Graphics processor unit hardware acceleration of Levenberg-Marquardt artificial neural network training

This paper makes two principal contributions. The first is that there appears to be no previous a description in the research literature of an artificial neural network implementation on a graphics processor unit (GPU) that uses the Levenberg-Marquardt (LM) training method. The second is an initial attempt at determining when it is computationally beneficial to exploit a GPU’s parallel nature in preference to the traditional implementation on a central processing unit (CPU). The paper describes the approach taken to successfully implement the LM method, discusses the advantages of this approach for GPU implementation and presents results that compare GPU and CPU performance on two test data sets

Geometrical-based lip-reading using template probabilistic multi-dimension dynamic time warping

By identifying lip movements and characterizing their associations with speech sounds, the performance of speech recognition systems can be improved, particularly when operating in noisy environments. In this paper, we present a geometrical-based automatic lip reading system that extracts the lip region from images using conventional techniques, but the contour itself is extracted using a novel application of a combination of border following and convex hull approaches. Classification is carried out using an enhanced dynamic time warping technique that has the ability to operate in multiple dimensions and a template probability technique that is able to compensate for differences in the way words are uttered in the training set. The performance of the new system has been assessed in recognition of the English digits 0 to 9 as available in the CUAVE database. The experimental results obtained from the new approach compared favorably with those of existing lip reading approaches, achieving a word recognition accuracy of up to 71% with the visual information being obtained from estimates of lip height, width and their ratio

Investigation of dimensionality reduction in a finger vein verification system

Popular methods of protecting access such as Personal Identification Numbers and smart cards are subject to security risks that result from accidental loss or being stolen. Risk can be reduced by adopting direct methods that identify the person and these are generally biometric methods, such as iris, face, voice and fingerprint recognition approaches. In this paper, a finger vein recognition method has been implemented in which the effect on performance has of using principal components analysis has been investigated. The data were obtained from the finger-vein database SDMULA-HMT and the images underwent contrast-limited adaptive histogram equalization and noise filtering for contrast improvement. The vein pattern was extracted using repeated line tracking and dimensionality reduction using principal components analysis to generate the feature vector. A ‘speeded-up robust features’ algorithm was used to determine the key points of interest and the Euclidean Distance was used to estimate similarity between database images. The results show that the use of a suitable number of principal components can improve the accuracy and reduce the computational overhead of the verification system

An applicable approach for extracting human heart rate and oxygen saturation during physical movements using a multi-wavelength illumination optoelectronic sensor system

© 2018 SPIE. The ability to gather physiological parameters such as heart rate (HR) and oxygen saturation (SpO2%) during physical movement allows to continuously monitor personal health status without disrupt their normal daily activities. Photoplethysmography (PPG) based pulse oximetry and similar principle devices are unable to extract the HR and SpO2% reliably during physical movement due to interference in the signals that arise from motion artefacts (MAs). In this research, a flexible reflectance multi-wavelength optoelectronic patch sensor (OEPS) has been developed to overcome the susceptibility of conventional pulse oximetry readings to MAs. The OEPS incorporates light embittered diodes as illumination sources with four different wavelengths, e.g. green, orange, red, and infrared unlike the conventional pulse oximetry devices that normally measure the skin absorption of only two wavelengths (red and infrared). The additional green and orange wavelengths were found to be distinguish to the absorption of deoxyhemoglobin (RHb) and oxyhemoglobin (HbO2). The reliability of extracting physiological parameters from the green and orange wavelengths is due to absorbed near to the surface of the skin, thereby shortening the optical path and so effectively reducing the influence of physical movements. To compensate of MAs, a three-axis accelerometer was used as a reference with help of adaptive filter to reduce MAs. The experiments were performed using 15 healthy subjects aged 20 to 30. The primary results show that there are no significant difference of heart rate and oxygen saturation measurements between commercial devices and OEPS Green (r=0.992), Orange(r=0.984), Red(r=0.952) and IR(r=0.97) and SpO2% (r = 0.982, p = 0.894)

Image fusion based multi resolution and frequency partition discrete cosine transform for palm vein recognition

