Volume 9 | Issue 10

 Abstract

In this paper a new electric braking system based on stopping time and energy regeneration is proposed for a brushless DC (BLDC) motor driven electric vehicle (EV). The new braking system is developed by integrating various regenerative methods and plugging. Aside from the present performance dimensions such as braking torque, boost ratio and maximum conversion ratio; stopping time and energy recovery for numerous methods are analysed in diverse running conditions. It is observed that the stopping time is less for plugging and increasing in the order two, three and single switch method. Besides, energy can be recovered more in single and three switch method. Based on these performances, a new braking strategy is put forward which combine all the regenerative braking methods including plugging and switch among themselves based on the depression of brake pedal. The effectiveness of the proposed method is shown in simulation results.

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 Keywords

Electric Vehicle, Regenerative Braking , BLDC Motor

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 Abstract

Gaussian belief propagation algorithm (GaBP) is one of the most important distributed algorithms in signal processing and statistical learning involving Markov networks. It is known that the algorithm correctly computes marginal density functions from a high dimensional joint density function over a Markov network in a finite number of iterations when the underlying Gaussian graph is acyclic. Analysis of convergence rate is an important factor. GaBP algorithm is shown to converge faster than classical iterative methods like Jacobi method,successive over relaxation.It is more recently known that walk summability approach extends for better convergence result.Convergence rate analysis of GaBP for markov network using walk summability approach and theoretical study of convergence rate analysis using laplacian operator are considered in this work.

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 Keywords

Gaussian belief propagation,Markov network,Convergence rate,Walk summability,Laplacian operator

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In speech enhancement, one of the most important tasks is the removal or reduction of background noise from a noisy signal. This paper discusses about various speech enhancement techniques and a general framework proposed to estimate short-time spectral amplitudes (STSA) of speech signals in noise by joint speech detection and estimation to remove or reduce background noise, without increasing signal distortion. By combining parametric detection and estimation theories, the main idea is to take into consideration speech presence and absence in each time-frequency bin to improve the performance of Bayesian estimators. The observed signal is frequently segmented, windowed and transformed into the time-frequency domain. Then, the clean signal coeffcients are usually retrieved by applying an enhancement algorithm to the noisy observations in this domain.

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 Keywords

speech enhancement, parametric method, joint detection and estimation, Bayesian estimator, minimum mean square error (MMSE).

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 Abstract

The need for home security alarm systems nowadays is a serious demand. As the number of crimes are increasing every day, there has to be something that will keep us safe. We are all aware of the high end security systems present in the market but they are not easily available to everyone. We therefore intend to provide a solution by constructing a cost efficient electronic system that has the capability of sensing the motion of the intruders and setting off the alarm. The basic idea behind this project is that all the bodies generate some heat energy in the form of infrared which is invisible to human eyes. But, it can be detected by electronic motion sensor. The project involves the use of Arduino, motion sensor, buzzer, LCD display and a simple program. The sensor detect any motion in its permissible range and triggers the alarm. It will also send the signal to Arduino which processes the signal and set off the alarm along with detection message on display. With this system we can easily set up a security alarm in our home for unwanted intruders.

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HOME SECURITY ALARM SYSTEM USING ARDUINO

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Text recognition is a major area of experimentation under image processing domain. It is a process by which the system locates any kind of text is present and extract them from an image. The extracted text must be converted to human readable form after several processing and if required is classified them into meaningful classes based on the content present. The platform used here in discussion is MATLAB. This paper provides a detailed study on the evolution of text detection in natural images. It analyzes and also discusses the different methods to overcome existing challenges in text detection. This paper presents the different types of datasets which are used to identify text from natural images and comparative study of different text detection methods. The paper is concluded by a method to recognize and classify the multi-oriented text present in an image based on MSER and CNN.

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 Keywords

MSER (Maximally Stable Extremal Regions), CNN (Convolution Neural Network)

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The aim of this paper is to explore an efficient image segmentation algorithm for medical images to reduce the physicians interpretation of Computer Tomography (CT) scan images. Medical imaging techniques which are modern generate large images that are extremely difficult to analyze manually. In this paper, image preprocessing is done using adaptive median filter and contrast limited adaptive histogram equalization .For Segmentation five important methods are used and they are k-means clustering, K-median clustering ,Particle swarm optimization(PSO), inertia-weighted particle swarm optimization(IWPSO), and guaranteed convergence particle swarm optimization(GCPSO).We will verify it using matlab and will get that GCPSO will give more accuracy of about 95.89%.

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Preprocessing of Lung Cancer Detection Using Image Segmentation by means of Evolutionary Algorithm

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Support Vector Machines (SVM), Melanoma, Deep learning, Dermoscopy, Benign and Malignant.

