An efficient implicit regularized Lagrangian twin support vector regression

Twin support vector regression (TSVR) and Lagrangian TSVR (LTSVR) satisfy only empirical risk minimization principle. Moreover, the matrices in their formulations are always positive semi-definite. To overcome these problems, we propose an efficient implicit Lagrangian formulation for the dual regularized twin support vector regression, called IRLTSVR for short. By introducing a regularization term to each objective function, the optimization problems in our IRLTSVR are positive definite and implement the structural risk minimization principle. Moreover, the 1-norm of the vector of slack variable is replaced with 2-norm to make the objective functions strongly convex. Our IRLTSVR solves two systems of linear equations instead of solving two quadratic programming problems (QPPs) in TSVR and one large QPP in SVR, which makes the learning speed of IRLTSVR faster than TSVR and SVR. Particularly, we compare three implementations of IRLTSVR with existing approaches. Computational results on several synthetic and real-world benchmark datasets clearly indicate the effectiveness and applicability of the IRLTSVR in comparison to SVR, TSVR and LTSVR.     

Computer Vision Techniques Applied for Diagnostic Analysis of Retinal OCT Images: A Review

The retina is a tiny layer at the posterior pole of an eye and is made up of tissues sensitive to light, these tissues generate nerve signals that pass through the optic nerve to the brain. A retinal disorder occurs when the retina malfunctions; glaucoma, diabetic retinopathy and pathologic myopia are retinal disorders and principal causes of blindness worldwide. These retinal disorders are often diagnosed and treated by an ophthalmologist. However, to accurately assess a retinal disease, ophthalmologist would need qualitative and quantitative analysis of the disease, it’s early and current statistics, but acquisition of these measurements are not possible through manual techniques, there should be automated computer aided diagnosis (CAD) systems to assist ophthalmologists. In this comprehensive review, an analysis and evaluation has been performed of different computer vision and image processing approaches applied to OCT images for automatic diagnosis of retinal disorders. We also reported disease causes, symptoms and pathologies manifestations within OCT images, which can serve as baseline knowledge for development of an automated CAD system. Hence, this disease specific review offers a good understanding to analyze visual impairments from retinal OCT images which will help researcher to design enhanced therapeutic systems for retinal disorders.     

A Novel Framework for Human Activity Recognition with Time Labelled Real Time Sensor Data

Human activity recognition is an effective approach for identifying the characteristics of historical data. In the past decades, different shallow classifiers and handcrafted features were used to identify the activities from the sensor data. These approaches are configured for offline processing and are not suitable for sequential data. This article proposes an adaptive framework for human activity recognition using a deep learning mechanism. This deep learning approach forms the deep belief network (DBN), which contains a visible layer and hidden layers. The processing of raw sensor data is performed by these layers and the activity is identified at the top most layers. The DBN is tested using the real time environment with the help of mobile devices that contain an accelerometer, a magnetometer, and a gyroscope. The results are analyzed with the metrics of precision, recall, and the F1-score. The results proved that the proposed method has a higher F1_score when compared to the existing approach.     

Mobile-cloud assisted framework for selective encryption of medical images with steganography for resource-constrained devices

In this paper, the problem of outsourcing the selective encryption of a medical image to cloud by resource-constrained devices such as smart phone is addressed, without revealing the cover image to cloud using steganography. In the proposed framework, the region of interest of the medical image is first detected using a visual saliency model. The detected important data is then embedded in a host image, producing a stego image which is outsourced to cloud for encryption. The cloud which has powerful resources, encrypts the image and sent back the encrypted marked image to the client. The client can then extract the selectively encrypted region of interest and can combine it with the region of non-interest to form a selectively encrypted image, which can be sent to medical specialists and healthcare centers. Experimental results and analysis validate the effectiveness of the proposed framework in terms of security, image quality, and computational complexity and verify its applicability in remote patient monitoring centers.     

Laminar convection in the annulus of a double-pipe with triangular fins

A steady and laminar convective flow has been numerically simulated in the fully-developed annular region of a finned double-pipe subjected to the constant heat flux boundary condition imposed at the inner-pipe wall. Finite element method has been employed in this study. Friction factor and Nusselt number have been studied as flow characteristics against variations in the ratio of radii of the inner and the outer pipes, the fin height, the fin half angle and the number of fins. The results show significant enhancement in the heat transfer rate in both the cases when sufficient pumping power is available and when it is not. The minimum and maximum increase in the product of friction factor and Reynolds number relative to the finless geometry is more than one time and more than 40 times respectively while gain in the relative value of Nusselt number lies in the range 1–177. This provides an evidence of more than four times enhancement in the heat transfer coefficient relative to that in the pressure loss as a result of extended fin surfaces.     

