Статьи журнала - International Journal of Intelligent Systems and Applications
Все статьи: 1159
Performance Analysis of Rayleigh and Rician Fading Channel Models using Matlab Simulation
Статья научная
An effort has been made to illustrate the performance comparison of the Rayleigh and Rician fading channel models by using MATLAB simulation in terms of source velocity and outage probability. We have developed algorithms for the Rayleigh and Rician fading channels, which computes the envelop and outage probability. The parameters such as source velocity and outage probability play very important role in the performance analysis and design of the digital communication systems over the multipath fading environment.
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Performance Analysis of VBLAST Based MIMO OFDM System in Vehicular Channel
Статья научная
Vehicular communication is emerging as an important ingredient for successful implement of Intelligent Transportation Systems(ITS). But the development of suitable communications systems plays an important role in mobility condition. The desired system should support dynamic wireless exchange of data between nearby vehicles or road side infrastructure. In this regards multiple input and multiple output (MIMO) orthogonal frequency domain multiplexing (OFDM) system is possibly the best solution. This paper deals with the performance analysis of the MIMO OFDM system in Nakagami-m channel with Doppler shift and also partial hardware implementation of baseband signal processing.
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Статья научная
This paper presents a comparative performance study of various analog integrated circuits (namely CC-II, DVCC, CDBA and CDTA) used with ISFET for monitoring the quality of water. The use of these active components makes the implementation simple and attractive. The functionality of the circuits are tested using Tanner simulator version 15 for a 70nm CMOS process model also the transfer functions realization for each is done on MATLAB R2011a version, the Very high speed integrated circuit Hardware description language(VHDL) code for all scheme is simulated on Xilinx ISE 10.1 and various simulation results are obtained and its is found that DVCC is most stable and consume maximum power whereas CC-II is the least stable and consumes minimum power amongst all the four deployed analog IC’s. Detailed simulation results are included in the paper to give insight into the research work carried out.
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Статья научная
We have proposed a new approach to identify segments, which can be executed simultaneously, or coextending to achieve high computational speed with optimized utilization of available resources. Our suggested approach is divided into four modules. In first module we have represented a program segment using Abstract Syntax Tree (AST) along with an algorithm for constructing AST and in second module, this AST has been converted into Program Dependence Graph (PDG), the detailed approach has been described in section II, The process of construction of PDG is divided into two steps: First we construct a Control Dependence Graph (CDG, In second step reachability definition algorithm has been used to identify data dependencies between the various modules of a program by constructing Data Dependence Graph (DDG). In third module an algorithm is suggested to identify parallel modules, i.e., the modules that can be executed simultaneously in the section III and in fourth module performance analysis is discussed through our approach along with the computation of time complexity and its comparison with sequential approach is demonstrated in a pictorial form.
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Performance Comparison of Hybrid GA-PSO Based Tuned IMMs for Maneuver Target Tracking
Статья научная
Target tracking is very important field of research as it has wider applications in defense as well as civilian applications. Kalman filter is generally used for such applications. When the process and measurements are non linear extensions of Kalman filters like Extended Kalman Filter, Unscented Kalman Filters are widely used. UKF can give estimations up to second order characteristics of random process. The target is maneuvering and switching among different models like constant velocity (CV), constant acceleration (CA) or constant turn (CT), Interactive Multiple Models (IMM) are employed. Implementation of IMM filters for any application is difficult because of initialization of Kalman filter i,e, tuning of filter has to be performed before applying to real time situations. It demands prior estimations of Noise covariance matrices which are left for engineering intuitions. This paper presents the nonlinear state estimation using IMM and tuning of the filter is done using bio-inspired algorithms like PSO GA and Hybrid GA-PSO.
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Performance Comparison of Various Robust Data Clustering Algorithms
Статья научная
Robust clustering techniques are real life clustering techniques for noisy data. They work efficiently in the presence of noise. Fuzzy C-means (FCM) is the first clustering algorithm, based upon fuzzy sets, proposed by J C Bezdek but it does not give accurate results in the presence of noise. In this paper, FCM and various robust clustering algorithms namely: Possibilistic C-Means (PCM), Possibilistic Fuzzy C-means (PFCM), Credibilistic Fuzzy C-means (CFCM), Noise Clustering (NC) and Density Oriented Fuzzy C-Means (DOFCM) are studied and compared based upon robust characteristics of a clustering algorithm. For the performance analysis of these algorithms in noisy environment, they are applied on various noisy synthetic data sets, standard data sets like DUNN data-set, Bensaid data set. In comparison to FCM, PCM, PFCM, CFCM, and NC, DOFCM clustering method identified outliers very well and selected more desirable cluster centroids.
