In-air handwriting is a rapidly emerging human–machine interactive paradigm that helps users to write and communicate naturally and intuitively in free space. In this paper, we develop a hybrid ...one-dimensional convolutional recurrent attention framework model for in-air handwritten Assamese word recognition (IAHAWR) which associates an encoder and a decoder framework for efficiently recognizing air-written words. The encoder is an assimilation of 1D convolutional neural network and bidirectional gated recurrent unit neural network for input trajectory feature sequence learning, while the decoder is an attention-based gated recurrent unit for predicting the target words. In contrast to conventional pen-based handwriting, in-air handwriting is intricate in the sense that the handwriting is finished in a single continuous stroke giving rise to many irrelevant motions called ligatures in between adjacent character strokes. So, we have imbibed a salient stroke extraction and a critical point detection scheme into the proposed system, which helps in removal of insignificant ligatures thus enhancing the recognition performance. Further, air-writing trajectories contain intermittent jitters and suffer wide variations in writing patterns due to unrestricted writing in free space. So, we incorporate a multistage word normalization methodology which generalizes the air-written patterns and aids in efficient recognition. We have assessed the performance of our proposed system on an air-written Assamese word dataset as well as some air-written Latin words. Experimental evaluation connotes that our proposed IAHAWR system can effectively procure characteristic information from air-writing sequences and provides comparable recognition accuracy and computational performance with that of other state-of-the-art recognition frameworks.
Dialects of languages demonstrate dependency on both speaker and sound-unit (phone)-related information, which encompasses the problem of dialect identification (DID) under the domain of language ...identification (LID). The DID task is more complicated than conventional LID, and it has been established that conventional acoustic features like perceptual linear prediction (PLP) and mel frequency cepstral coefficient (MFCC) features-which carry only phone-unit-related information-are not sufficient to address the problem of DID. The authors explore raw log critical band energy (LCBE) information obtained from critical band analysis of speech signals, which effectively carries both speaker and phone-unit-related information. A nonlinear feature extractor using multilayer perceptron (MLP) is designed to model the critical band information. Further, a neuro-fuzzy classifier (NFC) is configured to classify feature vectors into different dialectal classes to discriminate between finer variations. The objective is to investigate perceptually oriented information obtained from all critical bands to distinguish dialectal speech and the applicability of NFC for such problems. Experimental results are shown in terms of classification accuracy of four dialects of Assamese language, mostly spoken in Northeast India. A few baseline systems are developed using PLP and MFCC features along with a Gaussian Mixture Model (GMM)-based classifier. Experimental results prove the strength of the MLP-based nonlinear mapping of critical band information for dialect discrimination compared to the PLP-based autoregressive approximation and MFCC-based cepstral domain version of critical band energy.
Early diagnosis and classification of long term cardiac signals are crucial issues in the treatment of heart related disorders. The available number of medical professional are not sufficient to deal ...with the increase patients for which design of certain machine based diagnostics tools have been accepted as a viable option. Typical Electrocardiogram (ECG) machine is helpful for monitoring the heart abnormalities only for short interval of time. Therefore, it becomes necessary to design a system which captures relevant features of the ECG signal for use with certain classifiers. In our proposed system, ECG signal elements like Q, R and S peaks are detected and heart rate estimated using Linear Discriminant Analysis (LDA), Adaptive Linear Discriminant Analysis (ALDA) and Support Vector Machine (SVM). For our work we have been used MIT BIH (Standard Arrhythmia Database).
Cooperative non-orthogonal multiple access (NOMA) system has demonstrated the ability to improve spectral efficiency, massive connectivity, and spectral efficiency. In this study, the authors propose ...a novel dual-ordered NOMA in a decode and forward (DF) relaying network, where a dynamic channel ordering is considered at both source and relay nodes during the symbol transmission period. The proposed NOMA system uses instantaneous channel gain as the channel state information (CSI) measure for channel ordering of the NOMA users in contrast to the more popular statistical CSI measures. We have established the efficiency of the proposed method through investigation of three different performance metrics: outage probability, ergodic rate analysis, and fairness of the proposed scheme in a downlink NOMA network with DF relaying. The results obtained show superior performance of the double ordered NOMA as compared to the single-ordered NOMA.
In-air handwriting is a contemporary human computer interaction (HCI) technique which enables users to write and communicate in free space in a simple and intuitive manner. Air-written characters ...exhibit wide variations depending upon different writing styles of users and their speed of articulation, which presents a great challenge towards effective recognition of linguistic characters. So, in this paper we have proposed an ensemble model for in-air handwriting recognition which is based on convolutional neural network (CNN) and a long short-term memory neural network (LSTM-NN). The method collaborates overall character trajectory appearance modeling and temporal trajectory feature modeling for efficient recognition of varied types of air-written characters. In contrast to two-dimensional handwriting, in-air handwriting generally involves writing of characters interlinked by a continuous stroke, which makes segregation of intended writing activity from insignificant connecting motions an intricate task. So, a two-stage statistical framework is incorporated in the system for automatic detection and extraction of relevant writing segments from air-written characters. Identification of writing events from a continuous stream of air-written data is accomplished by formulating a Markov Random Field (MRF) model, while the segmentation of writing events into meaningful handwriting segments and redundant parts is performed by implementation of a Mahalanobis distance (MD) classifier. The proposed approach is assessed on an air-written character dataset comprising of Assamese vowels, consonants and numerals. The experimental results connote that our hybrid network can assimilate more information from the air-writing patterns and hence offer better recognition performance than the state-of-the-art approaches.
