash performs the worst since it presents a stronger hardening effect. Fracture energy is determined not to be suitable for evaluating the low-temperature performance of asphalt mastics since its results contradict the BBR and Tg tests. The maximum displacement at fracture can better characterize the brittleness of asphalt materials at low temperatures. The relaxation characteristic index has the strongest correlation with Tg of asphalt binders and mastics, followed by maximum displacement at fracture and comprehensive compliance parameter (Jc). The correlation coefficients are almost larger than 0.5, suggesting that relaxation time and relaxation rate can characterize the low-temperature properties of asphalt binders and mastics. 2022, RILEM. -
Low temperature synthesis of MoO3 nanoparticles by hydrothermal method: Investigation on their structural and optical properties
Molybdenum trioxide nanoparticles have recently achieved notable attention in optoelectronic and biomedical applications by virtue of their excellent structural, optical, electrical, and catalytic characteristics. The work presented here demonstrates the synthesis of orthorhombic MoO3 through the facile hydrothermal method at low temperature. Structural and optical characterization of the prepared sample was examined. XRD studies and Raman spectroscopy were carried out to study the structural behavior of the sample. The XRD peaks were concordant with the standard peaks of MoO3, which corresponds to the orthorhombic structure of MoO3. Micro-strain effects were also verified by the W-H method using UDM, UDEDM, and USDM. Raman spectroscopic data ascertained the orthorhombic phase of MoO3. From Tauc plot, a wide bandgap value (4.9 eV) was evaluated. In photoluminescence spectroscopy, peaks are related to the transition among the sub-bands of Mo5+ defects. Being a wide bandgap oxide semiconductor, MoO3 is a promising and worthy material for luminescence applications. 2022 -
Low-cost bio-waste carbon nanocomposites for sustainable electrochemical devices: A systematic review
Innovative brains have always drawn inspiration from nature while creating new designs. Animals and plants offer a variety of structures that are stronger, have higher energy sorption capacities, and have lower densities. These structures can inspire the creation of new, functional designs. Scientists have created structures by drawing inspiration from biological structures seen in nature. These structures have been demonstrated to significantly outperform conventional structures for use in the environmental and energy sectors. Due to their simple synthesis, adaptability, excellent performance, and variety of uses, including in light-harvesting systems, batteries, catalysis, bio-fuels, water, and air purification, and environmental monitoring, bio-fabricated materials have demonstrated several advantages. However, sensitive fabrication tools that can create bio-inspired structures and scale up manufacturing from laboratory-scale synthesis are urgently needed. A quick rundown of recent developments in bionanomaterials for different electrochemical systems, particularly the extensively researched rechargeable batteries, sensors, and supercapacitors, provided a discussion of the design principles for bionanomaterials, synthesis, and strategies for low-cost bio-inspired nanomaterial synthesis and device integration. A quick overview of the future research priorities is then suggested, followed by a critical analysis of the current problems. This review is anticipated to provide some understanding of biowaste-nanocomposites for electrochemical applications by taking cues from nature. 2024 Elsevier Ltd -
Low-frequency pulse-jitter measurement with the uGMRT I: PSR J0437-4715
High-precision pulsar timing observations are limited in their accuracy by the jitter noise that appears in the arrival time of pulses. Therefore, it is important to systematically characterise the amplitude of the jitter noise and its variation with frequency. In this paper, we provide jitter measurements from low-frequency wideband observations of PSR J0437 4715 using data obtained as part of the Indian Pulsar Timing Array experiment. We were able to detect jitter in both the 300-500 MHz and 1 260-1 460 MHz observations of the upgraded Giant Metrewave Radio Telescope (uGMRT). The former is the first jitter measurement for this pulsar below 700 MHz, and the latter is in good agreement with results from previous studies. In addition, at 300-500 MHz, we investigated the frequency dependence of the jitter by calculating the jitter for each sub-banded arrival time of pulses. We found that the jitter amplitude increases with frequency. This trend is opposite as compared to previous studies, indicating that there is a turnover at intermediate frequencies. It will be possible to investigate this in more detail with uGMRT observations at 550-750 MHz and future high-sensitive wideband observations from next generation telescopes, such as the Square Kilometre Array. We also explored the effect of jitter on the high precision dispersion measure (DM) measurements derived from short duration observations. We find that even though the DM precision will be better at lower frequencies due to the smaller amplitude of jitter noise, it will limit the DM precision for high signal-to-noise observations, which are of short durations. This limitation can be overcome by integrating for a long enough duration optimised for a given pulsar. The Author(s), 2024. Published by Cambridge University Press on behalf of Astronomical Society of Australia. -
Low-Velocity Impact Characteristics of GLARE Laminates with Different Sheet Thickness
