Neural networks are shown to be a viable implementation for joint channel equalisation and symbol detection in a vehicular network. Experimental results using a hardware-in-the-loop approach at 5.9GHz validate the efficacy of the proposed implementation following the 802.11p parameters using orthogonal frequency division multiplexing (OFDM). Further results are obtained using a more spectrally efficient waveform, namely spectrally efficient frequency division multiplexing (SEFDM), to show a trade-off between loss of orthogonality, and therefore bit-error rate (BER) performance, versus increased spectral efficiency to enable higher data rates or the ability to service more users. SEFDM is tested with compression factors ranging from 20% up to 60% bandwidth compression. The results show the neural network is able to achieve an acceptable BER performance in a highway non-line-of-sight (NLOS) channel which is a well established harsh and dynamic vehicular channel. This is further validated via measurements of the error vector magnitude.

This paper presents an adaptive heartrate-dependent heartwave-signal-based biometric identification. A reliable and continuous heartwave extraction method featuring the hybridized discrete waveform transform method with heartrate adaptive QT and PR intervals to perform comprehensive heartwave features extractions on more than 35 000 heartwave signal. The size of training data was determined and the hybridized Gaussian-mixture-model-hidden-Markov-model classification method was used in the classification. Dynamic thresholding criterial incorporating user-specific scores and heartrate were adopted. The identification process using dynamic thresholding criterial achieved a remarkable receiver operating characteristic of 0.89 in true positive rate and an equal error rate of 0.11.

This paper presents a heartwave based authentication method that utilizes an ensemble of deep belief networks (DBNs) under different parameters to increase the reliability of feature extraction. The multiview outputs are further embedded into a single view before inputting into a stacked DBN for classification. The result of the proposed novel architecture achieved a classification rate of 98.3% with 30% training data. Importantly, it is able to perform user classification using heartwave signals acquired under intense physical exercise where heart rate ranges from 50 bpm to as high as 180 bpm. Under extreme physical duress, the heartwave from an individual experiences extreme morphological variations that render conventional classification approaches nonapplicable.

Finger texture (FT) images acquired from different spectral lighting sensors reveal various features. This inspires the idea of establishing a recognition model between FT features collected using two different spectral lighting forms to provide high recognition performance. This can be implemented by establishing an efficient feature extraction and effective classifier, which can be applied to different FT patterns. So, an effective feature extraction method called the surrounded patterns code (SPC) is adopted. This method can collect the surrounded patterns around the main FT features. It is believed that these patterns are robust and valuable. Furthermore, a novel classifier termed the re‐enforced probabilistic neural network (RPNN) is proposed. It enhances the capability of the standard PNN and provides better recognition performance. Two types of FT images from the multi‐spectral Chinese Academy of Sciences Institute of Automation (CASIA) database were employed as two types of spectral sensors were used in the acquiring device: the white (WHT) light and spectral 460 nm of blue (BLU) light. Supporting comparisons were performed, analysed and discussed. The best results were recorded for the SPC by enhancing the equal error rates at 4% for spectral BLU and 2% for spectral WHT. These percentages have been reduced to 0% after utilising the RPNN.

This paper presents a novel computational intelligence vision sensing approach to estimate nutrient content in wheat leaves by analyzing color features of the leaves images captured on field with various lighting conditions. We propose the development of deep sparse extreme learning machines (DSELM) fusion and genetic algorithm (GA) to normalize plant images as well as to reduce color variability due to a variation of sunlight intensities. We also apply the DSELM in image segmentation to differentiate wheat leaves from a complex background. In this paper, four moments of color distribution of the leaves images (mean, variance, skewness, and kurtosis) are extracted and utilized as predictors in the nutrient estimation. We combine a number of DSELMs with committee machine and optimize them using the GA to estimate nitrogen content in wheat leaves. The results have shown the superiority of the proposed method in the term of quality and processing speed in all steps, i.e., color normalization, image segmentation, and nutrient prediction, as compared with other existing methods.

