Earticle

In this paper we presents the EPLS (Economic Production Lot Size) model which accounts for a production system producing perfect and imperfect quality items. Also, a single period multi-item volume flexible production model for deteriorating items with stochastic demand and stochastic imperfect production. Deterioration is taken as constant. Linear holding cost is considered. Shortages are permitted in inventory with partial backlogging. Profit maximization techniques are also used. The problem parameter effects upon the optimal solutions are examined numerically.

Three types of rough equalities were introduced by Novotny and Pawlak ([1, 2, 3]), which take care of approximate equalities of sets. These sets may not be equal in the usual sense. These notions were generalized by Tripathy, Mitra and Ojha ([10]), who introduced the concepts of rough equivalences of sets. These approximate equalities of sets capture equality of the concerned sets at a higher level than their corresponding rough equalities. Some more properties were proved in [11]. In this paper, we introduce two other types of approximate equalities of sets, called the approximate rough equivalences and approximate rough equality. We study some properties of these four types of approximate equalities and analyse their relevance from the application point of view.

IEEE 802.16d/e PHY layer adapted by WiMax forum includes multiple specifications, which make the standard flexible and adaptable to different frequency ranges. The PHY layer specifies some mandatory features and some optional features to be implemented to provide a reliable end-to-end link. The primary issue in implementation of PHY layer is robust performance in multipath fading environments. In this paper, the performance of WiMax PHY layer with turbo coding mechanisms is investigated and compared with the existing mechanisms. The results obtained show that turbo coding offers lower BER and enhance the performance of the PHY layer in mobile (multipath) environments.

With widespread availability of low cost GPS, cellular phones, satellite imagery, robotics, Web traffic monitoring devices, it is becoming possible to record and store data about the movement of people and objects at a large amount. While these data hide important knowledge for the enhancement of location and mobility oriented infrastructures and services, by themselves, they demand the necessary semantic embedding which would make fully automatic algorithmic analysis possible. Clustering algorithm is an important task in data mining. Clustering algorithms for these moving objects provide new and helpful information, such as Jam detection and significant Location identification. In this paper we present augmentation of relative density-based clustering algorithm for movement data or trajectory data. It provides a k-nearest neighbors clustering algorithm based on relative density, which efficiently resolves the problem of being very sensitive to the user-defined parameters in DBSCAN. In this paper we consider two real datasets of moving vehicles in Milan (Italy) and Athens (Greece) and extensive experiments were conducted.

Density based clustering algorithm is one of the primary methods for clustering in data mining. The clusters which are formed based on the density are easy to understand and it does not limit itself to the shapes of clusters. This paper gives a detailed survey of the existing density based algorithms namely DBSCAN, VDBSCAN, DVBSCAN, ST-DBSCAN and DBCLASD based on the essential parameters needed for a good clustering algorithm. We analyse the algorithms in terms of the parameters essential for creating meaningful clusters.

The objective of this paper is to classify recently released Indian coins of different denomination. The objective is to recognize the coins and count the total value of the coin in terms of Indian National Rupees (INR). The system designs coin recognition which uses by combining Robert’s edge detection method, Laplacian of Gaussion edge detection method, Canny edge detection method and Multi-Level Counter Propagation Neural Network (ML-CPNN) based on the coin Table 1. In this paper, it is proposed to introduce ML-CPNN approach. The features of old coins and new coins of different denominations are considered for classification. Indian Coins are released with different values and are classified based on different parameters of coin such as shape, size, surface, weight and so on. Some countries’ coins are having same parameters, but with different value. This paper concentrates on affine transformations such as simple gray level scaling, shearing, rotation etc. The coins are well recognized by zooming processes by which a coin size of the image is increased. To implement the coin classification, code is written in Matlab and tested with simulated results. A method is proposed for realizing a simple automatic coin recognition system more effectively. The Robert’s edge detection method gives 93% of accuracy and Laplacian of Gaussion method 95% of the result, the Canny edge detection method yields 97.25% result and the ML-CPNN approach yields 99.47% of recognition rate.

There have been substantially growing needs for storage space as there are more and more valuable and important stuff to be stored. The data which, originally, used to be processed manually and kept physically in the form of paper are now transformed into computerized data. However, these data keep increasing and within a certain period of time they become very large that they take more space to store. This situation causes serious problems in storing and transmitting image data. This research tries to find out the influence of wavelet to the Peak Signal to Noise Ratio (PSNR), and its level of decomposition towards the PSNR. The wavelet used are Daubechies family of Haar (Daubechies 1), Daubechies 2, Daubechies 3, Daubechies 4, Daubechies 5, and Coiflet families, as well as Symlet families. Test images used are 24-bit color image which are 512x512 in size. The wavelet which has the highest PSNR in each family is Haar, Coiflet 3, and Symlet 5. The effect of decomposition level towards PSNR is that the greater is the level of the decomposition, the smaller its PSNR becomes.

This paper presents a single-resistance-controlled sinusoidal equi-amplitude quadrature oscillator based on Current Feedback Operational Amplifier (CFOA) which has potential applications in modulation and detection. Most of the Quadrature Oscillators (QO) available in open literature are suffering with same problem. The ratio of two outputs of those QO is frequency dependent and not equal to unity. So this paper provides condition of equi-amplitude and verifies the results by PSPICE simulation. This circuit employs three CFOAs and all the passive elements are grounded, thus it is suitable for CMOS implementation.

In this paper river stage discharge models using Adaptive Neuro- Fuzzy Inference System (ANFIS) and Linear Multiple Regression (MLR) methods have been developed. This paper also investigates the best model to forecast river discharge. From the literature it is clear that ANN models and Fuzzy logic models are quite applicable on river stage discharge modelling. Hence this present study carried out for hybrid ANFIS models. Ten ANFIS models were developed out of which best five ANFIS models are selected. The developed models were trained, tested & validated on the data of Godavari river at Rajahmundry, Dhawalaishwaram Barrage site in Andhra Pradesh. Comparing observed data and the estimated data through developed ANFIS models, it has been proved that the developed ANFIS models predicted better results the traditional models, like MLR.

Urban data model (UDM) is a three-dimensional (3D) geography information systems (GIS) data model. The paper proposes the time class into UDM to manage historical of changes on the spatial properties of Point (0D), Line (1D), Surface (2D) and Body (3D) object. The changes would store explicit in database. The LOD (levels of detail) class of 3D objects adds also to show buildings at four levels from simple to complex. Event class in the model should record the reasons to create changes on the 3D objects in their evolution. The last model named