Recently many premature babies have lost their lives due to lack of proper monitoring of the incubator that leads to accidents. A neonatal incubator is an enclosed equipment where a pre-mature infant will be kept in a clean and controlled environment for observation and care. The biological parameters are monitored to ensure the safety of the babies and to prevent death rates. For monitoring the vital signs continuously for pre-mature infants in the hospital it requires sensors and electrodes which is said to be kept in contact to the patient and it can be displayed in a d monitor. Any abnormality in the parameter will be indicated by alarm system. In this survey, we concentrate on the available incubator monitoring systems, the biological parameters measured and analyse techniques used in real-time monitoring, transmission of the data.
<span lang="EN-US">Visible Light Communication (VLC) has become an accolade to its radio frequency counterpart. In VLC system, orthogonal frequency division multiplexing (OFDM) has drawn much attention, because of simple equalization, high spectral efficiency, high data rate and robustness to intersymbol interference (ISI). Besides, there are emerging applications that ought to be gotten with low latency and high reliability. To diminish power requirements with no transmission capacity extension, Trellis coded modulation (TCM) is utilized as a part of the framework in which the free distance of trellis diagram is equivalent to the minimum distance between the points of constellation focuses in partitioned subsets, which augments the coding gain up i.e. the performance parameter viably. TCM together with VLC-OFDM enhances the transmission execution in reasonable frameworks. In this paper, we propose OFDM which is based on TCM and is planned and exeuted for digitized OFDM frameworks by presenting delta sigma modulation (DSM) considering VLC channel. Simulation results show that the proposed TCM based VLC-OFDM offers incredible robustness against noises and nonlinear degradation.</span>
Tremors, a significant symptom of movement disorder, affects a part of the body ranging from slight to severe. These Tremors are symptoms of various neurological diseases such as Parkinson’s disease (PD), Essential tremors (ET), Physiological tremors (PT), Cerebellar tremor, Dystonic tremor, Psychogenic tremor, and many more. Tremor features and types differ for various neurological disorders. During the early stages of the disease, clinical examination of tremors plays a significant role in diagnose management. This work aims to develop a wearable assistive system with an Inertial Measurement Unit (IMU) sensor to classify the tremor of three different neurological disorders based on the tremor position and frequency. This research has been carried out in SRM Medical college and Research Centre with 15 patients. The type of neurodegenerative disease of the subject with tremor is evaluated based on the tremor position and tremor frequency level. The data is collected, transmitted, and processed using the IMU sensor with Internet of things (IoT) and Node MCU board. The decision tree algorithm is used for the classification of tremors. ET, PD, and PT tremors are classified based on the tremor frequency and tremor position. A high rate of accuracy is achieved for the developed system when compared with the Neurologist results. The proposed device quantitatively classified the tremor based on the frequency and position among the three different neurological disorders, i.e., ET, PD, and PT tremors.
Abstract:In the field of agriculture, plantation begins with ploughing the land and sowing seeds. The old traditional method plough attached to an OX and tractors needs human involvement to carry the process. The driving force behind this work is to reduce the human interference in the field of agriculture and to make it cost effective. In this work, apart of the land is taken into consideration and the robot introduced localizes the path and can navigate itself without human action. For ploughing, this robot is provided with tentacles attached with saw blades. The sowing mechanism initiates with long toothed gears actuated with motors. The complete body is divided into two parts the tail part acts as a container for seeds. The successor holds on all the electronics used for automating and actuation. The locomotion is provided with wheels covered under conveyor belts. Gears at the back of the robot rotate in equal speed with respect to each other with the saw blades. For each rotation every tooth on gear will take seeds and will drop them on field. Camera at the front end tracks the path for every fixed distance and at the minimum distance it takes the path preprogrammed. Introduction:
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