A. S. Hiwale

Bio: A. S. Hiwale is an academic researcher from Massachusetts Institute of Technology. The author has contributed to research in topic(s): MIMO & Block code. The author has an hindex of 5, co-authored 22 publication(s) receiving 69 citation(s). Previous affiliations of A. S. Hiwale include College of Engineering, Pune.

PAPR Reduction Scheme for OFDM

Abstract: This paper focused on peak to average power ratio (PAPR) reduction scheme for OFDM system. OFDM has several properties which make it an attractive modulation scheme for high speed transmission. the main draw back of OFDM is high PAPR. The high PAPR causes the interference and degraded the performance of the system while OFDM signal pass through the amplifier. Here a simple scheme clip and filter is used to reduce the PAPR of OFDM system. Simulation results are show that the improvement in performance of OFDM system by reducing the PAPR using clip and filter scheme.

Intelligent Twitter Spam Detection: A Hybrid Approach

Abstract: Over the years there has been a large upheaval in the social networking arena. Twitter being one of the most widely-used social networks in the world has always been a key target for intruders. Privacy concerns, stealing of important information and leakage of key credentials to spammers has been on the rise. In this paper, we have developed an Intelligent Twitter Spam Detection System which gives the precise details about spam profiles by identifying and detecting twitter spam. The system is a Hybrid approach as opposed to single-tier, single-classifier approaches which takes into account some unique feature sets before analyzing the tweets and also checks the links with Google Safe Browsing API for added security. This in turn leads to better tweet classification and improved as well as intelligent twitter spam detection.

EEG Spectral Analysis on OM Mantra Meditation: A Pilot Study

Abstract: Mantra meditation is easy to practice. “OM” Mantra is the highest sacred symbol in Hinduism. The present study investigated the temporal dynamics of oscillatory changes after OM mantra meditation. Twenty-three naive meditators were asked to perform loud OM chanting for 30 min and the EEG were subsequently recorded with closed eyes before and after it. To obtain new insights into the nature of the EEG after OM chanting, EEG signals were analyzed using spectral domain analysis. Statistical analysis was performed using repeated measures of analysis of variance. It did not reveal any specific band involvement into OM mantra meditation. But significantly increase in theta power was found after meditation when averaged across all brain regions. This is the main effect of OM mantra meditation. However, the theta power showed higher theta amplitude after condition at all regions in comparison to the before condition of meditation. Finding was similar to other studies documenting reduction in cortical arousal during a state of relaxation. The study argues for the potential role of loud ‘OM’ chanting in offering relaxation. It provides a new perspective of meditation to the naive meditators. This information may help to demystify meditation and encourage those considering this as beneficial practice.

Capacity and performance analysis of space-time block codes in Rayleigh fading channels

Abstract: Multi-Antenna systems are expected to play very important role in future multimedia wireless communication systems Such systems are predicted to provide tremendous improvement in spectrum utilization In this paper we consider the capacity analysis of Multiple-Input Multiple-Output (MIMO) systems Space-Time coding schemes are the practical signal design techniques to realize the information theoretic capacity limits of MIMO systems Here the orthogonal space-time block codes are considered for the capacity and error probability analysis of MIMO systems as a case study The numerical and simulation results obtained using MATLAB are presented for the Multi-antenna system channel capacity and bit-error probability in Rayleigh fading channels

SVM classification of EEG signal to analyze the effect of OM Mantra meditation on the brain

Abstract: Meditation can significantly contribute to improving physical and mental health in modern stressful life. "OM" mantra is very easy to practice for meditation .This study is undertaken to classify the EEG band to observe abrupt changes in band as an effect of Om mantra meditation. Twenty-three naive meditators were experimented to chant OM mantra for 30 min and EEG signal recorded before and after meditation. The stationary wavelet transform is used to exact five bands from the EEG. The different statistical features were calculated. SVM classifier with Radial Basis Kernel is employed to classify the band. Results show the significant changes in the delta band which represent the brain in deep sleep. Thus OM meditation gives the experience of deep sleep. Thus study can be helpful to give new direction towards the meditation.