An unsupervised machine learning algorithm based on nonnegative matrix factor Two-dimensional deconvolution (NMF2D) with approximated optimum model order is proposed. The proposed algorithm adapted under the hybrid framework that combines the generalized EM algorithm with multiplicative update. As the number of parameters in the NMF2D grows exponentially the number of frequency basis increases linearly, the issues of model-order fitness, initialization, and parameters estimation become ever more critical. This paper proposes a variational Bayesian method to optimize the number of components in the NMF2D by using the Gamma-Exponential process as the observation-latent model. In addition, it is shown that the proposed Gamma-Exponential process can be used to initialize the NMF2D parameters. Finally, the paper investigates the issue and advantages of using different window length. Experimental results for the synthetic convolutive mixtures and live recordings verify the competence of the proposed algorithm.

The estimation of nutrient content of plants is considerably important in agricultural practices, especially in enabling the application of precision farming. A plethora of methods has been used to estimate nitrogen amount in plants, including the utilization of computer vision. However, most of the image-based nitrogen estimation methods are conducted in controlled environments. These methods are not so practical, time consuming, and require many equipment. Therefore, there is a crucial need to develop a method to estimate nitrogen content of plants based on leaves images captured on field. It is a very challenging task since the intensity of sunlight is always changing and this leads to an inconsistent image capturing problem. In this paper, we develop a low-cost, simple, and accurate approach image-based nitrogen amount estimation. Plant images are captured directly under sunlight by using a conventional digital camera and are subject to a variation in lighting conditions. We propose a color constancy method using neural networks fusion and a genetic algorithm to normalize various plant images due to different sunlight intensities. A Macbeth color checker is utilized as the reference to normalize the color of the images. We also develop a combination of neural networks using a committee machine to estimate the nitrogen content in wheat leaves. Twelve statistical RGB color features are used as the input parameters for the nutrient estimation. The obtained result shows considerable better performance than the conventional gray-world and scale-by-max approaches, as well as linear model and single neural network methods. Finally, we show that our nutrient estimation approach is superior to the commonly used soil-plant analysis development meter based prediction.

Video-based multiple human tracking often involves several challenges, including target number variation, object occlusions, and noise corruption in sensor measurements. In this paper, we propose a novel method to address these challenges based on probability hypothesis density (PHD) filtering with a Markov chain Monte Carlo (MCMC) implementation. More specifically, a novel social force model (SFM) for describing the interaction between the targets is used to calculate the likelihood within the MCMC resampling step in the prediction step of the PHD filter, and a one class support vector machine (OCSVM) is then used in the update step to mitigate the noise in the measurements, where the SVM is trained with features from both color and oriented gradient histograms. The proposed method is evaluated and compared with state-of-the-art techniques using sequences from the CAVIAR, TUD, and PETS2009 datasets based on the mean Euclidean tracking error on each frame, the optimal subpattern assignment metric, and the multiple object tracking precision metric. The results show improved performance of the proposed method over the baseline algorithms, including the traditional particle PHD filtering method, the traditional SFM-based particle filtering method, multi-Bernoulli filtering, and an online-learning-based tracking method.

Traditional iris segmentation methods give good results when the iris images are taken under ideal imaging conditions. However, the segmentation accuracy of an iris recognition system significantly influences its performance especially in nonideal iris images. This paper proposes a novel segmentation method for nonideal iris images. Two algorithms are proposed for pupil segmentation in iris images which are captured under visible and near infrared light. Then, a fusion of an expanding and a shrinking active contour is developed for iris segmentation by integrating a new pressure force to the active contour model. Thereafter, a noncircular iris normalization scheme is adopted to effectively unwrap the segmented iris. In addition, a novel method for closed eye detection is proposed. The proposed scheme is robust in finding the exact iris boundary and isolating the eyelids of the iris images. Experimental results on CASIA V4.0, MMU2, UBIRIS V1, and UBIRIS V2 iris databases indicate a high level of accuracy using the proposed technique. Moreover, the comparison results with the state-of-the-art iris segmentation algorithms revealed considerable improvement in segmentation accuracy and recognition performance while being computationally more efficient.

