arXiv subjects

Alex Pappachen James

Publications and source records attributed to Alex Pappachen James.

54 records · Page 3Linked to original sources

arXiv2016

Speech Signal Analysis for the Estimation of Heart Rates Under Different Emotional States

A non-invasive method for the monitoring of heart activity can help to reduce the deaths caused by heart disorders such as stroke, arrhythmia and heart attack. The human voice can be considered as a biometric data that can be used for estimation of heart rate. In this paper, we propose a method for estimating the heart rate from human speech dynamically using voice signal analysis and by the development of an empirical linear predictor model. The correlation between the voice signal and heart rate are established by classifiers and prediction of the heart rates with or without emotions are done using linear models. The prediction accuracy was tested using the data collected from 15 subjects, it is about 4050 samples of speech signals and corresponding electrocardiogram samples. The proposed approach can use for early non-invasive detection of heart rate changes that can be correlated to an emotional state of the individual and also can be used as a tool for diagnosis of heart conditions in real-time situations.

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arXiv2016

RFID-Cloud Smart Cart System

The main purpose of this work is in reducing the queuing delays in major supermarkets or other shopping centers by means of an Electronic Smart Cart System which will introduce an intellectual approach to billing process through RFID technology. Smart Cart System is a cooperative performance of three separate systems: a website developed for the shopping market, electronic smart cart device and anti-theft RFID gates. This project focuses on developing the electronic smart cart device itself. It involves an embedded electronic hardware that consists of an OLED display, Arduino Mega 2560 board, a specifically designed PCB, a Wi-Fi module, 13.56 MHz HF RFID reader, a power supply and a shopping cart.

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arXiv2016

A Survey of Memristive Threshold Logic Circuits

In this paper, we review the different memristive threshold logic (MTL) circuits that are inspired from the synaptic action of flow of neurotransmitters in the biological brain. Brain like generalisation ability and area minimisation of these threshold logic circuits aim towards crossing the Moores law boundaries at device, circuits and systems levels.Fast switching memory, signal processing, control systems, programmable logic, image processing, reconfigurable computing, and pattern recognition are identified as some of the potential applications of MTL systems. The physical realization of nanoscale devices with memristive behaviour from materials like TiO2, ferroelectrics, silicon, and polymers has accelerated research effort in these application areas inspiring the scientific community to pursue design of high speed, low cost, low power and high density neuromorphic architectures.

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arXiv2016

Edge Detection for Pattern Recognition: A Survey

This review provides an overview of the literature on the edge detection methods for pattern recognition that inspire from the understanding of human vision. We note that edge detection is one of the most fundamental process within the low level vision and provides the basis for the higher level visual intelligence in primates. The recognition of the patterns within the images relate closely to the spatiotemporal processes of edge formations, and its implementation needs a crossdisciplanry approach in neuroscience, computing and pattern recognition. In this review, the edge detectors are grouped in as edge features, gradients and sketch models, and some example applications are provided for reference. We note a significant increase in the amount of published research in the last decade that utilizes edge features in a wide range of problems in computer vision and image understanding having a direct implication to pattern recognition with images.

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arXiv2015

Detection and Analysis of Emotion From Speech Signals

Recognizing emotion from speech has become one the active research themes in speech processing and in applications based on human-computer interaction. This paper conducts an experimental study on recognizing emotions from human speech. The emotions considered for the experiments include neutral, anger, joy and sadness. The distinuishability of emotional features in speech were studied first followed by emotion classification performed on a custom dataset. The classification was performed for different classifiers. One of the main feature attribute considered in the prepared dataset was the peak-to-peak distance obtained from the graphical representation of the speech signals. After performing the classification tests on a dataset formed from 30 different subjects, it was found that for getting better accuracy, one should consider the data collected from one person rather than considering the data from a group of people.

