Showing papers by "Mepco Schlenk Engineering College published in 2014"

A Discrete Firefly Algorithm for the Multi-Objective Hybrid Flowshop Scheduling Problems

Abstract: Hybrid flowshop scheduling problems include the generalization of flowshops with parallel machines in some stages. Hybrid flowshop scheduling problems are known to be NP-hard. Hence, researchers have proposed many heuristics and metaheuristic algorithms to tackle such challenging tasks. In this letter, a recently developed discrete firefly algorithm is extended to solve hybrid flowshop scheduling problems with two objectives. Makespan and mean flow time are the objective functions considered. Computational experiments are carried out to evaluate the performance of the proposed algorithm. The results show that the proposed algorithm outperforms many other metaheuristics in the literature.

Effect of graphite addition on mechanical behavior of Al6061/TiB2 hybrid composite using acoustic emission

Abstract: The present work includes the effect of addition of graphite for improving the properties of Al6061–TiB2 composite using the high-energy stir casting method. The characterization was performed with X-ray diffraction, energy dispersive spectrum and scanning electron microscope. The thermal and mechanical behaviors such as thermo gravimetric analysis/differential thermal analysis, hardness, tensile strength using acoustic emission and fatigue behaviors were investigated. The composite of composition Al6061–20% TiB2 with 2% graphite shows the greatest improvement in mechanical behavior. An Acoustic Emission system was employed in all the tests to monitor the acoustic energy release during the whole deformation process and some useful conclusions were drawn.

Diagnosis of diabetic retinopathy by employing image processing technique to detect exudates in retinal images

Abstract: Diabetic retinopathy (DR) is a microvascular complication of long-term diabetes and it is the major cause of visual impairment because of changes in blood vessels of the retina. Major vision loss because of DR is highly preventable with regular screening and timely intervention at the earlier stages. The presence of exudates is one of the primitive signs of DR and the detection of these exudates is the first step in automated screening for DR. Hence, exudates detection becomes a significant diagnostic task, in which digital retinal imaging plays a vital role. In this study, the authors propose an algorithm to detect the presence of exudates automatically and this helps the ophthalmologists in the diagnosis and follow-up of DR. Exudates are normally detected by their high grey-level variations and they have used an artificial neural network to perform this task by applying colour, size, shape and texture as the features. The performance of the authors algorithm has been prospectively tested by using DIARETDB1 database and evaluated by comparing the results with the ground-truth images annotated by expert ophthalmologists. They have obtained illustrative results of mean sensitivity 96.3%, mean specificity 99.8%, using lesion-based evaluation criterion and achieved a classification accuracy of 99.7%.

Retinal vessel segmentation employing ANN technique by Gabor and moment invariants-based features

Abstract: Diabetic retinopathy (DR) is the major ophthalmic pathological cause for loss of eye sight due to changes in blood vessel structure. The retinal blood vessel morphology helps to identify the successive stages of a number of sight threatening diseases and thereby paves a way to classify its severity. This paper presents an automated retinal vessel segmentation technique using neural network, which can be used in computer analysis of retinal images, e.g., in automated screening for diabetic retinopathy. Furthermore, the algorithm proposed in this paper can be used for the analysis of vascular structures of the human retina. Changes in retinal vasculature are one of the main symptoms of diseases like hypertension and diabetes mellitus. Since the size of typical retinal vessel is only a few pixels wide, it is critical to obtain precise measurements of vascular width using automated retinal image analysis. This method segments each image pixel as vessel or nonvessel, which in turn, used for automatic recognition of the vasculature in retinal images. Retinal blood vessels are identified by means of a multilayer perceptron neural network, for which the inputs are derived from the Gabor and moment invariants-based features. Back propagation algorithm, which provides an efficient technique to change the weights in a feed forward network is utilized in our method. The performance of our technique is evaluated and tested on publicly available DRIVE database and we have obtained illustrative vessel segmentation results for those images.

Numerical Simulation of Heat Flow in Friction Stud Welding of Dissimilar Metals

Abstract: Friction stud welding is a solid-state joining technique used for welding similar and dissimilar materials with high integrity. In friction stud welding, heat is generated by the conversion of mechanical energy into thermal energy at the interface of the work pieces during rotation under pressure. This complicated metallurgical process is accompanied by frictional heat generation, plastic deformation, cooling of high-temperature metal, and solid-state phase variation. Since the thermal cycle of friction stud welding is very short, simulation becomes a vital role to study the behaviour of materials. The simulations make it possible to observe the temperature distribution and heat transfer fields that take place during the process. In the present work, a three-dimensional nonlinear Finite Element-based heat flow model is developed for friction stud welding of aluminium and mild steel combination. The numerical model is validated with a temperature history at the weld interface measured using a non-contact-type infrared thermometer. During friction stud welding, temperature, temperature distribution, temperature gradient, heat transfer rate and their variations, govern welding parameters of a welding machine. Knowledge of them helps to determine optimum parameters and ways to improve the design and manufacture of welding machines. Hence, the developed numerical model could be used as a tool to study the thermal cycles during the process and it will be very much useful for the subsequent analysis of residual stress and distortion of welded joints.

