Arrhythmia (ARR) and Congestive Heart Failure (CHF) are the most common conditions that have delayed diagnoses in cardiovascular illnesses and the primary cause of death, these are compared with Normal Sinus Rhythm (NSR). Manually interpreting electrocardiogram (ECG) readings can lead to an early identification of various heart diseases. However, because ECG signals have so many different features, manual diagnosis is difficult. Patient lives could be saved with an accurate ARR and CHF group system. The signal classification problem is made simpler by the process of condensing the original signal from an ECG to a much fewer number of characteristics that work together to distinguish between several classes. The variations in variance for each of the three groups in the second-largest scale (second-lowest frequency) wavelet sub-band is examined. It makes use of a quadratic kernel multi-class SVM. This paper deals with two analyses. The whole set of data i.e. training and testing sets to determine the rate of misclassification and confusion matrix. With the best classification accuracy of 97.95%, the SVM divided the raw ECG signal data into three categories: NSR, ARR and CHF. The confusion matrix reveals the misclassification of one class to another i.e. one CHF record as NSR.

Conventional face recognition (FR) systems face challenges with varying poses, scales, and occlusions, particularly in dense environments where interpersonal occlusion is common. Existing methods using rectangular bounding boxes (BBs) often result in inaccurate detections and lower FR accuracy, particularly when landmark-based alignment fails. To address this, we propose a novel approach integrating Bound YOLO-v7 with a context module to improve face boundary marking, extend the receptive field, and preserve facial contours. Supported by a newly annotated boundary dataset, the method fills the gap in high-quality benchmark data for facial boundary segmentation. In the offline phase, Bound YOLO-v7 extracts face contours, while in the online phase, FaceNet identifies multiple faces in real time. The proposed method achieves a detection rate of 99.83% with mAP values of 0.995 and 0.979 for mAP@0.5 and mAP@0.5:0.95, respectively, and a confidence score of 0.42 ms at 41.3 ms. The inclusion of the context module results in mAP@0.5 scores of 99.5% (no occlusion), 96.0% (slight occlusion), and 89.0% (severe occlusion). This approach outperforms the existing method and balances detection accuracy and computational efficiency.

In dentistry, endodontics is the study of dental pulp and tissues surrounding the roots. Endodontic treatment is otherwise called root canal treatment. The importance of endodontics focuses on several therapies to protect human teeth from cavities or infections, injuries, and various oral diseases like oral cancer and periodontal disease. Over 3.5 billion people are affected by various oral diseases, 10% of the global population is affected by periodontal diseases, and 530 million children suffer from tooth decay. There are different types of root canal morphology and configurations in which multiple abnormalities exist, such as C-shaped canals, fusion of roots, dens invaginatus, distolingual root, taurodontism, root dilaceration, etc. AI plays a vital role in endodontic applications. Using AI for the prediction and diagnosis of periapical lesions, root fractures can be detected. Nowadays, AI is used to determine working length measurements, predict dental pulp stem cells, and guide retreatment procedures. Therefore, AI provides successful outcomes and improvements in diagnosis and prediction in root canal applications in day-to-day practices. This review chapter summarizes different deep learning techniques that can be implemented in various endodontic applications in detail to understand the pros and cons.

Precise segmentation of retinal blood vessels (RBVs) is pivotal in ophthalmology research, aiding in detecting diverse retinal abnormalities. This study proposes a contrast-limited adaptive histogram equalisation (CLAHE) technique to improve retinal image quality and visibility of microvascular structures. We aimed to determine the complexity of blood vessels using fractal dimensions (FD) and compare different metrics for their effectiveness. We employed the UNet architecture to separate blood vessels, and our results on the DRIVE retinal fundus image standard dataset showed an impressive accuracy rate of 97.24%, surpassing traditional filtering methods. Box counting, information, capacity, correlation, and probability dimensions are used in the FD analysis to help us understand the complex and irregular structures of retinal blood vessels. These metrics are valuable for detecting and monitoring retinal diseases in clinical settings. Our comparison with other techniques reveals promising results, particularly in the capacity and information dimensions, with statistical significance (P < 0.05). The potential of fractal dimensions as a screening tool for diabetic retinopathy underscores their importance in epidemiological studies.