Diffusion dynamics of open source software: An agent-based computational economics (ACE) approach

The rising popularity of open source software (OSS) calls for a better understanding of the drivers of its adoption and diffusion. In this research, we propose an integrated framework that simultaneously investigates a broad range of social and economic factors on the diffusion dynamics of OSS using an Agent Based Computational Economics (ACE) approach. We find that interoperability costs, variability of OSS support costs, and duration of PS upgrade cycle are major determinants of OSS diffusion. Furthermore, there are interaction effects between network topology, network density and interoperability costs, which strongly influence the diffusion dynamics of OSS. The proposed model can be used as a building block to further investigate complex competitive dynamics in software markets.     

3D block-based medial axis transform and chessboard distance transform based on dominance

Traditionally, the block-based medial axis transform (BB-MAT) and the chessboard distance transform (CDT) were usually viewed as two completely different image computation problems, especially for three dimensional (3D) space. In fact, there exist some equivalent properties between them. The relationship between both of them is first derived and proved in this paper. One of the significant properties is that CDT for 3D binary image V is equal to BB-MAT for image V' where it denotes the inverse image of V. In a parallel algorithm, a cost is defined as the product of the time complexity and the number of processors used. The main contribution of this work is to reduce the costs of 3D BB-MAT and 3D CDT problems proposed by Wang [65]. Based on the reverse-dominance technique which is redefined from dominance concept, we achieve the computation of the 3D CDT problem by implementing the 3D BB-MAT algorithm first. For a 3D binary image of size N³, our parallel algorithm can be run in O(logN) time using N³ processors on the concurrent read exclusive write (CREW) parallel random access machine (PRAM) model to solve both 3D BB-MAT and 3D CDT problems, respectively. The presented results for the cost are reduced in comparison with those of Wang's. To the best of our knowledge, this work is the lowest costs for the 3D BB-MAT and 3D CDT algorithms known. In parallel algorithms, the running time can be divided into computation time and communication time. The experimental results of the running, communication and computation times for the different problem sizes are implemented in an HP Superdome with SMP/CC-NUMA (symmetric multiprocessor/cache coherent non-uniform memory access) architecture. We conclude that the parallel computer (i.e., SMP/CC-NUMA architecture or cluster system) is more suitable for solving problems with a large amount of input size.     

Flexible and Autonomous Integrated System for Characterizing Metal Oxide Gas Sensor Response in Dynamic Environment

Characterization and calibration of gas sensor is a complex problem due to the dynamic behavior of gases and the limitations of current technology. This article reports a flexible, robust, and autonomous integrated system that is able to perform characterization on metal oxide-based gas sensors in dynamic environments. The system controls the concentration and flow of the relevant gases into the gas chamber and simultaneously measuring the sensor response. This feature allows the characterization of the sensor under continuous dynamic flow of gases similar to conditions on a robot or flow pipes. Several experiments have been performed on the system using hydrogen sulfide. The results provide information on the general characteristics of the sensor as well as its sensitivity. The noise levels were studied with different reference voltages. Overall, the results verify that the system is reliable and able to produce repeatable measurements.     

Selenium Speciation Studies from Soan-Sakesar Valley,Salt Range,Pakistan

Abstract

A regional study of Selenium (Se) speciation in surface waters, groundwaters, and saturation extracts from Soan-Sakesar valley Salt-Range, Pakistan was started in March 1994 and completed in October 1996. Groundwaters, streams, and springs had median concentrations of: total Se 48, 404.5, and 82 μg/l; Se^+VI 40, 350 and 51 μg/l; Se^+IV 8, 48 and 29 μg/l; Se^-II 0, 6.5 and 2.0 μg/l, respectively. The concentration of Se exceeded the recommended water quality guidelines for drinking and irrigation water of 10 and 20 μg/l, respectively. In saturation extracts, median total Se, Se^+VI, Se^+IV and Se^-II were 190, 146, 37 and 7.0 μg/l, respectively. Uchhali, Khabbaki and Jahlar lake water samples had the mean concentration of: total Se 2103, 670 and 297 μg/l; Se^+VI 1777, 470, and 233 μg/l; Se^+IV 291, 166 and 39 μg/l; Se^-II 35, 34 and 25 μg/l, respectively. The Sakesar limestone Formation of Eocene age rich in shales and fossils (median Se concentration 7.2 μg/kg) are considered to be the source of selenium that have enriched soils of Soan-Sakesar valley. Selenim speciation results show the abundance of selenate in all the environmental samples. A linear relationship between Se^+VI and pH (R²= 0.84, 0.58, 0.88, 0.82, and 0.91; significant at the 0.05 level) for groundwater, saturation extract, stream, spring, and lake waters were noted, suggesting that the Se^+VI in the samples is highly associated with pH. Linear relationships were established between Se^+VI and Mg, NO₃, and SO₄ for groundwater (R²= 0.24, 0.16, and 0.64; significant at the 0.01 level) and surface water (R²= 0.96, 0.14, and 0.91; significant at the 0.001 level). This suggests the high concentrations of Mg, NO₃ and SO₄, which strongly adsorb and compete with selenium for surface site, increases the Se^+VI fraction. The results show that the higher percentage of selenate on other species in the analyzed samples is favored by higher pH and oxidizing environments of the valley.