Improving the performance of the needleman-wunsch algorithm using parallelization and vectorization techniques

The Needleman-Wunsch (NW) is a dynamic programming algorithm used in the pairwise global alignment of two biological sequences. In this paper, three sets of parallel implementations of the NW algorithm are presented using a mixture of specialized software and hardware solutions: POSIX Threads-based, SIMD Extensions-based and a GPU-based implementations. The three implementations aim at improving the performance of the NW algorithm on large scale input without affecting its accuracy. Our experiments show that the GPU-based implementation is the best implementation as it achieves performance 72.5X faster than the sequential implementation, whereas the best performance achieved by the POSIX threads and the SIMD techniques are 2X and 18.2X faster than the sequential implementation, respectively.

     

An intelligent system for sorting pistachio nut varieties

An intelligent pistachio nut sorting system combining acoustic emissions analysis, Principal Component Analysis (PCA) and Multilayer Feedforward Neural Network (MFNN) classifier was developed and tested. To evaluate the performance of the system 3200 pistachio nuts from four native Iranian pistachio nut varieties were used. Each variety was consisted of 400 split-shells and 400 closed-shells nut. The nuts were randomly selected, slide down a chute, inclined 60° above the horizontal, on which nuts slide down to impact a steel plate and their acoustic signals were recorded from the impact. Sound signals in the time-domain are saved for subsequent analysis. The method is based on feature generation by Fast Fourier Transform (FFT), feature reduction by PCA and classification by MFNN. Features such as amplitude, phase and power spectrum of sound signals are computed via a 1024-point FFT. By using PCA more than 98% reduction in the dimension of feature vector is achieved. To find the optimal MFNN classifier, various topologies each having different number of neurons in the hidden layer were designed and evaluated. The best MFNN model had a 40–12–4 structure, that is, a network having one hidden layer with 40 neurons at its input, 12 neurons in the hidden layer and 4 neurons (pistachio varieties) in the output layer. The selection of the optimal model was based on the examination of mean square error, correlation coefficient and correct separation rate (CSR). The CSR or total weighted average in system accuracy for the 40–12–4 structure was 97.5%, that is, only 2.5% of nuts were misclassified.     

Building a multi-modal Arabic corpus (MMAC)

Traditionally, a corpus is a large structured set of text, electronically stored and processed. Corpora have become very important in the study of languages. They have opened new areas of linguistic research, which were unknown until recently. Corpora are also key to the development of optical character recognition (OCR) applications. Access to a corpus of both language and images is essential during OCR development, particularly while training and testing a recognition application. Excellent corpora have been developed for Latin-based languages, but few relate to the Arabic language. This limits the penetration of both corpus linguistics and OCR in Arabic-speaking countries. This paper describes the construction and provides a comprehensive study and analysis of a multi-modal Arabic corpus (MMAC) that is suitable for use in both OCR development and linguistics. MMAC currently contains six million Arabic words and, unlike previous corpora, also includes connected segments or pieces of Arabic words (PAWs) as well as naked pieces of Arabic words (NPAWs) and naked words (NWords); PAWs and Words without diacritical marks. Multi-modal data is generated from both text, gathered from a wide variety of sources, and images of existing documents. Text-based data is complemented by a set of artificially generated images showing each of the Words, NWords, PAWs and NPAWs involved. Applications are provided to generate a natural-looking degradation to the generated images. A ground truth annotation is offered for each such image, while natural images showing small paragraphs and full pages are augmented with representations of the text they depict. A statistical analysis and verification of the dataset has been carried out and is presented. MMAC was also tested using commercial OCR software and is publicly and freely available.     

Modelling and monitoring of passive control structures in human movement

Passive tissues, ligaments and cartilage are vital to human movement. Their contribution to stability, joint function and joint integrity is essential. The articulation of their functions and quantitative assessment of what they do in a healthy or injured state are important in athletics, orthopaedics, medicine and health. In this paper, the role of cartilage and ligaments in stability of natural contacts, connections and joints is articulated by including them in two very simple skeletal systems: one- and three-link rigid body systems. Based on the Newton–Euler equations, a state space presentation of the dynamics is discussed that allows inclusion of ligament and cartilage structures in the model, and allows for Lyapunov stability studies for the original and reduced systems. The connection constraints may be holonomic and non-holonomic depending on the structure of the passive elements. The development is pertinent to the eventual design of a computational framework for the study of human movement that involves computer models of all the relevant skeletal, neural and physiological elements of the central nervous system (CNS). Such a structure also permits testing of different hypotheses about the functional neuroanatomy of the CNS, and the study of the effects and dynamics of disease, deterioration, aging and injuries. The formulation here is applied to one- and three-link systems. Digital computer simulations of a two rigid body system are presented to demonstrate the feasibility and effectiveness of the approach and the methods.     