High blood pressure, or hypertension, has a direct association with retinal diseases, specifically hypertensive retinopathy (HR) and diabetic retinopathy (DR). HR is more prevalent in patients with severe and prolonged hypertension. Early identification of HR is crucial for assessing the risk of blindness. However, existing computerized techniques for diagnosing both HR and DR are limited. These methods often rely on conventional machine learning approaches, which are time-consuming and involve intricate image-processing steps. To address these challenges, we propose HDR-EfficientNet (HDR-EN), a deep learning algorithm to diagnose eye disorders effectively. This technique incorporates a spatial channel attention mechanism to enhance its ability to identify specific lesion regions and distinguish between various diseases. The HDR-EN model uses transfer learning methods to address the issues of imbalanced sample classes. Fractal dimension (FD) analysis is used to quantify differences in the retinal vessels, focusing on the optic cup and disc areas. FD values of the optic cup and disc play a crucial role in identifying HDR by revealing noticeable differences from healthy images, which are essential for precise classification. The results demonstrate high efficiency, with an average AUC of 0.98, an accuracy of 98%, a specificity of 96%, and a sensitivity of 95%. These findings indicate that the HDR-EN classifier could significantly diagnose HR and DR. In summary, the HDR-EN technique represents a deep learning-based method that offers improved accuracy and enhanced efficiency in detecting and categorizing retinal disorders.

The real-time Face Recognition (FR) techniques are limited to situations where only one face is visible in close proximity to the camera. Sometimes, the people under surveillance may not be directly facing the camera while walking. However, there is a growing need for FR technology to accurately identify multiple faces in crowded areas even when individuals are not facing the camera. An AI-based multiple-face recognition (MFR) system has been developed to improve performance by incorporating an increased number of pose variation samples. The developed system utilizes a pre-trained FaceNet architecture, which converts the face images into a compact Euclidean space of dimension 128 × 1. This study focuses on improving accuracy and decreasing computation time for multiple faces within the field of view. Results show that the FaceNet model has a high recognition rate of 99.7%. The system can recognize up to 10 faces in the field of view at a computational time of 1.21 s.

Tremors, involuntary rhythmic oscillations of body parts, can significantly impact individuals' quality of life and pose diagnostic challenges. This study focuses on differentiating among rest tremor, essential tremor, and cerebellar tremor, each associated with distinct neurological pathways and clinical characteristics. Clinicians face considerable challenges due to the similar symptoms exhibited by these tremor types. This paper aims to distinguish the characteristics of these tremors using an advanced algorithm developed with the CVZone library, based on the Mediapipe framework. The developed algorithm differentiates tremors by considering pose variations with 90% accuracy on PT data, 87.5% accuracy on ET data and 85.7% on CT data taken from multiple sources. The binomial test on the results demonstrated the algorithm's capability to differentiate tremors with a statistically significant p-value of 0.00039571, indicating robust performance in correctly identifying tremor types.

The Process of Grading and Segregation of chicken eggs in various breeds plays a Vital role to assess the standards of eggs that can enable the market to provide Quality eggs to the consumers. The current traditional grading mechanisms are manual and carried on observable traits like shell color and form, that are prone to human mistakes. These manual methods not only make the process cumbersome, but also raises the management cost. To overcome these challenges, this research aimed to differentiate Rhode Island red and white leghorn chicken eggs using non-destructive hyperspectral imaging techniques. Unlike manual inspection or invasive tagging, a nondestructive hyperspectral imaging setup captures a wide range of spectral information from chicken eggs, identifying minor differences in color and texture among different egg breeds in poultry farming. In this experiment, a sample set of 72 Rhode Island red and 72 white leghorn chicken eggs has been tested by using hyperspectral imaging. Spectral features of each breed say Rhode Island red and white leghorn chicken eggs have been identified to differentiate both the breeds.