Sclera blood veins have been investigated recently as a biometric trait which can be used in a recognition system. The sclera is the white and opaque outer protective part of the eye. This part of the eye has visible blood veins which are randomly distributed. This feature makes these blood veins a promising factor for eye recognition. The sclera has an advantage in that it can be captured using a visible-wavelength camera. Therefore, applications which may involve the sclera are wide ranging. The contribution of this paper is the design of a robust sclera recognition system with high accuracy. The system comprises of new sclera segmentation and occluded eye detection methods. We also propose an efficient method for vessel enhancement, extraction, and binarization. In the feature extraction and matching process stages, we additionally develop an efficient method, that is, orientation, scale, illumination, and deformation invariant. The obtained results using UBIRIS.v1 and UTIRIS databases show an advantage in terms of segmentation accuracy and computational complexity compared with state-of-the-art methods due to Thomas, Oh, Zhou, and Das.

In this paper, we propose a novel robust multiple human tracking approach based upon processing a video signal by utilizing a social force model to enhance the particle probability hypothesis density (PHD) filter. In traditional dynamic models, the states of targets are only predicted by their own history; however, in multiple human tracking, the information from interaction between targets and the intentions of each target can be employed to obtain more robust prediction. Furthermore, such information can mitigate the problems of collision and occlusion. The cardinality of variable number of targets can also be estimated by using the PHD filter, hence improving the overall accuracy of the multiple human tracker. In this work, a background subtraction step has also been employed to identify the new born targets and provide the measurement set for the PHD filter. To evaluate tracking performance, sequences from both the CAVIAR and PETS2009 datasets are employed for evaluation, which shows clear improvement of the proposed method over the conventional particle PHD filter.

We present a new approach for solving the single channel speech separation with the aid of an user-generated exemplar source that is recorded from a microphone. Our method deviates from the conventional model-based methods, which highly rely on speaker dependent training data. We readdress the problem by offering a new approach based on utterance dependent patterns extracted from the user-generated exemplar source. Our proposed approach is less restrictive, and does not require speaker dependent information and yet exceeds the performance of conventional model-based separation methods in separating male and male speech mixtures. We combine general speaker-independent (SI) features with specifically generated utterance-dependent (UD) features in a joint probability model. The UD features are initially extracted from the user-generated exemplar source and represented as statistical estimates. These estimates are calibrated based on information extracted from the mixture source to statistically represent the target source. The UD probability model is subsequently generated to target problems of ambiguity and to offer better cues for separation. The proposed algorithm is tested and compared with recent method using the GRID database and the Mocha-TIMIT database.

Unsupervised extraction of focused regions from images with low depth-of-field (DOF) is a problem without an efficient solution yet. In this paper, we propose an efficient unsupervised segmentation solution for this problem. The proposed approach which is based on ensemble clustering and graph-cut modeling aims to extract meaningful focused regions from a given image at two stages. In the first stage, a novel two-level based ensemble clustering technique is developed to classify image blocks into three constituent classes. As a result, object and background blocks are extracted. By considering certain pixels of object and background blocks as seeds, a constraint is provided for the next stage of the approach. In stage two, a minimal graph cuts is constructed by utilizing the max-flow method and using object and background seeds. Experimental results demonstrate that the proposed approach achieves an average F-measure of 91.7% and is computationally up to 2 times faster than existing unsupervised approaches.

A novel single-channel blind source separation (SCBSS) algorithm is presented. The proposed algorithm yields at least three benefits of the SCBSS solution: 1) resemblance of a stereo signal concept given by one microphone; 2) independent of initialization and a priori knowledge of the sources; and 3) it does not require iterative optimization. The separation process consists of two steps: 1) estimation of source characteristics, where the source signals are modeled by the autoregressive process and 2) construction of masks using only the single-channel mixture. A new pseudo-stereo mixture is formulated by weighting and time-shifting the original single-channel mixture. This creates an artificial mixing system whose parameters will be estimated through our proposed weighted complex 2-D histogram. In this paper, we derive the separability of the proposed mixture model. Conditions required for unique mask construction based on maximum likelihood are also identified. Finally, experimental testing on both synthetic and real-audio sources is conducted to verify that the proposed algorithm yields superior performance and is computationally very fast compared with existing methods.

