Heritage Institute of Technology

About: Heritage Institute of Technology is a based out in . It is known for research contribution in the topics: Support vector machine & Transconductance. The organization has 581 authors who have published 1045 publications receiving 8345 citations.

Lightweight user-adaptive handwriting recognizer for resource constrained handheld devices

Abstract: Here, we present our recent attempt to develop a lightweight handwriting recognizer suitable for resource constrained handheld devices. Such an application requires real-time recognition of handwritten characters produced on their touchscreens. The proposed approach is well suited for minimal user-lag on devices having only limited computing power in sharp contrast to standard laptops or desktop computers. Moreover, the approach is user-adaptive in the sense that it can adapt through user corrections to wrong predictions. With an increasing number of interactive corrections by the user, the recognition accuracy improves significantly. An input stroke is first re-sampled generating a fixed small number of sample points such that at most two critical points (points corresponding to high curvature) are preserved. We use their x- and y-coordinates as the feature vector and do not compute any other high-level feature vector. The squared Mahalanobis distance is used to identify each stroke of the input sample as one of several stroke categories pre-determined based on a large pool of training samples. The inverted covariance matrix and mean vector for a stroke class that are required for computing the Mahalanobis distance are pre-calculated and stored as Serialized Objects on the SD card of the device. A Look-Up Table (LUT) of stroke combinations as keys and corresponding character class as values is used for the final Unicode character output. In case of an incorrect character output, user corrections are used to automatically update the LUT adapting to the user's particular handwriting style.

Immune-Based Feature Selection in Rigid Medical Image Registration Using Supervised Neural Network

Abstract: Different radiological images like computed tomography (CT) and magnetic resonance (MR) are increasingly being used in medical science research for diagnosis and treatment This article presents an automatic image registration technique to register MR–MR images using gray-level co-occurrence matrix (GLCM) and neural networks This technique identifies different features of a brain image and its transformational counterpart GLCM-based image feature extraction is a co-occurrence-based method by which different feature parameters are obtained These parameters are calculated from the co-occurrence matrix along four directions, namely 0°, 45°, 90°, and 135° Six features are selected from a set of features using an artificial immune system-based optimized feature selection technique and these six parameters are fed into the proposed neural network Based on the principle of backpropagation algorithm, transformation parameters between the referenced and the sensed images are estimated To demonstrate the effectiveness of the proposed method, experiment is carried out on MR T1, T2 datasets, and the results are compared with two other existing medical image registration techniques The proposed method shows convincing results compared to others with respect to the estimation of underlying transformation parameters

Modeling and Simulation of Rotating Disk-Membrane Module in Ultrafiltration of Bovine Serum Albumin

Abstract: A dynamic mass transfer model, coupled with transient back transport flux for a Rotating Disk-Membrane (RDM) module has been developed in the present study. The simulation algorithm is capable of predicting the permeate flux (J), membrane surface concentration (c m ), and permeate concentration (c p ) under different parametric conditions of transmembrane pressure (TMP), feed concentration (c 0), stirrer speed (Ω2), and membrane speed of rotation (Ω1). The key feature of the proposed model is the analytical solution of the governing component balance equation. Additionally, the well-known osmotic pressure model and the Spiengler-Kedem black box model were used to describe the solvent transport through membrane. The proposed model was validated with the experimental results obtained in ultrafiltration of bovine serum albumin (BSA)/water solution conducted in a standard RDM module, fitted with polyethersulphone (PES) membrane of 30 kDa molecular weight cut-off (MWCO). The maximum absolute deviation of the m...

Gain enhancement of a slot antenna with a metamaterial superstrate structure

Abstract: This paper presents a technique to increase the gain of a slot antenna with the help of a single layer metamaterial (MTM) superstrate. The gain enhancement is achieved using the zero-index property of the metamaterial. The increased broadside gain for the proposed antenna is 6.42dB more in comparison to that of the reference slot antenna. The overall thickness of the MTM loaded antenna is 0.15λ0, where λ0 is the free-space wavelength at the resonance frequency of the antenna. A high efficiency of about 96.20% is obtained for proposed antenna. The loading of the metamaterial superstrate produces minimal effect on the cross polarization performance of the slot antenna.