Earticle

In this paper, a comprehensive study has been made to evaluate the performance of a Single Carrier Frequency Division Multiple Access (SC-FDMA) based wireless communication system. The 2-by-2 spatially multiplexed Turbo encoded SC-FDMA system under investigation incorporates Quasi-orthogonal space-time block coding (Q-STBC) scheme and three linear signal detection techniques (Equalizers) such as Minimum Mean Square Error (MMSE), Zero Forcing (ZF) and Q-less QR Decomposition under BPSK, DPSK, QPSK, DQPSK and QAM digital modulations. In the present simulated system, data transmission has been secured with implemented ECB cryptographic algorithm. It is noticeable from simulation results that a significant improvement of system performance is achieved with the Q-less QR Decomposition based signal detection scheme under QAM digital modulation technique. The results also clarify that the system performance deteriorates with increase in order of digital modulation and noise power as compared to signal power.

In this paper, a new modular active power filtering approach is proposed to eliminate harmonic currents and compensate reactive power. The method for identifying reference currents is based on FMVs “multi-variable filter”. This method uses two (FMVs) having the advantage of extracting harmonic directly from the axis, the first FMV (FMV Current) extracts the fundamental and individual harmonic component of the distorted line current signal and injects equal-but- opposite of each harmonic current into the line using a voltage source inverter VSI dedicated to that specific harmonic and the second FMV (FMV Voltage) estimates the fundamental component of the line voltage. Moreover the dc-side voltage is controlled by a fuzzy logic controller. The new approach has been illustrated in order to find the best way to reduce network harmonic currents and reactive power compensating of the connected load. All of the studies have been carried out through detail digital dynamic simulation using the MATLAB/Simulink Power System Toolbox.

Human face is generally viewed as most flexible model when it comes to the field of biometric applications. The reason being it is a cost effective one due to its easy data acquisition fundamentals as well as it being invariable when considered over a fixed time period. There have been various algorithms that have been invented for the purpose of facial recognition as well as demonstrated over last three decades which are mainly categorized into kernel based technique s, Vector Methods and so on. A unique binary string generated out of facial features of a person is called a template. This template is used in several applications like network security, public key cryptography and so on. The main challenge in this regard is that no two instances of the faces acquired at different time instance can be same. The templates are similar in regard to the hamming distance but are not same. Therefore it is important to propose an algorithm that presents a face recognition system with the invariant template generation. In this work we propose a unique security architecture where face features and subsequently the generated templates are used as the key for document security. A framework needs users to register with the system along with their face instances. The instances are used for training samples. Once a user selects a folder for encryption, all the files of the folders are encrypted with the templates generated from users face data. A decryption request needs to be authenticated through the face data and the template generated at the time of decryption is used for decrypting the encrypted files. Rinjdal method is used for the cryptographic framework. Frames are acquired in real time from camera and face part is segmented based on skin segmentation. Further Eigen face based template generation and matching is used for face recognition. Results show significant low FAR in comparison to FRR and improved performance in encryption process and recognition rate.

Name systems provide easy communication in large networks and relieve the network users and administrators from remembering long network addresses. But mobility, limited lifetime of user nodes and dynamic situations of MANETs make it impossible to use conventional NAME systems such as DNS on these kinds of networks. Numerous name systems are designed and proposed for MANETs, but most of these system are insecure and do not provide secure name services for upper layers. Name resolution is one of the main operations of that are conduct by client nodes to achieve the requested bindings from name systems. Vulnerability of MANETs to various security attacks, make it clear the need for a secure and reliable name resolution to protect the users against different security threats. In this paper, we present a solution to provide users with secure binding information that can use for secure name resolution. This solution is not dependent on any name systems and can be used with any previously designed naming scheme to enhance the security of name resolution even in offline state and link failures.

Automatic recognition of spoken words is one of the most challenging tasks in the field of speech recognition. The difficulty of this task is due to the acoustic similarity of many of the words and their syllabi. Accurate recognition requires the system to perform fine phonetic distinctions. This paper presents a technique for recognizing spoken words in Bangla. In this study we first derive feature from spoken words. This paper presents some technique for recognizing spoken words in Bangla. In this work we use MFCC, LPC, GMM and DTW.