Automatic segmentation of images with low depth of field (DOF) plays an important role in content-based multimedia applications. The proposed approach aims to separate the important objects (i.e., interest regions) of a given image from its defocused background in two stages. In stage one, image blocks are classified into object and background blocks using a novel cluster ensemble algorithm. By indicating the certain pixels (seeds) of the object and background blocks, a hard constraint is provided for the next stage of the approach. In stage two, a minimal graph cut is constructed using object and background seeds, which is based on the max-flow method. Experimental results for a wide range of busy-texture (i.e., noisy) and smooth regions demonstrate that the proposed approach provides better segmentation performance at higher speed compared with the state-of-the-art approaches.

A novel approach for adaptive regularization of 2-D nonnegative matrix factorization is presented. The proposed matrix factorization is developed under the framework of maximum a posteriori probability and is adaptively fine-tuned using the variational approach. The method enables: (1) a generalized criterion for variable sparseness to be imposed onto the solution; and (2) prior information to be explicitly incorporated into the basis features. The method is computationally efficient and has been demonstrated on two applications, that is, extracting features from image and separating single channel source mixture. In addition, it is shown that the basis features of an information-bearing matrix can be extracted more efficiently using the proposed regularized priors. Experimental tests have been rigorously conducted to verify the efficacy of the proposed method.

A novel approach to solve the single-channel source separation (SCSS) problem is presented. Most existing supervised SCSS methods resort exclusively to the independence waveform criteria as exemplified by training the prior information before the separation process. This poses a significant limiting factor to the applicability of these methods to real problem. Our proposed method does not require training knowledge for separating the mixture and it is based on decomposing the mixture into a series of oscillatory components termed as the intrinsic mode functions (IMFs). We show, in this paper, that the IMFs have several desirable properties unique to SCSS problem and how these properties can be advantaged to relax the constraints posed by the problem. In addition, we have derived a novel sparse non-negative matrix factorization to estimate the spectral bases and temporal codes of the sources. The proposed algorithm is a more complete and efficient approach to matrix factorization where a generalized criterion for variable sparseness is imposed onto the solution. Experimental testing has been conducted to show that the proposed method gives superior performance over other existing approaches.

In this paper, a novel solution is developed to solve the problem of separating noisy and post-nonlinearly distorted mixture. In the proposed work, the source signals are nonstationary and temporally correlated. A generative model based on hidden Markov model (HMM) is derived to track the nonstationarity of the source signal while the source signal itself is modeled by temporally correlated generalized Gaussian distribution (GGD) model. The maximum likelihood (ML) approach is developed to estimate the parameters of the proposed model by using the expectation maximization (EM) algorithm and the source signals are estimated by maximum a posteriori (MAP) approach. The strength of the proposed approach lies in the tracking of the nonstationarity of the source signal by HMM and the temporal correlation by the autoregressive (AR) source model. This has resulted in high performance accuracy, fast convergence and efficient implementation of the estimation algorithm. Simulations have been investigated to verify the effectiveness of the proposed algorithm and the results have shown significant improvement has been obtained when compared with nonlinear algorithm without using HMM.

Conventional biometric systems use the original biometrics for authentication, which exposes the users' identities to a risk of being compromised. The randomized Radon signature (RRS) is a cancellable biometric technique that protects face biometrics during authentication by using Radon transform and random projection. The extracted Radon signatures are generated from the parametric domain of the face and then projected into a random multi-space of a uniform distribution. The generated RRS templates are nonreversible, suitable for image-based statistical face classifiers and reissueable. In this paper the fisherface algorithm is used to conduct a comparison between the original face images and the RRS templates. Results have shown a dramatic 53.37% enhancement in the genuine and impostor distributions separation that leads to a 31.34% reduction in the equal error rate.