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Статья научная
High blood pressure (BP) monitoring Blood pressure (BP) is one of the common cardiovascular diseases and therefore the early high blood pressure (hypertension) detection, management, and prevention are mandatory. One promising method of continuous, non-invasive blood pressure estimation is photoplethysmography (PPG). In this study, a novel method was proposed to introduce the AlexNet framework into the time-frequency domain for classification of BP levels based on PPG signals. The study was conducted using the publicly available Figshare dataset which offers PPG signals, and the blood pressure labels against them. Data balancing techniques were used to alleviate class imbalances. Preprocessing and Feature Extraction of PPG Signals. The PPG signals were preprocessed with noise filtering and signals were then transformed from 1D-time to image to facilitate robust feature extraction. The proposed classification model, based on AlexNet showed the best result, with 98.89% accuracy, recall, and precision, and 99.44% specificity. This model outperformed alternative models (VGG16, DenseNet, ResNet50, GoogleNet) for classifying BP levels into the JNC 7 report standard categories normotension, prehypertension and hypertension. This study has two primary contributions. Initially, it demonstrates the efficacy of AlexNet model to extract meaningful features from PPG signals by its hierarchical convolutional and max-pooling layers thereby enabling accurate classification of BP levels. This study underscores the potential of deep learning and PPG signals for developing a highly accurate and truly non-invasive BP monitoring system. In the second aspect, the study offers a systematic assessment and comparison of the proposed over other well-known deep-learning networks, presenting the effectiveness of the AlexNet-based one. These results are of critical importance in the development of novel non-invasive BP monitoring modalities and optimization of cardiovascular health managements and personalized health cares.
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Статья научная
Advances in FPGA technology have dramatically increased the use of FPGAs for computer vision applications. Availability of on-chip processor (like PowerPC) made it possible to design embedded systems using FPGAs for video processing applications. The objective of this research is to evaluate the performance of different memory components available on FPGA boards for embedded/platform-based implementations of image/video processing applications. The clustering based change detection algorithm for Ubiquitous Multimedia Environment is selected for evaluating the effect of different memory components (DDR/BRAM) on performance of the system in terms of frame rate (frames per second).
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Статья научная
As the world evolves day by day with new technologies, there is a need to design a secure network in such a way that intruders and unauthorized persons should not have access to the network as well as information regarding the personnel in any firm. In this study, a new cryptographic technique for securing data transmission based on the LaplaceLaguerre polynomial (LLP) is developed and compared to an existing auto-associative neural network technique (AANNT).The performance of the LLPT and AANNT was tested with some selected files in a MATLAB environment and the results obtained provided comparative information (in respect of AANNT versus LLPT) as follows: encryption time (1.67 ms versus 3.9931s), decryption time (1.833 ms versus 2.1172s), throughput (26.2975 Kb/s versus 0.01098 Kb/s), memory consumption (3.349 KB versus 15.958 KB). From the compared results, it shows that AANNT offers a faster processing time, higher throughput, and takes up less memory space than the LLPT. However, cryptanalysis of the AANNT is possible if the network's weight and design are known; hence, the technique is unreliable for ensuring the data integrity and confidentiality of encrypted data. The proposed LLP cryptographic algorithm is designed to provide a higher security level by making the LLP algorithm computationally tedious to invert using the standard Laplace transform inversion method. When compared to the AANN-based cryptographic technique, cracking the algorithm to uncover the encryption key takes time. This shows the strength and robustness of the proposed LLP cryptographic algorithm against attacks, as well as its suitability for solving the problem of data privacy and security when compared to the AANN-based cryptographic algorithm.
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Performance Evaluation of Modified DBLA Using Dark Channel Prior & CLAHE
Статья научная
This paper has focused on the different image enhancement techniques. Image enhancement has found to be one of the most important vision applications because it has ability to enhance the visibility of images. It enhances the quality of poor pictures. Distinctive procedures have been proposed so far for improving the quality of the digital images. To enhance picture quality image enhancement can specifically improve and limit some data presented in the input picture. It is a kind of vision system which reductions picture commotion, kill antiquities, and keep up the informative parts. Its object is to open up certain picture characteristics for investigation, conclusion and further use. The main objective of this paper is to modify the DBLA using the dark channel prior and CLAHE to enhance the results further. The comparative analysis has shown the significant improvement over the CLAHE and the DBLA.