Fiber reinforcement with metallic face sheets is one of the recently implemented advanced materials in distinctive applications such as fender, bonnet, and chassis used in automotive sectors. While the reinforcement enhances the sustenance property of the laminate, the face sheets provide resistance to impact force. In most automotive sectors, drop-weight analysis at varying velocity range is performed to evaluate the damage characteristics of the vehicle body. The present work is aimed at studying the influence of low-velocity impact (LVI) on glass laminate aluminum-reinforced epoxy (GLARE) laminate. Three distinct thicknesses of Al-2024 T3 aluminum alloy (0.2, 0.3, and 0.4 mm) were chosen as the face sheet, the overall thickness was kept at 2.0 mm for all the cases. Absorbed energy and damage characteristics of GLARE for different energy was experimentally determined using drop-weight impact tester. From the results, it was found that GLARE laminate can sustain a maximum impact energy of around 20 J, beyond which damage in the form of cracks begin to occur at bottom face sheet also. It was also evident that laminate can sustain impact at a velocity of 3.13 m/s and beyond which it leads to delamination damage at 3.49 m/s. Further, it is noticed that GLARE laminate with 0.3 mm face sheet thickness has best results with reference to both absorbed energy and damage when compared with other thicknesses. Also, the sample B indicates the optimal surface texture when subjected to LVI damage obtained through scanning electron microscope (SEM). 2021 SAE International. -
LP norm regularized deep CNN classifier based on biwolf optimization for mitosis detection in histopathology images
Mitosis detection, a crucial biomedical process, faces challenges like cell morphology variability, poor contrast, overcrowding, and limited annotated dataset availability. This research presents a novel method for mitosis detection in histopathological images highlighting two important contributions using a Bi-wolf optimization-based LP norm regularized deep Convolutional neural network (CNN) model. This hybrid optimization protocol is the key to the precise calibration of model parameters and effective training, which translates into optimal classifier performance. The results reveal that this model achieves high accuracy, sensitivity, and specificity values of 96.69%, 91.89%, and 97.74% respectively. Bharati Vidyapeeth's Institute of Computer Applications and Management 2024. -
LRD: Loop Free Routing Using Distributed Intermediate Variable in Mobile Adhoc Network
One of the critical challenges in the design of the mobile adhoc networks is to design an efficient routing protocol. Mobility is an unique characteristics of wireless network, which leads to unreliable communication links and loss of data packets. We present a new algorithm, Loop Free Routing with DIV (LRD) is introduced which prevents loops and count to infinity problem using intermediate variables. In addition it finds the shortest path between source and destination. The analysis shows that DIV is compatible with all the routing protocol as it is independent of the underlying environment. The proposed algorithm LRD is compared with the existing algorithm of DIV to prove its applicability in the any routing environment. The simulation results show that LRD excels AODV routing protocol while considering throughput and packet delivery ratio. The new algorithm assures that the routing protocol is shortest loop-free path and outperforms all other loop-free routing algorithms previously proposed from the stand point of complexities and computations. Springer Nature Switzerland AG 2020. -
LRE-MMF: A novel multi-modal fusion algorithm for detecting neurodegeneration in Parkinson's disease among the geriatric population
Parkinson's disease (PD) is a prevalent neurological disorder characterized by progressive dopaminergic neuron loss, leading to both motor and non-motor symptoms. Early and accurate diagnosis is challenging due to the subtle and variable nature of early symptoms. This study aims to address these diagnostic challenges by proposing a novel method, Localized Region Extraction and Multi-Modal Fusion (LRE-MMF), designed to enhance diagnostic accuracy through the integration of structural MRI (sMRI) and resting-state functional MRI (rs-fMRI) data. The LRE-MMF method utilizes the complementary strengths of sMRI and rs-fMRI: sMRI provides detailed anatomical information, while rs-fMRI captures functional connectivity patterns. We applied this approach to a dataset consisting of 20 PD patients and 20 healthy controls (HC), all scanned with a 3 T MRI. The primary objective was to determine whether the integration of sMRI and rs-fMRI through the LRE-MMF method improves the classification accuracy between PD and HC subjects. LRE-MMF involves the division of imaging data into localized regions, followed by feature extraction and dimensionality reduction using Principal Component Analysis (PCA). The resulting features were fused and processed through a neural network to learn high-level representations. The model achieved an accuracy of 75 %, with a precision of 0.8125, recall of 0.65, and an AUC of 0.8875. The validation accuracy curves indicated good generalization, with significant brain regions identified, including the caudate, putamen, thalamus, supplementary motor area, and precuneus, as per the AAL atlas. These results demonstrate the potential of the LRE-MMF method for improving early diagnosis and understanding of PD by effectively utilizing both sMRI and rs-fMRI data. This approach could contribute to the development of more accurate diagnostic tools. 2024 The Authors -
LULC Analysis of Green Cover Loss in Bangalore
Urbanization of the cities especially the Indian City of Bangalore has led to the creation of an important discourse concerning development and conservation. The study carries out a detailed LULC study with special reference to Green Cover Loss in city of Bangalore. Using satellite images from 2014 to 2023 period and machine learning tools, the study establishes declines in green spaces with economic, environmental and health consequences of the city's uncontrolled expansion. The innovations afforded to the study regard methodologically on the use of ResNet50 for accurate LULC classification with an accuracy of 92% Hence the study reveals the interaction between urbanization and conservation, the efficiency of which requires policy adjustments that depend on existing knowledge. The study not only accustomizes the progression in the geography of Bangalore but it also shapes the technology and methodology for the further geospatial research in the areas under rapidly urbanizing in the future. 2024 IEEE. -
