Search

The daily routine problems that common man faces on roads while commuting are becoming a serious problem with each passing day. People get late and meet accidents. The model of autonomous car presented in this research paper aims to solve these issues by taking humans off the wheels, so that they do not have to drive anymore and the risk of accidents, getting late and traffic congestions can be reduced to a minimum. This car is able to follow the track, overtake other cars, detect obstacles, take sharp bends and turns, follow traffic signals and turn on its lights under low light conditions. Circuit diagrams for performing all these functions have been presented and the mechanical model of the car has also been shown in the paper, which is practically implemented and successfully run by the authors.

Machine translation of sign language into spoken languages is an important yet non-trivial task. The sheer variety of dialects that exist in any sign language makes it only harder to come up with a generalized sign language classification system. Though a lot of work has been done in this area previously but most of the approaches rely on intrusive hardware in the form of wired or colored gloves or are specific language/dialect dependent for accurate sign language interpretation. We propose a cost-effective, non-intrusive webcam based solution in which a person from any part of the world can train our system to make it learn the sign language in their own specific dialect, so that our software can then correctly translate the hand signs into a commonly spoken language, such as English. Image based hand gesture recognition carries sheer importance in this task. The heart of hand gesture recognition systems is the detection and extraction of the sign (hand gesture) from the input image stream. Our work uses functions like skin color based thresholding, contour detection and convexity defect for detection of hands and identification of important points on the hand respectively. The distance of these important contour points from the centroid of the hand becomes our feature vector against which we train our neural network. The system works in two phases. In the training phase the correspondence between users hand gestures against each sign language symbol is learnt using a feed forward neural network with back propagation learning algorithm. Once the training is complete, user is free to use our system for translation or communication with other people. Experimental results based on training and testing the system with numerous users show that the proposed method can work well for dialect-free sign language translation (numerals and alphabets) and gives us average recognition accuracies of around 65% and 55% with the maximum recognition accuracies rising upto 77% and 62% respectively.

This work presents a label-free bio-molecular detection technique based on real-time monitoring of the resonant frequency of micromechanical rotational mode disk resonators in a solution. Due to their extremely high mass sensitivities, adsorption of thin films or even molecular monolayers on the surface of such devices results in a measurable frequency shift. In this work, in order to insulate the electrical connections from the solution, protect the device against contaminants, and minimize the sample size requirements, MEMS resonators were encapsulated in PDMS-based micro-fluidic channels. To demonstrate the molecular detection capability, the strong non-covalent binding of Avidin with its ligand, biotin was utilized. Biotin chained with PLL-PEG was used to functionalize the silicon dioxide surface of the resonators. The positively charged PLL backbone sticks to the negatively charged silicon dioxide surface of the resonators and the PEG side chains stretch away from the surface. A frequency shift of 3400ppm after PLL-PEG-BIOTIN adsorption and 4000ppm after Avidin adsorption was observed. Similar procedures can be used to further attach biotinylated DNA strands or other proteins for real time label-free detection of a variety of bio-molecules.