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The aim of any fingerprint image compression technique is to achieve a maximum amount of compression with an adequate quality compressed image which is suitable for fingerprint recognition. Currently available techniques in the literature provide 100% recognition only up to a compression ratio of 180:1. The performance of any identification technique inherently depends on the techniques with which images are compressed. To improve the identification accuracy while the images are highly compressed, a multiwavelet-based identification approach is proposed in this paper. Both decimated and undecimated coefficients of SA4 (Symmetric Antisymmetric) multiwavelet are used as features for identification. A study is conducted on the identification performance of the multiwavelet transform with various sizes of images compressed using both wavelets and multiwavelets for fair comparison. It was noted that for images with size power of 2, the decimated multiwavelet-based compression and identification give a better performance compared to other combinations of compression/identification techniques whereas for images with size not a power of 2, the undecimated multiwavelet transform gives a better performance compared to other techniques. A 100% identification accuracy was achieved for the images from NIST-4, NITGEN, FVC2002DB3_B, FVC2004DB2_B and FVC2004DB1_B databases for compression ratios up to 520:1, 210:1, 445:1, 545:1 and 1995:1, respectively.

An automatic cDNA microarray image processing using an improved fuzzy clustering algorithm is presented in this paper. The spot segmentation algorithm proposed uses the gridding technique developed by the authors earlier, for finding the co-ordinates of each spot in an image. Automatic cropping of spots from microarray image is done using these co-ordinates. The present paper proposes an improved fuzzy clustering algorithm Possibility fuzzy local information c means (PFLICM) to segment the spot foreground (FG) from background (BG). The PFLICM improves fuzzy local information c means (FLICM) algorithm by incorporating typicality of a pixel along with gray level information and local spatial information. The performance of the algorithm is validated using a set of simulated cDNA microarray images added with different levels of AWGN noise. The strength of the algorithm is tested by computing the parameters such as the Segmentation matching factor (SMF), Probability of error (pe), Discrepancy distance (D) and Normal mean square error (NMSE). SMF value obtained for PFLICM algorithm shows an improvement of 0.9 % and 0.7 % for high noise and low noise microarray images respectively compared to FLICM algorithm. The PFLICM algorithm is also applied on real microarray images and gene expression values are computed.

A new way of reordering spatial orientation tree of SPIHT for improving compression efficiencies for monochrome and color images has been proposed. Reordering ensures that SPIHT algorithm codes more significant information in the initial bits. List of insignificant pixels and sets are initialized with fewer number of coefficients compared to conventional SPIHT for monochrome images. For color images an altered parent offspring relationship and an extra level of wavelet decomposition on chrominance planes were performed. PSNR improvement of 32.06% was achieved at 0.01 bpp for monochrome images and 19.76% for color images at 0.05 bpp compared to conventional schemes.

cDNA microarray image provides useful information about thousands of gene expressions simultaneously. Hence microarray image segmentation is an important task. In this paper, existing fuzzy clustering image segmentation methods in the literature have been tested for its suitability to perform segmentation of noisy cDNA microarray images. The algorithms considered for this purpose include fuzzy clustering based methods like, Fuzzy c-means (FCM), Possibilistic c means (PCM), Possibilistic fuzzy c means (PFCM) and Fuzzy local information c means (FLICM). The results of segmentation shows that FLICM is better in segmenting microarray spots compared to the other under the presence of noise.

In this article, techniques have been presented for faster evolution of wavelet lifting coefficients for fingerprint image compression (FIC). In addition to increasing the computational speed by 81.35%, the coefficients performed much better than the reported coefficients in literature. Generally, full-size images are used for evolving wavelet coefficients, which is time consuming. To overcome this, in this work, wavelets were evolved with resized, cropped, resized-average and cropped-average images. On comparing the peak- signal-to-noise-ratios (PSNR) offered by the evolved wavelets, it was found that the cropped images excelled the resized images and is in par with the results reported till date. Wavelet lifting coefficients evolved from an average of four 256 × 256 centre-cropped images took less than 1/5th the evolution time reported in literature. It produced an improvement of 1.009 dB in average PSNR. Improvement in average PSNR was observed for other compression ratios (CR) and degraded images as w...

