Location Aware Estimation Using Rover Technology

As compared to other traditional system awareness in different dimensions such as time awareness, user-awareness and device-awareness, the Location- aware computing is the automated tracker of location information, products and also provides services by analyzing the current location of the user. Rover Technology improves the system awareness by adding a users location to the existing other dimensions such as capabilities of a client equipment, user requirements, and also the time. The Rover systems software architecture is designed in such a way that it has scalability to maintain large number of clients. Rover client equipment can also be used for reservation and purchase of tickets to the museum for future visit. It also gives information about the related places based on user interest

Innovation of Touchless Touchscreen Technology in Automotive User Interface

Inside a car environment music system plays a major role for entertaining people. In this paper, a music system with touchless and vision based GUI is granted. This menu-driven UI is offered by controlling actions of the fist. Both these algorithms can be brought by the deep learning technique. Convolutional Neural Network (CNN) is used in the hand posture recognition technique to make the user interface interactive to perform the actions initiated by the user. Long-Term Recurrent Convolutional Neural Network (LRCNN) algorithm is used to ignite the touchless interface by the gestures. When a fist movement is carried out, a sequence is captured in the form of multiple image frames. So this can be accomplished using the deep learning technique. Sampled images are taken from the video sequence that is captured during the gesture recognition. Key frame extraction technique is adopted to obtain finer images from the video sequence using sparse learning. Sparse dictionary learning is used as it is individually optimized for the video sequence but, is expensive computationally

Location Aware Estimation Using Rover Technology

As compared to other traditional system awareness in different dimensions such as time awareness, user-awareness and device-awareness, the Location- aware computing is the automated tracker of location information, products and also provides services by analyzing the current location of the user. Rover Technology improves the system awareness by adding a users location to the existing other dimensions such as capabilities of a client equipment, user requirements, and also the time. The Rover systems software architecture is designed in such a way that it has scalability to maintain large number of clients. Rover client equipment can also be used for reservation and purchase of tickets to the museum for future visit. It also gives information about the related places based on user interest

An analysis-ready and quality controlled resource for pediatric brain white-matter research

We created a set of resources to enable research based on openly-available diffusion MRI (dMRI) data from the Healthy Brain Network (HBN) study. First, we curated the HBN dMRI data (N = 2747) into the Brain Imaging Data Structure and preprocessed it according to best-practices, including denoising and correcting for motion effects, susceptibility-related distortions, and eddy currents. Preprocessed, analysis-ready data was made openly available. Data quality plays a key role in the analysis of dMRI. To optimize QC and scale it to this large dataset, we trained a neural network through the combination of a small data subset scored by experts and a larger set scored by community scientists. The network performs QC highly concordant with that of experts on a held out set (ROC-AUC = 0.947). A further analysis of the neural network demonstrates that it relies on image features with relevance to QC. Altogether, this work both delivers resources to advance transdiagnostic research in brain connectivity and pediatric mental health, and establishes a novel paradigm for automated QC of large datasets

Simulation of Landsman Converter with Npc-Multilevel Inverter for Water Pump Applications

This study explains the operation and design of Landsman DC-DC converter employing maximum power point tracking (MPPT) controller for maximum power conversion in a solar Photovoltaic (PV) array. The output DC voltage of converter through inverter is fed to the induction motor based water pump. The above system is proposed by using a neutral point clamped (NPC) multilevel inverter for the DC to AC conversion. Incremental conductance algorithm is chosen to operate the PV array close to optimum operating point. The input voltage and current is sensed to achieve MPP. Output voltage is sensed to get required regulated output voltage. The controller is designed to obtain the regulated output at different load and irradiance conditions. The overall proposed system is simulated and the THD analysis is done for the output voltage of the inverter

3rd National Conference on Image Processing, Computing, Communication, Networking and Data Analytics

This volume contains contributed articles presented in the conference NCICCNDA 2018, organized by the Department of Computer Science and Engineering, GSSS Institute of Engineering and Technology for Women, Mysore, Karnataka (India) on 28th April 2018