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The aim of the current study was to assess a scrupulous pharmacognostic cram is to enhance the constructive information with regard to its species identification, characterization and standardization of a dynamic folklore therapeutic plant Pterolobium hexapetalum. It is one of the largest scrambling shrubs belonging to the Leguminosae family having significant medicinal properties. The aerial part of the fresh and healthy plant materials was collected from the Maruthamalai hill, Coimbatore District with a strong traditional background. The confined name of the species is “Karuindu” used as medicine in Siddha, Ayurveda and folk medicine for treating various illnesses. The external morphology of study species, the anatomy of leaf and stem, physicochemical examination of plant powder, powder with diverse chemical reagents, fluorescence analysis of therapeutic powder, ash values, mineral studies, heavy metal analysis, extractive values, phytochemical analysis, GCMS analytical studies were examined with the aim of drafting pharmacopeial principles. The studied plant contains 19 bioactive chemical constituents which confirm the healing domination. The analytical assessment of study species authenticates the presence of various bioactive constituents with a broad range of remedial properties which is used to treat manifold muddle as well it provides a comprehensive insight regarding the phytoconstituents profile which could be browbeaten for the maturity of plant-supported drugs.

India is the world’s third largest producer and third largest consumer of electricity [1]. The per person energy consumption in India in 2019 is 1181 kWh. With increasing demand and value for energy, it has become tedious to keep track of consumption of energy units. Smart Meters can override these difficulties by providing a more appropriate solution which includes accurate billing, better information on energy consumption thus leading to better utilization. These Smart energy meters are beneficial to both the consumer and the supplier. They provide a more efficient solution in determining tariffs for the latter. Internet of Things is an emerging technology and IoT based devices have revolutionized the field of electronics and IT. The proposed project is an Iot Based Smart Energy meter system developed to efficiently manage the energy consumption in households by avoiding wastage, to provide accurate information to small scale users and to avoid manual meter reading and automate bill data entry process.

Losing our belongings is probably the greatest bad dream for both air terminal specialists and people. The face is probably the least de manding approaches to separate the individual personality of one another. Face acknowledgment is an individual recognizable proof framework that utilizes individual qualities of an individual to distinguish the individual's character. Human face acknowledgment strategy essentially comprises of two stages, specifically face identification, where this procedure happens quickly in people, with the exception of under conditions where the item is situated at a short separation away, the following is the presentation, which perceive a face as people. Face acknowledgment frameworks have likewise been utilized by photograph the executives programming to recognize the subjects of photos, empowering highlights, for example, looking through pictures by individual, just as proposing photographs to be imparted to a particular contact if their quality were distinguished in a photograph. Facial acknowledgment is utilized as included security in specific sites, telephone applications, and installment strategies. In phrases of privateness the bag can be activated via owner's identification and also location may be tracked using GPS and GSM. on this bag within a small platform all the centers are applied collectively efficiently.

The proposed methods presents for the prediction of abnormal heart rhythm expending customary of electro cardio graph topographies and a radial basis algorithm. Generally and changeable techniques are used for find out and separation of the P-QRS-T waves. And also detect all intervals in the ECG signal and compared with stored record. Ventricular arrhythmia is predicted by analyzing ECG signal. Four methods are used to analysis the ECG signal namely the methods are ECG preprocessing, QRS complex detection, feature extraction and classification. By using above methods an accurate noise free ECG Signal is obtained. Proposed architecture of this project analysis the logic size, area and power consumption.