Breast cancer is one of the deadliest diseases among women ranging from young to old and second common disease that leads to death for women after lung cancer. In this paper investigates the effectiveness of thresholding, edge detection, region-based, and watershed-based segmentation techniques on breast thermal images captured from five distinct perspectives: front view, left at 45°, left at 90°, right at 45° and right at 90° views. The main objective of this research is to identify an appropriate segmentation technique that can improve the accuracy of breast cancer detection in noninvasively through thermal imaging. Each segmentation technique is applied on Region of Interest (ROI) breast thermal images to accurately delineate the breast abnormalities. Results suggest a suitable segmentation that is suitable to analyze breast thermal images with particular angle.

This paper presents a Model-Based Design (MBD) approach for the design and control of a customized manipulator intended for drilling and positioning of dental implants accurately with minimal human intervention. While per-forming an intra-oral surgery for a prolonged duration within a limited oral cavity, the tremor of dentist's hand is inevitable. As a result, wielding the drilling tool and inserting the dental implants safely in accurate position and orientation is highly challenging even for experienced dentists. Therefore, we introduce a customized manipulator that is designed ergonomically by taking in to account the dental chair specifications and anthropomorphic data such that it can be readily mounted onto the existing dental chair. The manipulator can be used to drill holes for dental inserts and position them with improved accuracy and safety. Further-more, a thorough multi-body motion analysis of the manipulator was carried out by creating a virtual prototype of the manipulator and simulating its controlled movements in various scenarios. The overall design was prepared and validated in simulation using Solid works, MATLAB and Simulink through Model Based Design (MBD) approach. The motion simulation results indicate that the manipulator could be built as a prototype readily.

An eco-friendly mechanism for disposing of sanitary waste is proposed by the current invention. Sensors, a microcontroller, and an incinerator make up the system. When operated by the microcontroller, the incinerator is set to accept an input signal from an IR Sensor installed inside a dispensing inlet. while it is being controlled by a microcontroller, which is where sanitary waste is distributed. Following that, an input line transports this sanitary waste to a burning chamber. To burn sanitary waste and produce gases and ash, which are then expelled through chimneys and collected in ashtrays, respectively, the waste is placed in a burning chamber. The use of UV light, charcoal, and cotton in chimneys helps to cut down on the discharge of odor-causing gases as well as carbon dioxide.

Malignancy, one of the leading causes of death worldwide, accounts for 9.6 million deaths in 2018. Around 1 out of 6 deaths are the direct result of the malignancy. Clinicians claim that age and breast density are two preliminary factors increasing the risk of cancer. The mortality rate brought about by malignant growth in low and high income countries is, for the most part, around 70%. Imaging techniques play a vital role in the detection, and staging, thereby helping in treatment decision making. This review paper presents a comprehensive survey involving a literature study about the evo-lution and efficacy of various breast cancer detection techniques. This work studies various procedures of imaging techniques such as mammograms, ultrasound, MRI, PET, CT, Terahertz Spectroscopy, Raman Spectroscopy, Optical coherence Tomography, Mass spectroscopy, diffuse reflectance spec-troscopy, and Infrared Thermography. Since cancer is a complicated illness with diverse pathophysiol-ogies, numerous modifications of the fundamental detection approach employed in each of these mo-dalities have been performed throughout the years to increase the detection efficiency. This paper co-vers basic preliminary results with FFPE breast cancer blocks of malignant and normal subjects using THz Techniques that are presented as proof of concept to carry out further research.