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Статья научная
Learning Disability (LD) is a classification including several disorders in which a child has difficulty in learning in a typical manner, usually caused by an unknown factor or factors. LD affects about 15% of children enrolled in schools. The prediction of learning disability is a complicated task since the identification of LD from diverse features or signs is a complicated problem. There is no cure for learning disabilities and they are life-long. The problems of children with specific learning disabilities have been a cause of concern to parents and teachers for some time. The aim of this paper is to develop a new algorithm for imputing missing values and to determine the significance of the missing value imputation method and dimensionality reduction method in the performance of fuzzy and neuro fuzzy classifiers with specific emphasis on prediction of learning disabilities in school age children. In the basic assessment method for prediction of LD, checklists are generally used and the data cases thus collected fully depends on the mood of children and may have also contain redundant as well as missing values. Therefore, in this study, we are proposing a new algorithm, viz. the correlation based new algorithm for imputing the missing values and Principal Component Analysis (PCA) for reducing the irrelevant attributes. After the study, it is found that, the preprocessing methods applied by us improves the quality of data and thereby increases the accuracy of the classifiers. The system is implemented in Math works Software Mat Lab 7.10. The results obtained from this study have illustrated that the developed missing value imputation method is very good contribution in prediction system and is capable of improving the performance of a classifier.
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Performance Improvement of Plant Identification Model based on PSO Segmentation
Статья научная
Plant identification has been a challenging task for many researchers. Several researches proposed various techniques for plant identification based on leaves shape. However, image segmentation is an essential and critical part of analyzing the leaves images. This paper, proposed an efficient plant species identification model using the digital images of leaves. The proposed identification model adopts the particle swarm optimization for leaves images segmentation. Then, feature selection process using information gain and discritization process are applied to the segmented image's features. The proposed model was evaluated on the Flavia dataset. Experimental results on different kind of classifiers show an improvement in the identification accuracy up to 98.7%.
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Статья научная
This paper describes the process of power system stabilizer (PSS) optimization by using bacterial foraging (BG) to improve the power system stability and damping out the oscillation during large and small disturbances in a multi-machine power system. The proposed PSS type is P. Kundur (Lead-Lag) with speed deviation as the input signal. BG used to optimize the PSS gains. The proposed BG based delta w lead-lag PSS (P. Kundur structure) (BG-PSS) evaluated in the well-known benchmark simulation problem P. Kundur 4-machines 11-buses 2-areas power system. The BG-PSS compared with MB-PSS with simplified settings: IEEE® type PSS4B according to IEEE Std. 421.5, Conventional Delta w PSS (as the proposed PSS without optimization) from P. Kundur, and Conventional Acceleration Power (Delta Pa) PSS to demonstrate its robustness and superiority versus the three PSSs types to damp out the inter-area oscillations in a multi-machine power system. The damping ratio and the real part of the eigenvalues used as the fitness function in the optimization process. The nonlinear simulation results obtained in the MATLAB / SIMULINK environment prove that the proposed PSS is highly effective, robust, & superior to the other used controllers in restrictive the inter-area oscillation in a large power system & to maintain the wide-area stability of the system. Also, the performance indices eigenvalue analysis, peak overshoot, settling time, and steady-state error used to validate the superior oscillation damping and fast recovered transient dynamic behavior over the three considered controllers.
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Статья научная
Most design optimization problems in engineering are in general extremely nonlinear and deal with various design variables under complex restrictions. Traditional mathematical optimization procedure may fail to find the optimum solution to real-world problems. Evolutionary Algorithms (EAs) can serve as an efficient approach for these types of optimization problems. In this paper, Particle Swarm Optimization (PSO), Differential Evolution (DE) and Cuckoo Search (CS) algorithms are used to find the optimal solution for some typical unimodal and multimodal benchmark functions. The source codes of all these algorithms are developed using C language and tested on a core i5, 2.4 GHz processor with 8 GB internal RAM. PSO algorithm has a simplicity of implementation and good convergence speed. In contrast, CS algorithm has good ability to find a global optimum solution. To use the advantages of CS and PSO algorithms, a hybrid algorithm of CS and PSO (CSPSO) is implemented and tested with the same benchmark functions. The experimental simulation results obtained by all these algorithms show that hybrid CSPSO outperforms with PSO, DE and CS algorithms.
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Статья научная
Due to the emergence of more data centric applications, the replication of data has become a more common phenomenon. In the similar context, recently, (PDDRA) a Pre-fetching based dynamic data replication algorithm is developed. The main idea is to pre-fetch some data using the heuristic algorithm before actual replication start to reduce latency In the algorithm further modifications (M-PDDRA) are suggested to minimize the delay in data replication. In this paper, M-PDDRA algorithm is tested under shared and output buffering scheme. Simulation results are presented to estimate the packet loss rate and average delay for both shared and output buffered schemes. The simulation results clearly reveal that the shared buffering with load balancing scheme is as good as output buffered scheme with much less buffering resources.
