Implementation of Omar Pigeon Space-Time (OPST) Algorithm to Mitigate the Interference and Peak-to-Average Power Ratio (PAPR) Using RPR Mobile and HST-HM in the 5G

Kumutha, D.; Thanarajan, Tamilvizhi; Alharbi, Meshal

doi:10.18280/ts.390520

Cited by 4 publications

(4 citation statements)

References 20 publications

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“…To eliminate frequent finger pricking, a CGM sensor is utilized to measure the diabetic patient's blood glucose level from the interstitial fluid. Because CGM sensors are implanted beneath the skin, they monitor interstitial glucose rather than blood glucose (BG).CGM sensors can be made "smart" by including algorithms that can send out notifications when glucose concentrations are expected to surpass normal range thresholds [19,20]. To improve the signal-to-noise ratio (SNR) of CGM data, it must be filtered.…”

Section: Proposed Methodologymentioning

confidence: 99%

Sensors Based Optimized Closed Loop Control Algorithm to Minimize Hypoglycemia/Hyperglycemia using 4-Variate Time Series Data

G.,

Sundarapandi,

et al. 2024

JNMES

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The goal of this work is to create a closed-loop control system that combines continuous glucose, carbohydrates, and physiological variable readings to regulate glucose levels to treat hyperglycemia and prevent hypoglycemia, as well as a hypoglycemia early alarm module. We use an Extended Kalman filter (EKF) to estimate time-series coefficients of type 1 diabetic patient's glucose levels using a 4-variate time series data such as glucose level, insulin dose, physical activities, and food consumption. An adaptive Kalman Filter algorithm is best suitable for sensor fusion and also for real-time data, which uses the series of measurement data over the period to tend to predict the unknown variable. We proposed to provide the mealtime bolus as a prolonged bolus, with a slice of the insulin dosage delivered before food and the remainder delivered after food. Our research entails the use of a glucose monitor (CGM), physical activity monitor (accelerometer sensor), carbohydrates monitor (biosensor), an automatic insulin infusion controller that calculates the quantity of insulin to be injected, and an insulin infusion drive. Low-Power and Lossy Routing (LPLR) with 6LoWPAN are proposed for efficient routing and private networks. Our model is evaluated using the UVa/Padova simulator for 25 patients. We conducted our experiments with 25 virtual patients with a mix of all age categories. The simulator's default sample time is one minute, but we have set the 5-minute sampling time. Overall, the proposed models are good at predicting hypoglycaemic (<70 mg/dl), normal glycaemic (>70 and 180 mg/dl), and hyperglycaemic (180 mg/dl) blood sugar levels.

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Section: Proposed Methodologymentioning

confidence: 99%

Sensors Based Optimized Closed Loop Control Algorithm to Minimize Hypoglycemia/Hyperglycemia using 4-Variate Time Series Data

G.,

Sundarapandi,

et al. 2024

JNMES

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“…( 14) [16,17]. where, W and b refers the weight and bias of entire deep network, 𝐽(𝑥, 𝑧) represents the LR cost amongst the classifier attained with input feature x and unsupervised outcome 𝑧̂ and W smc implies the weight and λ smc stands for the weight decomposed parameter [18,19]. By implementing finetuned, the weight and bias of SM are optimized together, and SM state was employed to the classifier.…”

Section: Softmax Classifiermentioning

confidence: 99%

Deep Learning Based Compression with Classification Model on CMOS Image Sensors

Palani,

Thanarajan,

Krishnamurthy

et al. 2023

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The complementary metal oxide semiconductor (CMOS) technique is widely used in modern manufacturing processes for the high compatibility. A novel metaheuristic with deep learning based compression with image classification model (MDL-CCIM) technique is developing to compress and classify the images captured by CMOS image sensors. The proposed MDL-CCIM technique follows two major processes, namely, butterfly compression and classification. Primarily, the BOA with LBG model is applied for image compression. Secondly, the DenseNet with softmax layer is employed for image classification. Finally, the hyper parameter tuning of the DenseNet model is optimally chosen by the Adam optimizer. A wide range of simulations was carried out to highlight the enhancement of the MDL-CCIM technique. The extensive comparative analysis reported the improved outcomes of the MDL-CCIM technique over the recent approaches. Hybrid DL models can be used for image classification purposes.

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“…In order to provide forecasts, NB classifiers use many probability metrics [21]. It's based on the assumption that the qualities that were artificially introduced have no causal relationship with one another [22]. An ANN is a kind of network with an output that is modelled after the neurons in the human brain [23].…”

Section: Introductionmentioning

confidence: 99%

Plant Disease Classification Based on ConvLSTM U-Net with Fully Connected Convolutional Layers

Alharbi¹,

Rajagopal²,

Rajendran³

et al. 2023

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Plants are susceptible to a variety of illnesses throughout their growth stages. One of the trickiest issues in agriculture is the early diagnosis of plant diseases. The entire output may be negatively impacted by infections if they are not discovered early on, which would lower farmers' profitability. Numerous researchers have proposed numerous cutting-edge solutions based on Deep Learning and Machine Learning techniques to address this issue. However, the majority of these systems either has poor classification accuracy rates or utilizes millions of training parameters. In this research, a novel model using ConvLSTM U Net-based automatic detection of plant disease is proposed. To the best of our knowledge, no state-of-the-art systems described in the literature have a hybrid system based on CAE and CNN to automatically identify plant diseases. The proposed model employed in this study is to identify the presence of Bacterial Spot disease in medicinal plants using the image of their leaves, but it may be extended to identifying any plant disease. The work conducted for this research employ a dataset that is readily accessible to get images of medicinal plant leaves. In comparison to previous methods described in the literature, the proposed ConLSTM U-Net model requires for less training parameters. As a consequence of this, the amount of time necessary to train the model for automatic plant disease detection and the amount of time required to diagnose the disease in plants using the trained model are both significantly decreased.

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Implementation of Omar Pigeon Space-Time (OPST) Algorithm to Mitigate the Interference and Peak-to-Average Power Ratio (PAPR) Using RPR Mobile and HST-HM in the 5G

Cited by 4 publications

References 20 publications

Sensors Based Optimized Closed Loop Control Algorithm to Minimize Hypoglycemia/Hyperglycemia using 4-Variate Time Series Data

Sensors Based Optimized Closed Loop Control Algorithm to Minimize Hypoglycemia/Hyperglycemia using 4-Variate Time Series Data

Deep Learning Based Compression with Classification Model on CMOS Image Sensors

Plant Disease Classification Based on ConvLSTM U-Net with Fully Connected Convolutional Layers

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