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arXiv2015

Adaptive Digital Scan Variable Pixels

The square and rectangular shape of the pixels in the digital images for sensing and display purposes introduces several inaccuracies in the representation of digital images. The major disadvantage of square pixel shapes is the inability to accurately capture and display the details in the objects having variable orientations to edges, shapes and regions. This effect can be observed by the inaccurate representation of diagonal edges in low resolution square pixel images. This paper explores a less investigated idea of using variable shaped pixels for improving visual quality of image scans without increasing the square pixel resolution. The proposed adaptive filtering technique reports an improvement in image PSNR.

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arXiv2015

Memristor Load Current Mirror Circuit

Simple current mirrors with semiconductor resistive loads suffer from large on-chip area, leakage currents and thermal effects. In this paper, we report the feasibility of using memristive loads as a replacement of semiconductor resistors in simplistic current mirror configuration. We report power, area and total harmonic distribution, and report the corner conditions on resistance tolerances.

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arXiv2015

A Review of Feature and Data Fusion with Medical Images

The fusion techniques that utilize multiple feature sets to form new features that are often more robust and contain useful information for future processing are referred to as feature fusion. The term data fusion is applied to the class of techniques used for combining decisions obtained from multiple feature sets to form global decisions. Feature and data fusion interchangeably represent two important classes of techniques that have proved to be of practical importance in a wide range of medical imaging problems

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arXiv2015

Spatial Stimuli Gradient Sketch Model

The inability of automated edge detection methods inspired from primal sketch models to accurately calculate object edges under the influence of pixel noise is an open problem. Extending the principles of image perception i.e. Weber-Fechner law, and Sheperd similarity law, we propose a new edge detection method and formulation that use perceived brightness and neighbourhood similarity calculations in the determination of robust object edges. The robustness of the detected edges is benchmark against Sobel, SIS, Kirsch, and Prewitt edge detection methods in an example face recognition problem showing statistically significant improvement in recognition accuracy and pixel noise tolerance.

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arXiv2014

Sparse distributed localized gradient fused features of objects

The sparse, hierarchical, and modular processing of natural signals is related to the ability of humans to recognize objects with high accuracy. In this study, we report a sparse feature processing and encoding method, which improved the recognition performance of an automated object recognition system. Randomly distributed localized gradient enhanced features were selected before employing aggregate functions for representation, where we used a modular and hierarchical approach to detect the object features. These object features were combined with a minimum distance classifier, thereby obtaining object recognition system accuracies of 93% using the Amsterdam library of object images (ALOI) database, 92% using the Columbia object image library (COIL)-100 database, and 69% using the PASCAL visual object challenge 2007 database. The object recognition performance was shown to be robust to variations in noise, object scaling, and object shifts. Finally, a comparison with eight existing object recognition methods indicated that our new method improved the recognition accuracy by 10% with ALOI, 8% with the COIL-100 database, and 10% with the PASCAL visual object challenge 2007 database.

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arXiv2014

Threshold Logic Computing: Memristive-CMOS Circuits for Fast Fourier Transform and Vedic Multiplication

Brain inspired circuits can provide an alternative solution to implement computing architectures taking advantage of fault tolerance and generalisation ability of logic gates. In this brief, we advance over the memristive threshold circuit configuration consisting of memristive averaging circuit in combination with operational amplifier and/or CMOS inverters in application to realizing complex computing circuits. The developed memristive threshold logic gates are used for designing FFT and multiplication circuits useful for modern microprocessors. Overall, the proposed threshold logic outperforms previous memristive-CMOS logic cells on every aspect, however, indicate a lower chip area, lower THD, and controllable leakage power, but a higher power dissipation with respect to CMOS logic.