Retinal vasculature feature extraction plays a critical role in the diagnosis and treatment of systemic conditions, particularly in the cases of diabetic retinopathy (DR). This research introduces an algorithm that utilizes segmented blood vessels in retinal images to identify and differentiate five stages of DR, including mild, moderate, severe and proliferative. The algorithm effectively extracts retinal blood vessels by integrating morphological operators and matched filters, yielding a more precise output. The algorithm's performance is evaluated using the database IDRiD, demonstrating precision and sensitivity scores comparable to those of a trained observer. A box-counting method was incorporated to measure the fractal dimension (FD) of DR-segmented vessel images at various stages to enhance the accuracy of DR staging. The FD analysis was applied to both thick and thin segments of the blood vessels, enabling the assessment of accuracy, sensitivity and specificity. The results indicate that the algorithm successfully identifies the different stages of DR with an accuracy of 93.65% for the mild stage, of 93.33% for the moderate and severe stages and of 92.71% for proliferative DR compared to the images without DR. The study reveals that the variation in FD between the thick and thin vessel components can be an effective biomarker for identifying the different stages of DR, contributing to a better understanding of disease progression. By combining morphological operators, matched filters and fractal dimension analysis, this research presents a promising approach for specialists involved in diagnosing and treating DR, eventually leading to improved patient care and consequences.

An electrocardiogram (ECG)can identify any cardiac activity abnormalities. The electrical signal produced when the heart muscles contract and relax, or the ECG, is contaminated with power line and instrument noise during recording. Wavelet algorithms can be used to denoise the ECG signal. For a successful denoised ECG, it is crucial to adjust the wavelet decomposition level. In this study, wavelet transformation technique is used to simulate noisy synthetic ECGs and denoise them. At each stage of decomposition, the Mean Square Error (MSE) between the clean synthetic ECG and denoised synthetic ECG is computed. According to the examination of MSEs, the level of wavelet decomposition can be optimized to produce an efficient denoised ECG output.

The application of Terahertz electromagnetic waves to diagnose oral cancer was investigated. A single case of formalin-fixed oral squamous cell carcinoma (malignant), ameloblastoma (benign), and odontogenic keratocyst was examined using terahertz pulsed spectroscopy in the frequency span of 0.1-2 THz. The measured absorption coefficient, refractive index, and the extinction coefficient were reported to be high for malignant samples than benign and cyst. The THz results are validated with hematoxylin and eosin-stained microscopic images. The results demonstrate that the THz signal was shown to be consistently higher for the malignant sample compared to benign and the cyst. These results indicate that THz signals responded to the cell density by eliminating the effect of water.

Object segmentation is the inevitable result of object recognition and semantic segmentation when moving from coarse to fine inference. Despite many efforts in segmenting instances, the quality of masks remains unsatisfactory. Because of the low pixel-to-edge ratio and the restricted spatial resolution of the feature map, the boundaries of the predicted version masks are typically inaccurate. Defining the object boundaries at the detailed pixel level is essential for instance segmentation. This paper aims a simple, yet conceptually effective, mask-based RCNN framework to generate accurate segmentation masks to overcome these challenges. Additionally, it can extract the foreground objects from the background and run at high inference speeds to meet the growing demand for accurate, real-time computer vision applications. This study introduces a new database called RSBFRS that includes unconstrained images and videos. Thus instance segmentation using Mask RCNN was implemented to mark/extract precise boundaries of moving person in a video and the maximum prediction score value of 0.9983 obtained. This efficient technique can serve as a basic support for future studies on instance-level detection and recognition.

The proposed robot aims to serve as a personal assistant for visually impaired people in obstacle avoidance, in identifying the person (known or unknown) with whom they are interacting with and in navigating. The robot has a special feature in truly locating the subject’s location using GPS. The novel feature of this robot is to identify people with whom the subject interacts. Facial detection and identification in real-time has been a challenge and achieved with accurate image processing using viola jones and SURF algorithms. An obstacle avoidance design has been implanted in the system with many sensors to guide in the correct path. Hence, the robot is a fusion of providing the best of the comfort and safety with minimal cost.

