Derivation of the (Closed-Form) Particular Solution of the Poisson’s Equation in 3D Using Oscillatory Radial Basis Function

Partial differential equations (PDEs) are useful for describing a wide variety of natural phenomena, but analytical solutions of these PDEs can often be difficult to obtain. As a result, many numerical approaches have been developed. Some of these numerical approaches are based on the particular solutions. Derivation of these particular solutions are challenging. This work is about how the Laplace operator can be written in a more convenient form when it is applied to radial basis functions and then use this form to derive the (closed-form) particular solution of the Poisson’s equation in 3D with the oscillatory radial function in the forcing term

Derivation of the (Closed-Form) Particular Solution of the Poisson’s Equation in 3D Using Oscillatory Radial Basis Function

Partial differential equations (PDEs) are useful for describing a wide variety of natural phenomena, but analytical solutions of these PDEs can often be difficult to obtain. As a result, many numerical approaches have been developed. Some of these numerical approaches are based on the particular solutions. Derivation of these particular solutions are challenging. This work is about how the Laplace operator can be written in a more convenient form when it is applied to radial basis functions and then use this form to derive the (closed-form) particular solution of the Poisson’s equation in 3D with the oscillatory radial function in the forcing term

MLTE Algorithm for Multicast Service Delivery in OFDMA Networks

Dispensing and overseeing radio resources to the multi-cast transmissions in OFDMA (orthogonal-frequency division-multiple-access) systems is testing exploration issue tended to by this paper. A sub-grouping technique, which separates the subscribers into subgroups as indicated by the accomplished channel quality, is considered to defeat the throughput confinements of conventional multicast data conveyance schemes. A low complexity algorithm intended to work with diverse resource allocation strategies, is additionally proposed to diminish the computational complexity of the subgroup development issue. Reproduction results, did by considering the long term evolution system taking into account OFDMA, affirm the adequacy of the proposed arrangement, which accomplishes a close ideal execution with a restricted computational load for the system. In this paper we are introducing the MLTE for improve the MBPS speed for fix network coverage at uniform and sparse. DOI: 10.17762/ijritcc2321-8169.150713

Deep Learning Based Automatic Vehicle License Plate Recognition System for Enhanced Vehicle Identification

An innovative Automatic Vehicle License Plate Recognition (AVLPR) system that effectively identifies vehicles using deep learning algorithms. Accurate and real-time license plate identification has grown in importance with the rise in demand for improved security and traffic management.The convolutional neural network (CNN) architecture used in the AVLPR system enables the model to automatically learn and extract discriminative characteristics from photos of license plates. To ensure the system's robustness and adaptability, the dataset utilized for training and validation includes a wide range of license plate designs, fonts, and lighting situations.We incorporate data augmentation approaches to accommodate differences in license plate orientation, scale, and perspective throughout the training process to improve recognition accuracy. Additionally, we use transfer learning to enhance the system's generalization abilities by refining the pre-trained model on a sizable dataset.A trustworthy and effective solution for vehicle identification duties is provided by the Deep Learning-Based Automatic Vehicle License Plate Recognition System. Deep learning approaches are used to guarantee precise and instantaneous recognition, making it suitable for many uses such as law enforcement, parking management, and intelligent transportation systems

Assessment of medication adherence among hypertensive patients: a cross-sectional study

Background: Hypertension affects around one billion individuals worldwide and is expected to increase by 29% to reach 1.56 billion by 2025. It is usually asymptomatic, chronic disorder needing lifelong treatment. The objective of this study was to study the medication adherence among hypertensive patients using hill-bone compliance to high blood pressure therapy scale (HILL-BONE CHBPTS) and to compare medication adherence in hypertensive patients with controlled and uncontrolled blood pressure.Methods: A cross-sectional, observational study was conducted for a period of one year in the Outpatient department of Medicine in a tertiary care hospital, Navi Mumbai. A total of 129 hypertensive patients who were on at least six months on antihypertensive medications were enrolled. Blood pressure was measured and details of drug therapy were noted. Medication adherence was assessed using HILL-BONE CHBPTS and respective scores were calculated.Results: HILL-BONE CHBPTS scores were on the higher side signifying poor medication adherence among hypertensive patients. HILL-BONE CHBPTS score correlated significantly in a positive direction with diastolic blood pressure, duration of treatment and the number of medications, As per JNC 8 recommendations, 58.9% (76) hypertensive patients were having blood pressure under control, whereas 41.1% (53) were having uncontrolled blood pressure. HILL-BONE CHBPTS scores were significantly higher (reflecting lower adherence) in hypertensive patients with uncontrolled blood pressure than those having optimally controlled blood pressure.Conclusions: Overall the medication adherence was poor in hypertensive patients. Adherence to therapeutic regimens is an important factor in blood pressure control among hypertensive patients and needs priority. Health education related to medication adherence needs be improved in hypertensive patients

ESCELL: Emergent Symbolic Cellular Language

We present ESCELL, a method for developing an emergent symbolic language of communication between multiple agents reasoning about cells. We show how agents are able to cooperate and communicate successfully in the form of symbols similar to human language to accomplish a task in the form of a referential game (Lewis' signaling game). In one form of the game, a sender and a receiver observe a set of cells from 5 different cell phenotypes. The sender is told one cell is a target and is allowed to send one symbol to the receiver from a fixed arbitrary vocabulary size. The receiver relies on the information in the symbol to identify the target cell. We train the sender and receiver networks to develop an innate emergent language between themselves to accomplish this task. We observe that the networks are able to successfully identify cells from 5 different phenotypes with an accuracy of 93.2%. We also introduce a new form of the signaling game where the sender is shown one image instead of all the images that the receiver sees. The networks successfully develop an emergent language to get an identification accuracy of 77.8%.Comment: IEEE International Symposium on Biomedical Imaging (IEEE ISBI 2020

A quantitative and qualitative approach to knowledge management – Episodic extraction, enhancement and creation of tacit knowledge

Capturing employee’s knowledge is of prime importance to any organization.If the knowledge is captured after a completion of entire task, quality and quantity of knowledge obtained from the expert decreases as it is human tendency to forget after a long interval of time.We, in this paper have developed a model emphasizing the need for episodic extraction, enhancement and creation of the tacit knowledge which will increase the quality and the quantity of the tapped tacit knowledge.With this model, the organizations will be able to capture the knowledge of employees in phased manner so that when an individual leaves the organization, the loss incurred by the organization is minimized to a great extent