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arXiv2014

Memristive Threshold Logic Circuit Design of Fast Moving Object Detection

Real-time detection of moving objects involves memorisation of features in the template image and their comparison with those in the test image. At high sampling rates, such techniques face the problems of high algorithmic complexity and component delays. We present a new resistive switching based threshold logic cell which encodes the pixels of a template image. The cell comprises a voltage divider circuit that programs the resistances of the memristors arranged in a single node threshold logic network and the output is encoded as a binary value using a CMOS inverter gate. When a test image is applied to the template-programmed cell, a mismatch in the respective pixels is seen as a change in the output voltage of the cell. The proposed cell when compared with CMOS equivalent implementation shows improved performance in area, leakage power, power dissipation and delay.

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arXiv2012

Improving feature selection algorithms using normalised feature histograms

The proposed feature selection method builds a histogram of the most stable features from random subsets of a training set and ranks the features based on a classifier based cross-validation. This approach reduces the instability of features obtained by conventional feature selection methods that occur with variation in training data and selection criteria. Classification results on four microarray and three image datasets using three major feature selection criteria and a naive Bayes classifier show considerable improvement over benchmark results.

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arXiv2012

A Neuron Based Switch: Application to Low Power Mixed Signal Circuits

Human brain is functionally and physically complex. This 'complexity' can be seen as a result of biological design process involving extensive use of concepts such as modularity and hierarchy. Over the past decade, deeper insights into the functioning of cortical neurons have led to the development of models that can be implemented in hardware. The implementation of biologically inspired spiking neuron networks in silicon can provide solutions to difficult cognitive tasks. The work reported in this paper is an application of a VLSI cortical neuron model for low power design. The VLSI implementation shown in this paper is based on the spike and burst firing pattern of cortex and follows the Izhikevich neuron model. This model is applied to a DC differential amplifier as practical application of power reduction

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arXiv2012

Feature selection using nearest attributes

Feature selection is an important problem in high-dimensional data analysis and classification. Conventional feature selection approaches focus on detecting the features based on a redundancy criterion using learning and feature searching schemes. In contrast, we present an approach that identifies the need to select features based on their discriminatory ability among classes. Area of overlap between inter-class and intra-class distances resulting from feature to feature comparison of an attribute is used as a measure of discriminatory ability of the feature. A set of nearest attributes in a pattern having the lowest area of overlap within a degree of tolerance defined by a selection threshold is selected to represent the best available discriminable features. State of the art recognition results are reported for pattern classification problems by using the proposed feature selection scheme with the nearest neighbour classifier. These results are reported with benchmark databases having high dimensional feature vectors in the problems involving images and micro array data.

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arXiv2012

Examplers based image fusion features for face recognition

Examplers of a face are formed from multiple gallery images of a person and are used in the process of classification of a test image. We incorporate such examplers in forming a biologically inspired local binary decisions on similarity based face recognition method. As opposed to single model approaches such as face averages the exampler based approach results in higher recognition accu- racies and stability. Using multiple training samples per person, the method shows the following recognition accuracies: 99.0% on AR, 99.5% on FERET, 99.5% on ORL, 99.3% on EYALE, 100.0% on YALE and 100.0% on CALTECH face databases. In addition to face recognition, the method also detects the natural variability in the face images which can find application in automatic tagging of face images.

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arXiv2012

Memory Based Machine Intelligence Techniques in VLSI hardware

We briefly introduce the memory based approaches to emulate machine intelligence in VLSI hardware, describing the challenges and advantages. Implementation of artificial intelligence techniques in VLSI hardware is a practical and difficult problem. Deep architectures, hierarchical temporal memories and memory networks are some of the contemporary approaches in this area of research. The techniques attempt to emulate low level intelligence tasks and aim at providing scalable solutions to high level intelligence problems such as sparse coding and contextual processing.

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arXiv2012

Cognitive Memory Network

A resistive memory network that has no crossover wiring is proposed to overcome the hardware limitations to size and functional complexity that is associated with conventional analogue neural networks. The proposed memory network is based on simple network cells that are arranged in a hierarchical modular architecture. Cognitive functionality of this network is demonstrated by an example of character recognition. The network is trained by an evolutionary process to completely recognise characters deformed by random noise, rotation, scaling and shifting

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