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Visible Light Communication Based Wide Range Indoor Fine Particulate Matter Monitoring System
SEJAN MOHAMMAD ABRAR SHAKIL,안진영,한대현,정완영 한국융합신호처리학회 2019 융합신호처리학회 논문지 (JISPS) Vol.20 No.1
Fine particulate matter known as PM 2.5 refers to the atmospheric particulate matter that has a diameter less than 2.5 micrometer identified as dangerous element for human health and its concentration can provide us a clear picture about air dust concentration. Humans stay indoor almost 90% of their life time and also there is no official indoor dust concentration data, so our study is focused on measuring the indoor air quality. Indoor dust data monitoring is very important in hospital environments beside that other places can also be considered for monitoring like classrooms, cements factories, computer server rooms, petrochemical storage etc. In this paper, visible light communication system is proposed by Manchester encoding technique for electromagnetic interference (EMI)-free indoor dust monitoring. Important indoor environment information like dust concentration is transferred by visible light channel in wide range. An average voltage-tracking technique is utilized for robust light detection to eliminate ambient light and low-frequency noise. The incoming light is recognized by a photo diode and are simultaneously processed by a receiver micro-controller. We can monitor indoor air quality in real-time and can take necessary action according to the result.
Mohammad Haris,Moh Sajid Ansari,Abrar Ahmad Khan 한국원예학회 2021 Horticulture, Environment, and Biotechnology Vol.62 No.5
This research was investigated to assess the eff ect of fl y ash (FA) on the growth, yield, biochemical attributes, and enzymaticantioxidant response of chickpea ( Cicer arietinum L. cv. Avrodhi). After evaluating soil and FA nutrient status byenergy-dispersive X-ray spectroscopy (EDX), diff erent concentrations of FA with soil were applied (5, 10, 15, 20, 25,and 30%). Chickpea plants grown at all FA-amended soil concentrations demonstrate signifi cant improvement ( p ≤ 0.05)in growth and yield biomass. Exhibition of enhancement in biochemical attributes such as photosynthetic pigments(chlorophyll a and b, total chlorophyll, and total carotenoid by 16.83, 24.50, 19.13, and 21.74%), total protein (48.54%),leghemoglobin (42.39%), nitrate reductase activity (18.35%), proline content (28.16%), and antioxidant enzymes activities. Moreover, an improvement in the stomatal pore of the FA treated plant also noticed by a scanning electron microscope(SEM). Overall, the fi ndings of this study indicate that the supplementation of FA contributed to increment inthese attributes at all the concentrations compared with control (without FA) ‘but maximally ~ up to FA 20%.’ However,plant growth and yield biomass decreased above the concentration of FA 20% because chickpea can balanced nutrientseffi ciency up to FA 20%.
Faizan Ullah,Muhammad Nadeem,Mohammad Abrar 한국인터넷정보학회 2024 KSII Transactions on Internet and Information Syst Vol.18 No.1
Gliomas are the most common malignant brain tumor and cause the most deaths. Manual brain tumor segmentation is expensive, time-consuming, error-prone, and dependent on the radiologist's expertise and experience. Manual brain tumor segmentation outcomes by different radiologists for the same patient may differ. Thus, more robust, and dependable methods are needed. Medical imaging researchers produced numerous semi-automatic and fully automatic brain tumor segmentation algorithms using ML pipelines and accurate (handcrafted feature-based, etc.) or data-driven strategies. Current methods use CNN or handmade features such symmetry analysis, alignment-based features analysis, or textural qualities. CNN approaches provide unsupervised features, while manual features model domain knowledge. Cascaded algorithms may outperform feature-based or data-driven like CNN methods. A revolutionary cascaded strategy is presented that intelligently supplies CNN with past information from handmade feature-based ML algorithms. Each patient receives manual ground truth and four MRI modalities (T1, T1c, T2, and FLAIR). Handcrafted characteristics and deep learning are used to segment brain tumors in a Global Convolutional Neural Network (GCNN). The proposed GCNN architecture with two parallel CNNs, CSPathways CNN (CSPCNN) and MRI Pathways CNN (MRIPCNN), segmented BraTS brain tumors with high accuracy. The proposed model achieved a Dice score of 87% higher than the state of the art. This research could improve brain tumor segmentation, helping clinicians diagnose and treat patients.