Digital libraries are platforms that allow users to access structured information in a knowledge-based repository of data and access. In the end, the availability of good content and easy accessibility and use will dr...
Digital libraries are platforms that allow users to access structured information in a knowledge-based repository of data and access. In the end, the availability of good content and easy accessibility and use will drive the production and growth of digital libraries. Significant advancements in IT and the rapid development of online data have generated an intense interest in tools to help users find desired data. This paper describes how, particularly in college libraries, a digital library can be built and enforced in an academic library. The specifications for hardware, software, processing and recovery technologies are addressed. An elegant user interface, combined with better browsing, insertion and reporting capabilities, functionality that anyone can access from anywhere. The library’s automatic update facility helps to get a clear idea of which books are accessed by the members, allows users to create a ’hard copy’ report. The Digital library system stores information such as name, address, ID number, date of birth of library members. The information of books such as book name, book number, topic to which it refers, author, edition, edition, year of publication, total number of publications, total number of books in the library can also be stored and accessed digitally.
The ability to drive a car is a significant step toward independence for individuals with impairments, however, the standard driving system placed in a car does not provide the functionality for the challenged driver ...
The ability to drive a car is a significant step toward independence for individuals with impairments, however, the standard driving system placed in a car does not provide the functionality for the challenged driver to operate the vehicle. Hence, in this project, we focused on designing a prototype of a car that works with the help of renewable energy like solar for people who have disabilities related to their legs or arms. It will be very helpful for those people who have physical disabilities to drive from one place to another without counting on anybody. The research shows that the issues about sustainable mobility are gaining increasing attention both in public opinion and specialists because of the major impact of automotive systems on carbon Dioxide production, climate change (global warming), and fuel exhaustion. So, by using renewable energy such as solar energy as source power it helps pollution-free, sustainable, and causes no greenhouse gases which will save the environment in the future. Probably the best development in the clinical field that helped both the old and the impeded is the portability vehicle. The expense of the vehicle may not be reasonable for an average person. In this case, an endeavor is made to manufacture a Solar Electric vehicle for disabled people at an ideal cost that can be used easily. It ensures the physically challenged people to be comfortable in travel and life. As solar energy is the main source on which we are focusing nowadays, because of many reasons like pollution, cost of fuel and also due to unavailability of the fuels. In the future solar energy will be the major source. The features have made in this solar electric vehicle are intelligent ones and are very useful for physically disabled people who want to be independent. In addition to that, the GSM is attached to the vehicle for monitoring disabled people. If the vehicle got any damage or the people is in out of control or any other irregular activities are found
Human computer Interaction(HCI) is becoming popular in this modern world. Their widespread use suggests that the ability to handle computers is perhaps equally essential for visually impaired as well as for sighted pe...
Human computer Interaction(HCI) is becoming popular in this modern world. Their widespread use suggests that the ability to handle computers is perhaps equally essential for visually impaired as well as for sighted persons. Even though a large amount of work has been performed in the gesture based human computer interface, blind users still feel it is tough to interact with computers. The major obstacle is the lack of knowledge about blind users preferences toward hand gestures. Mouse and keyboard are the basic input devices to interact with a computer. People who are sightless find it difficult to interact with these means of HCI. Though Braille systems are being used by blind people but this system has a disadvantage also. A Braille device has only 64 keys whereas a computer keyboard consists of 104 keys. In many applications the capability of deep learning techniques has been confirmed to outperform classic approaches. Accordingly, we use convolutional neural network to classify the hand gestures. The proposed system has four main phases: Data set collection, pre processing, feature extraction and classification. A hand gesture captured by the camera will be recognised and classified or mapped to corresponding symbol(alphabets, digits etc.). The matched output is saved in the file as well as audio feedback is given to the blind user. A real time application where this proposed system can be used is in competitive examination for blind people. The experiment results show that the prediction accuracy of hand gesture recognition goes upto 90% with samples around 332.
The current research is focused on the study of the effect of head shape geometrical deformations to the solution of the forward electroencephalographic (EEG) problem and its sensitivity response. A novel and accurate...