Evolving Support Vector Machines using Whale Optimization Algorithm for spam profiles detection on online social networks in different lingual contexts

Abstract: A new classification approach based Support Vector Machine is proposed for detecting spammers on Twitter.The proposed approach reveals the most influencing features in the process of identifying spammers.Different lingual contexts are studied: Arabic, English, Spanish, and Korean. Detecting spam profiles is considered as one of the most challenging issues in online social networks. The reason is that these profiles are not just a source for unwanted or bad advertisements, but could be a serious threat; as they could initiate malicious activities against other users. Realizing this threat, there is an incremental need for accurate and efficient spam detection models for online social networks. In this paper, a hybrid machine learning model based on Support Vector Machines and one of the recent metaheuristic algorithms called Whale Optimization Algorithm is proposed for the task of identifying spammers in online social networks. The proposed model performs automatic detection of spammers and gives an insight on the most influencing features during the detection process. Moreover, the model is applied and tested on different lingual datasets, where four datasets are collected from Twitter in four languages: Arabic, English, Spanish, and Korean. The experiments and results show that the proposed model outperforms many other algorithms in terms of accuracy, and provides very challenging results in terms of precision, recall, f-measure and AUC. While it also helps in identifying the most influencing features in the detection process.

Spectral analysis of the central nervous system effects of the relaxation respones elicited by autogenic training

Abstract: This study examined the effects of the relaxation response, elicited by autogenic training, on central nervous system (CNS) activity. We used computerized spectral analysis of EEG activity as a dependent measure. After baseline EEG data were obtained for all subjects, the experimental group practiced standard autogenic exercises for 15 experimental sessions with home practice. The control subjects received the same number of sessions under identical conditions, except that they listened to a pleasant radio show without home practice. Subjects were then posttested to assess the acute and chronic effects of autogenic training and the relaxation response on CNS activity. The results indicated significant acute effects differences between groups; the experimental group showed greater increases in theta and greater decreases in alpha percent total power. The results suggest that the relaxation response elicited by autogenic training produces significant acute changes in EEG activity and a characteristic spectral pattern; the results also suggest that focusing attention on a repetitive, internal stimulus is a key element in Benson's relaxation response model.

Science Of Yoga

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Two-Tier Channel Estimation Aided Near-Capacity MIMO Transceivers Relying on Norm-Based Joint Transmit and Receive Antenna Selection

Abstract: We propose a norm-based joint transmit and receive antenna selection (NBJTRAS) aided near-capacity multiple-input–multiple-output (MIMO) system relying on the assistance of a novel two-tier channel estimation scheme. Specifically, a rough estimate of the full MIMO channel is first generated using a low-complexity, low-training-overhead minimum mean square error based channel estimator, which relies on reusing a modest number of radio frequency (RF) chains. NBJTRAS is then carried out based on this initial full MIMO channel estimate. The NBJTRAS aided MIMO system is capable of significantly outperforming conventional MIMO systems equipped with the same modest number of RF chains while dispensing with the idealized simplifying assumption of having perfectly known channel state information (CSI). Moreover, the initial subset channel estimate associated with the selected subset MIMO channel matrix is then used for activating a powerful semi-blind joint channel estimation and turbo detector–decoder, in which the channel estimate is refined by a novel block-of-bits selection based soft-decision aided channel estimator (BBSB-SDACE) embedded in the iterative detection and decoding process. The joint channel estimation and turbo detection–decoding scheme operating with the aid of the proposed BBSB-SDACE channel estimator is capable of approaching the performance of the near-capacity maximum-likelihood (ML) turbo transceiver associated with perfect CSI. This is achieved without increasing the complexity of the ML turbo detection and decoding process.

Spam review detection using spiral cuckoo search clustering method

Abstract: Nowadays, online reviews play an important role in customer’s decision. Starting from buying a shirt from an e-commerce site to dining in a restaurant, online reviews has become a basis of selection. However, peoples are always in a hustle and bustle since they don’t have time to pay attention to the intrinsic details of products and services, thus the dependency on online reviews have been hiked. Due to reliance on online reviews, some people and organizations pompously generate spam reviews in order to promote or demote the reputation of a person/product/organization. Thus, it is impossible to identify whether a review is a spam or a ham by the naked eye and it is also impractical to classify all the reviews manually. Therefore, a spiral cuckoo search based clustering method has been introduced to discover spam reviews. The proposed method uses the strength of cuckoo search and Fermat spiral to resolve the convergence issue of cuckoo search method. The efficiency of the proposed method has been tested on four spam datasets and one Twitter spammer dataset. To validate the efficacy of proposed clustering method it is compared with six metaheuristics clustering methods namely; particle swarm optimization, differential evolution, genetic algorithm, cuckoo search, K-means, and improved cuckoo search. The experimental results and statistical analysis validate that the proposed method outruns the existing methods.