Thermography is a technique that offers high portability and poses absolutely no risk to the patient. Home monitoring of temperature has helped in the early diagnosis of foot ulcers in diabetic subjects. It is observed that the cost of a camera increases with the resolution, due to the enhancements made in the computation power, computer-based algorithms, and processing software. A need for an inexpensive, portable and easily usable self-monitoring tool for frequent examination and communication was achieved by developing this low-cost thermal imaging system. In this work, a low-cost, light-weight, and user-friendly thermal camera has been developed that can be used as a preliminary diagnostic tool for a wide range of applications such as skin cancer detection, monitoring thyroid disorders, identifying diabetic foot ulcers and fever screening. The device can be easily accessible and affordable to local clinics and homes, thereby making thermal imaging as a viable option for detection of abnormalities in temperature distribution over the human body in a non-contact and pain-free manner.

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.

This project aims to develop a self-powered, multipurpose system designed to measure an array of biomedical parameters like, SPO2, temperature and pulse rate which serves the need for Telemedicine application. The IOT based biomedical instrument helps in bridging the gap between a doctor and his patient by providing real time diagnosis and treatment through telecommunication technology. This instrument is designed to target individuals who are in fields which go hand in hand with high altitudes and having walking as the only means of transport. Fields like the armed forces, trekking etc. come in this bracket. Piezoelectric sensors are useful for such fields as these require a whole lot of activity but do not usually have active power sources to charge their health monitors. National defence personnel, who have to take care of their bodies in places like Siachen and other difficult terrains, require wearable health devices, now more than ever to continually monitor their health. Hence the design of a self-powered wearable health monitoring device proved to be of critical importance. As the device fulfils telemedicine requirements, it also provides sophisticated health care resources with solutions, allowing the soldier to return to duty and avoid medical evacuation.

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.

This work aims to design a High Efficient Maximum Power Point Tracking (MPPT) Solar Inverter. A boost converter is designed in the system to boost the power from the photovoltaic panel. By this experimental setup a room consisting of 500 Watts load (eight fluorescent tubes) is completely controlled. It is aimed to decrease the maintenance cost. A microcontroller is introduced for tracking the P&O (Perturb and Observe) algorithm used for tracking the maximum power point. The duty cycle for the operation of the boost convertor is optimally adjusted by using MPPT controller. There is a MPPT charge controller to charge the battery as well as fed to inverter which runs the load. Both the P&O scheme with the fixed variation for the reference current and the intelligent MPPT algorithm were able to identify the global Maximum power point, however the performance of the MPPT algorithm was better.

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 pre-programmed.

India’s growing economy and increasing demand for electricity has strongly increased challenge for grid-based systems for effective generation and transmission of energy. As energy management has become a worldwide concern, the heating, ventilating and air conditioning (HVAC) systems which constitute a significant part of annual total energy consumption in the world needs to be controlled. This work aims for the better energy management of HVAC systems by using different control strategies. Conventional controlling techniques like ON-OFF, Proportional, PI, PID controllers are used and compared with Artificial Intelligence techniques namely Fuzzy controller for the efficient use of HVAC system. The hardware circuit, QNET-HVAC has been taken as model for real time HVAC systems and control techniques are implemented for better temperature management.

Communication between the deaf and non-deaf has always been a very cumbersome task. This paper aims to cover the various prevailing methods of deaf-mute communication interpreter system. The two broad classification of the communication methodologies used by the deaf –mute people are - Wearable Communication Device and Online Learning System. Under Wearable communication method, there are Glove based system, Keypad method and Handicom Touch-screen. All the above mentioned three sub-divided methods make use of various sensors, accelerometer, a suitable micro-controller, a text to speech conversion module, a keypad and a touch-screen. The need for an external device to interpret the message between a deaf –mute and non-deaf-mute people can be overcome by the second method i.e online learning system. The Online Learning System has different methods under it, five of which are explained in this paper. The five sub-divided methods are- SLIM module, TESSA, Wi-See Technology, SWI_PELE System and Web-Sign Technology. The working of the individual components used and the operation of the whole system for the communication purpose has been explained in detail in this paper.

