Bleed-Through Removal in Document Images

When documents are written on both sides, quite often ink bleeds through the paper. This is a common phenomenon with old documents and low quality paper. With the presence of increased bleed-through, reading and deciphering the text becomes tedious. This thesis implements algorithms for reducing bleed-through distortion using techniques in digital image processing. A comparative study of three methods has been performed with the first being basic enhancement through thresholding. The next two methods are founded on a registration process which aims at working on both the recto and verso sides simultaneously. Firstly, both sides of the documents are digitized. The verso is then flipped and corrected so as to correspond to the coordinates of one side exactly with the coordinates of the original writing on the other which is done using an affine transformation of six parameters. The parameters are found by optimizing the alignment process. A restoration algorithm is then applied to remove bleed though areas on the desired side page. The third method proposes the use of cross correlation to handle the registration process. The accuracy of bleed-through correction is found to be largely dependent on the accuracy of alignment of the documents

Emotion classification in Parkinson's disease by higher-order spectra and power spectrum features using EEG signals: A comparative study

Deficits in the ability to process emotions characterize several neuropsychiatric disorders and are traits of Parkinson's disease (PD), and there is need for a method of quantifying emotion, which is currently performed by clinical diagnosis. Electroencephalogram (EEG) signals, being an activity of central nervous system (CNS), can reflect the underlying true emotional state of a person. This study applied machine-learning algorithms to categorize EEG emotional states in PD patients that would classify six basic emotions (happiness and sadness, fear, anger, surprise and disgust) in comparison with healthy controls (HC). Emotional EEG data were recorded from 20 PD patients and 20 healthy age-, education level- and sex-matched controls using multimodal (audio-visual) stimuli. The use of nonlinear features motivated by the higher-order spectra (HOS) has been reported to be a promising approach to classify the emotional states. In this work, we made the comparative study of the performance of k-nearest neighbor (kNN) and support vector machine (SVM) classifiers using the features derived from HOS and from the power spectrum. Analysis of variance (ANOVA) showed that power spectrum and HOS based features were statistically significant among the six emotional states (p < 0.0001). Classification results shows that using the selected HOS based features instead of power spectrum based features provided comparatively better accuracy for all the six classes with an overall accuracy of 70.10% ± 2.83% and 77.29% ± 1.73% for PD patients and HC in beta (13-30 Hz) band using SVM classifier. Besides, PD patients achieved less accuracy in the processing of negative emotions (sadness, fear, anger and disgust) than in processing of positive emotions (happiness, surprise) compared with HC. These results demonstrate the effectiveness of applying machine learning techniques to the classification of emotional states in PD patients in a user independent manner using EEG signals. The accuracy of the system can be improved by investigating the other HOS based features. This study might lead to a practical system for noninvasive assessment of the emotional impairments associated with neurological disorders

Photo-inhibition Effect from Strong Electron Withdrawing Nitro Group in N-[(E)(4-Bromophenyl)Methylidene]-4 Nitroaniline

Light induced effect of N-[(E)-(4-bromophenyl)methylidene]-4nitroaniline was investigated using UV-Vis spectrophotometer. This study revealed that the presence of strong electron withdrawing nitro group inhibited the photo-reactivity of the compound. Mainly, molecular structure and functional groups have tremendous influence on chromophoric compounds. The photoisomerization effect was not found in this compound, due to the photo-inhibition of nitro group present in the molecular system

Synthesis of New Liquid Crystals Embedded Gold Nanoparticles for Photoswitching Properties

A new series of liquid crystals decorated gold nanoparticles is synthesized whose molecular architecture has azobenzenes moieties as the peripheral units connected to gold nanoparticles (Au NPs) via alkyl groups. The morphology and mesomorphic properties were investigated by field emission scanning electron microscope, high-resolution transmission electron microscopy, differential scanning calorimetry and polarizing optical microscopy. The thiolated ligand molecules (3a–c) showed enantiotropic smectic A phase, whereas gold nanoparticles (5a–c) exhibit nematic and smectic A phase with monotropic nature. HR-TEM measurement showed that the functionalized Au NPs are of the average size of 2 nm and they are well dispersed without any aggregation. The trans-form of azo compounds showed a strong band in the UV region at ∼378 nm for the π-π∗ transition, and a weak band in the visible region at ∼472 nm due to the n-π∗ transition. These molecules exhibit attractive photoisomerization behaviour in which trans-cis transition takes about 15 s whereas the cis-trans transition requires about 45 min for compound 5c. The extent of reversible isomerization did not decay after 10 cycles, which proved that the photo-responsive properties of 5c were stable and repeatable. Therefore, these materials may be suitably exploited in the field of molecular switches and the optical storage devices

Crystal structure of methyl 1-methyl-2-oxospiro[indoline-3,2′-oxirane]-3′-carboxylate

Acknowledgements The authors thank Dr Babu Vargheese, SAIF, IIT, Madras, India, for the data collection.Peer reviewedPublisher PD