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FTSI System

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One of the prime reasons for vehicular accidents is due to undetected potholes and road humps.. Another reason for a huge number of accidents is drunk driving. Even a small amount of alcohol in blood can lead to mid-body imbalance. Also, even after an accident occurs timely medical is not given. Due to these reasons numerous lives are lost. Through this project we will be trying to provide a solution to these problems so that there is better safety for people inside and outside the vehicle. We are trying to achieve this by using transmitter-receiver modules. The transmitter section will be placed near the obstacles on the road which will provide the alert signal. Receiver section on the car will receive this and alert the driver. Drunk driving shall be prevented by an engine-lock system. Also, new potholes can be identified using the ultrasonic sensor which will be placed on the car.

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 Keywords

Accident Prevention, Alcohol Sensing, Engine lock, Ultrasonic Sensor, Pothole.

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 Abstract

The major problems that are faced by the cultivators are the diseases effecting on their crops. There is no such an authenticated and globalized technique for the detection and diagnosis of plant diseases. In this paper a Mobile diagnostic system for Chili plant was developed as an example to solve this problem. Here we take Chili plant as an example. Chili is one of the widely used as well as cultivated crop not only in our home garden, but also through out our country. India is one of the major producer of Chili crop among the whole world. With the rapid development of mobile service computing, it have an increasingly important role in our daily lives. Disease detection can be more user friendly when we utilize the mobile service computing technique for this purpose. So here we build an image dataset of 4 kinds of Chili diseases along with healthy leaves and healthy fruits. They are collected from the home garden. Then realize a Mobile diagnostic system for Chili diseases by constructing a Deep convolutional neural network (D CNN). The system realized using an Android app in our Mobile device, with which users can upload images and receive diagnostic results. Here the experimental results shows that the detection accuracy of the chili diseases exceeds 90 % and we can detect and receive diagnostic results in a few minutes using this system.

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 Keywords

Deep Convolutional Neural Network, Treatment advices, Mobile service computing, Android app

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 Abstract

The COVID-19 pandemic has spread throughout the world and changed all facets of our everyday lives dramatically The conventional method of keeping people at a safe distance in the Covid-19 Standard Operating System could not ensure everyone obeys the rule. An automatic social distancing system needs to be created to assist and train individuals to stay at a safe distance of at least 1 m. This paper proposes a wearable social distancing detector that uses a microcontroller with an ultrasonic sensor to detect the distance between two persons and provides a warning if the person fails to obey the rule. The system could perform social distancing detection accurately and can assist in theArduino UNO.

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 Keywords

Covid-19 , Ultrasonic Sensor ,Arduino UNO

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Covid-19 is the global pandemic that is adversely affecting the human life in this scenario. World is running for the overcoming this pandemic and for finding better treatments for it. Detecting this disease is major task for medical practitioners now a days. In this study we are focusing on preprocessing stages of multimodal imaging datas, mainly x-rays for the detection of this disease. And this preprocessed images are very helpful for using it in transfer learning methods to detect covid-19. We propose this preprocessing stage for creating an image dataset, which is trustworthy for developing and testing deep learning models for the detection of this disease. Unwanted noise in the images is removed in this stage so that detection can be made using specific features from them. As well as x-ray ,other two multimodal image datasets can be used to preprocess.

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 Keywords

Covid-19 detection, Image preprocessing, Multimodal imaging data

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Clean water is vital to our health, communities, and economy. People depend on clean water for their health. Our communities are impacted by water contamination. The macroscopic waste present in the water bodies is currently creating a high mode of threat to the use of this water for the people living in the corresponding areas. Abundant dumping of waste in to the water could affect the aquatic life in a threatening way. Presently there are a few methods existing for the purification of aquatic environment. But most of it is limited to micro-purification, chlorination and oxygenation of the water life. Most of the methods used to remove macroscopic waste are manual or machines which are manually operated. This method put forward a fully automated system to monitor and remove all the waste from the water bodies. A robot is designed to sense the waste using ultrasonic sensors and programmed to remove the macroscopic waste present in water.

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HC-sr 04 Ultrasonic ranging module, Gear motor, Arduino UNO, Robotic arm.

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Leaf Disease Detection Using Deep Convolutional Neural Networks Review and New Approach by VGG19.

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Identification and treatment of chronic wounds (CWs) are considered economic and social challenges, especially with respect to bedridden and elderly persons. CWs do not follow a predictive course of healing within a particular period. Their treatment and management costs are very high. Also,CWs decrease quality of life for patients, which cause severe pain and discomfort. The process of chronic wound healing is very complex and time consuming. Quantification of wound size plays a vital role for clinical wound treatment as the physical dimension of a wound is an important clue for wound assessment. The current techniques for wound area measurement are the ruler method and tracing which is mainly based on visual inspection, thus are not very accurate as well as time- consuming.. A computerized wound measurement system can provide a more accurate measurement, reduce bias and errors due to fatigue and can potentially reduce clinical workload .Here a efficient method for wound area segmentation based on ANN with color and texture Feature . The framework was trained and tested using 358 RGB images from Medetec wound database .Here we also proposed a simple but efficient method for wound area segmentation The satisfactory results obtained by this system make it a promising tool to assist in the field of clinical wound evaluation and suggest treatment .