The main aim of this paper is to decrease the mortality rate caused due to fire accidents that occur in locomotives. This is the impetus behind the design of our proposal. The main objective of this proposal is to rescue lives from such fire accidents at the earliest as possible, especially designed for moving locomotives. The over all proposed system has been segmented into four main parts: i) Fire Suppressing (Extinguishing) System ii) Actuating Mechanism iii) Alarming System iv) Telemetry System. The compartments are fitted with temperature sensor-LM35, smoke detector-21007581,microprocessor-MSP430FR5729 (Launch Pad),circuit breaker- TPS2311, solenoid vale, GSMmodule-SIM900,anda buzzer. The temperature sensor fitted in every compartment detects a temperature (>70oc), and these signals are sent to the microprocessor. Once the signals are received, the developed rescue system starts its action. The first action plan of the developed system is to pull the chain and stop the moving locomotive. Further the compartment is isolated from electrical supply by activating the circuit breaker. A moving train can be the main cause for the advancement of fire to the next compartments, and thereby increasing the death toll. Thus, a need arises to bring the fire under control. This is taken care by the above discussed developed chain actuating mechanism and the circuit breaker in this paper. Fire extinguishing system consists of a solenoid valve. Water from the cubicle tank is directed into the compartment via the solenoid valve to suppress the fire. GSM module is used for transmission of data to the official at a near by station house. The buzzer (alarm) performs the task of alerting the people.

This paper is aimed to cater the needs of rural areas, where it helps in comprehensive diagnosis of the patient without the doctor in the same geographical location. The developed system is generally composed of 4 main configurable elements-ECG Machine, Developed fuzzy decision support system and telemedicine communication protocol. A Fuzzy based decision support system has been developed for identifying different arrhythmias Sinus bradycardia, sinus tachycardia, supraventricular tachycardia (SVT), ventricular tachycardia (VT), Atrial flutter, ventricular fibrillation, Sinus exit block, Junctional escape beat, sinus arrhythmia, which forms the core of the paper. In this paper, the subject's Ventricular heart rate, QRS Width, QT and PR Interval collected from ECG waveform were passed to fuzzy analyzer developed. The developed fuzzy decision system helps in diagnosing and in the case of any abnormality it extends its help with a telemedicine application, by using web publishing tool. So that the doctor can pass the medical advice to the patient within the time. The developed fuzzy algorithm has been evaluated using mit-bih arrhythmia database and real time ECG's from 94 individuals. The fuzzy analyzer proved to be 90.8% accurate and also Web publishing tool has been successfully implemented.

The aim of this study is to determine the risks of various subjects to type 2 Diabetes and its dependence on the different subject records. A Fuzzy based system was designed to find the risk scores for diabetes based on risk score derived from Chennai Urban Rural Epidemiology Study (CURES). The risk score that has been adapted into the system is referred to as Indian Diabetes Risk Score (IDRS). The variables employed in it are age, gender, waist, exercise and history of diabetes. A database of subject records was collected from hundred random individuals from southern regions of India. A comparative study was performed on these records between the normal and fuzzified risk score based on IDRS. The program has been designed using Lab VIEW with Fuzzy System Designer being used for fuzzy rule execution. The details are transmitted online through web page to the physicians who can provide assistance in prevention of diabetes. The obtained risk scores of the subjects are used to improve the lifestyle and delay the onset of diabetes to the maximum possible. This system can be implemented in rural regions where experienced medical assistance may not be available. This system would form an ideal part of the current developments in medicine where physical physician presence is not required due to the buttress provided by advancements in computer technology. The aim of this study is to determine the risks of various subjects to type 2 Diabetes and its dependence on the different subject records. A Fuzzy based system was designed to find the risk scores for diabetes based on risk score derived from Chennai Urban Rural Epidemiology Study (CURES). The risk score that has been adapted into the system is referred to as Indian Diabetes Risk Score (IDRS). The variables employed in it are age, gender, waist, exercise and history of diabetes. A database of subject records was collected from hundred random individuals from southern regions of India. A comparative study was performed on these records between the normal and fuzzified risk score based on IDRS. The program has been designed using Lab VIEW with Fuzzy System Designer being used for fuzzy rule execution. The details are transmitted online through web page to the physicians who can provide assistance in prevention of diabetes. The obtained risk scores of the subjects are used to improve the lifestyle and delay the onset of diabetes to the maximum possible. This system can be implemented in rural regions where experienced medical assistance may not be available. This system would form an ideal part of the current developments in medicine where physical physician presence is not required due to the buttress provided by advancements in computer technology. © 2013 Science Publication.

