Abstract: This study investigates the cleaning performance of
high intensity 360 kHz frequency on removal of nano-dimensional
and sub-micron particles from various surfaces, uniformity of the
cleaning tank and run to run variation of cleaning process. The
uniformity of the cleaning tank was measured by two different
methods i.e. 1. ppbTM meter and 2. Liquid Particle Counting (LPC)
technique. The result indicates that the energy was distributed more
uniformly throughout the entire cleaning vessel even at the corners
and edges of the tank when megasonic sweeping technology is
applied. The result also shows that rinsing the parts with 360 kHz
frequency at final rinse gives lower particle counts, hence higher
cleaning efficiency as compared to other frequencies. When
megasonic sweeping technology is applied each piezoelectric
transducers will operate at their optimum resonant frequency and
generates stronger acoustic cavitational force and higher acoustic
streaming velocity. These combined forces are helping to enhance the
particle removal and at the same time improve the overall cleaning
performance. The multiple extractions study was also carried out for
various frequencies to measure the cleaning potential and asymptote
value.
Abstract: The sonochemical decolorization and degradation of azo dye Methyl violet using Fenton-s reagent in the presence of a high-frequency acoustic field has been investigated. Dyeing and textile effluents are the major sources of azo dyes, and are most troublesome among industrial wastewaters, causing imbalance in the eco-system. The effect of various operating conditions (initial concentration of dye, liquid-phase temperature, ultrasonic power and frequency and process time) on sonochemical degradation was investigated. Conversion was found to increase with increase in initial concentration, temperature, power level and frequency. Both horntype and tank-type sonicators were used, at various power levels (250W, 400W and 500W) for frequencies ranging from 20 kHz - 1000 kHz. A 'Process Intensification' parameter PI, was defined to quantify the enhancement of the degradation reaction by ultrasound when compared to control (i.e., without ultrasound). The present work clearly demonstrates that a high-frequency ultrasonic bath can be used to achieve higher process throughput and energy efficiency at a larger scale of operation.
Abstract: This paper presents a review on vision aided systems
and proposes an approach for visual rehabilitation using stereo vision
technology. The proposed system utilizes stereo vision, image
processing methodology and a sonification procedure to support
blind navigation. The developed system includes a wearable
computer, stereo cameras as vision sensor and stereo earphones, all
moulded in a helmet. The image of the scene infront of visually
handicapped is captured by the vision sensors. The captured images
are processed to enhance the important features in the scene in front,
for navigation assistance. The image processing is designed as model
of human vision by identifying the obstacles and their depth
information. The processed image is mapped on to musical stereo
sound for the blind-s understanding of the scene infront. The
developed method has been tested in the indoor and outdoor
environments and the proposed image processing methodology is
found to be effective for object identification.
Abstract: Motor imagery classification provides an important basis for designing Brain Machine Interfaces [BMI]. A BMI captures and decodes brain EEG signals and transforms human thought into actions. The ability of an individual to control his EEG through imaginary mental tasks enables him to control devices through the BMI. This paper presents a method to design a four state BMI using EEG signals recorded from the C3 and C4 locations. Principle features extracted through principle component analysis of the segmented EEG are analyzed using two novel classification algorithms using Elman recurrent neural network and functional link neural network. Performance of both classifiers is evaluated using a particle swarm optimization training algorithm; results are also compared with the conventional back propagation training algorithm. EEG motor imagery recorded from two subjects is used in the offline analysis. From overall classification performance it is observed that the BP algorithm has higher average classification of 93.5%, while the PSO algorithm has better training time and maximum classification. The proposed methods promises to provide a useful alternative general procedure for motor imagery classification