Biometrics has become a strong candidate to replace traditional authentication systems however biometric data in itself is vulnerable and requires protection. This paper presents a new method to protect face biometric data using one-way transformation in which original face images cannot be retrieved. The secure and reissueable templates are generated by utilizing the Radon transformed signatures of the face biometric and a multi-space random projection. Using an image-based statistical algorithm, authentication is conducted on the transformed templates without the need to reverse them back. Genuine and impostor distributions separation was also improved by 13.82% leading to a 41.35% reduction in the equal error rate.

In this paper, a novel solution is developed to solve blind source separation of postnonlinear convolutive mixtures. The proposed model extends the conventional linear instantaneous mixture model to include both convolutive mixing and postnonlinear distortion. The maximum-likelihood (ML) approach solution based on the expectation-maximization (EM) algorithm is developed to estimate the source signals and the parameters in the proposed nonlinear model. In the proposed solution, the sufficient statistics associated with the source signals are estimated in the E-step, while the model parameters are optimized through these statistics in the M-step. However, the complication resulted from the postnonlinear function associated with the mixture renders these statistics difficult to be formulated in a closed form and hence causes intractability in the parameter optimization. A computationally efficient algorithm is proposed which uses the extended Kalman smoother (EKS) to facilitate the E-step tractable and a set of self-updated polynomials is used as the nonlinearity estimator to facilitate closed form estimations of the parameters in the M-step. The theoretical foundation of the proposed solution has been rigorously developed and discussed in details. Both simulations and recorded speech signals have been carried out to verify the success and efficacy of the proposed algorithm. Remarkable improvement has been obtained when compared with the existing algorithms.

A novel polynomial-based neural network is proposed for nonlinear blind source separation. We focus our research on a recently presented mono-nonlinearity mixture where a linear mixing matrix is slotted into two mutually inverse nonlinearities. In this paper, we generalize the mono-nonlinearity mixing system to the situation where different nonlinearities are applied to the source signals. The theory of series reversion is merged with the neural network demixer to perform two layers of mutually inverse nonlinearities. The corresponding parameter learning algorithm for the proposed polynomial-based neural network demixer is also presented. Simulations have been carried out to verify the efficacy of the proposed approach. We demonstrate that the proposed network can successfully recover the original source signals in a blind mode under nonlinear mixing conditions

The post-nonlinear undercomplete blind signal separation problem is solved by a Bayesian approach in this paper. The proposed algorithm applies the generalized Gaussian model to approximate the prior distribution probability and a maximum a posteriori (MAP) based learning algorithm to estimate the source signals, mixing matrix and the nonlinearity of the mixing process. The mixing nonlinearity is modeled by a multilayer perceptron (MLP) neural network. In our proposed algorithm, the source signals, mixing matrix and the parameters of the MLP are iteratively updated in an alternate manner until they converges to a fixed value. The noise variance is regarded as the hyperparameter which is estimated in a closed form. Simulations based on real audio have been carried out to investigate the efficacy of the proposed algorithm. A performance gain of over 125% has been achieved when compared to linear approach

Linear independent component analysis (ICA) played an important role in the development of various signal processing techniques due to the inherent simplicity. However, the assumption of linear mixture is always violated in real life, which narrows down its applications. In this paper, the problem of nonlinear independent component analysis is considered. Based on a new type of nonlinear mixing model, we propose a two-stage series-based approach to recover the original source signals. The two-stage series-based algorithm offers significant advantages in terms of reduced computational complexity and better learning dynamics of the trajectory. Simulations have also been carried out to verify the efficacy of the proposed method

In this paper, a method of two dimensional Fisher principal component analysis (2D-FPCA) in the two dimensional principal component analysis (2DPCA) transformed space is analyzed and its nature is revealed, i.e., 2D-FPCA is equivalent to 2DPCA plus two dimensional Fisher linear discriminant analysis (2DFLD). Based on this result, a more transparent 2D FPCA algorithm is developed. That is, 2DPCA is performed first and then 2DFLD is used for the second feature extraction in the 2DPCA transformed space. Since 2D FPCA is based on the 2D image matrices, the vectorization of the image is not required. Thus, 2D FPCA optimizes the evaluation of the image matrices, the between and within matrices, by transforming them into a smaller 2DPCA space. In the linear discriminant analysis (LDA) based face recognition techniques, image representation and recognition is statistically dependent on the evaluation of the between and within matrices. This leads to the following benefits; the proposed 2D-FPCA yields greater recognition accuracy while reduces the overall computational complexity. Finally, the effectiveness of the proposed algorithm is verified using the ORL database as a benchmark. The new algorithm achieves a recognition rate of 95.50% compared to the recognition rate of 90.00% for the Fisherface method