EFFECTS OF YOGIC PRACTICE ON SELECTED BODY COMPOSITION VARIABLES AMONG MILD INTELLECTUALLY CHALLENGED PERSONS

Purpose of the study was to facilitate the Effects of yogic practices on selected body composition variables among intellectually challenged persons for this study twenty (N=20) male mild intellectually challenged persons were randomly selected as a subject from Paradise special school, Muttukadu in Chennai, India. Their Age ranged between 14-18 years. They were randomly divided in to two equal groups of ten (n=10) subjects each namely experimental group and Control group. Experimental group underwent to yogic practice for the period of twelve weeks and no training are given to the control group. Body composition variables such as Body Mass Index (BMI) and percentage body fat (%) were selected as dependent variables and independent variables were only yoga. The data was collected before and after the experimental treatment period. Analysis of Covariance (ANCOVA) statistical technique was used in this study. It was concluded that Body Mass Index (BMI) and percent body fat (%) level of experimental group were significantly altered due to the influence of twelve weeks practices of yoga when compare with control group of mild intellectually challenged persons.  Article visualizations

Inter-hemispheric EEG coherence analysis in Parkinson's disease : Assessing brain activity during emotion processing

Parkinson’s disease (PD) is not only characterized by its prominent motor symptoms but also associated with disturbances in cognitive and emotional functioning. The objective of the present study was to investigate the influence of emotion processing on inter-hemispheric electroencephalography (EEG) coherence in PD. Multimodal emotional stimuli (happiness, sadness, fear, anger, surprise, and disgust) were presented to 20 PD patients and 30 age-, education level-, and gender-matched healthy controls (HC) while EEG was recorded. Inter-hemispheric coherence was computed from seven homologous EEG electrode pairs (AF3–AF4, F7–F8, F3–F4, FC5–FC6, T7–T8, P7–P8, and O1–O2) for delta, theta, alpha, beta, and gamma frequency bands. In addition, subjective ratings were obtained for a representative of emotional stimuli. Interhemispherically, PD patients showed significantly lower coherence in theta, alpha, beta, and gamma frequency bands than HC during emotion processing. No significant changes were found in the delta frequency band coherence. We also found that PD patients were more impaired in recognizing negative emotions (sadness, fear, anger, and disgust) than relatively positive emotions (happiness and surprise). Behaviorally, PD patients did not show impairment in emotion recognition as measured by subjective ratings. These findings suggest that PD patients may have an impairment of inter-hemispheric functional connectivity (i.e., a decline in cortical connectivity) during emotion processing. This study may increase the awareness of EEG emotional response studies in clinical practice to uncover potential neurophysiologic abnormalities

Strategies for Social Media Marketing to Engage and Shape the Purchase Behavior of Gen Z

This study examines the impact of social media marketing on the purchase behavior of Generation Z (Gen Z) consumers in India. Leveraging platforms like Instagram, Facebook, and WhatsApp, social media marketing has emerged as a key driver of consumer engagement and influence. The Study focuses on Indian demographics, the research underscores the effectiveness of social media strategies in connecting with Gen Z consumers. The study identifies that consumer engagement and social media influence significantly impact purchase decisions. Findings emphasize the importance of influencer marketing, brand authenticity, and user-generated content in shaping Gen Z’s behavior

Issues in Delivering Morbidity Management for Lymphatic Filariasis Elimination: A Study in Pondicherry, South India

Lymphatic filariasis is a vector borne parasitic disease causing long term disability. The Global Programme to Eliminate Lymphatic Filariasis aims to achieve its objective through two strategies; Mass Drug Administration (MDA) to interrupt transmission and Morbidity Management (MM) to manage disability for those already affected. MDA is going on in full swing in endemic areas; but MM is lagging behind. An exploratory study was conducted in Pondicherry through focus group discussions to find out whether there are delivery issues if any, in the MM programme and get suggestions from end users. The study results show that MM has not received the same attention as MDA and there are shortcomings in the delivery mechanism of the programme. The importance of these findings are discussed and suggestions given for improving the programme

Agarose Derived Carbon Based Nanocomposite for Hydrogen Storage at Near-Ambient Conditions

Nanocomposites comprising of high surface area adsorption materials and nanosized transition metals have emerged as a promising strategy for hydrogen storage application due to their inherent ability to store atomic and molecular forms of hydrogen by invoking mechanisms like physisorption and spillover mechanism or Kubas interaction. The potential use of these materials for both transport and stationary applications depends on reaching the ultimate storage capacity and scalability. In addition to achieving good hydrogen storage capacity, it is also vital to explore novel and efficient synthesis routes to control the microstructure. Herein, a direct and simple thermal decomposition technique is reported to synthesize carbon-based nanocomposites, where nickel nanoparticles are dispersed in a porous carbon matrix. The structure, morphology, composition and nature of bonding in the samples were investigated using transmission electron microscopy, scanning electron microscopy, energy dispersive spectroscopy, X-ray diffraction and Raman spectroscopy. Sorption-desorption isotherms were used to study the hydrogen storage capacity of the nanocomposites at a moderate H2 pressure of 20 bar. Among the various nanocomposites examined, the best obtained storage capacity was 0.73 wt.% (against 0.11 wt.% for pure carbon sample) at 298 K with reversible cyclability. It is shown that the uniform dispersion of catalytic nanoparticles along with a high surface area carbon matrix helps in the enhancement of hydrogen storage capacity by a factor of 6.5 times over pure carbon.Comment: 23 pages, 9 figure