Secondary Intraocular Lens

Secondary intraocular lens (IOL) implantation has evolved over the past few decades. Several new techniques, lens options, and materials now exist. Careful patient selection is important to determine the optimal secondary IOL technique. Intraocular lens placement in the capsular bag is the most ideal followed by sulcus placement. However, the best option when no capsular support exists in an aphakic patient remains unclear. Surgeons should be aware of contraindications for each technique; however, there are several situations where anterior chamber intraocular lens (ACIOL), scleral-fixated intraocular lens (SFIOL), and iris fixation can all be used. In those cases, surgeon familiarity and comfort with the secondary IOL technique can determine the type of surgery performed

Blast vibration dependence on total explosives weight in open-pit blasting

It is well established that the blast design parameters, namely explosives weight per delay, distance between the blast site and monitoring location, delay interval, total explosives detonated in a blasting round, velocity of detonation (VOD) of explosives, burden, spacing, explosive column length, top stemming length, number of decks and their length, transmitting media and its geology, and scattering in the delay time of detonators, influence blast-induced vibrations. A study was conducted to assess the effect of total weight of explosive detonated in the blast in ground on the magnitude of blast vibrations at four big coal open-cast mines in India keeping all the parameters constant as stated above. Accordingly, experimental as well as production blasts were conducted at drag line and shovel benches. The results revealed that the magnitude of blast vibrations was influenced by the total amount of explosive detonated in a blast in ground at shorter distances regardless of maximum explosives weight per delay. This paper describes the result of a study carried out to investigate these effects at open-cast projects in India. The study involved 60 blasts with varying blast designs and 498 vibration data were recorded

Analysing the Performance of Divide-and-Conquer Algorithms on Multicore Processors

Multicore systems are widely gaining popularity because of the significant avail-ability and performance increase over the single core systems. Multicore systems have a lesser power consumption and heat generation than that of the multiple single core systems. The different compiler support provided by different vendors also make multicore programming one of the main area of research. The multicore programming utilises the power of multiple cores to parallelise a task. The widely used algorithm paradigms for multicore programming are the Divide and Conquer algorithms. The divide and conquer algorithms are candidate problem for the multicore programming because divide and conquer algorithm divides a problem into sub- problems which can be solved by distributing the sub-problems among the different cores and parallel solve them. A wide range of divide and conquer algorithm has been parallelized. In this paper, we have taken two of the widely used divide and conquer algorithms, quick sort and convex hull, parallel implemented them to analyse their performance gain in compared to the sequential version of the algorithm. The parallel implementations distribute the load onto the multiple cores, parallel work upon the loads and finally merge individual results of the each core. We have also proposed a scheme for efficient merging of the parallel sorted sub-arrays in the quick sort. We have taken the mean and standard deviation theory for efficient merging of the sorted sub-arrays. The OpenMP programming model has been used for the implementation of the programs. The processor architecture used for analysing the behaviour of the algorithm is a shared memory based processo

Investigating Open Issues in Swarm Intelligence for Mitigating Security Threats in MANET

The area of Mobile Adhoc Network (MANET) has being a demanded topic of research for more than a decade because of its attractive communication features associated with various issues. This paper primarily discusses on the security issues, which has been still unsolved after abundant research work. The paper basically stresses on the potential features of Swarm Intelligence (SI) and its associated techniques to mitigate the security issues. Majority of the previous researches based on SI has used Ant Colony Optimization (ACO) or Particle Swarm Optimization (PSO) extensively. Elaborated discussion on SI with respect to trust management, authentication, and attack models are made with support of some of the recent studies done in same area. The paper finally concludes by discussing the open issues and problem identification of the review

Identifying Appliances using NIALM with Minimum Features

Government of India has decided to install smart meters in fourteen states. Smart meters are required to identify home appliances to fulfill various tasks in the smart grid environment. Both intrusive and non-intrusive methods have been suggested for identification. However, intrusive method is not suitable for cost and privacy reasons. On the other hand, techniques using non-intrusive appliance load monitoring (NIALM) are yet to result in meaningful practical implementation. Two major challenges in NIALM research are the choice of features (load signatures of appliances), and the appropriate algorithm. Both have a direct impact on the cost of the smart meter. In this paper, we address the two issues and propose a procedure with only four features and a simple algorithm to identify appliances. Our experimental setup, on the recommended specifications of the internal electrical wiring in Indian residences, used common household appliances’ load signatures of active and reactive powers, harmonic components and their magnitudes. We show that these four features are essential and sufficient for implementation of NIALM with a simple algorithm. We have introduced a new approach of ‘multi point sensing’ and ‘group control’ rather than the ‘single point sensing’ and ‘individual control’, used so far in NIALM techniques.DOI:http://dx.doi.org/10.11591/ijece.v4i6.671

Detection Of Fetal Electrocardiogram from Multivariate Abdominal Recordings by using Wavelets and Neuro-Fuzzy Systems

The fetal electrocardiogram (FECG) signal reflects the electrical activity of the fetal heart. It contains  information  on  the health  status of the fetus and therefore, an early diagnosis of any cardiac defects before delivery (Specially in case of  labour pain) increases the effectiveness of the appropriate treatment. In this paper we consider one signal from the thoracic and another from abdomen of the mother. The artificial neural network fuzzy inference system (ANFIS) is used for estimating the FECG component from one abdominal ECG recording and one reference thoracic maternal electrocardiogram (MECG) signal. The obtained FECG is being enhanced by using wavelet transform. Key words: ECG, MECG, FECG, Neural network , Fuzzy logic, Membership function and Wavelet transform

A study on the lipid ratios and inflammatory markers in pre-diabetic and diabetic patients

Background: One of the leading causes of diabetic mortality is cardiovascular disease. Diabetes progression is preceded by pre-diabetic phase which is also at higher cardiovascular risk. Both hyperglycemia and atherosclerotic processes are inflammatory phenomenon. Keeping this in view, it was aimed to evaluate atherogenic indices and correlate them with inflammatory mediators.Methods: This study included 80 controls, 80 pre-diabetic and 80 diabetic patients. Anthropometric parameters (BMI, WHR) and blood parameters like fasting glucose, HbA1c, lipid profile (cholesterol, HDL, LDL TG, VLDL), adiponectin, IL-6, CRP, fibrinogen and uric acid were analysed.Results: Significantly high atherogenic indices were observed in pre-diabetic and diabetic subjects compared to healthy controls. The indices were also significantly correlated with BMI, fasting sugar, HbA1c, cholesterol, HDL, TG and LDL. The correlation with HDL was negative and with other parameters, the correlation was positive. In pre-diabetic patients, adiponectin showed significant negative correlation while fibrinogen and CRP showed significant positive correlation with cardiac risk indices. IL-6 was positively correlated only with AIP while correlation of uric acid with these indices was insignificant. In case of diabetic patients, the cardiac risk indices were significantly correlated with adiponectin, IL-6, CRP, fibrinogen and uric acid. The correlation with adiponectin was negative.Conclusions: The altered atherogenic indices and their significant association with inflammatory markers signify the direct association of inflammation with CVD risks. Thus, there is requirement of novel approaches that can retard inflammatory responses and arrest unwanted cardiac health outcomes