In this letter, a new type of nonlinear mixture is derived and developed into a multinonlinearity constrained mixing model. The proposed signal separation solution integrates the Theory of Series Reversion with a polynomial neural network whereby the hidden neurons are spanned by a set of mutually reversed activation functions. Simulations have been undertaken to support the theory of the proposed scheme and the results indicate promising performance.

Conventional assumptions of square mixing matrix and negligible noise adopted in blind signal separation do not always correspond with real applications. Signal detection from a small number of sensors is often required in signal and image modeling and biomedical applications. The paper proposes a new algorithm to estimate accurately signals from underdetermined mixtures with fewer restrictions and assumptions compared with existing techniques. The strength of this algorithm is that it does not adopt the conventional assumptions on the mixing, signals and noise. The algorithm is capable of separating orthogonal and non-orthogonal mixtures of both sparse and non-sparse signals with additional Gaussian or nonGaussian noise. This algorithm is also applicable to separating time-varying and instantaneous mixtures. Simulation results demonstrate the efficacy of the proposed algorithm for separation of time-varying mixtures in the presence of noise.

An electronic circuit is presented for a new type of neural network, which gives a recognition rate of over 100 kHz. The network is used to classify handwritten numerals, presented as Fourier and wavelet descriptors, and has been shown to train far quicker than the popular backpropagation network while maintaining classification accuracy.

A new type of neural network for recognition tasks is presented in this paper. The network, called the dynamic supervised forward-propagation network (DSFPN), is based on the forward only version of the counterpropagation network (CPN). The DSFPN, trains using a supervised algorithm and can grow dynamically during training, allowing subclasses in the training data to be learnt in an unsupervised manner. It is shown to train in times comparable to the CPN while giving better classification accuracies than the popular backpropagation network. Both Fourier descriptors and wavelet descriptors are used for image preprocessing and the wavelets are proven to give a far better performance.

A bandwidth efficient image codec is offered as a direct replacement for mobile radio speech codecs in second generation wireless systems, such as the Pan European GSM system. The image codec is contrived for Quarter Common Intermediate Format (QCIF) videophone sequences, and uses the 2D orthogonal wavelet transform to decompose the Displaced Frame Difference (DFD), into a four band structure using Quadrature Mirror Filters (QMF). The transformed coefficients are then compressed using classified vector quantisation (CVQ) and then multiplexed to a time division multiple access (TDMA) slot and modulated using Pilot Symbol Assisted Quadrature Amplitude Modulation (PSAQAM). The proposed image codec does not suffer from blocking effects that are visually disjointed and has therefore a major advantage over current discrete cosine transform (DCT) methods. A further advantage of using wavelet based transforms is that they require less hardware and are simpler to implement on a DSP chip than Fourier based methods. The method proposed is suitable for implementation in VLSI technology.

A concurrent error detection (CED) scheme has been proposed by Jou and Abraham (1988)for a radix-2 FFT network. The method uses a coding scheme to ensure that all the modelled faults are detectable. The same method has been applied to the prime factor algorithm (PFA) FFT network. The results show that the CED scheme is applicable to multi-factor PFA networks and it is efficient, only, for large N. Since the PFA is a minimum multiplication algorithm, it is logical to adopt a minimum multiplication CED scheme. For this purpose, a suitable CED scheme has been developed such that the data coding is no longer needed which results in less hardware and time overheads. The new scheme provides a stage-by-stage error detection without causing time delay and additional roundoff noise. The hardware overhead is substantially reduced with improved error detection performance since the scheme needs to consider the roundoff error of only one stage instead of the entire FFT network.