The aim is to design a complete online, automatic, intelligent and independent insulin delivery system. The proposed system calculates the amount of insulin by formulation based upon two linguistic factors i.e. weight and blood glucose levels. These factors have been used to develop a rule based fuzzy system in LabVIEW. The fuzziness brings a better regulation into the regime than a non fuzzy system as small changes in quantities of insulin can bring about effective command on the glucose levels. A user friendly interactive webpage has been designed that inculcates the fuzzy system for providing online doctor consultation. The insulin delivery has been simulated in LabVIEW where the quantity of insulin delivered to the patient can be controlled automatically or by the doctor. The system has been verified by taking a random sample of hundred insulin dependent individuals to test the effectiveness of the system. The developed fuzzy system was found to be more accurate than a non fuzzy system. This study is modeled for soluble human insulin type. The implementation of this system in real time with insulin pump will enable those in inaccessible areas and intensive care units to control the glucose levels automatically in an efficient manner. © 2013 Science Publications.

At present situation the mortality rate has been increased due to the rise in blood pressure. BP is the parameter which doesn't abide by a single range, but it depends upon the factors like age, family history, smoking, alcohol intake, pregnancy. Factors like smoking and intake of alcohol do have controllable aspects, which helps in providing a focus for patients looking to minimize their chances of developing hypertension. The importance behind this work is the development of an intelligent, accurate diagnosis of hyper and hypo blood pressure. Intelligent system used here is the fuzzy system which takes age, gender, body mass index, smoking habit, alcohol intake, pregnancy condition, family history as linguistic variables and diagnoses whether the person is having hyper and hypo tension. Apart from diagnosing, this system acts as a risk analyzer, i.e. it gives the risk factor of the subject proned to get BP in future. So by adapting a change in the life style the risk values of getting the blood pressure can be reduced. The unique feature of this system is that an individual can have self assessment of his bp condition without doctor in the nearby geological area. The analyzation of blood pressure value is done by fuzzy system designer available in LabVIEW. © 2012 IEEE.

Diabetes mellitus is a disease which needs constant and continued attention. The treatment of diabetes is patient specific and extreme care and caution is necessary for effective monitoring. The amount of insulin to be given to patients should be exact to their needs for obtaining the best results. The proposed system calculates the insulin required by using patient blood glucose levels and weight using fuzzy analysis. This quantity of insulin can be delivered to the patient using an insulin pump. The process has been simulated in LabVIEW. The insulin levels can be sent to the doctor by online access. It has been implemented and tested using each of the three different protocols web publishing, TCP-IP and datasocket connections separately. The doctor can advise further treatment and also suggest changes to the insulin quantity according to current glucose levels. The patient can also post questions for doctor consultation. © (2012) Trans Tech Publications, Switzerland.