Metal based pharmacologically active complexes of Cu(II), Ni(II) and Zn(II): synthesis, spectral, XRD, antimicrobial screening, DNA interaction and cleavage investigation.

Abstract: The present contribution reports a thorough characterization of newly obtained metallointercalators incorporating Schiff bases, formed by the condensation of N-acetoacetyl-o-toluidine with 1-amino-4-nitrobenzene (L(1))/1-amino-4-chlorobenzene (L(2)) as main ligand and 1,10-phenanthroline as co-ligand respectively. The characterization of newly formed metallointercalators has been done by (1)H NMR, UV-Vis, IR, EPR spectroscopy and molar conductivity studies. X-ray powder diffraction illustrates that they are crystalline nature. Binding interaction of these complexes with calf thymus (CT-DNA) has been investigated by emission, absorption, viscosity, cyclic voltammetry and differential pulse voltammetry. DNA binding experiments results reveal that the synthesized complexes interact with DNA through intercalative mode. The in vitro antibacterial and antifungal assay indicate that these complexes are good antimicrobial agents against various pathogens. The DNA cleavage exhibits that they act as efficient cleaving agents.

Mechanical Evaluation and Microstructure of Friction Stud Welded Aluminium–Mild steel Joints

Abstract: Friction stud welding of dissimilar metals has a variety of applications in the field of aerospace, automotive, nuclear engineering. Especially, in the field of automobile engineering, there is a requirement of joining a mild steel earthing pin to an aluminium car body. Hence, in this study, joining of aluminium (AA 6063) and mild steel (AISI 1030) was successfully carried out by friction welding technique. Experiments were conducted by varying friction time, and axial shortening distance and impact strength were measured. Macro and microstructural investigation was carried out using optical microscope, scanning electron microscope and energy dispersive X-ray analysis. Microhardness measurement shows increased plastic deformation of aluminium at the interfacial region. Optical micrograph reveals a dark region of about 240μm consisting of dynamic recrystallized grains on the aluminium side very near to the interfacial region. SEM micrograph and EDX analysis shows the presence of iron oxide on the aluminium side along with the formation of inter metallic compound FeAl at the joint interface. Hardness measured at the weld line is found to be higher than the hardness of the base metal due to increased plastic deformation.

Simultaneous scheduling of machines and tools in multimachine flexible manufacturing systems using artificial immune system algorithm

Abstract: This article deals with simultaneous scheduling of machines and tools in a multimachine flexible manufacturing system to generate best optimal sequences that minimise makespan. As flexible manufacturing system (FMS) is an integrated manufacturing facility, simultaneous scheduling of different components of FMS is essential. In this research work, attempts have been made to integrate machines and tools. The aim of this article is to address combined machine and tool scheduling in a FMS consisting of machines and a central tool magazine. Four heuristics, priority dispatching rules, modified nondelay schedule generation algorithm with six different priority dispatching rules, modified Giffler and Thompson algorithm and artificial immune system (AIS) algorithm, have been proposed for solving combined machine and tool problems with makespan as objective. The proposed heuristics are numerically tested on various problems and the results are compared. The result shows that AIS algorithm yields better results for...

Multi region based image retrieval system

Abstract: Multimedia information retrieval systems continue to be an active research area in the world of huge and voluminous data. The paramount challenge is to translate or convert a visual query from a human and find similar images or videos in large digital collection. In this paper, a technique of region based image retrieval, a branch of Content Based Image Retrieval, is proposed. The proposed model does not need prior knowledge or full semantic understanding of image content. It identifies significant regions in an image based on feature-based attention model which mimic viewer’s attention. The Curvelet Transform in combination with colour descriptors are used to represent each significant region in an image. Experimental results are analysed and compared with the state-of-the-art Region Based Image Retrieval Technique.

Mouse gesture based authentication using machine learning algorithm

Abstract: To authenticate a computer user, classification based on mouse operating behavior is proposed. It is used to perform the authentication task in initial login stage by capturing the mouse movements. The mouse operating behavior of users can be captured as coordinate axes and elapsed time, based on movement of the mouse. The obtained mouse behavior data consist of outliers and behavioral variability; these can be addressed by Peirce's criterion and Weighted Least Square Regression (WLSR) respectively. The features are extracted from that mouse behavior data to categorize the user's unique mouse behavior. The extracted features are analyzed and performs authentication using Learning Vector Quantization (LVQ). This LVQ Algorithm is used as a classifier, to find whether the given sample is authorized or unauthorized identity. The test result proves that, the proposed method WLSR with learning vector quantization classifier provides low error rates with good accuracy than the existing method.

Edge detection for illumination varying images using wavelet similarity

Abstract: This study presents a new method of edge detection (ED) in illumination varying images. The undesirable dependence because of uneven illumination is crucial in image ED. The proposed algorithm avoids this problem by incorporating a new weighted least-square regularisation with the wavelet filter directional responses. In order to obtain the edge information, the similarity between image neighbourhood pixels and wavelet filter is measured. This new method accomplishes the benefits of wavelets, which includes multi-scale, and wider design choice in the filter length. Figure of merit (F) and structural similarity index qualitatively compares its performances. The subjective and objective results describes that this algorithm performs better than other methods.