The rapid growth of technology has increased the demand for automated security systems. Due to the accessibility of the palm region and the unique characteristics of each individual's palm vein features, such biometrics have been receiving particular attention. In the published research relating to palm vein biometrics, usually only a single image is used to supply the data for recognition purposes. Previous experimental work has demonstrated that the fusion of multiple images is able to provide richer feature information resulting in an improved classification performance. However, although most of the image fusion techniques are able to preserve the vein pattern, the fused image is often blurred, the colors are distorted and the spatial resolution reduced. In this paper, the multi-resolution discrete cosine transform (MRDCT) and frequency partition DCT (FPDCT) image fusion are applied and are able to extract the finer details of vein patterns while reducing the presence of noise in the image. The performance shows that the use of MRDCT and FPDCT was able to improve recognition rate compared to using a single image. The equal error rate improvement is also significant, falling to 9% in 700nm image, 7% in 850nm image and 6% in 940nm image

Oxygen saturation measurements from green and orange illuminations of multi-wavelength optoelectronic patch sensors

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. Photoplethysmography (PPG) based pulse oximetry devices normally use red and infrared illuminations to obtain oxygen saturation (SpO2) readings. In addition, the presence of motion artefacts severely restricts the utility of pulse oximetry physiological measurements. In the current study, a combination of green and orange illuminations from a multi-wavelength optoelectronic patch sensor (mOEPS) was investigated in order to improve robustness to subjects’ movements in the extraction of SpO2 measurement. The experimental protocol with 31 healthy subjects was divided into two sub-protocols, and was designed to determine SpO2 measurement. The datasets for the first sub-protocol were collected from 15 subjects at rest, with the subjects free to move their hands. The datasets for the second sub-protocol with 16 subjects were collected during cycling and walking exercises. The results showed good agreement with SpO2 measurements (r = 0.98) in both sub-protocols. The outcomes promise a robust and cost-effective approach of physiological monitoring with the prospect of providing health monitoring that does not restrict user physical movements

Current state and future promise of m-Health [Book Review]

In delivering a much-needed book for the rapidly growing m-Health communities in both industry and academia, the authors draw on the extensive knowledge they have gained as a result of many years of experience of working at the forefront of the field. Indeed the lead author can be credited with coining the term m-Health

Encryption of text file using a user controlled automatically-generated key

Traditional symmetrical cryptographic algorithms generally provide an adequate degree of immunity to attacks aimed at revealing secret keys. A number of approaches exist for the automated generation of secret keys, but, for high security applications, some end users remain wary of approaches that are controlled by third parties. Consequently, there remains interest in certain high-security applications in being able to retain control over the method used for the generation of keys. In this paper, keys for both image encryption and decryption are obtained using the evolutionary computing tool Eureqa, in its modelling of pseudorandom input data. The secret keys generated by this approach and when applied to the encryption and decryption of gray-scale images are validated in a range of statistical tests, namely histogram, chisquare, correlation of adjacent pixel pairs, correlation between original and encrypted images, entropy and key sensitivity. Experimental results obtained from methods show that the proposed image encryption and decryption algorithms are secure and reliable, with the potential to be adapted to high-security image communication applications

Encryption of images file using a user controlled automatically-generated key

Traditional symmetrical cryptographic algorithms generally provide an adequate degree of immunity to attacks aimed at revealing secret keys. A number of approaches exist for the automated generation of secret keys, but, for high security applications, some end users remain wary of approaches that are controlled by third parties. Consequently, there remains interest in certain high-security applications in being able to retain control over the method used for the generation of keys. In this paper, keys for both cipher images, decryption images are obtained using the evolutionary computing tool Eureqa, in its modelling of pseudo-random input data. The secret keys generated by this approach and when applied to the encryption and decryption of gray-scale images are validated in a range of statistical tests, namely histogram, chi-square, correlation of adjacent pixel pairs, correlation between original and encrypted images, entropy and key sensitivity. Experimental results obtained from methods show that the proposed image encryption and decryption algorithms are secure and reliable, with the potential to be adapted to high-security image communication applications

Feature-fusion based audio-visual speech recognition using lip geometry features in noisy enviroment

Humans are often able to compensate for noise degradation and uncertainty in speech information by augmenting the received audio with visual information. Such bimodal perception generates a rich combination of information that can be used in the recognition of speech. However, due to wide variability in the lip movement involved in articulation, not all speech can be substantially improved by audio-visual integration. This paper describes a feature-fusion audio-visual speech recognition (AVSR) system that extracts lip geometry from the mouth region using a combination of skin color filter, border following and convex hull, and classification using a Hidden Markov Model. The comparison of the new approach with conventional audio-only system is made when operating under simulated ambient noise conditions that affect the spoken phrases. The experimental results demonstrate that, in the presence of audio noise, the audio-visual approach significantly improves speech recognition accuracy compared with audio-only approach