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Performance of medical image processing algorithms implemented in CUDA running on GPU based machine
Статья научная
This paper illustrates the design and performance evaluation of few algorithms used for analysing the medical image volumes on the massive parallel graphics processing unit (GPU) with compute unified device architecture (CUDA). These algorithms are selected from the general framework, devised for computer aided diagnostic (CAD) system. The CAD system used for analysing large medical image datasets are usually a pipeline processing that includes a variety of image processing operations. A MRI scanner captures the 3D human head into a series of 2D images. Considerable time spent in pre and post processing of these images. Noise filters, segmentation, image diffusion and enhancement are few such methods. The algorithms are chosen for study requires local information, available in few pixels or global information available in the entire image. These problems are best candidates for GPU implementation, since the parallelism is naturally provided by the proposed Per-Pixel Threading (PPT) or Per-Slice Threading (PST) operations. In this paper implement the algorithms for adaptive filtering, anisotropic diffusion, bilateral filtering, non-local means (NLM) filtering, K-Means segmentation and feature extraction in 1536 core’s NVIDIA GPU and estimated the speed up gained. Our experiments show that the GPU based implementation achieved typical speedup values in the range of 3-338 times compared to conventional central processing unit (CPU) processor in PPT model and up to 30 times in PST model.
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Performance-Based Adaptive Gradient Descent Optimal Coefficient Fuzzy Sliding Mode Methodology
Статья научная
Design a nonlinear controller for second order nonlinear uncertain dynamical systems is the main challenge in this paper. This paper focuses on the design and analysis of a chattering free Mamdani’s fuzzy-based tuning gradient descent optimal error-based fuzzy sliding mode controller for highly nonlinear dynamic six degrees of freedom robot manipulator, in presence of uncertainties. Conversely, pure sliding mode controller is used in many applications; it has two important drawbacks namely; chattering phenomenon and nonlinear equivalent dynamic formulation in uncertain dynamic parameter. In order to solve the uncertain nonlinear dynamic parameters, implement easily and avoid mathematical model base controller, Mamdani’s performance/error-based fuzzy logic methodology with two inputs and one output and 49 rules is applied to pure sliding mode controller. Pure sliding mode controller and error-based fuzzy sliding mode controller have difficulty in handling unstructured model uncertainties. To solve this problem applied fuzzy-based tuning method to error-based fuzzy sliding mode controller for adjusting the sliding surface gain. Since the sliding surface gain is adjusted by gradient descent optimization method. Fuzzy-based tuning gradient descent optimal error-based fuzzy sliding mode controller is stable model-free controller which eliminates the chattering phenomenon without to use the boundary layer saturation function. Lyapunov stability is proved in fuzzy-based tuning gradient descent optimal fuzzy sliding mode controller based on switching (sign) function. This controller has acceptable performance in presence of uncertainty (e.g., overshoot=0%, rise time=0.8 second, steady state error = 1e-9 and RMS error=1.8e-12).
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Persistence of Vision control using Arduino
Статья научная
This paper mainly emphasizes on the POV (Persistence Of Vision) technology. In current era in which energy is the main factor in designing all the applications, maximum and efficient use of the energy is very important. A POV display has many advantages over a traditional CRT, LCD or LED display, like power savings, less complexity, easy configuration, attractiveness etc. To overcome the drawback of old processor we have decided to implement the same display atop a new and advanced microprocessor, the Arduino duemilanove. This platform brings with it newer coding and a different understanding of peripherals. ARDUINO INTERFACE BOARDS provide us with a low-cost, easy-to use technology to create the project. We also aim to build the newer display to work with modern forms of interfaces. To accomplish this, we will be interfacing the display with an Android device. This project can be implemented with help of any Android Smartphone/tablet running Android 4.0+.
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Petri Net: A Tool for Modeling and Analyze Multi-agent Oriented Systems
Статья научная
Analysis and proper assessment of multi-agent system properties are very much important. In this paper, we discussed about methodologies for modeling, analysis and design of multi-agent oriented system with the help of Petri net. A Multi-agent system can be considered as a discrete-event dynamic system and Petri nets are used as a modeling tool to assess the structural properties of the multi-agent system. Petri net provides an assessment of the interaction properties of the multi-agent.
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Phone Duration Modeling of Affective Speech Using Support Vector Regression
Статья научная
In speech synthesis accurate modeling of prosody is important for producing high quality synthetic speech. One of the main aspects of prosody is phone duration. Robust phone duration modeling is a prerequisite for synthesizing emotional speech with natural sounding. In this work ten phone duration models are evaluated. These models belong to well known and widely used categories of algorithms, such as the decision trees, linear regression, lazy-learning algorithms and meta-learning algorithms. Furthermore, we investigate the effectiveness of Support Vector Regression (SVR) in phone duration modeling in the context of emotional speech. The evaluation of the eleven models is performed on a Modern Greek emotional speech database which consists of four categories of emotional speech (anger, fear, joy, sadness) plus neutral speech. The experimental results demonstrated that the SVR-based modeling outperforms the other ten models across all the four emotion categories. Specifically, the SVR model achieved an average relative reduction of 8% in terms of root mean square error (RMSE) throughout all emotional categories.
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