A sparse view approach for the reconstruction of object profile has been proposed for Microwave Diffraction Tomography. The forward scattered fields are acquired through sensing elements with the object illuminated from limited number of angles by a plane wave. A Re-weighted Basic Pursuit Algorithm is used to solve the inverse ill-posed problem. It is shown that the reconstructed image preserves the features of the original even after reducing the number of views by a factor of three. This further leads to reduction in data acquisition time and computational complexity.

Genetic algorithm based Fuzzy C Mean (GAFCM) technique is used to segment spots of complimentary DNA (c-DNA) microarray images for finding gene expression is proposed in this paper. To evaluate the performance of the algorithm, simulated microarray slides were generated whose actual mean values were known and is used for testing. K-means, Fuzzy C Means (FCM) and the proposed GAFCM algorithm were applied to the simulated images for the separation of the foreground (FG) spot signal information from background (BG) and the results were compared. The strength of the algorithm was tested by evaluating the segmentation matching factor, coefficient of determination, concordance correlation and gene expression values. From the results it is observed that the segmentation ability of GAFCM is better compared to FCM and K- Means algorithms.

An improved algorithm to compress fingerprint images based on sparse representation is proposed. The algorithm includes two parts namely, construction of the dictionary and the compression process. In order to construct the dictionary, recursive least squares dictionary learning algorithm (RLS-DLA) is used. In RLS-DLA, any given fingerprint is divided into small blocks called patches. Then sparse coding is performed on each patch and the dictionary is continuously updated. Each patch is represented as a linear combination of a few columns from the pre-constructed fingerprint dictionary, which leads to compression. To compute a linear expansion of the current patch, orthogonal projection of the patch on the pre-constructed dictionary element is done. Then the representation is quantized and encoded. The results obtained through RLS-DLA shows improvement of 2.98% in PSNR compared to K-singular value decomposition (K-SVD) dictionary learning algorithm.

Speech emotion recognition has an increasingly significant role in human — computer interfaces as well as in the communication among human beings. This paper presents the results of investigations in emotion recognition based on the prosodic features of 1050 segmental and 1400 supra segmental speech wave files in English. The investigations were done in neutral and six basic emotions collected from ten female speakers of Indian English. The features considered in this investigation are intensity, pitch, and duration or speech rate; which were statistically analyzed. The role of each feature in emotion recognition was quantitatively assessed in terms of the classification rates of the K-Nearest Neighbor, Naive Bayes and the Artificial Neural Network classifiers. At the segmental level, all emotions could be classified, with an average emotion classification rate of 95.91%, based on the prosodic feature set, and these results were validated. The obtained results indicate saving of time and effort by the classification of emotions from minimum inputs and is therefore significant. Besides, the existence of prosody has been acknowledged at the supra segmental level only, as per available literature. At the supra segmental level, all emotions have been recognized at an average classification of 91.96%.

In this paper an attempt has been made to determine the number of Premature Ventricular Contraction (PVC) cycles accurately from a given Electrocardiogram (ECG) using a wavelet constructed from multiple Gaussian functions. It is difficult to assess the ECGs of patients who are continuously monitored over a long period of time. Hence the proposed method of classification will be helpful to doctors to determine the severity of PVC in a patient. Principal Component Analysis (PCA) and a simple classifier have been used in addition to the specially developed wavelet transform. The proposed wavelet has been designed using multiple Gaussian functions which when summed up looks similar to that of a normal ECG. The number of Gaussians used depends on the number of peaks present in a normal ECG. The developed wavelet satisfied all the properties of a traditional continuous wavelet. The new wavelet was optimized using genetic algorithm (GA). ECG records from Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) database have been used for validation. Out of the 8694 ECG cycles used for evaluation, the classification algorithm responded with an accuracy of 97.77%. In order to compare the performance of the new wavelet, classification was also performed using the standard wavelets like morlet, meyer, bior3.9, db5, db3, sym3 and haar. The new wavelet outperforms the rest.