At present scenario BP is the emerging factor of mortality. This is the impetus behind the work that had been developed for an intelligent, accurate, continuous diagnosis of hyper blood pressure and hypo blood pressure. Here fuzzy system plays a role of intelligent system which takes age, gender, body mass index, smoking habit, alcohol intake, pregnancy condition, family history as linguistic variables and diagnoses whether the person is having hypotension or hypertension. Apart from diagnosing, the developed system also gives the risk level of getting hypotension or hypertension in future by taking the above linguistic variables. So by improving his/her's life style he/she can have a better control on blood pressure which can extend his or her lifespan. After diagnosing the system is programmed in such a way that it alarms immediately if there is an abnormability in the BP measurement so this helps in self assessment of blood pressure for the individual itself. This system also has an inbuilt provision of sending the analyzed data to the doctor, so that the person can get an online medication which saves the time, cost of travelling, so that the instant medication can be given to prevent the forthcoming adverse damage as early as possible. This system has been developed by using LabVIEW. The BP value is fed into the pc where the analysis of blood pressure value is done by fuzzy system designer available in LabVIEW. The fuzzy system analyzes the current status of the person by taking all the dependent linguistic variables and intelligently diagnose the BP status of the patient by itself. It also responds to the fluctuations in BP. This diagnosed data is send to the physician using web publishing tool in lab view. © 2011 IEEE.

Due to changing trends, there is an increasing risk of people having Cardiac Disorders. This is the impetus behind, for developing a system which can diagnose the cardiac disorder and also risk level of the patient, so that effective medication can be taken in the initial stages. In this paper, Atrial rate, Ventricular rate, QRS Width and PR Interval are extracted from ECG signal, so that arrhythmia disorders- Sinus tachycardia (ST), supra-ventricular tachycardia (SVT), ventricular tachycardia (VT), junctional tachycardia (JT), ventricular and Atrial fibrillation (VF & AF) have been diagnosed with their respective risk levels. So that the system acts as an risk analyzer, which tells how far the subject is prone to arrhythmia. LabVIEW signal express is used to read ECG and for analysis this information is passed to the Fuzzy Module. In the Fuzzy module Various "If-then rules" have been framed to identify the risk level of the patient. The Extracted information is then published to the client from the server by using a Online publishing tool. After passing the report developed by the system to the doctor,he or she can pass the medical advice to the server, i.e. generally the system where the patient ECG is extracted and analyzed. © 2011 Springer-Verlag.

This paper introduces a method of automatic multilevelcar parking system that parks the vehicle in reduced space using the conditions based on fuzzy logic controller. It possibly controls the traffic and avoids traffic congestion .This automatic multi-level vehicle stacking system also has online access to book the parking slot in advance which is interfaced with the LabVIEW. The vehicle placed at the entrance point is parked automatically according to the availability of slots. The car park consists of number of slots based on three different sizes. The car is moved to the particular slot with help of an elevator placed at the centre of the parking system. The elevator consists of an arm that moves along the vertical axis and can rotate along the multi stack parking system. LabVIEW is used to check the availability of slots, total number of cars parked the movement of the elevator and the position of the arm. The programming software tracks the location of the vehicle and returns the vehicle on request from the patron by the access system installed. Thus the paper shows the description of the automatic multi-level vehicle stacking with microcontroller and fuzzy mechanism using LabVIEW to achieve conventional parking system. ©2010 IEEE.

LabVIEW (Laboratory Virtual Instrument Engineering Workbench) is a graphical programming environment suited for high-level or system-level design. The primary difference between 'natural' instrumentation and virtual instrumentation is the software component of a virtual instrument. The software enables complex and expensive equipment to be replaced by simpler and less expensive hardware. This paper describes about the development and implementation of an ON/OFF controller for online temperature control experiment in real-time using the DataSocket communication protocol in LabVIEW. The proposed system is connected to the server computer using a Data Acquistion (DAQ) board. The objective of this experiment is to maintain the temperature inside a wooden box that is heated by a light bulb at some desired set-point value, which is selected by a remote client. The software for both the client and server computer is developed using DataSocket protocol in LabVIEW. The remote clients can monitor and control the temperature.