There has been a continuous emphasis on an energy efficient communication system design. With the advent of 5G communication technologies, along with a faster and reliable data transfer mechanisms, ...energy management and conservation is gaining more attention and is becoming a major and indispensable part of communication research. This papers highlights the contemporary technological developments in the field of RF energy harvesting in a cognitive and high data rate network. It has been observed that an efficient RF energy harvesting technology in a cognitive platform definitely leads towards a greener communication paradigm.
Aims: To investigate the association between Vitamin D receptor gene polymorphisms (BsmI, TaqI and FokI) and type 2 diabetes mellitus in patients in north eastern India. Settings and Design: This was ...a case control study with 40 cases of type 2 diabetes and 20 controls. Materials and Methods: Genomic DNA was extracted from blood and genotyped for the single nucleotide polymorphism (SNPs) of BsmI rs1544410, TaqI rs731236 and FokI rs2228570 by polymerase chain reaction and gene sequencing. Genotype distribution and allelic frequencies were compared between patients and controls. Data was expressed as mean ±standard deviation. Chi square test and t test were used to compare groups. Statistical analysis was done using SAS version 9.3 software. P value of <0.05 was considered significant. Results: Body weight and BMI were significantly associated with VDR polymorphisms BsmI and TaqI while BsmI was significantly associated with HbA1C. Vitamin D deficiency was significantly greater in cases than controls. The frequency of the heterozygous genotype of the BsmI polymorphism was significantly greater in type 2 diabetics than in controls. Conclusions: Vitamin D receptor polymorphisms are associated with type 2 diabetes in our population and require larger scale studies to be considered as possible risk factors or type 2 diabetes mellitus.
Phonemes are the smallest distinguishable unit of speech signal. Segmentation of a phoneme from its word counterpart is a fundamental and crucial part in speech processing because an initial phoneme ...is used to activate words starting with that phoneme. This work describes an artificial neural network-based algorithm developed for segmentation and classification of consonant phoneme of the Assamese language. The algorithm uses weight vectors, obtained by training self-organising map (SOM) with different number of iterations, as a segment of different phonemes constituting the word whose linear prediction coefficients samples are used for training. The algorithm shows an abrupt rise in success rate than the conventional discrete wavelet-based speech segmentation. A two-class probabilistic neural network problem carried out with clean Assamese phoneme is used to identify phoneme segment. The classification of the phoneme segment is alone as per the consonant phoneme structure of the Assamese language which consists of six phoneme families. Experimental results establish the superiority of the SOM-based segmentation over the discrete wavelet transform-based approach.
Scene text recognition is an application of Computer Vision that analyses the scene image and recognizes the text present on it. This task has many applications and will gain more importance if it ...can be used in handheld devices. The problem with existing methods is that if the model has a huge number of parameters and complex architectures, then the model will have a huge file size which will be problematic to deploy the application on mobile devices. Therefore, the aim of this paper is to propose a light-weight model that is a model with less number of parameters, small file size and less complexity that can be used in platforms with limited resources while achieving a comparable accuracy with those of the heavy weight models. The proposed models rely on deep learning to handle most of the steps automatically, consume less time and give precise results after facing many challenges. The proposed scene text recognition model is in the form of a Convolutional-Recurrent Neural network where the Convolution network extracts the features from the cropped images of scene text and the Recurrent network processes the sequential data of varying length present in the cropped images. After training, the scene text recognition model generates a weight file of 12 MB with 1 M parameters. To reduce number of parameters, weight of files and to show trade-off between efficiency and accuracy, MobileNetV2 is used in place of Convolution network that generates weight file of 6 MB with 0.5 M parameters. The performance on ICDAR 2013, IIIT 5K and Total-Text datasets shows that the proposed work performs well in detecting and recognizing texts from natural scene images.
With rise in device complexity and transmission rates, reliability in data recovery has become another critical issue requiring costly and computationally demanding mechanism. The popularity of ...artificial intelligence (AI) and its ubiquitousness have established the usefulness of design of data recovery schemes where device level complexity is less. Lower device complexity is being ensured by the use of AI driven data recovery. In this work, we focus on the design of such a mechanism where traditional process are replaced by a neuro-computing structure. The advantage is lower levels of device complexity but incorporation of a training latency. Experimental results have established the reliability of the proposed system.
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