Lectures on Cauchy’s Problem in Linear Partial Differential Equations. By J. Hadamard. Pp. viii+316. 15s.net. 1923. (Per Oxford University Press.)

Denoising is one of the key problems of image processingRecent research suggests that wavelet coefficients for natural images tend to have a Laplacian distribution functionIf wavelet transform used is orthogonal,noises in wavelet coefficients of those images corrupted by additive white Gaussian noises are also Gaussian and whiteImage denoising can be realized by filtering the coefficients based on Bayesian estimatorThis paper compares the linear estimator and Bayesian estimator in noise reductionSimulation results show that the latter has better performance

Bayesian denoising of visual images in the wavelet domain

Compressed sensing (CS) comprises a set of relatively new techniques that exploit the underlying structure of data sets allowing their reconstruction from compressed versions or incomplete information. CS reconstruction algorithms are essentially nonlinear, demanding heavy computation overhead and large storage memory, especially in the case of multidimensional signals. Excellent review papers discussing CS state-of-the-art theory and algorithms already exist in the literature, which mostly consider data sets in vector forms. In this paper, we give an overview of existing techniques with special focus on the treatment of multidimensional signals (tensors). We discuss recent trends that exploit the natural multidimensional structure of signals (tensors) achieving simple and efficient CS algorithms. The Kronecker structure of dictionaries is emphasized and its equivalence to the Tucker tensor decomposition is exploited allowing us to use tensor tools and models for CS. Several examples based on real world multidimensional signals are presented, illustrating common problems in signal processing such as the recovery of signals from compressed measurements for magnetic resonance imaging (MRI) signals or for hyper-spectral imaging, and the tensor completion problem (multidimensional inpainting). WIREs Data Mining Knowl Discov 2013, 3:355–380. doi: 10.1002/widm.1108



Conflict of interest: The authors have declared no conflicts of interest for this article.



For further resources related to this article, please visit the WIREs website.

Multidimensional compressed sensing and their applications

Peripheral blood smear analysis using image processing approach for diagnostic purposes: A review

Dysgraphia, a disorder affecting the written expression of symbols and words, negatively impacts the academic results of pupils as well as their overall well-being. The use of automated procedures can make dysgraphia testing available to larger populations, thereby facilitating early intervention for those who need it. In this paper, we employed a machine learning approach to identify handwriting deteriorated by dysgraphia. To achieve this goal, we collected a new handwriting dataset consisting of several handwriting tasks and extracted a broad range of features to capture different aspects of handwriting. These were fed to a machine learning algorithm to predict whether handwriting is affected by dysgraphia. We compared several machine learning algorithms and discovered that the best results were achieved by the adaptive boosting (AdaBoost) algorithm. The results show that machine learning can be used to detect dysgraphia with almost 80% accuracy, even when dealing with a heterogeneous set of subjects differing in age, sex and handedness.

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Dysgraphia detection through machine learning.

Dyslexia, Dysgraphia and Dyscalculia are significant learning disabilities that affect around 10% of children in the world. Despite the advancement of technology literacy in the community, limited attention has been given for screening and intervention of these disabilities using mobile applications in Sri Lanka. In this research, one of the first deep learning and machine learning based mobile applications, named “Pubudu” was developed for screening and intervention of dyslexia, dysgraphia and dyscalculia supporting local languages. In “Pubudu” we have followed up clinical screening and diagnostic procedures recommended by health professionals for screening and intervention. The screening of dyslexia, letter dysgraphia and numeric dysgraphia was carried out using deep neural network and the screening for dyscalculia was carried out using machine learning techniques. Intervention techniques are implemented using gamified environments. System testing was carried out using 50 differently abled children and 50 typical children. With the initial dataset 88%, 58%, 99% screening accuracies are achieved in neural networks for letter dysgraphia, dyslexia and numeric dysgraphia screening while dysgraphia, whereas 90% accuracy was achieved for dyscalculia. Handwritten letters and numbers were fed as inputs to CNN model in letter dysgraphia and numeric dysgraphia while embedded audio clips of letter pronunciation were fed in to voice recognition CNN model in dyslexia. “Pubudu” shows significant potential for screening and intervention of dyslexia, dysgraphia and dyscalculia in local languages motivating children and interactively making them able and would be an enabling app for most of the underprivileged children in Sri Lanka.

Pubudu: Deep Learning Based Screening And Intervention of Dyslexia, Dysgraphia And Dyscalculia

Autism spectrum disorder is an umbrella term for a group of neurodevelopmental disorders that is associated with impairments to social interaction, communication, and behaviour. Typically, autism s...

/pdf/a-predictive-model-for-paediatric-autism-screening-3gjkozobc6.pdf

A predictive model for paediatric autism screening

Cell morphology has been an active area in the field of bio-medical research. When applied for blood microscopic images, one can study blood cell characteristics and detect abnormalities. In this paper, we introduce an automatic, cost effective and accurate way of red blood cell analysis and evaluation through Blob detection, Morphology operations and Hough circle transformation techniques for identification of four common types of anemia. Our research has filled the gaps in the existing literature by developing an integrated system to Count RBC, Diagnose Elliptocytes, Microcytic, Macrocyte and Spherocytes Anemia, Detect abnormalities and Separate overlapped cells, automatically, accurately and efficiently. The result shows an insight in the manually processed results with 99.545% accuracy of RBC count. Each sub method is closely running in the range 91%-97% of accuracy. The achievements are highlighted as efficiency through automation, cost effective, elimination of human error and easy to manipulate.

/pdf/morphology-based-automatic-disease-analysis-through-2huruvlakt.pdf

Morphology Based Automatic Disease Analysis through Evaluation of Red Blood Cells

A critical assumption in applying Godard CMA algorithm for blind deconvolution and equalization is the assumption of an independently distributed source. Almost all the applications in the literature have based their implementations on this assumption. To our knowledge, no research has been done on the effect of source correlation on adaptive blind deblurring of images through CMA, and this paper addresses that gap, coming up with a novel model of addressing the source correlation problem in the image deblurring through CMA.

Minimum Kurtosis CMA Deconvolution for Blind Image Restoration

Though blood cell manipulation has been an interesting research area for many years, most of the techniques presented in literature produce poor segmentation results for images with high overlapped blood cells. In this paper, we introduce a fully automatic low cost and accurate system to identify four common types of anemia and report on blood cell count. The results of our system indicate a good impact with the manually processed results of 99.678% accuracy of Red Blood Cell count. The diagnosis of Elliptocytes, Microcytes, Macrocyte and Spherocytes anemia result in the range of 91%–97% accuracy.

Pradeepa Samarasinghe

Papers

Pubudu: Deep Learning Based Screening And Intervention of Dyslexia, Dysgraphia And Dyscalculia

A predictive model for paediatric autism screening

Morphology Based Automatic Disease Analysis through Evaluation of Red Blood Cells

Minimum Kurtosis CMA Deconvolution for Blind Image Restoration

Automatic anemia identification through morphological image processing