A Session based Multiple Image Hiding Technique using DWT and DCT

This work proposes Steganographic technique for hiding multiple images in a color image based on DWT and DCT. The cover image is decomposed into three separate color planes namely R, G and B. Individual planes are decomposed into subbands using DWT. DCT is applied in HH component of each plane. Secret images are dispersed among the selected DCT coefficients using a pseudo random sequence and a Session key. Secret images are extracted using the session key and the size of the images from the planer decomposed stego image. In this approach the stego image generated is of acceptable level of imperceptibility and distortion compared to the cover image and the overall security is high.Comment: 4 pages,16 figures, "Published with International Journal of Computer Applications (IJCA)

An Edge Assisted Robust Smart Traffic Management and Signalling System for Guiding Emergency Vehicles During Peak Hours

Congestion in traffic is an unavoidable circumstance in many cities in India and other countries. It is an issue of major concern. The steep rise in the number of automobiles on the roads followed by old infrastructure, accidents, pedestrian traffic, and traffic rule violations all add to challenging traffic conditions. Given these poor conditions of traffic, there is a critical need for automatically detecting and signaling systems. There are already various technologies that are used for traffic management and signaling systems like video analysis, infrared sensors, and wireless sensors. The main issue with these methods is they are very costly and high maintenance is required. In this paper, we have proposed a three-phase system that can guide emergency vehicles and manage traffic based on the degree of congestion. In the first phase, the system processes the captured images and calculates the Index value which is used to discover the degree of congestion. The Index value of a particular road depends on its width and the length up to which the camera captures images of that road. We have to take input for the parameters (length and width) while setting up the system. In the second phase, the system checks whether there are any emergency vehicles present or not in any lane. In the third phase, the whole processing and decision-making part is performed at the edge server. The proposed model is robust and it takes into consideration adverse weather conditions such as hazy, foggy, and windy. It works very efficiently in low light conditions also. The edge server is a strategically placed server that provides us with low latency and better connectivity. Using Edge technology in this traffic management system reduces the strain on cloud servers and the system becomes more reliable in real-time because the latency and bandwidth get reduced due to processing at the intermediate edge server.Comment: Accepted at the Doctoral Symposium on Human Centered Computing (Human-2023), February 25, 2023. To be published in "Springer Tracts in Human-Centered Computing

STEP: Spatial Temporal Graph Convolutional Networks for Emotion Perception from Gaits

We present a novel classifier network called STEP, to classify perceived human emotion from gaits, based on a Spatial Temporal Graph Convolutional Network (ST-GCN) architecture. Given an RGB video of an individual walking, our formulation implicitly exploits the gait features to classify the emotional state of the human into one of four emotions: happy, sad, angry, or neutral. We use hundreds of annotated real-world gait videos and augment them with thousands of annotated synthetic gaits generated using a novel generative network called STEP-Gen, built on an ST-GCN based Conditional Variational Autoencoder (CVAE). We incorporate a novel push-pull regularization loss in the CVAE formulation of STEP-Gen to generate realistic gaits and improve the classification accuracy of STEP. We also release a novel dataset (E-Gait), which consists of $2,177$ human gaits annotated with perceived emotions along with thousands of synthetic gaits. In practice, STEP can learn the affective features and exhibits classification accuracy of 89% on E-Gait, which is 14 - 30% more accurate over prior methods

Analysis on effect of shapes for microwave-assisted food processing of 2D samples

Paper presented to the 10th International Conference on Heat Transfer, Fluid Mechanics and Thermodynamics, Florida, 14-16 July 2014.Present work provides guidelines on forecasting heating patterns in microwave processed foods which influence their final properties and quality. Three different cross-sections with equal area have been considered, namely, circular, square (indicated as Type1) and square inclined at an angle of 45° with horizontal plane (indicated as Type 2). Have been assumed to be exposed to lateral and radially incident microwaves. Microwave power absorption within samples have been studied using dimensionless parameters, viz. (i) Nw: represents the effect of sample size on power absorption. (ii) fp and fw: represents the effect of dielectric properties on power absorption Food materials were classified into 4 Groups with their fp, fw, as low fp and low fw (Group 1), low fp and high fw (Group 2), high fp and low fw (Group 3), high fp and high fw (Group 4), where low fp (fw) represents fp (fw)<0.3, while high fp (fw) represents fp (fw)_0.3. Power and temperature profiles have been studied in representative materials from each Group. It is found that power absorption profiles for all groups of food and for all the shapes of circular, Type 1 and Type 2 occur in three regime in increasing order of sample size, i.e (i) thin regime: characterized by uniform power absorption (ii) intermediate regime: resonances in absorbed power and (iii) thick regime: exponential attenuation of power within sample. It is also found that, in general identical areas of all the three shapes give rise to identical power absorption at any given sample dimension. Formation and location of hot-spots within material is found to be dependent on the type of incidence, sample dimensions and cross-section of material.dc201