Performance evaluation of an improved hybrid genetic scatter search (IHGSS) algorithm for multistage hybrid flow shop scheduling problems with missing operations

Abstract: Multistage hybrid flow shop scheduling problems are considered in this paper. Each stage consists of several identical machines. The jobs are to be processed on any one of the machines at each stage. In most of the scheduling research works it is assumed that all jobs are to be processed at all stages. But in real-life industries some of the jobs may not be processed at some stages. Hence hybrid flow shop scheduling problems with missing operations are considered in this paper with the objective of determining a schedule that minimises makespan. The hybrid flow shop scheduling problems are non-deterministic polynomial time hard (NP-hard) problems. We propose a hybrid meta-heuristic algorithm, namely improved hybrid genetic scatter search (IHGSS) algorithm, based on genetic algorithm and scatter search algorithms. A case study problem of a leading steel furniture manufacturing company in India is presented to illustrate the proposed algorithm. Computational experiments show that the proposed IHGSS algorithm outperforms other heuristic and meta-heuristic algorithms.

Design of router using Reversible Logic in Quantum Cellular Automata

Abstract: Quantum Cellular Automata (QCA) is a promising future to the CMOS technology with ultra operating speed and low power consumption The realization of QCA with Reversible Logic is the Green Computing A router is the key component in the internet core for transmitting data packets Being the technology beyond CMOS, the work has been proposed to design a Router in QCA In addition to that, crossing in Router architecture is done in Multilayer with reversible logic This proposed architecture guarantees efficient data routing in Router Also it provides reduced cell area, high operating speed, and minimum complexity

Design of Band Pass Filter using active inductor for RF receiver front-end

Abstract: The Band Pass Filters are commonly used in wireless receivers and transmitter. The usage of spiral inductor in the band pass filters requires large chip area which can increase the band pass filter size and is difficult to obtain high Q-factor. This paper presents the active inductor based band pass filter using TSMC 0.18µm RF CMOS process. The band pass filter is realized using active inductor with suitable input and output buffer stages. The active inductor circuit uses the PMOS cascode structure as the negative transconductor of a gyrator. This structure provides the negative resistance to reduce the inductor loss with wide inductive bandwidth and high resonance frequency. The tuning of the center frequency is achieved through the controllable current sources. This active inductor demonstrates a maximum quality factor of 244 with a 154nH inductance. The simulation result of band pass filter designed at 100MHz has the gain of 6.129 dB and input return loss of −11.474 dB. The simulated IIP3 is −19 dBm and power consumed by the BPF is 28mW.

Solving tri-objective multistage hybrid flow shop scheduling problems using a discrete firefly algorithm

Abstract: Hybrid flow shop (HFS) scheduling problems turn into a fascinating area of research for the past several decades. However, most of the researchers consider only single objective and makespan is the most vital objective function. The real industrial scheduling problems involve multiple conflicting objectives. The HFS scheduling problems were proved to be NP-hard (non-deterministic polynomial time hard) problems. In this paper, we present a discrete firefly algorithm (DFA) to minimise the makespan, total flow time and machine idle time in a multistage HFS environment consisting of n jobs and k stages with identical parallel machines. The performance of the proposed algorithm is tested with a real industrial case study data and random problem instances. Computational results proved the effectiveness of the proposed algorithm.

Variants of RSA and their cryptanalysis

Abstract: In this paper, I present some of the variants of RSA and analyse the cryptanalytic attacks against these variants. The first variant I present is called the Efficient RSA, where the number of key generation exponents is increased compared to the original RSA. Another variant of RSA I present is called the Dependent RSA which is aimed at providing Semantic Security to the original RSA. A third variant of RSA which I call Carmichael RSA uses the Carmichael function instead of the Euler Phi Function. The other variants that discussed are the Shared RSA, the Multiprime RSA, the Common Prime RSA the CRT-RSA and the Rebalanced CRT-RSA. I discuss the cryptanalysis of these RSA systems along with the implementation aspects of the variants of the RSA Cryptosystem.

Robust real time face detection automatically from video sequence based on Haar features

Abstract: The automatic human face detection from sequences of video plays vital role in intelligent human computer interaction systems for video surveillance, face recognition, emotion recognition and face database management This paper proposes an automatic and robust method to detect human faces from the background that is capable of processing images rapidly while achieving high detection rates from video sequences Highlight of the face detection system is to identify and locate all faces regardless of their position, scale, orientation, lighting conditions, expressions etc The field of work is the incorporation of a normalization technique based on local histograms to alleviate a common problem in conventional face detection methods such as: inconsistent performance due to sensitivity to variation illuminations such as local shadowing, noise and occlusion Next the Haar-like rectangle features can be computed very rapidly using the integral image that is most suitable for face/non face classification In the final step, the face region is detected through a cascade of classifier consisting of detectors with Adaboost algorithm Experimental result is showing promising results by conducting the experiments on video sequence as against the existing work on images