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 Keywords

ANN,ColorFeature,ChronicWound.

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 Abstract

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 Keywords

COVID-19, SARS-CoV2, Blockchain, Artificial Intelligence, WHO

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As the demand in Machine Learning and AI keeps growing, new technologies will keep coming in the market which will impact our day-to-day activities, and one such technology is Virtual Assistant Bots or simply Chatbots. Chatbots have evolved from being Menu/Button based, to Keywords based and now Contextual based. The most advanced among all of the above is contextual based because it uses Machine Learning and Artificial Intelligence techniques to store and process the training models which help the chatbot to give better and appropriate response when user asks domain specific questions to the bot. The idea is to create a medical chatbot that can diagnose the disease and provide basic details about the disease before consulting a doctor. This will help to reduce healthcare costs and improve accessibility to medical knowledge through medical chatbot. The chatbots are computer programs that use natural language to interact with users. Our project focuses on providing the users immediate and accurate prediction of the diseases based on their symptoms. For the prediction of diseases, we have used Decision tree algorithm. Chatbots can play a major role in reshaping the healthcare industry by providing predictive diagnosis.

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 Keywords

Disease Prediction, Decision Tree Algorithm, Chatbot.

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 Abstract

Dependable individuals checking and human recognition is a significant issue in visual observation. Lately, the field has seen many advances, however, the arrangements have a few limitations: individuals should be moving, the foundation should be straightforward, and the picture goal should be high. We expect to foster a powerful strategy assessing the number of individuals and find each individual in a picture with confounded scenes. The primary focal point of this work is to discover intends to successfully manage the previously mentioned continuous issue utilizing the YOLO calculation to recognize the individual people in a video outline. The dataset is thus used to investigate the individual people and the tally of individual people is shown in the versatile application. Creating to make a productive framework to keep away from the surge in establishments, associations, and surprisingly open spots will help individuals better using time productively.

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Object Detection, YOLO, Firebase.

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An own house is a dream of many people. But when it comes to planning and construction of a house or building the challenges and troubles that we need to encounter are many. Which includes lack of trustworthy contractors, unavailability of laborers, unavailability of affordable and experienced architects or engineers, etc. If the owner is abroad or out of state the problem even worsens. Most of the people abroad normally assigned a contractor for the whole construction. But whether the contractors are trustworthy or not, we don't know! Many contractors quote very high and compromise the quality of raw materials. The construction of a home is a complex process. And that is where this application has come into the picture. This application includes everything for the construction of a building. From planning to flooring and painting, everything for the construction and accomplishment of a building is included. The customer can handle everything online. If the customer is abroad or out of state they can take part in each step of the construction like the selection of workers, engineers, materials online. Through this app, we aim to provide trustworthy, affordable, and hardworking employees and also help us to build our home without any complexities and delays. Customers can find all kinds of laborers that are needed for construction in this application. Any individual with some funds and this application can build their home without any complexities and with comfort.

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 Keywords

Android App, Home Construction.

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 Abstract

Poultry production in our state is still at meager development as proper updated information regarding the existing farms, feed availability and stock is not available properly. A need for a system that monitors these requirements and aids both farmers and customers in carrying out proper trading arises. Hence, we propose an application for the same based on blockchain technology which shall ensure data transparency and provenance tracking by ensuring legibility of data and keeping up proper updated information. The function of this application is mainly aimed to encourage the poultry famers of Kerala and fulfill their basic needs. Through this system the farmers will get to know about availability of feeds and ensure the quantity of the same. We also intend to provide the information about the total number of poultry farms or chicken centers and poultry feeding shops in Kerala along with details of each farm like the number of stocks to produce meats or eggs for food; the location of these farms and shops aiding in faster purchasing; the price of meat, eggs and feeds ensuring that price is not overruled by any one producer. Users shall rate the quality of shops as feedback to help identify quality shops and shall also promote good service. The availability of stock of feed/products can be monitored timely so as to cope with sudden unavailability or any adverse situation. Moreover, by checking these data the government can verify whether the farmers as well as their services are in a state of emergence or not. Thus, they can identify issues and bring about solutions to overcome the current scenario. Gradually, we can increase the number of farms in Kerala. Using blockchain, a distributed ledger for handling data rather than centralized methods will help in having a secure and tamper proof way of maintenance. The chance of overriding data and deleting or modifying data to create redundant or wrong information can be avoided by this.

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 Keywords

Blockchain, Ethereum, Smart-Contract.

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