Luminescence and energy storage characteristics of coke-based graphite oxide
The substantial escalation in both energy consumption and ecological crisis prompts the utilization of conventional pollution-causing energy resources towards a proficient mode of energy production and storage. The most polluting fossil fuel like coal possesses a highly ordered sp2 carbon clusters, that can be easily tailored into graphene derivatives promising for energy-related applications. However, the impact of crystallinity and quality of the precursor coke on the physicochemical characteristics of extracted carbon nanostructures need to be identified. Herein, we have prepared graphite oxide structures (GrO) from high-quality coal, coke via Improved Hummers' method eliminating the need for toxic NaNO3. The inherent defect states own by coke are also of high significance as it performs the role of various photoluminescence emission centers. The sp2 domains and different surface defects promote excitation independent and dependent luminescence substantiating the distinct multi-emission property of GrO. The extent of functionalization during the oxidation process has also significantly affected the thermal stability of the carbogenic structure. The symmetric galvanostatic charge-discharge curves and lower internal resistance present superior stability and fatser ion transport of as-synthesized GrO. A specific capacitance of 193F/g was obtained at 0.2A/g with excellent capacitance retention over 2500 cycles. The versatile attributes of the coke derived GrO validate its realizable optoelectronics and energy storage applications. 2020 Elsevier B.V. -
Lung Cancer Detecting using Radiomics Features and Machine Learning Algorithm
Lung Cancer Incidence across the globe is the second leading cancer type tallying to about 2,206,771 during 2020 and is estimated to rise to about 3,503,378 by 2040 for both male and female sexes and for all ages accounting to 11.4% as per Globocan 2020 [1]. It is the leading death-causing cancer. Lung Cancer [2] in broad terms encompasses Trachea, bronchus as well as lungs. Purpose: The study is aimed to understand Radiomics based approach in the identification as well as classification of CT Images with Lung Cancer when Machine Learning (ML) algorithms are applied. Method: CT Image from LIDC-IDRI [4] Dataset has been chosen. CT Image Dataset was balanced and image features by PyRadiomics library were collected. Various ML features classification algorithms are utilized to create models and matrices adopted in judging their accuracies. The models, distinctive capacity is assessed by receiver operating characteristics (ROC) analysis. Result: The Accuracy scores and ROC-AUC values obtained for various Classification Model are as follows, for Ada Boosting, the accuracy score was 0.9993 ROC-AUC was 0.9993 and followed by GBM, the accuracy score was 0.9993, was 0.9992. Conclusion: Extracting texture parameters on CT images as well as linking the Radiomics method with ML would categorize Lung Cancer commendably. 2023 IEEE. -
Lung cancer detection using image processing techniques
Lung cancer is one of the hazardous disease which leads to high death rates in the world. A cancer is an irregular growth of cells that can be characteristically derived from a single irregular cell and that may spread to whole part of the lung. So, it is necessary to find it at the earlier stages and take basic steps to cure.CT scan is one of the sensitive method used in the medical field for treating the patients. The quality of the image is very important for detection of lung cancer. Pre-processing of an image is a necessary process, as there is a difficulty in detecting cancer cells in an image due to the presence of noise and low-quality of images. To reduce the volume of these problems, diagnosis of lung cancer steps like image enhancement, image segmentation, feature extraction methods can be used. For processing and implementation of these methods Matlab tool has been used. This paper focuses on improving the quality of image and to optimise the work. Implementation is done using image processing toolbox that is available in Matlab tool.The whole idea of this research is to show the improved work in the existing system and to get more agreeable results. RJPT All right reserved. -
Lung Cancer Diagnosis from CT Images Based on Local Energy Based Shape Histogram (LESH) Feature Extration and Pre-processing
Lung cancer as of now is one of the dreaded diseases and it is destroying humanity never before. The mechanism of detecting the lung cancer will bring the level down of mortality and increase the life expectancy accuracy 13% from the detected cancer diagnosis from 24% of all cancer deaths. Although various methods are adopted to find the cancer, still there is a scope for improvement and the CT images are still preferred to find if there is any cancer in the body. The medical images are always a better one to find with the cancer in the human body. The proposed idea is, how we can improve the quality of the diagnosis form using pre-processing methods and Local energy shape histogram to improve the quality of the images. The deep learning methods are imported to find the varied results from the training process and finally to analyse the result. Medical examination is always part of our research and this result is always verified by the technicians. Major pre-processing techniques are used in this research work and they are discussed in this paper. The LESH technique is used to get better result in this research work and we will discuss how the image manipulation can be done to achieve better results from the CT images through various image processing methods. The construction of the proposed method will include smoothing of the images with median filters, enhancement of the image and finally segmentation of the images with LESH techniques. 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.