In this paper, an improved technique for evolving wavelet coefficients refined for compression and reconstruction of fingerprint images is presented. The FBI fingerprint compression standard [1, 2] uses the cdf 9/7 wavelet filter coefficients. Lifting scheme is an efficient way to represent classical wavelets with fewer filter coefficients [3, 4]. Here Genetic algorithm (GA) is used to evolve better lifting filter coefficients for cdf 9/7 wavelet to compress and reconstruct fingerprint images with better quality. Since the lifting filter coefficients are few in numbers compared to the corresponding classical wavelet filter coefficients, they are evolved at a faster rate using GA. A better reconstructed image quality in terms of Peak-Signal-to-Noise-Ratio (PSNR) is achieved with the best lifting filter coefficients evolved for a compression ratio 16∶1. These evolved coefficients perform well for other compression ratios also.

This paper discusses the implementation details of a child friendly, good quality, English text-to-speech (TTS) system that is phoneme-based, concatenative, easy to set up and use with little memory. Direct waveform concatenation and linear prediction coding (LPC) are used. Most existing TTS systems are unit-selection based, which use standard speech databases available in neutral adult voices. Here reduced memory is achieved by the concatenation of phonemes and by replacing phonetic wave files with their LPC coefficients. Linguistic analysis was used to reduce the algorithmic complexity instead of signal processing techniques. Sufficient degree of customization and generalization catering to the needs of the child user had been included through the provision for vocabulary and voice selection to suit the requisites of the child. Prosody had also been incorporated. This inexpensive TTS system was implemented in MATLAB, with the synthesis presented by means of a graphical user interface (GUI), thus making it child friendly. This can be used not only as an interesting language learning aid for the normal child but it also serves as a speech aid to the vocally disabled child. The quality of the synthesized speech was evaluated using the mean opinion score (MOS).

In this article, a multilevel hybrid approach which gives better accuracy for gender classification is presented. The first level uses discrete wavelet transform DWT, singular value decomposition SVD and principal component analysis PCA techniques to derive three independent sets of feature vectors for simultaneous gender classification by three independent neural networks. Coefficients similar to cdf9/7 DWT lifting coefficients and optimum values of initial seeds for the classifiers are evolved using genetic algorithm GA, for obtaining better feature vectors. Use of lifting coefficients causes faster evolution. In the second stage, the output is derived by decision formulated based on the outputs of the individual classifiers. With a database consisting left thumb impressions of 100 males and 100 females, an overall success rate of 93.94% and an average improvement of 5.24% accuracy over the existing classifiers is achieved. Use of feature vectors having lesser number of elements enhances the speed of operation of the classifier as well.

In this paper we present a new method for a visual cryptography scheme that uses random basis column pixel expansion technique. This method encoded the secret image into 'n' share images in a recursive manner, which have dimensions identical to the secret image. In this scheme, the encoded n shares further encoded into number of subshares recursively. It will increase the level of security of the encrypted message.

A method for automatic classification of Arrhythmias from Electrocardiogram based on features generated from a new Continuous Wavelet Transform (CWT) is presented in this paper. The classification performance was studied using the most commonly available database, the MIT-BIH arrhythmia database. The new wavelet for classification was evolved using Genetic Algorithm (GA). The optimum wavelet for classification was obtained after several runs of the GA algorithm. The class labeling was followed according to the Association for the Advancement of Medical Instrumentation (AAMI). The wavelet scales corresponding to the different frequency levels giving maximum classification performance was identified. Probabilistic Neural Network (PNN) classifier was used for classification. The proposed classification system offered an overall sensitivity of 97% for Normal beats (N), 75% for Supraventricular beats (Sv) and 93% for Ventricular beats (V) which is better than existing results reported in literature. This technique could exclusively identify some of the isolated abnormalities compared to other results reported.