Bamboo has being widely used as building material throughout the world. From traditional buildings to innovative architectural projects, bamboo has shown its suitability based on a combined low weight, high strength, beauty and durability. The properties of these species vary significantly. A successful application of bamboo in engineering firstly relies on the selection of a correct species. Therefore recognition of bamboo species is necessary before its efficient utilization. Species level identification of bamboos is a highly technical job done primarily by a systematic botanist based on morphological characteristics. However, recognition of the same can also be performed by computer. The bamboo Culm sheath shapes provide valuable data in identification of bamboo species. Automated recognition of bamboo has not yet been well established mainly due to lack of research in this area, non-availability and difficulty in obtaining the database. Using digital image processing and pattern recognition techniques, a supervised classification procedure of three different bamboo species has been developed. In the proposed work, an automated bamboo species recognition system based on shape features of bamboo Culm sheath has been developed using Fourier and Legendre moment classifier. A confusion matrix is created to quantify the class wise and the classifier accuracy. The performance of the classifier is compared based on the classifier accuracy and classwise accuracy. It is concluded the Fourier moment have significantly good results than the Legendre moment. The system can eliminate the need for laborious human recognition method requiring a plant taxonomist. The results obtained shows considerable recognition accuracy proving that the techniques used is suitable to be implemented for commercial purposes.

This paper presents the application of the Modified Particle Swarm Optimization (MPSO) technique for optimal settings zones for distance relay protect 400 kV single transmission line of Eastern Algerian transmission networks at Algerian Company of Electrical and Gas (Group Sonelgaz) compensated by Thyristor Controlled Series Capacitor (TCSC) connected at midpoint for minimized active and reactive power loss. The effects of TCSC insertion on the total impedance of a protected transmission line with respect to injected variable reactance value (XTCSC) in capacitive and inductive boost mode depending of the firing angle (α) is considered. The modified setting zone protection for three zones (Z1, Z2 and Z3) is have been investigate in order to prevent circuit breaker nuisance tripping and improve the performances of distance relay protection. In this work our aim is to compare the performance of the proposed MPSO algorithm with an analytical method (AM). The findings demonstrate the outstanding performance of the proposed MPSO in terms of computation speed, rate of convergence, and feasibility. The simulation results are compared with each other, and then the more perfect algorithm is considered.

The wireless networks are experiencing higher delay, higher error rate, and lower bandwidth compared to wired networks. The customers not only need mobility but also quality. It is a great challenge for the service providers to offer Quality of Service (QoS). The QoS parameters are divided into two categories such as Qualitative QoS parameters (reliability, security, etc.) and Quantitative QoS parameters (bandwidth, delay, jitter, etc). The requirements of the above parameters will vary from one application to another application. In this paper, we would like to propose an efficient technique to store the QoS requirements of data transfer and to evaluate the performance of data transfer with respect to delay. Certainly, the outcome of this paper will help to compare the expected QoS requirements with the actual QoS results, to understand performance degradation and to identify the parameters responsible for delay problem.

This paper deals with an optical character recognition (OCR) system of handwritten digit, with the use of neural networks (MLP multilayer perceptron). And a method of extraction of characteristics based on the digit form, this method is tested on the MNIST handwritten isolated digit database (60000 images in learning and 10000 images in test). This work has achieved approximately 80% of success rate for MNIST database identification.

This paper presents Dempster-Shafer Theory for insect diseases detection. Sustainable elimination of insect diseases as a public-health problem is feasible and requires continuous efforts and innovative approaches. In this research, we used Dempster-Shafer theory for detecting insect diseases and displaying the result of detection process. Insect diseases which include babesiosis, dengue fever, lyme, malaria, and west nile. We describe six symptoms as major symptoms which include fever, red urine, skin rash, paralysis, headache, and arthritis. Dempster-Shafer theory to quantify the degree of belief, our approach uses Dempster-Shafer theory to combine beliefs under conditions of uncertainty and ignorance, and allows quantitative measurement of the belief and plausibility in our identification result.

The Tennessee Eastman chemical process is a well-defined simulation of a chemical process that has been commonly used in process control research. As chemical process plants are getting more complex, the pressure on chemical engineers to develop accurate models for monitoring and control purposes is increased. In this paper, we explore the idea of using Genetic Programming (GP) technique to model the Tennessee Eastman (TE) Chemical Process Reactor. The process is decomposed to four subsystems. They are reactor level, reactor pressure, reactor cooling water temperature, and reactor temperature subsystems. GP found to have many advantages over other techniques in developing an automated process for industrial system modeling. A comparison between the applications of GP in modeling the TE chemical reactors subsystems with respect to other soft computing techniques such as Artificial Neural Networks (ANN), fuzzy Logic (FL) and Neuro-Gas and Neuro-PSO is provided.