The current research is focused on the study of the effect of head shape geometrical deformations to the solution of the forward electroencephalographic (EEG) problem and its sensitivity response. A novel and accurate analytical scheme has been developed by performing a perturbation analysis in the linear regime, where a homogenous three-shell spherical model, equipped with all the necessary mathematical tools, is introduced. The efficacy of the method is demonstrated by incorporating the numerical implementation of the obtained formulae in which both cases, with or without surface deviations, are presented and benchmarked. The suggested procedure provides us with a criterion, which recognizes whether surface perturbations will influence EEG recordings.
Underwater robots have the ability to go down the sea up to several meters of height without any fear of loss of human lives. These robots need autonomous control systems and guidance to carry out their tasks. One of ...
Underwater robots have the ability to go down the sea up to several meters of height without any fear of loss of human lives. These robots need autonomous control systems and guidance to carry out their tasks. One of the main objectives of any underwater robot is to reach a given depth under the water and also be able to maintain that depth throughout the operation period. In this paper a simple full state feedback controller was designed to control the underwater robot's depth, despite all the external forces and disturbances during a given mission.
Multiple works have applied deep learning to fringe projection profilometry (FPP) in recent years. However, to obtain a large amount of data from actual systems for training is still a tricky problem, and moreover, th...
详细信息
In the present situation COVID-19 global pandemic, an overall world got embrace with covid-19 virus. Moreover every country went under quarantine and firm mitigation measures by temporary shutdown in people crowded pl...
In the present situation COVID-19 global pandemic, an overall world got embrace with covid-19 virus. Moreover every country went under quarantine and firm mitigation measures by temporary shutdown in people crowded place like schools, restaurants, theatres and domestic and international transport facilities[1]. So government made these measures to be effective to support and compliance of public[2]. Advanced medical technology put up with new vaccines to control infection of virus. Vaccination is one of the most effective step to control the virus from affecting the entire population. This paper aims to explore the optimal effectiveness of Vaccination for novel COVID-19 non clinical approach. We need smart solutions and decisions to mitigate the coronavirus`s impact. We formulate the mathematical model to Analyse the vaccination effectiveness[3]. The objective function is designed to reduce both awareness of vaccination and effectiveness of vaccination using WHO data/ Indian dashboard data[4]. The degree of protection As a part we can analyse the risk factors of immunization using multivariate models using statistical analysis.
Compressive strength (CS) of concrete is a key quality factor that is being monitored continuously in all construction projects which use huge quantity of concrete throughout the world. Engineers need to be open minde...
Compressive strength (CS) of concrete is a key quality factor that is being monitored continuously in all construction projects which use huge quantity of concrete throughout the world. Engineers need to be open minded with the attitude of lifelonglearning to upskill with the ever-evolving softwareand hardware technologies. Seven day and twenty-eight-day compressive strengths of concrete samples with varied amounts of cement, blast furnace slag, fly ash, water, super plasticizer, coarse aggregate, and fine aggregate are studied. Multiple Linear Regression (MLR) models are fitted for this data in Microsoft Excel. Artificial Neural Networks (ANNs) in RStudio are developed for this data. The performances of both methods are compared. This paper takes care of Goal 12 of United Nations Sustainable Development of ensuring sustainable consumption and production patterns as environmentally degrading materials (flyash and blast furnace slag) are used.
Recent years many women are affected by breast cancer. Mammogram is one of the early breast cancer diagnosis techniques used to identify the abnormal regions of breast. The recommended research work uses Fuzzy C-means...
Recent years many women are affected by breast cancer. Mammogram is one of the early breast cancer diagnosis techniques used to identify the abnormal regions of breast. The recommended research work uses Fuzzy C-means segmentation algorithm to locate the wound area of mammogram breast image. Further the features of abnormal regions were extracted using Local Binary pattern (LBP) techniques. The statistical features are Entropy, Mean, RMS (Root Mean Square), correlation helps to train the neural network. Finally SVM (Support Vector Machine) classifier is utilized to categorize the abnormal regions of mammogram images with the accuracy rates of 86%.
暂无评论