Video Identification Based on Watermarking Schemes and Visual Cryptography

Related to the growth of data sharing on the Internet and the wide - spread use of digital media, multimedia security and copyright protection have become of broad interest. Visual cryptography () is a method of sharing a secret image between a group of participants, where certain groups of participants are defined as qualified and may combine their share of the image to obtain the original, and certain other groups are defined as prohibited, and even if they combine knowledge of their parts, they can’t obtain any information on the secret image. The visual cryptography is one of the techniques which used to transmit the secrete image under the cover picture. Human vision systems are connected to visual cryptography. The black and white image was originally used as a hidden image. In order to achieve the owner’s copy right security based on visual cryptography, a watermarking algorithm is presented. We suggest an approach in this paper to hide multiple images in video by meaningful shares using one binary share. With a common share, which we refer to as a smart key, we can decrypt several images simultaneously. Depending on a given share, the smart key decrypts several hidden images. The smart key is printed on transparency and the shares are involved in video and decryption is performed by physically superimposing the transparency on the video. Using binary, grayscale, and color images, we test the proposed method.     

Neural Networks Pipeline for Offline Machine Printed Arabic OCR

In the context of Arabic optical characters recognition, Arabic poses more challenges because of its cursive nature. We purpose a system for recognizing a document containing Arabic text, using a pipeline of three neural networks. The first network model predicts the font size of an Arabic word, then the word is normalized to an 18pt font size that will be used to train the next two models. The second model is used to segment a word into characters. The problem of words segmentation in the Arabic language, as in many similar cursive languages, presents a challenge to the OCR systems. This paper presents a multichannel neural network to solve the offline segmentation of machine-printed Arabic documents. The segmented characters are then fed as an input to a convolutional neural network for Arabic characters recognition. The font size prediction model produced a test accuracy of 99.1%. The accuracy of the segmentation model using one font is 98.9%, while four-font model showed 95.5% accuracy. The whole pipeline showed an accuracy of 94.38% on Arabic Transparent font of size 18pt from APTI data set.     

Adaptive and Predictive Control Strategies for Wind Turbine Systems: A Survey

The wind turbine (WT) is a renewable energy conversion device for transformation of kinetic energy from the wind to mechanical energy for subsequent use in different forms. This paper focuses on wind turbine control design strategies. The content is divided into the following parts: 1) An overview of the recent advances that have been made in the application of adaptive and model predictive control strategies for wind turbines. 2) Summarizes some important aspects of modeling of wind turbines for control studies. 3) Provides an outlook on the application of adaptive model predictive control for uncertain systems to stimulate new research interests for wind turbine systems. We provide an overall picture of the research results with evaluation of the merits/demerits.      

Persistent left superior vena cava: Two case reports and a review from nephrologists’ perspective

Thoracic venous anomalies without congenital heart anomalies are present in minority of the population, but they are frequent enough to be encountered while placing hemodialysis catheters through the jugular or subclavian veins. Persistent left superior vena cava is the most commonly seen anomaly and it is rarely noticed before the observation of an unusual course of hemodialysis catheter or guidewire on chest X‐ray. We present two patients with previously unspotted persistent left superior vena cava and uncomplicated hemodialysis catheter insertions through the internal jugular veins with good catheter functions. Review of the relevant literature from a nephrologists’ perspective with technical aspects is provided.     

On the Effect of Measurement Error with Linearly Increasing‐Type Variance on Simultaneous Monitoring of Process Mean and Variability

In most quality control applications, the errors generated from measurement system can adversely affect the ability of control charts in detecting out‐of‐control conditions. In this paper, the effect of measurement error with linearly increasing‐type variance on the performance of maximum exponentially weighted moving average and mean‐squared deviation (MAX‐EWMAMS) control chart is studied. For this purpose, different out‐of‐control scenarios including mean shifts, variance shifts, and simultaneous shifts in both are considered, and the detecting performance of the proposed approach is investigated through simulation study. The results of simulation study in terms of three criteria including average run length, standard deviation of run lengths, and the empirical distribution of run lengths prove that the measurement error with linearly increasing‐type variance can adversely affect the performance of MAX‐EWMAMS control. Copyright © 2015 John Wiley & Sons, Ltd.     

Structural,optical and electrical properties of PbS and PbSe quantum dot thin films

PbS and PbSe were prepared by hot injection method. The powders were used for preparing the corresponding films by using thermal evaporation technique. The structural, optical and electrical properties of PbS and PbSe thin films were investigated. The structural properties of PbS and PbSe were investigated by X-ray diffraction, transmission electron microscopy and energy dispersive X-ray techniques (EDX). PbS and PbSe films were found to have cubic rock salt structure. The particles size ranged from 1.32 to 2.26 nm for PbS and 1.28–2.48 nm for PbSe. EDX results showed that PbS films have rich sulphur content, while PbSe films have rich lead content. The optical constants (absorption coefficient and the refractive index) of the films were determined in the wavelength range 200–2500 nm. The optical energy band gap of PbS and PbSe films was determined as 3.25 and 2.20 eV, respectively. The refractive index, the optical dielectric constant and the ratio of charge carriers concentration to its effective mass were determined. The electrical resistivity, charge carriers concentration and carriers mobility of PbS at room temperature were determined as 0.55 Ω cm, 1.7 × 10¹⁶ cm^?3 and 656 cm² V^?1 s^?1, respectively, and for PbSe films they were determined as 0.4 Ω cm, 9 × 10¹⁵ cm^?3 and 1735 cm² V^?1 s^?1, respectively. These electrical parameters were investigated as a function of temperature.